[House Hearing, 114 Congress]
[From the U.S. Government Publishing Office]





         BIG DATA AND AGRICULTURE: INNOVATION AND IMPLICATIONS

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

                                HEARING

                               BEFORE THE

                        COMMITTEE ON AGRICULTURE
                        HOUSE OF REPRESENTATIVES

                    ONE HUNDRED FOURTEENTH CONGRESS

                             FIRST SESSION

                               __________

                            OCTOBER 28, 2015

                               __________

                           Serial No. 114-32


[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]


          Printed for the use of the Committee on Agriculture
                         agriculture.house.gov



                                  ______

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                        COMMITTEE ON AGRICULTURE

                  K. MICHAEL CONAWAY, Texas, Chairman

RANDY NEUGEBAUER, Texas,             COLLIN C. PETERSON, Minnesota, 
    Vice Chairman                    Ranking Minority Member
BOB GOODLATTE, Virginia              DAVID SCOTT, Georgia
FRANK D. LUCAS, Oklahoma             JIM COSTA, California
STEVE KING, Iowa                     TIMOTHY J. WALZ, Minnesota
MIKE ROGERS, Alabama                 MARCIA L. FUDGE, Ohio
GLENN THOMPSON, Pennsylvania         JAMES P. McGOVERN, Massachusetts
BOB GIBBS, Ohio                      SUZAN K. DelBENE, Washington
AUSTIN SCOTT, Georgia                FILEMON VELA, Texas
ERIC A. ``RICK'' CRAWFORD, Arkansas  MICHELLE LUJAN GRISHAM, New Mexico
SCOTT DesJARLAIS, Tennessee          ANN M. KUSTER, New Hampshire
CHRISTOPHER P. GIBSON, New York      RICHARD M. NOLAN, Minnesota
VICKY HARTZLER, Missouri             CHERI BUSTOS, Illinois
DAN BENISHEK, Michigan               SEAN PATRICK MALONEY, New York
JEFF DENHAM, California              ANN KIRKPATRICK, Arizona
DOUG LaMALFA, California             PETE AGUILAR, California
RODNEY DAVIS, Illinois               STACEY E. PLASKETT, Virgin Islands
TED S. YOHO, Florida                 ALMA S. ADAMS, North Carolina
JACKIE WALORSKI, Indiana             GWEN GRAHAM, Florida
RICK W. ALLEN, Georgia               BRAD ASHFORD, Nebraska
MIKE BOST, Illinois
DAVID ROUZER, North Carolina
RALPH LEE ABRAHAM, Louisiana
JOHN R. MOOLENAAR, Michigan
DAN NEWHOUSE, Washington
TRENT KELLY, Mississippi

                                 ______

                    Scott C. Graves, Staff Director

                Robert L. Larew, Minority Staff Director

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                             C O N T E N T S

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                                                                   Page
Conaway, Hon. K. Michael, a Representative in Congress from 
  Texas, opening statement.......................................     1
    Prepared statement...........................................     3
Lucas, Hon. Frank D., a Representative in Congress from Oklahoma, 
  opening statement..............................................     1
Peterson, Hon. Collin C., a Representative in Congress from 
  Minnesota, opening statement...................................     4

                               Witnesses

Hurst, Blake, President, Missouri Farm Bureau; Member, Board of 
  Directors, American Farm Bureau Federation, Tarkio, MO.........     6
    Prepared statement...........................................     8
    Submitted questions..........................................    71
Tiller, Billy, Director of Business Development and Co-Founder, 
  Grower Information Services Cooperative, Lubbock, TX...........    16
    Prepared statement...........................................    18
Stern, Ph.D., Michael K., President and Chief Operating Officer, 
  The Climate Corporation; Vice President, Monsanto, San 
  Francisco, CA..................................................    24
    Prepared statement...........................................    25
Rushing, Matt, Vice President, Advanced Technology Solutions 
  (ATS) Product Line, AGCO Corporation, Duluth, GA...............    26
    Prepared statement...........................................    28
    Submitted question...........................................    72
Ferrell, J.D., M.S., Shannon L., Associate Professor and Faculty 
  Teaching Fellow, Agricultural Law, Department of Agricultural 
  Economics, Oklahoma State University, Stillwater, OK...........    30
    Prepared statement...........................................    32
    Submitted question...........................................    73

                           Submitted Material

Deere & Company,* submitted statement............................    69
      
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    * Editor's note: John Deere was invited to testify but declined.

 
         BIG DATA AND AGRICULTURE: INNOVATION AND IMPLICATIONS

                              ----------                              


                      WEDNESDAY, OCTOBER 28, 2015

                          House of Representatives,
                                  Committee on Agriculture,
                                                   Washington, D.C.
    The Committee met, pursuant to call, at 10:00 a.m., in Room 
1300 of the Longworth House Office Building, Hon. K. Michael 
Conaway [Chairman of the Committee] presiding.
    Members present: Representatives Conaway, Neugebauer, 
Lucas, King, Gibbs, Austin Scott of Georgia, Crawford, 
DesJarlais, Gibson, Hartzler, Benishek, Denham, Davis, Yoho, 
Allen, Bost, Rouzer, Abraham, Moolenaar, Newhouse, Kelly, 
Peterson, David Scott of Georgia, Costa, Walz, McGovern, 
DelBene, Vela, Lujan Grisham, Kuster, Nolan, Bustos, 
Kirkpatrick, Aguilar, Plaskett, Graham, and Ashford.
    Staff present: Bart Fischer, Callie McAdams, Haley Graves, 
Jackie Barber, Matt Schertz, Mollie Wilken, Skylar Sowder, John 
Konya, Anne Simmons, Evan Jurkovich, Keith Jones, Mike Stranz, 
Nicole Scott, and Carly Reedholm.

OPENING STATEMENT OF HON. K. MICHAEL CONAWAY, A REPRESENTATIVE 
                     IN CONGRESS FROM TEXAS

    The Chairman. Good morning. I call the hearing to order.
    Before we start the hearing, I would like to recognize 
Frank Lucas for some conversation about the tragedy in 
Stillwater. Frank?

 OPENING STATEMENT OF HON. FRANK D. LUCAS, A REPRESENTATIVE IN 
                     CONGRESS FROM OKLAHOMA

    Mr. Lucas. Thank you, Mr. Chairman. Before we begin the 
hearing this morning, I want to take a couple of moments to 
reflect on the tragic events that unfolded at my alma mater, 
Oklahoma State University, this past weekend. As most of you 
know, a car plowed through a crowd of spectators at the 
homecoming parade Saturday morning.
    Our homecoming parade at Oklahoma State is like the biggest 
high school homecoming you have ever seen in your life. 
Enthusiasm, mass amounts of alums and supporters, and all those 
little kids up and down the line.
    Part of the group that was injured, and ultimately four of 
them lost their lives, two of those folks who did not survive 
the crash were Dr. Marvin Stone and his wife, Bonnie; both 
long-time employees of Oklahoma State. And it is fitting that 
we are having this hearing today on big data because Dr. Stone, 
a Regent's Professor of Biosystems and Agricultural Engineering 
in Oklahoma State, was a pioneer in the field. He was integral 
in developing new precision ag technology, such as the 
GreenSeeker technology, that helped pave the way for much of 
the innovation we see in the industry today. And while he 
retired in 2006 after 24 years of service at Oklahoma State, he 
remained very active in his profession and, ironically, was 
honored just this spring in this very room for his innovations 
in agriculture.
    The Division of Agricultural Sciences and Natural Resources 
at Oklahoma State will be honoring the Stones with a vigil on 
campus in Stillwater tonight, and I would ask that you keep all 
of those who were impacted by this terrible tragedy in your 
thoughts and prayers in the days ahead as funerals take place, 
as the survivors who are in critical condition still continue 
to mend themselves.
    And, Mr. Chairman, I would very respectfully ask, if the 
Committee could join me in a moment of silence for all those 
good folks lost.
    The Chairman. Thank you. Please now, join us for a moment 
of silence.
    Dear Heavenly Father, we thank you for the multitude of 
blessings you have bestowed upon us, including the blessing of 
peace that passes understanding. We ask for healing and comfort 
on those injured in the tragedy at Oklahoma State this past 
weekend, comfort for the grieving, and healing for those that 
need to be healed. Please be with that whole community and help 
them deal with this senseless tragedy. We also ask for wisdom, 
knowledge, and guidance that we might govern these great 
people, that we have big decisions to make this week. We ask 
for that wisdom and discernment that we may make those that 
honor you, and that our service will further your kingdom here 
on our Earth. Be with us this morning as we have this hearing. 
We ask these things in Jesus' name. Amen.
    Good morning everyone. This hearing of the Committee on 
Agriculture on big data and agriculture: the innovation and 
implications to come, will to order.
    Information technology is profoundly impacting every aspect 
of our lives. In so many ways, this is a good thing, but, as 
anyone who has had their identity stolen can tell you, it is 
not without its downsides.
    The same, of course, is true in the case of production 
agriculture. As we have learned in previous hearings, foreign 
countries do a lot to give their producers a leg-up over their 
competitors. By way of example, along with lower worker, 
consumer, and environmental standards, we have witnessed other 
countries manipulate their currencies, set up state trading 
enterprises, use subsidies, tariffs, and other non-tariff 
barriers to gain the upper hand in this competition. But, we 
too have some distinct advantages going for us. Some, like our 
infrastructure, are tangible, easy to see, while others, like a 
strong rule of law and a great entrepreneurial spirit, are 
usually just taken for granted. But every now and again, a 
game-changer comes along, and we in America have had an 
excellent track record of inventing them and using them early 
to our great advantage. This record has helped keep America's 
farmers and ranchers out in front of the pack.
    The United States has led the way in several major 
agricultural game changers, including the moldboard plow, the 
cotton gin, refrigeration, and the Green Revolution.
    Not long ago, we celebrated the addition of Norman 
Borlaug's statute to the Capitol. Of course, Dr. Borlaug's 
Green Revolution was a huge game-changer, introducing 
innovations that have saved billions of lives. Thanks to Dr. 
Borlaug, we are well positioned to be able to feed the nine 
billion people who will soon inhabit our planet, and we will 
meet this challenge using far fewer natural resources and 
inputs.
    Today, many believe that information technology, or big 
data as it has been called, is the next big game-changer for 
agriculture. Thanks to significant investments in precision 
agriculture technology by those companies represented here 
today, as well as countless others, producers now have more 
information about their farms at their fingertips than ever 
before.
    Big data, has what seems like boundless potential to 
improve the efficiency, profitability, and competitiveness of 
our nation's farmers and ranchers, while conserving natural 
resources and benefiting the environment.
    In fact, the benefits of big data have already been paying 
off, as we will hear about today. But, at least one of the 
reasons why potential benefits have not yet been fully realized 
is because farmers and ranchers are getting lots of information 
from lots of different places. Getting all of this information 
into one place where it can be easily accessed and used is 
critically important. I am very pleased that Billy Tiller, who 
is from my part of the country, is here today to talk about 
this impediment, and how he and other farmers are working to 
find farmer-friendly solutions in overcoming it.
    Beyond practical considerations, however, is the important 
question of how to protect producer privacy and private 
property rights.
    Thankfully, the law protects the privacy of most producer 
information that USDA gathers, but, of course, it does not 
cover information gathered by private entities. This has 
enormous implications that can, among other things, affect the 
commodities market, land values, and how farm policies operate, 
and it could potentially expose producers to frivolous and 
costly environmental litigation.
    My hope is that the Committee and our exceptional panel of 
witnesses will fully explore these and, perhaps, other relevant 
issues.
    In closing, I want to go back to what I think is a central 
point, and that is the fact that this data is the farmer's 
information, and as such, the farmer should own or, at bare 
minimum, control information about his operation.
    If we can achieve this important principle, I think we go a 
long way in ensuring that American agriculture harnesses the 
power of big data.
    [The prepared statement of Mr. Conaway follows:]

  Prepared Statement of Hon. K. Michael Conaway, a Representative in 
                          Congress from Texas
    Information technology is profoundly impacting every aspect of our 
lives.
    In so many ways this is a good thing. But, as anyone who's had 
their identity stolen can tell you, it is not without its downsides.
    The same, of course, is true in the case of production agriculture.
    As we have learned in previous hearings, foreign countries do a lot 
to give their producers a leg-up over their competitors. As a few 
examples, along with lower worker, consumer, and environmental 
standards, we have witnessed other countries manipulate their 
currencies, set up state trading enterprises, use subsidies, tariffs, 
and other non-tariff barriers in order to gain the upper hand.
    But, we, too, have some distinct advantages going for us. Some, 
like our infrastructure, are tangible and easy to see while others, 
like a strong rule of law and a great entrepreneurial spirit, are 
usually just taken for granted.
    But every now and again, a game-changer comes along. And we in 
America have had an excellent track record of inventing them and using 
them early to our great advantage. This record has helped keep 
America's farmers and ranchers out in front of the pack.
    The United States has led the way in several major agricultural 
game changers, including the moldboard plow, the cotton gin, 
refrigeration, and the Green Revolution.
    Not long ago, we celebrated the addition of Norman Borlaug's 
statute in the Capitol. Of course, Borlaug's ``Green Revolution'' was a 
huge game changer, introducing innovations that have saved billions of 
lives. Thanks to Borlaug, we are well positioned to be able to feed the 
nine billion people who will soon inhabit our planet and we will meet 
this challenge using far fewer natural resources and inputs.
    Today, many believe that information technology--or big data as it 
has been called--is the next big game changer for agriculture. Thanks 
to significant investments in precision agriculture technology by those 
companies represented here today, as well as countless others, 
producers now have more information about their farms at their 
fingertips than ever before.
    Big data has what seems like a boundless potential to improve the 
efficiency, profitability, and competitiveness of our nation's farmers 
and ranchers while conserving natural resources and benefiting the 
environment.
    In fact, the benefits of big data have already been paying off as 
we will hear about today.
    But, at least one of the reasons why potential benefits have not 
yet been fully realized is because farmers and ranchers are getting 
lots of information from lots of different places. Getting all of this 
information into one place where it can be easily accessed and used is 
critically important. And I am very pleased that Billy Tiller, who is 
from my part of the country, is here to talk about this impediment and 
how he and other farmers are working to find farmer-friendly solutions 
in overcoming it.
    Beyond practical considerations, however, is the important question 
of how to protect producer privacy and private property rights.
    Thankfully, the law protects the privacy of most producer 
information that USDA gathers. But that, of course, does not cover 
information gathered by private entities. This has enormous 
implications that can, among other things, affect the commodities 
market, land values, and how farm policies operate, and potentially 
expose producers to frivolous and costly environmental litigation.
    My hope is that the Committee and our exceptional panel of 
witnesses will fully explore these and, perhaps, other relevant issues.
    But, in closing, I want to go back to what I think is a central 
point, and that is the fact that this is the farmer's information. And, 
as such, the farmer should own or, at bare minimum, control information 
about his operation.
    If we can achieve this important principle, I think we go a long 
way in ensuring that American agriculture harnesses the power of big 
data.
    I would now recognize the Ranking Member, Mr. Peterson, for any 
comments he wishes to make.

    The Chairman. With that, I will recognize the Ranking 
Member for any comments that he would like to make.

OPENING STATEMENT OF HON. COLLIN C. PETERSON, A REPRESENTATIVE 
                   IN CONGRESS FROM MINNESOTA

    Mr. Peterson. Thank you, Mr. Chairman. And I welcome the 
witnesses to the Committee today, and I am looking forward to 
your testimony.
    There are a lot of interesting things happening with big 
data and agriculture, and they all have the potential to 
provide huge benefits, not just to farmers but to consumers, 
and to the economy as a whole.
    Adopting new technologies can make farming more efficient, 
enabling farmers to make wise use of inputs and help to keep 
their costs low. Technology can also help connect farmers with 
local businesses and consumers, opening up potential new 
business opportunities. Of course, there are concerns about 
these advances, particularly when it comes to privacy, and this 
is something we are going to have to keep an eye on. But I am 
encouraged that our commodity groups and agriculture technology 
providers have started a productive dialogue, and I hope that 
that relationship continues, and that we can learn more about 
these efforts during today's testimony.
    One final point, we need to take a look at what is 
happening with rural broadband. All of this technology is 
great, but it is not going to do anybody any good if we don't 
have reliable broadband. We made a lot of progress but there 
are still huge parts of the country that don't have reliable 
service, and in spite of all of the money that we have spent 
trying to get broadband into unserved areas, people continue to 
overbuild existing systems and spend the money in places that, 
in my opinion, they shouldn't. Somehow or another, we need to 
take this Universal Service Fund away from telephones and put 
it into broadband, and do what we did back in the 1930s where 
we got it to every house. And somehow or another, we have to 
figure out how to do this.
    So anyway, I think that there is a lot of interesting 
points for us to discuss today, and I thank our witnesses for 
appearing before this Committee. And yield back.
    The Chairman. I thank the gentleman.
    The chair would request that other Members submit their 
opening statements for the record so the witnesses may begin 
their testimony to ensure that there is ample time for 
questions.
    I now recognize Mrs. Hartzler to introduce our first 
witness.
    Mrs. Hartzler. Thank you, Mr. Chairman. I am very honored 
to be able to introduce our first witness, my friend and 
Missouri Farm Bureau President, Blake Hurst. Mr. Hurst, or 
Blake, farms in northwest Missouri. He has a 2 acre greenhouse 
with his wife, Julie, and daughter and two sons. But he is also 
more than just a farmer and a leader of the Farm Bureau in the 
state, he is also a very accomplished writer, and his 
articles--he has a gift of humor as well as getting his point 
across. And he has been featured in Wall Street Journal, Weekly 
Standard, Reader's Digest, Missouri Farm Bureau's Show Me 
Magazine, and many more publications. And he is certainly a 
leader among the American Farm Bureau Federation, and on issues 
relating to farmer data. He was one of our main pivotal voices 
last summer on my farm tour where we had a session dealing with 
big data. So I very much appreciate him and the honor to be 
able to introduce him today. Thank you for being here.
    The Chairman. Thank you. We also have Mr. Billy Tiller, the 
Director of Business Development, Co-Founder of Grower 
Information Services Cooperative in Lubbock, Texas. Dr. Michael 
Stern, President, Chief Operating Officer, The Climate 
Corporation and Vice President, Monsanto, San Francisco, 
California. Mr. Matt Rushing, who is the Vice President, 
Product Line, Advanced Technology Solutions, AGCO, Duluth, 
Georgia. And Mr. Shannon Ferrell, Associate Professor and 
Faculty Teaching Fellow, Agricultural Law Department of 
Agricultural Economics, Oklahoma State University, Stillwater, 
Oklahoma.
    Mr. Ferrell, I express our condolences on the death of your 
brother this past week, and our prayers for you and your family 
as you go through those circumstances. Thank you for being here 
this morning.
    Mr. Hurst, would you care to begin? Five minutes.

  STATEMENT OF BLAKE HURST, PRESIDENT, MISSOURI FARM BUREAU; 
 MEMBER, BOARD OF DIRECTORS, AMERICAN FARM BUREAU FEDERATION, 
                           TARKIO, MO

    Mr. Hurst. Thank you, Congresswoman Hartzler, for the warm 
welcome. Thank you, Chairman Conaway and Ranking Member 
Peterson, for holding this hearing.
    I am honored to represent Missouri Farm Bureau and the 
American Farm Bureau Federation, and share our members' views 
on big data. Included in my written testimony is an article I 
wrote a couple of years ago on the topic.
    Big data will lead to as much change in agriculture as the 
Green Revolution or biotechnology. Farmers have had access to 
precision technology for a number of years, but significant 
strides have been made in data collection and analytics. As a 
result, farmers using the technology are reporting higher 
yields, fewer inputs, more efficiency, less strain on the 
environment, and higher profits. Yet many are also expressing 
concerns about privacy, security, portability, and transparency 
in how their data is used, and who exactly has access.
    The questions about the new technology can be grouped into 
the following categories. Transparency: What information is 
being collected? Will the ATP, or ag technical provider, notify 
me, the farmer, if its policies and/or procedures change? With 
whom does the ATP share the information? Who else can obtain my 
data? Can I delete my data from a database? Can I easily switch 
among providers, which is a huge question? Am I the gatekeeper 
to data access? Of course, who is liable if there is a data 
breach? And is there value to this data to my farm, and can I 
capture some of that data, can I be paid for the data?
    In early 2014, the Farm Bureau invited six farm and 
commodity groups and six ag technology providers to meet to see 
if we could find a solution to the farmers' concern. We worked 
several months to develop 13 principles of data privacy and 
security, which are included in my written testimony.
    Farmers prefer this teamwork approach over regulatory or a 
legislative fix because we believe the market will provide the 
process to address problems if farmers have an equal footing 
with agribusinesses. If we rely on the government to make 
changes, the undue overhead may well irreversibly deter 
innovation.
    However, while we are not advocating for government 
involvement in regulating big data, our farmers are extremely 
interested in having the government being a data-driven partner 
so they can more easily use electronic technologies to access 
and utilize USDA programs such as having a one-stop sign-up for 
programs across multiple agencies like the Farm Service Agency, 
Natural Resources Conservation Service, Risk Management Agency. 
Through technology, the government can enable progress and 
efficiency. USDA needs better data technology and the authority 
and resources to use them to drive value for farmers' data. If 
we can accomplish that, we will jointly drive innovation, 
reduce economic burden on farmers, and reduce costs for USDA.
    You will note that we started this process with 12 
participants, and we have moved on from the beginning, we have 
had 35 different organizations sign on to our agreement. The 
first thing we did was set up a transparency evaluator. We 
agreed it would be useful to help farmers understand the 
documents they signed with ATPs and ag service providers, and 
to do so without hiring a lawyer. Consequently, we developed a 
transparency evaluator. I would describe it as a combination of 
a Consumer Reports review and a Good Housekeeping seal of 
approval.
    Before signing contracts, farmers should understand what 
will become of the data collected from their operation, 
including whether it is accessible to a Freedom of Information 
Act request, whether it is accessible to government agencies 
without permission, and whether it could be used to speculate 
in the commodities market. Farmers need to be able to determine 
if the benefits and relationship outweigh the privacy and 
security risks.
    We have also done work on ag data repositories. Today, most 
experts believe that 80 percent of the data we collect never 
leaves the tractor or combine, or is never entered into a 
database. A data repository will be developed so it is akin to 
a bank where one is free to deposit and withdraw data at will, 
a place where farmers can store their data for later use.
    While AFBF has not endorsed any ag data repository, we are 
working with some which are being developed so that producers 
have an opportunity to store their data in an open, neutral 
network. And we hope that one or more data repositories follow 
the principles in our data privacy and security document.
    We have had some skepticism from farmers about data 
repositories. The biggest concerns are security, providing 
agribusiness companies with one more avenue to sell to and 
increase their costs. I believe the farmer data has value, and 
by simply offering it to a repository we may not be able to 
capture that value. And if data is stored in an individual 
company database it is often difficult and often impossible to 
move to a different provider.
    In summary, the increasingly important role of precision 
agriculture and big data offer significant opportunities for 
farmers and ranchers. However, we must do everything we can to 
ensure producers own and control their data, can transparently 
ascertain what happens to the data, and have the ability to 
store the data in a safe and secure location.
    Thank you.
    [The prepared statement of Mr. Hurst follows:]

  Prepared Statement of Blake Hurst, President, Missouri Farm Bureau; 
Member, Board of Directors, American Farm Bureau Federation, Tarkio, MO
    Chairman Conaway and Ranking Member Peterson, thank you for the 
opportunity to testify today on the fast-paced expansion of innovation 
in ``big data,'' its implications and its use in production 
agriculture. I would like to begin my testimony by sharing an article I 
wrote nearly 2 years ago on this topic.

          Big data will make farming more environmentally responsible 
        and easier to regulate, but will lessen the sense of place 
        cherished by the local food movement.
          Nothing is more important in agriculture than place. What is 
        successful on one kind of soil in one kind of climate won't 
        necessarily work in another place with a different soil or 
        different weather patterns. Farmers have always gained the 
        knowledge necessary to understand a place through hard-won and 
        rarely transferable experience. What farmer Brown knows about 
        his land might travel down the road a few miles, but it is less 
        applicable on a similar farm in a different part of the 
        country. This idea of place is what drives the local food 
        movement. Wineries brag about the perfection of the marriage 
        between their varietals and soil. On our farm, every acre that 
        I've farmed for 35 years and that my father has farmed for 65 
        years has a story. We know which weeds grow where, when the wet 
        spots will appear, and we all remember that time the combine 
        caught on fire down by the hackberry tree. Farmers' personal 
        relationship to place, one of the salient facts that 
        distinguish agriculture, is about to change.
          Most combines traveling across fields in the Midwest this 
        fall had a GPS receiver located in the front of the cab. 
        Although agriculture has been experimenting with this 
        technology for a decade or so, only now is the industry 
        starting to consider all the uses of this transformative 
        technology. For several years, farmers have had the ability to 
        map yields with global positioning data. Using that 
        information, firms can design ``prescriptions'' for the farmer, 
        who uses the ``scrips'' to apply seed and fertilizer in varying 
        amounts across the field. Where the yield maps show soil with a 
        lower yield potential, the prescription calls for fewer seeds 
        and less fertilizer. This use of an individual farmer's data to 
        design a different program for each square meter in a field 
        spanning hundreds of acres could replace a farmer's decades of 
        experience with satellites and algorithms. What we have gained 
        in efficiency and by avoiding the overuse of scarce and 
        potentially environmentally damaging inputs, we may be losing 
        in the connections of the farm family to the ancestral place. 
        Precision technology will allow managers to cover more acres 
        more accurately and will likely lead to increasing size and 
        consolidation of farms. While Michael Pollan, Mark Bittman, and 
        Alice Waters continue to argue that we need to turn back the 
        clock on technology in agriculture, much of the world is moving 
        in a quite different direction.
          Advice for individual fields is only the beginning of the 
        uses for this technology. Agricultural equipment firms have run 
        pilot programs where data is uploaded every several hours to 
        the cloud, where it can be used . . . well, we don't really 
        know all the ways it can be used. If 1,000 machines randomly 
        spread across the Corn Belt were recording yield data on the 
        second day of harvest, that information would be extremely 
        valuable to traders dealing in agricultural futures. Traders 
        have traditionally relied on private surveys and U.S. 
        Department of Agriculture yield data. These yield estimates are 
        neither timely nor necessarily accurate. But now, real-time 
        yield data is available to whoever controls those databases. 
        The company involved says it will never share the data. Farmers 
        may want access to that data, however, and they may not be 
        averse to selling the information to the XYZ hedge fund either, 
        if the price is right--but that's only possible if farmers 
        retain ownership and control of the data.
          One of the most important issues around ``big data'' goes 
        directly to property rights. As Christopher Caldwell points out 
        in the Claremont Review of Books, just because Facebook, 
        MasterCard, or Google keeps track of what I searched for or 
        where I buy lunch, it is not altogether clear why they should 
        assume ownership of that data. For many of us, the convenience 
        and enjoyment we receive for free from Facebook or Google may 
        well be worth the loss of privacy.
          The value relationship between farmers and the companies that 
        collect their data is considerably different. The risks to 
        privacy that the farmer endures, such as his pesticide or GMO 
        usage that may be accepted practice but not politically 
        popular, are considerably greater than the fact that Amazon 
        knows I have a weakness for thrillers and murder mysteries. Not 
        only that, but the individual farmer's data has considerably 
        more value than the average consumer's data. Many farms are 
        fairly large businesses, spending hundreds of thousands on 
        fertilizer and seed and producing millions of dollars of crops. 
        It's not difficult to imagine a smart phone ad arriving within 
        seconds of a farmer encountering weed or insect damage while 
        he's harvesting his crop. Farmers' information is valuable to 
        the companies sponsoring ads, so farmers should be compensated 
        when their data is sold. Farmers need to protect their data and 
        make sure they bargain wisely as they share data with suppliers 
        and companies who desire access to their information.
          Farmers look forward to the ability to improve their yields 
        and efficiency by comparing their results to neighboring 
        producers. If my neighbor is receiving better results because 
        of superior seed selection or because he times applications of 
        inputs differently, then I'd really like to have that 
        information. But this knowledge can have other results. If 
        investors have data from all across the country, the access to 
        better information could correct any market imperfections in 
        the market for farmland. What has been a dispersed and 
        unorganized market will likely be more accurate and rational 
        with the advent of agricultural ``big data.'' Knowledge of soil 
        types, weather patterns, and productivity has been limited to 
        close neighbors, but now access to data maps will replace the 
        value of local knowledge. Owners of the database will have a 
        decided advantage when it comes to pricing agricultural inputs, 
        whether seed or farmland.
          Farmers are rightly concerned about data privacy. Even if an 
        individual operator does everything to the best of his ability, 
        following all the applicable rules, regulations, and best 
        management practices, there is still concern that the EPA or 
        one of the numerous environmental organizations that bedevil 
        agriculture might gain access to individual farm data through 
        subpoenas or an overall-clad Edward Snowden. This concern about 
        privacy will likely slow the adoption of the technology. The 
        data will be invaluable to regulators and to parties in future 
        litigation and it may also help protect farmers from 
        accusations of wrongdoing. Of course, some farmers will never 
        be comfortable sharing any kind of farm information with 
        strangers.
          Amazon made headlines with the news that it is beginning to 
        experiment with the use of drones for delivery of purchases to 
        customers. We're a long ways from Amazon CEO Jeff Bezos's ideas 
        about the delivery vehicle of the future, but it is fun to 
        think about what it might mean for agriculture. Nothing is more 
        irritating to farmers than having to stop harvesting and travel 
        dozens of miles for parts for their machines. With real-time 
        monitoring of machine data and drone delivery, the local 
        implement dealer may spot a bearing that is outside of the 
        recommended temperature range, recognize an impending part 
        failure, and dispatch a drone rescue mission before the actual 
        operator of the machine realizes he is in trouble. That's 
        unbelievably efficient, but more than a little spooky. Although 
        delivery by buzzing FedEx drones may be a part of the distant 
        future, drones will certainly be part of the data revolution in 
        agriculture in the here and now. Though the industry complained 
        loudly when they discovered that the EPA was using aerial 
        surveillance to monitor livestock firms, the advantages of 
        cheap and ubiquitous drones to monitor crop conditions and 
        forecast yields will be too valuable to ignore.
          Big data on farming will also likely affect the private-
        public partnership that brings us subsidized crop insurance. In 
        the present system, insurance rates are set to maximize 
        enrollment in the subsidized program, because encouraging 
        participation by producers is seen as a public good. Insurance 
        rates in marginal areas are lower than they would be if prices 
        reflected only actuarial risk. But with access to the data 
        about individual farms, insurance companies will be able to 
        identify the least risky, most productive farms, which will 
        likely buy less costly private insurance. This will end the 
        ability of the present crop insurance programs to spread risk 
        and will increase costs for farmers in more marginal areas, if 
        the government doesn't increase subsidies further.
          If a farmer can manage one machine guiding itself across a 
        field by satellite, applying inputs and measuring outputs, 
        reporting by-the-minute data on yields, oil temperature, and a 
        gazillion other data points, what is to stop that same farmer 
        from managing dozens of machines on farms the size of New 
        Hampshire? Tyler Cowen argues that we're about to see an even 
        wider disparity in incomes between the ten to 15 percent of the 
        population that can relate well to computers and the vast 
        majority of us who will deliver services to the computer-savvy 
        class. Farming may be one of the first industries to explore 
        the validity of Cowen's thesis. All of us involved in 
        agriculture will soon have to decide whether we want to occupy 
        the nostalgic niche providing artisanal beets and heritage pork 
        to Cowen's ten percent, or whether we'll roll the dice on 
        surviving the transition to a data-driven agriculture. Farming 
        will be more efficient, more environmentally responsible, and 
        easier to regulate and measure. But it won't be the same.

    I wanted you to have this article before we begin to share what 
Farm Bureau and other farm and commodity groups have been working on 
the past couple of years because it encapsulates the opportunities and 
challenges we all face--not just farmers and ranchers, but the 
agriculture technology providers (ATP) and other segments of the 
agricultural production and marketing chain. It is extremely likely 
that the big data movement and the innovative technologies and 
analytics it yields will lead to at least as much change in agriculture 
as did the Green Revolution and the adoption of biotechnology. Farmers 
using the technology are reporting higher yields, fewer inputs, more 
efficiency and, importantly, higher profits.
    Yet, many are also expressing concerns about privacy, security, 
portability and transparency in how their data is used and who, 
exactly, has access. While the questions about the new technology are 
numerous, they can be grouped into the following categories:

    Transparency

   What information is being collected?

   Will the ATP notify me (the farmer) if its policies and/or 
        procedures change?

   With whom does the ATP share the information?

   Who else can obtain my data?

    Control

   What control does the farmer have over the information that 
        is collected?

   Can I delete my data from an ATP's database?

   Can I easily switch among providers (and take my data with 
        me)?

    Security

   Am I the gatekeeper to data access?

   Who is liable if there is a data breach?

    Value

   What is the value of this data to the farm?

   Can I get paid for my data?
Principles of Data Privacy and Security
    In early 2014, the American Farm Bureau Federation (AFBF) initiated 
a working group by inviting six farm and commodity groups and six ATPs 
to discuss these issues and see if we could coalesce around some 
concepts and solutions to our members' challenges and concerns. The 
participants included:

   American Farm Bureau Federation;

   American Soybean Association;

   Beck's Hybrid Seed;

   Dow AgroScience;

   Dupont Pioneer;

   John Deere;

   Monsanto;

   National Association of Wheat Growers;

   National Corn Growers Association;

   National Cotton Council;

   National Farmers Union;

   Raven; and

   USA Rice.

    This group worked several months to develop 13 principles on 
privacy and security. I served as one of AFBF's four representatives on 
that group. We had significant discussion and frank debate on the 
issues. But more importantly, we had several ``learning moments'' that 
occurred simply from spending time with each other as the ATPs learned 
more about farmers' concerns and we gained insight into the ATPs' 
ability or inability to address each and all of those concerns. I would 
emphasize a critical point: farmers prefer this teamwork, ``business-
to-business'' approach over a regulatory or legislative ``fix'' because 
we believe the market will provide the process to address problems if 
farmers have an equal footing with agribusinesses. If we rely on the 
government to make changes, the undue overhead might irreversibly deter 
innovation.
    However, while we are not advocating for government involvement in 
regulating big data, our farmers are extremely interested in having the 
government be a data-driven partner so that they can more easily use 
electronic technologies to access and utilize USDA programs, such as 
having a one-stop sign-up for programs across multiple agencies rather 
than having to report to their crop insurance agent, the Farm Service 
Agency, Natural Resources Conservation Service, etc. Through 
technology, the government can enable progress and efficiency. USDA 
needs better data technologies and the authority and resources to use 
them to drive value for farmers' data. If we can accomplish that, we 
will jointly drive innovation, reduce economic burden on farmers, 
reduce administrative costs for USDA agencies and improve services. 
Everyone wins.
    You will note that we started this process with 12 participants. As 
we had intended from the beginning, when we completed our work on the 
principles document, it was shared with other groups to gauge their 
interest and see if they wanted to sign on indicating their support as 
well . . . Today, 35 groups have endorsed the principles. The latest 
document is attached for your further review.
    This was an extremely valuable process that allowed various 
segments to better understand the ``other side's views,'' work through 
differences and reach a workable conclusion. Beyond the principles 
document, the 35 groups have committed to ongoing engagement and 
dialogue regarding this rapidly developing technology.
Transparency Evaluator (TE)
    One of the first things that several of the participants agreed 
would be useful was a way to help farmers understand the formal 
agreements and/or contracts they sign to engage ATPs and/or ag service 
providers--and to do so without a legal background or hiring a lawyer 
to understand the details. This group made the decision to develop a 
Transparency Evaluator. In its simplest form, I would describe it as a 
combination of a Consumer Reports review and a Good Housekeeping Seal 
of Approval.
    This was a priority because many farmers are interested in using 
some form of data collection and storage, but virtually all are unaware 
of how their data is used after it leaves their farm--their immediate 
control, if you will.
    Farmers often sign a terms and conditions contract with companies 
that collect their data, a contract that typically exceeds 30 pages in 
length; some are even longer. It is virtually impossible to find the 
specific provision you may be interested in, such as ``will the ATP 
share my data'' in such a lengthy document and even more difficult if a 
farmer is trying to compare policies between companies or service 
providers.
    One of the driving motivations for the AFBF Board regarding the 
decision to engage in big data discussions was that use of this 
technology, in all its iterations, is a choice that belongs to each 
individual farmer. With that in mind, we determined our best course 
would be to encourage farmers, before signing a big data contract, to 
make sure they understand what will become of the data collected from 
their operations, including such important issues as:

   Who controls their data;

   Who can access it;

   Whether the aggregated or individual data can be shared or 
        sold;

   The ways a company intends to use the farmer's data;

   Whether it will be kept in a place that could make it 
        accessible to others via a Freedom of Information Act request;

   Whether farmers can get his data out of the system;

   Whether it is accessible to government agencies such as the 
        Environmental Protection Agency;

   Whether or not it could be used by ATPs to speculate in the 
        commodities market; and

   What happens to the data if the company is sold, acquired, 
        or dissolves.

    In short, farmers need to be able to determine whether the benefits 
outweigh the privacy and security risks associated with usage. By 
providing a tool to answer these questions, Farm Bureau can help 
farmers make informed decisions.
    Twenty farm and commodity organizations, ag service providers and 
ATPs have joined forces and provided financing to collaborate in the 
development of a TE. The TE will provide farmers with an easy-to-use 
mechanism to allow them to compare and contrast specific issues within 
the contracts presented to them by ATPs. The groups are:

   AGCO;

   AgConnections;

   American Farm Bureau Federation;

   American Soybean Association;

   CNH (Case New Holland);

   CropIMS;

   Dow AgroSciences;

   Dupont Pioneer;

   Farm Dog;

   Farmobile;

   Granular;

   GISC (Grower Information Services Cooperative);

   Growmark;

   Independent Data Management;

   John Deere;

   Monsanto;

   National Association of Wheat Growers;

   National Corn Growers Association;

   National Farmers Union; and

   National Sorghum Producers.

    While we are still in the development phase, the TE group has 
coalesced around a TE tool that will be simple and easy for farmers and 
ATPs to use. A key component in the development is, to the extent 
possible, match the questions/information available in the TE with the 
provisions endorsed in the Privacy and Security Principles.
    Farmers need a method to quickly understand the often-complicated 
privacy policies, terms and conditions and other documents that come 
with signing up for new precision agricultural services. Likewise, ATPs 
and ag service providers need an easily recognizable way to demonstrate 
to farmers that they mean what they say--that their marketing and 
promotional materials are consistent with the legal terms of the 
contract. The TE is being developed around a simple scorecard format to 
allow, for example, a farmer whose primary focus may be transparency 
concerns, to easily review that area of the TE and, if desired, click 
on a link to obtain more information from a particular ATP.
    The TE will provide answers to ten questions that provide the 
farmer with basic information about ownership, control and use of the 
data generated on his or her farm. These would be ``yes'' or ``no'' 
questions, with a link to the specific language in the actual contract 
to back up the answer if the farmer wishes to look at the specific 
contract language. While we have not yet finalized the questions, it is 
likely to include wording such as, ``Will the ATP obtain my consent 
before selling my data to persons or companies not parties to the 
agreement?'' and, ``Can I delete my data upon contract termination?'' 
Other questions could be about ownership, contract termination or 
portability.
    Products that have been through the transparency scorecard analysis 
and approved by the TE administrator would be eligible to use an annual 
TE seal, denoting compliance with the process. This is something that 
could be used on the ATP's product websites or in marketing materials, 
giving a farmer a quick method to determine how the privacy policy and 
other contract documents for the product relate to the data principles.
    While the original purpose of the TE was simple transparency of 
contracts, the members of the TE have discussed whether there should be 
a requirement for some level of adherence to the Privacy and Security 
Principles for Farm Data in exchange for awarding the seal of approval.
    The current process calls for the ATPs to be responsible for the 
initial completion of the transparency scorecard. ATPs would complete 
the transparency scorecard by answering the questions and providing 
hotlinks to their privacy policies and other contracts containing the 
answers to each of the ten questions. The ATPs would submit the forms 
upon completion via electronic means to the TE administrator, who would 
then undertake a legal review of the responses to verify their 
accuracy.
    This type of ATP self-certification at the beginning of the process 
has two advantages: it requires the ATP to engage in the process and, 
in the long term, we hope the scorecard will shape the privacy policies 
and other legal documents the ATPs attempt to certify.
    After submittal, the TE administrator's review would determine the 
completeness and accuracy of the transparency scorecard responses. 
Assuming that all answers are correct and links are functional, the TE 
administrator would notify the ATP that certification is appropriate 
and the seal is granted. If problems arise during the review of the 
ATP's scorecard responses, there will be opportunities for resubmission 
and an appeals process.
    Our goal is to have the TE operational next spring.
Ag Data Repositories
    Another big data issue on which Farm Bureau is focusing is the 
development of an ag data repository. Today, most experts believe that 
80 percent of a farmer's data is not removed from devices on the 
tractor or other machinery and that it is deleted before being 
transferred to storage in a database, effectively rendering it 
inaccessible and not usable.
    A data repository akin to a bank should be developed where an 
individual is free to deposit or withdraw funds at will. Farmers could 
use such a repository to store their data for later use, and also 
provide a means to share their data with a trusted service provider, an 
ATP, a university for research purposes, business partners or any 
others if they want. The repository should be able to aggregate, 
secure, store, clean and distribute production data with whomever the 
producer requests it be shared.
    While AFBF has not endorsed any particular ag data repository at 
this time, we are working with those who are developing them to share 
our thoughts on what type of system would work best so that producers 
have an opportunity to store their data in a secure, controlled and 
easily accessible location. To this end, it is also our hope to ensure 
one or more data repositories are developed and operated in a manner 
that, like the TE, adheres to the principles contained in the Data 
Privacy and Security document to the greatest extent possible.
    Some businesses already operate successful databases, but a 
generous portion of our members have expressed skepticism about 
allowing their data to be stored in those databases. The following are 
some of their biggest concerns:

  (1)  Concerns about data security and privacy.

  (2)  Providing agribusiness companies with their data gives those 
            companies another reason to target market to a producer and 
            potentially increase their cost of doing business.

  (3)  A belief that farmer data has value, and that by simply offering 
            it to a data service, they forgo opportunities to realize 
            this value. (At this time, very few companies have offered 
            to share any of the value they derive from a farmer's data 
            with the farmer.)

  (4)  If data is stored in an individual company database, it is often 
            difficult, if not impossible, to move-transport-producer 
            data from that ``data silo'' to another repository if a 
            farmer decides to change equipment dealers, seed dealers, 
            etc.

    Obviously, if historical data cannot be easily moved, the farmer is 
disadvantaged and innovation suffers.
    We are encouraging all ag data repositories in place or being 
developed to:

  (1)  store and protect agricultural production data;

  (2)  allow farmers to control their data and be responsible for 
            granting data access to others;

  (3)  per farmer agreement, to aggregate data in order for it to be 
            useful to outside parties interested in analytics;

  (4)  standardize and transfer aggregated data to agribusinesses to 
            create value;

  (5)  provide farmers with unrestricted access to their data;

  (6)  ensure and improve the participation of farmers in the creation 
            and pricing of new products and services;

  (7)  increase the value of agricultural data at the farm level and 
            improve the livelihood of farmers by capitalizing on this 
            new asset--much as farmers capitalize on other key assets 
            such as land, water, fertilizer and seed; and

  (8)  clean and certify the data to ensure a level of data quality so 
            that actionable information is available and poor decisions 
            are not made due to poor data--either now or in future 
            years.

    If these ideas are incorporated in a data repository, farmers will 
have more leverage with agribusinesses desiring to use their data than 
they do on their own. In addition, it will allow farmers to focus on 
farming--and ATPs, ag service providers, universities, etc., to focus 
on their core businesses while lowering costs to support their data-
related needs, products and services.
    If data repositories are properly developed, they will give farmers 
the ability to better manage and control their data, convert it into 
new products and services, increase their buying and selling power and 
capture more of their data's overall value. In short, it should enable 
farmers and their business partners to significantly expand their 
return on investments by unlocking the power of ag data.
    In summary, the increasingly important role of prescription 
agriculture and big data offers significant opportunities for farmers 
and ranchers to increase productivity and efficiencies. However, we 
must do everything we can to ensure that producers own and control 
their data, can transparently and easily ascertain what happens to 
their data, and have the ability to store the data in a safe and secure 
location so it can best be used to improve efficiency and productivity.
                               Attachment
Privacy and Security Principles for Farm Data
October 22, 2015

    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 January 23, 2015.

AGCO
Ag Connections, Inc.
AgSense
AgWorks
Ag Leader Technology
American Farm Bureau Federation
American Soybean Association
Beck's Hybrids
CNH Industrial
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 Sorghum Producers
North American Equipment Dealers Association
OnFarm
Raven Industries
Syngenta
The Climate Corporation--a division of Monsanto
USA Rice
Valley Irrigation
ZedX Inc.

    The Chairman. Thank you, Mr. Hurst.
    Mr. Tiller.

        STATEMENT OF BILLY TILLER, DIRECTOR OF BUSINESS
    DEVELOPMENT AND CO-FOUNDER, GROWER INFORMATION SERVICES 
                    COOPERATIVE, LUBBOCK, TX

    Mr. Tiller. It is a pleasure to be here today, Chairman 
Conaway, Ranking Member Peterson, and Members of the Committee.
    My name is Billy Tiller. I am actually a cotton and grain 
farmer from Texas, and even though I work with and helped found 
Grower Information Services Cooperative, that is how I would 
characterize myself. In fact, today, the weather is not right 
in Texas, and hasn't been the last week or so, but as I leave 
it cleared up, so my guys are quickly in the fields and I am 
not there. So my mind is drifting a little back to the farm in 
Texas.
    But what I want to talk about today is the fact, we are 
here on big data, and let me sort of tell you what happened.
    Back in 2010, really it was a few farmers working together 
and we were talking about just the simple thing that it was 
difficult to move our data around. An example would be, we had 
crop insurance and we are trying to move our production data to 
our crop agent, and it wasn't a simple task. I mean I would 
have to go over, had reams of paper that I am hand-delivering 
to crop insurance agents that they are re-keying into systems, 
and I actually had brought that data from a system where it was 
in a sequel server database. So my point is I was moving it, 
and actually it was difficult to move. So we were trying to 
actually streamline that process.
    Along the way of working with that, we started to identify 
some problems, and some problems I really hadn't started out 
thinking about, and those were problems of my own operation, 
which is about 6,000 acres. It is not one of the largest 
operations, and yet I was having difficulty with integration. 
And what I mean by that, I had many different systems that I 
was operating on the farm, and it was very difficult for me to 
integrate any of this data together to actually get--I wasn't 
thinking big data, I am just trying to get an analytical view 
of my own operation. And so what happened, I had a very 
incomplete data set because I had all these silos of data. In 
fact, on page 5 of the report, the testimony I gave you, I have 
a slide there and I would like to add one more silo there, one 
I forgot that I was thinking about yesterday is just paperwork. 
I have all this paper. We are digitizing a lot of it in my 
office today, but it is a silo of data that is missing there. 
But it is very hard to integrate and make good decisions when 
you can't bring all the data together.
    And so what was going on in 2010 and what is going on today 
are the exact same problems. It hasn't gotten fixed. And we 
have been working diligently around the problem. And what we 
did to actually try to fix the problem in our own sense, we 
formed a cooperative called Growers Information Services 
Cooperative. Much like the repositories that Blake was talking 
about, we are attempting to store data, and we have really just 
got our system up and running where we can do that, it has been 
a long process, and where we can store data centrally, begin to 
look at a view that is across platforms, not create all the 
tools for the market, but let the market create those tools, 
but let us store them in a central location.
    The other thing that the cooperative brought was we wanted 
to geospatially reference this information. Now, there is a 
land layer that lies at Farm Service Agency that they created, 
and really is an unbelievable task they went about to create 
the Common Land Unit. It is what we run our farm programs 
against, and also the same land layers used now with the 
approved insurance providers to operate our crop insurance. So 
we have map-based reporting, and we went down that road. It is 
a great geospatial way to reference things. It is a standard 
that we needed in agriculture, and so we chose that map to bear 
a standard.
    Now, as I move forward, that is the reasons we did it, 
along with now we can see big data and we share revenue as 
growers together around the safety and privacy around data. 
What about data privacy. Let me say this. There is machine data 
out there, and this is very disconcerting to me, that when I 
buy a machine, I might not always own the data that might come 
off my machine. I can't hardly believe that I paid money for 
this. It would be like you buying your Kodak camera back in the 
day and then realizing that Kodak might want the pictures off 
the camera. It doesn't make sense to me, and I don't think it 
makes sense to you either. You should own the data if you 
bought the controller.
    And so there are those things around data ownership. Also I 
would like to say that the landscape is changing. I mean 
quickly changing. And what I mean by that is, think about how 
quickly we went from smartphones to where we are talking about 
the cloud, and that has just been in the last 2 or 3 years.
    So as I talk to you today, Members of the Committee, I want 
to complement what has gone on in the prior farm bills. And in 
2008, Section 1619 set out to protect us as farmers around the 
geospatial information on our farm, and I would ask you all to 
continue in that. I would ask that you continue to safeguard 
that information. I think it should be allowed to be accessed 
by the farmer, and that is what we are working to actually use 
that. We use that information at GiSC because we step into the 
shoes of the grower, and we have an MOU with USDA where we have 
been working diligently to decide how can you get real-time 
data into farmers' hands. And USDA needs help there, we need 
help there, so there is a great partnership going on between 
the two of us, and I can see that work continuing to move 
forward into the future.
    So I would summarize by saying please continue your work 
around privacy, understanding also that we don't need bumper 
rails, and you all know that. We want innovation, but somebody 
has to protect us, and we would like FSA to continue to update 
the Common Land Unit layer and do those things around that.
    So anyway, I have a lot more at the end of my testimony, 
but four points I would like you--you can read those, but thank 
you for the time here.
    [The prepared statement of Mr. Tiller follows:]

 Prepared Statement of Billy Tiller, Director of Business Development 
  and Co-Founder, Grower Information Services Cooperative, Lubbock, TX
    Good morning. My name is Billy Tiller, and I am a 4th generation 
family farmer in Lamb and Bailey Counties of Texas. For those of you 
who like geography like me, we are about a 1 hour drive northwest of 
Lubbock, Texas near a very little town called Bula, just south of a 
bigger little town called Sudan. It is a great area. We grow non-
irrigated crops--mainly cotton, grain sorghum, and sunflowers. I have 
also run cattle, have presided over the operations at a local bank, and 
for the past 5 years now, I have been working around innovations in, 
and analyzing the implications of ``Big Data in Agriculture''--the 
topic of your hearing today. Let me say that I am very honored to be 
with you.
    I am here today as a Co-Founder and the current Director of 
Business Development for Grower Information Services Cooperative 
(GiSC--www.GiSC.coop). GISC is a farmer-owned and farmer-led 
cooperative that is built around the idea that information--the data--
generated from the farming operation has tremendous value, and farmers 
should be put in the best position possible to harvest this value. In a 
sentence, GISC seeks to accomplish grower data ownership by giving the 
grower better tools to index, store, protect, share and thereby use 
their data.
    This idea that information and even raw data generated from the 
farm can be a valuable commodity is not necessarily new, but the pace 
of technology and innovation sweeping through the sector keeps this 
reality and world of possibilities ever changing. GISC's timing has 
been very fortunate. In the testimony that follows, I will explain why 
we came to the conclusion that growers need a cooperative to handle 
data, the services we are providing today, and the challenges we see 
for the future.
Brief History of GiSC
    The concept of GiSC began in 2010, as discussion between myself and 
the other co-founder Monty Edwards. Monty was a very progressive and 
dynamic young crop insurance agent who also happened to be a fifth 
generation farmer and good friend. As we struggled with the immense 
paperwork involved in FSA and Crop Insurance, he and I began developing 
a way to move information more efficiently between my farm and certain 
farm service providers. During this exercise we realized the problem in 
agriculture was not so much the need for more technology, but the need 
for integration of current and future technologies to provide me an 
``end-to-end'' view of my farm's operations.
    We concluded that ``big data'' would only benefit the family 
operation if we as farmers had a means to organize the data. We also 
concluded that farmers could only find value in the developing 
agricultural data market if they had a means to aggregate their data 
and this needed to be done with a trusted entity. Therefore, in the 
early days of GiSC, we settled on two areas of focus:

  1.  Develop a secure data platform which could integrate and store 
            data from the myriad of technologies adopted by the ag 
            community. This same platform would also need to allow 
            growers to share data with others while providing them sole 
            control over the parameters of data sharing.

  2.  Formally launch GiSC to be a friend of the farmer/rancher and 
            begin to create a plan for data governance with the 
            grower's interest in mind, including the premise that the 
            grower owns all the data that originates on his operation 
            or his operation's activities.

    We all know the last 100 years of history have been marked by some 
major revolutions in agriculture. The mechanical revolution brought my 
father and his father innovations that changed the very fabric of 
civilization. This same ever-improving mechanization has brought me 
climate controlled cabs, more (mechanical) horsepower, and much 
improved safety mechanisms, all of which have improved life on the 
farm. My father always said, ``Son, you are living in the golden age of 
farming.''
    We have since witnessed giant leaps in scientific and agronomic 
innovations: from hybrid seeds, to better fertilizers, herbicides, 
pesticides, and fungicides. In the last 20 years, we have seen the 
another wave of scientific revolution involving biotechnology. All of 
these innovations have made farming more productive and have made the 
farmer a better steward of the land, as we have reduced the use of 
water, fuel, herbicides and pesticides. These scientific innovations 
continue today as further advances in biotechnology are pushing the 
upper boundaries of yield and stretching perceived water limitations 
through advances in genetics.
    I value all these experiences tremendously. I value them for the 
tremendous impact they have had for humanity. I also value them because 
they have shaped my thoughts about how to make sure that future 
innovations are in the best interest of agriculture producers.
    As I testify here today, I believe another revolution in 
agriculture is occurring now--and that is the Information Revolution. 
It is built on precision agriculture, which involves the integration of 
computing power, satellites, and software that is increasingly being 
utilized to bring the American farmer into a ``brave new world'' of 
automation and operational analysis. It involves GPS guidance systems, 
recording operational activity in fields, and programmed applications 
customizable at the field and subfield levels. Indeed, we are 
accelerating toward a time when the producer will utilize all available 
sources of information, deciphered intelligently to operate more 
efficiently and decisively. This is the ``big data'' opportunity within 
agriculture.
    So the Information Revolution is happening. This is very exciting. 
But there are some problems and hurdles to overcome.
    We at GiSC think precision agriculture as we know it today has one 
fundamental drawback. It creates what is really an overwhelming amount 
of data that is difficult to assimilate, especially without tools to 
integrate and synchronize data created by various sources. So the data-
poor environment of agriculture's past is now data-rich, but we lack 
any real effective way to handle all the information that is being 
funneled into the agricultural producers' management systems.
    Too much information is almost impossible to manage, especially 
since the individual producer's data is an island. The farmer can get 
his hands on more information about his farming operation than at any 
other time in history, but that information is currently for his eyes 
only. The farmer is at a loss as to how to accomplish the task of 
sharing his information with another party.
    The information age has brought not only information from internal 
sources that are at the producer's disposal, but also information from 
many outside sources. He receives data and information from the Farm 
Service Agency, crop insurance agents, accountants, chemical vendors, 
spray pilots, fertilizer dealers, cotton gins, marketing pools, grain 
elevators, equipment dealers, crop consultants, real estate brokers, 
etc. The list goes on and on.
    Now look at the grower's data dilemma: not only does the grower 
have his own island of incompatible and unassimilated data, but there 
are also third party data islands. The grower needs both to provide 
data and receive data from those parties. This is the core reason GiSC 
was formed--to be the solution that bridges these islands, integrating 
and assimilating the grower's disparate data and providing digital 
connections with those that provide services to his operation.
GiSC Today
    As noted, GiSC is a data cooperative owned by growers. It was 
founded on the notion that growers need an easy way to securely store 
and access their information, and to share that information with those 
who serve and support them. GiSC, in every sense, is ``Built by 
Growers, For Growers''TM.
    The cooperative was birthed as an idea in 2010, but formally 
chartered in late 2012. Today we have 1,300 members in 37 states and 
are growing daily. The map below illustrates GiSC's footprint.
GiSC Footprint



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          * States with Members in Light Gray.

    Beyond 1,300 growers in membership, we estimate that we have had 
direct personal communications with over 10,000 growers. These 
conversations indicated that 99.9% of those growers think forming GiSC 
was a good idea. Some thought it was such a good idea, they joined 
immediately. Even more exciting, most that did not join immediately 
left us with the impression that they would join soon after we deployed 
our platform technology, AgXchangeTM. This platform for 
growers has just been deployed and is available to all growers who are 
members of GiSC.
    The point is that GiSC is gaining real traction, and in 2015, we 
have begun to transition the operations from mostly volunteer work by 
the early founders, to employees that spend every day answering 
growers' questions concerning data, systems, and privacy. In the summer 
of 2015, GiSC announced the hiring or Mr. Jason Ward to be the first 
Executive Director of GiSC. Mr. Ward brings 2 decades of experience in 
marketing and agricultural cooperative management, and will lead the 
staff in service to the grower members as the AgXchangeTM 
platform is deployed.
    Upon joining GiSC, Mr. Ward summed up our current mission well, 
stating, ``Information is the new, and emerging, cash crop for 
agriculture and I believe the grower should be at the forefront of that 
movement. The first step for every grower is to make sure he or she is 
taking an active role in owning and controlling his or her data.''
    This mission is being carried out through three primary objectives:

  (1)  Establish the precedent that growers should own and control the 
            information and data related to their production 
            agriculture operations.

  (2)  Offer growers a private and secure cloud-based platform called 
            AgXchangeTM (www.AgXchange.com), where they can 
            store all of the information related to a their operation, 
            and provide their trusted third parties a communication 
            channel for exchanging data, digital documents, and 
            information. AgXchangeTM, will have the 
            functionality to organize a grower's information 
            geographically by a map of a grower's farm or land units. 
            Central to GiSC's mission, the grower will be in control--
            the grower will dictate who may send data to the grower's 
            data repository or access the data in the repository and 
            may limit the access granted to his or her repository.

  (3)  Return value back to the grower members of GiSC. As the network 
            of information and connections increases in 
            AgXchangeTM, the value of that network 
            increases. GiSC, will deliver patronage dividends back to 
            its grower members from profits generated.
The Importance of Indexing Data and the CLU
    I operate a farm that produces reams of data from many sources. In 
fact, I am producing and processing more data than at any other time in 
my history, because it is so easy with devices such as my smartphone. 
Many only consider my Precision Ag data as the data that runs the farm, 
but it is much more encompassing. Here is view of my farm data in 
various silos outside of GISC:
Types of Farm Data


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    Much of the information I have today is cloud-based precision 
agriculture data, but much of my data is still in paper form or a 
digital form of paper such as a pdf file. I must be able to utilize 
both.
    How can we index this data in a way that helps GiSC provide a big 
data picture for agriculture? The sensible answer is this: tie it back 
to the land; use a map; geospatially reference as much data as 
possible. Farmers have always kept track of things by farms or land 
units. The land unit operates much like a factory, where all 
manufacturing is taking place during the growing season for a 
geographic location. Accordingly, we needed to reference any data we 
could back to the land unit. All of the components of 
AgXchangeTM hinge on this most basic unit.
    Many operational maps that growers use, including precision ag 
maps, were researched, as the organizational backbone for geospatial 
referenced data. The conclusion of this research was that one map was 
head and shoulders above the rest for operating as that backbone: the 
Farm Service Agency (FSA) Common Land Unit (CLU) map layer. All U.S. 
farms registered with FSA have been geospatially defined as a unit or 
units, known as Common Land Unit(s). I state this matter of fact, but 
this was a monumental undertaking. FSA employees from across the U.S., 
in a coordinated effort, drew the boundary lines for the CLUs. This was 
perhaps one of the greatest feats ever accomplished by FSA without much 
public knowledge. FSA manages and keeps this CLU updated for its use to 
administer farm programs and increasingly for RMA to index crop 
insurance to the same land unit.
    As stated above, GiSC and its member farmers saw the CLU maps as 
the solution to index all data. However, the CLU maps and data are 
still not readily available even to the farmers it is meant to serve.
    The 2008 Farm Bill restricted public access to the CLU layer when 
connected to any personally identifiable information. We at GiSC 
strongly agree with limiting public access to grower's farm data, and I 
personally appreciate the steps taken by Congress in this prior farm 
bill to protect me. The 2008 Farm Bill also provided a needed 
exception, allowing the grower to request his CLU data from FSA. This 
was good for the grower in principle, but there is no simple method for 
growers to access their CLU data, much less an affordable and easy-to-
use GIS system to view or use the CLU map layer.
    GiSC has worked diligently with FSA since 2012 to understand what 
would be needed by FSA to share the CLU and other farmer information 
with a grower, and GiSC has developed a strong relationship with FSA 
and its staff during the process. FSA has thoughtfully worked to find 
ways to move this process forward, while also being very careful to 
protect producer privacy. Through a Memorandum of Understanding between 
GiSC and FSA, we are now receiving some producers' CLU information on 
behalf of the growers, with their consent. We expect this capability to 
continue to expand as we work through the legal and technical issues 
with FSA.
    The farmer's CLU land layer, integrated into GiSC's platform, makes 
for a very user friendly system. This is the start of how GiSC can help 
farmers manage their many silos of data and index it in a way that can 
make the data useful. It provides a meaningful way to display the 
information for the grower and those with which he or she wishes to 
share data within our system. We expect that FSA will make even greater 
strides in 2016 for delivery of real time data to their customers, and 
this will, in turn, benefit GiSC's membership as GiSC is able to 
deliver more data and data analytics to its farmer members.
    Finally, I would also be remiss if I did not thank this Committee 
for including some very important provision in the 2014 Farm Bill to 
provide resources for and generally promote the electronic exchange of 
data between farmers and the USDA.
The Importance of Aggregating and the AgXchangeTM Platform
    The map-based CLU layer alone does not provide a farmer with 
avenues to interact; therefore, a system needs to be in in place to 
utilize it. Providing such a system was the inspiration for 
AgXchangeTM, and continues to be one of the fundamental 
value propositions of the AgXchangeTM platform.
AgXchange Platform View


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    As stated earlier, we at GiSC determined there was a need in the 
industry for a grower-controlled platform that would be open to all 
service and technology providers to participate. This would provide a 
neutral technology tool, allowing growers to easily collect data from 
all of the proprietary systems and disparate clouds, organize and 
translate it into something meaningful. The CLU layer is the organizing 
point that makes geo-referencing possible, but it is the 
AgXchangeTM that empowers growers to be better decision 
makers, and enables service and technology providers to give us better 
products and services.


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    GiSC is attempting to move the industry in the direction of 
enabling growers to have an end-to-end view of their operations just 
like an Enterprise Resource Planning (ERP) platform in other 
industries. But what has been lacking is a technology neutral middleman 
that can solve the industry's data acquisition and integration problem. 
GiSC and its partners aim to fill that gap and be the aggregator of 
agricultural data, whether it is from John Deere equipment, Case IH 
equipment, or any other precision data technology provider.
Last Word to Farmers in this New World of Big Data
    Farmers need a data aggregator and data integrator to help them 
reap all the benefits of big data and its implications to agriculture. 
We cannot just sit on the sidelines and wonder how it will all turn 
out, trusting that the tremendous for-profit agriculture technology 
providers will use our information only for our good rather than 
returns to their own share-holders. We need to be proactive by joining 
forces with groups such as GiSC, to give farmers a voice.
    Growers must have access to data they own and they must devise 
applications and paths to bring the data back to their barn. We must 
remain vigilant as growers with the agreements that are currently being 
utilized by some vendors that take the rights to our data and our 
future data if we use the software or hardware of that particular 
vendor. We also need to realize that some of these agreements give 
these companies the right to a worldwide license to use our data in any 
way they please and in most cases for free.
    To this point, it is important that all farmers know the important 
work that has been done--thanks in large part to the leadership of the 
American Farm Bureau, to bring all parties--grower groups and 
technology providers--to the table to hammer out a set of principles 
that should govern contracts in this area. This was and is an important 
piece of work for growers everywhere.
    Subsequent to the agreement on Principles for Data Privacy, GiSC is 
currently involved in an initiative alongside commodity groups and 
Agricultural Technology Providers (ATPs) to develop an easy to 
understand metric that informs producers what they are agreeing to when 
they sign or click to accept data terms and use conditions from ATPs. 
We feel it is imperative that producers know upfront who has access to 
and can share their data so they can make informed decisions about the 
products and services they deploy on their operation.
    Finally, I would just say to all growers everywhere that you will 
be impacted by the Information Revolution, whether you choose to 
participate or not. Information is powerful, and we do not want to be 
at the mercy of others, nor should we be information-poor as growers. 
The farmer must remain the premier fount of knowledge and information 
about his farm.
Last Word to the U.S. House Committee on Agriculture
    As the Committee continues to weigh innovations and implications of 
big data in agriculture, GiSC would encourage you to keep some 
important principles in mind.
    First, please be aware of the critical importance of the provisions 
of the 2008 Farm Bill which protect producer privacy around any 
geospatial data. While we support efforts to make it easier for a 
producer to attain his or her CLU and related farm-level data, we do 
not believe there is a legitimate public purpose to be gained in 
sharing such information with others who might ask. We appreciate that 
you understand that there is a right to privacy in our farm locations 
and our CLUs.
    Second, we believe it is important to keep USDA in the middle of 
maintaining the standards for agricultural data and the most up-to-date 
statistics available to maintain transparency and sanctity in the 
markets. Objective and standardized measures and sets of data create a 
level playing field and thus benefit all participants in the 
marketplace. GiSC believes in this principle, and it is why we are 
indexing our data around the CLU.
    Third, while USDA's role in the quality and standards for data is 
important, we believe the marketplace should be the source of new 
innovations in the world of big data. There are worlds of opportunity, 
and there needs to be profit drivers that continue to fuel the research 
and development needed that will continue this information revolution. 
Maintaining strong independent family farms is also key to keeping 
balance in this marketplace. To this end, we hope that you will 
continue listen to the commodity and grower organizations that have the 
grower's interest at heart.
    Finally, I would ask that you continue to look for ways to automate 
the process of data delivery from USDA to the growers. GiSC is a 
willing partner in the task, and we will continue to work hand in glove 
with USDA to try and understand how to keep the grower in the driver's 
seat of this new digital world of big data.
    Thank you for the opportunity to tell you about the work of GiSC 
and our efforts on behalf of the American farmer. Thank you for all the 
hard work you do on behalf agriculture and for the best interest of 
this great nation.

    The Chairman. Thank you, Mr. Tiller.
    Mr. Stern--or, Dr. Stern, excuse me, 5 minutes.

   STATEMENT OF MICHAEL K. STERN, Ph.D., PRESIDENT AND CHIEF 
  OPERATING OFFICER, THE CLIMATE CORPORATION; VICE PRESIDENT, 
                  MONSANTO, SAN FRANCISCO, CA

    Dr. Stern. Thank you Chairman Conaway, Ranking Member 
Peterson, and Members of the Committee, for inviting me to 
participate in today's hearing.
    Your interest in the use of grower data and farm data 
analysis comes at an exciting time as agricultural data 
technology is being made available to farmers in ways it has 
never been done before. We are on the cusp of a digital 
revolution in production agriculture driven by the digitization 
of farm information that will drive a new wave of agricultural 
innovation and productivity.
    The mission of The Climate Corporation is to help all the 
world's farmers sustainably increase productivity through the 
use of digital tools. Accordingly, The Climate Corporation 
looks at the actions farmers take every day, and the roughly 40 
big decisions that farmers make every year on their farm. For 
example, what type of seeds to plant, where and when to plant 
those seeds, what is the optimal seeding population, and when 
and how much fertilizer should be applied, just to mention a 
few. The use of data can provide important, fact-driven 
information and insights to farmers to enable them to maximize 
yield, optimize their use of resources, and save money. What 
you might refer to as farmer data, or precision agriculture, is 
what I think about as digital agriculture; by using data 
science and software engineering, we transform data into 
insights for growers to help them make more informed decisions 
about what is happening in each part of each field. Our 
proprietary Climate FieldView PlatformTM uses real-
time and historic crop and weather data to deliver customized 
insights that help farmers make important agronomic decisions 
with confidence.
    So how do we actually do this? By combining publicly and 
privately available information on weather, soil, and land with 
agronomic practices and farm equipment information provided by 
our farmer customers, we build complex models to analyze all of 
this data and provide insights for farmers to help them make 
real-time decisions that will result in greater efficiencies 
and increased productivity. All of this means that we are 
analyzing a vast amount of data for the farmer to help distill 
that information into usable insights. For example, we have 
developed our Nitrogen Advisor to monitor the movement of 
nitrogen-based fertilizers through the field from fall 
application to spring planting and beyond. This digital tool 
will provide insights to help farmers determine whether they 
have sufficient fertility in the field during the growing 
season to meet their yield objectives. Our Field Health Advisor 
uses satellite imagery to provide high contrast digital maps 
that help farmers spot trends and potential problems in their 
fields before they impact yield. The end result is to provide 
growers with more data-driven information to more sustainably 
increase the productivity of their operations.
    As a company that will utilize our farmer customer data in 
the course of developing these transformational digital tools, 
we take our commitment to safeguarding that data very 
seriously. In June of 2014, The Climate Corporation published 
our data privacy policy which is customer-focused, transparent, 
and makes it clear what we will and won't do with farmer data.
    Our policy states that the company will make it easy for 
farmers to control who can access the data they provide and for 
what purpose. We will only use a farmer's data to deliver and 
improve the services for which they are subscribing. We will 
ensure safeguards are in place to protect farmer information 
from outside parties. We will not sell customer-provided data 
to third parties. The farmer owns this data. And finally, we 
will enable farmers to easily remove that data from our systems 
if they choose to no longer do business with us.
    In addition, about a year ago, we endorsed a set of 
principles for data privacy that we and other industry 
participants developed with the American Farm Bureau. We are 
proud of the work that was accomplished, and we are pleased 
that our collaboration with grower organizations and other 
companies continues as we create a system to verify for our 
customers that we are meeting the standards we have endorsed.
    The promise of digital agriculture is to help American 
farmers and farmers around the world to more sustainably 
convert natural resources into food. It is why we are in 
business. We believe that the digital ag revolution and The 
Climate Corporation's unique technologies will drive innovation 
to help achieve these important goals.
    Thank you for the opportunity to share my thoughts with you 
this morning.
    [The prepared statement of Dr. Stern follows:]

   Prepared Statement of Michael K. Stern, Ph.D., President and Chief
 Operating Officer, The Climate Corporation; Vice President, Monsanto, 
                           San Francisco, CA
    Thank you Chairman Conaway, Ranking Member Peterson, and Members of 
the Committee for inviting me to participate in today's hearing. Your 
interest in the use of grower data and farm data analysis comes at an 
exciting time as agricultural data technology is being made available 
to farmers in a way that's never been done before. We are on the cusp 
of a digital revolution in production agriculture, driven by the 
digitization of farm information, that will drive a new wave of 
agricultural innovation and productivity.
    The mission of The Climate Corporation is to help all the world's 
farmers sustainably increase productivity through the use of digital 
tools. Accordingly, The Climate Corporation looks at the actions 
farmers take every day and the roughly 40 big decisions that farmers 
make every year on their farm. For example, what type of seeds to 
plant, when to plant, what is the optimal seeding population and when 
and how much fertilizer should be applied, to mention a few. The use of 
data can provide important, fact-driven information and insights to 
farmers to enable them to maximize yield, optimize their use of 
resources, and save money.
    What you might refer to as farmer data, or precision agriculture, 
is what I think about as digital agriculture--by using data science and 
software engineering we transform data into insights for growers to 
help them make more informed decisions about what's happening in each 
part of each field. Our proprietary Climate FieldView 
PlatformTM uses real-time and historical crop and weather 
data to deliver customized insights that help farmers make important 
agronomic decisions with confidence. This information can be visualized 
in the cab of their tractor or in their fields to support the complex 
and important decisions they make throughout the season.
    How do we do actually do this?
    By combining publicly and privately available information on 
weather, soil, and land with agronomic practices and farm equipment 
information provided by our farmer customers, we build complex models 
to analyze all of this data and provide insights for farmers to help 
them make real time decisions that will result in greater efficiencies 
and increased productivity. All of this means that we are analyzing a 
vast amount of data for the farmer to help distill that information 
into usable insights. For example, we have developed our Nitrogen 
Advisor to monitor the movement on nitrogen based fertilizers through 
the field from fall application to spring planting and beyond. This 
digital tool will provide insights to help farmers determine whether 
they have sufficient fertility in the field during the growing season 
to meet their yield objectives. Our Field Health Advisor uses satellite 
imagery to provide high contrast digital maps that help farmers spot 
trends and potential problems in their fields before they impact yield. 
The end result is to provide growers with more data driven information 
to more sustainably increase the productivity of their operations.
    As a company that will utilize our farmer customer's data in the 
course of developing these transformational digital tools, we take our 
commitment to safe-guarding that data very seriously. In January of 
2014 The Climate Corporation published our data privacy policy which is 
customer-focused, transparent, and makes it clear what we will and 
won't do with farmers' data.
    Our policy states that the company will make it easy for farmers to 
control who can access the data they provide and for what purpose. We 
will only use a farmer's data to deliver and improve the services for 
which they are subscribing. We will ensure safeguards are in place to 
protect farmer information from outside parties. We will not sell 
customer-provided data to third parties and finally we will enable 
farmers to easily remove that data from our systems if they choose to 
no longer do business with us.
    In addition, about a year ago, we endorsed a set of principles for 
data privacy that we and other industry participants developed with the 
American Farm Bureau. The purpose of this set of principles is to 
further assure farmers that The Climate Corporation takes their privacy 
and security concerns as seriously as they do. These principles give 
farmers a framework on how to assess privacy policies as they consider 
doing business with data companies. We are proud of the work that was 
accomplished here, and we are pleased that our collaboration with 
grower organizations continues as we create a system to verify to our 
customers that we are meeting the standards we have endorsed.
    The promise of digital agriculture is to help American farmers and 
farmers around the world to more sustainably convert natural resources 
into food. It's why we are in this business. We believe that the 
digital ag revolution and The Climate Corporation's unique technologies 
will drive innovation to help achieve these important goals. Thank you 
for the opportunity to share my thoughts with you today.

    Mr. Neugebauer [presiding.] Mr. Rushing, you are recognized 
for 5 minutes.

STATEMENT OF MATT RUSHING, VICE PRESIDENT, ADVANCED TECHNOLOGY 
               SOLUTIONS (ATS) PRODUCT LINE, AGCO
                    CORPORATION, DULUTH, GA

    Mr. Rushing. Chairman Conaway, Ranking Member Peterson, and 
Members of the Committee, thank you for the opportunity to 
testify on behalf of AGCO Corporation.
    AGCO supports more productive farming across every phase of 
the crop cycle, through a full line of equipment, precision 
farming technologies, and services. Nearly 700 of our 3,100 
dealers are located here in the United States, and support 
AGCO's vision to deliver high-tech solutions for professional 
farmers feeding the world.
    We are at a hinge point in global agriculture. Growers must 
increase food production, as you know, between now and 2050 by 
60 to 70 percent in order to feed the growing population. We 
must do all this with less.
    Precision farming technologies are focused on inputs and 
environmental impacts, while optimizing the farm operation and 
lowering growers' input costs, improving overall efficiency, 
and maintaining stewardship of the land.
    Advanced sensors and sensor fusion continue to enable 
better data acquisition, better insights into input deployment 
and yield, cloud computing, and wireless connectivity allows 
for more efficient analysis and more granular management of 
land, machines, and inputs.
    These technologies create a tremendous amount of data that 
is not always fully utilized by growers. We submit to the 
Committee that smart connected farm equipment and growers' 
ability to effectively manage and use this data is at the 
forefront of the next farming revolution. Machinery that 
collects it, farm managers and agronomists who can analyze it, 
and on and off-board technologies that transfer it, read it, 
and put it into action will be the next tools farmers use to 
unlock the value of their data.
    So harnessing this data has the potential to be the next 
big driver for farm productivity gains, similar to the 
transition we saw 100 years ago when we moved from horses to 
machinery. With these developments must come shared standards 
for accessing, processing, and ownership of this data. 
Expansion of rural broadband, which was mentioned earlier, 
Internet access, which enables farm equipment connectivity is 
very critical. Progression towards and adherence to industry-
wide farm data formats and quality standards enables growers to 
effectively work with agriculture service providers to increase 
farm efficiency. Ownership is another key piece of this farm 
data discussion.
    AGCO and many other leaders in the industry assert that the 
farmer owns and should have control and responsibility for the 
data generated by his or her operation. Aside from the 
technical barriers, farmers must perceive the value of big data 
in their operations. Like any other industry going through a 
big data conversion or revolution, stakeholders must see to 
believe. Adoption of precision farming tools and services is 
driving the realization that data benefits and has a return on 
investment. Agricultural equipment and service providers must 
continue to demonstrate the value of data, and make it tangible 
across a wide variety of operations that exist. Data on its own 
is not valuable.
    Given these challenges, it is up to us as leaders in the 
industry to develop and advocate for technology that achieves a 
secure and standardized and adaptable environment. Before we 
can do all that, we must demystify this area of big data. We 
must educate the industry and growers themselves on what farm 
data is and how it can effectively be used. AGCO's focus is on 
helping growers make sense of their data, and in keeping it 
private so they can use it how they want to maximize its 
potential. We are implementing strategic focuses around the 
world on developing equipment that is accurately recording the 
data parameters required for farm managers to engage in 
analytics and enable better decision-making, while ensuring the 
smart equipment can implement management plans derived from the 
data.
    To respect the grower's data privacy choices, we have also 
chosen to transmit the data in two ways, through two pipelines; 
one machine data, and one for more sensitive agronomic data. We 
call this strategic initiative Fuse', AGCO's open 
approach to precision agriculture that optimizes the farm.
    AGCO applauds the Committee for highlighting this important 
topic. It is an exciting time to be part of the agricultural 
industry. New technology and innovations and ways to utilize 
data are propelling growers' productivity and efficiency. We 
are experiencing an unprecedented level of cooperation among 
farmer advocacy groups, industry associations, biotech 
companies, equipment manufacturers, and technology providers, 
all coming together to help growers utilize the data to better 
feed the world.
    We look forward to your continued support. Thank you.
    [The prepared statement of Mr. Rushing follows:]

Prepared Statement of Matt Rushing, Vice President, Advanced Technology 
       Solutions (ATS) Product Line, AGCO Corporation, Duluth, GA
    Mr. Chairman and Members of the Committee: thank you for the 
opportunity to testify today on behalf of AGCO Corporation. My goal 
here is to offer you some perspective into the area of agricultural 
data: what it is, the potential it holds for helping growers increase 
productivity, some challenges the industry faces and, most importantly, 
the exciting opportunity before us if we help our nation's growers 
leverage their data effectively.
    Founded in 1990 with worldwide headquarters just north of Atlanta, 
GA, AGCO is a global leader in the design, manufacture and distribution 
of agriculture equipment in over 140 countries. We support more 
productive farming across every phase of the crop cycle through a full 
line of equipment, precision technologies and services. Nearly 700 of 
our 3,100 dealers are based in the U.S. AGCO's vision is to deliver 
high tech solutions for professional farmers feeding the world. This 
means everything we do supports growers in their efforts to feed the 
rising population.
I. Precision Farming and the Role of Data
    Farmers face a number of challenges that modern agriculture helps 
meet, while also creating some unprecedented dilemmas. We are at a 
hinge point in the global agriculture industry. Our customers--
growers--must increase food production 60-70% between 2005 and 2050 
(United Nations Food and Agriculture Organization Report: http://
www.fao.org/docrep/016/ap106e/ap106e.pdf) in order to feed the global 
population; they must do more with less. AGCO is driving one of the 
next phases of evolution for the modern farm through the advent of 
technology-enabled services to help farmers optimize and fine-tune 
their operations like never before. Most precision farming technologies 
that have been widely adopted today focus on minimizing waste of fuel, 
water, chemicals, seeds, fertilizers or time, and reducing soil and 
water pollution. (Using the right amount of water is critical in light 
of growing demand and damaging droughts.) Thus, the use of data in 
farming optimizes across several aspects of the farm operation, 
lowering growers' costs, improving overall efficiency and improving 
stewardship of the land.
    These technologies have also created tremendous amounts of data 
that has so far not been fully utilized by most growers. The data will 
be leveraged to drive decisions on selecting the best crop varieties 
for each individual zone in a field. Fertilization and crop protection 
plans best suited for those plants in those specific field conditions 
are combined with recommendations for the optimal timing of each field 
operation. Machinery that collects it, farm managers and agronomists 
who analyze it, and on- and off-board technologies that transfer it, 
read it and put it into action will be the next tools farmers use to 
unlock the value in their data.
    Harnessing this data has the potential to be the next big driver in 
productivity gains, similar to the transition more than 100 years ago 
from horses to tractors, and later from mechanical to electronic 
machines. Improved sensors and sensor fusion enable better data 
acquisition and better insights into input deployment. Cloud computing 
and wireless connectivity allows for more efficient analysis and more 
granular management of land, machines and inputs. AGCO submits to this 
Committee that smart, connected machines and growers' ability to 
effectively manage and use farm data is at the forefront of the next 
farming revolution.
II. Challenges to Effective Use of Agricultural Data
    With such change must come shared standards for accessing, 
processing and ownership of this data. In terms of access, expansion of 
rural broadband/Internet access which enables farm equipment 
connectivity is critical to the continued progression of evolving 
farming practices which lead to increased food, fuel and fiber 
production. In terms of processing, adherence to industry-wide farm 
data formats and quality standards enables growers to efficiently work 
with the agriculture service providers (ASPs) to increase farm 
efficiency. Today, farm data is highly varied and follows different and 
often proprietary formats which dramatically limit growers' ability to 
work with their data. Key agriculture industry associations and 
initiatives, of which AGCO and others testifying here today are proud 
to be a part, are working hard to get the ``small data'' right in order 
to improve data portability and interoperability, streamlining farmers' 
ability to utilize it. Ownership is another key piece of this farm data 
discussion. AGCO and many of the other key players in the industry 
assert that the farmer owns and should have control and responsibility 
for the data generated by his or her operation.
    Aside from technical barriers, farmers must perceive the value of 
`big data' in their operations. Like in other industries going through 
a similar `big data revolution,' stakeholders must see to believe. 
Adoption of precision farming tools and services is driving the 
realization of data benefits and return on investment. Agricultural 
equipment and service providers must continue to demonstrate the value 
of data--make it tangible across the wide variety of operations that 
exist.
    Given these challenges, it is up to us leaders in the industry to 
develop and advocate for technology that achieves a secure, 
standardized yet adaptable environment. As you'll hear from those of us 
testifying today, and others in the industry, there are many exciting 
recent and currently underway developments to get us there.
III. What AGCO Is Doing To Help Farmers Overcome Data Hurdles
    Before we can do all that, we must demystify this idea of ``big 
data.'' We must educate the industry and growers themselves on what 
farm data is. Many generate and use data every day and don't even 
realize it. There's a good deal of confusion and some fear of the 
unknown surrounding agricultural data. AGCO's focus is on helping 
growers make sense of their data, and keeping it private so they can 
use it how they want, to maximize its potential. AGCO leads and 
participates in critically important agriculture industry associations 
and initiatives that are working to address these issues through 
information sharing and education. Much of a farmer's concern over his 
or her data comes from the nature of the farm business itself. Most 
other industries would consider this type of information to be 
proprietary or trade secret, however, due to the relationship between a 
farmer and his or her operation, farmers see it as personal data. This 
data also falls into a few categories. Agronomic data is the record of 
what was done in each field, and operational results. Machine data is 
information about the performance and operational settings of the 
equipment that was used. There are also other categories such as 
weather data, financial information, supply chain information and 
several others, but machine and agronomic are generally the most 
discussed.
    In terms of technology development, AGCO is actively implementing 
its strategic decision to focus on engineering equipment that 
accurately records the data parameters required for farm managers to 
engage in robust analytics that enable better decision making, while 
ensuring this smart equipment can then implement management plans 
derived from that data. To respect growers' data privacy choices, we've 
separated our data pipelines; one for machine data, and one for more 
sensitive agronomic data. For agronomic data AGCO has chosen to not 
aggregate, evaluate or even store the data other than to facilitate the 
transfer between the machine and the software that the grower or the 
grower's advisors use to manage the information. The second data 
``pipe'' is for machine data; we encourage growers to share this 
information with us and our dealers. Machine data is less sensitive to 
growers since it generally is difficult to use to determine any of that 
farmer's ``secret sauce'' in producing their crop, or determining their 
profitability. This data can be used to provide services for improved 
uptime as well as optimization for efficient operation. Machine data is 
also valuable for equipment manufacturers like AGCO to use when 
developing the next generation of farm equipment.
    We call this strategic initiative Fuse'--AGCO's open 
approach to precision agriculture that optimizes the farm, providing 
mixed-fleet operations improved access to farm data and better 
connections to trusted service providers. This enables more informed 
business decisions, reduced input costs, and improved yields and 
profitability. Within this strategy, Fuse Technologies is the 
technology foundation--tools--including machine guidance, telematics 
and advanced sensors to create smart, connected machines, fine-tuned 
for each application that can communicate with farm managers, third 
party service providers, and each other. On top of this technology 
foundation, AGCO's dealers are now beginning to offer Fuse Connected 
Services, which combines the right machines, technology, parts, service 
and support to help customers optimize their operation and maximize 
uptime through preventative maintenance, machine condition monitoring 
and year-round consultation. This system is highly flexible--our 
customers who have the ability to manage their data on their own can 
leverage our tools to do it themselves, while those who prefer extra 
support can get it from their AGCO dealers.
    AGCO's strategy is made possible in large part through a focus on 
mobility, and our pioneering open approach. Our tools and technologies 
are easy-to-use and developed for maximum accessibility from the farm 
office, in the field or on the go. We co-develop with a wide range of 
industry partners and suppliers from Silicon Valley to the Corn Belt--
allowing for advanced, nimble and quick-to-market innovations that will 
help growers keep pace with the farming data revolution. Our open 
approach also allows growers to choose the service providers they work 
with, while maintaining a high level of data privacy and security.
IV. Conclusion
    As farm sizes increase, data will enable growers to continually 
optimize and become data-driven managers of their fields. By developing 
technologies to capture, process and utilize farm data, OEMs like AGCO 
and other suppliers will help growers become not only qualitative but 
quantitative experts of their land, using the knowledge gleaned from 
their data to truly optimize their operations and improve productivity, 
putting the right amount of inputs in the right spots in the field, at 
the right time. Agricultural data is the ultimate grower tool to 
minimize risk and increase profitability while enabling them to become 
better stewards of the land.
    AGCO applauds this Committee for highlighting this important topic. 
It's an exciting time to be part of the agriculture industry--new 
technology innovations and ways to utilize data are propelling growers' 
productivity and efficiency. We are experiencing an unprecedented level 
of cooperation among farmer advocacy groups, industry associations, 
biotech companies, equipment manufacturers and technology providers--
all coming together to help growers utilize data to feed the world. We 
look forward to your continued support.

    Mr. Neugebauer. I thank the gentleman.
    Now, Mr. Ferrell, you are recognized for 5 minutes.

    STATEMENT OF SHANNON L. FERRELL, J.D., M.S., ASSOCIATE 
             PROFESSOR AND FACULTY TEACHING FELLOW,
          AGRICULTURAL LAW, DEPARTMENT OF AGRICULTURAL
      ECONOMICS, OKLAHOMA STATE UNIVERSITY, STILLWATER, OK

    Mr. Ferrell. Mr. Chairman, Ranking Member Peterson, and 
Members of the Committee, thank you very much for this 
opportunity to speak to you. I want to commend you for your 
wisdom. You put the lawyer at the end, which indicates to me 
you have probably done this before.
    I also want to echo the comments of the Chairman and 
Congressman Lucas with respect to Dr. Stone. I would be remiss 
if I didn't mention the impact that Dr. Stone had on me. We are 
having this conversation today largely because of some of the 
work that Dr. Stone did. In addition to the accomplishments 
that Congressman Lucas raised to our attention, Dr. Stone was 
also instrumental in developing SAE Standard J1939, which 
sounds like a bunch of alphabet soup, but it is the framework 
upon which machine data is basically built and transmitted. And 
so we really wouldn't be having this conversation today without 
Dr. Stone's work.
    As the Congressman alluded to also, my brother passed away 
earlier this week, but I wanted to come here and speak today in 
spite of that because the last conversation I had with him was 
about how we could apply some of these principles to his cattle 
marketing strategies. And so I thought it was fitting for the 
memory of both Dr. Stone and my brother that I come speak to 
you today.
    Many of the previous speakers have already made many of the 
points I was going to make about the opportunities that big 
data provides, and some of the parameters of the consensus-
driven discussions that we have had in the industry, which I am 
very encouraged by, so I am going to move my comments more 
directly towards a couple of the legal questions that I think 
that are on everyone's mind with respect to this data. First, 
what does the law really have to say currently about the 
ownership of agricultural data; and second, what protections 
are out there for the privacy of that data, generated and 
shared by farmers and ranchers?
    So on the first point of whether the law really gives us an 
ownership interest in agricultural data, I will give the 
classic law professor answer of: it depends, which I know is 
kind of a punt, but I will put it this way. In the regime of 
Federal law, the trademark, patent, and copyright, there is 
really no good fit for agricultural data. It really just 
doesn't have any protection under those various umbras. And so 
what we would then look to would be to state law, and 
specifically the Uniform Trade Secrets Act. And one could make 
an argument, although it is not necessarily a slam-dunk 
argument, that agricultural data could be protected under state 
law as a trade secret, however, that is not really, again, a 
very clear fit. And so if Congress chose to act on that and to 
enhance those protections, one thing that could be done would 
be to perhaps adapt the Uniform Trade Secrets Act on a Federal 
level, or provide a more clear legal definition of where 
agricultural data fits in the concepts provided by the UTSA, 
and provide a more clear protection of agricultural data within 
that framework.
    On the privacy side, there really isn't a good fit under 
Federal law for agricultural data either. Health information 
has HIPAA, financial information has Gramm-Leach-Bliley and the 
Fair Credit Reporting Act, but agricultural data really has no 
place to go at the Federal level defining the level of privacy 
protection. So, ways that Congress could address that issue 
are, first, to enact legislation that clearly and narrowly 
defines the circumstances under which disclosure of 
agricultural data can be compelled by Federal agencies, and 
also the circumstances under which Federal agencies would be 
allowed to disclose that information specifically with regard 
to the Freedom of Information Act. And second, to strengthen 
the safeguards that would prevent inadvertent disclosure of 
agricultural data held by Federal agencies or the unauthorized 
access to that data by other parties. And again, I think that 
the current consensus process is doing a good job of developing 
those protections on the private side, and I really think 
Congress should lend its support to that action as well.
    We really have seen tremendous strides through 
collaboration with all the stakeholders in this industry, and I 
think that Congress could also be very well served and could 
advance the cause of big data's adoption in agriculture if we 
support those public consensus-driven efforts led by American 
Farm Bureau Federation and lots of the service providers in the 
industry.
    With that, I want to, again, extend my appreciation to 
Chairman Conaway, Ranking Member Peterson, and the Members of 
the Committee. I greatly appreciate this opportunity and look 
forward to answering any questions that you may have.
    Thank you.
    [The prepared statement of Mr. Ferrell follows:]

    Prepared Statement of Shannon L. Ferrell, J.D., M.S., Associate 
 Professor and Faculty Teaching Fellow, Agricultural Law Department of
   Agricultural Economics, Oklahoma State University, Stillwater, OK
Executive Summary
    Today's technology affords farmers the ability to instantaneously 
collect data about almost every facet of their cropping operations from 
planting through harvest. Many agricultural producers have concerns 
about their rights in this data and their privacy if they choose to 
share their information to take advantage of the numerous tools 
afforded by the big data revolution as they struggle with how to 
balance the advantages of automatic and continuous uploading of that 
data to other parties such as equipment dealers, input vendors, and 
consultants with the potential loss of confidentiality in such 
transfers.
    The current intellectual property framework fails to provide a 
clear niche for agricultural data in the realms of trademark, patent, 
or copyright law. Agricultural data may fit within the realm of trade 
secret, but that fit is, at best, arguable. To the extent Congress 
wishes to enhance the intellectual property rights held by agricultural 
producers in agricultural data, adaptation of the Uniform Trade Secrets 
Act to accommodate the unique characteristics of agricultural data may 
be a viable approach.
    The greater concern may be in the privacy issues surrounding the 
sharing of agricultural data through big data applications. Current 
Federal privacy laws do not directly address one's privacy rights with 
respect to information like agricultural data. Ways in which Congress 
can directly address privacy issues in this field is (1) to enact 
legislation clearly and narrowly defining the circumstances under which 
production of agricultural data can be compelled by Federal agencies 
and the circumstances under which agricultural data held by Federal 
agencies can be disclosed, and (2) strengthening the safeguards 
preventing the inadvertent disclosure of agricultural data held by 
Federal agencies or the unauthorized access of that data by outside 
parties.
    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, 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 Plews Shadley Racher & 
Braun LLP firm in Indianapolis, Mr. Ryan Jenlink of the Conley Rose, PC 
firm in Plano, Texas, and Mr. Matthew Steinert of Steinert Farms, LLC 
in Covington, Oklahoma contributed greatly to 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 accident last Saturday at the University's 
homecoming parade. I hope this testimony honors his memory, the 
contributions he made to this field, to the U.S. agriculture industry, 
and to all of his students.
Issue Analysis
1. Introduction
    I would like to thank Chairman Conaway, Ranking Member Peterson, 
and the Members of the Committee for the opportunity to present my 
observations on the legal issues surrounding the concept of big data 
and its application to data collected by 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 
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. Framework for Legal Issues Surrounding Big Data in Agriculture
    The concept of big data has exploded in a relatively short period 
of time. As a result, the national dialogue continues to develop both 
common definitions for the core terms in the discussion and the central 
issues of the discussion. Since these definitions and issues continue 
to evolve, my testimony today will provide some framing for both.
2.1  Defining Core Terms in the Big Data Discussion
    Two terms immediately rise to the top in an examination of the 
agricultural data discussion: Big data and agricultural data itself.
    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.'' \1\ 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 firms being aggregated by service providers). 
Big data can be defined in the agricultural context to mean the 
analysis of large numbers of data points both from a producer's own 
operation and from other operations to discover actionable information 
at the farm level and to identify trends at the regional or industrial 
level.
---------------------------------------------------------------------------
    \1\ Arthur, Lisa. 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.
---------------------------------------------------------------------------
    Another term vital to the discussion is agricultural data. The 
concept of agricultural data is almost too broad to define, but looking 
at research in the field and conversations surrounding agricultural 
data as part of the big data debate indicates the term centers around 
two more specific concepts: telematics 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.
    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 in regard 
to either crop production or management of the crop enterprise. Crop 
production services could include fertilizer or chemical applicators, 
custom cultivators, 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  Framing the Legal Issues Surrounding Big Data in Agriculture
    The issues involved in the discussion of big data in Agriculture is 
almost innumerable, but many can be captured under the umbrella of two 
over-arching concepts: ownership of agricultural data, and protections 
against the unauthorized disclosure of agricultural data. Although each 
of these issues is discussed in greater detail later in this testimony, 
a brief framing of each issue is provided here.
    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 
have already provided substantial economic returns to farmers.\2\ 
Improved sensing of soil conditions, crop health, and yields has led to 
significantly improved management information for agricultural 
producers.
---------------------------------------------------------------------------
    \2\ See, e.g., Matthew Darr, ``Big Data and Big Opportunities,'' 
paper presented at Precision Ag 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 
``data.'' 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 1 below.\3\
---------------------------------------------------------------------------
    \3\ 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 1: Comparison of Primary and Secondary Agricultural Data Uses
------------------------------------------------------------------------
        Data                Primary Use              Secondary Use
------------------------------------------------------------------------
Yield monitor data    Documenting yields; on-  Genetic, environmental,
                       farm seed trials         management effect
                                                (GEM) analyses
Soil sample data      Fertilizer decisions     Regional environmental
                                                compliance
Scouting              Spray decisions          Regional analytics
------------------------------------------------------------------------

    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 said farm in 1 year, given the environment of that 
farm for that year and the management practices employed during that 
year. Big data aggregation of similar data across hundreds or even 
thousands of farms allows for the evaluation of that seed hybrid across 
tens of thousands of permutations of these factors, enabling both seed 
companies and agricultural producers to learn in 1 or 2 years what 
would take decades of collections by use of traditional seed trials. 
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 particular 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.
    Bringing about the full economic benefits of big data in 
agriculture require 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.\4\ The agricultural data industry 
is working tirelessly to create those systems. Perhaps the issue of 
greater concern to this hearing is not 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. Doing so 
requires answers to questions of what rights they 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?
---------------------------------------------------------------------------
    \4\ See generally George G. Judge, et al., Introduction to the 
Theory and Practice of Econometrics (2nd ed, 1988), 96.

---------------------------------------------------------------------------
    2.2.1  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. Thus, this issue might be 
framed as ``Who owns data generated about an agricultural producer's 
operation?''

    2.2.2  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. 
While aggregation may in some ways actually reduce the disclosure or 
discovery of information about any one farm, 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.\5\ 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?'' \6\
---------------------------------------------------------------------------
    \5\ Smith, Lars. 2006. ``RFID and other embedded technologies: who 
owns the data?'' Santa Clara Computer and High Technology Law Journal.
    \6\ Peterson, Rodney. 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 15, 2014.
---------------------------------------------------------------------------
    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.\7\ Since trademark is not relevant to a discussion about 
data,\8\ the analysis will focus on patent, copyright, and trade 
secret.
---------------------------------------------------------------------------
    \7\ U.S. Constitution, Article I,  8, clause 8.
    \8\ 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.  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.  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'').\9\ 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.
---------------------------------------------------------------------------
    \9\ 35 U.S.C.  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.\10\
---------------------------------------------------------------------------
    \10\ 17 U.S.C.  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,\11\ 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.\12\ 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.
---------------------------------------------------------------------------
    \11\ 499 U.S. 340 (1991).
    \12\ 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.\13\ 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 \14\ regarding copyright protection 
systems in vehicle software.\15\
---------------------------------------------------------------------------
    \13\ See generally Marshall A. Leaffer, Understanding Copyright 
Law, 109-110 (5th ed. 2011).
    \14\ 17 U.S.C.  512, 1201-1205, 1301-1332; 28 U.S.C.  4001.
    \15\ 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 October 25, 2015). 
Compare Kyle 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 October 25, 2015).
---------------------------------------------------------------------------
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 
appropriately 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.\16\ 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.\17\ 
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.
---------------------------------------------------------------------------
    \16\ 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.
    \17\ See Bruce 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 data is 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.'' \18\ The question becomes what 
constitutes ``reasonable steps'' to keep continuously uploaded data 
protected. 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.
---------------------------------------------------------------------------
    \18\ Smith, supra note 4, citing Restatement of Unfair Competition 
(Third)  757 (1995).
---------------------------------------------------------------------------
    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.
    While 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.\19\
---------------------------------------------------------------------------
    \19\ 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).\20\
---------------------------------------------------------------------------
    \20\ Julianne 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 
accessed May 21, 2015).
---------------------------------------------------------------------------
    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 to read machine 
diagnostic codes for repairs.\21\ 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.\22\
---------------------------------------------------------------------------
    \21\ Interview with Dr. John Fulton, Ohio State University 
Department of Food, Agricultural, and Biological Engineering, July 6, 
2015.
    \22\ See Progressive Corporation, ``Snapshot' Terms and 
Conditions,'' https://www.progressive.com/auto/snapshot-terms-
conditions/ (last visited July 6, 2015).
---------------------------------------------------------------------------
    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.\23\ Note, that several companies now provide means for re-
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.\24\ 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.
---------------------------------------------------------------------------
    \23\ See interview with Dr. Marvin Stone (June 10, 2015).
    \24\ 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.\25\ Obtaining 
agronomic data via a physical connection to an implement poses a task 
manageable for someone knowledgeable in SAE J1939 and ISO 11783 \26\ 
technology.\27\ 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.
---------------------------------------------------------------------------
    \25\ A relatively quick search of Google will yield many GPS 
receiver units for less than $50.
    \26\ SAE International, ``The SAE J1939 Communications Network: An 
Overview of the J1939 Family of Standards and How they are Used,'' 5 
(white paper), available at http://www.sae.org/misc/pdfs/J1939.pdf 
(last visited October 25, 2015). 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.
    \27\ Mikko 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 
July 11, 2015).
---------------------------------------------------------------------------
    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. 
First, virtually all cellular signals are encrypted when transmitted 
and decrypted at the cellular tower; \28\ 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 \29\ ). 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.\30\
---------------------------------------------------------------------------
    \28\ 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 October 8, 
2015).
    \29\ See Craig Timberg & Ashkan 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-bf45-61f69f
54fc5f_story.html (last visited July 1, 2015).
    \30\ See Clemens 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.'' \31\ 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'') \32\ 
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) \33\ 
could also be used as a potential prosecutorial tool for those 
attempting to intercept agricultural data during the data transmission 
process.
---------------------------------------------------------------------------
    \31\ Sam Sammataro, ``Cybersecurity for Small or Regional Law 
Firms,'' paper presented at American Agricultural Law Association 
Annual Symposium, Charleston, South Carolina (October 23, 2015).
    \32\ 18 U.S.C.  1030 et seq.
    \33\ 18 U.S.C.  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.\34\ 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.
---------------------------------------------------------------------------
    \34\ Sara 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 May 21, 2015).
---------------------------------------------------------------------------
    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.\35\ For example, the Federal Health Insurance Portability and 
Accountability Act (``HIPAA'') \36\ provides privacy rights and 
restrictions against disclosure of health information; the Gramm-Leach 
Bliley Act (also known as the Financial Modernization Act of 1999) \37\ 
and Fair Credit Reporting Act \38\ protect financial information from 
disclosure; the Privacy Act of 1974 \39\ 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.
---------------------------------------------------------------------------
    \35\ Todd 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).
    \36\ 42 U.S.C.  300gg, 29 U.S.C.  1181 et seq. and 42 U.S.C.  
1320d et seq.
    \37\ 15 U.S.C.  6803.
    \38\ 15 U.S.C.  1681 et seq.
    \39\ 5 U.S.C.  552a.
---------------------------------------------------------------------------
5. Potential Policy Responses To Address Big Data in Agriculture
    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, and, 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'' 
\40\ 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.\41\ 
The next step is to see continued cooperation among groups such as 
these in integrating their principles in legally-binding service 
agreements.
---------------------------------------------------------------------------
    \40\ American Farm Bureau Federation, ``Privacy and Security 
Principles for Farm Data,'' November 13, 2014 (revised May 5, 2015). 
Available at http://www.fb.org/tmp/uploads/
PrivacyAndSecurityPrinciplesForFarmData.pdf (last visited October 25, 
2015).
    \41\ Open Ag Data Alliance, ``Principals and Use Cases,'' http://
openag.io/about-us/principals-use-cases/ (last visited October 25, 
2015).
---------------------------------------------------------------------------
    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 
\42\ or other circumstances.
---------------------------------------------------------------------------
    \42\ 5 U.S.C.  552.
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    Finally, although outside the direct scope of a discussion of legal 
issues in agricultural use of big data tools, rural access to wireless 
broadband services is crucial to fully utilizing the potential of 
agricultural data systems. Congress should be encouraged to continue 
its efforts to expand access to this vital utility.
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 Committee for its foresight in 
addressing these issues, and sincerely thank the Committee, Chairman 
Conaway, and Ranking Member Peterson for the opportunity to address you 
today.

    Mr. Neugebauer. Thank you. I would remind other Members 
that they will be recognized for questioning in order of 
seniority for Members who were here at the start of the 
hearing. After that, Members will be recognized in order of 
arrival. I appreciate Members' understanding.
    And with that, the chair would recognize himself for 5 
minutes.
    Mr. Tiller, one of the big concerns in west Texas and in 
other parts of the country is, obviously, water. And one of the 
things that is exciting about data that we are gathering is: 
how we can more efficiently utilize that in the future, and 
make sure that we are using that resource in an appropriate 
way, both environmentally but also financially because when you 
are running those wells, it is costing money. But could you 
kind of just briefly describe some of the technology out there 
and how producers could utilize that?
    Mr. Tiller. Yes, there is actually a lot of technology 
today that--and I have seen more in the last couple of years 
around--especially when we are talking center pivot irrigation, 
drip irrigation, that actually monitor, operate those pivots in 
ways where should you irrigate, should you not. I have often 
said that when we get to binary decisions, when you have a yes/
no, should the pivot be on or not. And that is going to come 
about because of algorithms that come out of various companies. 
I think many times we need to look harder at the bottom line. 
It is very hard when you have limited water like west Texas. 
There may only be 300 gallons a minute that you can pump and 
you are trying to irrigate this field. It is not a lot of 
water. But there are ways to look at the bottom line using data 
really to decide in this pivot, in this area of land, can we 
maximize profitability, not yield, can we maximize 
profitability. And that is only answered with the right 
numbers.
    I am very encouraged as I talk to growers, they understand 
we are in hard times and the best way we can handle these 
issues is to understand these underlying costs. And so with 
that said, that is going to be data-driven.
    There are a lot of devices, quickly, that have to do with 
measuring soil moisture. There are devices that measure 
temperature in the canopy. All these things can be used to help 
these guys with irrigation.
    Mr. Neugebauer. And so, obviously, the more data that we 
can gather from a broader spectrum, the more important that 
information is and more useful it is. What are some of the 
impediments that you all are finding to getting producers to 
embrace this technology, and more importantly, wanting to share 
their data?
    Mr. Tiller. In our part of the world, because there is such 
limited water and it is not in great capacities, many growers 
don't employ that exact technology that I am talking about 
because they think that I only have so much water, gee, I 
couldn't manage it anyway. When am I going to cut it off? But I 
am starting to see some that are, telling neighbors. It is sort 
of word-of-mouth. Someone realizes that someone is getting a 
heads-up by actually employing some of those. I have been in 
Nebraska, great grower there that employs a ton of technology, 
but he has a lot more water to work with.
    But with that said, with better weather data analytics, 
deciding when it might rain and actually understanding weather 
patterns better, integrating those things back into the system, 
I think that is going to be a game-changer in trying to save 
water. But again, I think it is profitability down at that land 
layer when we are going to decide how do we plant this and save 
our water resource, and be profitable at the same time.
    Mr. Neugebauer. Yes. And I think one of the things that we 
were--I think maybe Mr. Rushing or--I can't remember which--who 
was talking about that there are kind of two sets of data out 
there; there is the financial data and then there is the 
operational data. And, obviously, being able to integrate both 
of those pieces of information is important to using it to be, 
as you mentioned, more profitable.
    And so in the future as you are collecting the operational 
data, that subset is used for certain kind of purchases in the 
financial, so when we start granting access, how are we going 
to position that where the producer can look at his data and he 
can look at other people's data but not attribute it to one 
individual in making that proprietary--who wants to--Dr. Stern?
    Dr. Stern. Yes. You have characterized it well, that there 
are different--and it is probably more than just two sets of 
data. Ultimately, we believe this has to be a decision that is 
made by the grower around what data needs to be shared, and 
then it is the responsibility of us in the industry, or others 
associated with receiving that data, to make sure that the data 
that they are getting is used for the purposes under which they 
have agreed for us to use it. The question then is can you get 
more value by aggregating a lot of that data, and when you talk 
to growers, they are very, very open to the concept of 
aggregating and anonymizing data----
    Mr. Neugebauer. Yes.
    Dr. Stern.--because they recognize that that can bring 
benefits back to their farm. Generally, when I talk to growers 
they say, hey, look, I don't want you to sell it to anybody, I 
don't want marketers calling me at dinnertime, those types of 
things, but if you can do things by aggregating data that is 
going to help me be more productive. I am really willing to do 
that. So the framework in that understanding at the grower 
level is there.
    Mr. Neugebauer. I thank the gentleman.
    I now recognize the Ranking Member, Mr. Peterson, for----
    Mr. Peterson. I thank----
    Mr. Neugebauer.--5 minutes.
    Mr. Peterson. I thank the gentleman.
    I don't even know where to start. As somebody that has 
tried to get USDA to be able to talk to each other, even within 
the Department, and this is frustrating, I am concerned about 
how we are going to come up with a format that is available to 
everybody that is standard. It looks like everybody is kind of 
going off in different directions, which is a problem. And I 
don't know, are they moving--are you guys moving all this data 
to the cloud, because that is where this is going to end up? Is 
that going on, instead of trying to put it on individual 
servers and whatever?
    Dr. Stern. Yes, I--absolutely, that--I mean when you think 
about where data is going, ultimately it is going to be going 
to the cloud. And with respect to The Climate Corporation, it 
is going to The Climate Corporation cloud. And there needs to 
be a mechanism by which, across the industry there is easy 
movement of data. It gets back to the concept that a grower 
owns their data, and they should be able to transfer it from 
one cloud to the other. If they are working with us 1 year, if 
they are working with AGCO, whatever it is, and so there needs 
to be standards. We have proposed something called the Open Ag 
Data Alliance that begins to set standards, very similar to the 
broader software industry, around how data can move freely 
from, if you will, one cloud to the other.
    Mr. Peterson. So you are beginning that?
    Dr. Stern. We are beginning that.
    Mr. Peterson. So how far along is it?
    Dr. Stern. I would still say it is in early days. I think 
we have a bunch of companies working with that. It is being 
actually run out of Purdue University. But it is early days in 
the system, but for this to be really efficient and to be able 
to allow growers to do what they need to do----
    Mr. Peterson. Are you going to be able to incorporate the 
information that some of these companies are now developing 
with drones, which is probably the most exciting thing that has 
happened here, is that going to be able to be integrated into 
this?
    Dr. Stern. Absolutely. I think the concept of being able to 
use drones in agriculture is something that we need to think 
seriously about. How do we allow growers to be able to access 
that technology safely and appropriately, but it will be a 
game-changer rather than a----
    Mr. Peterson. That is a whole other question, how we keep 
these yahoos from screwing up this thing, that are flying these 
things around causing----
    Dr. Stern. Exactly. Yes.
    Mr. Peterson.--trouble. But the other thing I am concerned 
about, there is a lot of information out there that is going to 
get wound into this regarding the environmental issues and we 
have just got this terrible fight going on. In my opinion, we 
are making huge progress in agriculture in terms of developing 
technology and so forth, but, frankly, in the environmental 
area, these people are trying to go back 200 years. And that is 
what they are pushing is no technology that it has got to be 
natural, we have a fight going on in Minnesota over buffer zone 
strips to clean the water quality, when the best thing we could 
do is till the land, and they are fighting us on that. So how 
do we get these environmental people to start using technology 
to help the environment instead of fighting us, instead of 
saying that the only way this is going to be good is if we go 
back to Buffalo Commons and have everything in grass and 
buffalo running around?
    Mr. Ferrell. Well, and one means of addressing that, and 
one of the most exciting promises of big data is the fact that 
it allows for regional compliance amongst producers when it 
comes to environmental issues to really be a true possibility. 
For example, if we had the kind of sensing technologies that we 
have talked about today at the farm level, and can integrate 
information at a more regional level through some of the big 
data aggregation technologies that Dr. Stern was mentioning, we 
could really have meaningful impact on non-point source 
pollution issues, which is something that we have struggled 
with for a long time. And we have been making strides with 
incentive-based approaches, and the incentive-based approach is 
a good way to do that, but we have a way of giving the 
incentive-based approach real teeth if we can have a level of 
regional coordination that allows farmers to do much better 
farm level nutrient management that in turn provides regional 
results. And that is one of the really exciting promises that 
this has for environmental compliance in agriculture.
    Mr. Peterson. Do you think these ideologues that have their 
mind made up about everything are going to pay attention to the 
actual data and not just go off on their tangents?
    Mr. Ferrell. Hope springs eternal, sir.
    Mr. Peterson. Mr. Hurst?
    Mr. Hurst. Yes, one of the points I guess I would like to 
make, and maybe for people--we are assuming a level of 
knowledge here that may not be--everybody may not have. The 
first thing you need to understand about this is it cuts our 
use of inputs in a very, very real sense.
    Mr. Peterson. I know.
    Mr. Hurst. We have a yield record from a certain place in 
the field for 5 or 10 years, we find out that that place is 
less productive than the average place in the field, and now I 
have the ability to shut any input that I am applying to that 
place in the field, I use less because it will be ineffective 
there. So it is a huge environmental----
    Mr. Peterson. Well, I understand that. The problem is we 
have a lot of folks in the environmental community that do not 
want to recognize this.
    Mr. Hurst. Sure.
    Mr. Peterson. That is the problem. I don't know what we do 
to bring those people into the 21st century, but that is a 
whole other fight.
    I yield back.
    Mr. Neugebauer. I thank the gentleman.
    Now the gentleman from Oklahoma, Mr. Lucas, is recognized 
for 5 minutes.
    Mr. Lucas. Thank you, Mr. Chairman.
    And, Mr. Ferrell, just for the sake of our colleagues here, 
you and I are a little bit different in age, but raised at two 
different ends of the same highway in western Oklahoma. Could 
you expand for a moment on your earlier comments? You are in a 
position where you teach, where you interact with the 
constituents back in Oklahoma at Oklahoma State. What kind of 
questions are you getting from the folks back home about these 
very issues, and how are you responding to those questions?
    Mr. Ferrell. Well, the questions that we are seeing in 
western Oklahoma are largely indicative of the questions that I 
have heard from the other witnesses here today. I think farmers 
are excited about the opportunities that big data analytics can 
provide them, and that is tempered a little bit by their 
trepidation in that they don't really understand the mechanics 
of how that works. And we are kind of inherently private. 
Farmers and ranchers are very kind, generous, hardworking 
people, but sometimes they don't want everyone else knowing 
their business, and there is just a lack of understanding about 
what someone else is able to discern about my business when I 
participate in one of the systems that we are talking about 
today. And realistically, there shouldn't be a lot of concern 
because when we are talking about the aggregation technologies 
at use, we see that the aggregation actually protects anonymity 
of the individual producer. So their concerns are very 
understandable and well-founded, but for the most part, we can 
address those through education. I think the more that farmers 
and ranchers understand the mechanics of how data is shared and 
analyzed, they would see that a lot of those concerns don't 
have to be concerns for them. And I think that is really 
important because we have kind of a chicken and the egg issue 
wrapped in a trust issue in that, for big data to really work, 
it has to be big. We have to have large numbers of producers 
that are willing to share their data in these large robust 
datasets for us to really get that advantage. And the irony in 
that is that you can see more insights about your own farm, as 
Mr. Hurst was saying, by looking at lots of other farms as 
well. I think education is going to be a big piece of that.
    I have devoted a lot of my work to educating the farmers 
and ranchers on the legal side of things; what really are your 
legal rights with respect to your data, how do you protect 
those. As I mentioned earlier, the public dialogue that we have 
had has been excellent. And the next step is for us to continue 
that dialogue, and move from the principles that we have had 
established through the Farm Bureau dialogue and start 
maintaining the dialogue with our service providers to see 
those principles are actually integrated into the agreements, 
those legally binding service agreements that farmers and 
ranchers are signing on to, and that we also educate farmers 
and ranchers about how to read those things. Not everybody 
reads their iTunes use agreement when they want to download 
that next song, but you are agreeing to everything that says, 
and you have to take some time to actually read the fine print 
on that, and hopefully will help them do that reading.
    Mr. Lucas. You mentioned in your opening testimony the 
potential need for statute changes, whether Federal or at 
whatever level, to help facilitate this. You wear a hat both as 
an ag economic Professor, and as an attorney, how focused 
should we be and how aggressive should we be thinking about 
those kind of statutory issues, or is that still just a little 
bit away until some other things clarify themselves?
    Mr. Ferrell. Well, I think that, again, I have been 
tremendously encouraged by the efforts that the stakeholders in 
the industry have undertaken to really advance that dialogue, 
and very quickly too, in that space. I think perhaps the low-
hanging fruit for Congress is to address some of the data 
concerns that farmers have with respect to data held by Federal 
agencies. The EPA lawsuit of a couple of years ago where we saw 
a lot of information from our livestock operations put out 
there, the disclosure of lots of the farm subsidy information 
by the Environmental Working Group has led to farmers having a 
little bit of trepidation about that information. And so one of 
the ways to enhance the trust level, at least on the Federal 
side, is to perhaps more clearly delineate what information is 
and is not subject to FOIA requests that farmers are 
transmitting to agencies like FSA and NRCS, et cetera, and to 
really make clear what information that the Federal Government 
can and cannot request for production from those public service 
providers as well.
    Mr. Lucas. Thank you, Mr. Ferrell.
    I yield back the balance of my time, Mr. Chairman.
    Mr. Neugebauer. The gentlewoman from Washington, Ms. 
DelBene, for 5 minutes.
    Ms. DelBene. Thank you, Mr. Chairman. And thanks to all of 
you for being here today. We really appreciate it.
    My district is a good example. My district is home to a lot 
of big technology companies, as well as a lot of agriculture, a 
lot of specialty crop agriculture, and we are already seeing 
some of the merger there. I think one thing that is incredibly 
important for folks to understand is technology isn't kind of 
its own separate area anymore. It is kind of basic 
infrastructure, and we need to think of it as basic 
infrastructure and the types of businesses that are running, 
but also understand how best to use that. And as someone who 
worked in technology for many years, the talk about developing 
standards is something that we have gone through many times 
before, and hopefully can inform some of the work that all of 
you are doing to make sure we are doing the right things in 
this area in particular.
    I think this hearing also shows that privacy is definitely 
not an issue of any one particular industry; it is an issue 
that we have, and it is not just a technology issue, it is 
everyone's issue. Just like bulk collection of information from 
an ordinary citizen by a Federal agency has been a great 
concern, and something we focused on in the other committee I 
am on, the Judiciary Committee, so would the collection of 
precision agriculture data and the release of that data.
    Mr. Ferrell, you talk about a few things in your written 
testimony as well. We know that for digital information, we 
don't always have the same standard of protection of digital 
information that we do of physical information, which is why we 
have seen some folks more concerned about information going to 
the cloud or being used digitally. One example you bring up is 
the Electronic Communications Privacy Act. That was a law that 
was written in 1986. A lot has changed since 1986, especially 
about the way we communicate electronically, yet we still have 
not updated that law. And so can you tell me a little bit how 
updates to laws like the Electronic Communications Privacy Act 
and others would have an impact and potentially help folks feel 
more comfortable with big data and technology in agriculture?
    Mr. Ferrell. Certainly, and you make a very good point. 
First, things have changed a little bit in information 
communication since 1986. And second, people have privacy 
concerns almost omnidirectionally. I kind of wonder how 
Facebook knows what I just searched on Amazon. I thought that 
seems kind of weird. And so this is a concern with respect to 
agricultural data that is unique to agriculture, but it is 
indicative of concerns that cut across almost all industry 
sectors and private individuals as well.
    And so one of the struggles that we face, going forward, if 
we want to enhance some of the statutory protections on privacy 
is defining what is agricultural data, because we could argue 
that it is unique in that it contains information that is 
generated by a producer, and their activities, their management 
decisions on the farm, and that is provided to a service 
provider in the expectation that they are going to receive a 
direct benefit from that. You can make an analogy to your 
Amazon purchasing history in that, well, I went to Amazon, I 
wanted to get these products, and so those products provide a 
benefit to me, but then Amazon has that information, uses it to 
make suggested sales, in some way may share that information 
with other organizations, and then I kind of start to feel a 
little bit differently about that.
    So agricultural data has some similarities with that, but 
also has some uniqueness, and the particular problem that we 
face is, do we want to do that on an industry-by-industry 
basis, or is the better approach to step back a little bit and 
say, well, what protections do we want to have in place for 
what we might basically call consumer-generated data. That is 
your Amazon purchasing history, that may include your farm's 
data, but we could also argue that includes your financial 
reporting data and your credit score, things of that sort.
    So we really need to have a dialogue about what are the 
rights of the individual with respect to data generated by 
their activity, but perhaps collected by a third party, whether 
that is directly or indirectly----
    Ms. DelBene. Now----
    Mr. Ferrell.--and so I think----
    Ms. DelBene.--a warrant standard probably for digital data, 
like we have for physical data, might also kind of be another 
place we can start.
    Mr. Ferrell. I would definitely agree with that.
    Ms. DelBene. Dr. Stern, you talked about some of the 
standards that were being developed, and when we talk about 
security in particular, and also a lot of these standards, they 
are moving targets, right, where you are getting new 
technologies, what you might think is the most secure 
infrastructure you could put in place today, may change 
tomorrow. How are you adapting those, knowing that this is a 
dynamic environment and things are going to be continually 
changing?
    Dr. Stern. Sure. At The Climate Cooperation, we have a 
dedicated team of cybersecurity specialists, and so we are 
constantly looking at industry best practices and new 
technology. You are exactly right, I mean this is a very, very 
rapidly moving area as we talk about digital ag and, of course, 
the threats associated that could come in around us with 
respect to data security are evolving rapidly. So I feel like 
this is going to be just an area that the entire industry needs 
to be vigilant about, and continue to work on.
    I think there is space for us to collaborate industry-wide, 
competitors or not, to figure out how do we go ahead and 
safeguard data. And while the OADA Project, which is about how 
does data get transmitted, which is more about of an API type 
of approach, I do think there is work to do on how do we really 
work across the industry on data security because it evolves 
daily.
    Ms. DelBene. Yes.
    Thank you. My time has expired. I yield back, Mr. Chairman.
    Mr. Neugebauer. I thank the gentlewoman.
    Now the gentleman from Iowa, Mr. King, is recognized for 5 
minutes.
    Mr. King. Thank you, Mr. Chairman. And I thank the 
witnesses.
    And I turn first to Dr. Stern, I am over here, Dr. Stern. 
The Climate Corporation of San Francisco is on that list. Is 
that part of a Google initiative that has come together with 
Monsanto that is part of this package of The Climate 
Corporation?
    Dr. Stern. No. The Climate Corporation is--if I understand 
the question, is a wholly owned subsidiary of Monsanto.
    Mr. King. Yes.
    Dr. Stern. And maybe I misunderstood----
    Mr. King. Well, was it generated within Monsanto, or was 
it----
    Dr. Stern. Okay. No, it was a private company that we 
purchased 2 years ago just about now, and it was started 6 
years earlier, and predominantly was developing this technology 
for the crop insurance business. And we both felt, from The 
Climate Corporation and Monsanto, saw how you could use big 
data and analytics to actually influence a lot more operations 
on the farm than just insurance.
    Mr. King. Could you tell us just a little bit about the 
genesis of Climate Corporation forming, who the brains are 
behind that?
    Dr. Stern. Yes, sure. So the founders were executives at 
Google 10 years ago or so and left. David Friedberg, who is the 
CEO of the company, started a company called WeatherBill, which 
was really focused on, hey, there is a bunch of industries out 
there that weather impacts their success; golf courses, ski 
resorts, bike rentals, that type of thing. And so that was the 
genesis of the business. It evolved into a core competency of 
weather prediction, weather forecasting, weather data. And 
today, we still get three million weather feeds a day which 
feeds into the agronomic model. So that was the origin. And----
    Mr. King. I appreciate that. That puts that together and 
links up with the memory that I have of that.
    And now I want to just try, if I can summarize what you can 
do with Climate Corporation and the association with Monsanto. 
And I am just thinking in terms of, I come from the heart of 
the Corn Belt, so----
    Dr. Stern. Right.
    Mr. King.--so I think in terms of this. Monitoring rainfall 
in a grid across the field, and monitoring the humidity and the 
sunshine, the growing days that we have, the growing units that 
we have, and then being able to send maybe a text or an e-mail 
to the producer that says you have a window here that your 
nitrogen has either leached down or been uptake into the plant, 
and you have a window here of 18 hours before you are going to 
get 1\1/2\" rain, you had 20 pounds in. Is that some of what 
you do?
    Dr. Stern. You nailed it. That is exactly what we do. And 
there is tremendous use cases right now, and they will just 
give them the Nitrogen Advisor in the fall application. We just 
had a large grower in central Illinois that just did that. They 
looked at the Nitrogen Advisor, they recognized through the 
modeling, that if they--and what the weather is, if they 
applied last week, they were going to lose about 20 pounds 
because a rainstorm was coming in. They delayed that 
application for a week and they felt they saved a lot of money 
and put the right amount of nitrogen down that was going to 
ultimately not leach. So I think that is exactly right how 
you----
    Mr. King. This science squeezes more production and gives 
you an opportunity to ration inputs to match up with the 
weather patterns that you have seen and the ones that you see 
coming.
    Dr. Stern. Yes. That is exactly--it----
    Mr. King. And can you monitor also then for either insect 
or plant disease?
    Dr. Stern. Yes, we have a program right now developing what 
we call--looking at disease and stress and pests, and a lot of 
that will come from satellite imagery. It could come from drone 
information over the field. It also utilizes our understanding 
of the germplasm, what is planted there, different--just like 
humans have different susceptibility to different diseases, 
well, different types--different germplasms of corn have 
different susceptibilities. And so the power of big data and 
data analytics is you can bring all that information together; 
weather, soil, humidity, what is being planted in the field, 
and begin to make predictions about what the outcomes could be 
and allow growers to make better decisions. And so pests and 
disease are--a lot of diseases are windborne. So just 
understanding wind and wind direction, you could be able to go 
ahead and help growers, if you will, downwind in understanding 
that disease pathogens could be coming their way.
    Mr. King. Then I would like to turn to Mr. Tiller, and I 
thought yours was an excellent testimony, and please tell Monte 
that, but I was fascinated because it is the narrative that you 
have is that you are out there as a producer, fourth generation 
farmer, you saw the need to integrate this information. When I 
first saw that need in our construction company, I went in and 
tried to find somebody in business that could integrate that 
information. They told me what you need is a wife. Well, I had 
one, but she wasn't on that task. So----
    Mr. Tiller. Well, you are scaring me. I don't know where 
this is going, so----
    Mr. King. Well, I think that you have really brought a big 
picture here together, and it sounds to me like it is all the 
data that you could possibly ask for, and want and need, 
brought together, integrated from sources that are formed 
elsewhere, but synced together through macros and relational 
databases that you can use in a fashion that is friendly--user-
friendly. In the seconds that I have, I would just like to ask, 
how much of what Dr. Stern has proposed is already integrated 
into your operation?
    Mr. Tiller. Actually, what he is proposing, this is 
cutting-edge stuff. I mean Climate Corp, there are some others 
that are all developing algorithms and processes to do this. We 
call that the best of tools. Climate Corp may win that best of 
tools. That doesn't affect us because we have a database of 
information that we hope someday Climate Corp rides against. 
And what I mean, ride against that data. And we may want to 
store it, and we will have to work through those things. And I 
think as we grow in numbers of growers who want to do it this 
way, we will have companies want to engage us in that pattern. 
But there are--Climate Corp and others that are literally--it 
is cutting-edge stuff I am watching, you mentioned drones, and 
if those get cleared by FAA, you are going to see literally a 
lot more go on of plant health monitoring where you don't have 
to worry about cloud cover and those sorts of things. So does 
that answer your question?
    Mr. King. It does, and I appreciate all of your testimony.
    And I yield back.
    The Chairman [presiding.] The gentleman's time has expired, 
actually.
    Ms. Kuster, 5 minutes.
    Ms. Kuster. Thank you. Thank you for being with us.
    I am from New Hampshire, it is a much smaller state, with 
much smaller farms. We have 4,400 total farms, 47,000 acres. So 
that is probably describing one farm of my colleagues here. And 
we have a strong agriculture economy, and I am very excited to 
report that we are actually growing, a five percent increase, 
in new farms. A lot of young people coming, starting new farms.
    So I would just love if any of you could comment on whether 
there is any application to a smaller farm model or a 
collection of farms in a smaller farm economic, and how that 
might help. We have a lot of specialty crops, a lot of value-
added products; maple syrup and such, and just if there is 
anything you could comment.
    Dr. Stern. So just in general, the neat thing about this 
technology, it is scalable. It is precision ag, and so it gets 
down to not just a single field, but even subfield level. So 
whether you are farming 10 acres or whether you are farming 
10,000 acres, the value proposition and what these tools can be 
used for on the farm is the same.
    First of all, it spans the farm size issue which is great. 
With respect to specialty crops, early on, right now, we are 
looking at some specialty crop applications. It is the 
magnitude of the data that you need around those crops----
    Ms. Kuster. Yes.
    Dr. Stern.--in order to go ahead and begin to understand 
how you can build algorithms, but, at least for us right now, 
our focus, as you can imagine, is on the large row crops, but 
we certainly believe these tools are applicable. And in 
California, there are other companies that are actually looking 
at some of the high value specialty crops that are grown in the 
Central Valley, so I am very optimistic that these technologies 
will find their way into both specialty crops and small-holder 
farmers in the U.S. as well as to broad acre crops.
    Mr. Tiller. Could I add a little bit to that also?
    Ms. Kuster. Sure, yes.
    Mr. Tiller. We have taken the view at Grower Information 
Services Cooperative that all those farmers are important. We 
have had some very small farmers come to us and want to be part 
of that, and we encourage them to do so. And the point would be 
that I would say, around financial information, I don't care 
what size your farm is you need a profit and loss statement. 
And so every organization needs that. It doesn't matter if you 
are a million acres or you are 200, you have to do those 
things.
    So how many factors do you have? Maybe you only have one 
field. That is still the same. You are taking it down to the 
field level, you are trying to decide is it profitable, what 
can we do with it, using data analytics to do that. And so it 
works. It is scalable. So I just want to make that point that 
we encourage small growers to join us.
    Mr. Hurst. And one of the exciting things about this is the 
technologies tend to go down in price, right?
    Mr. Tiller. Yes.
    Mr. Hurst. So that is the only thing that--I mean my 
combine doesn't go down in price when I trade it off, or 
anything else that I buy, but my iPhone does, right? So we have 
a chance that the technology here will decrease in price, 
rather than increase.
    Ms. Kuster. And then just adding to that. I have a lot of 
dairy farmers, I am just wondering if you have had any 
experience in livestock or dairy?
    Mr. Rushing. I think from an AGCO point of view, we also 
have a business called GSI. Part of that business is also 
producing protein systems----
    Ms. Kuster. Yes.
    Mr. Rushing.--that support feeding poultry and pork 
producers. And we are seeing a lot of value come out of those 
operations as far as data is concerned as well. Imagine, you 
can watch the operation throughout the year, understand what 
the best conditions are for producing the best chickens or the 
best pigs, and then recreate those conditions based on that 
information. And now if you can aggregate that information with 
other pork and poultry producers you can start to recreate that 
on every farm. So the value of data goes from row crops to high 
value crops, all the way down to livestock growers, and even in 
the dairies we are seeing the same thing on the dairy side by, 
again, just recreating those same conditions where you got the 
best results.
    Ms. Kuster. Well, as you were talking, it reminds me that 
historically, we didn't call it data, we called it experience, 
and it was passed down from generation to generation, and we 
have a lot of young farmers so this is a new way for them to 
come into it, but just the reference to having a wife reminds 
me of a very quick story about, I was in a dairy barn and we 
were talking about the--they are actually birthing calves all 
year long, and I said, well, how do you know in this small 
operation if there is a problem with a birthing, and do you 
have somebody who stays up 24/7, how do you manage that? And 
the farm spouse looked up and she said, well, you see that 
window right there, and I said, yes, and she goes, that is our 
bedroom, closest to the barn. She said, he can sleep through a 
normal birth that is not difficult, but as soon as he hears her 
having a hard time, he is up and out in the barn. So I thought, 
with all the technology in the world, you can't beat a system 
like that.
    But thank you.
    The Chairman. The gentlelady yields back.
    Mr. Gibbs, 5 minutes.
    Mr. Gibbs. Thank you, Mr. Chairman. Thank you for holding 
this hearing. Thank you for the witnesses.
    I just want to first of all reiterate what the Ranking 
Member was saying about the environmentalists and that issue, 
and it just needs to be said again that technology has improved 
production and also it protects the environment, because we can 
pinpoint our inputs and, for people who might not know, you can 
be riding in a combine at 5 miles an hour shelling corn, and 
get real-time data on moisture, yield, and I mean it is just 
incredible.
    And so that leads to the next part, when Mr. Ferrell 
mentioned about the EPA let those records go of the livestock 
farmers and some other cases, it is paramount that we protect 
this data for several reasons. Obviously, it is a privacy 
issue, but I also think when you get this big data amassed 
enough, and before the trends of what is happening in the 
market conditions and the markets is made public, if they are 
in the wrong person's hands, you are opened up for market 
manipulation and all kinds of problems. So I think that the 
industry has to work with our elected officials and get this 
right because technology is moving fast and we have to get that 
right.
    I am trying to, I guess, understand the technology we put 
up in the cloud, and Dr. Stern, for the farmers to be able to 
use this, because you get all this data and, it doesn't do us a 
whole lot of good. I am a farmer so I can say it doesn't do us 
a whole lot of good because you can't use it unless you can 
really analyze it, and that is what they have to go to your 
respective entities to do that. And so you are very supportive, 
all of you there, to have protections in place. I, as a farmer, 
can say here is our contract, you can't upload that, at least 
maybe in the aggregate maybe you can but not in the individual 
cases, and you are all agreeable on that, right?
    Go ahead, Dr. Stern, if you have a comment.
    Dr. Stern. Yes, to be clear, the way it kind of works right 
now in the system is that we have a contract, an agreement, 
with an individual grower who owns the data. They agree to 
share that data, and in the agreement it is very clear on what 
we will use the data for. And the Farm Bureau standards also 
help an awful lot on making sure growers can have a lens from 
which to go ahead and look at those agreements.
    Once that data is uploaded into the cloud, okay, the 
concept to aggregate it allows us to go ahead and look at it as 
a whole. The individual data itself helps us look specifically 
back at their farm, and it is typically an input into a broader 
model that we have developed that allows that specific 
information to help go back and give specific----
    Mr. Gibbs. I think it is also clear to me that we need to 
pass legislation so even the government can't come in and do 
it.
    Dr. Stern. Yes.
    Mr. Gibbs. Are we agreeable?
    Dr. Stern. I would agree that----
    Mr. Gibbs. Yes.
    Dr. Stern.--we----
    Mr. Gibbs. Mr. Ferrell?
    Mr. Ferrell. Well, I just want to address one point that 
you made, Congressman, and it is a good point: one of the 
concerns that producers have had is the potential for market 
manipulation because if you want to know what the corn market 
is going to do, it sure would be nice if you had the means of 
instantaneously knowing what the corn harvest exactly looked 
like.
    Mr. Gibbs. Yes, it is like insider information.
    Mr. Ferrell. And I am glad that you used that term because 
I have actually kind of researched that issue a little bit, and 
it is not, by definition, insider information, it is just 
really good market intelligence. And so the current legal 
framework that they have really wouldn't prohibit anyone from 
doing that. So that may be one thing that we need to address is 
if we are going to entrust someone with the capability of being 
a data aggregator, do they, should they have the ability to use 
that information in the commodities marketplace. That is just 
another policy----
    Mr. Gibbs. And, Mr. Hurst----
    Mr. Ferrell.--issue that has been raised.
    Mr. Gibbs.--I know you----
    Mr. Hurst. Yes. I guess we all agree that the farmer owns 
the data. It becomes a little more complicated than that. Does 
the landlord own the data? Does the crop rent tenant own the 
data? Does the cash rent landlord own the data? Does the 
applicator that is driving a machine through my field that I 
have hired, is that my data or his data, is he transmitting it, 
is it leaving my farm? I don't know. Lots of stuff still to be 
worked out.
    Mr. Gibbs. Of course, my opinion is if the farmer is paying 
cash rent, it is the farmer's data. I would lean that way, 
unless someone else----
    Dr. Stern. That is exactly how we look at it as well, that 
it is between--agreements between us and the farmer, and it is 
up to the farmer within their land lease agreement to determine 
with the landowner their own arrangement around the data.
    Mr. Gibbs. Yes. Yes. And, Mr. Hurst, I am sure you are 
enjoying your presidency of the Missouri Farm Bureau. In my 
past life, I was President of the Ohio Farm Bureau, and I knew 
one of your predecessors, and lived in the boot heel of 
Missouri.
    Mr. Hurst. President Cruz.
    Mr. Gibbs. That is right. Thanks.
    Thanks. I yield back.
    The Chairman. The gentleman yields back.
    Mr. Aguilar, 5 minutes.
    Mr. Aguilar. Thank you, Mr. Chairman. Thank you to the 
panel for joining.
    I represent an area in southern California. And I was 
picking up, and, Mr. Hurst, you mentioned it in your testimony, 
and we have just kind of elaborated on that as well, but the 
discussion of your term was a data-driven partner, and just 
kind of understanding what that could mean. In my district, is 
the corporate headquarters for ESRI, which is a geographic 
information systems company that is quite large, and their 
relationship--and they often partner with USDA. And you 
mentioned this discussion of a data-driven partner, and I want 
to just expand on what that could look like, and could there be 
a role, while I am completely in favor of making sure that this 
does remain the rights of the farmers, is there a role for that 
data-driven partner to play a role in connecting the USDA 
program or aggregating the data that can be helpful, because as 
Mr. Ferrell mentioned, this depends on having that large number 
of inputs that would be necessary, and is that a role that 
these technology companies can play?
    Mr. Hurst. Yes. As far as the USDA, it would be very handy. 
When I make my report, my crops each spring, a report is 
generated at the FSA. I literally walk that or drive it, my 
crop insurance agent is quite some distance away, and he enters 
those figures by hand. So I have traveled to the office, the 
FSA office 20 miles from home, made that--given him--them that 
information, they don't have the ability to talk to my crop 
insurance agent, and all of this information resides on my 
thumb drive that I have for my planner, which tells me exactly 
how many acres of all these crops I planted, and nobody can 
talk to each other. All that information is already accessible 
to the FSA, to my crop insurance agent, but I have no way to 
transfer it in an efficient manner, and that would be 
extraordinarily helpful.
    Mr. Aguilar. Dr. Stern?
    Dr. Stern. Yes, I would completely agree with Mr. Hurst. 
This technology will drive efficiencies through agricultural 
production in a variety of different ways, not just simply 
productivity gains on the farm, but the interactions with the 
crop insurance agencies, USDA, FSA, is a great example. All of 
this data is digitized. The farm is digitized. It is going to 
be stored in places and it is going to be organized. And there 
are a lot of opportunities to be more efficient from the grower 
perspective and from the government perspective, by us working 
together with the USDA to find ways that this specific 
information can be transferred electronically. I mean the 
technology is there to do it.
    Mr. Aguilar. Right.
    Dr. Stern. And we hear that from growers all the time, and 
it is an area that we, in fact, we feel that the tools that we 
are developing can actually be employed almost now to help do 
that. So it is a big opportunity in our opinion.
    Mr. Aguilar. Thank you.
    Mr. Ferrell, I----
    Mr. Ferrell. No, I would just completely agree. And one of 
the things that Mr. Rushing and I were actually thinking of 
while we were having that discussion was two issues that are 
out there, and Dr. Stern alluded to this, the Open Ag Data 
Alliance and the Ag Gateway Program, which are two--I don't 
know if open source is necessarily the right word to use, I 
wouldn't use that, but collaborative efforts to develop some of 
those data transmission and storage standards to really 
facilitate some of the data transfers that Mr. Hurst and Dr. 
Stern were referring to.
    Mr. Rushing. Yes, if you look at the history of farm 
equipment, you can remember years ago when you went to hook up 
an implement in the tree line and it didn't have the right 
couplers on the end of it. And then we standardized to one type 
of coupler in the industry. It is a simple example, but then 
after that you saw ISOBUS come. And ISOBUS now allowed data to 
transfer freely between different brands and different products 
and--of different types of equipment.
    I think now what you are seeing with the Ag Gateway 
Initiative, through SPADE and also a project called ADAPT, is 
to come up with standardized approaches to transmitting data. I 
know that many of the government entities are also involved in 
those discussions as well as farm software providers, farm 
machinery manufacturers, all those guys are together now and 
looking at ways that they can standardize on how that data is 
transferred and used.
    There are also things outside of the OADA I know that is in 
the industry now, and those things are like also data co-ops. 
So data cooperatives are starting to come up, sponsored by 
different universities so that there can be some neutral places 
where anybody that wants to consume that data can come in and 
consume it based on the farmer's permissions.
    Mr. Aguilar. Great. Thank you so much.
    Mr. Chairman, I yield back.
    The Chairman. The gentleman yields back.
    Mr. Benishek, 5 minutes.
    Mr. Benishek. Thank you, Mr. Chairman.
    Well, this sort of reminds me of--I am a doctor, so this 
information technology and data kind of is in the medical field 
and how you can't get the information from one place to 
another. So tell me--there are two questions that the testimony 
has gotten me thinking about, and one of them is the ownership 
of the data. One of you mentioned the fact that if John Deere--
or I don't know who the ownership of the tractor is, that maybe 
you don't own the data, which seems about as logical as if you 
buy a computer, that the data on the computer should be yours 
not Microsoft or whatever. Right? So tell me how does that 
actually occur? I mean I don't understand it, frankly.
    Mr. Tiller. I saw----
    Mr. Benishek. Who would sign a contract like that?
    Mr. Tiller. Yes. It is a contractual agreement. I don't 
necessarily personally like it. Most farmers I know, and I 
would like to get Blake's take here in a moment, they don't 
personally like that, but at the moment there are--and they are 
not alone, they are not alone. I mean John Deere's agreement 
does--they use the word control. So they take the word own out 
and they say you can control your data.
    We at Grower Information Services Cooperative take the 
position that the grower needs to own his data. And I used to 
use the example of, you park your car in your neighbor's garage 
long enough, he will forget that it is under your control, and 
before long he will think he owns it. And maybe he does. So, 
Blake, what are your thoughts on that?
    Mr. Hurst. Well, there is a controversy about the ability 
to go in and work on the software, which is if they are very 
large corporations they don't want you modifying their software 
and then the data.
    One of the other questions that comes--the ownership 
question that comes up, I understand that I own my data, but 
when it goes into a database, do I own \1/100\ or 1/
1,000 or 1/
1,000,000 of that 
database? I have a feeling that that may be a place where some 
controversy could occur because the farmer might well feel that 
he still had some ownership interest in that database. The 
person that holds the database may have the opposite opinion.
    Mr. Benishek. You mentioned a co-op. Now, to me the data 
should be like a farm co-op.
    Mr. Tiller. That is the way we operate.
    Mr. Benishek.--do you know what I mean? That is----
    Mr. Tiller. Same principle as a corn marketing 
cooperative----
    Mr. Benishek. Right. Right.
    Mr. Tiller.--at Growers, we--at GiSC we are a data 
marketing cooperative. We are trying to be the data aggregator 
who we can bring that data together.
    I want to make a statement around the aggregate data. I 
mean it is a huge question, and once we aggregate data and we 
have let's say we have 10,000 growers that are put together, 
because I want the data sets to be large enough where everybody 
is anonymized. It is kind of like putting sugar in a cake, once 
you bake the cake, show me how you are going to get the sugar 
out. You can't. So we have to be big boys in agriculture. As a 
farmer, I have a large operation. When I sign an agreement and 
say you can aggregate, I can't really expect to go back and 
pull that out. I am just wanting to make sure the farmer's 
educated and understands that. That is all.
    Mr. Benishek. Right.
    Mr. Tiller. I mean growers, we are very set on let's make 
sure we educate them so that they understand, once they have 
agreed to this aggregation, what that means.
    Mr. Benishek. There are so many questions that come up 
about this to me. So does the--if you buy this John Deere 
tractor, that you sign this contract, is that like a 20 year 
deal then or the tractor can last a long time.
    Mr. Tiller. As long as you want to use the controller 
there. I mean there is sort of--I don't know how to best 
explain it because it is even confusing to me, even though I 
have dealt in the issue the last 4 or 5 years watching it 
evolve. It is the position they have taken, for whatever 
reason, I can't answer for them, I wish they were here to 
answer for themselves around it, but it is a position they have 
taken. I mean and they have been very stern that they make 
great technology by the way they do it.
    Mr. Benishek. Well, the other question that comes up is the 
interoperability of the data. You guys have kind of talked a 
little bit about it, so is it over different platforms, is it 
coordinated, is----
    Mr. Tiller. It is all proprietary data. There is very 
seldom--there are a few formats, but for the----
    Mr. Benishek. Well, one county has predominance of one 
company, the next county over might use a different company, 
they can't aggregate that data and make----
    Mr. Tiller. It is----
    Mr. Benishek.--use of it?
    Mr. Tiller. It is very tough, but you can do binary 
transformations where you can create----
    Mr. Benishek. Right. Right. Right.
    Mr. Tiller.--your own way to actually----
    Mr. Benishek. Yes. Yes.
    Mr. Tiller.--take that data in and consume it, but for the 
most part, you have Ag Gateway and you have OADA, two 
different--I call them standards groups, trying to develop 
standards around how we can make this happen. And it is in the 
works, and it will be in the works----
    Mr. Benishek. Well, this is----
    Mr. Tiller.--for years----
    Mr. Benishek.--a problem across all this data, not only for 
you guys but for medicine, and that is----
    Mr. Tiller. Yes.
    Mr. Benishek.--that is exactly the problem we face here, 
and it is--to me, you are being held hostage by the, I don't 
know, I call them data weenies, because they are the only 
people that know how to work it, and you have to pay them----
    Mr. Tiller. You could be and that is----
    Mr. Benishek.--so much a month and----
    Mr. Tiller.--and that is a portability issue. So when I 
want to leave a particular chemical company or--I am talking 
about regional vendors, and I have data on their digital 
platform and I am ready to move, and I am unhappy with them and 
I want to go to someone else, will they make my data portable 
so I can leave. Some will, some won't, and some will make it 
portable. It is not a very good format for me to consume it 
with another set of software. So----
    Mr. Benishek. Thank you.
    Mr. Tiller.--that is really where it goes.
    Mr. Benishek. Thank you, my friend.
    The Chairman. The gentleman's time as expired.
    Ms. Plaskett, 5 minutes.
    Ms. Plaskett. Yes, thank you, Mr. Chairman. Thank you, 
witnesses, for this lively discussion on a topic which is 
difficult at best for some of us.
    But I wanted to expound on a question that my colleague, 
Ms. Kuster, brought up to you with regard to smaller farmers. 
And one of the questions was, we talked about the benefit, that 
there is a benefit to the smaller farmers in doing this, but is 
there a decreased benefit to the smaller farmers in relation to 
the larger ones in terms of the data collection, and the cost-
benefit that goes into them doing it in comparison to the 
larger farmers, does it push them out of the market of benefit 
in doing this?
    Mr. Ferrell. I will take a swing at that one. I will take 
off my lawyer hat and put on my ag economics Professor hat.
    Ms. Plaskett. Those are big hats.
    Mr. Ferrell. Yes, they are and it takes a large hat rack to 
keep them all straight.
    I think there really are some important benefits for our 
smaller producers, and the reason that I say that is because 
any time that you are dealing with volatility in the 
commodities market, which is arguably the entire history of 
forever, when you have that volatility, the producers in the 
long run that come out ahead are the low-cost producers because 
they can withstand those changes in prices, and if they can 
stay the most efficient low-cost producers, they are the ones 
that are going to survive.
    Typically, that has put small producers at a disadvantage 
because they just don't have the economies of scale, they may 
not have the capital structure to withstand those kind of 
buffeting influences of the market. But with the kind of data 
tools that we have talked about today, to some extent they have 
the same decision-making capacities that a much larger 
operation might have. If they can have access to the insights 
afforded by some of the big data analytics that we have 
discussed, they can make management decisions with the same 
level of precision and information intelligence that the larger 
producers can. So I think that there are some real advantages 
for the small producers from those technologies.
    Now, that is if we are looking at coming from big data down 
to the individual level.
    Ms. Plaskett. Yes.
    Mr. Ferrell. But that is a two-way street, and one way that 
have to really participate in making those management decisions 
as best we can is by having good farm-level data----
    Ms. Plaskett. Yes.
    Mr. Ferrell.--and that requires sensing technologies that 
we have talked about, and sometimes that it is on the larger 
equipment that may be beyond the operational scale----
    Ms. Plaskett. Right.
    Mr. Ferrell.--of those smaller producers. So that is kind 
of the bad news for small farms and ranches, but the good news 
is, as we mentioned earlier, as that technology drives forward, 
we are seeing ever-decreasing costs of integrating that 
technology. We are seeing it put on smaller and smaller 
implements and--sorry, implements and tractors. Those----
    Ms. Plaskett. This----
    Mr. Ferrell.--types of things.
    Ms. Plaskett. This may be where cooperatives really work in 
favor of----
    Mr. Ferrell. Yes.
    Ms. Plaskett.--the smaller farmers----
    Mr. Ferrell. Yes.
    Ms. Plaskett.--banding together in terms of doing the data.
    Mr. Ferrell. Absolutely. I think we are going to see those 
costs driven down, and we are going to see more access to more 
types of equipment systems that have that technology. So it is 
only going to get closer and closer to the small producer.
    Dr. Stern. Just another comment on that. For instance, if 
you look at our Nitrogen Advisor, that is built on a very large 
data set. An overwhelming majority of it is publicly available 
data, or data that we ourselves invested in in our research 
farms to generate. So the scale piece that if you have a larger 
farm, or for some reason they have more information, it is 
going to drive more information for them versus a small-holder 
farmer, that is not really the basis of it. We are looking at 
it field by field. I would say also the way we price the 
products are per acre. Ultimately growers are going to make 
their decision on whether or not that is the appropriate value. 
But I truly believe the technology is very scalable, and I 
completely agree with Mr. Ferrell that in some ways it is a 
leveling technology.
    Ms. Plaskett. Right. The other question I had was, we have 
talked specifically about crops and farmers, can you talk about 
how this translates to livestock?
    Mr. Rushing. As I mentioned earlier, there are a lot of 
opportunities to utilize this data loop inside of things like 
poultry, beef production, and pork production.
    Ms. Plaskett. Can you give an example of that?
    Mr. Rushing. So, for example, if you can identify within 
the crop cycle of raising, say, poultry, you can identify 
specific environments or specific conditions where you produced 
the best chickens. And you can identify that throughout the 
crop cycle, identify what you did as far as feed----
    Ms. Plaskett. Yes.
    Mr. Rushing.--what the temperature was in the house, what 
nutrients you used, then you can recreate those conditions 
based on understanding that cycle and when those events 
happened.
    So that is one place where we have seen it being used. We 
have also seen it being used in milk production where you are 
able to see what types of feeds are used, and then be able to 
recreate those same conditions to replicate the productivity.
    Ms. Plaskett. Okay, Dr. Stern----
    Dr. Stern. I will----
    Ms. Plaskett.--you would like to----
    Dr. Stern. I will give one more example.
    Ms. Plaskett. Yes.
    Dr. Stern. For an integrated farmer who is producing--maybe 
they are a dairy farmer but they are also producing row crops, 
again, our Nitrogen Advisor allows them to use the manure that 
they will be spreading on their field as an input into that 
calculation. So we take that calculation into the algorithm and 
it allows us then to understand overall fertility in the 
fields, so they don't necessarily have to go and apply any more 
nitrogen fertilizer to that field. So that is a little bit of a 
different twist, but it is how livestock and row crop 
operations can integrate with the----
    Ms. Plaskett. Thank----
    Dr. Stern.--technology.
    Ms. Plaskett. Thank you so much.
    Thank you, Mr. Chairman.
    The Chairman. The gentlelady's time has expired.
    Mr. Davis, 5 minutes.
    Mr. Davis. Thank you, Mr. Chairman.
    Mr. Hurst, thanks for being here. Your written testimony 
mentions some key differences in big data in agriculture from 
the big data that is collected about individuals, say, from 
Google searches or social media interaction. The difference 
seems to be not just in the data that is collected, but also 
the risk that the collection of that data imposes, say, for 
information maybe on some pesticide applications or use of GMO 
seeds. Is there anything that we haven't asked you regarding 
these aspects of data that you would like to relay to the 
Committee that can be helpful in us determining future 
policies?
    Mr. Hurst. Yes, the point I was trying to make was that if 
Google or Amazon knows that I like to read murder mysteries, it 
is a value to them as a marketer, but it is not something I 
care if anybody else knows.
    It may be that farmers are nervous about public knowledge 
of the applications they use of pesticides, even though they 
are applying them well within the prescribed limits and 
following all the labels. So this information is more sensitive 
in that sense. It comes very close to the same level of 
sensitivity as financial information or Social Security 
Numbers, or any of those things.
    And one of the other points that I guess I was trying to 
make when talking about is we freely give that information to 
Facebook, right? I read somewhere that every customer or every 
member of Facebook, their value is worth $20 to Facebook. So 
you think about the average person's buying power and then you 
multiply that by 20 or 30 or 40 to get to the buying power of 
farms, and our information has a great deal of value as 
farmers. It would really be nice as we go through this process 
that we get--keep in mind that it would be helpful to 
agriculture if we were able to monetize that in ways other than 
just the benefits it makes for my productive capabilities.
    Mr. Davis. Well, thank you, Mr. Hurst.
    Dr. Stern, you mentioned that a farmer in central Illinois, 
the breadbasket of America, of course, since I represent there, 
was able to use precision agriculture and data to determine 
when it would be correct--when it would be best to put nitrogen 
in the field. I hope that that farmer was in my district. But 
can you explain, besides the fact that all good things happen 
right out of central Illinois, can you explain to us how we can 
make that more useful as we move into the future?
    Dr. Stern. Sure. And this farmer was just south of 
Springfield, so I don't know whether or not that is----
    Mr. Davis. Probably my home county.
    Dr. Stern. Yes.
    Mr. Davis. Christian County in Taylorville.
    Dr. Stern. I think we----
    Mr. Davis. The best farmers.
    Dr. Stern. Yes. And in my testimony I said we are just on 
the cusp of this digital revolution, and we are in the very 
early days in this technology. This is just a simple example of 
timing, understanding how much nitrogen was left in their field 
after the growing season, because that is what the Advisor does 
in understanding weather. In the future, if we just stick to 
nitrogen or fertility in general, so it is not just nitrogen, 
whether it be phosphorus and potassium, as we span the scope, 
we will have a better picture of fertility in the field, and we 
will get--right now, our Advisor looks at the entire field.
    Mr. Davis. Yes.
    Dr. Stern. We are going to get to subfield levels where we 
are looking at soil maps or other information that is generated 
in the field that allows us to say this part of the field has a 
different fertility profile than this part of the field. And 
the third piece of information that will come in will be around 
the genetics and what is being planted. Different genetic lines 
of corn will respond to nitrogen differently.
    So when you begin to bring all this together, and this is 
in our roadmap of products that we are going to be developing, 
you can begin to see more of an operating plan that we can work 
with growers to develop that covers fertility broadly on a 
subfield level, all the way to how to optimize what is being 
planted, both from a--seeding population as well----
    Mr. Davis. Okay.
    Dr. Stern.--as where seeds are being planted. So that is 
just a little snapshot of----
    Mr. Davis. Well----
    Dr. Stern.--I think the power of the technology.
    Mr. Davis. Thank you. And I would urge each of you as we 
leave this hearing today to also ensure that when you come back 
to us, help us understand what government can do to halt 
technology like this, and what we can do to stop that from 
being implemented out here in Washington, D.C., at the policy 
level.
    And in my last few seconds, Mr. Ferrell, my colleague from 
Oklahoma is gone, so I was going to ask you to explain to him 
what an iTunes agreement was, but--yes, yes. But has anybody 
told you that you sound just like our former colleague, Cory 
Gardner? I had my head down and I was thinking Cory is in this 
place.
    Mr. Ferrell. I would defer to my colleague, Mr. Fischer. I 
will leave it to him to let me know that fact. He had not yet 
apprised me of that, but I wouldn't be surprised.
    Mr. Davis. Well, those of you who may not know who Cory 
Gardner is, he got demoted to the U.S. Senate.
    So I yield back the balance of my time.
    The Chairman. The gentleman's time has expired.
    Mr. Yoho, 5 minutes.
    Mr. Yoho. Thank you, Mr. Chairman. Gentlemen, I appreciate 
you being here.
    It is fascinating to see the advancement of technology and 
how fast it is going.
    Dr. Stern, the information you are getting is coming from 
satellites. Are those your own, or are you tapped into the 
LANDSAT satellites of NASA's?
    Dr. Stern. Yes, part of the information we get is from 
satellites, but we actually purchase satellite imagery from a 
variety of different venders that are not our satellites.
    Mr. Yoho. Yes, we did an ag seminar in our district and--
showing the farmers what they can get off the LANDSAT. And, of 
course, they can gather all this information on their own. Of 
course, the thing they are missing is the algorithms that 
assimilate that stuff to come out with the recommendations, it 
is a whole different ball game. You can get the raw data but 
put it into practical terms.
    Mr. Ferrell, you had brought up the CAFO situations, the 
concentrated animal feeding units. And we know what happened 
with the EPA, they gave that information out mistakenly. And we 
just need to make sure that information when it is collected, 
that the farmer or the person that it pertains to is protected. 
I think there has to be a way that we up here can protect the 
citizens. Yes, people have a right to know some things, but in 
situations like that, it is a national security situation. If 
you look at one of our big feeding operations, and of course, I 
come from the State of Florida, and we have some of the ranches 
down there, hundreds of thousands of acres, it is a very 
precarious situation if somebody were to get into that. And 
working off what my colleague here, Mr. Davis, had brought up 
was that if you guys are out there in the industry, you are out 
there in the field, coming up with the ideas that we can 
institute on this end to protect you out there so that you can 
continue to do what you are doing. And one of the things in our 
district is we have six drone companies. One of them is 
developing software right now where they can go over a farm 
field and they can take an image of the cattle, and they can 
predict how many young calves there are, what the average body 
weight is, and it is just going to revolutionize the ag 
industry. But in order to be able to do that, they have to have 
the permission and the policies in place so that they can fly 
the drone. And so, again, use this Committee as something to 
move that legislation forward so that we can benefit all of 
agriculture, yet protect the privacy of the neighbors and of 
the individual.
    Where do you see this going? I mean what do you see the 
biggest challenge that we are seeing? I mean you have mentioned 
a lot of that, and we will start with you, Mr. Ferrell, the 
impediments maybe in the industry?
    Mr. Ferrell. I say this with a bias of someone who works in 
Cooperative Extension, so I am out there always talking to 
those producers, and I really think, at least at this point in 
time, the barrier may be almost informational. And what I mean 
by that is that farmers can see the benefits that this 
technology promises, but there is just a hesitancy to engage 
with that technology because either they feel that the current 
safeguards aren't adequate, or they just don't understand what 
those safeguards are. And so really, the technology is being 
driven incredibly quickly. I think it will be there when the 
producer is ready, and one of the best things that we can do is 
to have continued educational efforts to make sure those 
producers read and understand those agreements in the contracts 
they enter into with the service providers so that they feel 
comfortable with the protections that they have, and to 
facilitate the dialogue that we already have in establishing 
some of those basic principles of data ownership, rights, 
privacy, and disclosure of uses, and really overarching that 
principle of transparency.
    Mr. Yoho. Okay, I go along with the lines of Mr. Gibbs, if 
I am paying for that information, that information is mine. 
And, the service agreements and all that, I know that has to be 
worked out and those are things that we have to look at.
    Mr. Rushing, do you have any thoughts on that?
    Mr. Rushing. Yes, one of the big challenges is going to be 
data standardization; making sure that there is specific 
formats that everyone can use, and that is going to open up 
more choices for the farmer to be able to choose what types of 
equipment, what types of products, what types of services that 
he wants to be able to use as well. So if any support can come, 
it is in helping establish those data standards within the 
industry and also across the world, so that we can build this 
equipment and the services and products to communicate with 
each other.
    Mr. Yoho. Do you feel that is something that should be done 
in a private industry, those standards, keep the government out 
of it because we don't want to show up and say we are here from 
the government to help you?
    Mr. Rushing. Definitely. Definitely.
    Mr. Yoho. Okay.
    Mr. Rushing. It has to be done by the industry. And a lot 
of the industry organizations we talked about today are working 
in that direction, but it can't come fast enough.
    Mr. Yoho. Anybody else want to weigh-in in the last 30 
seconds?
    Dr. Stern. Yes, I would just add that, ultimately, growers 
are always looking for new technology to optimize their 
operation, and this is really new technology. And so they are 
going to need to work with it for a little bit and see the 
value that these digital tools bring to their farm. In doing 
that, they will become more trustworthy of it, they will 
understand the value that it brings, and they will be more 
engaged in the technology.
    I will just say 70 percent of growers out there right now 
are touching different pieces of this technology, so they are 
very receptive to it.
    Last, broadband, with respect to how this Committee could 
help, expanding broadband.
    You have to move the data around.
    Mr. Yoho. Thank you.
    Dr. Stern. That is important.
    The Chairman. The gentleman's time has expired.
    Mr. Kelly.
    Mr. Kelly. The thing that is bad about being last is--thank 
you, Mr. Chairman--or very close to last, is most of the great 
questions have been asked.
    But I have been thinking about this thing while you have 
talked, and there are so many competing interests here. You 
have data, which is the farmers'. They own the farm, they own 
the yield that comes off of that crop, they own several things. 
You have Climate Corporation, which owns a lot of the weather 
data and those type of things. There are soil samples which may 
be owned by a whole lot of different people. And then you have 
the algorithms, and the things that turn that data from being 
data into actionable information or something that you can use. 
And then you have the collector. John Deere owns the tractor 
that has the GPS on that owns that. It is very difficult to 
make sure that each one of those parties is represented in the 
correct way that doesn't give them an unfair competitive 
advantage over the other. I shouldn't be able to sell you my 
product and use that as an unfair competitive advantage to make 
sure that you use only this, whether it be from any one of 
those sources.
    So do any of you, and, Mr. Ferrell, how do we keep people 
from using that as an unfair competitive advantage, and how do 
we make sure that the smaller farmers or the farmers who are 
sometimes not as technology savvy or don't have as much 
information, how do we make sure they are educated when they 
make those decisions of how to sell that?
    Mr. Ferrell. You saved the good question for last. I don't 
think they took all the good ones. I think that was an 
excellent question.
    And it is tough because, I was actually just looking at 
some information earlier this week that showed the number of 
companies that were evolving in the space of ag data 
management, transfer, analysis, and it is pretty large. It is 
not going to stay that way. We are going to see industry 
consolidation. We almost always see industry consolidation as 
we go on. And that is tough because the marketplace gives you 
choices when you have choices that you can make with your 
dollar. And at least at this point in time, we let the consumer 
kind of pick who is going to best serve their needs and their 
ownership interests in that data. That consolidation is going 
to come.
    I keep going back to the concept that we have had with 
success thus far in the dialogue amongst all the stakeholders, 
and thus far, that has really served this industry well. I am 
impressed by that in the fact that we have seen the concerns of 
the consumer, here the farmer and rancher, represented really 
well and very early on in this process. I have been really 
amazed at how quickly we have come to a consensus in the 
industry about some of these principles.
    I think the key to the question that you are raising is to 
maintain that consensus process and to make sure that those 
principles are embodied in the contracts that these service 
providers are going to be using, because we can have principles 
all day long, but they are not legally enforceable until they 
are in that agreement that that farmer or rancher has signed. 
And so that is part of it. I think the other part of it is, 
like we talked about, making sure that farmers and ranchers 
understand what that framework looks like, and making sure that 
they make educated decisions about which service provider they 
choose based on which service provider best fits their needs 
and their interests in that data.
    Mr. Kelly. I will open it up to everyone, but it is very 
important to me that we don't allow people to make uneducated 
decisions about what they are giving away, and there is a value 
to all of those products. And it is also important that one 
person, because of information or because of the size of their 
organization, that they don't use that either as an unfair 
advantage, or also that we share those profits that should be 
shared. And I am kind of looking out for protecting the little 
guy. I want to make sure that someone is not taking an unfair 
advantage of them. Any other ideas?
    Mr. Tiller. Well, that is the reason for something like a 
data cooperative where you can actually give that small grower, 
or all the growers, literally power of a voice, very educated 
around what is going on in the industry. This is a very 
evolving industry. I mean just from what you are hearing here, 
you can begin to see very quickly what is going on. I mean, of 
course, I am proposing what we are doing, but as a grower, I 
mean there was a reason that I wanted to go down this path. I 
didn't think that I could stand alone and really keep that 
place of significance, where I really said it should be a 
grower-centered world where I am talking about my data.
    Mr. Hurst. As we went through the--I am sorry.
    Mr. Kelly. I just have time for one final point.
    While doing that and taking care of the small guy, we also 
don't need to stifle innovation by doing that, and I understand 
that too. And if you can comment real briefly, Mr. Hurst, and I 
yield back the rest of my time after your answer.
    Mr. Hurst. Yes, our transparency evaluations, all the 
principles we have developed, all those things were to make 
sure the farmers had good information when they made these 
decisions. Thanks.
    The Chairman. The gentleman's time has expired.
    Mr. Austin Scott, 5 minutes.
    Mr. Austin Scott of Georgia. Thank you, Mr. Chairman. And, 
gentlemen, thank you for being here today.
    One of the things that we talk about with regard to the 
data, whether it is control or ownership, one of the other 
things that we are going to make sure--or need to make sure 
that we address is if that data is submitted to any of the 
government agencies, is whether it is or is not subject to the 
Freedom of Information Act. And certainly, that is private data 
and somehow we need to make sure that we get that language 
correct when we do that.
    So I want to ask you this question, Mr. Rushing, because 
AGCO has taken the position that the farmers should only 
control the data. I certainly agree with you on that. Other 
companies have taken a different position on that. Why has AGCO 
taken the position that you have, and how is the farmers' 
ownership of that data protected in the agreements, and why 
would other companies take a different approach?
    Mr. Rushing. So the first thing to make sure that we 
realize is AGCO is a machinery company, so we are focused on 
machinery and assets. We are not necessarily so focused on crop 
production data. We can't provide goods and services that are 
going to benefit the grower by understanding a lot of that crop 
production data. It is our job to take the prescription that is 
developed or capture the data in the field in regards to yield 
or how something was applied, and then utilize that in the 
machines, so make sure the machine is capable. So our position 
has been that the grower or the farmer owns the data. He will 
give whomever he wants permission to utilize that data, and as 
a result, we built two pipes. We build a pipe that is 
specifically focused on the machine because if I can see that 
farmer's machinery data, say, for example, machinery health, I 
can then respond to that farmer with additional services that 
are going to benefit him. One of the biggest challenges farmers 
have is downtime.
    Mr. Austin Scott of Georgia. Yes.
    Mr. Rushing. If they are down in the field and they are not 
operating, then it is just like a factory being down, it is not 
being productive. So how we can utilize that information based 
on how the machines are performing, we can come back and we 
provide services to keep that machine running, keep it 
repaired, keep it optimized, keep it performing like it is 
supposed to be performing in the field.
    So from that respect, we want to make sure that when we say 
to the farmer you own your data, but if you will let us see it 
we can provide you value in return through these services and 
through these other opportunities.
    For crop production data though, we can't. So what we say 
to the farmer, it is your data, and not only is it your data, 
we are going to facilitate your transfer of that information to 
whomever you want to transfer it to. And also in the process, 
as it is transferred through the pipe, it is deleted. We will 
never look at it, we will never use it, we will never try to 
understand your operation from an agronomic standpoint because 
we can't provide you any value in that regard. And that is 
basically why we have taken the position that we have taken.
    Now, we are making connections to a lot of folks here on 
this panel to make sure that they can consume that data if the 
farmer chooses, but again, the farmer will make that choice, 
whether it is machinery data shared with us or it is agronomic 
data or task data shared with some other ag service providers.
    Mr. Austin Scott of Georgia. And so if I buy one of your 
machines, is there a certain computer system that I have to use 
for your machine, or can I purchase different computer systems 
that might collect the data, and would that be available on 
other companies who don't share your belief that the farmers 
should own the data? If I buy a machine where the company 
thinks they own the data, do I have the ability to take their 
data collection out and put my own in?
    Mr. Rushing. The farmer can always select an aftermarket 
solution that he can plug into the machine to collect specific 
amounts of data. It might not necessarily be everything on the 
machine. The OEM, or the original equipment manufacturer, has a 
lot of access to the technology on the machine that an 
aftermarket provider wouldn't have, but there is an option for 
a farmer to buy an aftermarket solution and install it on the 
machine.
    Mr. Austin Scott of Georgia. Yes. Okay.
    I don't have any further questions, Mr. Chairman. Thank 
you, and I yield the remainder of my time.
    The Chairman. The gentleman yields back. Thank you.
    Mr. Allen, for 5 minutes.
    Mr. Allen. Thank you, Mr. Chairman. And I will be quick 
here. I just wanted to welcome Mr. Rushing to our hearing 
today. Nice to have a fellow Georgian----
    Mr. Rushing. Thank you.
    Mr. Allen.--up here with us. And thank you for being here. 
Thanks to all for your testimony. And I would just encourage 
you to do this; to all get together and let's solve this 
problem so the farmer gets the information he needs, but also 
he gets paid for furnishing that information. It is a fair 
deal, and I encourage you all to get together and work this out 
as you know what happens when Congress gets involved. I think 
it would be better if you could do this privately, and I thank 
you for your work.
    I yield back.
    The Chairman. The gentleman yields back.
    I want to thank our panel. This has been a refreshing 
hearing this morning. You are not coming here looking for 
solutions, you are coming here simply to tell the Agriculture 
Committee about things that are working in the private-sector. 
I echo Mr. Allen's comments. The private-sector does a better 
job than Congress can when it comes to fixing all the problems 
that you are already recognizing. The collegial manner in which 
you are working across interests is encouraging to me. There 
may very well be some things that Congress needs to do to 
protect markets and others. I have been told that the high-
frequency traders figured out if they got their servers closer 
to the market, they can save a couple of segments of a blink of 
an eye, and then they could execute their deals quicker. This 
agricultural data, particularly during the harvest, is 
stunningly valuable. As we walk this path, we will need your 
help and others' help in the industry to make sure that we 
don't have unintended consequences by people trying to exploit 
big data.
    The other thing is that early adopters, like Mr. Tiller and 
others, are willing to make the investment ahead of time 
because they see the vision down the road. Later adopters must 
make this decision on a cost-benefit analysis. The money they 
save from investing in big data must be greater than the costs. 
I think what we have heard this morning is that it is becoming 
more affordable, and more folks want to adopt it, simply based 
on the current cost-benefit analysis and the desire to be on 
the leading edge.
    Under the rules of the Committee, the record for today's 
hearing will remain open for 10 calendar days to receive 
additional material and supplemental written responses from the 
witnesses to any questions posed by a Member.
    This hearing of the Committee on Agriculture is adjourned. 
Thank you.
    [Whereupon, at 11:51 a.m., the Committee was adjourned.]
    [Material submitted for inclusion in the record follows:]
                Submitted Statement by Deere & Company *
---------------------------------------------------------------------------
    * Editor's note: John Deere was invited to testify but declined.
---------------------------------------------------------------------------
    Deere & Company (``John Deere'') respectfully submits these 
comments for the record as part of the Committee's October 28, 2015 
hearing on the subject of ``Big Data and Its Role in Agriculture.''
    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, 
technology 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 self-propelled 
machines to the farm almost 100 years ago. Insights producers generate 
from 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, agricultural 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, give 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%.\1\
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    \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/.
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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, 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 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 
machine data, production data and personal information, and are 
incorporated into every customer's John Deere Operations Center 
services contract. These principles are designed to ensure the customer 
is always in control of whether and how his data can be used, by whom, 
and for how long. These principles are:

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

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

  3.  Deere utilizes customer business 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 customer business data is determined solely by 
            the customer's designated account preferences and through 
            contractual agreements with John Deere.

    John Deere believes farmers own the information generated by their 
farming operations. However, farming is a complex, dynamic industry. 
Farmers use Deere's tools and offerings in many different ways, which 
may complicate 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. For 
example:

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

    Different circumstances can make determining who owns data 
complicated and unclear. 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 business 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 Other 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 
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. Customers may choose to allow Deere 
to anonymize Production Data and share it with agronomists, service 
providers and other input providers, for purposes of benchmarking, 
product performance reports or set ups under similar conditions.
    Other Data are data that are identified for special handling 
because of their more sensitive nature, such as variable rate 
prescriptions, user-entered notes and user-formatted reports. Other 
Data may not be anonymized for external sharing, even if a customer 
opts to allow John Deere to anonymize and share Machine and Production 
Data. 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.
    Finally, it should also be noted that, without essential broadband 
connectivity to croplands, many of the potential benefits of ``big 
data'' in 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.
    Deere & Company appreciates the Committee's consideration of its 
views, and looks forward to working with the Committee on these 
important issues.
                                 ______
                                 
                          Submitted Questions
Response from Blake Hurst, President, Missouri Farm Bureau; Member, 
        Board of Directors, American Farm Bureau Federation
Questions Submitted by Hon. David Rouzer, a Representative in Congress 
        from North Carolina
    Question 1. Mr. Hurst, I have farmers in my district who are 
concerned with how ownership of data could affect the price of 
machinery. For example, if a tractor company owns the software in their 
tractors and a farmer was to trade his tractor to a dealer that is not 
with the same tractor company, how would these dealers handle the 
software? Would this make the trade-in value depreciate substantially 
since the dealer in this scenario would not actually own the software 
within these tractors?
    Answer. I'm afraid that I cannot answer this question with any 
degree of confidence. As I understand it, the concern goes to the 
software that operate the tractor, not the data generated by the 
farmer's use of the machine. If I trade my J.D. to a competing dealer, 
and he does not expect to be able to work on that machine when he sells 
it to another farmer, because he doesn't have access to work on the 
software that controls the machine, that prospective loss of revenue 
could mean that I would receive less for the machine I trade in than I 
would if went back to a J.D. dealer. But I'm afraid I'm totally 
speculating here, and have no data to back up my speculation. On a 
personal note, when we trade something that's been used on our farm, it 
normally old enough and worn out enough that is has little value.

    Question 2. Mr. Hurst, another concern I have heard from my farmers 
is the ability of on-farm mechanics to repair broken equipment on the 
fly without concern for the legal implications of altering the 
``implied license to operate the vehicle.'' I have heard that farmers 
can't just repair the equipment without having a technician from the 
tractor company come out to the farm due to the computer based 
programming aspect of these machines. Are their ways to rectify this 
concern?
    Answer. I think that is a concern for those farmers who feel 
comfortable with the software, and might be able to make repairs 
themselves. There is no doubt that repairs are more expensive because 
of the highly technical nature of the machines. So, if the internal 
workings of the engine need repair, all of us are forced to use 
technicians from the company that sold us the tractor. I don't see an 
easy solution to this problem, which of course is the same challenge 
faced by back yard mechanics who a generation ago could work on their 
cars, but now must take it to a dealer who has the diagnostic equipment 
needed to figure out what's going on.
Question Submitted by Hon. Ralph Lee Abraham, a Representative in 
        Congress from Louisiana
    Question. The value proposition of the data collected on a farm is 
an interesting one. Are farmers getting paid for their data now? How 
can farmers potentially leverage their data as a revenue source?
    Answer. No, farmers are not being paid for their data, at least as 
far as I know. There is at least one start up which is attempting to 
use a data repository to market data from individual farmers. As you 
might imagine, there is resistance from folks with an interest in the 
data. Their value proposition involves the farmer paying them to 
manipulate, store, and prepare ``prescriptions'' for individual 
farmers. It will be very interesting to see how the market develops.
Response from Matt Rushing, Vice President, Advanced Technology 
        Solutions (ATS) Product Line, AGCO Corporation
Question Submitted by Hon. David Rouzer, a Representative in Congress 
        from North Carolina
    Question. Mr. Rushing, it seems the data connected to the physical 
piece of equipment has more influence on value and ownership than 
actually owning the equipment. How can companies, especially the farm 
equipment companies, reassure farmers that they own the physical 
equipment that they purchase?
    Answer. Farmers own the equipment that they purchase. AGCO's 
position is that they also own the data they collect. And while data 
will continue to be a bigger and bigger part of the value proposition 
in agriculture, it will still be the machines that actually engage the 
ground. AGCO strives to deliver the most open policy in the industry of 
optimizing ground engagement through data management and connections to 
different technologies. This openness will ensure that customers have 
ever-increasing choice to customize their operations for maximum 
effect.
    The only complexity regarding ownership arises because there are 
legal, regulatory, environmental, and safety requirements that 
equipment manufacturers must meet to ensure the safe and compliant 
operation of the machines. Consequently, there are certain software 
licenses associated with the equipment to protect the integrity of the 
overall machine electronic architecture to ensure meeting these 
requirements. Just as for certain consumer products--like a computer or 
mobile device--the purchased hardware is clearly the property of the 
customer, but the consumer is not buying the accompanying operating 
software itself, but rather a license to use it.
Question Submitted by Hon. Ralph Lee Abraham, a Representative in 
        Congress from Louisiana
    Question. A farmer will be the first to tell you that there is a 
strong interest on the farm in preserving and protecting natural 
resources, including soil and water. How can you foresee the data that 
is being collected on the farm being used to improve conservation of 
natural resources?
    Answer. The use of data in farming can help farmers optimize how 
they manage their fields and crops--allowing them to reduce waste all 
around, including that of water, and over-application of chemicals.
    Big data will play an important role as weather conditions tend to 
be unpredictable and volatile. The analysis of macro climate trends and 
improved forecasting will enable growers to select drought tolerant 
varieties, or possibly faster growing crops to mitigate weather risk. 
Additionally the data can help them better manage irrigation and other 
nutrient and seed applications to best fit the expectations for the 
coming growing season and time farming activities in-between weather 
events so the plants are able to get the maximum benefit from 
fertilizers and chemicals with minimal loss due to wind and rain. 
Related to this, modeling of the spread of disease and insects should 
enable more prescribed applications based on true threats and not 
preventative applications that are done more as insurance policies.
    The technology on machines will also be able to accurately record 
what was done, and where, within a field. This information is valuable 
for reporting and traceability purposes and also serves to inform 
models used to plan the next pass across the field. These models are 
then able to take into account numerous variables ensuring the right 
amount of nutrients are put in the right places in the field. This 
helps maximize yield and ensure no inputs are wasted or end up in an 
environmentally sensitive area.
Response from Shannon Ferrell, J.D., M.S., Associate Professor and 
        Faculty Teaching Fellow, Agricultural Law Department of 
        Agricultural Economics, Oklahoma State University
Question Submitted by Hon. Ralph Lee Abraham, a Representative in 
        Congress from Louisiana
    Question. How does big data fit into the current farm economy?
    Answer.

          At this moment, I would characterize the role of big data in 
        the farm economy as ``emerging'' and its role in agriculture as 
        ``limited, but growing rapidly and poised for even faster 
        growth.'' 

    Since the mid-1990's with the emergence of a number of precision 
agriculture tools and sensor technologies beginning to be integrated to 
agricultural implements, farmers have been starting to accumulate data 
at the farm level. Fairly shortly thereafter, farmers began sharing 
that data with service providers such as crop consultants, and 
databases containing larger numbers of farms began to emerge. This laid 
the foundation for big data in agriculture as we know it today. 
Conversely, the ability to collect data about a larger range of 
parameters for both the field and machinery, and to wirelessly transmit 
that data to a consultant or other service provider in real-time is a 
relatively recent development. That capability, coupled with 
significant advances in the analytical systems available to service 
providers aggregating this data, will likely lead to significant 
expansions in the integration of big data tools on our farms and 
ranches. Although there is only limited research on the actual economic 
impact to individual farms and ranches from these technologies, 
anecdotal evidence suggests many farmers who are already using these 
tools are experiencing significant improvements to their decision-
making capabilities, and with that, improved profitability. With the 
expansion of big data tools--which I would anticipate to continue 
rapidly over the next 5 to 10 years--I believe we would begin to see 
more widespread impacts to the farm economy as a broader cross-section 
of producers increase their efficiencies through such tools.

    Question a. When we had higher commodity prices, to what extent 
were farmers adopting the use of big data?
    Answer.

          Recent periods of high commodity prices likely laid the 
        foundation for adoption of big data tools through the purchase 
        of new farm equipment integrating improved sensors and data 
        communications equipment; indeed, many farmers may have joined 
        big data systems through such purchases without intentionally 
        doing so.

    With the most recent periods of increased commodity prices (2008-
2009 and 2012-2014) coinciding with fairly generous Internal Revenue 
Code Section 179 allowances for depreciation of capital assets, 
equipment manufacturers saw significantly increased sales of new 
tractors and combines, many of which included improved sensors for 
machine parameters (including harvest yield sensing) and for their 
external environment. These machines, at an increasing rate, also had 
integrated cellular modems that could be used to transmit data from 
these sensors to a service provider. In addition, for a number of 
years, farm equipment have been equipped to upload machinery 
diagnostics back to the manufacturer regardless of the farmer being 
cognizant of this data transfer. Thus, while the most recent periods of 
higher commodity prices may not have led directly to increased adoption 
of big data tools, it is quite likely they laid the foundation for 
increased adoption of such tools by enabling agricultural producers to 
procure the equipment needed to facilitate that adoption at a later 
date.
Crop Farm Received and Paid Indexes, All Items by Quarter--United 
        States: 2011=100
        
        
        
        
        [GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
        
       
          USDA-NASS, 10/25/2015.
          http://www.nass.usda.gov/Charts_and_Maps/Agricultural_Prices/
        cropfarm.php.

    Question b. How has that been affected by the recent downturn in 
the farm economy?
    Answer.

          While recent data suggests declines in new farm equipment 
        purchases, the downturn in commodity prices may actually speed 
        adoption of big data tools as farmers seek ways to increase 
        efficiency and reduce operating costs.

    By all accounts, the recent downturn in commodity prices has had a 
significant negative impact on new agricultural equipment sales, but as 
mentioned above, recent favorable conditions may have put data-enabled 
equipment in the hands of many agricultural producers.

                                           [October 2015 Flash Report]
                                        [United States Unit Retail Sales]
       [Copyright, AEM. All rights reserved. If data is referenced, please acknowledge AEM as the source.]
----------------------------------------------------------------------------------------------------------------
                                    October                             YTD--October                 Beginning
                    ----------------------------------------------------------------------------- Inventory Oct.
                        2015         2014         %Chg         2015         2014         %Chg          2015
----------------------------------------------------------------------------------------------------------------
2WD Farm Tractors:
  < 40 HP                11,469        9,305         23.3      105,443       97,564          8.1          65,586
  40 < 100 HP             5,931        5,851          1.4       50,671       50,830         ^0.3          33,048
  100+ HP                 2,717        3,853        ^29.5       20,829       27,259        ^23.6          10,226
                    --------------------------------------------------------------------------------------------
    Total 2WD Farm       20,117       19,009          5.8      176,943      175,653          0.7         108,860
     Tractors
                    --------------------------------------------------------------------------------------------
4WD Farm Tractors           391          507        ^22.9        2,562        4,426        ^42.1           1,048
                    ============================================================================================
  Total Farm             20,508       19,516          5.1      179,505      180,079         ^0.3         109,908
   Tractors
                    ============================================================================================
Self-Prop Combines          457          572        ^20.1        4,489        6,938        ^35.3           1,397
----------------------------------------------------------------------------------------------------------------
[These data are, in part, estimates that are subject to revisions when final detailed data become available.
  Because of the seasonal nature of the industry, comparisons of monthly data from one period to another should
  be done with extreme caution. These data represent the machines in each product category being sold at retail
  in the fifty states and District of Columbia by most, but not all, of the manufacturers.]
Source: Association of Equipment Manufacturers, http://www.aem.org/AllDocuments/AEM/MI/Reports/
  15%2010%20USAG.pdf.

    With this in mind, the downturn in agricultural commodity prices 
might actually increase the adoption of big data tools. The rationale 
for such a scenario is that big data tools hold the potential to help 
producers make much more efficient input and machinery management 
decisions, thus decreasing their overall operating costs and helping 
them preserve as much profitability as possible given the prevailing 
market conditions. As history has repeatedly shown, the farmer and 
ranchers best-positioned to handle difficult times are the consistently 
low-cost producers. In order for big data tools to provide this 
potential benefit to producers, though, the companies providing them 
must have price points that make them cost-effective in the current 
market environment. Many service providers offer big data services at 
no direct costs to the farmer, or at least offer a `freemium' version 
such that the farmer does not pay a fee for an entry level service. 
Upcharge services are sometimes available for farms desiring additional 
services. Other service providers charge a nominal annual per farm fee. 
These pricing structures are set to attract as many farmers, and 
farmers' fields, as possible so that a critical mass of farmers enroll 
in the system. The idea is that when the system has fewer than the 
critical mass of farmers, then farmers do not have adequate incentives 
to participate; and that when the system has at least a critical mass 
of members, then additional farmers have clear incentives to enroll.
    Continued risk-management education programs to help producers 
understand how to effectively use such tools for their own operations 
will also be vital to adoption of measures that can help preserve farm 
profitability.

                                  [all]