[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
(ii)
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
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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
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
* 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
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
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.
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
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\
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\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.
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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.
---------------------------------------------------------------------------
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 *
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* Editor's note: John Deere was invited to testify but declined.
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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]