    [Senate Hearing 115-645]
    [From the U.S. Government Publishing Office]

                                                         S. Hrg. 115-645

                          TECHNOLOGY IN AGRICULTURE:
                              DATA-DRIVEN FARMING

    =======================================================================

                                    HEARING

                                  BEFORE THE

                      SUBCOMMITTEE ON CONSUMER PROTECTION,
                           PRODUCT SAFETY, INSURANCE,
                               AND DATA SECURITY

                                     OF THE

                             COMMITTEE ON COMMERCE,
                          SCIENCE, AND TRANSPORTATION
                              UNITED STATES SENATE

                         ONE HUNDRED FIFTEENTH CONGRESS

                                 FIRST SESSION

                                   __________

                               NOVEMBER 14, 2017

                                   __________

        Printed for the use of the Committee on Commerce, Science, and
                                 Transportation

    [GRAPHIC NOT AVAILABLE IN TIFF FORMAT]

                    Available online: http://www.govinfo.gov

                                   __________

                        U.S. GOVERNMENT PUBLISHING OFFICE
    37-228 PDF                  WASHINGTON : 2019

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           SENATE COMMITTEE ON COMMERCE, SCIENCE, AND TRANSPORTATION

                         ONE HUNDRED FIFTEENTH CONGRESS

                                 FIRST SESSION

                       JOHN THUNE, South Dakota, Chairman
    ROGER F. WICKER, Mississippi         BILL NELSON, Florida, Ranking
    ROY BLUNT, Missouri                  MARIA CANTWELL, Washington
    TED CRUZ, Texas                      AMY KLOBUCHAR, Minnesota
    DEB FISCHER, Nebraska                RICHARD BLUMENTHAL, Connecticut
    JERRY MORAN, Kansas                  BRIAN SCHATZ, Hawaii
    DAN SULLIVAN, Alaska                 EDWARD MARKEY, Massachusetts
    DEAN HELLER, Nevada                  CORY BOOKER, New Jersey
    JAMES INHOFE, Oklahoma               TOM UDALL, New Mexico
    MIKE LEE, Utah                       GARY PETERS, Michigan
    RON JOHNSON, Wisconsin               TAMMY BALDWIN, Wisconsin
    SHELLEY MOORE CAPITO, West Virginia  TAMMY DUCKWORTH, Illinois
    CORY GARDNER, Colorado               MAGGIE HASSAN, New Hampshire
    TODD YOUNG, Indiana                  CATHERINE CORTEZ MASTO, Nevada
                           Nick Rossi, Staff Director
                     Adrian Arnakis, Deputy Staff Director
                        Jason Van Beek, General Counsel
                     Kim Lipsky, Democratic Staff Director
                  Chris Day, Democratic Deputy Staff Director
                          Renae Black, Senior Counsel

                                    ------

      SUBCOMMITTEE ON CONSUMER PROTECTION, PRODUCT SAFETY, INSURANCE, AND
                                 DATA SECURITY

    JERRY MORAN, Kansas, Chairman        RICHARD BLUMENTHAL, Connecticut,
    ROY BLUNT, Missouri                      Ranking
    TED CRUZ, Texas                      AMY KLOBUCHAR, Minnesota
    DEB FISCHER, Nebraska                EDWARD MARKEY, Massachusetts
    DEAN HELLER, Nevada                  CORY BOOKER, New Jersey
    JAMES INHOFE, Oklahoma               TOM UDALL, New Mexico
    MIKE LEE, Utah                       TAMMY DUCKWORTH, Illinois
    SHELLEY MOORE CAPITO, West Virginia  MAGGIE HASSAN, New Hampshire
    TODD YOUNG, Indiana                  CATHERINE CORTEZ MASTO, Nevada

                                C O N T E N T S

                                  ----------
                                                                       Page
    Hearing held on November 14, 2017................................     1
    Statement of Senator Moran.......................................     1
        Prepared statement...........................................     2
    Statement of Senator Blumenthal..................................     3
    Statement of Senator Inhofe......................................     4
    Statement of Senator Young.......................................     4
    Statement of Senator Fischer.....................................    51
        Press Release dated November 8, 2017 entitled ``Lindsay Corp
          President and CEO Speaks to U.S. Senate Committee''........    51
        Report entitled ``FieldNET AdvisorTM Irrigation
          Management Decision Support Tool'' by Lindsay Corporation..    52
    Statement of Senator Klobuchar...................................    61
    Statement of Senator Lee.........................................    63

                                   Witnesses

    Justin Knopf, Vice President, Kansas Association of Wheat Growers     5
        Prepared statement...........................................     6
    Jason G. Tatge, Co-Founder, President and CEO, Farmobile.........     9
        Prepared statement...........................................    11
    Shannon L. Ferrell, J.D., M.S., Associate Professor, Oklahoma
      State University Department of Agricultural Economics..........    15
        Prepared statement...........................................    18
    Todd J. Janzen, President, Janzen Agricultural Law LLC...........    34
        Prepared statement...........................................    36
    Dr. Dorota Haman, Ph.D., Professor and Chair, Agricultural and
      Biological Engineering, Institute of Food and Agricultural
      Sciences, University of Florida (UF/IFAS)......................    45
        Prepared statement...........................................    46

                                    Appendix

    Hon. Bill Nelson, U.S. Senator from Florida, prepared statement..    77
    Timothy Hassinger, President and CEO, Lindsay Corporation,
      prepared statement.............................................    78
    Deere & Company, prepared statement..............................    78
    Response to written questions submitted to Justin Knopf by:
        Hon. Jerry Moran.............................................    81
        Hon. Catherine Cortez Masto..................................    83
    Response to written questions submitted to Dr. Shannon Ferrell
      by:
        Hon. Jerry Moran.............................................    84
        Hon. Catherine Cortez Masto..................................    89
    Response to written questions submitted to Todd J. Janzen by:
        Hon. Jerry Moran.............................................    92
        Hon. Catherine Cortez Masto..................................    93
    Response to written questions submitted to Dr. Dorota Haman by:
        Hon. Jerry Moran.............................................    94
        Hon. Bill Nelson.............................................    95
        Hon. Catherine Cortez Masto..................................    98

                          TECHNOLOGY IN AGRICULTURE:
                              DATA-DRIVEN FARMING

                                  ----------

                           TUESDAY, NOVEMBER 14, 2017

                                   U.S. Senate,
          Subcommittee on Consumer Protection, Product
                  Safety, Insurance, and Data Security,
            Committee on Commerce, Science, and Transportation,
                                                        Washington, DC.
        The Subcommittee met, pursuant to notice, at 2:32 p.m. in
    room SR-253, Russell Senate Office Building, Hon. Jerry Moran,
    Chairman of the Subcommittee, presiding.
        Present: Senators Moran [presiding], Blumenthal, Blunt,
    Fischer, Inhofe, Lee, Young, Klobuchar, Hassan, and Cortez
    Masto.

                OPENING STATEMENT OF HON. JERRY MORAN,
                        U.S. SENATOR FROM KANSAS

        Senator Moran. Good afternoon, everyone. Thank you for
    joining us this morning. Our Subcommittee's hearing is on
    ``Technology in Agriculture: Data-Driven Farming.'' That's this
    Subcommittee, and that hearing will come to order.
        The agricultural community's adoption of field sensors,
    drones, satellite imagery, advanced machinery, and similar
    technology is increasing at an incredible pace. Our Commerce
    Committee and this Subcommittee have been paying a lot of
    attention to those issues. And the result of that increasing
    pace is greater crop yields and improved sustainable practices
    in farming. The most profitable farms are often the most
    sustainable ones. This rapidly evolving technology will have a
    vital role in preserving farmers' most important assets--their
    land--with the potential increase farmers' margins to
    unprecedented levels.
        The collection and analysis of data has enabled farmers to
    reduce costs through more efficient applications of inputs like
    fertilizers and pesticides; improve production decisions
    through enhanced recordkeeping and more accurate yield
    predictions; and enhance land stewardship and sustainable
    practices by removing inefficiencies in planting, harvesting,
    water use, and the allocation of other resources. With an
    increasing volume of quality data, in tandem with improved data
    analysis, data-collection technology has the potential to
    dramatically increase farm productivity and profitability.
        The collection and use of such data raises issues regarding
    control of the data, transparency of agreements between farmers
    and data firms, and barriers to expanding internet access to
    rural America.
        Additionally, as data collection and sharing practices
    become more popular across the ag economy, farmers are well-
    positioned to benefit from their ``commoditization'' of data
    collected from their land, especially as equipment
    manufacturers, service providers, cooperatives and other
    businesses seek access to that data.
        The goal for this hearing is to educate and empower our
    nation's farmers to understand the value of the information
    they are creating, and certainly to allow Members of Congress
    to have a better understanding of the current lay of the land
    and what the future holds.
        It's my pleasure to introduce the panel today, and I thank
    you all for being here.
        Justin Knopf is a farmer from Gypsum, Kansas, right in the
    middle of our state. He grows wheat, alfalfa, soybeans, grain
    sorghum, corn, and multi-species of cover crops. As part of his
    sustainability-focused farming operations, he practices what is
    referred to commonly as ``no-till'' farming and utilizes a
    variety of technologies that assist his monitoring efforts to
    be a good steward of the land while improving his yield.
        Jason Tatge is the Co-Founder and CEO of Farmobile, a
    technology firm based in Overland Park, Kansas; that's a suburb
    of Kansas City. His company's services provide farmers with
    real-time access to ownership of current and historical data
    pertaining to their land. By providing a user-friendly,
    simplified, and comprehensive overview of relevant data,
    Farmobile's customers are able to make educated decisions in a
    much more timely fashion.
        Dr. Shannon Ferrell is an Associate Professor at Oklahoma
    State in the Department of Agricultural Economics. He also
    serves as an agricultural industry representative to the
    Oklahoma Environmental Quality Board, which oversees operations
    of the Oklahoma Department of Environmental Quality. And the
    Senator from Oklahoma will have an opportunity to introduce Dr.
    Ferrell shortly.
        Mr. Todd Janzen is President of Janzen Agricultural Law,
    LLC, and the Administrator of the Ag Data Transparency project.
    This project makes available the Ag Data Transparency
    Evaluator, which aims to provide clarity to consumers as to
    what businesses do with the data that is shared with them all.
        And, finally, Dr. Dorota Haman is Professor and Chair of
    the Department of Agriculture and Biological Engineering at the
    University of Florida. She specializes in irrigation water
    management and efficiencies, and has been an active leader in
    providing irrigation technologies in developing countries, in
    the Americas, and in Africa.
        [The prepared statement of Senator Moran follows:]

        Prepared Statement of Hon. Jerry Moran, U.S. Senator from Kansas
        Good afternoon. Welcome to the Subcommittee's hearing on
    ``Technology in Agriculture: Data-Driven Farming.'' The Subcommittee
    will come to order.
        Thank you for being here today to discuss the advancements and
    benefits of agricultural technology and the potential of ``Big Data''
    in farming.
        The agricultural community's adoption of field sensors, drones,
    satellite imagery, advanced machinery and similar technology is
    increasing at an incredible pace to increase crop yields and improve
    sustainable practices. The most profitable farms are often the most
    sustainable ones. This rapidly evolving technology will have a vital
    role in preserving farmers' most important asset, their land, with the
    potential to increase farmers' margins to unprecedented levels.
        The collection and analysis of data has enabled farmers to reduce
    costs through more efficient applications of inputs like fertilizers
    and pesticides; improve production decisions through enhanced
    recordkeeping and more accurate yield predictions; and enhance land
    stewardship and sustainable practices by removing inefficiencies in
    planting, harvesting, water use, and the allocation of other resources.
    With an increasing volume of quality data, in tandem with improved data
    analysis, data-collecting technology has the potential to drastically
    increase farm productivity and profitability.
        The collection and use of such data raises issues regarding control
    of the data, the transparency of agreements between farmers and data
    firms and barriers to expanding Internet access in rural areas.
        Additionally, as data collection and sharing practices become more
    popular across the agriculture economy, farmers are well-positioned to
    benefit from the ``commoditization'' of data collected from their land,
    especially as equipment manufacturers, service providers, cooperatives,
    and other businesses seek to access and utilize this data.
        My goal for this hearing is to educate and empower our Nation's
    farmers to understand the value of the information they are creating.
        It is my pleasure to introduce our panel today. Thank you all for
    being here.
        Mr. Justin Knopf is a farmer from Gypsum, Kansas, and he grows
    wheat, alfalfa, soybeans, grain sorghum, corn, and multi-species cover
    crops. As a part of his sustainability-focused farming operations, he
    practices what is commonly referred to as ``no-till'' farming and
    utilizes a variety of technologies that assist his monitoring efforts
    to be a good steward of the land while improving his yield.
        Mr. Jason Tatge is the Co-Founder and CEO of Farmobile, a
    technology firm based in Overland Park, Kansas. His company's services
    provide farmers with real-time access to and ownership of current and
    historical data pertaining to their land. By providing a user-friendly,
    simplified yet comprehensive overview of relevant data, Farmobile's
    customers are able to make educated decisions in a timely fashion.
        Dr. Shannon Ferrell is an Associate Professor at Oklahoma State
    University Department of Agricultural Economics. He also serves as the
    agricultural industry representative to the Oklahoma Environmental
    Quality Board, which oversees the operation of the Oklahoma Department
    of Environmental Quality.
        Mr. Todd Janzen is President of Janzen Agricultural Law, LLC and
    the Administrator of the Ag Data Transparency project. This project
    makes available the Ag Data Transparency Evaluator, which aims to
    provide clarity to consumers as to what businesses do with the data
    that is shared with them.
        Dr. Dorota Haman is a Professor and Chair of the Department of
    Agricultural and Biological Engineering at the University of Florida.
    She specializes in irrigation water management and efficiencies, and
    has been an active leader in providing irrigation technology to
    developing countries in the Americas and Africa.
        I look forward to hearing the testimonies of this expert witness
    panel. I now turn to my colleague Ranking Member Blumenthal for his
    opening remarks.

        Senator Moran. I look forward to hearing the testimony of
    these expert witnesses. And before we do that, let me turn to
    the Ranking Member, the Senator from Connecticut, Mr.
    Blumenthal.

                 STATEMENT OF HON. RICHARD BLUMENTHAL,
                     U.S. SENATOR FROM CONNECTICUT

        Senator Blumenthal. Thank you, Mr. Chairman.
        And thank you to the witnesses for being here today. And
    thank you to the Chairman for having this hearing.
        I represent the State of Connecticut in the United States
    Senate. And I want to welcome witnesses from the states that do
    a different type of farming. We do have farming in the State of
    Connecticut. And I have an additional connection to what you
    folks do for a living, which is that my grandfather had a farm,
    and my first job literally was shoveling manure on his farm at
    the age of probably about 7 or 8 years old.
        I would bet the most complicated piece of machinery on his
    farm in the 1950s and 1960s was the radio in his house, and the
    idea of data and farming being in the same sentence would have
    totally perplexed him. But, in fact, data has enabled us to
    increase yield and productivity in ways that would have been
    unimaginable to him and many farmers of his generation and
    maybe the generation afterward. And the benefits have been
    widely shared by America and the world because America's
    farmers have led the world in using technology to raise
    productivity and yield.
        At the same time, the advances in data have raised
    questions about who owns it, who controls it, how do we protect
    privacy, and how do we prevent others from, in effect,
    profiteering at the expense of our farmers, who really should
    be the ones who own that data and control it?
        So these kinds of questions bring us here today. And I
    thank you for shedding some light on an enormously important
    and complex topic that occupies this Committee in a number of
    different realms, and this one is certainly one of the
    preeminently important ones.
        Thank you very much.
        Senator Moran. Mr. Blumenthal, thank you very much. Thank
    you for explaining how you got your start in politics.
        [Laughter.]
        Senator Moran. I would recognize the Senator from Oklahoma
    for purposes of an introduction.

                     STATEMENT OF HON. JIM INHOFE,
                       U.S. SENATOR FROM OKLAHOMA

        Senator Inhofe. Well, since I don't know anything about Dr.
    Ferrell, I won't introduce him except to say that he is here on
    behalf of Oklahoma State University, and he's got to be a good
    guy if he's with the Oklahoma State University. Thank you.
        Senator Moran. He was smart enough, Senator Inhofe, to wear
    the KU tie, however.
        [Laughter.]
        Senator Moran. Perhaps that's just patriotic.
        Senator Young has the floor for purposes of introduction.

                     STATEMENT OF HON. TODD YOUNG,
                       U.S. SENATOR FROM INDIANA

        Senator Young. Thank you, Mr. Chairman. I appreciate you
    holding this very timely hearing on the agricultural technology
    and digitalization, if I can say that word, of the farm.
        I am happy to introduce Todd Janzen, a fellow Hoosier and
    expert in the field. He is the President of Janzen Agricultural
    Law in Indiana, and his experience in the industry began at an
    early age. Todd grew up on a grain and livestock farm in
    Kansas, where he learned the ins and outs of the industry.
    After graduating from Bethel College in Kansas, Todd made his
    way to the great Hoosier state and attended my alma mater,
    Indiana University's McKinney School of Law, where he began his
    law career in Indianapolis.
        In addition to his work at Janzen Ag Law, Todd serves as
    General Counsel for the Indiana Dairy Producers. He's a member
    of the Indiana Farm Bureau Property Rights Policy Committee,
    and previously he sat on the Board of the Council for
    Agriculture, Science, and Technology.
        Todd maintains a blog on law and technology issues facing
    the agricultural industry, and his writing has been republished
    by numerous journals and news sources. Todd is the
    Administrator of the Ag Data Transparency project, which is
    making ag data publicly available to farmers all across the
    world. These insights will be especially relevant today I know
    as the Committee discusses ag technology and examines the many
    potential benefits and considerations that lie ahead.
        I want to thank Mr. Janzen for taking the time to testify.
    And I look forward to hearing the entire panel's discussion
    this afternoon.
        Senator Moran. Thank you both for those introductions, and
    let us now hear from our witnesses. We'll start with Mr. Knopf
    and work our way to his left.

              STATEMENT OF JUSTIN KNOPF, VICE PRESIDENT,
                  KANSAS ASSOCIATION OF WHEAT GROWERS

        Mr. Knopf. Mr. Chairman and Ranking Member Blumenthal,
    members of the Subcommittee, thank you for the opportunity to
    address you today. My name is Justin Knopf. I am a fifth
    generation farmer from Gypsum, Kansas, as Chairman Moran
    mentioned.
        I also currently serve as Vice President for Kansas
    Association of Wheat Growers. Working alongside my dad and
    brother, we grow wheat, alfalfa, soybeans, grain sorghum, corn,
    and multi-species cover crops across our 4,500-acre farm.
        Like most U.S. farms, we are a family farm. As my father
    has always said, the most important thing we raise on our farm
    is children. My wife Lindsey and I have two daughters and a
    son, and my brother and his wife have two sons. We utilize a
    holistic approach in our farm management, rooted in values of
    faith and family with a multi-generational view. This
    decisionmaking process examines not only our economic returns,
    but also the returns to natural and human resources.
        For the past 15-plus years, our dry land operation has
    utilized a cropping system focused on continuous no-till
    practices and cropping rotations. This system protects the
    soil, allows biology in the soil to thrive, is more resilient
    to extreme weather, and increases carbon content in soils by
    sequestering carbon dioxide from the atmosphere.
        Never before has our society had the access to data and
    information that we have today. The same is true in
    agriculture. Data collection, processing, and the utilization
    of data for improved decisionmaking has become a core
    competency for many, if not the majority of farmers. The amount
    of knowledge per acre and amount of knowledge about each acre
    are significant drivers in the amount of profit per acre.
        There are three main areas on our farm where data shapes
    our decisionmaking and has impact: economic sustainability,
    environmental stewardship, and transparency with consumers. And
    I'd like to share a brief example of each with you.
        As you know, the current low commodity prices equate to a
    difficult economic reality on the farm. Managing costs is
    critical right now. We are utilizing data to divide fields into
    specific management zones; for instance, high-, average-, and
    low-producing areas of the field. When growing corn, seed is
    one of more expensive inputs. We are prescribing different
    planting populations for each yield zone, planting more seed in
    areas of the field that consistently produce more, maximizing
    our return on that investment, and planting less seeds in areas
    of the field that are consistently lower producing, lowering
    our costs on those acres.
        Farmers understand the importance of environmental
    stewardship and protecting our natural resources for future
    generations. That being said, we've made our fair share of
    mistakes and always have room for improvement. On our farm, we
    have a significant focus on protecting the soil and improving
    its health and resiliency. Cover crops are an important tool in
    this endeavor. Utilizing data collection equipment on our
    machinery to carry out on-farm research trials has enabled us
    to better quantify the impact of cover crops to subsequent crop
    yields and other agronomic factors.
        Consumers today, and, therefore, the supply chain, are
    increasingly interested in how their food is produced and that
    it's done in a way that corresponds with their values. This
    past year, we enrolled our wheat acres into a sustainability
    program with ADM. Basically, we entered field information,
    things such as the amount of fertilizer rates used in each
    field and yield data into a web-based software. The software
    utilizes sustainability metrics designed by the collaborative
    group field to market, and then gives us quantifiable
    environmental impact metrics on our farm, benchmarks on how our
    farm compared to other farms, and then the supply chain
    receives the aggregated data.
        The agricultural economy is at a crossroads right now,
    depressed prices, increased costs, and rising debt levels are
    creating economic angst. The average age of farmers continues
    to increase while the number of us continues to decrease. There
    is this great challenge of intensifying our farming system and
    doing so in a way that is sustainable, if not restorative, to
    our natural resources. Consumers are increasingly removed from
    the farm and wary of technological innovations in farming.
        These are all significant challenges. However, the minds
    and spirits engaged in what will be the next generation of
    agriculture are as bright as ever. There will be a record
    percentage of farms transitioning to this next generation in
    the coming decade. This transition represents a great
    opportunity for change and innovation not only in improved
    productivity, but also in environmental stewardship. It is
    critical that we collaborate, learn, and adapt in order that we
    may have continual improvements.
        I appreciate genuinely the opportunity to share the value
    of data with you today. And I appreciate that Congress is
    willing to listen to the people who may be impacted by future
    legislation.
        [The prepared statement of Mr. Knopf follows:]

              Prepared Statement of Justin Knopf, Vice President,
                      Kansas Association of Wheat Growers
        Mr. Chairman, Ranking Member Blumenthal, and Members of the
    Subcommittee, thank you for the opportunity to address you today. My
    name is Justin Knopf and I am a fifth generation farmer from Gypsum,
    Kansas. I also serve as the Vice President of the Kansas Association of
    Wheat Growers. Working alongside my dad and my brother, we grow wheat,
    alfalfa, soybeans, grain sorghum, corn, and multi-species cover crops
    across our 4500 acre farm.
        As my father has always said, we also grow people on our farm. For
    my brother and I, farming is a lifelong learning process. The local
    young people who find a summer job with us grow in responsibility, work
    ethic, and perspective. My wife Lindsey and I have three young
    children, two daughters and a son, and my brother also has two young
    sons. Our farm is not unlike most farms in the United States. According
    to the USDA, farming is still overwhelmingly comprised of family-owned
    businesses. 99 percent of U.S. farms are family farms, and they account
    for 89 percent of farm production.\1\ The USDA also estimates that
    small farms make up 90 percent of the farm count and operate nearly
    half of America's farmland.\2\
    ---------------------------------------------------------------------------
        \1\ https://www.ers.usda.gov/webdocs/publications/eib164/eib-
    164.pdf
        \2\ https://www.ers.usda.gov/webdocs/publications/eib164/eib-
    164.pdf
    ---------------------------------------------------------------------------
        We utilize a holistic approach to our farm management, rooted in
    values of faith and family, with a multi-generational view. Farmers
    understand the need for good stewardship and conservation. This is what
    we do every day. We depend on clean water and healthy soils to make a
    living and feed the world. This decision making process examines not
    only our economic returns, but also the returns to natural and human
    resources. For the past fifteen plus years, our dry land operation has
    utilized a cropping system focused on continuous no-till practices and
    crop rotations. This system protects the soil, allows soil biology to
    thrive, is more resilient to extreme weather, and increases Carbon
    content in soils by sequestering CO2 from the atmosphere.
        Although we have been blessed with some bountiful harvests in the
    last few years, the current economic reality on the farm is difficult
    and the coming years are shaping up to be some tough times. Farm income
    levels are at their lowest point since 1985. Net farm income dropped 95
    percent from 2014 to 2015, and net farm debt levels have increased 25
    percent over the last 3 years.\3\ This downturn has largely been caused
    by low commodity prices, which are due to record highs in both local
    and worldwide production over the past two years.\4\ These production
    levels have increased supply, while overall demand has waned, due to a
    strong U.S. dollar and decreasing exports.\5\ Another major factor is
    that while revenues have only gone down, the cost of production and
    expenses have gone up. From 2009 to 2015 the cost of production has
    increased almost 50 percent.\6\ This rise in costs has forced farmers
    to look for ways to find efficiencies and minimize costs. Our ability
    to adapt to changes is what will keep us going when times get tough.
    ---------------------------------------------------------------------------
        \3\ http://www.agmanager.info/kfma/state-summaries
        \4\ https://www.wsj.com/articles/whats-behind-the-glut-in-
    agricultural-commodities-1476670
    020
        \5\ https://www.wsj.com/articles/the-next-american-farm-bust-is-
    upon-us-1486572488
        \6\ http://www.agmanager.info/kfma/state-summaries/2015-state-
    summary-detailed-cost-summary
    ---------------------------------------------------------------------------
        According to the United Nations there will be 9.1 billion people on
    the planet in the year 2050.\7\ One of the more significant long term
    challenges facing our world is how we feed a growing global population.
    Food security isn't just an agricultural issue; it is a national
    security issue. As farmers, we must find a way to produce more food, on
    less land, with less water, all while protecting our soils and natural
    resources. As stewards of the land it is our job to find ways to do
    more with less. It will take all available tools to meet these
    challenges. Agricultural innovations, like technological improvements,
    seed technology, and on farm efficiencies, are all important. Research
    within private entities and public institutions is critical. Perhaps
    most fundamental is collaboration with others, an eagerness to learn,
    and a willingness to adapt.
    ---------------------------------------------------------------------------
        \7\ http://www.fao.org/fileadmin/templates/wsfs/docs/expert_paper/
    How_to_Feed_the_World
    _in_2050.pdf
    ---------------------------------------------------------------------------
        Never before has our society had the access to data and information
    that we have today. Data is all around us, and there is value in it
    all. While a record of Google searches and websites visited may be
    useless history to me, analysts and marketers see valuable information
    that allows them to adjust the content they create. The same, of
    course, is true in agriculture. While some may see a jargon-filled
    spreadsheet or just a bunch of various colors on a field map, I see
    ways to maximize efficiency in my operation, both for my pocketbook, as
    well as for the land that provides the livelihood of my family. Data
    collection, data processing, and the utilization of data for improved
    decision making has become a core competency for many, if not a
    majority of, farmers.
        The obvious benefit of data is the ability to make improved
    management decisions. Data has become an important layer in our
    decision making process and a driver in our economical sustainability
    and environmental stewardship. The amount of knowledge per acre, and
    amount of knowledge about each acre, are significant drivers in the
    amount of profit per acre.
        There are three main types of data we utilize on our farm.
    Microdata is data we collect and produce that is specific to our farm.
    Service provider data is data that is provided to us by service
    partners that is specific to our farm. And Macrodata, or big data, is
    data we provide to others and they, in return, give us an idea of what
    is happening in the industry on a larger scale.
        Specifically on our farm, we collect and utilize this data in a
    number of different ways. As on many farms, our seeding, spraying, and
    harvesting equipment all has hardware and software that measures and
    records spatially what is being done or happening in the field.
    Performing on-farm research with sound scientific and statistical
    principles is one way we use this technology. For example, we were able
    to quantify the impacts of cover crops on subsequent crop yields, which
    has led to a broader adoption of cover crop practices. We utilize
    satellite imagery as a way to help us identify management zones within
    a field and predict yield variability. These management zones allow us
    to modify our seeding rates based on the productivity of the land,
    which lowers our seed cost on acres that are less productive. We use
    zone soil sampling to quantify soil fertility levels in differing areas
    of the field, which allows us to fertilize based off specific soil
    conditions and fertility levels. It allows us to focus inputs on the
    areas that need them and to avoid applications on areas that don't. We
    enter data into ADM's sustainable wheat program, and in return we
    receive sustainability metrics based off the field to market
    calculator. Through this program we are able to use key sustainability
    outcomes and metrics and benchmark our farm's performance to others in
    the program.
        I also share economic, cost, and revenue data with the Kansas State
    University (K-State) Farm Management program and receive informational
    data back on my farm's profitability in relation to other like-sized
    no-till farms. This program allows me the ability to know how my
    business is doing in relation to others in the industry, what business
    strategies I should implement to become more profitable, or what
    investments I should make in my business.
        The quality and the quantity of data in agriculture, and its
    importance, is driving the improvement of farming practices and its
    value will only continue to grow. It is vital that our stakeholders and
    collaborators work alongside our public research institutions, such as
    K-State, to continue to develop the tools farmers need to be
    successful. Private industry is rapidly expanding in this space and the
    technology is changing by the day. Competition for the ``digital acre''
    is increasing and it is rapidly driving innovation.
        For example, we can now use crop sensors mounted to sprayers that
    utilize algorithms, developed by K-State and other land-grant
    institutions, that tell us in real time how much nitrogen each plant
    needs, while giving credit to biological nitrogen that already exists
    in the plant. As the sprayer travels through the field the sensors will
    tell us in real time how much nitrogen the plants in that spot need.
    This technology allows us to put the right amount of nitrogen in the
    right place which saves money and increases environmental stewardship.
        There are also proprietary tools from companies such as Pioneer and
    Monsanto that utilize soil and weather data to predict a crop's
    nitrogen needs and the amount of available nitrogen in the soil. This
    data helps farmers tune the timing and quantity of fertilizer
    applications to increase efficiencies.
        A researcher from Kansas State has been utilizing land on our farm
    and others to test and develop a sensor that can quickly and
    efficiently quantify soil water-holding capacity differences across a
    field. As water is typically one of our most limiting factors for crop
    production on many farms in the Great Plains, efficient access to this
    information would be very valuable in developing management zones and
    insight in how to best manage each area within the field.
        Data is also important to those off the farm as well. Consumers
    have an ever growing interest in their food. They want to know more
    about how their food is produced, how it is processed, and if it is
    being grown in a way that aligns with their values. Our use of data
    allows us to tell our story to the consumer and enables us to do so
    with transparency like never before.
        However, as we begin to find new ways to collect and utilize this
    valuable data we need to make sure we protect the ownership interests
    and rights of farmers. We need to make sure that government and
    regulatory agencies do not try and access proprietary data that is
    critical to a farmer's business. We need to make sure third party
    dealers and vendors do not try and take ownership of data that was
    generated and collected by the farmer. Finally, we need to ensure that
    the privacy rights and ownership interests of the farmer are respected
    by all those who may want to access this data. The last thing we need
    is for those who are not aligned with our farm interests to twist and
    misconstrue what we do on the farm.
        The agricultural economy is at a crossroads right now. Depressed
    prices, increased costs, and rising debt levels are creating economic
    angst. The average age of farmers continues to increase while the
    number of us continues to decrease. There is this great challenge
    intensifying our farming system, but doing so in a way that is
    sustainable if not restorative to our natural resources. Consumers are
    increasingly removed from the farm and wary of technological
    innovations in farming. However, the minds and spirits engaged in
    agriculture and farming are as bright as ever. There will a record
    percentage of farms transitioning to the next generation in the coming
    decade. This transition represents a great opportunity for change and
    innovation, not only in improved productivity, but also in
    environmental stewardship. It is critical that we collaborate, learn,
    and adapt in order that we may have continual improvement. I appreciate
    the opportunity to share the value of data with you today, and I
    appreciate that congress is listening to the people who may be impacted
    by future legislation. There is an immense amount of technology, both
    here and on the horizon, that will allow American farmers to continue
    to meet these challenges. This drive for continual improvement and
    understanding of the complex biological ecosystem we farm with is what
    will allow my children, and their children after them, to continue
    feeding the world and protecting the natural resources long after I am
    gone. I urge you to continue to listen as your shape future
    legislation.

        Senator Moran. Thank you, Justin.
        And now Mr. Tatge.

      STATEMENT OF JASON G. TATGE, CO-FOUNDER, PRESIDENT AND CEO,
                               FARMOBILE

        Mr. Tatge. Chairman and members of the Subcommittee, thank
    you for the invitation today to share my ideas about how to
    improve the collection, standardization, and interoperability
    of agricultural data for the benefit of every American.
        My name is Jason Tatge, Co-Founder, President, and CEO of
    Farmobile. We help farmers collect and organize data so they
    can use it to better manage their own operations, share the
    data with their trusted partners, and even license their data
    to vetted third parties to create a brand new revenue stream
    for their business.
        I've been on over 200 farms in the Midwest in the last few
    years, and I've had hundreds of conversations with farmers
    defining the future of global agriculture. I'd like to share
    what I've learned from listening to the men and women on the
    ground.
        One of the most important topics we discuss on the farm is
    data. Who has it? Who does it belong to? And what's it being
    used for?
        Through my conversations, I've come to believe that for the
    sake of a safer, more productive food future, farmers need to
    be able to own their data outright. Farm data needs to be
    accessible in real time and compatible with multiple systems.
    In short, we need a standard for the agricultural industry.
        I will start with the question of ownership. Big
    agricultural companies know it will benefit them to own the
    digital content coming out of the farmers' fields. When a U.S.
    farmer spends hundreds of thousands of dollars on a new piece
    of equipment, the largest manufacturers profit from the initial
    sale of the equipment, then they profit again from the data
    generated from the farmer using that equipment. This is wrong,
    because the data being collected by many big ag companies is
    the farmers' proprietary intellectual property. It is a unique
    formula or secret recipe for operating their successful
    businesses.
        Attempts to get big players in the ag industry to
    voluntarily enact transparent data policies have been slow. In
    fact, organizations who played a big role in drafting the best
    practices for data transparency have failed to sign on or adopt
    them. Asking a farmer for their secret recipe would be bad
    enough, but tricking them into signing it away, the unique
    formula, with complicated legal agreements, is appalling to
    most, and the main reason I sit here.
        The potential value in this data to farmers and
    agricultural communities writ large cannot be overstated. If
    farmers own their data and can license it multiple times, we're
    talking about an opportunity to create an estimated billion
    dollar annual new revenue stream returning that money to rural
    agricultural communities of America.
        To further illustrate the importance of data ownership, I
    want to talk about the value data represents for a farmer.
    Here's an example. We know the genetic yield potential for corn
    in the U.S. is over 500 bushels per acre, yet the national
    average for corn is only about 170 bushels. Why is this? The
    answer lies in the data.
        Farmer-controlled digital records to document the farming
    practices will help U.S. agriculture and individual farmers
    determine best practices for maximizing their yields. Those
    records enable farmers to make better decisions, identify
    efficiencies, boost productivity, and mitigate risks as well as
    aid the industry in streamlining the manual process required to
    participate in Federal programs and crop insurance.
        These kinds of ag data benefits, however, require
    agriculture to get past the roadblocks of data
    interoperability. Many farmers operate mixed fleets today, and
    that means they have separate data systems for each equipment
    brand. Ag companies make it incredibly labor-intensive to move
    data out of one system and into another. This stifles
    competition and customer choice in an already dramatically
    shrinking landscape of agricultural giants.
        I believe we need to properly align incentive structures
    and drive standardization across the industry. Every farmer has
    a right to access and use their data regardless of where it
    came from or what system contains it. We should encourage the
    flow of information that could help farmers and their trusted
    advisers to make the best decisions for their farms and the
    food industry at large.
        The power of data can go way beyond the field. When farm
    data is available in real time in a standardized portable
    format, like Farmobile's electronic field records, there's a
    huge potential to reduce the volatility in commodity markets.
        Agricultural markets are volatile for good reason: there's
    a massive lag time in getting the information. The USDA is the
    gold standard, but even the USDA sometimes takes 3 weeks to get
    out information in the right form for release, but most row
    crops are only alive for about 90 days. Faster information will
    dramatically reduce the volatility in the markets and enable
    traditional risk management strategies, like hedging, to work
    again for farmers and the agricultural businesses that rely on
    these commodities to produce their products.
        In 1960, John F. Kennedy said, ``The farmer is the only man
    in our economy who has to buy everything at retail, sell
    everything at wholesale, and pay the freight both ways.'' It's
    unfortunate that this statement is still very true today.
        But disruption is coming in the form of technology. We need
    to make sure that our legal system keeps up with the technology
    available and is informed on the formative debates that will
    define the future in my industry. Most farmers I talk with
    believe we have 2 to 3 years to figure this out, or they will
    lose. Farmers need policies that safeguard data rights, are
    interoperable, and improve data access to drive efficiencies in
    innovation and food production. As you consider these issues
    further, ask yourselves, ``Why is it ever okay for others to
    own or control farmers' data? And how do we enact policies that
    create true data interoperability?''
        I firmly believe that done right, data is the answer to
    advancing agriculture and the entire food industry while
    protecting America's farmers.
        I appreciate your openness to ideas and action from the
    private sector as well as administrative and legislative
    change. I look forward to working with the industry as well as
    the members of the Committee to advance this vision. Thank you.
        [The prepared statement of Mr. Tatge follows:]

     Prepared Statement of Jason G. Tatge, Co-founder, President and CEO,
                                   Farmobile
        Chairman and Members of the Subcommittee:

        Thank you for the invitation, today, to share my ideas about how to
    improve the collection, standardization and interoperability of
    agricultural data for the benefit of every American.
        My name is Jason Tatge, co-founder, president and CEO of
    Farmobile--a relatively small agtech startup company, from Kansas, with
    a creative business model that turns our farmer customers data into a
    monetizable commodity and shares the revenue with the farmers. We
    recently celebrated our four-year anniversary and employ over 40 people
    with plans to add at least 20 more over the next 12 months. Farmobile
    offers a ``data as a service'' subscription that properly aligns our
    company's future success with our farmers' success. Practically
    speaking, we help farmers to collect and organize their data so they
    can use it to better manage their own operations, share their collected
    data with their trusted partners and/or sell their data to interested
    third parties, the same way musicians can sell their music.
        I've been on over 200 farms in the Midwest in the last few years
    and am completely amazed by how ridiculously awesome these people are
    at growing our food. These folks are the ``rock stars'' of global
    agriculture.
        One of the most common topics we discuss when on the farm is data
    ownership. Many are confused over how we've gotten to this current
    place or when data ownership even become a question They are confused
    about who has access to their data and what they are doing with it. All
    have an expressed interest in being able to establish a value for the
    data they generate. As one farmer, David Seba from Cleveland, Missouri
    told me, ``Big ag has been collecting our data for so long, that
    there's this attitude that the way we farm carries no value. Well, it
    does. For farmers, the field is our business and the way we manage it
    is our formula for success. So, why is it okay for these companies to
    claim the data as theirs and then sell it without our knowledge?''
        At Farmobile we are proud to be working alongside some of the most
    innovative farmers in the world, and we are passionate about providing
    these farmers the opportunity to establish ownership and directly
    profit from the data generated from their field activities, if they
    choose to sell licenses to their data.
    Introduction
        Whether you represent the 2 percent of the U.S. population who farm
    or the non-farm constituents who--like all of us--eat, farm data will
    become a digital currency that impacts both farmers and food buyers.
    Today, I'll share my thoughts about the state of the industry, and the
    needs, risks and opportunities we have.
        Data and analytics are disrupting and changing most industries.
    From grocery shopping to political campaigns, the world is forever
    changed by data. Farming is no different, although I'd suggest we are a
    few years behind other industries when it comes to data collection.
    That's changing fast, and we have a lot to learn from other industries
    that have already made the move from analog to digital, like
    healthcare.
        A big part of farming today is being able to manage a large mixed
    fleet of equipment. Real-time data connectivity empowers farmers to
    remotely manage their logistics like never before by using any Internet
    connected device. While adoption of precision agriculture technologies
    has been on the rise for years, now that it's available in real-time,
    adoption has accelerated because farmers quickly ``see'' the value of
    data.
        Real-time data is the ``game changer'' for the future of farming
    because of the ability to gain insights and react ``right now'' during
    the season. This is the foundational driver to improve yields, lower
    input costs, strengthen stewardship and pave the way for cutting-edge
    programs like yield guarantees that enable seed and chemical companies
    to ``share the performance risks'' associated with their recommended
    products.
        Big ag companies certainly agree that the industry is going
    digital. Look no further than public statements made by ag business
    giants framing this opportunity for their shareholders. They absolutely
    know it will benefit them to own the digital content coming out of the
    farmers' fields.
        But this comes with a cost to the farmers, not only do these big
    companies expect to get the data for free, but they also create
    ``silos'' for the data and make it very difficult to get that data back
    out from their systems. This stifles competition and customer choice in
    an already dramatically shrinking landscape of agricultural giants,
    whose recent mergers have reduced the big six to the big four.
        Farmers are just beginning to understand that their data has value
    outside the perimeter of their operations, and that data ownership and
    a neutral digital strategy is necessary to be competitive today.
    Because of this, some farmers are starting to ask tough questions about
    data: Who owns it? How will it be used? How do I extract maximum value
    from it? And, most importantly, how do I put a ``fence'' around my data
    so that it's protected for future generations?\1\
        This brings me to the first opportunity and risk for farmers--data
    ownership.
    Farmers and Data Ownership
        As business owners, farmers face a very real risk from many ag
    companies with whom they do business because: 1) companies gain access,
    control and sometimes ownership over the farmer's private data; and 2)
    these companies can lock farmers into their data policies.
        At the center of this growing concern is the method in which ag
    companies typically collect and use a farmer's data. To understand it,
    let me provide a consumer-facing illustration that everyone
    understands--Google.
        When I choose to use Google to search the web (for free), I
    understand that Google is collecting information about me through my
    interaction with their technology. I know that Google turns this
    collected data into information by combining it with other datasets.
    Further, I realize Google makes money from selling this information to
    marketers that want to learn more about me. In spite of this, I choose
    to use Google search because it is of value to me, and it's free.\2\
        On the other hand, when I purchase a license to use Microsoft
    Office, I gain access to tools like Microsoft Word, Microsoft Excel and
    Microsoft PowerPoint. These tools provide value to me. When I use these
    tools, Microsoft does not get rights to the content I create. Could you
    imagine the types of congressional hearings we'd be having on that
    topic--if Microsoft treated its customers the way big ag treats their
    customers?
        When a U.S. farmer spends hundreds of thousands of dollars on a new
    piece of equipment, the largest manufacturers profit from the initial
    equipment sale PLUS they profit from the data generated from the farmer
    using that equipment. The collection of this data often happens without
    the farmer's knowledge due to complex and heavy-handed user agreements.
        While the fact that Google is collecting search data doesn't bother
    me as a consumer, the stakes are much higher and far different in the
    farmer example. The data being collected by many big ag companies is
    the farmer's Intellectual Property--the special and unique formula or
    ``secret recipe'' for operating their successful business.
        Imagine if we, as a user of Google, asked for its search engine
    algorithms. Or, as a customer of Microsoft, if we asked for its source
    code to the Microsoft Office Suite? Asking a farmer for their ``secret
    recipe'' would be bad enough, but tricking them into signing away that
    unique formula with complicated legal agreements is appalling to most
    and the main reason I am here today.
        We believe farming practices represent Intellectual Property that
    could be copyright protectable. Yet, today, it is difficult to
    establish who owns this information because farmers are caught in the
    habit of unknowingly giving this data for free when they sign
    complicated legal agreements pertaining to an entirely different
    subject. It is my personal motivation to help farmers by providing
    alternatives with upside potential.
        I've been working for the better part of three years with the
    American Farm Bureau Federation (AFBF) to address these issues. The
    AFBF has shown great leadership in trying to bring transparency to
    these confusing legal contracts farmers are required to sign. Working
    with commodity groups, farm organizations and agriculture technology
    providers, the AFBF established the Privacy and Security Principles for
    Farm Data in November 2014. Thirty-seven different organizations
    participated in drafting the ``Core Principles'' document. Many of
    these organizations were very opinionated around the wording of the Ag
    Data Transparency Evaluator's ten questions, but only nine of these
    companies have agreed to become Ag Data Transparent! The ones who
    haven't signed are challenging the very need for ``ownership'' of farm
    data to be defined in the ``Core Principals.''
        Make no mistake about it, these companies are intentionally
    delaying participation because they hope this issue will blow over and
    farmers will continue to operate the way they have in the past--by
    unknowingly checking a box in a legal contract in order to take
    delivery of their product.
        Missouri Farm Bureau President Blake Hurst of Tarkio, Missouri
    describes the situation like this: ``So much of what we do is done by
    habit. As soon as we get in the habit of giving that data away, no
    company is going to remark on the fact that it is a heck of a good deal
    for them. If we don't start out doing it the right way, it will be very
    harmful to farmers in the future.''
        In agriculture, we are at a point in time where there is a great
    opportunity to ``do the right things for the right reasons'' on behalf
    of the people who produce our food.
    Data Interoperability
        We know the genetic yield potential for corn in the U.S. is over
    500 bushels per acre, yet the national average for yield is about 170
    bushels per acre. Having farmer-controlled digital records (such as
    Electronic Field Records) to document farming practices will help U.S.
    agriculture better determine best practices farmers. Those records
    enable farmers to make better decisions, identify efficiencies, boost
    productivity and mitigate risks, as well as aid the industry in
    streamlining the manual processes required to participate in Federal
    programs and crop insurance.
        These kinds of ag data benefits, however, require agriculture to
    get past roadblocks to data interoperability and over the ``not-
    invented-here'' syndrome. Farmers need a uniform standard that allows
    data to be portable and enables them and their trusted service
    providers to make real use of the information.
        The need for data interoperability is not a new issue. My written
    remarks contain an excerpt from the testimony of the late Neal
    Patterson, who spoke before the Senate Committee on Health, Education,
    Labor and Pensions in June of 2015.\3\ Neal was a personal friend and
    mentor of mine as well as co-founder and CEO for Cerner, a leading
    health information technology company.
        Neal believed, as I do, in the parallels between Electronic Health
    Records and Electronic Field Records. His testimony stated: ``The
    intersection of healthcare and IT is one of the most important in
    modern society. Every citizen touches and depends on both.'' \3\
        I absolutely believe the same is true for agriculture, everyone
    eats. Every farmer has a right to access and use their data, regardless
    of where it came from or what system contains it. We should encourage
    the flow of information that could help farmers--and their trusted
    advisors--to make better-informed decisions about their businesses and
    food production.
        In agriculture, sensor technology and communication protocols exist
    for data to move quickly across different systems; however, many
    existing companies are not interested in building tools that would
    allow standard data to move efficiently. At Farmobile, we build
    technology that supports interoperability; we are a neutral provider
    that enables farmers to compare ``apples to apples'' when looking at
    products and services offered to them.
        It is not by accident that big ag companies use their war chests of
    cash to hold farmer data hostage in their platform. They make it very
    labor intensive to move the data from one system to another. I believe
    in properly aligning incentive structures to drive standardization and
    financially benefit farmers--who are the creators of Electronic Field
    Records. The Electronic Field Record is a universal commodity in
    support of digitizing agriculture, and both farmers and consumers
    benefit.
        Farmobile is the first company to build a business model around the
    monetization of standardized farm data whereby farmers share in the
    revenue, and data buyers can drive further innovation as the consumers
    of this valuable information. This is a powerful new revenue
    opportunity--a true win-win for farmers and the industry. (Figure 1)
    \4\
        The idea of farmers harvesting their data and selling it as a new
    ``crop'' is a game-changer. It adds economic strength to rural
    communities, and also contributes to food safety--which is in the
    national security conversation.
    Real-time Data and the Impact on Commodity Markets
        After graduating with a Bachelor of Arts in Financial Economics
    from Gustavus Adolphus College in St. Peter, Minnesota, I spent the
    next 20 years trading agricultural commodities--the pure economic
    theory of supply and demand fascinated me and still does today. I first
    traded for the Pillsbury Company and then for a large regional player,
    The Scoular Company. When my career began, commodity trading was done
    ``in the pits'' using an open outcry system. There was an inherent time
    delay to disseminate pricing data--first from the pits in Chicago to
    the local grain buyers, then from the grain buyers to the farmers. This
    created an unfair advantage for those, who could afford to pay for the
    real time pricing feeds. For years, this opportunity was used to take
    advantage of additional margin--and the farmers paid the freight for
    decades.
        The last 10 years of my trading career were all about challenging
    the status quo in the commodity trading world and changing sides from
    being the buyer to helping the seller. My company helped farmers become
    better grain marketers by utilizing new technology, which enabled them
    to take advantage of real-time data feeds in their marketing plans.
    This opportunity was fueled by the Chicago Mercantile Exchange
    acquisition of the Chicago Board of Trade which rapidly accelerated the
    use of electronic trading and hedging.
        Today a similar opportunity exists to ramp up the creation of farm
    data into ``tradeable'' information in the form of Electronic Field
    Records. To work, the data must be interoperable and available in real-
    time to those who desire to purchase it. This data liquidity will
    dramatically accelerate the foundational science to help solve the
    looming global food challenge and identify best practices, minimize
    environmental impact and maximize nutritional content of food being
    produced. Every time this information is ``sold,'' it is with the
    explicit permission of the farmer, and the farmer who created it shares
    in the revenue. The same digital information can be sold multiple times
    with an opportunity to create an estimated $1billion annually of new
    revenue returned to rural agricultural communities of America.
        Once you get something faster, you rarely go back. The commodity
    markets are no different. I will challenge anyone to debate the notion
    that real-time data, data ownership and interoperability would not be
    good for the farmer.
        The reason that there is so much volatility in the agricultural
    markets is because of the massive time lag in getting the information.
    The USDA is the gold standard in historical commodity information.
    However, this information is released three weeks after it is observed
    due to the process required to get that information in the right place.
    The delay causes much of the volatility given the fact that most row
    crops are alive about ninety days and it takes about 21 days to get the
    data from the county offices to the markets in the form of USDA
    reports.
        The technology exists today to get that information to the market
    daily. Faster information will dramatically reduce volatility in the
    markets and enable traditional risk management strategies, like
    hedging, to work again for farmers and the agricultural businesses that
    rely on these commodities to produce their products.
        Many large commercial grain trading companies have reported
    significant losses in the markets recently as traditional hedging
    practices are introducing more risk than they are reducing. Faster
    access to better information will help normalize markets and monetarily
    benefit the farmers who choose to sell licenses to their information.
    Conclusion
        I'd like to conclude by revisiting history. It is 1960 and John
    Fitzgerald Kennedy is running for president when he visits a group of
    farmers in Senator Thune's home state of South Dakota and he says:
    ``The farmer is the only man in our economy who has to buy everything
    at retail, sell everything he sells at wholesale, and pay the freight
    both ways.''
        It's pretty incredible to think that--with all the change we've
    seen in the last 57 years--this statement is, unfortunately, as true
    today as it was then.
        But disruption is coming and it's coming in the form of technology.
    We need to make sure that our legal system keeps up with the technology
    available. Most farmers I talk with think we have probably two to five
    years to figure this out, or they will lose.
        Thank you for your time today. I hope my testimony sheds some light
    on what is happening in the industry and I look forward to continued
    conversations about the many ways we can help the farmer finally stop
    paying the freight both ways. I firmly believe that, done right, data
    is the answer.

      1.  Farmers need policies that safeguard their data rights, and allow
            interoperability and accessibility to drive efficiencies and
            innovation in food production.
      2.  As you review this topic, ask yourselves:

         Why is it ever ``o.k.'' for others to own or control a
                farmer's data?

         How do we affect policies for true data
                interoperability?

        I appreciate your openness to ideas and action from the private
    sector, as well as administrative and legislative change. I look
    forward to working with the industry, as well as members of the
    Committee, to advance this vision.
        Thank you.
        cc: Addendum
                                    Addendum
    Additional References
        \1\ The Problem of Vendor Lock-In for Ag, http://bit.ly/2xHeie5
        \2\ Farmobile: Changing the Game in Ag Data, http://bit.ly/2oObquw
        \3\ Testimony of Neal L. Patterson, Co-founder, Chairman and CEO of
    Cerner Corporation, U.S. Senate Committee on Health, Education, Labor
    and Pensions, Hearing: Health Information Exchange: A Path Towards
    Improving the Quality and Value of Health Care for Patients, June 10,
    2015, http://bit.ly/2zLI1YB
        \4\ Farmobile's Business Model (Figure 1)

        (Figure 1, Farmobile's Business Model)

        Senator Moran. Thank you, Jason.
        Dr. Ferrell.

        STATEMENT OF SHANNON L. FERRELL, J.D., M.S., ASSOCIATE
    PROFESSOR, OKLAHOMA STATE UNIVERSITY DEPARTMENT OF AGRICULTURAL
                               ECONOMICS

        Dr. Ferrell. Subcommittee Chairman Moran, Ranking Member
    Blumenthal, and members of the Subcommittee, thank you for the
    opportunity to present my observations in the collection and
    utilization of data in agriculture, the opportunities and
    challenges that presents, and the legal issues surrounding
    agricultural data collection, transmission, and use.
        The new frontier in agriculture presents a fascinating and
    sometimes paradoxical mix of cutting-edge technology, recent
    legal changes, and centuries-old common law.
        Farm equipment rolls off the assembly line with a suite of
    sensors and transmitters enabling it to share unprecedented
    amounts of farm-level or ``Small Data,'' that gives farmers the
    ability to proactively manage risks that heretofore they may
    have found unmanageable and, in some cases, even unknowable.
        At the same time, this perfusion of Small Data can now be
    aggregated by many means into what we call agricultural ``Big
    Data.'' Analysis of big data in agriculture holds many
    potential advantages for producers, who can apply Big Data
    insights to their individual operations, and at the same time,
    we now have the opportunity for better market analysis, as Mr.
    Tatge was just saying, and Mr. Knopf as well, and we can now
    manage agricultural risk at a national and potentially even
    global scale in a few years.
        Within the policy and academic realms, Big Data holds the
    potential for us to provide more timely responses to industry
    crises and have better evaluation of farm policy impacts and
    food programs. So Big Data may eventually be able to predict
    many crises before they even emerge.
        However, agricultural data faces a peculiar chicken-and-
    the-egg problem in that the development of datasets
    sufficiently large to take full advantage of all the
    opportunities ag data opposes requires participation by a large
    number of producers. At the same time, farmers are often
    reluctant to participate in those agricultural data systems if
    they're concerned about the share of the value that they're
    going to receive for the contribution of their data.
        Further, despite a little potential shown by agricultural
    data, the current technological, economic, and legal
    environment raises some issues about how the value of
    agricultural data will be shared between data aggregators and
    producers, as Mr. Tatge just mentioned, so producers receiving
    what they deem to be sufficient value is going to be a gateway
    issue for us having a critical mass of producers making data
    contributions to really truly understand the value that
    agricultural data could pose for our industry.
        So addressing the concerns of producers with respect to
    their rights and data, the value it creates, and their privacy
    if they choose to share their information is vital. Farmers
    often express concerns like this collectively under the
    question of, ``Who owns their data?'' And that may not be the
    question that has a clear answer in our current intellectual
    property framework, although, arguably, there is a colorable
    argument to be made that they do own their data. The question
    is, ``What does that ownership actually mean?''
        So that question of ownership may not be as important as
    ensuring farmers always have access to their data once it has
    been shared, that they can receive value from its use, and they
    can feel comfortable with the level privacy or that they're,
    conversely, comfortable with the lack of privacy that they're
    going to experience as a result of sharing those agricultural
    data platforms.
        Well, agricultural technology making use of data grows at
    an exponential rate, but the technology and policies for
    protecting data has not. For example, opting out of data
    collection is going to grow increasingly difficult as more and
    more of even the used equipment machinery fleet has embedded
    technologies that make data sharing something you must opt out
    of rather than opting into. And, indeed, many producers may not
    even know that they have an option of opting out of data
    sharing if they so choose.
        Further, in some circumstances, it may be almost impossible
    to truly anonymize data once it has been shared, because with
    the addition of some publicly available data, we could almost
    interpolate everything that you would want to know about that
    operation from what data has been shared, even though,
    ostensibly, it was supposed to be amalgamated with others and
    rendered anonymous.
        The resolution of these issues may depend on the relative
    bargaining power of those at the table when data use agreements
    are negotiated, and historically farmers have been at something
    of a disadvantage in that regard. However, significant steps
    are already underway to facilitate consensus among industry
    stakeholders regarding those issues, and you'll hear about some
    of those steps today, and you already have heard some of those
    in passing from the previous witnesses.
        There are a number of ways I think the Subcommittee and
    Congress, as a whole, can facilitate the realization of
    agricultural data's true potential.
        First, Congress can support continuing efforts to build
    industry consensus between farmers, equipment manufacturers,
    and data service providers. Whether through consensus or with
    legislation, Congress could also consider support of a clear
    framework of right-to-know issues with respect to how your data
    is being used, the right to opt out of data collection if you
    so choose, guidelines for the disclosure of agricultural data
    uses by service providers, and protections against the
    disclosure of data. It can also fund research and educational
    efforts to help agricultural producers make informed decisions
    about how to engage agricultural data systems and how to
    develop protections for agricultural data shared with service
    providers and the government.
        Finally, if the agricultural data revolution is to realize
    its true potential, sustained efforts to build and maintain a
    robust broadband Internet infrastructure for rural America must
    be sustained, as you mentioned, Chairman. The current support
    of rural broadband access from a supply perspective is having a
    positive impact, but we also need to support demand-side
    drivers for rural Internet access as well. Widely available
    wireless and hardwired broadband connectivity both are crucial
    to realizing the potential of agricultural data as well as
    maintaining the economic opportunities that can revitalize
    rural America.
        Chairman Moran, Ranking Member Blumenthal, and members of
    the Committee, thank you for the opportunity to share, and I
    look forward to helping you explore this issue further.
        [The prepared statement of Dr. Ferrell follows:]

        Prepared Statement of Shannon L. Ferrell, J.D., M.S., Associate
     Professor--Oklahoma State University Dept. of Agricultural Economics;
                                    Jointly
      prepared by the witness and Dr. Terry Griffin, Assistant Professor,
            Kansas State University Dept. of Agricultural Economics
    Executive Summary
        Today's technology affords farmers the ability to instantaneously
    collect data about almost every facet of their cropping (and
    increasingly, their livestock operations) year-round. As a result,
    there has been unprecedented growth in the amount of data collected at
    the farm level. This farm-level ``Small Data'' increasingly provides
    management insights to agricultural producers allowing them to manage
    more risk factors than ever before. At the same time, this profusion of
    Small Data can now be aggregated by many means to create agricultural
    ``Big Data.'' Analysis of Big Data in agriculture holds many potential
    advantages for producers and creates the opportunity for better
    macroeconomic analysis of farm policy tools, food programs, and
    management of agricultural risk at a national scale.
        The current technological, economic, and legal environments raise
    issues about how the value of agricultural data will be captured among
    the agricultural producers generating the data and the agricultural
    technology providers (ATPs) aggregating it. Producers receiving what
    they deem to be sufficient value for their data contributions is
    critical as a potential gateway issue for making those contributions;
    without large, robust participation in agricultural data systems, such
    systems will fail to reach their full potential.
        Thus, addressing the concerns of agricultural producers with
    respect to their rights in data, the value it creates, and their
    privacy if they choose to share their information is vital to see that
    the agricultural industry collectively maximizes the value of these
    data technologies. Farmers often express these concerns collectively as
    a concern about who ``owns'' their data, and there are no clear answers
    in the current intellectual property framework. However, the question
    of agricultural data ownership may not be as important as ensuring
    farmers always have access to their data can receive value from its
    use, and can feel comfortable with the level of privacy--or lack
    thereof--that can be afforded to those participating in Big Data
    platforms.
        Significant steps are already underway to facilitate consensus
    among industry stakeholders regarding these issues. This Committee and
    Congress as a whole may best be able to facilitate the realization of
    Big Data's potential advantages to U.S. agriculture through support of
    this consensus effort, support of educational efforts to help
    agricultural producers make informed decisions about how to engage with
    Big Data systems, continued development of more robust protections for
    agricultural data shared with the government, and continued support of
    improved broadband access in rural areas.
    Acknowledgements
        Dr. Terry Griffin of Kansas State University's Department of
    Agricultural Economics was instrumental in the preparation of this
    testimony, and his assistance in the creation of this document is
    gratefully acknowledged. Dr. John Fulton of Ohio State University's
    Department of Food, Agricultural, and Biological Engineering, Ms.
    Maureen Kelly Moseman, Adjunct Professor of Law at the University of
    Nebraska College of Law, Mr. Todd Janzen of the Janzen Agricultural Law
    firm, Mr. Ryan Jenlink of the Harness, Dickey & Pierce law firm, Dr.
    Keith Coble of the Mississippi State University Department of
    Agricultural Economics, Dr. Ashok Mishra of the Arizona State
    University Morrison School of Agribusiness, and Mr. Matthew Steinert of
    Steinert Farms, LLC also provided vital input in the development of
    this testimony.
        Perhaps the greatest contribution to this testimony and my
    understanding of agricultural data systems, though, was made by Dr.
    Marvin Stone. Dr. Stone was a giant in the agricultural data field,
    contributing tremendously to the development of the Green Seeker
    technology that significantly advanced machine-sensing of plant health.
    He was also instrumental in the development of the SAE J1939 standard
    that forms the foundation for many of the machine data technologies at
    the heart of this discussion. Beyond being a giant in the field we
    examine here today, Dr. Stone was a mentor to myself and hundreds of
    other students at Oklahoma State University. He and his wife were both
    killed in the tragic Oklahoma State University homecoming parade
    accident of October 24, 2015. I hope this testimony honors his memory,
    the contributions he made to this field, to the U.S. agriculture
    industry, and to all his students.
    Issue Analysis
    1. Introduction
        I would like to thank Subcommittee Chairman Moran, Ranking Member
    Blumenthal, and the Members of the Committee for the opportunity to
    present my observations on the collection and utilization of data in
    agriculture and the legal issues surrounding the concept of Big Data
    and its application to U.S. farmers and ranchers. This new frontier in
    agriculture presents a fascinating and sometimes paradoxical mix of
    cutting edge technology, recent legal changes, and centuries-old
    doctrines of common law. In my testimony today, I will discuss how both
    ``Small Data'' and ``Big Data'' in agriculture are being utilized by
    agricultural producers and what lies just over the horizon for those
    technologies. I will also discuss some of the opportunities and
    challenges posed by the advancements in agricultural data technology.
    Then, I lay a framework for discussing the legal issues surrounding Big
    Data in agriculture, discuss how the current U.S. legal environment
    addresses ownership and privacy rights in agricultural data, and
    suggest some potential avenues for policy responses that may facilitate
    the economic advantages to be gained from the application of Big Data
    principles to agricultural data while dealing with the concerns
    associated with such applications.
    2. The growth of Small Data and Big Data in production agriculture
        The concept of Big Data has exploded in a relatively short period
    of time. However, there would be no Big Data in agriculture were it not
    for Small Data. Since these definitions and the issues surrounding data
    use in agriculture continue to evolve, my testimony today will provide
    some framing for both.
    2.1 Defining core terms in the Data-Driven Farming discussion
        Three terms immediately rise to the top in an examination of the
    agricultural data discussion: agricultural data, Small Data, and Big
    Data. Taken together, the use of Small Data and Big Data in agriculture
    is increasingly referred to as ``digital agriculture.''
        The concept of agricultural data is almost too broad to define, but
    looking at research in the field and conversations surrounding
    agricultural data indicates the term centers around two more specific
    concepts: ``telematics'' or ``machine'' data and ``agronomic'' data.
    Telematics data (sometimes called ``machine data'') refers to the
    information an agricultural implement (such as a planter) or self-
    propelled vehicle (such as a tractor or combine) collects about itself.
    Almost by definition, telematics data comes from agricultural equipment
    owned, operated, or hired under contract by the agricultural producer.
    Agronomic data refers to information about a crop or its environment,
    such as ``as-planted'' information from a seed planter, ``as-applied''
    information from a fertilizer sprayer, yield data from a grain combine,
    and so on. While agronomic data resembles telematics data in that much
    of it is gleaned directly from agricultural implements, agronomic data
    can also be obtained from many other sources such as hand-held sensors,
    aerial platforms such as manned survey flights or flights by unmanned
    aerial systems (UAS, commonly called ``drones''), and even satellite
    imagery.
        Another piece of the agricultural data puzzle is so-called
    ``metadata,'' which includes management information such as seeding
    depth, seed placement, cultivar, machinery diagnostics, time and
    motion, dates of tillage, planting, scouting, spraying, and input
    application. In addition to data on the products and how those products
    are applied, information on external environmental circumstances such
    as weather including precipitation events, evapotranspiration, and heat
    unit accumulation help to round out the complete agricultural data
    package.\1\
    ---------------------------------------------------------------------------
        \1\ T. Griffin, et al., ``Big Data Considerations for Rural
    Property Professionals.'' Journal of the American Society of Farm
    Managers and Rural Appraisers, 2016:167, 168.
    ---------------------------------------------------------------------------
        Beyond these data sources, numerous other data sources continue to
    emerge in the agricultural data space. Work continues to build data
    collection technology in the livestock industries, ranging from GPS-
    enabled cattle ear tags to ``bolus'' sensors that can be swallowed by
    animals to provide health data. Some would argue that vendor-generated
    data about producers might also fit into this category; such data could
    include everything from payment history data to customer relationship
    management (CRM) information (does the producer try to negotiate input
    prices, have preferences for some products over others, typically buy
    inputs from one salesperson versus others, etc.).
        Agricultural data is the foundation for Small Data systems. In
    simplest terms, farms use ``Small Data'' when data are isolated to the
    fields where the data originated. Farmers who use information
    technology to conduct their own on-farm experiments, document yield
    penalties from poor drainage, or negotiate crop share agreements are
    using data that is considered ``small.''
        Perhaps ironically, the evolution and revolution in agricultural
    Big Data comes from the expansion of ``Small Data'' in agriculture.\2\
    There has been remarkable growth in producers' ability to collect data
    pertaining only to their own operation through the growth of techniques
    and technologies such as grid soil sampling, telematics systems for
    farm equipment, Global Positioning Systems (GPS)/Global Navigation
    Satellite Systems (GNSS), farm aerial imagery acquired via small
    unmanned aerial systems (sUAS) and the like. Producer adoption of these
    information technologies has increased dramatically in recent years,\3\
    giving rise to a profusion of agricultural data heretofore unseen.\4\
    ---------------------------------------------------------------------------
        \2\ K. Coble, T. Griffin, A. Misrha, and S. Ferrell, ``Big Data in
    Agriculture: A Challenge for the Future,'' forthcoming in Applied
    Economics and Policy Perspectives (accepted for publication October 20,
    2017).
        \3\ T. Griffin, Miller, N.J., Bergtold, J., Shanoyan, A., Sharda,
    A., and Ciampitti, I.A. 2017. Farm's Sequence of Adoption of
    Information-Intensive Precision Agricultural Technology. Applied
    Engineering in Agriculture 33(4):521-527 doi: 10.13031/aea.12228.
        \4\ B. Erickson, and D. Widmar. 2015. Precision Agricultural
    Services Dealership Survey Results. West Lafayette, Indiana, Purdue
    University, August. Accessed June 21, 2016: http://
    agribusiness.purdue.edu/files/resources/2015-crop-life-purdue-
    precision-dealer-survey.pdf
    ---------------------------------------------------------------------------
        The new abundance of field-level information provided by these
    technologies could improve the ability of producers to make profit-
    maximizing decisions benefitting the producer operating the field, i.e.
    Small Data.\5\ However, pooling the datasets of hundreds or thousands
    of fields could hold a much greater potential value both to individual
    producers and the agricultural industry as a whole. Agricultural Big
    Data--farm data that has been combined into an aggregate form--has the
    potential to reveal undiscovered insights. Currently, only limited
    quantitative evidence exists regarding the value of assembling data
    from precision agriculture technology into a community; however,
    indirect evidence suggests farm data has economic value.
    ---------------------------------------------------------------------------
        \5\ Griffin, supra note 1.
    ---------------------------------------------------------------------------
        While the term Big Data is relatively new, it refers to a concept
    that is not. There are many definitions for the term, but a straight-
    forward one might be ``a collection of data from traditional and
    digital sources inside and outside your company that represents a
    source for ongoing discovery and analysis.'' \6\ While this definition
    sounds much like traditional data analysis (and it is), recent advances
    in both data collection and transmission increase the analytical power
    of data analysis procedures by orders of magnitude. The ``big'' in Big
    Data comes from the fact data sets continue to grow exponentially both
    in breadth (with more and more firms collecting data) and depth (with
    data from more and more sources long the food supply chain being
    aggregated by more firms). Conceptually, Big Data is defined as the
    analysis of datasets requiring advanced tools to manage the data due to
    four factors: volume, velocity, variety, and veracity.
    ---------------------------------------------------------------------------
        \6\ L. Arthur. 2013. What is big data? Forbes, CMO Network blog
    entry. Available at http://www.forbes.com/sites/lisaarthur/2013/08/15/
    what-is-big-data/, last accessed November 15, 2014.

                       Table 1: Big Data defining factors
    ------------------------------------------------------------------------
          Factor                             Definition
    ------------------------------------------------------------------------
    Volume              The sheer amount of data precludes its storage on a
                         single computer system; analytic software must
                         aggregate the data from multiple systems
    ------------------------------------------------------------------------
    Velocity            New data enters the analysis continuously at high
                         rates of transmission.
    ------------------------------------------------------------------------
    Variety             Data is aggregated from a variety of sources, many
                         of which may use different data formats.
    ------------------------------------------------------------------------
    Veracity            Accuracy of the data is vital to correct analysis,
                         while the data source may apply varying (or no)
                         methods of data validation. Thus the Big Data
                         system may have to independently validate the data
                         or make assumptions about its accuracy.
    ------------------------------------------------------------------------

        Agricultural data has arguably already crossed over into the realm
    of Big Data as measured by these factors.
        Existing technologies can already generate over 10 MB of data per
    acre, and when extrapolated over the 90 million acres of corn ground in
    the U.S., this means 900 terabytes (TB, 1 TB being equal to 1,000,000
    MB) of data could be generated on corn acres alone.\7\ A student at
    Ohio State University recently completed the ``Terra Byte'' project to
    determine how much data could be garnered from one corn plant, with a
    resulting 18.4 gigabytes) of data; over a 100 acre corn field, this
    would be the equivalent of 60 petabytes (PB, 1 PB being equal to
    1,000,000,000 MB).\8\ Already, the commodity dataset has grown too
    large to be transported via broadband connections, or even physically
    via external hard drives, meaning analytical software must go to the
    data \9\ Thus, the volume requirement for Big Data is satisfied.
    ---------------------------------------------------------------------------
        \7\ T. Griffin, ``Can Agricultural or Farm Data Be Considered Big
    Data?'' Kansas State University, https://www.agmanager.info/machinery/
    precision-agriculture/precision-ag-farm-data-blog
    /can%C2%A0agricultural%C2%A0or%C2%A0farm%C2%A0data%C2%A0be (last
    visited November 8, 2017).
        \8\ M. Brookhart and M. Reese. ``World Record for Data Collection
    Set by OSU Precision Ag Team.'' Ohio Country Journal, October 11, 2017,
    http://ocj.com/2017/10/world-record-for-data-collection-set-by-osu-
    precision-ag-team/#.Wd45GMwR0Qo.twitter (last visited November 8,
    2017).
        \9\ Grifin, supra note 7.
    ---------------------------------------------------------------------------
        Looking only at as-planted data collected from planters via
    telematics, 5.5 MB of data on location, speed, cultivar, and other geo-
    spatial and meta-data are collected for each acre planted. During
    planting seasons, the size of the aggregated farm data community
    becomes much larger every day. Although agricultural operations are
    seasonal, it should be recognized that even for commodity crops like
    corn, cotton, soybean, rice, and wheat that peak planting times differ
    for each such that as-planted data are collected during several months
    of the year rather than all at once. In addition to planting, other
    field operations such as tillage, spray applications, and harvest occur
    at other times during the season; each operation adding to the
    community of data. Thus, Griffin observes, planting data alone would
    satisfy the ``velocity'' component of Big Data.\10\ By the same token,
    each of these data points are being collected by different brands of
    equipment using different file formats and supplemented using manually-
    collected data such as soil samples, all of which may be reported in
    non-standard formats, satisfying the ``variety component.'' \11\
    ---------------------------------------------------------------------------
        \10\ Id.
        \11\ Id.
    ---------------------------------------------------------------------------
        That leaves the ``veracity'' component and agricultural data can
    certainly pose veracity challenges. Such challenges arise from the
    problems inherent in trying to measure biological processes by
    mechanical means. Data quality has been a contentious topic in
    precision agriculture for decades; especially regarding raw yield
    monitor data and other farm data collected by on-the-go sensors. A part
    of the debate on the veracity of yield data involves whether the farmer
    or combine operator properly calibrates the yield monitor. Therefore,
    both sensors and human error influence farm data quality. Given this,
    agricultural data appears to more than satisfy the Big Data test.\12\
    ---------------------------------------------------------------------------
        \12\ Id.
    ---------------------------------------------------------------------------
        Although not as prominent to the discussion as Big Data and
    agricultural data, another important term to define is service
    provider. Service provider (sometimes called an ``Agricultural
    Technology Provider'' or ``ATP'') is the term frequently used to
    describe a party external to the farm providing some service regarding
    either crop production or management of the crop enterprise. Crop
    production services could include fertilizer or chemical applicators,
    custom operators, or harvest contractors whose equipment generate
    agricultural data regarding the farm. Management services include
    traditional services such as crop consulting and scouting, but
    increasingly include services targeted specifically at data collection
    and analysis.
    2.2 Opportunities and Challenges arising from Small and Big Data use in

            Agriculture
        It is important to note this discussion would not occur were it not
    for the tremendous potential the nascent farm data revolution promises.
    Existing technologies such as real-time kinematics (RTK) and ``auto-
    steer'' (sometimes referred to as ``GNSS-enabled navigation technology)
    have already provided substantial economic returns to farmers.\13\
    Improved sensing of soil conditions, crop health, and yields has led to
    significantly improved management information for agricultural
    producers. As mentioned above, this represents Small Data, with data
    generated--and decisions made--at the farm level.
    ---------------------------------------------------------------------------
        \13\ See, e.g., M. Darr, ``Big Data and Big Opportunities,'' paper
    presented at PrecisionAg Big Data Conference, August 21, 2014 (Ames,
    Iowa).
    ---------------------------------------------------------------------------
        To date, much of the gains from improved sensing technologies and
    their sharing with service providers have come from eliminating
    inefficiencies in the utilization of agronomic and machinery inputs.
    Put another way, we have seen significant increases in the use of Small
    Data.
        Small Data sees a variety of farm-level uses. Data kept isolated to
    the originating farm has value, but the value of that data is limited
    to just that farm or potentially to farms in relative proximity. The
    primary uses of farm data are those for which the data were initially
    generated such as documenting within-field site-specific yields with a
    yield monitor.\14\ Typically, primary uses of data are restricted to
    the field that the data originated; consider the analogy of using a
    computer when that computer is not connected to the Internet. Primary
    data uses are ``local'' to the field or operation from which they
    originate and are not connected to data from other areas.
    ---------------------------------------------------------------------------
        \14\ Note that secondary uses of data will be discussed later in
    this testimony.
    ---------------------------------------------------------------------------
        Considerable effort has been made by farmers, researchers, and
    others from within and external to the agricultural industry to
    profitably utilize data generated from precision agricultural
    technologies. The majority of these efforts have historically focused
    on one-field-at-a-time or maybe even at the whole farm level but for
    only that one farm. The value of farm data when isolated to a specific
    farm has been limited and only of value to that particular farm (or
    some value for the next farmer of the land). At the very least, the
    value of that data decays very quickly with distance from the field.
        Indeed, it is possible that the site-specific value of farm data
    might actually play a role in farmland values themselves. Griffin and
    Taylor \15\ explored how big data could impact farmland values and
    rental rates, stating ``It remains unclear whether the `data premium'
    [for farmland conveyed with a significant farm-specific dataset] will
    be a true premium (an amount added to the market price of land) or a
    penalty (an amount deducted from the market price of land). In the
    short-run, early movers who choose to provide data to land buyers may
    see a premium. However, as the transfer of data with a land sale
    becomes more common, a penalty to land parcels without data may become
    more common.'' They also describe how biophysical data, such as
    historical yield, soil test results, and other production data have
    been included in farmland sales and/or rental agreements, but they
    suggest these data have not substantially influenced farmland values
    nor are sufficient to be considered ``big.'' These historical data
    could be annual whole-field yield written on paper or site-specific
    geospatial data including GPS yield monitor data or grid soil samples
    in either electronic form or printed maps. Although the above mentioned
    data may provide evidence of historical productivity and soil amendment
    utilization, they do not impact farmland values directly. Farmland
    values and rental rates will likely be a function of both quantity and
    quality of geospatial metadata once the big data sector of the
    agriculture industry matures.
    ---------------------------------------------------------------------------
        \15\ T. Griffin., and Taylor, M.R. (2015). Precision Agriculture
    Data Impact on Farmland Values: Big Data in Ag. K-State Department of
    Agricultural Economics AgManagerInfo AM-TWG-PRAG-4.2015 Online: http://
    www.agmanager.info/crops/prodecon/precision/PrecisionAgData
    _FarmlandValues.pdf., last visited November 8, 2017.
    ---------------------------------------------------------------------------
        Farmers have made use of precision agriculture technology and farm
    data in a variety of ways, and oftentimes in ways that the
    manufacturers had not anticipated. An early report on how farmers used
    yield monitors indicated the primary uses of yield data include but not
    limited to: 1) conduct on-farm experiments, 2) tile drainage decisions,
    and 3) split crop share rents.\16\ To estimate the value of farm data
    for each of these examples, the alternative decision making process
    must be evaluated. However, back of the napkin extreme examples make
    the point that the value of the above scenarios are finite and limited
    to a single farm.
    ---------------------------------------------------------------------------
        \16\ T. Griffin, ``Farmers' Use of Yield Monitors,'' University of
    Arkansas Fact Sheet FSA36, available at https://www.uaex.edu/
    publications/pdf/FSA-36.pdf (last visited November 8, 2017).
    ---------------------------------------------------------------------------
        Perhaps the most dramatic gains lie ahead, though, as agriculture
    puts the ``Big'' in Big Data by compiling datasets of sufficient size
    to enable much more robust statistical analyses of multiple factors
    influencing commodity production. Examples of how the aggregation of
    farm data across large datasets can significantly increase value to
    farmers are illustrated in Table 2 below.\17\
    ---------------------------------------------------------------------------
        \17\ Table and scenarios taken from Terry Griffin, ``Big Data
    Considerations for Agricultural Attorneys,'' paper presented at
    American Agricultural Law Association Annual Symposium, October 23,
    2015 (Charleston, South Carolina).

                           Table 2: Comparison of Primary and Secondary Agricultural Data Uses
    ----------------------------------------------------------------------------------------------------------------
              Data                Primary Use ``Small Data''                  Secondary Use ``Big Data''
    ----------------------------------------------------------------------------------------------------------------
    Yield monitor data        Documenting yields; on-farm seed    Genetic, environmental, management effect (G x E x
                               trials                              M) analyses
    ----------------------------------------------------------------------------------------------------------------
    Soil sample data          Fertilizer decisions                Regional environmental compliance
    ----------------------------------------------------------------------------------------------------------------
    Scouting                  Spray decisions                     Regional analytics of pest patterns
    ----------------------------------------------------------------------------------------------------------------

        As an example of initial or primary use of farm data, yield monitor
    data on one farm can help document the farm's productivity on a field-
    by-field basis and can illustrate how a seed hybrid performed on that
    farm in one year, given the environment of that farm for that year and
    the management practices employed during that year. Interesting
    opportunities arise when that data is ``re-used'' in Big Data
    aggregation with similar data across hundreds or even thousands of
    farms, and this aggregation creates the bridge linking Small and Big
    Data.
        Such aggregation allows for the evaluation of that cultivar across
    tens of thousands of permutations of factors such as management
    practices, soil type, and climate. This enables both seed companies and
    agricultural producers to learn via observational data in one or two
    years what would take decades of collections by use of traditional seed
    trials via experimentation. Soil sample data coupled with yield data
    can inform an agricultural producer about the nutrient uptake of the
    crop on his or her farm, but Big Data could allow all the agricultural
    producers in a region to effectively tackle nutrient loading to
    impaired water bodies through voluntary management of non-point
    pollution. Crop scouting can help an individual agricultural producer
    make decisions about the application of a specific pesticide, but Big
    Data could allow a crop industry to spot trends in plant pathogens that
    could be used to head off the spread of potentially devastating plant
    health threats. The true maturity of Big Data in agriculture may come
    when the value of secondary uses realize greater aggregate economic
    value than the primary uses of the data.\18\
    ---------------------------------------------------------------------------
        \18\ V. Mayer-Schonberger, and K. Cukier, Big Data: A Revolution
    That Will Transform How We Live, Work, and Think, Kindle Edition.
    Houghton Mifflin Harcourt Publishing Company, New York, NY. 257 pp.
    2014.
    ---------------------------------------------------------------------------
        The integration of Small Data and Big Data at the farm level could
    hold important implications for farms competitiveness.\19\ Early
    adopters of big data in other industries (such as healthcare,
    transportation, and retail) are shown to have gained a competitive
    advantage within their industries and have realized significant
    increases in operating margins.\20\ There is an emerging discussion in
    the agribusiness industry and its literature about the potential of big
    data and its capacity to change the basis of competition in
    agriculture.\21\ This belief is based on the previous trends in the
    history of innovations powering productivity and enhancing
    competitiveness in the agri-food supply chain, enabled by information
    and communication technology (ICT). Among such examples is precision
    agriculture powered by GPS, remote sensing, and variable rate
    technology (VRT) technologies in crop farming. While the adopters of
    ICT-based applications in agricultural production were primarily
    motivated by the efficiency gains, they also have laid the foundation
    for the big data infrastructure within agriculture. As a result, modern
    farms are generating, or have a capacity to generate, a substantial
    amount of agricultural production data. This data becomes an important
    intangible resource alongside the physical and human resources, which
    if managed effectively, can produce substantial value for the farming
    operation. The important question to ask is under which circumstances
    the data, as an intangible resource, can become a source of competitive
    advantage?
    ---------------------------------------------------------------------------
        \19\ This discussion of agricultural data and competitive issues is
    taken from Griffin, et al., supra note 1.
        \20\ J. Manyika, Chui, M., Brown, B., Bughin, J., Dobbs, R.,
    Roxburgh, C., & Byers, A. H. (2011). ``Big data: The next frontier for
    innovation, competition, and productivity.'' McKinsey Global Group
    report, available at https://www.mckinsey.com/business-functions/
    digital-mckinsey/our-insights/big-data-the-next-frontier-for-
    innovation, last visited November 8, 2017).
        \21\ S. Sonka. (2014). Big Data and the Ag Sector: More than Lots
    of Numbers. International Food and Agribusiness Management Review,
    17(1), 1-20. Available online at http://www.ifama.org/files/IFAMR/
    Vol%2017/Issue%201/(1)%2020130114.pdf, last visited November 8, 2017.
    ---------------------------------------------------------------------------
        Beyond the benefits of Big Data to production agriculture, it also
    presents the agricultural economics community with numerous
    opportunities to enhance and expand the analysis of numerous
    microeconomic, macroeconomic, and agricultural policy issues.\22\ For
    example, microeconomic farm management issues could now be analyzed by
    aggregating data across thousands of farms using management decisions
    as variables instead of using a farm-by-farm case study approach. Food
    program evaluations, regulatory impact analysis, and demand estimation
    could be accomplished by rapid aggregation and analysis of grocery
    store UPC scanner data. Geospatial analysis of crop yields could lead
    to improved precision in the pricing of crop insurance products. Broad
    environmental sensor networks coupled with farm data could
    significantly enhance the ability to manage crop fertilizer
    applications to minimize nutrient runoff impacts.
    ---------------------------------------------------------------------------
        \22\ The following examples are taken from K. Coble, T. Griffin, A.
    Misrha, and S. Ferrell, ``Big Data in Agriculture: A Challenge for the
    Future,'' forthcoming in Applied Economics and Policy Perspectives
    (accepted for publication October 20, 2017).
    ---------------------------------------------------------------------------
        To understand the potential policy implications of Big Data's
    growth in agriculture, one must recall that one of the defining
    characteristics of agricultural Big Data is combining data from
    multiple farms into a community. A leading reason for this is that each
    farmer becomes a variable (rather than a constant) once a critical mass
    of farms is in the community. When farm data were isolated to a single
    farm, then there was no opportunity to evaluate the management
    practices specific to that farmer, i.e. the management was held
    constant.
        Farm data must be aggregated to perform community analysis. A
    leading example of community analysis is evaluating how a product (G
    for ``genetics,'' from classic varietal or hybrid tests) in a given
    location (E for ``environment,'' including soils, weather, and other
    uncontrolled factors) under the farm's production practices (M for
    ``management,'' including controlled factors such as planting dates,
    seeding rates, timing of operations, tillage practices and many
    others). When farm data are not aggregated across numerous farms, then
    the data remain `small' and the value is limited since the M in
    analysis known as GxExM, is not a viable variable (only the traditional
    GxE). When data are aggregated such that M is a variable to the
    analysis GxExM, insights can be discovered for a majority of
    participants. Examples of previously unknown discoveries may include
    which products or bundle of products (seed, fungicides, planting dates)
    maximize profitability for a given region under specific farm
    production practices.
        Each player (and each group of players) benefit differently with
    respect to the big data system. One must consider how these different
    players benefit to comprehend how the value of Big Data systems may be
    captured relative to the data contributors (farmers) and aggregators
    (ATPs). The economics of networks are important to fully understand the
    value gained from the big data community. The value of the data
    community depends not only on the quality of the data but on how many
    others participate in the system. Data from numerous farms aggregated
    into a community are more valuable than data from any one individual
    farm. In the long run, the aggregator controlling the flow of data
    enjoys the majority of the value. Other groups, such as those offering
    analytic services of the aggregated data, enjoy their value capture
    especially in the short run. Once a critical mass of farms are in the
    data community, i.e. the long run, farmers' bargaining power with the
    data aggregator likely will be greatly reduced.
        In the long-run the majority of the value will be enjoyed by the
    one controlling the data community, i.e. the data service provider.
    Other players such as input manufacturers, retailers, and advisors may
    enjoy their own levels of varying value capture. The important part to
    be cognizant is that 1) the farmer is not the only player at the big
    data table and 2) the farmer is not likely to receive the vast majority
    of the value from participating in the big data system. However, that
    is not to say that farmers will not still see potentially important
    benefits from the analyses provided by Big Data systems. Such systems
    pose the opportunity of providing potentially unprecedented insights to
    inform farm management decisions, decreasing production risk, and
    potentially reducing financial and market risks as well.
        While there are countless potentially positive uses of Big Data
    tools, any tool can also be misused. Farmers, ranchers, and other
    participants in the agricultural industry have expressed concerns about
    several potential misuses of agricultural data beyond the mere
    disclosure of confidential information (discussed below). Some
    producers worry that the ability of equipment manufacturers to access a
    significant amount of data about their operations, giving the
    manufacturers the ability to interpolate the farmer's financial
    condition and use such information to an unfair advantage in
    transactions with the farmer or to alter the balance of negotiating
    power in the manufacturer's favor for any number of transactions.
    Others worry about government agencies taking advantage of aggregated
    datasets to acquire information that the producer could not be
    compelled to produce without a formal legal process. Yet another
    concern is that falsified data could be introduced into individual or
    aggregated agricultural datasets to skew environmental assessments of
    farm performance.
        One additional Big Data challenge worries both producers and
    economists. As stated in Coble, et. al: \23\
    ---------------------------------------------------------------------------
        \23\ Supra, note 4.

            The Holy Grail for market participants is to get perfect
            information as soon as it is knowable, and preferably before it
            is knowable to others. While Big Data has a long, long way to
            go before achieving this, bigger steps toward that goal are
            being taken faster than ever before. Thus, a significant
            concern with aggregating agricultural data is whether--either
            legitimately or not--a small number of market participants (or
            a single actor) could get access to information sufficient to
            move (or even manipulate) markets faster than, or to the
            exclusion of, other market participants. While there are
            numerous rules in place to deal with a broad range of market-
            manipulating activities, none of these current rules
            contemplate the type of actions that could take place with a
            sufficiently large aggregated dataset. Currently, there are
            various rules restricting insider trading (see 17 C.F.R.
            Sec. 1.59(a), 17 C.F.R. Sec. 1.3(ee)), and government employees
            are prohibited from using data for financial gain that has not
            been disseminated to the public (7 U.S.C. Sec. 6c(a)(3)).
            However, there are no rules governing ``very good market
            information'' such as that which could be obtained through
            completely legal means by aggregating sufficient telematics
            data (as an example). As a result, research on the potential
            market effects of growing market asymmetries that could be
            triggered by growing Big Data aggregations and the implications
            of policies restricting the use of aggregated data in commodity
            market transactions could do much to inform the development of
    ---------------------------------------------------------------------------
            law in the arena.

        Only time and experience will tell whether these concerns are well-
    founded, but the fact they exist may well impact producers' willingness
    to participate in Big Data systems, and thus impact the future of the
    industry. Most industry observers believe the benefits to individual
    producers and the agricultural industry as a whole far outweigh the
    potential risks. However, bringing about the full economic benefits of
    Big Data in agriculture requires a robust system by which large numbers
    of agricultural producers can share their data since the predictive
    power of statistical analysis increases with the number of observations
    available for each variable examined.\24\ The agricultural data
    industry is working tirelessly to create those systems. The issue is
    one of trust--farmers must feel they can trust Big Data systems before
    they will participate. Thus, the issue of most concern to this hearing
    may not be whether we will have systems that can accept and analyze
    that data; it is perhaps how Congress can facilitate the development of
    an environment in which farmers will share their data. Metcalfe's Law
    states that the value of a network is proportionate to the number of
    its members. Put another way, Facebook has little value if you are its
    only member, but it has tremendous value when populated by millions of
    members. Thus, agricultural producers can only harness the value of Big
    Data if we can foster an environment in which they are comfortable
    sharing their data. However, that participation might be inevitable
    given the increasing prevalence of data-collection technologies. As
    Griffin and Shanoyan observe, going ``off the grid'' with respect to
    agricultural data may be possible in the near term, but eventually will
    require farmers to use then-antiquated technology, placing them at
    further competitive disadvantage.\25\
    ---------------------------------------------------------------------------
        \24\ See generally George G. Judge, et al, Introduction to the
    Theory and Practice of Econometrics (2nd ed, 1988), 96.
        \25\ T. Griffin and A. Shanoyan, ``Is Going Off the Grid Possible
    in the Age of Farm Data?'' Kansas State University, https://
    www.agmanager.info/machinery/precision-agriculture/precision-ag-farm-
    data-blog/going-grid-possible-age-farm-data (last visited November 7,
    2017).
    ---------------------------------------------------------------------------
        Given this potential inevitability of data sharing, one must turn
    to questions of what rights farmers can retain in their shared data. Do
    they retain ownership of their information? Is there any hope of
    retaining their privacy in that information once it is shared?
    2.3 Framing the legal issues surrounding data in agriculture
        The issues involved in the discussion of data in agriculture are
    almost innumerable, but many can be brought under the umbrella of two
    over-arching concepts: agricultural data ownership, and protections
    against its unauthorized disclosure. Although each of these issues is
    discussed in greater detail later in this testimony, a brief framing of
    each issue is provided here.
    2.3.1. Ownership of agricultural data

        As agricultural producers began to realize the information they
    were generating (and, in some cases, sharing with service providers)
    had potential economic value, questions began to arise regarding who
    had the superior ``ownership'' right to that information, given that
    multiple parties had a hand in its creation. Further, the realization
    of agricultural data's value changed the relative negotiation power
    between parties. This is an important concept; if their data is shared
    by someone other than them with a third party, that sharing may cause
    the farmer to lose negotiation power with vendors, landlords, and the
    like as a result. Thus, farmers may wish to assert ``ownership'' of
    data so as to exercise one of the rights of property ownership, namely,
    to exclude others from its use. Thus, this issue might be framed as
    ``Who owns data generated about an agricultural producer's operation?''
    2.3.2. Privacy rights for agricultural data

        As discussed in more detail below, it is possible--and even likely
    -the greatest economic value of agricultural data to the farm owner
    comes not from his or her own analysis of the data but from its
    aggregation with data from hundreds or even thousands of other farms
    (in a true Big Data model) to provide management information and trend
    identification that could not be derived from any smaller dataset. For
    example, one of the most common analyses provided by ATPs to farmers
    are ``comparative analytics'' (for example, benchmarking performance
    relative to similarly-situated operations). While that might have some
    economic value for the producer, much greater benefits await via
    advanced analysis. The balance of negotiating power between the farmer
    and the aggregator will eventually determine what proportion of the
    analyses conducted benefit each party. While aggregation may in some
    ways reduce the disclosure or discovery of information about any one
    farm (through the anonymization of individual farm data by aggregation
    with many other farms), it naturally also raises fears about the
    release of that information (whether the result of intentional activity
    such as database hacking or an accidental disclosure). This leads to
    the second question: ``What protections prevent the disclosure of
    agricultural data to outside parties?''
    3. Current Legal Framework for Ownership of Agricultural Data
        The United States has one of the most robust systems of property
    rights in the world, empowered by a legal system making it easy
    (relatively speaking) to enforce those rights. Thus, the first place
    many look for a means of protecting one's data from misappropriation
    and/or misuse is the property right system. This requires one to
    examine who ``owns'' agricultural data. The answer to the question is
    not simple, though, as traditional notions of property ownership find
    challenge in their application to pure information.
        The notion of property ownership typically involves some form of
    six interests, including the right to possess (occupy or hold), use
    (interact with, alter, or manipulate), enjoy (in this context, profit
    from), exclude others from, transfer, and consume or destroy. Some of
    these interests do not fit, or at least do not fit well, with data
    ownership. Excluding others from data, for example, is difficult,
    particularly when it is possible for many people to ``possess'' the
    property without diminishing its value to the others, just as the value
    of a book to one person may not be diminished by the fact other people
    own the same book.\26\ Thus, the better question may be ``What are the
    rights and responsibilities of the parties in a data disclosure
    relationship with respect to that data?'' \27\
    ---------------------------------------------------------------------------
        \26\ L. Smith. 2006. ``RFID and other embedded technologies: who
    owns the data?'' Santa Clara Computer and High Technology Law Journal
        \27\ R. Peterson. 2013. ``Can data governance address the conundrum
    of who owns data?'' Educause blog, http://www.educause.edu/blogs/
    rodney/can-data-governance-address-conundrum-who-owns-data, last
    accessed November 8, 2017.
    ---------------------------------------------------------------------------
        Data is difficult to define as a form of property, but it most
    closely resembles intellectual property. As a result, the intellectual
    property framework serves as a useful starting point to define what
    rights a farmer might have to their agricultural data. Intellectual
    property can be divided into four categories: (1) trademark, (2)
    patent, (3) copyright, and (4) trade secret. The first three areas
    compose the realm of Federal intellectual property law as they are
    defined by the Constitution as areas in which Congress has legislative
    authority.\28\ Since trademark is not relevant to a discussion about
    data,\29\ the analysis will focus on patent, copyright, and trade
    secret.
    ---------------------------------------------------------------------------
        \28\ U.S. Constitution, Article I, Sec. 8, clause 8.
        \29\ The Federal Trademark Act (sometimes called the Lanham Act)
    defines trademark as ``any word, name, symbol, or device, or any
    combination thereof . . . to identify and distinguish his or her goods,
    including a unique product, from those manufactured or sold by others
    and to indicate the source of the goods, even if that source is
    unknown.'' 15 U.S.C. Sec. 1127.
    ---------------------------------------------------------------------------
    3.1 Application of patent law to agricultural data
        The U.S. Patent Act states ``whoever invents or discovers any new
    and useful process, machine, manufacture, or composition of matter, or
    any new and useful improvement thereof, may obtain a patent therefor''
    (35 U.S.C. Sec. 101). Generally, for an invention to be patentable, it
    must be useful (capable of performing its intended purpose), novel
    (different from existing knowledge in the field), and non-obvious
    (somewhat difficult to define, but as set forth in the Patent Act, ``a
    patent may not be obtained. . .if the differences between the subject
    matter sought to be patented and the prior art are such that the
    subject matter as a whole would have been obvious at the time the
    invention was made to a person having ordinary skill in the art to
    which said subject matter pertains'').\30\ Patent serves as a poor fit
    for a model of agricultural data ownership since it protects
    ``inventions.'' Raw data, such as agricultural data, would not satisfy
    the definition of invention.
    ---------------------------------------------------------------------------
        \30\ 35 U.S.C. Sec. Sec. 102, 103.
    ---------------------------------------------------------------------------
        It should be noted patentable inventions could be derived from the
    analysis of agricultural data. While this does not mean the data itself
    is patentable, it does suggest that any agreement governing the
    disclosure of agricultural data by the agricultural producer should
    address who holds the rights to inventions so derived.
    3.2 Application of copyright law to agricultural data
        The Federal Copyright Act states the following:
        Copyright protection subsists, in accordance with this title, in
    original works of authorship fixed in any tangible medium of
    expression, now known or later developed, from which they can be
    perceived, reproduced, or otherwise communicated, either directly or
    with the aid of a machine or device. Works of authorship include the
    following categories:

            literary works;

            musical works, including any accompanying words;

            dramatic works, including any accompanying music;

            pantomimes and choreographic works;

            pictorial, graphic, and sculptural works;

            motion pictures and other audiovisual works;

            sound recordings; and

            architectural works.\31\
    ---------------------------------------------------------------------------
        \31\ 17 U.S.C. Sec. 102(a).

        More so than trademark and patent, the copyright model at least
    resembles a model applicable to agricultural data. At the same time,
    however, the model also has numerous problems in addressing
    agricultural data. First, the list of ``works of authorship'' provided
    in the statute strongly suggests a creative component is important to
    the copyrightable material. Second, the term ``original works of
    authorship'' also has been interpreted to require some element of
    creative input by the author of the copyrighted material. This
    requirement was highlighted in the case of Fiest Publications Inc. v.
    Rural Telephone Service Company,\32\ where the U.S. Supreme Court held
    the Copyright Act does not protect individual facts. In Fiest, the
    question was whether a pure telephone directory (consisting solely of a
    list of telephone numbers, organized alphabetically by the holder's
    last name) was copyrightable. Since the directory consisted solely of
    pure data and was organized in the only practical way to organize such
    data, the Supreme Court held the work did not satisfy the creative
    requirements of the Copyright Act.\33\ This ruling affirmed the
    principle that raw facts and data, in and of themselves, are not
    copyrightable. Put another way, the fact that ABC Plumbing's telephone
    number is 555-1234 is not copyrightable. However, an author can add
    creative components to facts and data such as illustrations,
    commentary, or alternative organization systems and can copyright the
    creative components even if they cannot copyright the underlying facts
    and data. Continuing the analogy, ABC's phone number alone is not
    copyrightable, but a Yellow Pages ad with ABC Plumbing's number
    accompanied by a logo and a description of the company's services would
    be copyrightable.
    ---------------------------------------------------------------------------
        \32\ 499 U.S. 340 (1991).
        \33\ See id.
    ---------------------------------------------------------------------------
        Agricultural data in and of itself may not be copyrightable, but it
    can lead to copyrightable works. For example, agricultural data may not
    be copyrightable, but a report summarizing the data and adding
    recommendations for action might be. Again, then, it is incumbent upon
    those disclosing agricultural data to include language in their
    agreements with the receiving party to define the rights to such works
    derived from the data.
        A separate issue regarding copyrights deriving from agricultural
    data also continues to emerge. Increasingly, the original agricultural
    data is never even disclosed to the agricultural producer; rather, the
    data has been processed into a report or a new form through use of a
    computer algorithm. Quite simply, agricultural producers may often
    receive a completely computer-generated report with no human author.
    This requires moving into the realm of copyrights in computer generated
    works--an area that is far from settled.\34\ The evolution of
    understanding who holds the rights to computer-generated works with
    regard to agricultural data played out recently in the discussions
    surrounding comments by Deere & Company on proposed exemptions to the
    Digital Millennium Copyright Act \35\ regarding copyright protection
    systems in vehicle software.\36\
    ---------------------------------------------------------------------------
        \34\ See generally M. Leaffer, Understanding Copyright Law, 109-110
    (5th ed. 2011).
        \35\ 17 U.S.C. Sec. Sec. 512, 1201-1205, 1301-1332; 28 U.S.C.
    Sec. 4001
        \36\ See Deere & Company, ``Long Comment Regarding a Proposed
    Exemption Under 17 U.S.C. 1201'' (2015). Available at http://
    copyright.gov/1201/2015/comments-032715/class%2022/
    John_Deere_Class22_1201_2014.pdf (last visited November 8, 2017).
    Compare K. Weins, Wired (Business Blog Section, online edition)
    (editorial) ``We Can't Let John Deere Destroy the Very Idea of
    Ownership,'' April 21, 2015. http://www.wired.com/2015/04/dmca-
    ownership-john-deere/ (last visited November 8, 2017).
    ---------------------------------------------------------------------------
    3.3 Application of trade secret law to agricultural data
        While trademark, patent, and copyright do not appear to fit as
    models for farm data ownership, trade secret has the potential to serve
    the agriculture industry's concerns regarding rights in data shared
    with Big Data service providers. Importantly, trade secret is a
    function of state law (unlike trademark, patent, and copyright, which
    are all creatures of Federal law). At the time of this testimony, all
    but three states have adopted the Uniform Trade Secrets Act, providing
    a degree of consistency in trade secret law across most states.
        Under the Uniform Trade Secrets Act (``UTSA''), a ``trade secret''
    is defined as:

             information, including a formula, pattern, compilation,
            program, device, method, technique, or process, that:

            (i) derives independent economic value, actual or potential,
            from not being generally known to, and not being readily
            ascertainable by proper means by, other persons who can obtain
            economic value from its disclosure or use, and

            (ii) is the subject of efforts that are reasonable under the
            circumstances to maintain its secrecy.

        Importantly, this definition makes clear ``information . . .
    pattern[s], [and] compilation[s]'' can be protected as trade secret.
    This, at last, affords hope of a protective model for farm data. This
    is not to say that trade secret is a perfect model for protecting farm
    data, however. Note the two additional requirements of trade secret:
    first, the information has actual or potential economic value from not
    being known to other parties, and second, it is the subject of
    reasonable efforts to maintain the secret.
        The first provision requires that to be protected as a trade
    secret, farm data such as planting rates, harvest yields, or outlines
    of fields and machinery paths must have economic value because such
    information is not generally known. While a farmer may (or may not)
    have a privacy interest in this information, the question remains as to
    whether the economic value of that information derives, at least in
    part, from being a secret. The counterargument to that point is the
    economic value of the information comes from the farmer's analysis of
    that information and the application of that analysis to his or her own
    operation--a value completely independent of what anyone else does with
    the information--and that the information for that farm, standing
    alone, has no economic value to anyone else since that information is
    useless to anyone not farming that particular farm.\37\ One can see
    this first element poses problems for the trade secret model. It should
    be noted here there is a clear economic benefit to the collection of
    farm data; otherwise companies would not be investing billions of
    dollars to position themselves in the agricultural data industry.\38\
    This represents a question yet to be answered clearly by the body of
    trade secret law: whether one can have trade secret protection in
    information that standing alone has no economic value to other parties,
    but does have such value when aggregated with similar data from other
    parties.
    ---------------------------------------------------------------------------
        \37\ An agricultural producer could, hypothetically, use such data
    to bid rented agricultural land away from another tenant if they could
    somehow demonstrate they could provide the landowner with evidence they
    could increase the landowner's returns. However, this seems a tenuous
    argument for the economic value element of the UTSA test and has no
    application at all in a scenario with owned agricultural land.
        \38\ See B. Upbin, Forbes (Tech business blog), ``Monsanto Buys
    Climate Corp for $930 Million,'' October 2, 2013. http://
    www.forbes.com/sites/bruceupbin/2013/10/02/monsanto-buys-climate-corp-
    for-930-million/.
    ---------------------------------------------------------------------------
        The second provision--the data be subject to reasonable efforts to
    maintain its secrecy--also finds problems in an environment where the
    data is continuously uploaded to another party without the intervention
    of the disclosing party. The fact that data are disclosed to another
    party does not mean it cannot be protected as a trade secret; if that
    were the case, there would be little need for much of trade secret law.
    Rather, the question is how and to whom the information is disclosed.
    As noted in the Restatement (Third) of Unfair Competition's comments on
    the Uniform Trade Secret Act, ``. . . the owner is not required to go
    to extraordinary lengths to maintain secrecy; all that is needed is
    that he or she takes reasonable steps to ensure that the information
    does not become generally known.'' \39\
    ---------------------------------------------------------------------------
        \39\ Smith, supra note 4, citing Restatement of Unfair Competition
    (Third) Sec. 757 (1995).
    ---------------------------------------------------------------------------
        The question becomes what constitutes ``reasonable steps'' to keep
    continuously uploaded data protected, or data that is voluntarily
    shared with a Big Data ATP. Almost certainly this means there must be
    some form of agreement in place between the disclosing party and the
    receiving party regarding how the receiving party must treat the
    received information, including to whom (if anyone) the receiving party
    may disclose that information. Such agreements are discussed in greater
    detail below. However, there is some question as to whether any
    agreement could protect the trade secret claim for data that was
    disclosed to an ATP. When one discusses farm data privacy, one often
    consider the concept of remaining anonymous. However, in the Big Data
    world anonymity is no longer achievable, at least in the same manner as
    it once was. Mayer-Schonberger and Cukier describe how even sanitized
    data can reveal the identity of individuals by combining additional
    layers of (probably publicly available) data. Given the prevalence of
    public geospatial data, data from USDA, and plat maps, it is possible
    in many circumstances to use those data layers with a sanitized
    community of farm data to reveal all the data that were intended to
    remain anonymous. As a result, one could argue sharing data with an
    aggregator essentially renders it ineligible as a trade secret
    (regardless of a non-disclosure agreement with the aggregator) since
    the receiver cannot make a reasonable guarantee that the data can be
    kept secret.\40\ This concept has implications not only for the
    potential application of trade secret principles to agricultural data,
    but to broader privacy policy concerns as well.
    ---------------------------------------------------------------------------
        \40\ Griffin and Shanoyan, supra note 20.
    ---------------------------------------------------------------------------
        Assuming for the moment that trade secret protection can be
    obtained for agricultural data, one should consider the use of a ``non-
    disclosure agreement'' when sharing data with an ATP. While an explicit
    written ``non-disclosure agreement'' (or ``NDA'') is not necessary to
    claim trade secret protection, such an agreement is almost certainly a
    good idea if an agricultural producer wishes to retain a protectable
    ownership interest in their data if such an interest exists. Not only
    can such an agreement clarify a number of issues unique to the
    relationship between the disclosing and receiving parties, but also can
    address numerous novel issues in the current information environment
    that trade secret law have not yet reached.
        The concept of NDAs as separate agreements may be practicable for
    one-on-one relationships, such as those between agricultural producers
    and smaller consulting firms, negotiating separate agreements with
    multiple entities poses significant transaction costs. This problem is
    particularly magnified when one considers larger corporate service
    providers who would face the issue of negotiating tens of thousands of
    NDAs. Unsurprisingly, such entities choose to create standard
    agreements in their form contracts. While certainly understandable,
    this in turn creates the ``opt-out problem'' wherein a farmer who
    believes the form contract does not adequately protect his or her
    interests is forced to either agree to the form or do without the
    product or service--which may be the only product or service compatible
    with a significant portion of the very expensive equipment he or she
    already owns or uses. This then provokes the discussion of whether such
    contracts are enforceable or are, instead, adhesion contracts. There is
    yet to be found consistency among Federal courts as to the
    enforceability of such software use agreements.\41\
    ---------------------------------------------------------------------------
        \41\ The asymmetry of EULA's has led to allegations they represent
    ``adhesion contracts'' and should not be enforceable as a matter of
    policy. However, some courts have found insufficient evidence of
    adhesion and held such agreements enforceable. Compare cases finding
    EULAs enforceable: Ariz. Cartridge Remanufacturers Ass'n v. Lexmark
    Int'l, Inc., 421 F.3d 981 (9th Cir., 2005); ProCD, Inc. v. Zeidenberg,
    86 F.3d 1447 (7th Cir. 1996); Microsoft v. Harmony Computers, 846 F.
    Supp. 208 (E.D.N.Y. 1994); Novell v. Network Trade Center, 25 F. Supp.
    2d. 1218 (D. Utah, 1997) with cases finding EULAs unenforceable: Step-
    Saver Data Systems Inc. v. Wyse Technology, 939 F.2d 91 (3rd Cir.
    1991); Vault Corp. v. Quaid Software Ltd. 847 F.2d 255 (5th Cir. 1988);
    Klocek v. Gateway, Inc., 104 F. Supp. 2d 1332 (D. Kan. 2000).
    ---------------------------------------------------------------------------
        To conclude the trade secret analysis, colorable arguments exist
    both for and against the proposition farm data poses an ``ownable'' and
    protectable trade secret. That said, this option provides the best
    doctrinal fit among the traditional intellectual property forms, and
    farmers wishing to preserve whatever rights they do indeed have in that
    data seem best advised to use the trade secret model to inform the
    their protective measures. Even so, use of trade secret doctrine as a
    protective measure for agricultural data has drawbacks in the lack of
    consistency among states in trade secret law (although the UTSA has
    done much to add consistency to the field) and the fact it is often a
    ``backward looking'' and costly solution since trade secret must
    frequently be used to seek damages (which are often difficult to both
    prove and quantify) through litigation after a disclosure has already
    been made.
    4. Current Legal Framework for Privacy Rights in Agricultural Data
        Those concerned about the disclosure of personal data can certainly
    cite a number of damaging data breach examples. Recent history suggests
    many of the real threats in data transfers come from insufficient
    controls to prevent the disclosure of personally identifiable
    information (``PII'') to outside parties and inadequate agreements on
    the uses of data by parties to whom it is disclosed.
        To the extent producers regard agricultural data as proprietary,
    their concerns about its disclosure naturally invite a review of the
    release or theft of proprietary information in other sectors. One need
    not look far into the past to find numerous examples of the disclosure
    of PII, whether merely inadvertent or the result of targeted hacks.
    Attacks on companies' payment systems have resulted in the credit card
    information of hundreds of millions of customers from Adobe Systems
    (150 million customers), Heartland Payment Systems (130 million
    customers), TJX (parent company of TJ Maxx and Marshalls, 94 million
    customers), TRW Information Systems (credit reporting company, 90
    million customers), Sony (70 million customers) each of which dwarf
    breaches attracting more media attention such as Home Depot (56 million
    customers) and Target (40 million customers).\42\ Perhaps the most
    troubling data breach in recent history, though, was the 2017 Equifax
    data breach, which exposed a large array of personal and financial data
    for over 143 million.\43\ The Equifax breach is especially troubling
    for many consumers, as Equifax was entrusted with the most sensitive
    personal information consumers could provide, and was supposed to serve
    as a secure repository for that information. It is reasonable to
    surmise that particular breach was a significant setback for the trust
    of agricultural producers in systems that could collect their financial
    data.
    ---------------------------------------------------------------------------
        \42\ J. Pepitone, ``5 of the Biggest-ever Credit Card Hacks,''
    (2013) CNN Money, available at http://money.cnn.com/gallery/technology/
    security/2013/12/19/biggest-credit-card-hacks/ (last visited November
    8, 2017).
        \43\ Federal Trade Commission, ``The Equifax Data Breach: What to
    Do.'' https://www.consumer.ftc.gov/blog/2017/09/equifax-data-breach-
    what-do (last visited November 8, 2017).
    ---------------------------------------------------------------------------
        To some extent, there may be a very limited reasonable
    ``expectation of privacy'' in agricultural data since a significant
    segment of such data is available from public sources or sources
    obtainable from public vantage points (such as aerial or satellite
    imagery). Nevertheless, there remains an also-significant segment of
    data for which an argument could be made that a privacy interest
    exists. The challenge may be figuring out who has the best ability to
    protect that data from disclosure.
        The greatest risk of data breaches for agricultural producers may
    be attacks against aggregators, since attacks against individual farm
    systems pose very high barriers relative to the amount of data such an
    attack could obtain. Theoretically, a hacker could tap into the
    tractor/implement network (also called the tractor/implement bus) using
    a number of commercially-available technologies allow farmers to plug
    into the network and access Controller Area Network (``CAN'') messages
    directly; for example, one could purchase a CAN message reader (``CAN
    sniffer'') to read machine diagnostic codes for repairs.\44\ Someone
    wishing to ``steal'' data would likely not want to be present to
    retrieve the data from the device, though, and would likely prefer to
    use a CAN data logger coupled with a device to wirelessly transmit the
    data. Many data loggers are available to the public as well; for
    example, the ``Snapshot'' device used by Progressive Insurance for
    some insurance programs is simply a CAN data logger plugged into a
    vehicle's On-Board Diagnostic (OBD-II) port.\45\ Alternatively, of
    course, if one wanted to steal large amounts of agricultural data at
    once, one could attempt to hack a cellular network provider used by an
    equipment manufacturer to carry their data signals. Further, it should
    be noted the equipment manufacturer likely has no ability to specify or
    enforce the security protocols used to safeguard such cellular
    transmissions.
    ---------------------------------------------------------------------------
        \44\ Interview with Dr. John Fulton, Ohio State University
    Department of Food, Agricultural, and Biological Engineering, July 6,
    2015.
        \45\ See Progressive Corporation, ``Snapshot Terms and
    Conditions,'' https://www.pro
    gressive.com/auto/snapshot-terms-conditions/ (last visited November 8,
    2017).
    ---------------------------------------------------------------------------
        While such an approach would work for standard messages transmitted
    over the bus, it would not work for proprietary messages. To decode
    such messages, the prospective hacker would have to develop a system
    for decoding the information being provided from the task controller
    for the implement, and that task would take almost as much work (if not
    more) than the work in developing the task controller system in the
    first place.\46\ Note, that several companies now provide means for
    reverse-engineering proprietary CAN messages (such as those related to
    crop yield) so farmers can automatically transfer yield data to the
    cloud. Such technology could also be used to decode other proprietary
    information.\47\ Perhaps ironically, the growth of proprietary data
    network protocols that lead to complaints about the lack of
    interoperability of farm equipment systems could also provide greater
    protection against data breaches.
    ---------------------------------------------------------------------------
        \46\ See interview with Dr. Marvin Stone (June 10, 2015).
        \47\ Interview with Dr. John Fulton, Ohio State University
    Department of Food, Agricultural, and Biological Engineering, July 6,
    2015.
    ---------------------------------------------------------------------------
        Additionally, the Global Positioning System ``GPS'' receiver in
    most systems connects directly to the implement's task controller. As a
    result, a ``bug'' might receive information about the commands sent to
    the implement but without the associated location data, rendering it
    meaningless. The bug would require its own GPS receiver along with
    implement data (the configuration and dimensions of the implement),
    which today could be done for a modest equipment cost.\48\ Obtaining
    agronomic data via a physical connection to an implement poses a task
    manageable for someone knowledgeable in SAE J1939 and ISO 11783 \49\
    technology.\50\ However, building and deploying such a device poses a
    significant amount of effort (to say nothing of the potentially-
    criminal trespass involved in deploying it) in relation to the prospect
    of collecting data on only one farm.
    ---------------------------------------------------------------------------
        \48\ A relatively quick search of Google will yield many GPS
    receiver units for less than $50.
        \49\ SAE International, ``The SAE J1939 Communications Network: An
    Overview of the J 1939 Family of Standards and How they are Used,'' 5
    (white paper), available at http://www.sae.org/misc/pdfs/J1939.pdf
    (last visited November 8, 2017). See also International Organization
    for Standardization, ISO Draft International Standard ISO/DIS 11783:
    Tractors and machinery for agriculture and forestry--serial control and
    communications data network (2012). The ISO 11783 standard is often
    referred to as the ``ISOBUS standard'' and defines how the on-board
    computer networks on most agricultural equipment works and how their
    individual components work together. Combined, SAE J1939 and ISO 11783
    govern much of how the data-collection network on any agricultural
    equipment works.
        \50\ M. Miettien, ``Implementation of ISO 11783 Compatible Task
    Controller,'' XVI CIGR (International Commission of Agricultural and
    Biosystems Engineering) World Congress, Bonn, Germany (2006), available
    at http://users.aalto.fi/ttoksane/pub/2006_CIGR20062.pdf (last visited
    November 8, 2017).
    ---------------------------------------------------------------------------
        As illustrated from this discussion, a number of factors in the
    configuration and operation of farm data networks limit the
    opportunities for hackers to take agricultural data directly from the
    agricultural producer. Admittedly, most producers put little thought
    into their systems being physically hacked but worry instead about
    their data being accessed through an intercepted cellular signal. They
    might also worry about a bad actor hacking the system to implant false
    data. First, virtually all cellular signals are encrypted when
    transmitted and decrypted at the cellular tower;\51\ without the
    decryption key, interpreting any data transmitted would be difficult
    (although not impossible for a sophisticated hacker; recent news has
    highlighted the ability of some groups to do so \52\). The use of data
    encryption through a secure sockets layer (``SSL'') protocol by the
    farmer and his or her service provider in data transfers adds another
    difficult-to-break security barrier to interception of the data.\53\
    ---------------------------------------------------------------------------
        \51\ For a primer on the process of encoding and decoding cellular
    signals, see How Stuff Works, ``How Cell Phones Work,'' http://
    electronics.howstuffworks.com/cell-phone.htm (last visited November 8,
    2017).
        \52\ See C. Timberg & A. Soltani, By Cracking Cellphone Code, NSA
    Has Ability to Decode Private Conversations, The Washington Post,
    December 13, 2013. Online edition, available at http://
    www.washingtonpost.com/business/technology/by-cracking-cellphone-code-
    nsa-has-capacity-for-decoding-private-conversations/2013/12/13/
    e119b598-612f-11e3-bf45-61f69f54fc5f_story.html (last visited November
    8, 2017).
        \53\ See C. Heinrich, Secure Socket Layer (SSL), in Encylopedia of
    Cryptography and Security 1135 (Henck C.A. van Tilborg, Sushil Jajodia,
    eds., 2011)
    ---------------------------------------------------------------------------
        Most agricultural data disclosed to a service provider is likely in
    the form of telematics data, raw data regarding crop production, GIS
    information about the farm, and other similar types. Conversely,
    hackers frequently go after large concentrations of data with easily-
    converted financial value, such as credit card information. Thus, it
    may be difficult for hackers to make a ``quick buck'' from agricultural
    data making it a less-appealing target of attack. Nevertheless, an
    adage in computer security is ``where there is value, there will be a
    hacker.'' \54\ As a result, systems storing agricultural data are less
    likely to be directly attacked, but farmers are understandably
    concerned that PII may be stolen if, for example, their vendor account
    information is somehow linked to their agricultural data or if their
    account information is stored with a third party that is a more
    appealing target. Depending on the type of computer at issue and its
    common use, the Federal Computer Fraud and Abuse Act (``CFAA'') \55\
    may provide a means of prosecuting unauthorized access of the computer
    in the event agricultural data linked to PII is compromised. Discussed
    below, the Federal Electronic Communications Privacy Act (ECPA) \56\
    could also be used as a potential prosecutorial tool for those
    attempting to intercept agricultural data during the data transmission
    process.
    ---------------------------------------------------------------------------
        \54\ S. Sammataro, ``Cybersecurity for Small or Regional Law
    Firms,'' paper presented at American Agricultural Law Association
    Annual Symposium, Charleston, South Carolina (October 23, 2015).
        \55\ 18 U.S.C. Sec. Sec. 1030 et seq.
        \56\ 18 U.S.C. Sec. Sec. 2510 et seq.
    ---------------------------------------------------------------------------
        The theft of PII by criminals is one threat posed by data
    transfers, but so too is the inadvertent, or perhaps intentional but
    misinformed, disclosure of data by the party receiving that data. Take,
    for example, the disclosure of thousands of farmers' and ranchers'
    names, home addresses, GPS coordinates and personal contact
    information'' by EPA in response to a Freedom of Information Act (FOIA)
    request regarding concentrated animal feeding operations (CAFOs) which
    prompted a lawsuit from the American Farm Bureau Federation and
    National Pork Producers Council alleging the agency overstepped its
    authority in doing so.\57\ While this event represents the disclosure
    of information by an enforcement agency, many farmers fear the
    converse--that an enforcement agency could compel a data-receiving
    party to disclose information even if such disclosure were not legally
    required. Another concern is whether an adverse party in litigation (or
    even a party contemplating litigation) could persuade a party holding a
    farmer's data to disclose the data as an aid to their case, again even
    if such disclosure was not legally required.
    ---------------------------------------------------------------------------
        \57\ S. Wyant, ``Farm Groups File Lawsuit to Stop EPA Release of
    Farmers' Personal Data.'' Agri-Pulse (2013), available at http://
    www.agri-pulse.com/Farm-groups-file-lawsuit-to-stop-EPA-release-of-
    farmers-personal-data-07082013.asp (last visited November 8, 2017).
    ---------------------------------------------------------------------------
        Much work remains to be done on defining governmental safeguards
    against disclosures, and even more work remains to be done in defining
    how the government can obtain electronic data. Although laws such as
    the ECPA (heavily modified by the USA Patriot Act) govern the
    acquisition of information through intercepted communications, there is
    little law to prevent a government agency from simply requesting data
    from a service provider. Anecdotal evidence suggests service providers
    and their legal counsel continue to struggle in defining parameters for
    how to respond to non-subpoenaed requests for data by government
    agencies.
        All these issues surround restrictions on the taking of information
    by some unauthorized (or at least questionable) means. While there are
    at least some laws potentially applicable in these circumstances, there
    are no laws defining an inherent privacy right in agricultural
    data.\58\ For example, the Federal Health Insurance Portability and
    Accountability Act (``HIPAA'') \59\ provides privacy rights and
    restrictions against disclosure of health information; the Gramm-Leach
    Bliley Act (also known as the Financial Modernization Act of 1999) \60\
    and Fair Credit Reporting Act \61\ protect financial information from
    disclosure; the Privacy Act of 1974 \62\ restricts disclosures of
    personal information by held by the Federal government. As of now,
    though, there are large categories of agricultural data that may fall
    between the cracks of these laws with no Federal (and in most cases, no
    state) protections against its disclosure.
    ---------------------------------------------------------------------------
        \58\ T. Janzen, ``Legal Issues Surrounding Farm Data Ownership,
    Transfer, and Control,'' paper presented at American Agricultural Law
    Association Annual Symposium, Charleston, South Carolina (October 23,
    2015).
        \59\ 42 U.S.C. Sec. 300gg, 29 U.S.C. Sec. Sec. 1181 et seq. and 42
    U.S.C. Sec. Sec. 1320d et seq.
        \60\ 15 U.S.C Sec. 6803.
        \61\ 15 U.S.C. Sec. Sec. 1681 et seq.
        \62\ 5 U.S.C. Sec. 552a.
    ---------------------------------------------------------------------------
    5. Potential Policy Responses to Address Agricultural Data Issues
        Having reviewed the current legal environment surrounding the
    ownership rights and privacy protections relevant to agricultural data,
    what can this Committee and Congress do to enable U.S. farmers and
    ranchers to take maximum economic advantage of Big Data tools? As
    referenced above, Big Data cannot be Big Data without ``buy-in'' to the
    system from large numbers of agricultural producers. In these beginning
    years of agricultural data systems, there are many ATPs vying for
    farmers and their acreages to enroll in their systems. As the system
    matures, this relationship will likely shift, and there will be few (or
    perhaps only one) ATP and the vast majority of farms may be
    participating. Nevertheless, for the maturation process to begin,
    agricultural producers must ``buy in'' to the system. At a fundamental
    level, that buy-in requires trust in the system from those producers.
    That trust, in turn, likely requires answers to the questions of
    ownership and privacy in agricultural data.
        None of the Federal intellectual property laws directly address who
    holds a protectable intellectual property right in agricultural data.
    Arguably, the most appropriate fit may be found in state law under the
    UTSA, although the applicability of that law is questionable as well.
    The UTSA may provide a useful map to any Congressional efforts to help
    define ownership rights in agricultural data. Passage of statutory law
    defining ownership of ``agricultural data'' may be a daunting task
    given the complexity of the current Federal and state intellectual
    property framework (which also draws from centuries of common law).
    Thus, it may be advisable instead to use a consensus-driven approach
    among agricultural producers and service providers to define
    agricultural data rights. The coalition led by the American Farm Bureau
    Federation and its ``Privacy and Security Principles for Farm Data''
    \63\ represents a tremendous step forward on this issue. Other groups,
    such as the Open Ag Data Alliance, continue to build coalitions on the
    technical side of the Big Data issue to develop systems and standards
    embodying the principles of interoperability, security and privacy.\64\
    The next step is to see continued cooperation among groups such as
    these in integrating their principles in legally-binding service
    agreements.
    ---------------------------------------------------------------------------
        \63\ American Farm Bureau Federation, ``Privacy and Security
    Principles for Farm Data,'' November 13, 2014 (revised April 1, 2016).
    Available at https://www.fb.org/issues/technology/data-privacy/privacy-
    and-security-principles-for-farm-data (last visited November 8, 2017).
        \64\ Open Ag Data Alliance, ``Principals and Use Cases,'' http://
    openag.io/about-us/principals-use-cases/ (last visited November 8,
    2017).
    ---------------------------------------------------------------------------
        Another collaborative effort to help agricultural producers
    evaluate the data policies and protections of data service providers
    has been the Ag Data Transparency Evaluator, coordinated by the
    American Farm Bureau Federation, which requires service providers to
    undergo a ten-factor review (based in part on the Privacy and Security
    Principles, with the review self-reported by the service provider) with
    a satisfactory review resulting in the ``Ag Data Transparent''
    seal.\65\ Congressional support of this and other efforts to equip
    farmers and ranchers in evaluating the data tools available can help
    foster trust, encourage Big Data participation, and drive many of the
    potential advantages Big Data services have to offer.
    ---------------------------------------------------------------------------
        \65\ See www.agdatatransparent.com (last visited November 8, 2017).
    ---------------------------------------------------------------------------
        Modern agricultural producers are expected to be proficient in a
    broad array of the disciplines of science and business, but few have a
    background in intellectual property law. Support of educational
    programs to help these producers understand the legal issues at play in
    Big Data service agreements could do much to help increase trust,
    advance the consensus process, and empower producers to make informed
    decisions about the cost-benefit analysis of sharing their data under
    those service agreements. The consensus process may also provide a
    vehicle for developing an understanding among all stakeholders as to
    the privacy protections necessary and appropriate to protect
    agricultural data, which occupies a unique space between purely
    personal and business information. Such information does not readily
    fit into the existing framework of Federal privacy laws, and as
    business information, may not belong in such a framework.
        One matter in which Congressional action may be directly applied is
    the development of clearer guidelines regarding the production of
    agricultural data held by private data aggregators, more robust
    safeguards against inadvertent disclosure or intentional hacking by
    outside parties, and clear guidance on when disclosure of government-
    held data is, and is not, required under the Freedom of Information Act
    \66\ or other circumstances.
    ---------------------------------------------------------------------------
        \66\ 5 U.S.C. Sec. 552.
    ---------------------------------------------------------------------------
        Finally, although outside the direct scope of a discussion of legal
    issues in agricultural use of agricultural data tools, rural access to
    wireless broadband services is crucial to fully utilizing the potential
    of agricultural data systems. Before the rapid adoption and usage of
    agricultural data technologies will occur, the lack of this enabling
    technology must be addressed. The expansion of connectivity across the
    U.S. has been a priority, but access has grown slowly. This is
    especially true in the major crop producing regions. The majority of
    data transfer occurs over cellular systems, but there are worldwide
    initiatives to provide wireless connectivity via satellite, balloons,
    and other platforms. Regardless of platform, the agricultural industry
    relies upon wireless connectivity to support big data systems.
        Telematics allows data to be wirelessly uploaded and downloaded
    between farm machinery and online servers. However, limited
    connectivity is a barrier to adoption leading to potential economic
    losses.\67\ Whitacre et al. addressed the current connectedness of
    agricultural production areas.\68\ It was these areas that were
    impacted by the United States Federal Communications Commission (FCC)
    updated definition of connectivity that could be considered broadband
    in January 2015. The definition changed from 4 Megabits per second
    (Mbps) download and 1 Mbps upload to 25 Mbps download and 3 Mbps
    upload. Although broadband speeds did not instantly change, the level
    of connectivity that service providers could advertise as `broadband'
    changed. The faster speeds required to be considered broadband brought
    light to connectivity barriers, especially with respect to connectivity
    gaps in rural areas where agricultural production occurs. Specifically,
    the 25 Mbps download speed requirement negates the majority of United
    States wireless connections from being classified as broadband.
    ---------------------------------------------------------------------------
        \67\ Griffin, T.W., and Mark, T.B. (2014). ``Value of Connectivity
    in Rural Areas: Case of Precision Agriculture Data.'' International
    Conference on Precision Agriculture. July 20-23, 2014. Sacramento, CA.
        \68\ Whitacre, B.E., Mark, T.B., and Griffin, T.W. (2014). How
    Connected are Our Farms? Choices. Online: http://
    www.choicesmagazine.org/choices-magazine/submitted-articles/how-
    connected-are-our-farms.
    ---------------------------------------------------------------------------
        However, the vast majority of data being passed between farm
    equipment and online servers is uploaded rather than downloaded; and
    upload speeds are typically only a fraction of download speeds. For
    some types of data such as machine diagnostics and prescriptions,
    current speeds may be adequate. However, yield data and specifically
    imagery data may require connectivity speeds in excess of what is
    currently available. In summary, a concerted national policy effort
    must be made to expand broadband access in rural areas for a number of
    important rural development purposes, not the least of which is to
    facilitate the potential economic advantages to be gained by
    integration of agricultural data technologies on farms and ranches.
    Concluding Remarks
        The application of Big Data to agricultural production holds the
    potential to improve the profitability of U.S. agriculture and to
    better prepare its farmers and ranchers to handle the inherent risks of
    the industry. Additionally, Big Data could play a vital role in the
    further development of tools and techniques necessary to feed an ever-
    growing, hungry world. I commend this Subcommittee for its foresight in
    addressing these issues, and sincerely thank the Subcommittee, Chairman
    Moran, and Ranking Member Blumenthal for the opportunity to address you
    today.

        Senator Moran. Thank you very much, Doctor.
        Mr. Janzen, welcome.

                STATEMENT OF TODD J. JANZEN, PRESIDENT,
                      JANZEN AGRICULTURAL LAW LLC

        Mr. Janzen. Thank you, Mr. Chairman, Ranking Member
    Blumenthal, and members of the Subcommittee. My name is Todd
    Janzen. I'm an attorney, a private practice attorney, in
    Indianapolis, Indiana. And the firm that I work at, Janzen
    Agricultural Law LLC, specializes in helping farmers, ag
    technology providers, and also agribusinesses.
        But I speak here today not only as an attorney, but also as
    somebody who grew up on a farm in south-central Kansas, a grain
    and livestock farm. And so I'm particularly attuned to the
    issues that farmers are facing on a legal front.
        Let me start, though, by talking about the types of data
    that are being collected by the various ag data platforms that
    are out there because I think that's helpful in understanding
    the framework here.
        So there are various streams of data I like to say that
    come off of fields and farms, and this can include land data,
    agronomic data, weather data, management data, machine data,
    and also livestock data, which would be information about
    genetics or feed consumption by animals.
        But what's really changed in the last 5 years is not just
    that farmers are generating this information, but now farmers
    are taking this information and storing it in cloud-based
    servers that are not located on the farm. And so this transfer
    of information off the farm I think is a pretty monumental
    transition in history for U.S. agriculture.
        But with this transfer of information, there have also been
    some groups that have started to raise concerns, you know,
    about farmers losing out on the benefit of knowing all this and
    having all this information on their farm.
        A poll by American Farm Bureau in 2016 identified a number
    of issues that farmers faced, but I classify them into three
    big categories. One is a lack of trust with a lot of these ag
    technology providers that are on the market today. Second would
    be a loss of control to these companies. And then the third
    would be something that some other panelists have already
    mentioned, which is just the overall complexity of the
    agreements that farmers are being asked to sign. I heard a
    farmer yesterday describe this as the aggressive fine print.
        So to address these problems, American Farm Bureau,
    National Farmers Union, and the commodity groups from a number
    of national organizations came together and said, ``Let's
    create a set of core principles that we can all agree on.'' So
    in 2014, they introduced a document called the ``Privacy and
    Security Principles for Farm Data,'' which really outlined some
    core principles that they wanted to see adopted by the
    industry.
        And 37 different companies and organizations signed onto
    these core principles by pledging that they would incorporate
    them into their contracts, but here we are today, and we don't
    yet have all 37 of those who have really met the challenge that
    they agreed to take on years ago.
        So as a follow up to this effort, American Farm Bureau,
    Farmers Union, and others came together and said, ``Let's
    create some sort of certification process where we can
    recognize companies that are adhering to these core principles
    for agricultural data.'' And so out of that came this Ag Data
    Transparency Evaluator process, which I've been fortunate
    enough to be a part of. And I want to just briefly describe how
    that works so that it's clear to the Subcommittee.
        Companies that want to be certified as ``Ag Data
    Transparent'' can submit their contracts and answer a 10-
    question form about how they use farmers' ag data. That is then
    reviewed by an independent third-party administrator, and
    currently Janzen Agricultural Law is that administrator. So we
    take that information and we review it, and we ultimately
    determine if companies are being transparent with how they are
    using agricultural data or if they're not being transparent, in
    which case, we send them back and ask them to do it over.
        But if they are transparent, then they are awarded the ``Ag
    Data Transparent'' seal of approval. And you may see this on
    some companies' marketing materials. And I'll mention
    Farmobile, one of the very first companies to go through this
    process and has been awarded the ``Ag Data Transparent'' seal
    of approval.
        So we review these questions, such as, ``What data is being
    collected?'' Does the company obtain consent before the data is
    transferred in or out of that platform? And then also, ``Can a
    farmer delete their data if they're finished using that
    platform?'' And then we post the results of this question-and-
    answer to the AgData
    Transparent.com website so that farmers can review this and
    make informed decisions before they go down the road of sending
    their data to one of these companies.
        So here we are today. We've had nine companies go through
    this evaluation process and be certified ``Ag Data
    Transparent.'' There is still a lot of work to do because there
    are a lot of companies that have said they want to do this
    process or maybe they've committed to it, but yet they haven't
    followed through and become certified.
        So, Mr. Chairman and members of the Subcommittee, I'm
    honored to be here today, and I look forward to your questions.
    I think this is a very important issue for farmers, and I hope
    that we can address them.
        Thank you.
        [The prepared statement of Mr. Janzen follows:]

               Prepared Statement of Todd J. Janzen, President,
                          Janzen Agricultural Law LLC
        Good afternoon Chairman Moran, Ranking Member Blumenthal, and
    members of the Subcommittee. My name is Todd J. Janzen, I am the
    president and attorney with Janzen Agricultural Law, LLC, a law firm
    based in Indianapolis, Indiana that serves the needs of America's
    farmers, ag technology providers, and agribusinesses.
        One of the reasons we founded Janzen Ag Law in 2015 was that we
    wanted to be at the forefront of the changes that have been occurring
    on the farm for the past few years. Farms are becoming more digital
    every day, and together with that digitalization is a movement of
    agricultural data stored on computers in the farm office to cloud-based
    data storage devices. Agricultural data (ag data) can be many things,
    including yield data, soil data, planting information, weather data,
    financial data, etc. This marks the first time in history that the
    majority of the information that farmers generate and use on their
    farms has been moved into the hands of companies outside the farm.
        As a result, we are seeing a digital land-rush occurring across the
    United States. The past few years have seen millions of dollars pour
    into ag data startups from Silicon Valley, to Kansas City, to North
    Carolina. Historic legacy agricultural companies, such as John Deere,
    are also at the forefront of this movement by expanding their product
    offerings to include cloud-based data storage platforms. All of these
    companies are scrambling to get the most acres of data into their
    platforms so that when consolidation of ag technology providers (ATPs)
    begins, they are in the strongest position.
        In the race to the cloud, we must also be cautious so that the
    American farmer is not left behind. Today I will address the issues
    facing farmers as digitalization occurs and how the industry has begun
    to address these issues.
    Issues Facing Farmers as Ag Data Moves into the Cloud
        American Farm Bureau Federation (Farm Bureau) conducted a poll of
    over 400 farmers in 2016 to understand their issues concerning ag data
    privacy, security, and control. The poll highlighted what are
    essentially three issues that continue to come up when asking farmers
    about ag data concerns:
    1. Lack of Trust
        Seventy-seven percent (77 percent) of farmers expressed concern
    about which entities can access their farm data after the data is
    uploaded to cloud-based servers. The same percentage expressed concern
    about whether uploading the data could cause it to be used for
    regulatory purposes.
        Sixty-seven percent (67 percent) of farmers said they consider how
    outside parties will use their ag data when deciding whether to entrust
    their data with a certain ATP.
        A farmer's lack of trust can come from many sources, but I
    speculate it originates in two places. Many ag data companies are new.
    Ag data startups lack the goodwill that older agricultural companies
    have spent years building. They have new sales associates who are
    strangers to the farm, or in some instances, strangers to agriculture.
    They are viewed as outsiders.
        Older, long-established agricultural companies do not suffer from a
    general lack of trust with the farmer, since they have spent years
    building that relationship. But when a seed company, equipment
    manufacturer, or ag retailer begins offering an ag data platform to
    store the farmer's ag data, farmers often are skeptical about whether
    the storage provider is trying to help the farmer raise a better crop
    or using the ag data to sell the farmer more or higher-priced goods and
    services. This skepticism may erode a farmer's trust.
    2. Concern with Losing Control
        Farmers are also concerned that uploading their ag data to cloud-
    based platforms means they will lose control over downstream uses.
    Sixty-six percent (66 percent) of respondents in the Farm Bureau poll
    believe farmers should share in the potential financial benefits from
    the use of their data beyond the direct value they may realize on their
    farm.
        Farmers raised concerns that ATPs could use their ag data to gain
    an unfair advantage in the marketplace. Sixty-one percent (61 percent)
    of farmers expressed worry that ATPs could use their data to influence
    market decisions.
        These concerns arise from a fundamental legal truth about ag data--
    there are no laws that specifically protect farmers' privacy and
    security concerns. Ag data is not typically ``personally identifiable
    information,'' such that it would be protected by state laws which
    prevent misuse of personal information like name, address, and phone
    number. Nor does ag data fit into a class of data that Congress has
    chosen to protect legally, such as medical information (HIPAA).
    Finally, ag data does not neatly fit into existing legal protections
    for intellectual property, such as patents, trademarks, or copyrights.
    Ag data ultimately may be deemed a trade secret under existing state
    and Federal trade secret laws, but that will depend upon whether courts
    interpret existing statutes to include information such as agronomic
    data.
        These uncertainties mean that the contracts between farmers and ag
    tech providers are very important. These contracts will determine
    farmers' rights in the ag data their farms create.
    3. Frustration with Complexity of Current Legal Agreements
        Fifty-nine (59 percent) percent of farmers were confused about
    whether current legal agreements allowed ATPs to use their ag data to
    market other services, equipment, or inputs back to them. Zippy Duvall,
    president of Farm Bureau, said: ``This indicates a higher level of
    clarity and transparency is needed to secure grower confidence. One of
    the topics I hear most about from farmers on the data issue is having a
    clear understanding about the details of `Terms and Conditions' and
    `Privacy Policy' documents we all sign when buying new electronics. You
    should not have to hire an attorney before you are comfortable signing
    a contract with an ag technology provider.''
        Our experience as a law firm working in this area confirms that
    this is a real problem for farmers and ATPs. There is no standard
    agreement that governs ag data transfer, use, and control by ATPs.
    Instead, technology companies have adapted other forms of legal
    agreements to try to address the issues associated with moving ag data
    into cloud-based platforms, but with limited success. A farmer seeking
    to compare two similar products today might find that they are governed
    by two very different sets of contracts.
        This only adds to a farmer's confusion. If we want to make
    technology easy to embrace and use--and we do--then we need to simplify
    the contracts farmers sign when implementing new ag data technology on
    the farm. Contracts that no one reads and understands set the stage for
    problems down the road.
    How the Ag Industry is Addressing Farmers' Concerns
    1. The Privacy and Security Principles for Farm Data
        Farm Bureau, National Farmer's Union, and national commodity
    organizations for corn, soybeans, wheat, and sorghum, led an effort in
    2014 to establish fundamental principles for companies working in the
    ag data space. These organizations held a series of meetings where
    roundtable discussions occurred among industry stakeholders, such as
    John Deere, CNH Industrial, AGCO, Monsanto, DuPont Pioneer, Beck's
    Hybrids, Dow Agrosciences, Farmobile, and other ag technology
    providers. The culmination of these efforts was the drafting of the
    ``Privacy and Security Principles for Farm Data,'' also known today as
    ag data's ``Core Principles.''
        The Core Principles address thirteen key elements related to ag
    data. These include:

       Education

       Ownership

       Collection, Access and Control

       Notice

       Transparency and Consistency

       Choice

       Portability

       Terms and Definitions

       Disclosure, Use, and Sale Limitation

       Data Retention and Availability

       Contract Termination

       Unlawful or Anti-Competitive Activities

       Liability & Security Safeguards

        After releasing the Core Principles in 2014, Farm Bureau asked
    companies to voluntarily ``sign on'' to the document. As of October
    2017, the following organizations and companies have agreed to
    implement the Core Principles into their contracts with farmers.

    AGCO
    Ag Connections, Inc.
    Agrible, Inc.*
    AgSense
    AgWorks
    Ag Leader Technology
    American Farm Bureau Fed.
    American Soybean Assoc.
    Beck's Hybrids*
    CNH Industrial
    Conservis*
    Crop IMS
    CropMetrics
    Dow AgroSciences LLC
    DuPont Pioneer
    Farm Dog
    Farmobile LLC*
    Granular*
    Grower Information Services Cooperative
    GROWMARK, Inc.*
    Independent Data Management LLC*
    ohn Deere
    Mapshots, Inc.
    National Assoc. of Wheat Growers
    National Barley Growers Assoc.
    National Corn Growers Assoc.
    National Cotton Council
    National Farmers Union
    National Potato Council
    National Sorghum Producers
    North American Equipment Dealers Assoc.
    OnFarm
    Raven Industries
    Reinke Manufacturing Co., Inc.
    Syngenta
    The Climate Corporation--a division of Monsanto
    USA Rice Federation
    Valley Irrigation
    ZedX Inc.

    *Company certified to be Ag Data Transparent. For more information,
    visit www.agdatatransparent.com
        A copy of the Core Principles is attached as Exhibit A.

    2. The Ag Data Transparent Effort
        Having the Core Principles in place was a great starting point for
    the ag data industry to address farmers' concerns with ag data privacy,
    use, and control. However, the Core Principles are only guidelines, and
    only valuable if companies incorporate the Core Principles into their
    contracts with farmers. Therefore, following the release of the Core
    Principles, several farm groups and industry stakeholders worked
    together to create an independent verification tool that could help
    farmers determine if ag tech providers are abiding by the Core
    Principles. This tool is called the Ag Data Transparency Evaluator. It
    is a simple three-step process:

       Participating companies must answer 10 questions about how
            they store, use, and transfer ag data.

       The 10 question answer form is reviewed by an independent
            third party for transparency and completeness.

       If the evaluation is acceptable, the company is awarded the
            ``Ag Data Transparent'' seal of approval for use on its future
            marketing materials.

        Participation is voluntary, but all companies that signed onto the
    Core Principles have been asked to participate in the Ag Data
    Transparent effort as well.
        a. The 10 Question Evaluation. Here is a list of the 10 questions
    that each participant is asked to answer as part of the evaluation:

        Answers to all questions except for question 1 are ``yes'' or
    ``no,'' but companies are also given space to explain their answer.
        b. Reviewing the 10 Question Evaluation.
        After an ag tech company completes the 10 question evaluation form,
    the company submits its answers to an independent third party evaluator
    to determine compliance. Janzen Agricultural Law LLC is the law firm
    that has been selected to conduct the evaluations. After reviewing a
    company's answers, we typically go back to that company with
    suggestions for improving its contracts and policies to bring into
    compliance with Core Principles. Companies then make those revisions to
    their contracts and policies and resubmit their 10 question form. Once
    a company's answers align with the Core Principles, we send an official
    letter designating the company as ``Ag Data Transparent'' and
    authorizing use of the seal of approval.
        The final, approved 10 question answer forms are posted on the Ag
    Data Transparent website at www.AgDataTransparent.com Farmers can
    research and review companies' answers online. The website requires no
    log in and is free to use. An example of the home page is attached as
    Exhibit B.
        c. The Ag Data Transparent Seal of Approval
        Companies that undergo evaluation and are approved as ``Ag Data
    Transparent'' may then use the seal of approval on their websites and
    marketing materials. To date, nine companies have completed the
    evaluation and been approved as ``Ag Data Transparent.'' These nine
    companies are:

       AgDNA

       AgIntegrated, Inc.

       Agrible, Inc.

       Beck's Hybrids

       Conservis Corporation

       Farmobile

       Granular

       GROWMARK

       Independent Data Management LLC

        The participants are diverse, from a Silicon Valley ag tech
    startup, to a Midwestern seed company, to one of the Nation's largest
    farm cooperatives and ag retailers. These companies may use the Ag Data
    Transparent seal on their websites, denoting their compliance with the
    Core Principles. Farmers who see the seal of approval will know the
    company went through the time and effort to certify its contract.

        The Ag Data Transparent process addresses farmers' three main
    concerns with ag data. First, the process instills trust. No company
    submits its contracts to a voluntary evaluation unless the company is
    willing to revise its contracts, as necessary, to bring them into
    compliance with the Core Principles. Second, loss of control is
    addressed by requiring tech providers to obtain farmer consent before
    transferring data to third parties. Finally, farmers' complexity
    frustration is addressed by condensing all of a tech provider's
    contracts into a 10 question form that answers the questions farmers
    want to know. The Ag Data Transparent process makes contracts better.
        d. Who is behind the Ag Data Transparent effort?
        The Ag Data Transparent effort is governed by a non-profit
    corporation, the Ag Data Transparency Evaluator Inc. The corporate
    bylaws create two classes of directors: (1) farm organizations that are
    made up of farmer-member organizations; and (2) diverse ag technology
    providers, referred to as ``industry partners.'' The farm organizations
    are American Farm Bureau Federation, American Soybean Association,
    National Corn Growers Association, National Farmers Union, National
    Sorghum Producers, National Association of Wheat Growers and National
    Potato Council. The industry partner board members are ag technology
    providers ranging from large corporations, medium-sized companies, and
    ag tech startup organizations.

        Janzen Agricultural Law LLC, which serves as the administrator of
    the program and conducts the evaluation reviews, is not a board member.
    3. The Ag Data Use Policy
        Our law firm also drafts terms of service, license agreements,
    privacy policies, and other contracts for ag technology providers. This
    work has confirmed many concerns facing farmers today when it comes to
    ag data. We see how companies struggle to communicate clearly how they
    intend to store, use, and transfer ag data.
        For these reasons, we have encouraged companies to draft ``data use
    policies'' or ``data use agreements'' for their farmers. In a typical
    data use contract, the technology provider addresses all of the issues
    raised by the 10 questions and the Core Principles. For example, a data
    use policy will explain what information the provider collects and what
    permission is required before the provider transfers that data to
    another party.
        From our standpoint, the Ag Data Transparent effort has helped
    drive more technology providers into creating data use policies. Thus,
    the effort has paid dividends even for some companies that have not
    participated in evaluations because it has caused them to rethink how
    they are contracting with farmers.
    Conclusion
        The Ag Data Transparent effort is great step towards bringing
    transparency to ag data contracts between farmers and their technology
    providers. Wider participation would certainly help the effort, but
    that is up to the industry. Out of the dozens of ag tech providers with
    cloud-based platforms on the market today, only nine have completed the
    certification process. A few companies are in the process of
    certifying, but uptake could be better.
        Farmers should ask their technology providers why they have not
    earned that Ag Data Transparent seal. This Subcommittee should ask
    technology providers this question as well when they come before you to
    testify in future hearings.
        Thank you, Mr. Chairman and Ranking Member for your time and
    attention to this important issue. I look forward to answering any
    questions you may have for me.
                                     ______

                                   Exhibit A
                 Privacy and Security Principles for Farm Data
                          (Ag Data's Core Principles)
    November 2014
        The recent evolution of precision agriculture and farm data is
    providing farmers with tools, which can help to increase productivity
    and profitability.
        As that technology continues to evolve, the undersigned
    organizations and companies believe the following data principles
    should be adopted by each Agriculture Technology Provider (ATP).
        It is imperative that an ATP's principles, policies and practices
    be consistent with each company's contracts with farmers. The
    undersigned organizations are committed to ongoing engagement and
    dialogue regarding this rapidly developing technology.
        Education: Grower education is valuable to ensure clarity between
    all parties and stakeholders. Grower organizations and industry should
    work to develop programs, which help to create educated customers who
    understand their rights and responsibilities. ATPs should strive to
    draft contracts using simple, easy to understand language.
        Ownership: We believe farmers own information generated on their
    farming operations. However, it is the responsibility of the farmer to
    agree upon data use and sharing with the other stakeholders with an
    economic interest, such as the tenant, landowner, cooperative, owner of
    the precision agriculture system hardware, and/or ATP etc. The farmer
    contracting with the ATP is responsible for ensuring that only the data
    they own or have permission to use is included in the account with the
    ATP.
        Collection, Access and Control: An ATP's collection, access and use
    of farm data should be granted only with the affirmative and explicit
    consent of the farmer. This will be by contract agreements, whether
    signed or digital.
        Notice: Farmers must be notified that their data is being collected
    and about how the farm data will be disclosed and used. This notice
    must be provided in an easily located and readily accessible format.
        Transparency and Consistency: ATPs shall notify farmers about the
    purposes for which they collect and use farm data. They should provide
    information about how farmers can contact the ATP with any inquiries or
    complaints, the types of third parties to which they disclose the data
    and the choices the ATP offers for limiting its use and disclosure.
        An ATP's principles, policies and practices should be transparent
    and fully consistent with the terms and conditions in their legal
    contracts. An ATP will not change the customer's contract without his
    or her agreement.
        Choice: ATPs should explain the effects and abilities of a farmer's
    decision to opt in, opt out or disable the availability of services and
    features offered by the ATP. If multiple options are offered, farmers
    should be able to choose some, all, or none of the options offered.
    ATPs should provide farmers with a clear understanding of what services
    and features may or may not be enabled when they make certain choices.
        Portability: Within the context of the agreement and retention
    policy, farmers should be able to retrieve their data for storage or
    use in other systems, with the exception of the data that has been made
    anonymous or aggregated and is no longer specifically identifiable.
    Non-anonymized or non-aggregated data should be easy for farmers to
    receive their data back at their discretion.
        Terms and Definitions: Farmers should know with whom they are
    contracting if the ATP contract involves sharing with third parties,
    partners, business partners, ATP partners, or affiliates. ATPs should
    clearly explain the following definitions in a consistent manner in all
    of their respective agreements: (1) farm data; (2) third party; (3)
    partner; (4) business partner; (5) ATP partners; (6) affiliate; (7)
    data account holder; (8) original customer data. If these definitions
    are not used, ATPs should define each alternative term in the contract
    and privacy policy. ATPs should strive to use clear language for their
    terms, conditions and agreements.
        Disclosure, Use and Sale Limitation: An ATP will not sell and/or
    disclose non-aggregated farm data to a third party without first
    securing a legally binding commitment to be bound by the same terms and
    conditions as the ATP has with the farmer. Farmers must be notified if
    such a sale is going to take place and have the option to opt out or
    have their data removed prior to that sale. An ATP will not share or
    disclose original farm data with a third party in any manner that is
    inconsistent with the contract with the farmer. If the agreement with
    the third party is not the same as the agreement with the ATP, farmers
    must be presented with the third party's terms for agreement or
    rejection.
        Data Retention and Availability: Each ATP should provide for the
    removal, secure destruction and return of original farm data from the
    farmer's account upon the request of the farmer or after a pre-agreed
    period of time. The ATP should include a requirement that farmers have
    access to the data that an ATP holds during that data retention period.
    ATPs should document personally identifiable data retention and
    availability policies and disposal procedures, and specify requirements
    of data under policies and procedures.
        Contract Termination: Farmers should be allowed to discontinue a
    service or halt the collection of data at any time subject to
    appropriate ongoing obligations. Procedures for termination of services
    should be clearly defined in the contract.
        Unlawful or Anti-Competitive Activities: ATPs should not use the
    data for unlawful or anti-competitive activities, such as a prohibition
    on the use of farm data by the ATP to speculate in commodity markets.
        Liability & Security Safeguards: The ATP should clearly define
    terms of liability. Farm data should be protected with reasonable
    security safeguards against risks such as loss or unauthorized access,
    destruction, use, modification or disclosure. Polices for notification
    and response in the event of a breach should be established.
        The undersigned organizations for the Privacy and Security
    Principles of Farm Data as of April 1, 2016.
    AGCO
    Ag Connections, Inc.
    Agrible, Inc.*
    AgSense
    AgWorks
    Ag Leader Technology
    American Farm Bureau Federation
    American Soybean Association
    Beck's Hybrids*
    CNH Industrial
    Conservis*
    Crop IMS
    CropMetrics
    Dow AgroSciences LLC
    DuPont Pioneer
    Farm Dog
    Farmobile LLC*
    Granular*
    Grower Information Services Cooperative
    GROWMARK, Inc.*
    Independent Data Management LLC*
    John Deere
    Mapshots, Inc.
    National Association of Wheat Growers
    National Barley Growers Association
    National Corn Growers Association
    National Cotton Council
    National Farmers Union
    National Potato Council
    National Sorghum Producers
    North American Equipment Dealers Association
    OnFarm
    Raven Industries
    Reinke Manufacturing Co., INC.
    Syngenta
    The Climate Corporation--a division of Monsanto
    USA Rice Federation
    Valley Irrigation
    ZedX Inc.

    *Company that has also certified its policy is compliant with the Ag
    Data Transparency Evaluator. For more information, visit
    www.agdatatransparent.com
                                     ______

                                   Exhibit B
                          Ag Data Transparent Homepage

                           www.AgDataTransparent.com

        Senator Moran. Thank you very much.
        Dr. Haman.

            STATEMENT OF DR. DOROTA HAMAN, Ph.D., PROFESSOR

          AND CHAIR, AGRICULTURAL AND BIOLOGICAL ENGINEERING,

              INSTITUTE OF FOOD AND AGRICULTURAL SCIENCES,

                    UNIVERSITY OF FLORIDA (UF/IFAS)

        Dr. Haman. Chairman Moran, Ranking Member Blumenthal, and
    members of the Committee, thank you very much for this
    invitation to talk about technology in agriculture and data-
    driven farming.
        My name is Dorota Haman, and I am Professor and Chair of
    Agricultural Engineering at the University of Florida, and I
    have been working there since 1985. Agriculture is a major
    economic driver in Florida. Florida agriculture is very diverse
    and focused on specialty crops with most farms smaller and
    complex, and they are much more complex than large Midwest
    farms dedicated to crops like soybean, corn, or wheat.
        This diversity makes introduction of new technologies more
    complicated, and data collection is also more complicated, and
    analytics are more complicated. Many Agricultural Technology
    Providers are focused on agronomic crops, not on specialty
    crops, such as citrus, tomatoes, strawberries, blueberries, and
    other fruit and vegetables produced in Florida.
        Data-driven farming is the future of agriculture.
    Furthermore, it is becoming clear that the future of
    agricultural operation will embrace the concept of Internet of
    Things, a system of interrelated computing devices, machines,
    robots, sensors, actuators, and network connectivity. Many
    technologies are needed to bring agricultural operation to this
    new level, and many of them have been available for some time,
    but they are now becoming economical to introduce in
    agriculture. Farmers will need to accept these technologies to
    be competitive.
        New farm technologies and monitoring equipment are already
    producing enormous amount of data at a wide variety of spatial
    and temporal scales. Raw data are not very useful, but become
    very valuable if appropriate algorithms are developed and
    applied. Process data used by farmers are also valuable to
    others, including insurance companies and commodity markets.
        A level of data standardization will be necessary for
    optimal sharing and utilization, including a common pool
    infrastructure to facilitate transfer and integration of data
    from different sources. Common pool infrastructure would also
    facilitate collection of information for the USDA National
    Statistics Service.
        There is no doubt that standardization and openness of
    platforms would accelerate solution development and innovation.
    However, data ownership and privacy and security of information
    in relation to agricultural Big Data analytics must be
    addressed.
        It can be argued that access to high-quality farm data
    gives large agribusiness advantage over small farmers with
    limited resources. Open source analytics developed by public
    institutions, such as universities and Cooperative Extension
    Service using public funds, for example, USDA funds, NSF funds,
    would provide the solution for those farmers.
        These are exciting times for agriculture. Scientists and
    engineers have been focusing on research in the area of
    robotics, remote sensing, machine vision, machine learning, for
    many years. Progress has been made on early estimation of
    yields for specialty crops, such as citrus, strawberries. This
    was done using autonomous vehicles and machine vision. These
    techniques need to be adapted for other specialty crops.
        Extensive weather data and crop models help growers
    evaluate climate change adaptation strategies. We have
    developed technology to remotely diagnose citrus ``greening,''
    a devastating citrus disease. Data-driven technologies can also
    improve farm safety through use of alerts and wearable sensors,
    and save lives.
        I want to thank you for your time. I'm happy to answer any
    questions you might have.
        [The prepared statement of Dr. Haman follows:]

      Prepared Statement of Dr. Dorota Haman, Ph.D., Professor and Chair,
        Agricultural and Biological Engineering, Institute of Food and
             Agricultural Sciences, University of Florida (UF/IFAS)
        Chairman Moran, Ranking Member Blumenthal and Members of the
    Committee, thank you for the invitation to talk about technology in
    agriculture and data-driven farming. My name is Dorota Haman. I am a
    Professor and Chair of Agricultural and Biological Engineering
    Department (ABE) at the University of Florida (UF) in Gainesville,
    Florida.
        I was educated as an agricultural engineer at Michigan State
    University and I have been living in Florida and working at the
    University of Florida since 1985. My research, teaching and extension
    work has been focused on water management in irrigated agriculture.
    Since 2007, I have been in a leadership position in Agricultural and
    Biological Engineering at UF and have been working with an
    interdisciplinary group of scientists and engineers. I have also been
    involved in the American Society of Agricultural and Biological
    Engineers (ASABE) for over 30 years, serving on various technical
    committees and on the Board of Trustees and I am Fellow of this
    organization. This testimony represents my view on the emerging, and
    rapidly growing area of data-driven agriculture.
        Technology is rapidly changing the way we live, work and interact.
    This is also true in agriculture. The way we farm and produce food and
    fiber is experiencing a rapid change and it will only undergo more
    dramatic change in the near future. Agricultural operations and
    machinery are becoming more automated, computerized and data-driven.
    The future of agricultural operations will include the Internet of
    Things (IoT), defined as a system of interrelated computing devices,
    machines, robots, sensors, actuators and network connectivity. The
    transfer of data in this system does not require human-to-human or
    human-to-computer interaction.
        More and more sensors are introduced every day to agricultural
    operations and monitoring systems. Increasingly, these sensors are
    connected through wireless communications to the internet, making data
    available in farm databases and mapping systems, and, more importantly,
    for enabling analysis of what affects what, why, when and how.
        Many technologies that are needed to bring agricultural operations
    to this new level have been available for some time, but they are only
    now becoming economical for introduction into agriculture. The
    convergence of technologies in other fields is rapidly bringing down
    the cost of devices and sensors, and therefore making agricultural
    applications economically feasible. For example, sensors built as
    components of costly medical devices are now economical for farmers.
    The medical sensor may cost several hundred (if not thousands) of
    dollars but a simplified, maybe less accurate form, but totally
    adequate for the agricultural application, is becoming available for a
    small fraction of the price. The use of unmanned aerial vehicles (UAVs
    or Drones) in agricultural operations is another good example of
    technology becoming affordable. A drone that costs a few hundred
    dollars today, was sold for thousands of dollars a few years ago.
        Agriculture is a major economic driver in Florida. Agricultural
    research and extension at the University of Florida (UF) provides
    knowledge, innovation and technology transfer that supports 2.2 million
    agriculture-related jobs and direct industry output of $148 billion in
    Florida (2014). Florida agriculture is very diverse and focused on
    specialty crops with most farms smaller and more complex than large
    Midwest farms dedicated to crops like soybean, corn or wheat. This
    diversity of production often makes introduction of new technologies
    more complicated for data collection and analytics. Many Agricultural
    Technology Providers (ATP) are focusing on agronomic crops not on
    specialty crops such as citrus, tomatoes, strawberries, blueberries and
    many other fruits and vegetables produced in Florida.
        Florida is also a significant milk producer. Many modern dairy
    farms are highly computerized and data-driven to optimize their
    operation. Last week, I visited one of the dairies in North Florida.
    Each calf is tagged at birth and monitored throughout its entire life
    on the farm. Calves are fed by robots that adjust the formula of their
    feed based on need at their individual stage of life. The feed, health,
    milk production, milk quality, location etc. of every cow are monitored
    and available when needed. Solid waste is converted to high quality
    compost sold to the ornamental industry and the liquid waste is
    recycled through field irrigation to produce more feed for the cows.
    This is just a glimpse where modern agriculture is now and where it is
    going.
        A successful peanut grower in Levy County, remotely controls
    center-pivot irrigation systems in response to soil moisture probe
    data, from anywhere in the world where there is internet, saving time
    and labor. Remote video systems on center-pivots allow visual
    monitoring for security and system operation. Enhanced GPS controlled
    auto-steering systems on tractors on this farm are accurate to less
    than an inch and virtually hands-off. They precisely map the location
    of planting, for precise and efficient harvesting several months later.
    Such precise operations eliminate over-application and overlap of
    pesticides and fertilizers. Soil can be sampled automatically and
    indexed to GPS coordinates to regulate a variable fertilizer spreader.
    These efficiencies save water, fertilizer and money and reduce adverse
    impacts on the environment.
        Responding to Florida growers' needs, scientists and engineers have
    been focusing on research in the area of robotics, remote sensing and
    machine vision for many years. Early yield estimation is critical for
    harvest planning and marketing. Great progress has been made on
    estimating the yields of various specialty crops such as citrus and
    strawberries using autonomous vehicles and machine vision. These
    techniques need to be adapted to other specialty crops.
        Huanglongbing (HLB)--commonly called ``greening'' is a devastating
    disease that has had significant impact on citrus yield and quality in
    Florida. Unfortunately, so far, no cure has been reported for HLB. The
    first critical step for successful control of HLB is its detection and
    diagnosis. It has been demonstrated that high-resolution aerial imaging
    using a low-cost UAV or drones can detect the disease. Recently, a
    vision sensor was introduced and successfully tested at UF for early
    detection of HLB. Since the majority of Florida citrus is already
    showing symptoms of the disease, research emphasis is shifting to
    genetic solutions to the HLB problem.
        Sensor-based management of water delivery through irrigation has
    been implemented on many farms across the U.S. and in Florida.
    Precision irrigation offers the potential for improving irrigation
    efficiency through localized water delivery based on plant needs,
    weather predictions and soil moisture sensors. Reported water savings
    due to sensor control of irrigation and precise application of water
    are on average 60 percent. This results in money savings to a farmer
    and reduced use of precious water resources.
        Data-driven technologies can also improve farm safety. Wearable
    sensors, now under development, can save lives and reduce harm to farm
    workers. Alert systems that can detect personal overheating, or inform
    about approaching thunderstorms and lightening (typical in Florida),
    will also increase safety in the fields. Nine out of the 10 hottest
    years on record have occurred in the past decade and, according to the
    Centers for Disease Control and Prevention, farmworkers are more than
    20 times at risk for heat-related deaths compared to other occupational
    groups. Wearable sensors can also alert workers to chemical exposure
    through direct contact during application, contact with residue on
    plants, or through drift from nearby applications. The wearable sensors
    can be also used on various machines, drones and robots to provide
    immediate safety intervention.
        Weather data systems have been essential for efficient management
    of agricultural systems. A system of automatic weather stations and
    satellite data provide excellent management tools for Florida growers.
    The cost of a sophisticated blue-tooth-enabled weather station is now
    less than $1000. This monitoring will become more critical as climate
    change leads to increased changes in seasonal temperatures, rainfall
    patterns, and the frequency and severity of storms. For example, in the
    last few winters, Florida growers have been affected by insufficient
    chill-hours to optimize production of temperate fruits such as
    blueberries, peaches, and strawberries. Adapting to these changes,
    Florida blueberry growers are experimenting with ``evergreen''
    production of blueberries that does not require chilling. Agriculture
    in Florida is not only sensitive to the manifestations of climate
    change mentioned above, but also to the salt water intrusions in
    coastal irrigation wells as a result of sea-level rise.
        Data quality has always been a key issue in farm management
    information systems, and is more challenging in an era of Big real-time
    data. Intelligent processing and analytics of Big Data is challenging
    because of the large amount of often unstructured, heterogeneous data
    which requires a smart interplay between skilled data-scientists and
    domain experts. At present, new farm technologies and monitoring
    equipment are producing enormous amounts of data at a wide variety of
    spatial and temporal scales. Raw data sets are cumbersome and not
    directly useful, but become very valuable if appropriate algorithms are
    developed and applied. Data analytics, frequently provided by ATPs, are
    being developed for agricultural applications and are a necessary step
    to make these data valuable to growers. These processed data are also
    very valuable to others including insurance companies and commodity
    markets. A level of data standardization will be necessary for optimal
    sharing and utilization, including a common pool infrastructure to
    facilitate transfer and integration of data from different sources/
    companies.
        New technologies are introducing new problems and issues that need
    careful consideration. There is no question that openness of platforms
    would accelerate solution development and innovation. However, data
    ownership, and related privacy and security issues are problems that
    are frequently discussed in relation to Big Data and analytics. These
    concerns need to be addressed, realizing that enforcement may slow down
    innovation.
        It can be argued that access to high quality data, that allows for
    predictive business modeling of every aspect of farming, gives large
    agribusinesses advantage over farmers, especially small farmers, who do
    not have sufficient resources to pay for data analytics.
        Leveling the playing field for smaller farmers, such as Florida
    farmers who produce specialty crops, through the use of open source
    analytics developed by public institutions such as universities and
    Cooperative Extension Service using public funds (e.g., USDA, NSF) is
    an option, providing data processing though utilities (apps),
    interactive models and maps.
        In summary:

       Agricultural operations and machinery are becoming more
            automated, computerized and data-driven. It is becoming clear
            that the future of agricultural operations will embrace the
            concept of Internet of Things (IoT).

       Technology is becoming less expensive, and economical in
            agricultural operations. Farmers will need to adapt to be
            competitive.

       Farmer-collected raw data become valuable if appropriate
            algorithms are developed and applied. Data analytics,
            frequently provided by ATPs, are necessary to make data
            valuable to growers.

       The processing and analytics of agricultural Big Data is in
            its infancy, and is challenging because of the large amount of
            often unstructured, heterogeneous data which requires a smart
            interplay between skilled data-scientists and domain experts. A
            common pool infrastructure should be developed to enhance
            sharing and integration.

       Land-grant universities and Cooperative Extension Service
            may provide a public platform for data processing though
            utilities (apps), interactive models, and maps.

       Data ownership, and related privacy and security issues, are
            problems that are frequently discussed in relation to Big Data
            and analytics. These concerns need to be addressed.

        I want to thank you for taking the time to focus on technological
    innovations in agriculture. Thank you for inviting me to testify today.
    I would be happy to answer any questions that you might have.

        Senator Moran. I want to thank you for your time. Thank you
    very much. And there will be questions.
        I think I'll turn now to the Ranking Member for purposes of
    his questions, and we'll then deal with the other Members of
    the Senate who are here, and I'll go toward the end.
        So, Senator Blumenthal.
        Senator Blumenthal. Thanks, Mr. Chairman.
        And thank all of you for your testimony. And I think that
    you have all identified one of the key issues here, ``How do we
    enable farmers to maintain control, either through some kind of
    opt-in consent or some other means?''
        And I just want to call your attention to the fact and ask
    your comment on the vote that the Senate took not so long ago
    on a resolution to overturn the Federal Communication broadband
    privacy rules, which I found extraordinarily regrettable. These
    rules were repealed, which constituted, in my view, an attack
    on consumer rights to privacy, but also these rules underlay
    the potential for protection of privacy in your industry as
    well.
        So I want to ask each of you whether you've considered that
    all of these efforts may be undermined by the repeal of the
    FCC's broadband privacy rules because they leave no baseline
    protection, privacy protection, in place, and broadband
    providers can sell and share farmer data just as they can other
    consumer data, and whether you might recommend that we and
    principally the FCC reinstate those rules? And I'll ask that
    question of all of you.
        You look like you're ready to answer, Dr. Ferrell.
        Dr. Ferrell. Well, and the lens through which I've examined
    a lot of the protections for agricultural data is interesting
    because there is not really a specific existing statute that
    would address the type of data that we're discussing here.
        If my tractor uses a cellular signal and basically makes a
    phone call to a service provider, there are Federal rules
    governing telephone communications that would protect that
    transmission. If I e-mail a file to my crop consultant, there
    are protections for that as well.
        But if we're just talking about the transmission of
    telematics data, that really doesn't fall comfortably into any
    realm, especially when we start to combine and package that
    agricultural data with other things.
        One example I sometimes give is that if my data service
    provider has raw agronomic data about my farm, but they also
    have a customer relationship management software program that
    pairs with that data, well, now there might be protectable, you
    know, personally identifiable information that is now coupled
    with my farm, that's a very different privacy issue than if
    we're just talking about agronomic data.
        And so I think one thing that we may have to do is--I kind
    of agree that we need more robust consumer protections for data
    generally, and I think specifically when we talk about
    agricultural data, we've got a lot of work to do definitionally
    in defining what this kind of data looks like. It's not health
    data under HIPAA, it's not financial data under the Fair Credit
    Reporting Act, it's something unique unto itself that I think
    may deserve some efforts to actually define individually and
    define what those protections should be.
        Senator Blumenthal. You are certainly correct that there
    are lots of different kinds of data, even within farming, but
    the principles I think have to be framed in a way that they're
    applicable to all of these kinds of data. And you're right
    also, there may not be a specific statute here, but the
    principles have to be embodied at some point in regulations or
    rules or statutes, and the authority is there for the FCC to
    adopt protections.
        So I would just invite you to think about what those
    protections should be, because right now a provider could paint
    an extraordinarily detailed picture of a farmer's business
    practices, from the type of feed he may use, the type of crops
    he grows, the type of fuel he uses, how many workers, how many
    livestock.
        And I recall well a lunch I had, referring back to my early
    experience, with my grandfather, who had a farm--by the way,
    Senator Fischer, just south of Omaha--and the lunch was at the
    stockyards, and I was asking someone having lunch with us, one
    of my grandfather's friends, how many cattle he was feeding.
    And my grandfather nudged me and gave me that look of
    disapproval that grandfathers do when they are not enamored of
    the line of questioning, and he explained afterward that's like
    asking somebody how many dollars he has in his bank account.
    And that sense of privacy is broken, I think, by many of these
    practices, and farmers are very, very respectful about their
    privacy, with good reason.
        Thank you.
        Senator Moran. Senator Blumenthal, thank you.
        The Senator from Nebraska, Senator Fischer.

                    STATEMENT OF HON. DEB FISCHER,
                       U.S. SENATOR FROM NEBRASKA

        Senator Fischer. Thank you, Mr. Chairman.
        And thank you, Ranking Member Blumenthal.
        As a cattle rancher, I thank you for your respect to my
    privacy.
        I would like to thank both of you for calling the hearing
    today so we can focus on managing that Big Data with new
    technologies for our Nation's farmers and ranchers. Not only
    will this help boost productivity, but advancements in digital
    analytics can improve how we feed our communities and conserve
    our natural resources. In fact, Tim Hassinger of Lindsay
    Corporation, from Omaha, Nebraska, noted at last week's
    Internet of Things hearing that the combined yield enhancement
    and resource savings from new technologies can increase the
    American farmers' profits by an average of $40 per acre.
        And I would like to please submit for the record follow-up
    materials that Lindsay has provided regarding its research on
    data analytics and irrigation management, Mr. Chairman.
        Senator Moran. Without objection.
        Senator Fischer. Thank you.
        [The information referred to follows:]

    FOR IMMEDIATE RELEASE--November 8, 2017

         Lindsay Corp President and CEO Speaks to U.S. Senate Committee

    Tim Hassinger Addresses the Need for Improved Access to Rural Broadband
            Service

        (OMAHA, Neb.)--November 2017--Tim Hassinger, president and CEO of
    Nebraska-based Lindsay Corporation, offered testimony during a hearing
    yesterday conducted by the U.S. Senate Committee on Commerce, Science
    and Transportation. The hearing, titled Advancing the Internet of
    Things in Rural America, focused on the benefits of the Internet of
    Things (IoT) in rural communities and the infrastructure needs
    necessary to advance the IoT market to ensure rural America has access
    to products and devices that are driving the digital economy.
        ``Like all business owners, farmers in rural communities need the
    ability to go online,'' Hassinger said. ``The Internet fuels the
    innovative, advanced technology that will help America's farmers meet
    the food, fuel and fiber needs of our rapidly growing global
    population.''
        Hassinger's testimony contends that with reliable, high-speed
    Internet access, farmers can take advantage of tools that deliver
    hyper-local weather forecasts, real time data on soil moisture
    conditions and GPS for planting and irrigation management. They can
    also take advantage of a myriad of emerging technologies available from
    Lindsay Corporation and other American manufacturers. The testimony
    further explains that these innovations enable efficiencies through
    remote data collection, transfer and analysis from connected devices
    like soil moisture sensors, weather stations and cloud-based tools.
        ``At Lindsay Corporation, we are developing and deploying
    technologies to help growers produce more with less. For example, our
    FieldNET and FieldNET AdvisorTM remote irrigation
    management and decision support tools help farmers decide precisely
    when, where and how much to irrigate--to help them maximize yields
    while reducing overwatering and related input costs and nutrient
    losses,'' Hassinger said. ``But we know the technology is only as good
    as the farmer's ability to access it.''
        According to the Federal Communications Commission Broadband Access
    Report, an estimated 39 percent of the rural population (23.4 million
    Americans) lack access to broadband that meets today's benchmark speeds
    of 25 Mbps for downloads and 3 Mbps for uploads. By contrast, only 4
    percent of urban Americans lack access to 25/3 Mbps broadband.
        ``While cities and municipalities typically have access to several
    high-speed Internet service providers, that access often ends at the
    city limits. Those living in rural communities must depend on radio
    networks, satellite or cell service--all of which typically operate at
    lower speeds, limiting connectivity,'' Hassinger said. ``All farmers
    are faced with the pressure to increase yields while conserving
    resources. The lack of reliable broadband hinders their ability to
    adopt the new technologies that will help them optimize their
    operations and compete in the global marketplace.''

    Hassinger's full testimony can viewed at https://commerce.senate.gov.
    (https://commerce.senate.gov.)
                                     Report
    [GRAPHICS NOT AVAILABLE IN TIFF FORMAT]

        Senator Fischer. Mr. Knopf, in your testimony, you describe
    your farm management operations. And what benefit do you see on
    your farm from implementing Big Data technology? How do you
    analyze it? Do you have input from your nutritionists, your
    equipment dealer, local co-op? How do you get it all put
    together?
        Mr. Knopf. That's a good question. There's a lot of
    information to try to analyze. And so in our farm--that answer
    is going to look different for each individual farm, of course,
    on the strengths and talents and the passions of that
    individual farm operator. Some farmers are very passionate
    about their data, very passionate about precision agriculture,
    and they want to be very hands-on in that process. And those
    are also I think the guys that are very passionate about the
    ownership and transparency of that data. They want it on a
    server in their office.
        There's the other spectrum of farmers--and we're kind of in
    the middle, let me say that--there's the other spectrum of
    farmers, and sometimes it changes based on age demographics as
    well. I've noticed in my community, in my experience, and
    thinking about our own farm, the older generation is much more
    private about their data, very unwilling to share it. The
    younger generation is--you know, they grew up with smartphones,
    and Google is tracking them all the time and telling them
    things about themselves that they didn't know already, and so
    they're more used to that. So there's a generational change
    there as well.
        For us, we're kind of in the middle of--I have an agronomic
    background from Kansas State. I have a young man that works--we
    have one full-time employee that also is an agronomist. And we
    do most of our data analysis in-house. But our data is stored
    in a software system that is web-based, and it's subscription-
    based. And that was a big change for us about 2 years ago, 3 to
    4 years ago, is transitioning our data from being stored in-
    house to web-based, which is somewhat of an uncomfortable step
    because you feel like you're losing control, and that's why the
    transparency is so important. But we are still processing a lot
    of the data and analyzing a lot of it in-house. But there is a
    growing amount of service providers dealing with that. We
    collaborate a lot with our land grant with Kansas State
    University, and a lot of their research we collaborate with and
    host on our farm. So that's a very important collaboration. And
    I appreciate two representatives here from universities as
    well.
        Senator Fischer. And as you noted in your testimony, the ag
    economy is in a downward cycle right now, it's hurting. I also
    know that there's a lack of access to broadband, and we have a
    lot of slow downloading speeds. And how does that affect your
    bottom line----
        Mr. Knopf. That has a----
        Senator Fischer.--when you have this slow access?
        Mr. Knopf. Yes. Excuse me. That has a very significant
    impact. In fact, the county that we live in is just now
    receiving fiber optics ran to farms. In fact, it's not even
    hooked up yet, it's just being ran right--right now, being
    buried, because a lot of my peers that live in more--we have a
    community of about 40- to 50,000 within our county, but yet
    there is still a lot of rural part of the county. So a lot of
    my colleagues that live in rural counties in Kansas qualified
    for grants from the government to help build that initial
    infrastructure.
        Well, our county happened to not qualify because of the
    urban center that we had within that county, and we have some
    broadband antennas on top of elevators and towers and so forth,
    but it is very unreliable and fairly slow. So that has limited
    our utilization and access of data, particularly in web-based
    software platforms, and been an economic disadvantage in
    utilization of data because we're kind of caught in the middle
    of that process.
        Senator Fischer. Thank you.
        Thank you, Mr. Chairman.
        Senator Moran. You're welcome.
        Senator Klobuchar.

                   STATEMENT OF HON. AMY KLOBUCHAR,
                      U.S. SENATOR FROM MINNESOTA

        Senator Klobuchar. Thank you, Mr. Chairman. Thanks for
    allowing me to go, too. And just to follow up with Senator
    Fischer, and we've worked on some of these broadband issues, I,
    like Senator Fischer, have heard many stories from my state
    about the need for better broadband access. And I always
    remember a precision ag company out of Willmar, Minnesota, that
    actually told farmers to go to the restaurant parking lot
    because they would get better Internet.
        And when you think about it, you know, way back about 10
    years ago people were just trying to get Internet so they could
    write their grandkid an e-mail or something, and it has changed
    so much that if they don't have that ability, they're just not
    going to be able to do their work basically, and that's
    everything from Jennie-O in Minnesota measuring the temperature
    in barns for turkeys--I mention turkeys, we are number one in
    turkeys in Minnesota, and Thanksgiving is upon us--and other
    things that we've seen that it's just really a must-do for
    these farmers.
        Mr. Ferrell, most broadband connections provide faster
    download than upload speed. Why is upload speed important for
    ag data? And how are reliability and bandwidth needs of farmers
    different than the average consumer?
        Dr. Ferrell. Well, you're very right, Senator, in that
    we've seen tremendous gains in terms of what rural broadband
    access has been able to offer in terms of download speeds, and
    that's been good for farmers acquiring information. But as the
    witnesses have mentioned today, we're gathering tremendously
    more data that has to be uploaded. And in certain cases, in
    some applications, we need that data to be uploaded in real
    time.
        And so even if you're in a broadband-connected accessible
    area, your farm equipment needs to be able to access wireless
    broadband speeds as well to upload that data and receive real-
    time updates about things like cropping prescriptions, like Mr.
    Knopf was mentioning.
        As you're doing intensive management of a specific field,
    you need to have advanced broadband upload speeds to be
    collecting that information, getting real-time recommendations
    back, so you can make on-the-fly adjustments to things like
    seed population, fertilizer application, pesticide application,
    and things of that sort.
        So the download speeds are good, but as the ag data
    revolution continues, upload speeds I think will be even more
    important because of the amount of data that we're pushing to
    the cloud rather than receiving from it.
        Senator Klobuchar. Thank you.
        Dr. Haman, I just recently visited, in fact, just a week
    ago, Aker Technologies in Winnebago, Minnesota. You may not
    have been there before, but they're developing this innovative
    drone technology to help farmers be more efficient when
    applying fertilizer, pesticides, and water. It saves money. It
    leads to more sustainable farming because they're going to be
    able to see where they need the water, where they need the
    pesticide, and not do one-size-fits-all for their whole fields,
    nor do they have the resources, especially small farmers, to be
    able to walk through every row.
        The FAA recently introduced a pilot program to explore the
    use of drones far beyond the line-of-sight operations in
    limited cases. For farmers managing large tracts of land, this
    could be a valuable option in the future. And in your
    testimony, you mentioned the use of drones to combat citrus
    diseases in Florida. Could additional drone monitoring help
    prevent the spread of diseases?
        Dr. Haman. Absolutely, yes. They absolutely. We already are
    using drones, for example, in forestry. And in forestry, the
    drones can detect the areas where the disease is likely, where
    the plants are stressed. Using different spectra, you can
    detect stress which is from water or stress which is due to
    disease, and separate that. And we are right now doing research
    to detect specific stresses due to specific deficiencies of
    different nutrients and different--so possibilities are
    enormous, and as the cameras are getting smaller, it makes also
    big difference because they can be carried on small drones.
        Senator Klobuchar. Exactly. And that's what this concept is
    going on at this company.
        Dr. Haman. Yes.
        Senator Klobuchar. Last question, Mr. Knopf. The Minnesota
    Corn Research & Promotion Council has been working with local
    farmers to test innovative conservation approaches, pilot new
    technology applications, by promoting the adoption of precision
    ag technology, like drone monitoring, smart irrigation, that I
    was just talking to the doctor about how we can help farmers
    with conservation.
        Could you talk about how Federal research initiatives or
    land-grant universities partner with industry to further
    advance innovation? Quickly.
        Mr. Knopf. I really appreciate that question, and it is
    invaluable and becoming increasingly invaluable to both
    economic sustainability and environmental stewardship. We host
    yearly on our farm between four to a dozen experiments in
    collaboration with Kansas State. Their resources are limited
    more as time goes on because of budgets, so farmer-private
    university collaboration is increasingly important.
        Senator Klobuchar. Thank you very much.
        Thank you, Mr. Chairman and Senator Blumenthal.
        Senator Moran. You're very welcome. Thank you, Senator
    Klobuchar.
        Senator Lee.

                      STATEMENT OF HON. MIKE LEE,
                         U.S. SENATOR FROM UTAH

        Senator Lee. Thank you very much. Thanks to all of you for
    being here. We're witnessing a technological revolution. It's
    very significant. I think it's going to soon be the case we'll
    look back at what we're experiencing today and realize that
    this was just the tip of the iceberg. And it's one that I think
    has the ability to allow farmers and ranchers enjoy cutting-
    edge technology and make their operations more effective, more
    productive, so that they can feed more people and do what they
    do.
        I want to focus today a little bit on the application of
    unmanned aerial vehicle technology, drone technology, as we
    sometimes refer to it, to agriculture.
        Mr. Knopf, I want to follow up on some questions Senator
    Klobuchar asked you about. Tell us what--how this has potential
    application in your field?
        Mr. Knopf. We happen to be 15 miles from the Polytechnic
    campus of Kansas State University, which is in Salina, and they
    are one of the leading institutions for UAS and UAV
    programming. For the last couple years, they have been carrying
    out a pilot project alongside the FAA with a grant they
    received on some extended line-of-sight operations for drones,
    which was already brought up, and looking at some of those
    regulations and how that can work.
        So specifically what that means to our farm, other
    collaboration that we're doing with Kansas State from the
    Agronomy Department is looking at things such as being able to
    sense NDVI ratings, which is a plant health greenness index,
    and across the field in real time to very quickly pick up any
    problem areas in the field, to quickly address issues that need
    to be addressed before they make more significant impact. And
    it's just a better, more efficient, precise way to collect data
    across a wide scale. So there are a lot of implications in
    that, and I think satellite imagery is also an increasing area
    of data that is going to be easily accessible as well that will
    have a lot of applications.
        Senator Lee. Thank you. And following up on that, Dr.
    Ferrell, you mentioned in your testimony that drones are one
    technology, one useful piece of technology, that can be used to
    gather agronomic data. Can you explain how important this is
    for farmers, why it's important?
        Dr. Ferrell. Yes. I think it's incredibly important because
    it's constantly being called upon for farmers to do more with
    less, and that's not just in terms of input, sometimes it's in
    terms of labor as well. You know, we've talked about the use of
    UAS and drones to scout crop conditions, and certainly that
    enables one farmer to cover potentially thousands of acres in a
    day whereas if they were trying to do that on the ground by
    scouting, that might take days out of their schedule, and they
    just don't have the manpower to handle that.
        But another important aspect of UAS use that we haven't
    talked about much is actually in livestock operations,
    especially if you're talking about large, you know, pasture-
    based operations, sometimes using drones to check cattle can
    save a tremendous amount of time and resources, and enable us
    to identify cattle that are in poor health, get them treated
    quickly, and get them back to health.
        So I think there are unprecedented opportunities there for
    us in the agricultural sector.
        Senator Lee. It could help a lot of people in their
    operations.
        Now, Mr. Janzen, your law firm represents people in
    regulatory compliance and registration. What can you tell us
    about what the largest regulatory barriers are to farmers and
    ranchers using drone technology, or for drone technology more
    broadly being implemented in the United States?
        Mr. Janzen. Thank you, Senator. I think right now probably
    some of the largest barriers for farmers would be the cost of
    the drones and also the ability to use the data and get real
    results back that they can put into practice on their
    particular farms. And I think that there was a lot of hype when
    drones first started to become very popular, and, you know, now
    here we are today, and we're trying to figure out how to best
    use that information today. So I don't know currently that
    there is an enormous regulatory hurdle for use of those drones
    on the farm.
        Senator Lee. What about the FAA's restriction on the use of
    drone technology beyond line of sight?
        Mr. Janzen. Oh, yes.
        Senator Lee. Has that had an impact on people's adoption of
    this technological platform?
        Mr. Janzen. That's a very good point. Yes, it probably is
    creating some restrictions on people wanting to use the
    technology because that is a barrier to, you know, a single
    farmer who may want to use this technology on their farm.
        Senator Lee. OK. It's one of the reasons why I've been a
    big believer in and an advocate for legislation that would help
    provide clarity on drone regulation. This is a technology that
    has tremendous promise, not just for the U.S. economy, but for
    humanity, but because the technology is developing in the way
    that it is here in the United States, it can offer some real
    benefits to the United States specifically.
        As we've seen in other areas, one of the things that can
    hold back technological innovation is stifling government
    regulation, and so that's why I've been an advocate on that.
        Thank you, Mr. Chairman. I see my time has expired.
        Senator Moran. Thank you, Senator Lee.
        Let me start I think with Mr. Tatge, although I'm happy to
    have a response from any of you. The promise, or a promise,
    from data collection and its usage is its increasing value,
    and, therefore, added profitability to production agriculture.
        So in your testimony, Mr. Tatge, you estimated that a
    billion dollars of annual revenue could be returned to rural
    communities from multiple sales of that information.
        Mr. Tatge. Yes, sir.
        Senator Moran. So how do you envision Mr. Knopf earning
    additional income as a result of collecting data on his farm?
        Mr. Tatge. So we create what is called an electronic field
    record. An electronic field record is a map basically of what
    happened on what part of the field, whether it be seeding rates
    that are being planted, whether it be application rates from
    sprays that may have been applied to the crop, and then from
    harvest as well. And once you get those--that data in a state
    where it's in a standard, and we call this electronic field
    record, we can make that available for sale.
        So when we make that available for sale, companies come to
    us, and we've already sold data for farmers, will come to us
    and say, ``I would like to buy that layer of data, and this is
    what I'm willing to pay.'' And then we go back to the farmers
    and we tell them who the buyer is of that data and how much
    they're willing to pay, and the farmer chooses if they want
    to--let's think of it as licensing a copy of that data to that
    company. And if they do, we split the revenue with the farmers.
        So we've turned it into a profit center for basically
    taking digital exhaust and packaging it to get it to go to the
    people who have a desire to acquire it.
        Senator Moran. So you're the market. You're the
    intermediary between those who have something to sell and those
    who have something they want to buy?
        Mr. Tatge. Yes, sir. We're also helping with the collection
    of the data in the first place to get it standardized in a way
    that makes it easily portable or interoperable. Because our job
    is not to do any analytics on data, our job is to help farmers
    get the best dataset that they can get in one place and in a
    format that it can move wherever they direct it to go.
        So when our focus is exclusively on being able to focus on
    how can we get the best dataset into a database, and then how
    can we get that data to flow into whatever system the farmer
    directs us to send it? If they're working with their local
    agronomist, we've got several examples where a farmer is
    automatically sending data to their agronomist without having
    to do anything. The day they get done spraying the field, this
    file is going directly to their agronomist, and there's no
    charge for that.
        The farmer can choose to share that data with anyone they
    want. It's when you get enough of this data packaged together,
    though, that's when you bring the opportunities for
    monetization of the data, just like a brand-new commodity.
        Senator Moran. That's not much value to individual data in
    and of itself, an individual farmer's data, but if you can
    combine it with broad data, bigger data----
        Mr. Tatge. Yes, there are values to it on the farm itself
    for your operations, making sure you're running your machines
    as efficiently as you can, fuel usage, idle time. It's a
    different value prop to the farmers. Then there's a value to
    sharing that data with trusted advisers, and it adds increased
    service opportunities and increased ability to react during the
    in-season mode to make some changes to hopefully put down any
    threats that come up or minimize their impact.
        And then to the market, there's a greater opportunity to
    really start to identify and make these datasets available for
    us to understand best practices. Are we planning--how come when
    the farmer rips open the bag, we are--we lose all this genetic
    yield potential? Is it because they're planting the seeds too
    deep? Are they planting them too shallow? Are they planting
    them at the wrong time? Are they planting them too early? Are
    they planting them too late?
        There are all of these things that get opened up with the
    data for us to identify what the best practices are and what
    the best pieces of equipment are to help drive efficiencies.
    It's very exciting. But that is how you can create a revenue
    stream going back there because the major ag companies that you
    see today, they could easily change their marketing budgets.
    Instead of buying commercials and hats and jackets and boots--
    right?--we could change that into let's be open and honest with
    our customers, let's buy their data from them so that we have
    the opportunity to service them better.
        Senator Moran. Who are the purchasers of data today? And
    who do you expect them to be in the future?
        Mr. Tatge. They range from a wide range of data analytics
    companies as well as insurance and reinsurance companies trying
    to identify risk levels and some of that. They're also
    equipment manufacturers. So there are a bunch of people that
    are trying to figure out and benchmark data against each other,
    and they're getting access to data that they've never had
    before.
        Senator Moran. Is this function that you're helping create,
    is it similar to what USDA does as they collect data and then
    make it public for purposes of farmer decisionmaking----
        Mr. Tatge. Yes, it can be. You know, the USDA has got the--
    I mean, they are the gold standard of data, and they are the
    ones--when you see the markets move a lot, a lot of times it's
    because a USDA report came out, and it wasn't what the market
    was anticipating, and that causes a lot of the volatility
    there.
        Traders love the volatility, and I was one for several
    years. But farmers traditionally, and the companies that use
    that raw commodity as the base for their product, like a flour
    mill, does not care for the volatility. So getting that
    volatility out of the market and getting that information to
    the market quicker is a huge opportunity to help reduce, I
    think, overall volatility in the markets.
        Senator Moran. In the billion dollars that you estimated,
    does that include all three--the value of all three components
    of that data that you described?
        Mr. Tatge. That includes--no, that is the value of the
    marketplace side of the data only.
        Senator Moran. And you base that estimate on what?
        Mr. Tatge. We base that estimate on 25 percent of the
    acreage in the four major row crops--which would be corn,
    beans, wheat, and cotton--and being able to monetize that data
    four to five times on a turn. Once one person buys the data,
    the others want to buy it as well.
        Our problem right now is the fact that we don't have enough
    data. And when you go into a new market like this, when you're
    creating a new marketplace, supply brings demand. So that's one
    of the things that we're working very hard with right now to
    address.
        Senator Moran. And this is a question for again maybe you,
    Mr. Tatge. So when a farmer buys a piece of equipment, at what
    point in time--what year, what model year, of equipment
    collects the data? Where did we start? How recent of a
    development is this?
        Mr. Tatge. So I think in 2011 is when most of the new
    equipment started coming with a modem in it. I think it was a
    2G modem at that time. So I would say from 2011 and newer, most
    of the equipment that is sold today has a modem in it. But we
    have an aftermarket solution that allows you go to back to
    roughly 2002 on most of the pieces of equipment. So as long as
    there are sensors on it, we have a piece of hardware that can
    allow us to make that older piece of equipment more modern in
    its form of collecting data.
        Senator Moran. So when you say there's a shortage of data,
    is it because farmers are using equipment that's older than
    that, the technology isn't utilized on their farm, or they
    don't know or utilize the information, the data, that they
    have?
        Mr. Tatge. It's the second one more so. I would estimate
    that 80 percent of the data generated in cabs of equipment
    today does not make it out of the cab ever, and that's a
    roadblock. The industry lacks--I'm going to use a navigation
    app for me. So Google Maps is something that I use just about
    everywhere. I used it to get here.
        And when I went to buy my new pickup, I couldn't get it
    without the electronics in it, without the navigation system. I
    wanted to because it was expensive and I didn't need it. But I
    couldn't buy that truck without it. My wife has a car as well
    that we've had the same challenge on not buying the navigation
    system. But we ended up buying two of them that we don't use.
    And the reason we use Google Maps is because of the interface
    and it is portable and it can move with me.
        The ag industry lacks a common interface of that nature to
    be able to get this data out of the cab. So when you only plant
    once a year, I mean, you kind of forget how the technology
    works until next year. And that's one of the challenges that we
    have, and we have the opportunity with the technology available
    today to make those tools smarter and easier for digital
    immigrants.
        Senator Moran. Thank you very much.
        Do any of you have anything to add in regard to how this
    use of data, the monetization of information, means money
    returning to rural America?
        Well said, Mr. Janzen?
        Mr. Janzen. Yes. I would add a little bit to that. I always
    tell people, because we get asked this question a lot, ``What
    is the real value of the farm data?'' Right?
        And I always say imagine that you had two 80-acre parcels
    of land that you had grown corn and soybeans and wheat on for
    the last 20 years, and, you know, one of those you have
    accurate data records for how things were planted, when they
    were fertilized, when you applied pesticides, and one you did
    not. You know, how much more would somebody be willing to pay
    for that information at the time of sale than the parcel that's
    identical that doesn't have any of this that goes along with
    it?
        And so I think the answer is certainly something, right? I
    mean, it's certainly worth something. Is it worth $20 an acre?
    $50? $100? I don't think we really know that yet because it's
    such a young industry. And I also think we haven't done a good
    enough job of collecting that information year after year after
    year to really see the value. But we will get there and there
    will be a time when that is a valuable commodity and certainly
    people are willing to pay more for it.
        Senator Moran. Mr. Knopf?
        Mr. Knopf. Mr. Chairman, I may quickly mention that I think
    it's important to keep in mind that the value may not always be
    just monetary. I think one of the things we're seeing in
    private industry right now is there is tremendous competition
    to grasp and collect that digital acre. Sometimes the value
    going back to the farmer is in benchmarking against other farms
    on our performance from a yield standpoint or a sustainability
    standpoint. Other times, the value coming back to the farmer by
    sharing his data is access to a model that that data can be
    implemented to that has been designed by Big Data that a
    private industry has paid to capture.
        And so it may be access to a model or access to something
    other than monetary value, it may also be the nugget given back
    to farmers for sharing data.
        Senator Moran. When Mr. Tatge says that 80 percent of the
    information collected from a piece of farm equipment is not
    being utilized, is that by the farmer? Is the information still
    being utilized by the manufacturer, the seller of the farm
    equipment?
        Mr. Knopf. From my perspective, in most of our equipment,
    it is not automatically, as far as I know, automatically being
    sent somewhere even though there are modems in the machine.
    And, yes, I think most of it is that it's not being utilized by
    the farmer.
        You know, we only have so much time. I think a lot of this
    data utilization is going to have to be, with the exception of
    very large farms, more of it's going to be outsourced to
    service providers, which is a new economic opportunity for
    rural America, and the drone industry as well.
        Because the farmer just literally doesn't have the time to
    manage all of this data to take it out of the cab and, you
    know, I can't--my dad, we know the age of farmers, and this is
    going to be an important transition, as that generational
    change happens, there is going to be a lot of change as we see
    that transition happen, but the current generation of farmers,
    it's a big technological hurdle to cross for most farmers.
        Senator Moran. Because of the access to this information,
    farmers can be more efficient and, therefore, more profitable
    is the theory, and I think the facts I think will demonstrate
    that. Does that increase the chances that farms get larger, or
    does it make it more likely that the medium-sized farm becomes
    more efficient and, therefore, more sustainable economically?
        Dr. Ferrell, you look interested.
        Dr. Ferrell. We've actually speculated about that in some
    of the academic research. And there's definitely the argument
    to be made that says there are efficiencies to be had, and
    those efficiencies might be magnified at the larger scale of
    farms. And so from one perspective, that could actually
    accelerate the growth and the scale of farming, and that's one
    thing that's true.
        It depends on what we talked about earlier, which was,
    ``How do we go about sharing the value and the gains from those
    technological advances between the aggregators and the
    individual farmers?'' It might actually be that this technology
    might enable our medium and smaller producers to capture some
    of the efficiencies that heretofore were only available to
    those larger producers and might actually keep them more
    viable. I think there are probably more macroeconomic trends
    that are going to still push toward larger operations, but I
    think this could be an important source of invigoration for
    those small and medium producers as well.
        Senator Moran. That's encouraging.
        Senator Blumenthal has some additional questions. We'll
    have more one more round. Senator Blumenthal will ask his
    question, I'll ask mine, and we'll try to wrap this up.
        Senator Blumenthal. Thank you.
        I think, Dr. Ferrell, you were the one who used the term
    ``aggressive fine print.'' Or Mr. Janzen, sorry.
        [Laughter.]
        Senator Blumenthal. And let me address this question to the
    panel, but first to you. What do you worry about in those
    contracts, performers in the fine print, and is one of those
    worries mandatory arbitration clauses, which, as you know,
    deprive any potential party and litigant of the right to go to
    court under some circumstances, often very broad circumstances,
    and often contained in the fine print?
        Mr. Janzen. Thank you. Yes. Well, as an attorney, I worry a
    lot about this, and I get asked to draft these types of
    contracts by ag technology providers that hire our services.
    And so we think about this a lot. And, you know, what I always
    tell my clients is you want the contract to be something that
    the farmer can understand when they read it. And that's very
    different than I think the traditional route that we've gone in
    this country.
        I mean, we all get the updates on our cell phones all the
    time, and nobody reads those, you know, and that's a problem,
    and it's a real problem when the information is not just, you
    know, the stuff that you're clicking on your cell phone, but
    it's actually proprietary information that belongs to a farmer.
        So, you know, I worry a lot about the complexity of these
    agreements, and I worry a lot that the industry adopts standard
    form contracts for these new agricultural technologies when
    it's like putting a square peg in a round hole and it really
    doesn't work.
        Regarding arbitration provisions, I haven't seen that as
    widely used as probably in some other areas, but they are
    definitely in there. And, yes, that certainly could be a
    problem. You know, as somebody whose Equifax data was breached,
    I can certainly relate to that issue.
        I would say the other thing that I concern myself with is a
    choice of forum provision in a contract because if anyone has
    to go to New York City to adjudicate a dispute, and they live
    in Wyoming, you already know the outcome of that, right? So
    that's the other thing that I worry about in these standard
    form contracts.
        Senator Blumenthal. Any other responses?
        Dr. Ferrell, you're nodding.
        Dr. Ferrell. Oh, I would just agree. We've seen those in
    lots of contracts. I've seen a lot of those in some of the ag
    data contracts. And interestingly enough, I've seen those a lot
    in oil and gas contracts, wind energy contracts, solar
    contracts, and there is some limited empirical evidence that
    shows that those arbitration clauses really work to disfavor
    the landowner in those circumstances because not only is the
    choice of forum usually a remote urban location as opposed to
    the home court of the producer, but also the arbitration
    process usually kind of tends to stack the deck in favor of the
    more--and I don't want to call these unsophisticated, that's
    not what I intend to say, but corporate entities tend to see
    arbitration more, are very familiar with that process, and use
    it to their advantage I would say generally.
        Senator Blumenthal. They're more experienced in it.
        Dr. Ferrell. Yes, sir.
        Senator Blumenthal. Especially if it's mandatory.
        Dr. Ferrell. Yes, sir.
        Senator Blumenthal. Thank you.
        Senator Moran. Mr. Knopf, does K-State Extension, for
    example, have any ability to educate you and other farmers in
    regard to data? Is that happening?
        Mr. Knopf. K-State Research and Extension has data
    specialists. I'm very impressed with Dr. Ferrell's knowledge.
    Come north a little bit if you----
        [Laughter.]
        Mr. Knopf. So, yes, we have precision agricultural
    specialists, but, again, it's--again, it's another--it's a
    whole other realm that they have historically not dealt within,
    and the amount of capital and things to just fund positions is
    I think limited in how extensively they can take on that roll.
        Senator Moran. Is there anyone who is vying for your data?
    Does somebody want to buy it?
        Mr. Knopf. Nobody has offered me any money for it yet.
        [Laughter.]
        Mr. Knopf. But, yes, certainly private companies are not as
    much directly, but very indirectly competing for access to my
    data, and they don't really know, I think in many instances,
    yet what they're going to do with it because it's the amount
    that they can collect. The bigger the amount is, the more
    value. Whatever that value is, it's about scale. And so I think
    my perception as a farmer is a lot of times they don't know or
    aren't letting on what they're going to do with it, but once
    they have a bigger scale of it, there's inherently going to be
    an incredible amount of value there. And the competition for me
    to just hand over my data card is very high.
        Senator Moran. Is it possible that a monopoly could arise
    owning the data, that the information is available only to a
    select few? Could an exchange end up being the purchaser of
    data and know more than others who are interested in the
    markets?
        Dr. Ferrell.
        Dr. Ferrell. That's a perfect opportunity for me to loop in
    the fact that Dr. Terry Griffin at K-State, their precision
    cropping systems economist, actually taught me a lot about this
    issue specifically. And if you look at the trends that we've
    seen in other technology providers, take, for example,
    operating systems for personal computers, you start with lots
    of those systems out there, and over time, through
    acquisitions, market share, and the fact that, as Mr. Tatge
    said, we want to see interoperability, that's why so many of us
    either have a Mac or a PC, is because eventually those two
    operating systems kind of boil down, and I can create a
    Microsoft Office document and share it with everybody who's on
    the Committee with me here fairly easily.
        And so lots of factors, economic and practical, tend to
    drive us toward that, either monopoly or monopsony, that small
    group of providers that are eventually going to take over.
        There are lots of reasons to suspect you would probably see
    that in the data space as well, and that's why I think that the
    dialogue between the producers and the aggregators is so
    important, is to make sure that as that consolidation occurs,
    that we maintain some parity in the bargaining position between
    the users and those aggregators.
        Senator Moran. That could be a potential difference between
    USDA information and data that's collected through this process
    we're talking about. The access could be much more limited in
    this setting?
        Dr. Ferrell. I think so. And the interesting hypothetical
    situation is, you know, we have USDA data trying to provide us
    information about crop conditions, acres planted, things of
    that sort, and, you know, as the other witnesses mentioned,
    that's a huge driver of both market information and volatility
    as well. What happens if someday USDA is, for lack of a better
    word, outmoded by private aggregators of that information that
    have much better, much more rapid market intelligence? How
    would that influence markets?
        Senator Moran. Right. I mean, that's not a question I
    thought of until I was hearing the panel today. Information is
    valuable, but there can be a corner on the market, which then
    changes the nature of the market.
        Mr. Tatge. So I talk about this and debate this a lot. I
    traded commodities for several years. The interesting thing is
    that if a farmer opts-in to sharing their data or selling their
    data, let's say licensing their data, to a commodity brokerage
    house, they're getting their profit, they're taking the risk
    off the table right away because they're getting a piece of it,
    regardless of where the market goes.
        So I would argue that the data flowing to the market
    quicker is going to reduce volatility and give the farmers
    additional reasons and ways to monetize that data that they
    haven't probably thought of in the past.
        The farmer is the first consumer of that data, if you will.
    When you're driving through your combine, you're the first
    consumer of that data. OK? So you might think, ``Oh, my yields
    are much better than I thought. I should hurry up and sell
    some.'' Right? He--that's a good use of that first--that first,
    you know, consumer of that data. The problem is you're not
    quite certain enough if it's just that field or if it's just
    your area. But if we can have a larger pool of that data,
    suddenly that becomes really relevant for you in your marketing
    decisions as well as the market at a whole.
        I absolutely have no--I have no doubt that getting
    information--once you get something faster, you never go back.
    OK? And I believe that will be the same in the markets and the
    information that we're collecting as well.
        Mr. Knopf. Mr. Chairman, I may--you've been a--you
    understand regulation and how that impacts farming systems, and
    I think that's one important note that hasn't been perhaps
    mentioned, is we also need to recognize the risk of--as access
    of data increases, I think we need to be cognizant of the risk
    that that--the risk that that presents in utilizing that data
    down to the farm level perhaps to regulate a farmer on specific
    practices even though we're working within a biological system.
        There are going to be large swings across years based on
    rainfall, on how much nitrogen we use per bushel of wheat per
    se. So this year is going to look very different than last
    year. And when folks don't understand a biological system and
    weather variation and just look at a dataset, it can be an easy
    thing to immediately think, well, how can we get them to always
    just use this much nitrogen per bushel of wheat? So I think
    that's an important thing to think about as well.
        Senator Moran. These markets, of course, are international.
    And is the market for data international? Is the same thing
    we're talking about here happening in Brazil and Argentina and
    Europe?
        Mr. Tatge. I think hypothetically, absolutely. There is
    going to be a lot of different regulations and rules, and it's
    going to take a while to understand how each country wants to
    deal with their data that's collected. But I absolutely think
    this is a global opportunity for sure.
        Senator Moran. Let me just ask a few questions that I think
    can be quick. Is my understanding correct that there really is
    not an agency of the Federal Government that protects farmers
    or regulates the collection of data? Is that the conclusion of
    what I heard you all say?
        Dr. Ferrell. That's correct, sir.
        Senator Moran. And I guess I should ask the next question:
    Should there be?
        Dr. Haman?
        Dr. Haman. I think that it is very important, one thing is
    very important, that the data is standardized because the
    closer the formats of the data, the possibility of comparing
    that is extremely important. Right now, we are getting a lot of
    data which is really not very good. And even farmers would
    admit that the data, raw data, is something which they cannot
    process.
        I have mentioned that we are moving toward Internet of
    Things or Internet of agriculture. At that point, we remove the
    factor of human being in a lot of situations because machines
    talk to each other and the computers talk to each other.
    Sensors are talking directly to the computers.
        So some kind of standardization will become reality anyway.
    That's also true of the data which is coming from the
    satellites because all of that has to be downscaled because
    it's on a scale which is not really practical for farmers. But
    there's a lot of work going on, on downscaling the data which
    is coming from satellites.
        But all these databases have to talk to each other, and
    that's very difficult to do if there are a lot of small--it
    seems to me if there are a lot of small entities, small
    companies, which are organizing the data differently, then
    there is a problem. Of course, sooner or later, the Microsoft
    or an Apple is going to emerge and just take over. This is
    probably what's going to happen, but the sooner we make this
    data comparable, the easier it will be for everybody.
        That also creates kind of a positive situation for very
    small farmers and for farmers with specialty crops because the
    big companies are not interested in data on specialty crops,
    and this is where farmers don't have such large farms, but
    there is a lot of very good data going on there.
        And I just recently heard that in Brazil they are tagging
    individual trees in the orchard and collect data on individual
    trees. We are not doing that right now, but maybe somebody else
    is doing. So the interest is all over the world. Germany is
    investing a lot of money into data as well.
        Senator Moran. Thank you very much.
        Dr. Haman. Thank you.
        Senator Moran. I can see USDA is the agency or the
    department that would be involved in the standardization of
    data.
        Dr. Haman. Probably.
        Senator Moran. That's a slightly different question than
    where I was going, but an important one. And ultimately that
    could be of great value to FSA and to RMA, risk management and
    to farm programs, if the Federal Government actually had the
    information in which they're basing payments, crop losses, or
    crop production, standardized. My question, slightly different
    than that, is no one is out there protecting the farmer.
        And, Mr. Janzen, my understanding of what you do is related
    to transparency, not related to privacy, not related to
    standardization. You've got a--I mean, ``niche'' makes it sound
    less important, but you have an area that you're dealing with
    related to transparency, which is important, but nothing
    broader than that, and no one else is doing what you're doing
    in those other areas, right?
        Mr. Janzen. That's essentially right. I would say we do
    deal with privacy issues because the Transparency Evaluator
    does ask questions that really get at privacy as well and
    protecting that.
        Senator Moran. OK.
        Mr. Janzen. And going back to your question about
    regulation, I think that the Transparency Evaluator really is
    sort of an attempt at the industry to police itself so that
    Congress doesn't have to step in. Now, for that to work, we
    need full buy-in from the industry, and right now I would say
    we have a good first step, but we need wider buy-in from other
    players.
        Senator Moran. When you indicated earlier that there is
    uncertainty as to who owns the data, whose job, whose
    responsibility is it to clear up this issue of data ownership?
        Dr. Ferrell. Well, it's interesting, we've actually tried
    to explore this. There is not really a way to trademark it,
    patent it. You really can't copyright data because copyright
    requires an element of creativity, and data is raw facts, which
    we have a Supreme Court and President that says you cannot
    copyright true facts.
        So that leaves us in the realm of trade secret, and this is
    something that Mr. Janzen and I have talked about quite a bit.
    We think there's a colorable argument to be made that farm data
    is a trade secret. If it is a trade secret, there are a lot of
    strings that come with keeping it a trade secret. And trade
    secret is also a function a state law, not Federal law. So you
    run into some problems with that being consistent across
    states.
        Now, we do have the Uniform Trade Secret Act, which the
    last time I had checked, 48 states had adopted, and the
    remaining 2 states were considering it. But there might be an
    advantage toward a Federal protection there to keep that
    consistent across states and to allow, in a way, more
    interoperability as well because we would have a consistent
    application of those principles no matter where you were.
        Senator Moran. Senator Blumenthal and witnesses and
    audience, this is I think my last question, to give you hope.
        [Laughter.]
        Senator Moran. So my question was going to be about, what
    are the risks and consequences of data breach, data security,
    insecurity? And this question of about, ``Who owns data?''
    causes me to ask, so if someone steals your data, is there a
    question about whether that's a theft because who owns it? So
    is it that uncertain?
        Dr. Ferrell. There's a very valid question with that
    because if you are claiming that your ag data is a trade
    secret, number one, that means it has to derive independent
    economic value from not being known. In other words, the data
    actually has to be more valuable because people don't know it
    rather than people are--you get value from it, but so does
    someone else as well.
        And the argument there is to say, you know, if I have data
    about Mr. Knopf's farm, that doesn't do me any good on my farm
    because I don't have his farm, I have mine. And so the one side
    of that argument is that, well, so what if ag data gets
    disclosed because that's data that's incredibly location-
    specific to your farm, and it gets out there?
        But the other argument is, well, if you know more about my
    farm, you might be able to bid resources away from me. You
    might go to my landlord and say, ``Wow, Ferrell is doing a
    terrible job of farming your place, I could do a much better
    job, and I've got the data to back it up.''
        So there's an argument there that disclosing the data might
    actually reduce its economic value. But the point of that is
    it's really nebulous, and you would spend a lot of time trying
    to make a very legal argument about whether or not you even had
    that ownership in the absence of something more carefully
    defining what ag data ownership really meant.
        Senator Moran. So in today's world of cyber breaches, cyber
    attacks, and data breaches, is there something we should worry
    about in the realm of agricultural production data?
        Mr. Janzen. It seems to me--oh, go ahead. I was going to
    say it seems to me like the most likely candidate for hacking
    into and obtaining disclosure of agricultural data from someone
    that shouldn't have it would probably be some form of corporate
    espionage or, you know, a competitor of one company thinking
    that if they could get their hands on that information, it may
    give them a leg-up in the marketplace. And, you know, I don't
    think we would see that from a United States company, but it's
    very possible that it could be from a foreign company that
    wants to get their hands on information to give them a leg-up.
        Senator Moran. I can see some production facilities who
    would love to keep their information private.
        Mr. Tatge. I think one of the big questions that's yet
    unanswered is the companies that have data, and they've had it,
    collected it for years, what happens to that data when those
    companies are sold? That's the question that is looming out
    there as far as if we've collected data for a long time, and we
    go to sell our company, part of the value of that company is
    based upon the data that you've collected. And did you obtain
    it in ways that were described and really covered under some
    form of agreement? And I would say for the most part today that
    doesn't exist.
        Senator Moran. Justin, Mr. Knopf, if you sold your farm in
    the future, I don't imagine you have enough data at the moment
    to make the data itself valuable, does the data go with the
    farm, or that's a separate commodity?
        Mr. Knopf. Well, that's a fair question. I haven't really
    conceived that question.
        Senator Blumenthal. Why is it not like any other property--
    --
        Mr. Knopf. I think----
        Senator Blumenthal.--the grain that remains in the silo or
    the gas in your tractor or----
        Mr. Knopf. Yes.
        Senator Blumenthal. Now, the interesting question, though,
    is--and we haven't really talked about it--mentioning the word
    ``copyright,'' what about the software in your tractor? When
    you sell your farm and you sell that equipment, you're also
    selling the tractor, but the software belongs to GM or John
    Deere or whoever made the machine, and you can get in trouble
    if you start tinkering with the software even on your car, let
    alone your tractor. So this is a common problem in terms of who
    owns what when we're talking about Big Data.
        Mr. Knopf. Yes, two questions there. Chairman Moran's
    question, I think a lot of it is the value is still very much
    being defined, and it's rapidly changing every day in the
    marketplace because I think as we're--right now, farmers are
    still struggling to know what the value is in that data and how
    to use it, and there's a lot of personal intuition about
    looking at that data from your farm that has to--that another
    person from outside your farm that doesn't have the
    understanding that you do of your farm is not going to see the
    same value that you do in your data. But as models develop and
    software develops to better automate that process, that data
    will become more valuable and an independent value proposition.
        And, Mr. Blumenthal, that's a very appropriate question,
    and perhaps you all talked about this as the right--the right
    to repair, and that's a whole other realm of, you know, when
    our tractors break down with some--a sensor or a piece of
    software that's running something mechanical, we get a code,
    the tractor stops moving. We have to call out the service
    repairman from the implement company that has the only access
    to that software to be able to reset that code. There is no
    third-party competition within the access of that right to
    repair that specific code.
        Now, I understand protecting intellectual property rights
    and innovation and being able to collect and based on your--
    recoup value and innovation and protecting that, but there is
    some--there is also a balance there between at some point in
    that machinery allowing third parties to enter that software
    and to be able to access it for repairs as well.
        Mr. Tatge. One example, just to add on to that, that I've
    heard quite a bit is if you were to buy a piece of land in your
    general area, how many years does it take you to get it to
    perform the way it was performing for the individual before? On
    average, I hear it's probably 3 or 4 years for you to really
    get to know that piece of dirt.
        When you think about that, when you go to resell or when
    you go to market a piece of land, having that history and
    giving the formula as to what was used before I think could
    greatly allow the new acquirer to be able to bring that up to
    the same production standards much faster than it would be on
    their own.
        Mr. Knopf. And we've always had that, you know, we've
    always had soil samples and more kind of raw non-digital data
    on our farms in the past, and that typically, depending on the
    transaction, if it's a friendly transaction, sometimes it will
    just be given over, but as that value increases, that becomes
    more of a very appropriate question.
        Senator Moran. Senator Blumenthal, I take no offense at you
    indicating now this is the more interesting question, I guess
    because truth is a defense.
        [Laughter.]
        Senator Moran. Thank you for an interesting question.
        Senator Blumenthal. Thank you all for your participation
    today. And I want to thank the Chairman for having this
    hearing.
        Senator Moran. Mr. Blumenthal, thank you very much. Thanks
    for your participation.
        The hearing record will remain open--let me--I always ask
    this question, Does anyone, any witness, have anything they
    want to say before we close today's hearing? Anything we
    missed?
        [No audible response.]
        Senator Moran. Great. The hearing record will remain open
    for 2 weeks. During this time, Senators are asked to submit any
    questions for the record. Upon receipt, the witnesses are
    requested to submit their written answers to the Committee as
    soon as possible.
        That concludes today's hearing. I thank you all for your
    presence.
        [Whereupon, at 4:07 p.m., the hearing was adjourned.]

                                A P P E N D I X

       Prepared Statement of Hon. Bill Nelson, U.S. Senator from Florida
        Thank you, Mr. Chairman, and thank you for holding this hearing to
    discuss a topic that is certainly of paramount importance to the State
    of Florida.
        Mr. Chairman, agriculture is one of the most important pillars of
    Florida's economy, with an annual economic impact of over $120 billion
    dollars. We have over 47,000 farms and ranches, which employ over 2
    million people. Many people know about Florida Oranges, which by the
    way make up 60 percent of the total U.S. value, but in addition to
    oranges, we are also ranked #1 in value of production for snap beans,
    cucumbers, grapefruit, sugarcane, fresh market tomatoes, and
    watermelons.
        So I am very pleased to be holding a hearing that discusses how we
    can make farming more productive, more efficient, and more sustainable
    through the use of data. Human activity is drastically changing our
    world's climate, and not for the better, and we will have to use data
    and analysis to adapt agricultural practices and protect workers.
        In Florida, farmers use precision agriculture to monitor the health
    and development of beef cattle and dairy cows. They use it to assess
    soil health in peanut and cotton fields.
        And if a fascinating experiment continues to show promise, citrus
    growers might soon be using lasers to inject pesticides directly into
    the leaves of citrus trees. Last January I visited with researchers
    from the University of Florida Citrus Research and Education Center in
    Lake Alfred, to see this incredible technology in person. A laser makes
    a tiny incision in the leaf of an infected citrus tree, and then a
    machine sprays a bactericide directly into the laser cut to get it into
    the tree's system more effectively.
        Research like this couldn't come at a more crucial moment for the
    citrus industry. Last week, the U.S. Department of Agriculture updated
    its estimate for the 2017-2018 season, lowering the expected harvest to
    50 million boxes. The initial prediction for this season was already a
    decades-low 54 million boxes.
        And that was before Hurricane Irma swept through the state in early
    September. Farmers and ranchers are still trying to calculate total
    losses and figure out how to salvage the season. The initial reports
    are devastating: $760 million in losses for citrus growers, and $2.5
    billion in total agriculture losses in Florida. I'm working very
    closely with Senator Rubio and others to make sure that the next
    supplemental package includes disaster aid for these farmers so that
    they don't go out of business.
        In the meantime, this hearing can explore how precision agriculture
    is playing a role in our fight to find a cure for citrus greening,
    canker, laurel wilt, and other pests and diseases.
        Lastly, this committee has a long history of examining the issues
    of ``big data'' and how it affects consumers and our economy. Whether
    it's a consumer shopping online, or a farmer tending to his or her
    fields, companies are collecting information and monetizing that
    information for their purposes. Just as I have long advocated for
    consumers when they are using their smartphone or shopping at the mall,
    they should have a say in if and how their information is collected and
    used.
        Lastly, I'd like to welcome our distinguished witness panel today,
    but I want to particularly recognize Dr. Dorota Haman (Door-OH-tah HAH-
    man). Dr. Haman is Professor and Chair of the Department of Agriculture
    and Biological Engineering at the University of Florida. She is a
    renowned expert as an agricultural engineer in advanced irrigation
    management and technologies. I look forward to hearing Dr. Haman and
    the testimony from all of our witnesses today.
        Mr. Chairman, I am excited to see the new ways in which
    technological advances can increase agricultural productivity and
    efficiency in my state and in the rest of the country. Thank you for
    holding this important hearing.
                                     ______

          Prepared Statement of Timothy Hassinger, President and CEO,
                              Lindsay Corporation
        Thank you Subcommittee Chairman Moran, Ranking Member Blumenthal,
    Senator Deb Fischer and all of the members of the subcommittee for this
    opportunity to submit this written testimony.
        My name is Tim Hassinger. I am the president and chief executive
    officer of Nebraska-based Lindsay Corporation--a leading manufacturer
    of center pivot and lateral move agricultural irrigation systems. For
    more than 50 years, Lindsay Corporation has been at the forefront of
    research and the development of products and services designed to meet
    the world's rapidly growing agriculture and transportation needs.
        As you may know, it's estimated that by 2050, the global demand for
    food will be 60 percent higher than it is today. To meet this daunting
    challenge, it's imperative that we develop and deploy technologies that
    will help growers produce more with less, while preserving water and
    other natural resources.
        Data-driven farming is the key to allowing growers to meet the
    food, fuel and fiber needs of the rapidly growing global population.
    With the touch of a button or swipe of a finger, farmers who have
    broadband access can:

       Receive commodity price information;

       Monitor and respond to changing weather conditions;

       Use GPS for planting and irrigation management;

       Get real time data on soil and moisture conditions;

       Connect with other farmers and agriculture experts, and

       Store and analyze data to increase sustainability and
            productivity.

        They can also take advantage of a myriad of new technologies now
    available from Lindsay Corporation and other American manufacturers.
    Among other things, these innovations enable remote data collection,
    transfer and analysis from connected devices like soil moisture
    sensors, weather stations and cloud-based support tools. Farmers are
    using this information to streamline their operations, maximize
    efficiency and increase productivity.
        We work with farmers every day, so we know the power that comes
    with the ability to leverage big data. We now offer technology that
    helps farmers decide precisely when, where and how much to irrigate--
    maximizing yields while reducing overwatering and related input costs
    and nutrient losses.
        In recently conducted field studies, our researchers found that
    remote telemetry streamlined growing operations in several key ways,
    including:

       3 percent increase in corn yield (driving profit of $25 per
            acre);

       17 percent reduction in water usage (saving more than 9.25
            million gallons on a 130 acre field);

       $10/acre reduction in energy costs; and

       75 percent reduction in time spent going back and forth to
            the fields (another $5/acre saved).

        This combination of yield enhancement and resource savings can
    increase American farmers' profits by an average of $40 per acre--
    profits that can be reinvested in their operation and in their local
    economy.
        Data-driven technologies allow growers to increase yields while
    conserving water and other natural resources. These technologies are no
    longer luxuries. Rather, they are critical tools needed to increase the
    overall operational efficiency and productivity needed to complete in
    the global marketplace.
                                     ______

                     Prepared Statement of Deere & Company
        Deere & Company (``John Deere'') respectfully submits these
    comments for the record as part of the Subcommittee's November 14, 2017
    hearing on the subject of ``Technology in Agriculture: Data-Driven
    Farming.''
        John Deere is a global leader in the manufacture of agricultural,
    construction, turf and forestry equipment. Deere provides advanced
    agricultural and other equipment and services to customers that
    cultivate, harvest, transform, enrich and build upon the land to meet
    the world's dramatically increasing need for food, fuel, fiber and
    infrastructure. Deere has been providing innovative equipment and
    services to customers since 1837, and today is pioneering state-of-the-
    art data and information solutions designed to greatly enhance
    productivity and sustainability.
    The Value of Data-Enabled Agriculture
        John Deere believes that the growth of data-enabled agriculture is
    as transformational today as was the introduction of mechanization to
    the farm almost one hundred years ago. Insights generated from producer
    data will be critical to meeting the goal to produce enough food and
    build the infrastructure required to sustain a growing global
    population. Properly used, producer data has the potential to greatly
    improve precision, productivity, profitability, and sustainability on
    the farm.
        American farmers face constant pressure to improve efficiency,
    environmental stewardship, and output. For this purpose, farmers look
    to advanced smart farming technology solutions, including solutions
    that take advantage of mobile and fixed broadband access. Today,
    producers are able to farm to within a few centimeters of accuracy
    thanks to innovative GPS-enabled positioning systems that are now
    standard on virtually all modern farming equipment, as supplemented
    with data available from satellite signals. Using these high precision
    techniques, advanced agricultural equipment and services now include
    technology that provides real-time agronomic data that can be analyzed
    to optimize the precise amount of seed, fertilizer and pesticides
    needed, reduce costs for fuel, labor, water, and identify best
    practices for fields in a given location. (Deere's Precision Ag
    Technologies, for instance, gives farmers access to detailed agronomic
    information in the field essential for improved decision-making with
    respect to managing costs and recourses.)
        Where possible, producers use data and communication technologies
    to interact with customers and vendors, follow commodity markets,
    obtain real-time information on field conditions, weather and other
    environmental factors, and manage fleets and regulatory compliance.
    Farmers can also employ innovative machine-to-machine (``M2M'')
    operations in the field and machine-to-farm (``M2F'') from the field
    that enable producers to make significant improvements in real-time
    productivity and cost management.
        Today, these technologies are making an enormous contribution to
    improved use of limited resources, regulatory compliance and ag
    sustainability. Precision technologies are enabling more efficient,
    prescriptive use of soils, water, fertilizer, herbicides and fuel by
    allowing producers to tailor farming practices and applications to the
    specific conditions of an individual field.
        For example, when the farmer leaves his field in the fall, he is
    able to share harvest yields directly and immediately with trusted
    agronomist advisors. This helps the advisor to prescribe the
    appropriate amount of nutrients to be added back to the soil, based
    only on what the farmer took off at harvest, and ensure those nutrients
    are added and incorporated before winter. The farmer can also make
    decisions on which seeds to buy for next year, taking advantage of
    early order price discounts. By reducing inputs, improving resource
    management, minimizing land impacts and lowering costs, these
    technologies are delivering the promise of sustainability on the farm.
        The economic impact of these technologies is significant. According
    to recent reports, data-driven decisions about irrigation,
    fertilization and harvesting can increase corn farm profitability by $5
    to $100 per acre, and a recent 6-month pilot study found precision
    agriculture improved overall crop productivity by 15 percent.\1\
    ---------------------------------------------------------------------------
        \1\ See Kurt Marko, Forbes, Precision Agriculture Eats Data, CPUC
    Cycles: It's a Perfect Fit for Cloud Services (Aug. 25, 2015),
    available at: http://www.forbes.com/sites/kurtmarko/2015/08/25/
    precision-ag-cloud/.
    ---------------------------------------------------------------------------
    The Importance of Data Privacy
        In addition to offering a full line of innovative, high-quality
    agricultural equipment to producer customers worldwide, John Deere
    provides data and data application services that support customer
    business needs and the optimal utilization of Deere machines. These
    services are provided through Deere's proprietary data management
    platform, the John Deere Operations Center.
        John Deere believes that all involved in the generation and use of
    data and data services should have effective processes in place to
    ensure privacy, security and ultimate control for the producer. Deere
    has been actively engaged with individual customers, grower
    organizations, ag service providers, agronomists and many others to
    develop practices and processes that ensure producer privacy and
    control, while making data processing, analysis, and use as seamless as
    possible. Deere believes that the market participants across this value
    chain--through collaboration, private agreement and mutual trust--are
    best able to develop and implement the necessary practices and
    protocols that protect producers and serve commercial needs. To this
    end, Deere has developed a set of business data principles that govern
    its use of production data, machine data and administrative, and are
    incorporated into every customer's John Deere Operations Center
    services contract. These principles are designed to give the customer
    the ability to control whether and how his/her data can be used, by
    whom, and for how long.
        These principles are:

      1.  Deere provides data and end-user application services for one
            reason: to support the business needs of its producer customers
            and improve the use of Deere equipment and technologies.

      2.  The producer's data should be differentiated into machine,
            production, and administrative data, and each data subset
            should be managed in accord with these important distinctions.

      3.  Deere utilizes customer data only with the customer's consent, in
            order to improve grower productivity and profitability, and to
            optimize the utilization of John Deere products and services in
            the customer's farming operations.

      4.  The producer customer retains control of his business data
            including whether, what and how his data is used and shared.
            The customer may withdraw this consent or request that data be
            deleted from his account at any time.

      5.  Any disclosure of explicit customer business data is determined
            solely by the customer's designated account preferences and
            through contractual agreements with John Deere.

        Farming is a complex, dynamic industry and this makes the notion of
    farm data ownership complex as well. Farmers use Deere's tools and
    offerings in many different ways, which further complicates the issue
    of ownership. Expectations, relationships, contracts and laws regarding
    data control and ownership vary from place to place, operation to
    operation and even on a single farm. Companies that assert that farmers
    own their data are not being transparent--it is not as simple as that,
    which is why John Deere is focused on efforts to ensure that farmers
    control their data.
        Real-world circumstances that make data ownership complicated and
    uncertain include:

       Custom harvesters or equipment operators who may have the
            right to share production data.

       Landlord and/or tenants who may have the right to share some
            or all production data from a farm.

       Agronomists and other consultants who may have the right to
            share data.

       A farmer who may buy licenses to use commercial prescription
            files, other technologies, or seed hybrids that the farmer does
            not own.

        This is why Deere believes that customer control of the data is the
    most important issue. Deere's data management services and applications
    are designed to ensure customer control of their farm's data.
        There are important distinctions between the types of data that are
    generated through integrated ag technologies, and Deere and its
    customers agree to manage these differentiated data sets accordingly.
    John Deere segregates customer data into three subsets--Machine Data,
    Production Data, and Administrative Data.

       Machine Data are data that generally relate to how equipment
            is functioning (fuel consumption, vehicle diagnostic, engine
            performance). This data may be utilized, with the customer's
            consent, in original or aggregated, anonymized form to
            proactively address equipment issues and improve the customer's
            experience with the machine.

       Production Data relate to the work being performed by the
            customer, and enable Deere to administer services the customer
            has opted into, such as field tasks, location history or
            wireless data transfer. By using our systems, customers agree
            to allow Deere to create aggregated and anonymized Production
            Data sets. These anonymized data sets are proprietary to John
            Deere. John Deere is free to use and disclose the anonymized
            data, and John Deere may promote information and services
            derived from anonymized data. Anonymized data is never
            traceable back to a specific customer.

       Administrative Data is information that helps Deere support
            a customer's account and activities in our system. Examples of
            administrative data are: data sharing permissions, account
            users, machines and licenses connected to accounts, acres or
            file sizes.

        These distinctions are a critical part of the data management
    process. They preserve customer control while distinguishing the
    sensitivities associated with certain data sets. They are reflected in
    the contractual agreements between John Deere and its customers.
        It should be noted that the marketplace for technology around data
    collection, transmission, storage and use is evolving rapidly and will
    continue to evolve in the years to come. Producers will continue to be
    presented with new options and product offerings that can deliver even
    greater value, while rewarding the most innovative technology and
    service providers at the same time. This can best happen through the
    collaborative private sector efforts of market participants, without
    the specter of more rigid standards or codes imposed from outside that
    could stifle innovation. The Ag Data Transparency Evaluator, created
    and managed in conjunction with the American Farm Bureau Federation, is
    a good example of this private sector effort. John Deere played an
    integral part in the creation and implementation of this tool. We are
    actively working with this group to achieve its ``seal of approval.''
    Initially, discussions around the requirement to recognize farmers'
    ownership, rather than control, of data slowed our progress. After much
    discussion, Deere believes we have greater alignment with this group
    and look forward to adding our name to the list of companies that have
    gone through the transparency process.
        Finally, it should also be noted that, without essential broadband
    connectivity in croplands, many of the potential benefits of data-
    driven agriculture can never be realized. Real-time ag services using
    data generated on the farm are dependent on reliable, high-speed wired
    and wireless connections to the Internet--connections that in turn
    depend on a robust rural broadband infrastructure that is currently
    lacking in many parts of the country. More attention must be given at
    the Federal level to ensure that the build-out of wireless broadband
    infrastructure, including connectivity in the fields where farmers and
    equipment operate, is achieved.
        At the heart of John Deere's efforts and principles around data-
    enabled agriculture lies our history of, and commitment to, helping
    those linked to the land. Since 1837, John Deere has been building
    lasting relationships with agricultural producers based on our core
    values of integrity, quality, innovation, and commitment. Deere
    believes that the trust we have established with producers, built up
    over these 180 years, is exponentially more important than the value we
    might derive from producer data.
        Deere & Company appreciates this opportunity to share its views,
    and looks forward to working with the Subcommittee on these important
    issues.
                                     ______

        Response to Written Questions Submitted by Hon. Jerry Moran to
                                  Justin Knopf
        Question 1. Your testimony described the usage of data as a
    ``driver of economical sustainability and environmental stewardship.''
    As a Kansas farmer focused on both being a good steward of the land and
    making a living to provide for your family, could you please further
    describe your efforts to balance sustainable farming practices with
    improving efficiencies to increase profits? Can the two goals go hand
    in hand?
        Answer. Yes, often the two goals can go hand in hand. I believe
    most ``sustainable farming practices'' will have positive impacts on
    profit in the long term. The challenge is ``the long term'' might be
    ten years, or twenty, or a lifetime. There are numerous examples of
    sustainable farming practices which evidence would point to having a
    long term positive impact on profit, yet be a possible net cost in the
    short term. These types of practices will tend to have slower adoption
    curves. An example of one of these practices on our farm would be cover
    crops. They are crops planted between our main grain crops solely
    intended to provide environmental benefits to soil protection and
    health. The seed and investment in time and machinery to plant them is
    a significant cost and we have yet to document a yield or economic
    benefit to the subsequent grain crop. However, evidence and agronomic
    principles predict that across time, the environmental and biological
    benefits from cover crops in our climate and soils will improve the
    resiliency of our farm and perhaps the productivity, and therefore
    profit. In the meantime, we utilize data from both on farm research
    trials and field scale to evaluate what cover crops in which part of
    our cropping sequence will have the most impact with the least amount
    of cost. We then start with limited acreage and hopefully scale the
    practice to broader implementation as we learn and begin to reap some
    benefits over time.
        There are other examples of sustainable farming practices that will
    also improve efficiencies to increase profit as well in the short term.
    An example from our farm is zone management. We utilize various sources
    of data, typically multi-year yield maps, satellite imagery, and soil
    maps, to divide a field into zones based on productivity. Then, the
    more productive zones of the field receive the right amount of
    fertilizer to sustain that productivity while fertilizer rates are cut
    on less productive zones. Right away, we have improved the efficiency
    of our fertilizer which increases our return on that investment, plus
    reduces fertilizer carryover and loss into the environment.

        Question 2. How does data collection and sharing specifically
    assist farmers in striking the appropriate balance, including
    innovations in live-time monitoring of crops and measurements of
    surrounding conditions?
        Answer. Data collection and sharing improves our understanding of
    how crops are impacted by certain factors such as weather events,
    management decisions, soil types, etc. Obviously some of these factors
    are outside of the farmer's control, but access to improved live-time
    monitoring of crops and measurement of surrounding conditions can help
    farmers be more proactive in predicting crop response to these factors
    or events and lead to more timely and improved decision making. One
    quick example, this past summer we had a summer hail storm that
    significantly reduced soybean stands in its path. We utilized satellite
    images of the impacted fields that were available several days after
    the storm to fine tune our scouting and decision making about where to
    replant and where we could salvage the stand of soybeans.

        Question 3. Your testimony divides the data that your farm
    specifically uses into three categories: microdata, service provider
    data, and macrodata. Will you please describe how farmers and their
    operations benefit from each category of data?
        Answer. Microdata-this is data a farmer collects from his own
    operation and is specific to his operation. This likely helps better
    characterize specific aspects or management factors unique to his farm,
    leading to improved decision making.
        Service provider data-this is data that is produced by a service
    provider outside of the farm. Likely, there will be data from the farm
    shared with or collected by the service provider, but then the service
    provider will utilize data from that farm and perhaps integrate it with
    data from other farms and/or a proprietary algorithm or internal data
    set to provide analyzed data back to the farm in order to help with
    improved decision making by the farmer.
        Macrodata-this is ``big data'' collected from many farms likely
    across a broad geography. The farmer may or may not have contributed
    data from his farm, however, there are insights gained from the sheer
    volume of data that may not be possible if the data set was not so
    large. The insights may be more universal in nature yet still applied
    by many farmers to improve decision making.

        Question 4. In a 2016 poll conducted by the American Farm Bureau
    Federation, regarding the loss of control over downstream uses of data,
    sixty-six percent of the farmers polled expressed concern about not
    being compensated for the potential benefits from the use of their data
    beyond the direct value they may realize on their farm. Meanwhile,
    sixty-one percent of the farmers were concerned that agricultural
    technology providers (ATPs) could use their data to influence market
    decisions. Which of the two concerns do you believe is the greatest
    threat to farmer profitability and well-being, and what should be done
    to alleviate these concerns?
        Answer. I don't know which of these two concerns is the greatest
    threat to the farmer. As the statistics indicate, both are of
    significant concern to many farmers. Farmers are accustomed to dealing
    with concrete things we can put our hands on-tractors, soil, grain.
    Data is very abstract and therefore more difficult for farmers to
    quantify the value of, although most of us certainly recognize it does
    have value. As was mentioned in the hearing, I believe one of the most
    important steps to reducing the threat of non-compensation is
    transparency and understandable communication up front before data
    transactions and agreements take place. It is important the farmer can
    quickly and easily understand what is happening with his data and the
    parameters of any data agreement he is considering. I also believe it's
    important to recognize that farmers may be compensated for their data
    in forms other than money. Compensation may be access to insights
    gleaned from the larger data set they are contributing to, or access to
    a proprietary decision making tool.
        I believe most would agree that there is power in data and
    recognize the consolidation in agricultural companies, which is likely
    why farmers feel concern about their data being used to influence
    market decisions. Consolidation and concentration of data is perhaps
    something that should be monitored.

        Question 5. With connectivity being crucial to the successful
    implementation of the technology we have discussed today and almost 30
    percent of farms not receiving adequate broadband connection according
    to the USDA's Farm Computer Usage and Ownership August 2017 report,
    what role can this Committee play in closing the gap to make sure all
    of our farms are able to benefit from broadband and innovative
    technologies? Do you see a role for advanced wireless networks in
    achieving that goal?
        Answer. Our farm is one of the 30 percent not receiving adequate
    broadband connection. We do have a broadband connection, but to date it
    is slow, not very reliable, and with only one provider choice, there is
    no competition to drive improvement. Nearly all data management
    software has now become web based so as the amount of data on the farm
    needed to be uploaded and downloaded exponentially increases, effective
    utilization of data and implementation of technology becomes impossible
    without reliable and high speed data transfer technology. My time as a
    farmer is very limited as it is, especially during the growing season,
    so I cannot afford to sit and wait on a slow data connection. There
    have been numerous times I've had to abandon a project because of slow
    data transfer. However, I am hopeful as a local communications company
    has undertaken the project of running new fiber optics to rural
    residences and farms in our community. This will help with effective
    data transfer from our farm office, but it will take advanced wireless
    networks to achieve this goal in the field from mobile devices. It is
    my hope this committee would have a renewed commitment to learning
    where these gaps still exist and assisting small local companies, such
    as Home Communications, Inc. in Galva, Kansas, along with wireless
    network providers in closing those access gaps to fast and reliable
    data networks.
                                     ______

    Response to Written Questions Submitted by Hon. Catherine Cortez Masto
                                to Justin Knopf
        Question 1. Obviously in the west we have some greater agriculture
    challenges than other regions, and wildfires are one of them that we've
    seen have detrimental effects over and over again. In many cases, these
    fires have a multiplier effect on production and for things like future
    flooding. And, in general, water is also another constant challenge.
    Are there technologies that can help measure and account for drought
    conditions, or measure the volatility of fires within parched forests
    or grasslands before we have fires that potentially get out of control?
    Are there any associations with improving broader weather prediction or
    forecasts? And are there other specifics where you foresee this data
    and technologies helping us get a better handle on our climate change
    crisis?
        Answer. It has and continues to be heart breaking to see and hear
    reports from the west on the devastation caused by these wildfires.
    We've had devastation in Kansas as well from wildfires recently.
    Thousands upon thousands of acres of grassland have been burned,
    countless cattle and miles of fencing lost, and homes and barns that
    have been passed down through families on the prairie for generations
    destroyed. There are certainly technologies that can provide data to
    help measure and account for drought and predict risk of fire. While
    I'm certainly not the best person to speak to these technologies, I can
    give you an example from Kansas that is helping. Since 1986, Kansas
    State University has managed a network of high tech weather stations,
    called the Kansas Mesonet. Through recent efforts to grow the number of
    stations in the network, they now have coverage across the state. These
    stations are not only monitoring and recording typical weather data,
    but also soil moisture, which is of course helpful in characterizing
    drought, and when used in conjunction with relative humidity and wind
    speed, has been a helpful proactive warning for high risk of grassland
    fires. I believe increased number of weather stations such as these
    that are available for farmers, ranchers, emergency preparedness
    personnel, and the public to access will hopefully help quantify and
    characterize more extreme weather, in turn leading to increased
    preparedness.
        There are technologies being implemented on farms that are scalable
    enough to help mitigate climate change. Two specific examples are
    improved fertilizer management through using the right source, rate,
    time, and place for plant nutrient applications, which significantly
    reduces Nitrous Oxide emissions, and also no-till farming practices
    which sequesters Carbon Dioxide from the atmosphere in the soil. With
    the help of Dr. Charles Rice, soil microbiologist at Kansas State
    University and world-renowned soil Carbon expert, I recently calculated
    that through building soil organic matter by implementing a no-till
    based cropping system, our family farm has offset the average Carbon
    emissions for roughly 4000 Americans. The soil is one of the biggest
    Carbon sinks on the planet and practices such as no-till that increase
    the organic matter of the soil not only offsets Carbon Dioxide, but
    also improves the ability of the soil to capture and hold water,
    reduces erosion, and allows soil biology to thrive, all leading to a
    more productive and resilient system.

        Question 2. There were many references to the environmental
    benefits of agricultural data. Are we in a position yet where we can
    authoritatively quantify the environmental benefits experienced by the
    use and attention to these technologies and data analysis? For example,
    is this science proven to the point that we should be creating
    incentives in the farm bill conservation title for their utilization to
    keep pristine watersheds like Lake Tahoe, or water quantity in drought
    areas, solidified for the decades to come? Or is there a place for this
    use in connection with the Federal crop insurance program?
        Answer. Yes, we are now able to much better quantify environmental
    benefits experienced through utilization of technology, data, improved
    decision making, and effective conservation practices. However, the
    challenge becomes as geography, climate, soils, and numerous other
    things change from state to state, region to region, and even farm to
    farm, the outcomes and impacts of practices will vary widely.
    Therefore, the ``right'' decision for meaningful and long lasting
    environmental impact on my farm may be the ``wrong'' decision for a
    farm 100 miles west of me. Farmers are operating within an incredibly
    complex biological system with an infinite number of relationships all
    impacting each other. This is why it becomes so difficult to legislate
    effective change to a biological system. That being said, I do believe
    incentives can be incredibly effective at driving long lasting and
    meaningful change if they are flexible and tailored to a local level by
    local experts and advisors whom farmers trust. I have personally
    utilized the Conservation Stewardship Program in the farm bill to help
    offset some of the risk and short term costs in utilizing cover crops,
    which has allowed me to implement the practice on more acres. And I do
    believe crop insurance could be an effective avenue to offer incentives
    for implementing conservation practices, but they must be practices
    that are proven to be effective, economic, and increase reliance at a
    local level. As you know, the devil is always in the details.
                                     ______

        Response to Written Questions Submitted by Hon. Jerry Moran to
                              Dr. Shannon Ferrell
        Question 1. In your testimony, you state that as participation in
    the data community increases to a critical mass, farmers' bargaining
    power with the data service providers likely will be greatly reduced
    and a majority of the value will be enjoyed by the providers. You then
    state that for farmers to take maximum economic advantage of Big Data
    tools, large numbers of farmers must ``buy-in'' and participate in the
    data community. Where is most of this value enjoyed by the data service
    providers derived from? What can be done to mitigate the disparate
    levels of value received, especially for producers?
        Answer. Witness response to Question 1, part A: Where is most of
    this value enjoyed by the data service providers derived from?
        Before discussing the source of the value enjoyed by data service
    providers through data compilation and deployment of Big Data tools, it
    is important to discuss the value of data at the farm level in the form
    of Small Data. At the individual farm level, farmers may take advantage
    of today's data collection and analysis tools to run on-farm
    experiments with respect to seed varieties, fertilizer applications,
    moisture management, and so on. They can also use data to calculate
    crop shares for rent, look for improved efficiencies in equipment,
    management, conservation practices, and so on. In these uses, farmers
    capture all of the data's value on-farm in the form of increased
    returns or reduced costs.
        While these ``micro'' level benefits can be considerable for the
    individual producer, there can also be significant value derived
    through the use of Big Data systems and analytical techniques at the
    ``macro'' level when data from hundreds or even thousands of operations
    are aggregated. As mentioned in the written testimony, Big Data
    analytics can be used to much more rapidly develop hybrids by running
    trials across multiple soils and environmental conditions, developing
    more accurate and robust models for predicting risk factors such as
    weather patterns and production numbers, and development of improved
    agricultural equipment. All of these items can be derived from the
    aggregation of data from farmers and provide value back to those
    farmers.
        When one examines the potential economic values of data for the
    data service providers, there are five primary sources to consider:

      (1)  Data service providers can derive revenue from the services they
            provide directly to the farmer that provided the data. This
            could be in the form of service fees for things like data
            collection and validation, creating a repository of the
            farmer's data that can be easily shared with other parties to
            whom the farmer would like to provide access (such as a crop
            consultant, landlord, etc.), or in the case of a data service
            provider that also serves another role such as a crop
            consultant, providing reports, prescriptions, or
            recommendations to the farmer based upon the data.

      (2)  The data service provider could derive revenue from using the
            data to market goods or services to the farmer. For example, if
            the data service provider is a subsidiary of a seed company,
            the farmer's data could help the seed company make seed
            recommendations that are a good fit for the farmer's operation.
            This form of focused marketing can sometimes benefit the farmer
            as well. For example, consider purchasing a mixing bowl for
            your kitchen on Amazon. Based on that purchase, Amazon suggests
            a whisk and baking sheet that are commonly purchased with your
            mixing bowl that have gotten favorable reviews from people who
            purchased all three items simultaneously. If you also make the
            suggested purchases and enjoy them, it is a ``win-win''
            transaction for both you and Amazon. Similarly, purchasing a
            seed variety that performs well for your farm and increases
            profits creates a win-win for you and the seed company.
            However, companies can also use a farmer's data to provide
            recommendations while extracting more profit from the producer.
            As another example, seed companies already use information
            about producers to know which seed varieties are better suited
            to the farmer's land and charge them more for that variety than
            they would charge another producer. With increasing access to
            producer data, input suppliers could continue to derive more
            precise information about a producers willingness to pay (or
            ability to pay) for their inputs and adjust their pricing
            accordingly.

      (3)  Data service providers might provide data products or services
            based on farmers' data to other companies. For example, data
            service providers might sell reports or predictive models to
            insurance underwriters might help them price crop insurance
            products. These reports or models might be derived from
            farmers' data but their sale would not necessarily involve the
            transfer of the farmers' data.

      (4)  Data service providers can function as data aggregators and then
            sell the farmer's data to third parties, deriving revenue from
            those sales. In some cases, such data service providers may pay
            the farmer for their data, but in other cases they may charge
            the farmer a fee for their data collection services (while
            providing some analytical services or reports back to the
            farmer) and also derive a fee from the sale of the data to
            other entities.

      (5)  Eventually, if and when enough farms join data networks, a fifth
            use could come of that data--use of that information for
            significant transactions in commodities markets. A hypothetical
            example would be a data service provider who had access to a
            sufficiently large sample of farms to make accurate predictions
            of eventual U.S. crop yield who then takes positions in the
            commodities markets well before anyone else would be able to
            access that information.

        A potential sixth value source is from the data service provider
    positioning itself as an acquisition target with the purchasing company
    getting either the data it holds and/or the subscription relationships
    with the farmers it serves. Economic theory and historical precedent
    both suggest that we will see an evolution in the agricultural data
    industry starting with a large number of service providers vying to
    engage farmers because, as Metcalfe's law suggests, the value of their
    data networks will increase as a square of the number of their network
    participants. Better-capitalized firms or firms with another
    competitive advantage will acquire other firms until eventually only a
    handful of dominant service providers--or even a singular monopolistic
    provider--emerges. In the course of this evolution, the more farmers
    and data a company can acquire, the more attractive they become as an
    acquisition target. While some firms are certainly pursing the strategy
    of becoming one of the dominant providers, it is equally certain that
    other firms are seeking simply to be acquired.

        Witness response to Question 1, part B: What can be done to
    mitigate the disparate levels of value received, especially for
    producers?
        Answer. Research continues to determine both the value of
    agricultural data in the aggregate and what proportion of that value is
    captured by the farmer relative to others in the value chain. As with
    the current USDA estimate that farmers capture 15.6 cents of the food
    dollar, it is likely farmers will not capture a large proportion of
    data values since they are relatively small, ``atomistic'' players in
    the market with little bargaining power and face significant barriers
    to the kind of collective action necessary to increase that bargaining
    power.
        Having said that, farmers' bargaining power may be at a maximum
    right now. As mentioned in the response to Part A of Question 1, most
    data service providers recognize they are in a race to acquire access
    to the data of as many farmers and their acres as they can, as quickly
    as possible. Some are approaching this with the strategy of a
    telemarketer telling a prospect ``sign up today, because this offer
    will be gone tomorrow!'' However, farmers should be thinking like
    economists, and carefully weighing the benefits presented by any
    particular service provider with the value they can receive from that
    provider's services (or, indeed, the payment the provider is offering
    to secure the farmer's data). To that end, farmers should ask five
    questions of any prospective data service provider:

      (1)  How many growers/farms/fields/acres are in the data service
            provider's data community? The higher the number, the greater
            the value it can potentially provide to the individual farmer.

      (2)  What analytics conducted on the community will benefit my farm?
            This aims at the direct ability of the data service provider to
            increase the farmer's ability to make profitable decisions,
            regardless of any external benefits.

      (3)  What data quality control standards are being used? If the data
            service provider is not taking strong measures to ensure the
            quality of the data in their community, it cannot provide
            reliable insights to the producer. To quote the age-old
            computer principle: ``garbage in, garbage out.''

      (4)  What uses will be made of my data? This question has a number of
            implications discussed in the responses to other questions
            below, but here its purpose is to help the producer gauge what
            potential values the data service provider may be trying to
            capture that will not directly benefit the producer (or may
            actually increase costs for the producer).

      (5)  What assurances can the data service provider make that the
            farmer's data will not be provided to third parties without the
            ability of the producer to share in the revenues from that
            transaction? Consider the analogy of a farmer giving an
            unrestricted easement to an oil pipeline company. The farmer
            may get a payment for the easement, but the oil pipeline
            company may be able to sell co-located easements to a natural
            gas company, a telephone company, a fiber optic company, and so
            on, without the farmer having any ability to capture the value
            realized from those transactions. Similarly, if a farmer does
            not have an agreement restricting ``downstream'' uses of his or
            her data, they only have one opportunity to capture the value
            of that data.

        While farmer's negotiating power may be at a peak, it may also be a
    matter of timing to make sure farmers do not completely miss out on the
    ability to capture the value of their data. As the industry continues
    to evolve, we will likely see a progression going from a) farmers
    paying data service providers for their services, b) data service
    providers realizing they need more and more farmers in their network
    and thus reducing their costs, potentially to nothing, to join, c)
    companies actually paying farmers for their data, but then d) companies
    securing a critical mass of farms or acres to have sufficiently robust
    data networks that they no longer need additional farmers to join.
        In the end, the most effective means of helping farmers secure the
    maximum amount of their data's value may be educational efforts to help
    them determine the value of their data, evaluate data service providers
    and their agreements, and make informed choices about their data
    sharing relationships.

        Question 2. You note the irony in the growth of proprietary data
    network protocols that lead to complaints about the lack of
    interoperability of farm equipment systems also providing greater
    protection against data breaches. What measures can be taken to
    continue improving the interoperability of data collection without
    sacrificing security?
        Answer. Witness response to Question 2: There are two primary
    reasons computer viruses are far more problematic for computers using a
    Windows operating system than an Apple OS system: first, far more
    machines use Windows, and second, the nature of Windows architecture
    permits more access points to its code than Apple OS. At the same time,
    one could also make the argument that all computer users have
    benefitted from consolidation in the computer industry in that so many
    programs are now interoperable and data can be shared among what is now
    billions of other users with relatively little friction. By analogy,
    farmers and data service providers may both benefit from consolidation
    in the industry and the increased interoperability it provides.
        Such consolidation may also ``consolidate'' security concerns as
    more eggs are kept in fewer baskets. This is the Windows problem
    again--since there are far more Windows-based computers in the world,
    virus writers devote more resources to viruses that target them. Thus,
    as consolidation continues in the agricultural data sector, increased
    research and development efforts will be needed to make sure that these
    ``fewer baskets'' are guarded by increasingly robust security tools.
    Congressional support of these research efforts would benefit not only
    agricultural producers but a vast number of Americans who rely on data
    security to keep their personal information secure.
        The increasing automation of agricultural data collection and
    transmission tasks might actually serve as a means of increasing data
    security. The second reason Windows computers suffer from so many
    security issues is the Windows operating system was not built with
    security as one of its primary concerns, since its foundations were
    built before networked computing was a primary use of PCs. As a result,
    there are far more access points to its code that virus writers can use
    to insert malicious instructions. Conversely, there are fewer access
    points in Apple OS--a system built from the ground up for networked,
    multi-user applications that thus requires explicit user permission for
    code to be activated. The analogy in agricultural data is that the
    tighter integration of data networks across agricultural equipment
    creates fewer intervention opportunities for third parties. The market
    will likely continue to drive this integration. With that said, the
    issue of user's data security needs to be continuously brought to the
    attention of hardware and software developers so they can keep security
    as a foundational principle in their designs.
        Another tool that might aid the security of farm data networks may
    not seem to be directly connected to the security issue, but certainly
    is: the expanded development of broadband cellular networks in rural
    areas. Cellular transmissions are encrypted by the nature of the
    processes that make cellular communication work; this has the most
    beneficial side effect of adding to the security of the transmission.
    However, in many rural areas, sufficient cellular signal or bandwidth
    is not available to make use of many current agricultural data
    technologies, requiring farmers to manually download and transmit their
    data using much less secure methods. Expansion of rural broadband
    cellular access would have the dual benefit of making agricultural data
    tools more accessible and profitable for farmers while also reducing
    the security risks associated with collecting and transmitting
    agricultural data.

        Question 3. In your testimony, you note that the current legal
    framework for ownership of agricultural data is inadequate for the
    transfer and aggregation of agricultural data. Is the agricultural data
    space unique enough to require specific legislation regarding ownership
    and property rights, or is a novel combination of existing property
    ownership laws more appropriate and adequate?
        Answer. Witness response to Question 3: One of the first questions
    farmers always ask about their data is ``do I own it?'' It is a natural
    question to ask, since farmers depend on access to land whether it is
    owned or leased, and thus are closely attuned to property rights.
    However, traditional notions of ownership break down to some extent
    with agricultural data. Thus, the better question for the farmer to ask
    may be ``what rights do I have with respect to my data, and what rights
    to others have with respect to it. The most critical element of this
    may be what rights (and abilities) does the farmer have to exclude
    others from access to the data.
        If one thinks about it, the notion of ``privacy'' is really a
    function of one's ability to exclude others from access to information.
    For example, HIPAA's provisions regarding health information naturally
    couples with the notion of privacy in one's health matters, as they
    pertain to matters of one's own body. Thus, HIPAA provides a legal
    right to exclude others from access to health information without
    explicit consent for the disclosure of that data. The Fair Credit
    Reporting Act (FCRA) covers matters of financial information, which
    also couples with traditional notions of privacy and the right to
    exclude others from access to one's financial records.
        However, the barriers start to blur a bit with the FCRA in that
    financial transactions mean reaching out to and communicating with
    another party; we recognize that others may have some limited right to
    access our financial information if that information is relevant to
    their financial risk in some respect, such as loaning us money.
    Therefore, we allow credit bureaus to collect financial information and
    to disclose that information to others so they may make credit
    decisions about us.
        The credit reporting analogy is important to understanding whether
    we need a specific legal framework for agricultural data. Whenever
    someone requests credit, they are required to ask if the potential
    borrower will give consent to the lender to access a credit report.
    Thus, for a third party to make use of the financial data, they must
    have the consent of the person about which the data is collected.
    Conversely, though, in many farm data agreements, the farmer may not
    have the right to approve or deny access to their data to
    ``downstream'' users.
        One could argue that agricultural data is significantly different
    from HIPAA-protected health information (about the workings and
    condition of one's own body) or financial data, but one should also
    consider the fact that farmers lack the ability to protect their
    information from disclosure. Put another way, it would be impossible
    for farmers to try and keep confidential much of their production
    information and practices because they can literally be seen from
    aerial or satellite imagery. With relatively little effort one can tell
    how many acres a particular farmer owns and what proportion of it is in
    what crop for a given year (compare this to a bicycle maker or a coffee
    shop--it would be extraordinarily difficult to determine the volume or
    nature of their business, or to even tell one business from the other
    without continuous monitoring of the business at high image
    resolutions, simply because their businesses have roofs and farms
    cannot). It could be argued then that perhaps farmers should be given
    more protections than other businesses because one could derive a
    significant amount of financial information about them from publically
    available resources, to say nothing of the improved ability to do so if
    one coupled data sources from a data service provider that transferred
    that information without the farmer's consent.
        However, if one did desire to provide enhanced protections for
    agricultural data and allow farmers to exclude others from their data
    without explicit consent, two significant barriers loom. First, one
    would have to define the type of agricultural data subject to the
    protection. This would be challenging, given the broad diversity of
    data that can be collected and transmitted on farms today. Second, it
    would be difficult to define when such consent would be needed. Would
    the farmer have to give consent to any data transfer to another party?
    There would be significant transactional costs in such an approach.
    Further, there are doubtlessly data uses that will be available in the
    near future that might not even be conceivable today; it would be quite
    challenging to give informed consent in an up-front data use agreement
    when one doesn't know what data uses might be possible in the future.
        The current legal framework might be serviceable as an interim tool
    to help provide farmers some grounds for the excludability of
    agricultural data, and enhancements to that framework may be possible.
    In the near term, though, perhaps one way to help farmers maintain
    control of their data is additional research into encryption algorithms
    that give farmers a key that would be required to access the
    information--this would put more control over downstream uses back in
    the hands of the farmers, and also give them an increased ability to
    participate in the value received for data transactions.

        Question 4. As more and more firms enter the agriculture-technology
    space and interact with data used by and/or generated by farmers, the
    need for clarity and consistency on privacy principles is growing. For
    these new entrants, can you suggest any best practices these firms
    should engage upon to ensure their data privacy procedures properly
    convey the data's expected use?
        Answer. Witness response to Question 4: Much use is made in the
    agricultural data industry of the word ``transparency'' but there can
    often be much ambiguity in what that term means. The greatest value of
    that term, in the witness' opinion, is to err on the side of disclosure
    to the farmer when discussing the internal and external uses that the
    data service provider will be making of the farmer's data. Those uses
    should be disclosed clearly in language that is understandable by
    farmers with a wide range of experiences and educational backgrounds.
    One such example may be a Truth in Lending Act (TILA) disclosure.
    Though an agricultural data use agreement might not bear a clear
    analogy to a lending transaction, TILA makes clear the potential
    impacts of the lending transaction and the borrower's rights and
    remedies. Data service providers could benefit from making sure their
    data use agreements have similar levels of clarity.
        Another principle beyond the clarity of the disclosure is its
    frequency. Using another financial analogy, individuals can use credit
    monitoring services to receive notifications when someone makes a
    credit inquiry about them. Data service providers could also provide
    notices when an external entity has made a request to access the
    farmer's data, or when a new internal use is made of the data. Robust
    notification procedures can also help farmers take protective actions
    in the event of a data breach.
        As mentioned in the response to Question 3, there arises the issue
    of informed consent when a new data use arises that was not
    contemplated by the original data use agreement. Though it might
    increase transactional costs, the simple answer to this problem is to
    require disclosure of a potential new use and secure the farmer's
    consent to the use before it is implemented. The counterpoint to this
    approach, however, is that its increased transactional costs might make
    companies implementing it less competitive than those who do not.
        Finally, as new companies enter the agricultural data sector, they
    would do well to avail themselves of the efforts of farm groups,
    existing data service providers, and equipment manufacturers to develop
    consensus on the principles that should govern agricultural data
    management. The Privacy and Security Principles Farm Data developed by
    the American Farm Bureau Federation and the Ag Data Transparency
    Evaluator are two good starts for companies to use in developing their
    operating policies and procedures. Both of these tools continue to
    develop, and the dialogue can provide greater benefits to the
    agricultural industry with increased participation from more farmers
    and data firms.

        Question 5. While much of the data we discussed in the hearing is
    generated on farm and captured by farmers or their equipment,
    significant quantities of data is publicly available and critically
    important to inform risk modeling, yield prediction, etc. in both the
    public and private sector. How can we encourage the continued use of
    this type of data, and even grow our sources, while ensuring that
    farmers understand their role in this process?
        Answer. Witness response to Question 5: Perhaps the best steps that
    can be made toward this goal are to continue funding of research and
    extension efforts through our Land Grant universities to help producers
    understand the value of the data resources to their decision making
    processes. For example, the witness is currently the principal
    investigator on a Southern Risk Management Education Center grant
    funded through USDA-NIFA to develop a handbook and decision tools that
    can help producers understand the value of agricultural data tools and
    help them make informed choices about their uses (SRMEC Agreement
    21667-19).
        Additional research on how agricultural data systems can be made
    more robust, reliable, and accurate can also add to the volume and
    quality of publically-available data. For example, the most commonly-
    logged seed variety on planter data systems is the variety that comes
    first alphabetically on the system's drop down list. This means that
    producers sometimes inadvertently (although potentially carelessly or
    intentionally) select data inputs that are inaccurate, which in turn
    affects all downstream uses of their data. Research of tools to help
    improve data accuracy will not only increase profitability for
    producers, but will also improve the data and decision tools available
    to the industry.
                                     ______

    Response to Written Questions Submitted by Hon. Catherine Cortez Masto
                             to Dr. Shannon Ferrell
        Question 1. Ranching is a part of the fabric of rural Nevada's
    frontier. We have operations under generations of family management.
    While we heard many of the virtues of row crop farming and big data
    during the hearing, can you outline the advancements and prospects for
    animal agriculture? Is there also an advantage of agricultural data in
    ensuring a higher bar of food safety as well, for farmers, ranchers and
    consumers?
        Answer. While most of the discussion around agricultural data and
    Big Data in agriculture has revolved around applications for crop
    systems, the data revolution holds tremendous implications for
    livestock producers and the consumers of their products. Space does not
    permit a full exploration of all the potential avenues by which new
    data technologies could impact the livestock industry, but below are a
    few examples.
        Animal traceability: One example of how data technologies are being
    used right now in other countries is to provide robust animal
    traceability from farm to fork. For example, the bovine spongiform
    encephalopathy (BSE, sometimes called ``mad cow disease'') outbreak in
    the U.K. during the late 1990s spurred the implementation of an animal
    identification system that can trace an animal throughout its life from
    birth to the retail packaging in which its cuts are sold. Put another
    way, a steak could be traced back to the retailer, wholesaler, meat
    processor, feedlot, and cow-calf operation at which the calf was born.
    That level of traceability has the potential to prevent losses that
    could reach billions of dollars in the event of a BSE discovery in the
    United States by quickly isolating every other animal with which a
    diseased animal came into contact and drastically reducing the number
    of animals that would have to be quarantined or destroyed to prevent
    spread of the disease. It could simultaneously determine the wholesaler
    and retailer location of any potentially dangerous food products. Quick
    and accurate disease traceability thus has important implications for
    both livestock producers and consumers.
        Disease traceability is an important example of the loss-prevention
    capability of data technologies, but there are also important value-
    added applications as well. The same technology can enhance the ability
    of livestock producers to provide age and source verification of beef
    products, enhancing their ability to market their beef as a branded
    product rather than a commodity. Traceability also allows ranchers to
    show compliance with certifications and standards to demonstrate to
    consumers that given affinity traits have been maintained throughout
    the beef supply chain.
        Keeping of such traceability information is possible with manual
    technologies, but is made faster, easier, and much more accurate and
    reliable by automation technologies such as radio frequency
    identification (RFID) tags for animals coupled with automated readers
    and loggers. Those technologies, working together with an already
    robust supply chain data system in the wholesaler and retailer sector,
    have significant potential to rapidly improve traceability within the
    U.S. beef system.
        Managing production inputs: One of the reasons the use of data
    technologies has received so much attention in crop production is that
    it is relatively easier (though one hates to apply the term ``easy'' to
    the significant work in technological research and application that has
    occurred there) to deploy sensors and data collection/transmission
    systems on tractors, planters, sprayers, and combines than it is to
    cattle. However, advancements in sensors and computers continue to make
    it easier to use these advanced data technologies in livestock
    applications.
        In addition to the traceability systems mentioned above, we
    continue to see advances in sensor systems allowing ranchers to learn
    more and more about their production input use. The corn farmer cares
    about seed variety, water uptake, fertilizer inputs, and disease
    pressure as variables in determining their crop yield. A robust genetic
    database and traceability system coupled with wearable sensors could
    help a rancher track a cow's water and feed intake and health. We
    already have technologies allowing for feed and water tracking in
    closed environments like a feed yard or dairy, but advancements
    continue toward wearable sensor technologies that would allow similar
    tracking in open pasture environments.
        Beyond tracking these factors, such sensors could also be coupled
    with other data systems to provide significantly enhanced management
    information for ranchers to see what production variables yield the
    greatest financial returns. An example of this is a program here in
    Oklahoma called the Oklahoma Quality Beef Network (OQBN). Through a
    number of manual protocols, producers participating in the OQBN have
    the information needed to show compliance with a number of value-added
    programs giving them access to premiums in the marketplace for their
    cattle. Participation in the program also gives ranchers the ability to
    analyze the costs and returns of their production practices. OQBN
    represents another system that could be enhanced by the deployment of
    data technologies and a strong animal traceability system. For example,
    as an animal is given the OQBN vaccine regimen, each vaccine could be
    scanned as administered to the cow after the cow's RFID scan; those
    vaccines would then be part of the cow's OQBN record to show compliance
    with its vaccination requirements, and that information could be
    displayed at the auction as the cow comes into the ring.
        Managing range cattle production: At the risk of stating the
    obvious, managing range cattle production is hard, and those challenges
    are multiplied in Nevada by the significant size and/or rugged
    topography of ranches. Many of the sensor technologies referenced
    herein require cattle to be close to a collection point such as a
    watering point or feed area, but those points are difficult to come by
    in range production. Even if a rancher could put a transceiver near
    common watering points or feeding areas on a range, those transceivers
    depend on cellular data networks to collect and transmit data. The
    expansion of rural broadband cellular coverage could increase the
    opportunities for producers to take advantage of these technologies,
    however. Broader and stronger networks would allow many more
    opportunities for range cattle producers to take advantage of these
    data technologies.
        Another technology that could be of great benefit to range cattle
    producers is unmanned aerial systems (UAS or ``drones''). In their most
    straightforward application, UAS could be used to help ranchers check
    on cattle much more quickly than they could by ground vehicle or
    horseback. This could include everything from taking pictures or video
    to count cattle, inspect fences, or check on water availability. This
    is particularly true in rugged, mountainous terrain that is difficult
    to access.
        If the UAS were equipped with data transceivers backed by a strong
    rural broadband network, the UAS could even collect and relay the
    sensor data mentioned above, or could alert a rancher to a cow that was
    ill and in need of immediate treatment. While some current UAS
    technologies could already help in this regard, current FAA regulations
    only allow most UAS to be operated by ``direct line of sight'' which
    means the pilot must have direct visual contact with the UAS. That rule
    makes some of these applications impractical, particularly in
    mountainous terrain. Continued improvements in camera and transmitter
    technologies could soon make a ``point of view'' camera mounted on the
    UAS an acceptable substitute for direct visual contact, though,
    permitting amendment of the FAA regulations to allow longer flight
    ranges.
        Consumer-side data: Much has been discussed in terms of how Big
    Data technologies can create models to help crop production, but there
    is also significant potential for those technologies to impact beef
    production, and indeed, consumption. We already have large volumes of
    universal product code (UPC) scanner data from retail outlets that can
    tell us much about where and when certain food products are consumed.
    However, if the beef industry were to couple that data and supply chain
    information with the traceability elements mentioned above, the amount
    of data available for analysis would grow exponentially, and tremendous
    insights could be derived about beef demand, consumption patterns, and
    other factors. Big Data could also provide significant insights into
    the effectiveness of food policy such as the use of SNAP EBT benefits
    for certain types of foods, the nutrient profile of those purchases,
    and consumer incentives.

        Question 2. There were many references to the environmental
    benefits of agricultural data. Are we in a position yet where we can
    authoritatively quantify the environmental benefits experienced by the
    use and attention to these technologies and data analysis? For example,
    is this science proven to the point that we should be creating
    incentives in the farm bill conservation title for their utilization to
    keep pristine watersheds like Lake Tahoe, or water quantity in drought
    areas, solidified for the decades to come? Or is there a place for this
    use in connection with the Federal crop insurance program?
        Answer. The most concise answer is ``we're not there yet, but we're
    getting closer rapidly.''
        At the ``micro'' or farm scale, we may indeed be getting close to
    having the technology in place to help producers demonstrate compliance
    with environmental requirements. For example, say a producer has a
    feedlot that meets the definition of a Concentrated Animal Feeding
    Operation (CAFO) under the Clean Water Act and wants to show their
    compliance with the CAFO's nutrient management plan (NMP). A grid-
    sampled soil test of the field to receive animal waste could create a
    precise prescription for how much of each macronutrient should be
    applied to that field. Sensors on the tractor used to inject liquefied
    animal waste to crop ground, coupled with a nutrient analysis of the
    waste, could show the amount of nitrogen, phosphorous, and potassium
    applied using variable-rate technologies to comply with the
    prescription. Later, the crop harvested from the ground where the
    wastes were applied is reported using a combine's yield monitor data,
    which can show the nutrient uptake of the crop and thus the nutrient
    balance for the soil.
        This scenario demonstrates the technologies available to manage
    agricultural environmental factors on the input side, but if we want to
    reliably automate environmental compliance efforts, we must also have
    strong sensor networks in and around environmental receptors such as
    streams and lakes, particularly at points where watersheds connect.
    This will require continued development of environmental sensors and
    again, strong rural broadband networks over which to transmit the data.
        The discussion to this point has focused on farm-level compliance,
    but what about compliance at a ``macro'' or regional level, such as
    demonstrating compliance of a watershed with an agriculturally-based
    total maximum daily load (TMDL)? We may have a bit to go before we can
    reliability show compliance on a regional level, simply because doing
    so would require 1) the continued penetration of sensor and
    transmission technology into more agricultural implements and 2) the
    interoperability of those systems to ``speak the same language'' so
    insights could be derived across multiple farms, rather than on a farm-
    by-farm basis. However, with that said, these factors continue to
    advance rapidly, and we may cross that threshold soon. Such
    technologies could soon facilitate the ability of farmers to manage
    nutrients in impaired watersheds. For example, in a TMDL-limited
    watershed with a ``cap and trade'' system, producers could validate how
    much of each nutrient they have applied to show their compliance with
    the nutrient limitations they received or traded.
        In any of these scenarios, it is important to note that the
    accuracy of any compliance system depends on the proper calibration and
    operation of the sensors used. While automation may make compliance
    much easier, if automation is ever used to demonstrate compliance with
    a regulatory program, it should also be paired with the opportunity for
    a producer to review their submissions and explain anomalies. If a
    sensor suffers a malfunction, it could show a producer applied far more
    than their allocation of a nutrient, even though the proper amount was
    actually applied. Conversely, a third party might try to ``hack'' a
    tractor system or the data collection to implant false data. Any
    compliance system created in the future should make allowances for how
    to handle these anomalies.
        With respect to the crop insurance program, there are tremendous
    opportunities to hone the actuarial models for the system and to
    facilitate produce's demonstration of compliance with insurance program
    requirements afforded by both Small Data and Big Data systems. Big Data
    analytics can continue to drive gains in accuracy for models in setting
    premiums and cost management, while Small Data can help producers
    accurately report yields (again, presupposing proper calibration and
    operation of the equipment sensors).

        Question 3. Obviously in the west we have some greater agriculture
    challenges than other regions, and wildfires are one of them that we've
    seen have detrimental effects over and over again. In many cases, these
    fires have a multiplier effect on production and for things like future
    flooding. And, in general, water is also another constant challenge.
    Are there technologies that can help measure and account for drought
    conditions, or measure the volatility of fires within parched forests
    or grasslands before we have fires that potentially get out of control?
    Are there any associations with improving broader weather prediction or
    forecasts? And are there other specifics where you foresee this data
    and technologies helping us get a better handle on our climate change
    crisis?
        Answer. There are indeed a number of technologies that can help
    address these concerns. One present example of a weather and climate
    monitoring system that has provided tremendous benefits for my home
    state is the Oklahoma Mesonet. The Mesonet is a system of weather
    stations spread throughout the state, with at least one (if not more)
    such stations placed in every county. These stations fill in the
    significant geographic gaps between NOAA weather monitoring stations,
    and allow us to collect data on dozens of weather and climate
    parameters. These observations are fed into a number of models that
    help us keep close tabs on a number of factors for everything from fire
    weather hazards to drought monitoring and evaporative losses. The
    Oklahoma Mesonet has already helped our meteorologists and
    climatologists refine their predictive models and led to a tremendous
    output of research into Oklahoma weather and climate issues. The
    application of similar technologies in Nevada could provide significant
    improvements to prediction of fire weather conditions with the
    potential to issue advisories and reduce the risk of ignition sources.
    Similarly, the evaporation, soil moisture, and mesoscale models could
    help refine the use of scarce water resources. To help illustrate the
    applications of the Mesonet and our models, such as the fire-weather
    model, I have attached a summary prepared for you by Mr. Al Sutherland,
    coordinator of Mesonet Agricultural Data and Products, with the
    assistance of Dr. J.D. Carlson, lead investigator on the OK-FIRE model.
        Further, in the future, the reduction in cost of reliable weather
    sensors could mean individual landowners could have their own weather
    stations (as an increasing number of Oklahoma farmers and ranchers do)
    that could be interoperable with Mesonet stations. This would have the
    effect of filling in even more gaps in the network, increasing the
    precision of its observations and providing even better information for
    predictive models. Indeed, weather monitoring is an arena in which
    there is tremendous opportunity for improved sensor systems and Big
    Data to have a positive impact in climate management.
                                     ______

        Response to Written Questions Submitted by Hon. Jerry Moran to
                                 Todd J. Janzen
        Question 1. As administrator of the Ag Data Transparency Evaluator,
    you are familiar with the lack of trust and confusion that many farmers
    experience in identifying what exactly is done with data collected from
    their land. Can you please describe considerations among industry
    stakeholders that led to publishing the ``Core Principles'' that are
    incorporated into the Evaluator?
        Answer. The main drivers behind the ``Core Principles'' for ag data
    were the concerns from farmer-members of national farm organizations,
    such as American Farm Bureau Federation, National Farmers Union,
    National Corn Growers, National Association of Wheat Growers, American
    Soybean Association, and National Sorghum Producers. These organization
    spearheaded the effort to develop the Core Principles because their
    members wanted a basic framework around how ag data is collected and
    shared.

        Question 2. Your testimony states that only nine companies
    (including Farmobile) are currently approved as ``Ag Data Transparent''
    according to the Ag Data Transparency Evaluator's formal process. Why
    have not more companies voluntarily completed the evaluation,
    especially given the fact that nearly 40 companies participated in
    drafting the ``Core Principles?''
        Answer. This is a question best addressed to those companies that
    have not participated. I can only speculate as to their delay in
    participation. My belief is that these companies want more control over
    farmers' data than they are willing to publicly admit. Therefore, it is
    easier to remain quiet and say nothing than subject themselves to the
    Ag Data Transparent process.

        Question 3. How can we incentivize more active participation by
    industry stakeholders to complete this evaluation?
        Answer. I think the fear that Congress might step in and regulate
    the privacy and collection of ag data is something that will drive more
    companies to participate. As the value of the Ag Data Transparent brand
    increases over time, that will drive more participation as well.

        Question 4. As agricultural data becomes more valuable to entities
    outside of the farmers that collect it, data security concerns are
    likely to grow exponentially while criminals with all types of motives
    seek to illegally gain access to and capture privately-owned data. How
    do you foresee data security practices in the agricultural industry
    evolving as a result?
        Answer. Data security in the ag data space must progress at the
    same rate as data security in the non-agricultural space. Ag tech
    companies should not think that they are immune to security challenges.

        Question 5. Are there any specific security traits to agricultural
    data that need to be accounted for steps going forward?
        Answer. Ag data can contain proprietary information, which makes it
    different than other types of consumer-type data that may not be
    proprietary.

        Question 6. In a 2016 poll conducted by the American Farm Bureau
    Federation, regarding the loss of control over downstream uses of data,
    sixty-six percent of the farmers polled expressed concern about not
    being compensated for the potential benefits from the use of their data
    beyond the direct value they may realize on their farm. Meanwhile,
    sixty-one percent of the farmers were concerned that agricultural
    technology providers (ATPs) could use their data to influence market
    decisions. Which of the two concerns do you believe is the greatest
    threat to farmer profitability and well-being, and what should be done
    to alleviate these concerns?
        Answer. I believe the greatest threat to the farmer is that ATPs
    will be able to influence the ag markets by using ag data, but without
    making that same g data available to farmers. That would put certain
    holders of information in a superior position to the average farmer.

        Question 7. As more and more firms enter the agriculture-technology
    space and interact with data used by and/or generated by farmers, the
    need for clarity and consistency on privacy principles is growing. For
    these new entrants, can you suggest any best practices these firms
    should engage upon to ensure their data privacy procedures properly
    convey the data's expected use?
        Answer. New firms in the ag data space should do two things when
    they begin to collect data. First, they should determine their guiding
    principles for how they intent to treat ag data. Second, they should
    develop easy to understand data use policies that they can share with
    farmers that explain how the firm intends to use the farmer's data.

        Question 8. As more and more firms enter the agriculture-technology
    space and interact with data used by and/or generated by farmers, the
    need for clarity and consistency on privacy principles is growing. For
    these new entrants, can you suggest any best practices these firms
    should engage upon to ensure their data privacy procedures properly
    convey the data's expected use?
        Answer. New firms in the ag data space should do two things when
    they begin to collect data. First, they should determine their guiding
    principles for how they intent to treat ag data. Second, they should
    develop easy to understand data use policies that they can share with
    farmers that explain how the firm intends to use the farmer's data.

        Question 9. While much of the data we discussed in the hearing is
    generated on farm and captured by farmers or their equipment,
    significant quantities of data is publicly available and critically
    important to inform risk modeling, yield prediction, etc. in both the
    public and private sector. How can we encourage the continued use of
    this type of data, and even grow our sources, while ensuring that
    farmers understand their role in this process?
        Answer. Witness did not respond.
                                     ______

    Response to Written Questions Submitted by Hon. Catherine Cortez Masto
                               to Todd J. Janzen
        Question 1. Given the dramatic benefits of agricultural data that
    were outlined in the hearing, are we addressing these advancement in
    our high school agricultural education and training efforts? Or at the
    technical or community college education level? And much of what we
    heard about, and what we think about in the innovation sector, requires
    more students and a workforce who can work with the technology, data
    analytics, and various other computer science skill sets. Is that
    accurate from your perspectives? Are there ways in your mind we can
    better incentivize and be developing the workforce we'll need to see
    the great promise of what we're talking about today? Including the
    cyber security needed to protect agricultural and ag-related business
    in this sector?
        Answer. I am not involved with high school or secondary education
    and cannot therefore directly speak to this question. From my personal
    experience, more could be done to drive more students into science and
    technology focused careers, including agriculture. National and state
    FFA organizations do a lot to help foster growth in this area, but
    these are non-profit organizations that rely on donations and
    volunteers.

        Question 2. There were many references to the environmental
    benefits of agricultural data. Are we in a position yet where we can
    authoritatively quantify the environmental benefits experienced by the
    use and attention to these technologies and data analysis? For example,
    is this science proven to the point that we should be creating
    incentives in the farm bill conservation title for their utilization to
    keep pristine watersheds like Lake Tahoe, or water quantity in drought
    areas, solidified for the decades to come? Or is there a place for this
    use in connection with the Federal crop insurance program?
        Answer. The data collection and analytic tools today are already
    smart enough to make meaningful differences in environmental
    protection. For example, we could measure fertilizer use on farmland
    and fertilizer run-off from field tiles and other pathways to determine
    proper fertilizer application rates, assuring that as little as
    possible is lost. Likewise, we can compare fertilizer usage and yield
    across fields to determine proper application rates and timing to
    maximize fertilizer and soil resources.
        There could certainly be more use of data in the Federal crop
    insurance program. Deb Casurella at Independent Data Management LLC is
    an expert on this subject and would be the right person to testify on
    this topic.

        Question 3. So if we acknowledge the virtues of agricultural data,
    and that small data can even be used to market the sale of land or an
    operation, what safeguards in place to verify this information so a
    potential land buyer isn't defrauded by skewed land performance data or
    analytics that inflated the profitability of the land?
        Answer. The best way to verify that ag data is not fraudulent would
    be to insist on receiving the original raw data from a seller, or
    obtain a copy of the same data from a third-party source. Even then,
    there would still be a question whether the machine that produced the
    raw data was properly calibrated. One way to verify that would be
    benchmark the seller's data with other data in the same area. Any
    dramatic difference could be due to data manipulation.

        Question 4. Have agribusinesses been utilizing varying strategies
    to collect big data in agriculture? What was the old typical procedure
    for companies to obtain information from farmers, or their customers,
    on the progress or performance of their product? And are there
    situations where the producers are forced into a data collection
    program by seed, fertilizer or equipment companies? Or can they opt-
    out?
        Answer. Many of the larger agribusiness companies have been
    collecting agricultural data from customers for years. Often, they
    collected this information because they were providing services to
    farmers, such as fertilizer of pesticide application. They collected
    data during such activities and then retained it for their own records.
        I am not aware of any specific situations where companies require
    ag data submission as a condition of using a machine or device,
    although I think that will inevitably happen.
                                     ______

        Response to Written Questions Submitted by Hon. Jerry Moran to
                                Dr. Dorota Haman
        Question 1. One of the aspects of your background I found
    interesting is the work you've done with farmers in developing nations,
    specifically as it relates to irrigation. I chair the Senate Hunger
    Caucus and have worked for many years on the issue of reducing food
    insecurity in the world. I believe that agriculture development
    initiatives that help countries feed themselves is a key part of the
    long-term strategy to end global hunger. Can you elaborate on your work
    with farmers in developing countries, specifically as it relates to
    using water more efficiently, and how that work has reduced food
    insecurity?
        Answer. I would like to take this opportunity to thank Senator
    Moran for asking this question and for his bipartisan leadership on the
    Senate Hunger Caucus. Food assistance, and other support provided by
    the U.S. all over the world, leads to reduction of global food
    insecurity.
        My expertise is in agricultural engineering with a focus on water
    management and irrigation. As reported by the Food and Agriculture
    Organization (FAO) of the United Nations and by the World Bank,
    approximately 70 percent of fresh water usage is for agriculture. Most
    of agricultural irrigations systems are poorly designed and poorly
    managed. Even the best irrigation systems, if not maintained and
    carefully managed, are inefficient. Most of the irrigation systems
    over-apply water and there is a potential to improve efficiencies
    through technology and education.
        My major effort has been focused on Florida growers and specialty
    crop production in Florida. I have also been a university teacher
    working with the next generation of farmers, academics and irrigation
    specialists. I teach people how to design, manage and maintain
    irrigation systems. My work has been focusing on efficient systems such
    as microirrigation and sprinklers. These systems are usually used for
    higher value, specialty crops such as fruits and vegetables but can
    also be adapted for small farmers in developing countries.
        My international work has been largely in education. I have worked
    for FAO in Zimbabwe designing curriculum and lab experiments for a six-
    months intensive course focused on planning, design, maintenance and
    management of irrigation for smallholder farmers. I have taught two 2-
    week courses in Egypt. I have spent 3 months in Mexico and 4 months in
    Chile investigating and teaching efficient methods of irrigation. In
    addition, my students have worked with farmers in India, Ecuador,
    Columbia and Poland.
        As an example, one of my students, working in Jamaica on his
    Masters project, implemented a simple drip system for calaloo (Jamaican
    spinach) and cucumbers. The increases in yield and reduction in water
    usage were significant. After the experiment was finished, the farmer
    adopted the system on the entire farm.

        Question 2. What opportunities exist, if any, to take the
    technology being used today by large-scale U.S. farmers and use it to
    help smallholder farmers in developing nations be more productive and
    sustainable?
        Answer. New technologies can eliminate many maintenance mistakes
    through automation and sensor control. New technologies can provide
    inexpensive alerts, and in the future, automatic intervention.
        Technology leapfrogging is likely in agriculture in developing
    countries. Use of cell phones in developing countries is often cited as
    an example of leapfrogging. Apps and advisory programs, built and
    available from the open sources, can be made available on the
    smartphones. Solar phone chargers are becoming available even in very
    remote locations without an electrical grid. Access to quality data is
    critical for development of Apps and tools that can be available to
    poor farmers. One of the examples of an open platform is the BioSense
    Institute in Serbia. This project was funded from the European program
    Horizon 2020.
                                     ______

        Response to Written Questions Submitted by Hon. Bill Nelson to
                                Dr. Dorota Haman
        Question 1. In addition to improving crop yields, how can big-data
    be used to protect farm workers from heat-related injuries? Is the
    farming industry using technology and data to improve worker safety?
        Answer. In collaboration with the Farmworker Association of
    Florida, Dr. Linda McCauley, Emory University in Atlanta (GA), has
    established the Los Girasoles heat stress study. Los Girasoles, funded
    by the National Institute for Occupational Safety and Health (NIOSH),
    is a four-year project (now in its third year) that aims to better
    understand how agricultural workers respond to heat stress and collect
    better data on the magnitude of heat-related illnesses like heat stroke
    in agricultural work.
        The data have been used to develop the Heat Related Prevention
    Illness training for the PISCA (FSU-FWAF) project and the Farm Labor
    Supervisor Training (FLST) in IFAS/UF.
        An APHA policy statement have been drafted and approved. A local
    representative is sponsoring local legislation to promote a regulation
    of heat stress. A pilot study has been conducted with FSU looking at
    alternative clothing. Data on kidney functions as related to heat
    stress have been collected by researchers last year and will be
    continued next year.
        Large amounts of physiological data collected by Los Girasoles was
    presented at several conferences and meetings. This is a list of
    current publications on the topic:

      1.  Flocks, J., Mac, V., Runkle, J., Tovar-Aguilar, A., Economos, J.,
            & McCauley, L. (2013). Female farmworkers' perceptions of heat-
            related illness and pregnancy health. Journal of agromedicine,
            18(4), 350-358.

      2.  Mutic, A., Mix, M., Elon, L., Mutic, J., Economos, J., Flocks, J.
            Tovar-Aguilar, A., & McCauley, L. (2017). Classification of
            Heat-Related Illness Symptoms among Florida Farmworkers.
            Journal of nursing scholarship.doi:10.1111/jnu.12355

      3.  Mac, V., Tovar-Aguilar, A., Flocks, J., Economos, E., Hertzberg,
            V., & McCauley, L. (2017). Heat exposure in Central Florida
            fernery workers: results of a feasibility study. Journal of
            agromedicine, 22(2), 89-99.

      4.  Hertzberg, V., Mac, V., Elon, L., Mutic, N., Mutic, A., Peterman,
            K., Tovar-Aguilar, A., Economos, E., Flocks, J., & McCauley, L.
            (2017). Novel Analytic Methods Needed for Real-Time Continuous
            Core Body Temperature Data. Western journal of nursing
            research, 39(1), 95-111.

        Wearable sensors similar to Fitbit can measure temperature and
    heart beat (or other biometrics) to alert workers to dangerous
    conditions. Sensors that are built into the clothing are also under
    development. CorTemp http://www.hqinc.net/ has been used for body core
    temperature, which also allows capture of heart rate signals, in
    addition to button temperature sensors in and outside workers'
    clothing, including ActiGraphs (medical-grade wearable activity and
    sleep monitoring solutions for the research community).
        In addition to Florida, there are teams in California, Tennessee,
    and Sinaloa (Mexico) who are using similar equipment. To improve
    diagnostics, researchers are collecting blood, urine, BMI, HBP, body
    fat percentage, and temperature at workers' homes.

        Question 2. Florida has faced both record droughts and record
    rainfall in the past few years. Can you describe how technology can
    help farmers prepare for and adapt to wild swings in weather?
        Answer. Technology can help farmers prepare and adapt to wild
    swings of weather in several ways. First it can help inform farmers of
    upcoming weather conditions in a timely and user-friendly way. It can
    also help farmers monitor conditions on a farm to better track the risk
    of pests and diseases, soil moisture conditions, and other potential
    yield-reduction factors that may require control measures. However,
    collecting data in an efficient and cost-effective way is just the
    first step in the process. Data must be translated into information
    through the use of mathematical models, machine learning, artificial
    intelligence and other data analytics to infer information from this
    new ``data rich'' environment. Data and information only have value if
    used to drive a farmers' decisions. That is why we need to provide
    farmers with tools that are customized to their farming environment and
    conditions.
        It is also important to highlight that we need to develop and
    promote technologies and management practices that help farmers become
    more resilient to climate variability and change: http://
    agroclimate.org/fact-sheets/climate/
        To adapt to these extremes, systems should be designed to a higher
    standard than is currently normal. For example, drainage systems should
    be designed to handle extreme rainfall events. At the same time, to
    cope with extreme drought events, efficient irrigation systems and
    additional water supplies should be identified.

        Question 3. How can big data help us prepare for and adapt to the
    effects of climate change?
        Answer. Large-scale geo-reference data can be very helpful in
    identification of the long-term trends in weather patterns and climate
    change. With new ways of data collection through satellites, drones,
    robots and cheap sensors, there will be improved data collection and
    analysis.
        Reliable and early prediction of both drought and rainfall based on
    climate models and global and local weather data (that are becoming
    more dense and precise) are critical for dealing with extreme weather
    events. Forecasting weather for the next few days or a week, the next
    rain front, the next heat wave, the path of the next hurricane and
    trends in climate change over the next decades use the same climate
    models which are build, tested and updated with billions of weather/
    climate data from tens of thousands of weather stations and sensors
    from land, ships, airplanes, ocean floats at various depths, research
    stations, and satellites. These are examples of big data already used
    and applied in daily life.
        Increasing accuracy of weather event predictions is critical to the
    timeliness of preparations, decisions and interventions. For example,
    expectation of unusual rainfall may require lowering of the water table
    in the field or draining of water reservoir to create more space for
    upcoming rain. Timing of this operation is critical. For example,
    draining water storage in preparation for extreme rain cannot be done
    too early or the plants may be stressed to the point of yield
    reduction. The accuracy of prediction is critical to successful
    preparation and big data from numerous sensors, as well as remote
    sensing, can increase the accuracy of prediction.
        Technology can also be helpful in managing scarce resources during
    drought and regulating water application to optimize incomes under
    drought conditions. Sufficient lead-time is important for
    implementation of water storage to minimize the impact of drought on
    production.
        It is important to remember that improved short-term forecasts (on
    the scale of days up to the entire season) can be very useful for
    management decisions and can mitigate the impact of an extreme event.
        Big data may be able to help in analyzing reports of the impacts of
    extreme events. Currently, state climatologists and the National
    Drought Mitigation Center rely on somewhat subjective reports of
    drought impacts. Big data can help in analyzing diverse reports of
    impacts in a more objective manner.

        Question 4. Can big data help us reduce greenhouse gas emissions
    from agriculture?
        Answer. Big data allow us to see the patterns and fine-tune systems
    to make them more efficient. Well-known examples of the agricultural
    sources of greenhouse gas emissions are paddy-rice production and
    animal production. Another is oxidation of the organic material in
    cultivated soils.
        To shift from the paddy-rice, scientists are working on increasing
    production of upland rice that is not flooded and this growing system
    contributes much less to atmospheric greenhouse gasses. Increased
    efficiency in animal production and waste management may be beneficial
    to reduction of methane emissions. Efficient management of these
    agricultural systems is critical and the information provided by big
    data can help us optimize the systems through mathematical modeling.
    For example, by increasing the land-surface area covered with plants
    and reducing deforestation we can increase removal of
    CO2 from the atmosphere. New technologies, including
    robotics and low-energy LED lights for protected (indoor) plant
    production, are opening possibilities for vertical growth (in
    multistore buildings) of high value vegetables and fruits in urban
    areas. This production may benefit from CO2 enrichment
    (using industrially produced CO2) at the same time
    preventing its release into the atmosphere.
        To reduce CO2 in the atmosphere, numerous management
    strategies have been discussed including using plants to draw carbon
    out of the atmosphere and developing techniques to hold it in the soil.
    These strategies vary in effectiveness across different climates, soils
    and geographies. Sequestration of carbon is achieved through
    transferring atmospheric carbon into the soil via plant photosynthesis.
    Soil carbon must then be protected as effectively as possible from
    microbial activity that will release the carbon back to the air. Most,
    if not all, of the management techniques (operations) that promote
    carbon sequestration also improve soil aggregation, water retention,
    soil fertility, and food security.

        Question 5. Should we be thinking about ``big data'' and how
    farmers control their information the same way we think about consumers
    using the Internet or conducting financial transactions?
        Answer. I believe that these factors need to be carefully analyzed
    and a set of acceptable rules should be developed and established. I
    believe that it is important to make sure that sufficient high-quality
    data are available as an open source to allow for creating free access
    to critical information (or at very reasonable cost) especially for
    small, family farms and for farmers in developing countries. For
    example, in Europe, all the data created under the Horizon 2020 program
    are open access and the program includes a lot of agricultural big
    data. Another example of this type of platform is the BioSense
    Institute in Serbia.

        Question 6. Can you describe how extension services like the one at
    the University of Florida help famers--particularly small farmers, new
    farmers, and specialty crop farmers--access the same type of
    information that corporate farmers have?
        Answer. The University of Florida Institute of Food and
    Agricultural Sciences (IFAS) has been engaged in developing services
    and decision aids to help Florida farmers, including small farmers,
    improve resource use efficiency and reduce risk associated with climate
    variability and change. AgroClimate.org (http://www.agroclimate.org)
    for example provides climate related information and dynamic
    application tools that interact with a climate database system for the
    Southeastern U.S.A. Information includes climate forecasts combined
    with risk management tools for a range of crops, forestry, pasture, and
    livestock. It has been quite successful and adopted by farmers in
    Florida to decide when to apply fungicide to strawberry fields based on
    weather conditions or to track the accumulation of chill hours during
    the winter in farms growing temperate fruits such as blueberries and
    peaches. AgroClimate integrates weather and climate data from public
    sources such as the Florida Automated Weather Network (FAWN) and
    gridded weather data from NOAA that covers the entire U.S.A. and the
    globe. Several mobile phone apps have been developed based on the
    weather and climate database services provided by AgroClimate including
    the Smart Irrigation Apps http://www.smartirriga
    tionapps.org) that help farmers in Florida and Georgia schedule
    irrigation of crops such as citrus, strawberries, cotton and turf;
    thereby saving water and reducing leaching of nutrients into the
    groundwater.
        At UF, we are planning to create a similar portal for safety
    information and safety practices in agriculture. We are in the process
    of hiring a new faculty member to work on this project.

        Question 7. Do you think that consumer concerns about genetic
    engineering and GMO crops might create a backlash against precision
    technology, particularly as it relates to our food supply?
        Answer. I believe that this is unlikely. Precision agriculture is
    not directly linked to genetic engineering or GMO. In fact, due to
    precise management of resources (water, nutrients, pesticides and other
    chemicals, etc.), available technologies benefit the environment and
    reduce input costs. Precision technologies have been used for years in
    agriculture. Data availability, especially Big Data, makes these
    systems more precise, allows for faster and better decisions, and
    increases operation and production efficiency. These technologies do
    not introduce anything new or foreign into food chain. New genetic
    technologies, such as CRISPR (gene editing) seems to be less
    controversial and more likely to be accepted by the general public.
                                     ______

     Response to Written Question Submitted by Hon. Catherine Cortez Masto
                              to Dr. Dorota Haman
        Question. There were many references to the environmental benefits
    of agricultural data. Are we in a position yet where we can
    authoritatively quantify the environmental benefits experienced by the
    use and attention to these technologies and data analysis? For example,
    is this science proven to the point that we should be creating
    incentives in the farm bill conservation title for their utilization to
    keep pristine watersheds like Lake Tahoe, or water quantity in drought
    areas, solidified for the decades to come? Or is there a place for this
    use in connection with the Federal crop insurance program?
        Answer. I do not believe that we are yet ready for inclusion of
    specific incentives, policies and regulations. Scientists are working
    on information that can improve crop insurance program but we are not
    ready to implement it yet. The new technologies and sensors are
    providing abundant and potentially high quality agricultural data that
    can be very beneficial (in the future) to evaluate the environmental
    impact of agriculture and hopefully optimize the entire system to
    reduce the impact on the environment. Massive data require the
    development of adequate models, and these are not yet mature but are
    rapidly evolving. At this point, model development is one of the major
    challenges in big data analytics. The concept of analyzing big data
    relies excessively on ``blind'' machine learning, where ``black boxes''
    of data are ``mined'' in the hope that the process will ``tell us what
    it contains''. The problem is that these large data sets are of highly
    dimensional (complex) and there are many possible combinations of the
    drivers that could potentially produce similar results. Identifying the
    correct relationships requires experts with mature and relevant
    conceptual models to drive the search process. In addition, ``dimension
    reduction'' (selecting a smaller set of really important factors
    suitable for management) is a critical step.
        Interdisciplinary approach is necessary to assure that big data are
    appropriately analyzed. Teams of subject-matter experts need to team up
    with machine-learning experts (from statistics and informatics) to
    identify the correct solutions to the problems. The challenge today is
    that because of high potential economic gains, sensors, data, and
    machine learning has progressed rapidly but has not teamed up with
    content experts for specific problems. This is particularly true in
    Agriculture and Environmental Sciences, which are particularly complex
    as they are based on open and largely uncontrolled systems (as opposed
    to other artificial or human-controlled settings).

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