U.S. GOVERNMENT PUBLISHING OFFICE

37–228 PDF WASHINGTON : 2019

S. HRG. 115–645 

TECHNOLOGY IN AGRICULTURE: 

DATA-DRIVEN FARMING 

HEARING 

BEFORE THE

SUBCOMMITTEE ON CONSUMER PROTECTION, 

PRODUCT SAFETY, INSURANCE, 

AND DATA SECURITY 

OF THE

COMMITTEE ON COMMERCE, 

SCIENCE, AND TRANSPORTATION 

UNITED STATES SENATE 

ONE HUNDRED FIFTEENTH CONGRESS 

FIRST SESSION 

NOVEMBER 14, 2017 

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

( 

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

(II) 

SENATE COMMITTEE ON COMMERCE, SCIENCE, AND TRANSPORTATION 

ONE HUNDRED FIFTEENTH CONGRESS 

FIRST SESSION 

JOHN THUNE, South Dakota, Chairman 

ROGER F. WICKER, Mississippi 

ROY BLUNT, Missouri 

TED CRUZ, Texas 

DEB FISCHER, Nebraska 

JERRY MORAN, Kansas 

DAN SULLIVAN, Alaska 

DEAN HELLER, Nevada 

JAMES INHOFE, Oklahoma 

MIKE LEE, Utah 

RON JOHNSON, Wisconsin 

SHELLEY MOORE CAPITO, West Virginia 

CORY GARDNER, Colorado 

TODD YOUNG, Indiana 

BILL NELSON, Florida, Ranking 

MARIA CANTWELL, Washington 

AMY KLOBUCHAR, Minnesota 

RICHARD BLUMENTHAL, Connecticut 

BRIAN SCHATZ, Hawaii 

EDWARD MARKEY, Massachusetts 

CORY BOOKER, New Jersey 

TOM UDALL, New Mexico 

GARY PETERS, Michigan 

TAMMY BALDWIN, Wisconsin 

TAMMY DUCKWORTH, Illinois 

MAGGIE HASSAN, New Hampshire 

CATHERINE CORTEZ MASTO, Nevada 

NICK ROSSI, Staff Director 

ADRIAN ARNAKIS, Deputy Staff Director 

JASON VAN BEEK, General Counsel 

KIM LIPSKY, Democratic Staff Director 

CHRIS DAY, Democratic Deputy Staff Director 

RENAE BLACK, Senior Counsel 

SUBCOMMITTEE ON CONSUMER PROTECTION, PRODUCT SAFETY, 

INSURANCE, AND DATA SECURITY 

JERRY MORAN, Kansas, Chairman 

ROY BLUNT, Missouri 

TED CRUZ, Texas 

DEB FISCHER, Nebraska 

DEAN HELLER, Nevada 

JAMES INHOFE, Oklahoma 

MIKE LEE, Utah 

SHELLEY MOORE CAPITO, West Virginia 

TODD YOUNG, Indiana 

RICHARD BLUMENTHAL, Connecticut, 

Ranking 

AMY KLOBUCHAR, Minnesota 

EDWARD MARKEY, Massachusetts 

CORY BOOKER, New Jersey 

TOM UDALL, New Mexico 

TAMMY DUCKWORTH, Illinois 

MAGGIE HASSAN, New Hampshire 

CATHERINE CORTEZ MASTO, Nevada 

(III) 

C O N T E N T S 

Page 

Hearing held on November 14, 2017 ...................................................................... 1 

Statement of Senator Moran .................................................................................. 1 

Prepared statement .......................................................................................... 2 

Statement of Senator Blumenthal .......................................................................... 3 

Statement of Senator Inhofe ................................................................................... 4 

Statement of Senator Young ................................................................................... 4 

Statement of Senator Fischer ................................................................................. 51 

Press Release dated November 8, 2017 entitled ‘‘Lindsay Corp President 

and CEO Speaks to U.S. Senate Committee’’ ............................................. 51 

Report entitled ‘‘FieldNET AdvisorTM Irrigation Management Decision 

Support Tool’’ by Lindsay Corporation ........................................................ 52 

Statement of Senator Klobuchar ............................................................................ 61 

Statement of Senator Lee ....................................................................................... 63 

WITNESSES

Justin Knopf, Vice President, Kansas Association of Wheat Growers ................ 5 

Prepared statement .......................................................................................... 6 

Jason G. Tatge, Co-Founder, President and CEO, Farmobile ............................. 9 

Prepared statement .......................................................................................... 11 

Shannon L. Ferrell, J.D., M.S., Associate Professor, Oklahoma State University Department of Agricultural Economics ....................................................... 15 

Prepared statement .......................................................................................... 18 

Todd J. Janzen, President, Janzen Agricultural Law LLC .................................. 34 

Prepared statement .......................................................................................... 36 

Dr. Dorota Haman, Ph.D., Professor and Chair, Agricultural and Biological 

Engineering, Institute of Food and Agricultural Sciences, University of 

Florida (UF/IFAS) ................................................................................................ 45 

Prepared statement .......................................................................................... 46 

APPENDIX

Hon. Bill Nelson, U.S. Senator from Florida, prepared statement ...................... 77 

Timothy Hassinger, President and CEO, Lindsay Corporation, prepared statement ...................................................................................................................... 78 

Deere & Company, prepared statement ................................................................ 78 

Response to written questions submitted to Justin Knopf by: 

Hon. Jerry Moran ............................................................................................. 81 

Hon. Catherine Cortez Masto .......................................................................... 83 

Response to written questions submitted to Dr. Shannon Ferrell by: 

Hon. Jerry Moran ............................................................................................. 84 

Hon. Catherine Cortez Masto .......................................................................... 89 

Response to written questions submitted to Todd J. Janzen by: 

Hon. Jerry Moran ............................................................................................. 92 

Hon. Catherine Cortez Masto .......................................................................... 93 

Response to written questions submitted to Dr. Dorota Haman by: 

Hon. Jerry Moran ............................................................................................. 94 

Hon. Bill Nelson ............................................................................................... 95 

Hon. Catherine Cortez Masto .......................................................................... 98 

(1) 

TECHNOLOGY IN AGRICULTURE: 

DATA-DRIVEN FARMING 

TUESDAY, NOVEMBER 14, 2017 

U.S. SENATE, 

SUBCOMMITTEE ON CONSUMER PROTECTION, PRODUCT

SAFETY, INSURANCE, AND DATA SECURITY, 

COMMITTEE ON COMMERCE, SCIENCE, AND TRANSPORTATION, 

Washington, DC. 

The Subcommittee met, pursuant to notice, at 2:32 p.m. in room 

SR–253, Russell Senate Office Building, Hon. Jerry Moran, Chairman of the Subcommittee, presiding. 

Present: Senators Moran [presiding], Blumenthal, Blunt, Fischer, 

Inhofe, Lee, Young, Klobuchar, Hassan, and Cortez Masto. 

OPENING STATEMENT OF HON. JERRY MORAN, 

U.S. SENATOR FROM KANSAS 

Senator MORAN. Good afternoon, everyone. Thank you for joining 

us this morning. Our Subcommittee’s hearing is on ‘‘Technology in 

Agriculture: Data-Driven Farming.’’ That’s this Subcommittee, and 

that hearing will come to order. 

The agricultural community’s adoption of field sensors, drones, 

satellite imagery, advanced machinery, and similar technology is 

increasing at an incredible pace. Our Commerce Committee and 

this Subcommittee have been paying a lot of attention to those 

issues. And the result of that increasing pace is greater crop yields 

and improved sustainable practices in farming. The most profitable 

farms are often the most sustainable ones. This rapidly evolving 

technology will have a vital role in preserving farmers’ most important assets—their land—with the potential increase farmers’ margins to unprecedented levels. 

The collection and analysis of data has enabled farmers to reduce 

costs through more efficient applications of inputs like fertilizers 

and pesticides; improve production decisions through enhanced recordkeeping and more accurate yield predictions; and enhance land 

stewardship and sustainable practices by removing inefficiencies in 

planting, harvesting, water use, and the allocation of other resources. With an increasing volume of quality data, in tandem with 

improved data analysis, data-collection technology has the potential 

to dramatically increase farm productivity and profitability. 

The collection and use of such data raises issues regarding control of the data, transparency of agreements between farmers and 

data firms, and barriers to expanding internet access to rural 

America. 

2 

Additionally, as data collection and sharing practices become 

more popular across the ag economy, farmers are well-positioned to 

benefit from their ‘‘commoditization’’ of data collected from their 

land, especially as equipment manufacturers, service providers, cooperatives and other businesses seek access to that data. 

The goal for this hearing is to educate and empower our nation’s 

farmers to understand the value of the information they are creating, and certainly to allow Members of Congress to have a better 

understanding of the current lay of the land and what the future 

holds. 

It’s my pleasure to introduce the panel today, and I thank you 

all for being here. 

Justin Knopf is a farmer from Gypsum, Kansas, right in the middle of our state. He grows wheat, alfalfa, soybeans, grain sorghum, 

corn, and multi-species of cover crops. As part of his sustainabilityfocused farming operations, he practices what is referred to commonly as ‘‘no-till’’ farming and utilizes a variety of technologies 

that assist his monitoring efforts to be a good steward of the land 

while improving his yield. 

Jason Tatge is the Co-Founder and CEO of Farmobile, a technology firm based in Overland Park, Kansas; that’s a suburb of 

Kansas City. His company’s services provide farmers with real-time 

access to ownership of current and historical data pertaining to 

their land. By providing a user-friendly, simplified, and comprehensive overview of relevant data, Farmobile’s customers are able to 

make educated decisions in a much more timely fashion. 

Dr. Shannon Ferrell is an Associate Professor at Oklahoma State 

in the Department of Agricultural Economics. He also serves as an 

agricultural industry representative to the Oklahoma Environmental Quality Board, which oversees operations of the Oklahoma 

Department of Environmental Quality. And the Senator from Oklahoma will have an opportunity to introduce Dr. Ferrell shortly. 

Mr. Todd Janzen is President of Janzen Agricultural Law, LLC, 

and the Administrator of the Ag Data Transparency project. This 

project makes available the Ag Data Transparency Evaluator, 

which aims to provide clarity to consumers as to what businesses 

do with the data that is shared with them all. 

And, finally, Dr. Dorota Haman is Professor and Chair of the Department of Agriculture and Biological Engineering at the University of Florida. She specializes in irrigation water management and 

efficiencies, and has been an active leader in providing irrigation 

technologies in developing countries, in the Americas, and in Africa. 

[The prepared statement of Senator Moran follows:] 

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

Good afternoon. Welcome to the Subcommittee’s hearing on ‘‘Technology in Agriculture: Data-Driven Farming.’’ The Subcommittee will come to order. 

Thank you for being here today to discuss the advancements and benefits of agricultural technology and the potential of ‘‘Big Data’’ in farming. 

The agricultural community’s adoption of field sensors, drones, satellite imagery, 

advanced machinery and similar technology is increasing at an incredible pace to 

increase crop yields and improve sustainable practices. The most profitable farms 

are often the most sustainable ones. This rapidly evolving technology will have a 

vital role in preserving farmers’ most important asset, their land, with the potential 

to increase farmers’ margins to unprecedented levels. 

3 

The collection and analysis of data has enabled farmers to reduce costs through 

more efficient applications of inputs like fertilizers and pesticides; improve production decisions through enhanced recordkeeping and more accurate yield predictions; 

and enhance land stewardship and sustainable practices by removing inefficiencies 

in planting, harvesting, water use, and the allocation of other resources. With an 

increasing volume of quality data, in tandem with improved data analysis, data-collecting technology has the potential to drastically increase farm productivity and 

profitability. 

The collection and use of such data raises issues regarding control of the data, 

the transparency of agreements between farmers and data firms and barriers to expanding Internet access in rural areas. 

Additionally, as data collection and sharing practices become more popular across 

the agriculture economy, farmers are well-positioned to benefit from the 

‘‘commoditization’’ of data collected from their land, especially as equipment manufacturers, service providers, cooperatives, and other businesses seek to access and 

utilize this data. 

My goal for this hearing is to educate and empower our Nation’s farmers to understand the value of the information they are creating. 

It is my pleasure to introduce our panel today. Thank you all for being here. 

Mr. Justin Knopf is a farmer from Gypsum, Kansas, and he grows wheat, alfalfa, 

soybeans, grain sorghum, corn, and multi-species cover crops. As a part of his sustainability-focused farming operations, he practices what is commonly referred to as 

‘‘no-till’’ farming and utilizes a variety of technologies that assist his monitoring efforts to be a good steward of the land while improving his yield. 

Mr. Jason Tatge is the Co-Founder and CEO of Farmobile, a technology firm 

based in Overland Park, Kansas. His company’s services provide farmers with realtime access to and ownership of current and historical data pertaining to their land. 

By providing a user-friendly, simplified yet comprehensive overview of relevant 

data, Farmobile’s customers are able to make educated decisions in a timely fashion. 

Dr. Shannon Ferrell is an Associate Professor at Oklahoma State University Department of Agricultural Economics. He also serves as the agricultural industry representative to the Oklahoma Environmental Quality Board, which oversees the operation of the Oklahoma Department of Environmental Quality. 

Mr. Todd Janzen is President of Janzen Agricultural Law, LLC and the Administrator of the Ag Data Transparency project. This project makes available the Ag 

Data Transparency Evaluator, which aims to provide clarity to consumers as to 

what businesses do with the data that is shared with them. 

Dr. Dorota Haman is a Professor and Chair of the Department of Agricultural and 

Biological Engineering at the University of Florida. She specializes in irrigation 

water management and efficiencies, and has been an active leader in providing irrigation technology to developing countries in the Americas and Africa. 

I look forward to hearing the testimonies of this expert witness panel. I now turn 

to my colleague Ranking Member Blumenthal for his opening remarks. 

Senator MORAN. I look forward to hearing the testimony of these 

expert witnesses. And before we do that, let me turn to the Ranking Member, the Senator from Connecticut, Mr. Blumenthal. 

STATEMENT OF HON. RICHARD BLUMENTHAL, 

U.S. SENATOR FROM CONNECTICUT 

Senator BLUMENTHAL. Thank you, Mr. Chairman. 

And thank you to the witnesses for being here today. And thank 

you to the Chairman for having this hearing. 

I represent the State of Connecticut in the United States Senate. 

And I want to welcome witnesses from the states that do a different type of farming. We do have farming in the State of Connecticut. And I have an additional connection to what you folks do 

for a living, which is that my grandfather had a farm, and my first 

job literally was shoveling manure on his farm at the age of probably about 7 or 8 years old. 

I would bet the most complicated piece of machinery on his farm 

in the 1950s and 1960s was the radio in his house, and the idea 

of data and farming being in the same sentence would have totally 

4 

perplexed him. But, in fact, data has enabled us to increase yield 

and productivity in ways that would have been unimaginable to 

him and many farmers of his generation and maybe the generation 

afterward. And the benefits have been widely shared by America 

and the world because America’s farmers have led the world in 

using technology to raise productivity and yield. 

At the same time, the advances in data have raised questions 

about who owns it, who controls it, how do we protect privacy, and 

how do we prevent others from, in effect, profiteering at the expense of our farmers, who really should be the ones who own that 

data and control it? 

So these kinds of questions bring us here today. And I thank you 

for shedding some light on an enormously important and complex 

topic that occupies this Committee in a number of different realms, 

and this one is certainly one of the preeminently important ones. 

Thank you very much. 

Senator MORAN. Mr. Blumenthal, thank you very much. Thank 

you for explaining how you got your start in politics. 

[Laughter.] 

Senator MORAN. I would recognize the Senator from Oklahoma 

for purposes of an introduction. 

STATEMENT OF HON. JIM INHOFE, 

U.S. SENATOR FROM OKLAHOMA 

Senator INHOFE. Well, since I don’t know anything about Dr. 

Ferrell, I won’t introduce him except to say that he is here on behalf of Oklahoma State University, and he’s got to be a good guy 

if he’s with the Oklahoma State University. Thank you. 

Senator MORAN. He was smart enough, Senator Inhofe, to wear 

the KU tie, however. 

[Laughter.] 

Senator MORAN. Perhaps that’s just patriotic. 

Senator Young has the floor for purposes of introduction. 

STATEMENT OF HON. TODD YOUNG, 

U.S. SENATOR FROM INDIANA 

Senator YOUNG. Thank you, Mr. Chairman. I appreciate you 

holding this very timely hearing on the agricultural technology and 

digitalization, if I can say that word, of the farm. 

I am happy to introduce Todd Janzen, a fellow Hoosier and expert in the field. He is the President of Janzen Agricultural Law 

in Indiana, and his experience in the industry began at an early 

age. Todd grew up on a grain and livestock farm in Kansas, where 

he learned the ins and outs of the industry. After graduating from 

Bethel College in Kansas, Todd made his way to the great Hoosier 

state and attended my alma mater, Indiana University’s McKinney 

School of Law, where he began his law career in Indianapolis. 

In addition to his work at Janzen Ag Law, Todd serves as General Counsel for the Indiana Dairy Producers. He’s a member of the 

Indiana Farm Bureau Property Rights Policy Committee, and previously he sat on the Board of the Council for Agriculture, Science, 

and Technology. 

Todd maintains a blog on law and technology issues facing the 

agricultural industry, and his writing has been republished by nuVerDate Nov 24 2008 08:08 Aug 05, 2019 Jkt 000000 PO 00000 Frm 00008 Fmt 6633 Sfmt 6601 S:\GPO\DOCS\37228.TXT JACKIE
5 

merous journals and news sources. Todd is the Administrator of 

the Ag Data Transparency project, which is making ag data publicly available to farmers all across the world. These insights will 

be especially relevant today I know as the Committee discusses ag 

technology and examines the many potential benefits and considerations that lie ahead. 

I want to thank Mr. Janzen for taking the time to testify. And 

I look forward to hearing the entire panel’s discussion this afternoon. 

Senator MORAN. Thank you both for those introductions, and let 

us now hear from our witnesses. We’ll start with Mr. Knopf and 

work our way to his left. 

STATEMENT OF JUSTIN KNOPF, VICE PRESIDENT, 

KANSAS ASSOCIATION OF WHEAT GROWERS 

Mr. KNOPF. Mr. Chairman and Ranking Member Blumenthal, 

members of the Subcommittee, thank you for the opportunity to address you today. My name is Justin Knopf. I am a fifth generation 

farmer from Gypsum, Kansas, as Chairman Moran mentioned. 

I also currently serve as Vice President for Kansas Association 

of Wheat Growers. Working alongside my dad and brother, we 

grow wheat, alfalfa, soybeans, grain sorghum, corn, and multi-species cover crops across our 4,500-acre farm. 

Like most U.S. farms, we are a family farm. As my father has 

always said, the most important thing we raise on our farm is children. My wife Lindsey and I have two daughters and a son, and 

my brother and his wife have two sons. We utilize a holistic approach in our farm management, rooted in values of faith and family with a multi-generational view. This decisionmaking process examines not only our economic returns, but also the returns to natural and human resources. 

For the past 15-plus years, our dry land operation has utilized 

a cropping system focused on continuous no-till practices and cropping rotations. This system protects the soil, allows biology in the 

soil to thrive, is more resilient to extreme weather, and increases 

carbon content in soils by sequestering carbon dioxide from the atmosphere. 

Never before has our society had the access to data and information that we have today. The same is true in agriculture. Data collection, processing, and the utilization of data for improved decisionmaking has become a core competency for many, if not the majority of farmers. The amount of knowledge per acre and amount 

of knowledge about each acre are significant drivers in the amount 

of profit per acre. 

There are three main areas on our farm where data shapes our 

decisionmaking and has impact: economic sustainability, environmental stewardship, and transparency with consumers. And I’d 

like to share a brief example of each with you. 

As you know, the current low commodity prices equate to a difficult economic reality on the farm. Managing costs is critical right 

now. We are utilizing data to divide fields into specific management zones; for instance, high-, average-, and low-producing areas 

of the field. When growing corn, seed is one of more expensive inputs. We are prescribing different planting populations for each 

6 

yield zone, planting more seed in areas of the field that consistently produce more, maximizing our return on that investment, 

and planting less seeds in areas of the field that are consistently 

lower producing, lowering our costs on those acres. 

Farmers understand the importance of environmental stewardship and protecting our natural resources for future generations. 

That being said, we’ve made our fair share of mistakes and always 

have room for improvement. On our farm, we have a significant 

focus on protecting the soil and improving its health and resiliency. 

Cover crops are an important tool in this endeavor. Utilizing data 

collection equipment on our machinery to carry out on-farm research trials has enabled us to better quantify the impact of cover 

crops to subsequent crop yields and other agronomic factors. 

Consumers today, and, therefore, the supply chain, are increasingly interested in how their food is produced and that it’s done in 

a way that corresponds with their values. This past year, we enrolled our wheat acres into a sustainability program with ADM. 

Basically, we entered field information, things such as the amount 

of fertilizer rates used in each field and yield data into a web-based 

software. The software utilizes sustainability metrics designed by 

the collaborative group field to market, and then gives us quantifiable environmental impact metrics on our farm, benchmarks on 

how our farm compared to other farms, and then the supply chain 

receives the aggregated data. 

The agricultural economy is at a crossroads right now, depressed 

prices, increased costs, and rising debt levels are creating economic 

angst. The average age of farmers continues to increase while the 

number of us continues to decrease. There is this great challenge 

of intensifying our farming system and doing so in a way that is 

sustainable, if not restorative, to our natural resources. Consumers 

are increasingly removed from the farm and wary of technological 

innovations in farming. 

These are all significant challenges. However, the minds and 

spirits engaged in what will be the next generation of agriculture 

are as bright as ever. There will be a record percentage of farms 

transitioning to this next generation in the coming decade. This 

transition represents a great opportunity for change and innovation 

not only in improved productivity, but also in environmental stewardship. It is critical that we collaborate, learn, and adapt in order 

that we may have continual improvements. 

I appreciate genuinely the opportunity to share the value of data 

with you today. And I appreciate that Congress is willing to listen 

to the people who may be impacted by future legislation. 

[The prepared statement of Mr. Knopf follows:] 

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

Mr. Chairman, Ranking Member Blumenthal, and Members of the Subcommittee, 

thank you for the opportunity to address you today. My name is Justin Knopf and 

I am a fifth generation farmer from Gypsum, Kansas. I also serve as the Vice President of the Kansas Association of Wheat Growers. Working alongside my dad and 

my brother, we grow wheat, alfalfa, soybeans, grain sorghum, corn, and multi-species cover crops across our 4500 acre farm. 

As my father has always said, we also grow people on our farm. For my brother 

and I, farming is a lifelong learning process. The local young people who find a summer job with us grow in responsibility, work ethic, and perspective. My wife Lindsey 

7 

1https://www.ers.usda.gov/webdocs/publications/eib164/eib-164.pdf 

2https://www.ers.usda.gov/webdocs/publications/eib164/eib-164.pdf 

3http://www.agmanager.info/kfma/state-summaries 

4https://www.wsj.com/articles/whats-behind-the-glut-in-agricultural-commodities-1476670 

020 5https://www.wsj.com/articles/the-next-american-farm-bust-is-upon-us-1486572488 

6http://www.agmanager.info/kfma/state-summaries/2015-state-summary-detailed-cost-summary 7http://www.fao.org/fileadmin/templates/wsfs/docs/expert_paper/How_to_Feed_the_World 

_in_2050.pdf 

and I have three young children, two daughters and a son, and my brother also has 

two young sons. Our farm is not unlike most farms in the United States. According 

to the USDA, farming is still overwhelmingly comprised of family-owned businesses. 

99 percent of U.S. farms are family farms, and they account for 89 percent of farm 

production.1 The USDA also estimates that small farms make up 90 percent of the 

farm count and operate nearly half of America’s farmland.2

We utilize a holistic approach to our farm management, rooted in values of faith 

and family, with a multi-generational view. Farmers understand the need for good 

stewardship and conservation. This is what we do every day. We depend on clean 

water and healthy soils to make a living and feed the world. This decision making 

process examines not only our economic returns, but also the returns to natural and 

human resources. For the past fifteen plus years, our dry land operation has utilized 

a cropping system focused on continuous no-till practices and crop rotations. This 

system protects the soil, allows soil biology to thrive, is more resilient to extreme 

weather, and increases Carbon content in soils by sequestering CO2 from the atmosphere. 

Although we have been blessed with some bountiful harvests in the last few 

years, the current economic reality on the farm is difficult and the coming years are 

shaping up to be some tough times. Farm income levels are at their lowest point 

since 1985. Net farm income dropped 95 percent from 2014 to 2015, and net farm 

debt levels have increased 25 percent over the last 3 years.3 This downturn has 

largely been caused by low commodity prices, which are due to record highs in both 

local and worldwide production over the past two years.4 These production levels 

have increased supply, while overall demand has waned, due to a strong U.S. dollar 

and decreasing exports.5 Another major factor is that while revenues have only gone 

down, the cost of production and expenses have gone up. From 2009 to 2015 the 

cost of production has increased almost 50 percent.6 This rise in costs has forced 

farmers to look for ways to find efficiencies and minimize costs. Our ability to adapt 

to changes is what will keep us going when times get tough. 

According to the United Nations there will be 9.1 billion people on the planet in 

the year 2050.7 One of the more significant long term challenges facing our world 

is how we feed a growing global population. Food security isn’t just an agricultural 

issue; it is a national security issue. As farmers, we must find a way to produce 

more food, on less land, with less water, all while protecting our soils and natural 

resources. As stewards of the land it is our job to find ways to do more with less. 

It will take all available tools to meet these challenges. Agricultural innovations, 

like technological improvements, seed technology, and on farm efficiencies, are all 

important. Research within private entities and public institutions is critical. Perhaps most fundamental is collaboration with others, an eagerness to learn, and a 

willingness to adapt. 

Never before has our society had the access to data and information that we have 

today. Data is all around us, and there is value in it all. While a record of Google 

searches and websites visited may be useless history to me, analysts and marketers 

see valuable information that allows them to adjust the content they create. The 

same, of course, is true in agriculture. While some may see a jargon-filled spreadsheet or just a bunch of various colors on a field map, I see ways to maximize efficiency in my operation, both for my pocketbook, as well as for the land that provides 

the livelihood of my family. Data collection, data processing, and the utilization of 

data for improved decision making has become a core competency for many, if not 

a majority of, farmers. 

The obvious benefit of data is the ability to make improved management decisions. Data has become an important layer in our decision making process and a 

driver in our economical sustainability and environmental stewardship. The amount 

of knowledge per acre, and amount of knowledge about each acre, are significant 

drivers in the amount of profit per acre. 

There are three main types of data we utilize on our farm. Microdata is data we 

collect and produce that is specific to our farm. Service provider data is data that 

is provided to us by service partners that is specific to our farm. And Macrodata, 

8 

or big data, is data we provide to others and they, in return, give us an idea of what 

is happening in the industry on a larger scale. 

Specifically on our farm, we collect and utilize this data in a number of different 

ways. As on many farms, our seeding, spraying, and harvesting equipment all has 

hardware and software that measures and records spatially what is being done or 

happening in the field. Performing on-farm research with sound scientific and statistical principles is one way we use this technology. For example, we were able to 

quantify the impacts of cover crops on subsequent crop yields, which has led to a 

broader adoption of cover crop practices. We utilize satellite imagery as a way to 

help us identify management zones within a field and predict yield variability. 

These management zones allow us to modify our seeding rates based on the productivity of the land, which lowers our seed cost on acres that are less productive. We 

use zone soil sampling to quantify soil fertility levels in differing areas of the field, 

which allows us to fertilize based off specific soil conditions and fertility levels. It 

allows us to focus inputs on the areas that need them and to avoid applications on 

areas that don’t. We enter data into ADM’s sustainable wheat program, and in return we receive sustainability metrics based off the field to market calculator. 

Through this program we are able to use key sustainability outcomes and metrics 

and benchmark our farm’s performance to others in the program. 

I also share economic, cost, and revenue data with the Kansas State University 

(K-State) Farm Management program and receive informational data back on my 

farm’s profitability in relation to other like-sized no-till farms. This program allows 

me the ability to know how my business is doing in relation to others in the industry, what business strategies I should implement to become more profitable, or what 

investments I should make in my business. 

The quality and the quantity of data in agriculture, and its importance, is driving 

the improvement of farming practices and its value will only continue to grow. It 

is vital that our stakeholders and collaborators work alongside our public research 

institutions, such as K-State, to continue to develop the tools farmers need to be 

successful. Private industry is rapidly expanding in this space and the technology 

is changing by the day. Competition for the ‘‘digital acre’’ is increasing and it is rapidly driving innovation. 

For example, we can now use crop sensors mounted to sprayers that utilize algorithms, developed by K-State and other land-grant institutions, that tell us in real 

time how much nitrogen each plant needs, while giving credit to biological nitrogen 

that already exists in the plant. As the sprayer travels through the field the sensors 

will tell us in real time how much nitrogen the plants in that spot need. This technology allows us to put the right amount of nitrogen in the right place which saves 

money and increases environmental stewardship. 

There are also proprietary tools from companies such as Pioneer and Monsanto 

that utilize soil and weather data to predict a crop’s nitrogen needs and the amount 

of available nitrogen in the soil. This data helps farmers tune the timing and quantity of fertilizer applications to increase efficiencies. 

A researcher from Kansas State has been utilizing land on our farm and others 

to test and develop a sensor that can quickly and efficiently quantify soil waterholding capacity differences across a field. As water is typically one of our most limiting factors for crop production on many farms in the Great Plains, efficient access 

to this information would be very valuable in developing management zones and insight in how to best manage each area within the field. 

Data is also important to those off the farm as well. Consumers have an ever 

growing interest in their food. They want to know more about how their food is produced, how it is processed, and if it is being grown in a way that aligns with their 

values. Our use of data allows us to tell our story to the consumer and enables us 

to do so with transparency like never before. 

However, as we begin to find new ways to collect and utilize this valuable data 

we need to make sure we protect the ownership interests and rights of farmers. We 

need to make sure that government and regulatory agencies do not try and access 

proprietary data that is critical to a farmer’s business. We need to make sure third 

party dealers and vendors do not try and take ownership of data that was generated 

and collected by the farmer. Finally, we need to ensure that the privacy rights and 

ownership interests of the farmer are respected by all those who may want to access 

this data. The last thing we need is for those who are not aligned with our farm 

interests to twist and misconstrue what we do on the farm. 

The agricultural economy is at a crossroads right now. Depressed prices, increased 

costs, and rising debt levels are creating economic angst. The average age of farmers 

continues to increase while the number of us continues to decrease. There is this 

great challenge intensifying our farming system, but doing so in a way that is sustainable if not restorative to our natural resources. Consumers are increasingly reVerDate Nov 24 2008 08:08 Aug 05, 2019 Jkt 000000 PO 00000 Frm 00012 Fmt 6633 Sfmt 6621 S:\GPO\DOCS\37228.TXT JACKIE
9 

moved from the farm and wary of technological innovations in farming. However, 

the minds and spirits engaged in agriculture and farming are as bright as ever. 

There will a record percentage of farms transitioning to the next generation in the 

coming decade. This transition represents a great opportunity for change and innovation, not only in improved productivity, but also in environmental stewardship. 

It is critical that we collaborate, learn, and adapt in order that we may have continual improvement. I appreciate the opportunity to share the value of data with 

you today, and I appreciate that congress is listening to the people who may be impacted by future legislation. There is an immense amount of technology, both here 

and on the horizon, that will allow American farmers to continue to meet these challenges. This drive for continual improvement and understanding of the complex biological ecosystem we farm with is what will allow my children, and their children 

after them, to continue feeding the world and protecting the natural resources long 

after I am gone. I urge you to continue to listen as your shape future legislation. 

Senator MORAN. Thank you, Justin. 

And now Mr. Tatge. 

STATEMENT OF JASON G. TATGE, CO-FOUNDER, PRESIDENT 

AND CEO, FARMOBILE 

Mr. TATGE. Chairman and members of the Subcommittee, thank 

you for the invitation today to share my ideas about how to improve the collection, standardization, and interoperability of agricultural data for the benefit of every American. 

My name is Jason Tatge, Co-Founder, President, and CEO of 

Farmobile. We help farmers collect and organize data so they can 

use it to better manage their own operations, share the data with 

their trusted partners, and even license their data to vetted third 

parties to create a brand new revenue stream for their business. 

I’ve been on over 200 farms in the Midwest in the last few years, 

and I’ve had hundreds of conversations with farmers defining the 

future of global agriculture. I’d like to share what I’ve learned from 

listening to the men and women on the ground. 

One of the most important topics we discuss on the farm is data. 

Who has it? Who does it belong to? And what’s it being used for? 

Through my conversations, I’ve come to believe that for the sake 

of a safer, more productive food future, farmers need to be able to 

own their data outright. Farm data needs to be accessible in real 

time and compatible with multiple systems. In short, we need a 

standard for the agricultural industry. 

I will start with the question of ownership. Big agricultural companies know it will benefit them to own the digital content coming 

out of the farmers’ fields. When a U.S. farmer spends hundreds of 

thousands of dollars on a new piece of equipment, the largest manufacturers profit from the initial sale of the equipment, then they 

profit again from the data generated from the farmer using that 

equipment. This is wrong, because the data being collected by 

many big ag companies is the farmers’ proprietary intellectual 

property. It is a unique formula or secret recipe for operating their 

successful businesses. 

Attempts to get big players in the ag industry to voluntarily 

enact transparent data policies have been slow. In fact, organizations who played a big role in drafting the best practices for data 

transparency have failed to sign on or adopt them. Asking a farmer 

for their secret recipe would be bad enough, but tricking them into 

signing it away, the unique formula, with complicated legal agreements, is appalling to most, and the main reason I sit here. 

10 

The potential value in this data to farmers and agricultural communities writ large cannot be overstated. If farmers own their data 

and can license it multiple times, we’re talking about an opportunity to create an estimated billion dollar annual new revenue 

stream returning that money to rural agricultural communities of 

America. 

To further illustrate the importance of data ownership, I want to 

talk about the value data represents for a farmer. Here’s an example. We know the genetic yield potential for corn in the U.S. is over 

500 bushels per acre, yet the national average for corn is only 

about 170 bushels. Why is this? The answer lies in the data. 

Farmer-controlled digital records to document the farming practices will help U.S. agriculture and individual farmers determine 

best practices for maximizing their yields. Those records enable 

farmers to make better decisions, identify efficiencies, boost productivity, and mitigate risks as well as aid the industry in streamlining the manual process required to participate in Federal programs and crop insurance. 

These kinds of ag data benefits, however, require agriculture to 

get past the roadblocks of data interoperability. Many farmers operate mixed fleets today, and that means they have separate data 

systems for each equipment brand. Ag companies make it incredibly labor-intensive to move data out of one system and into another. This stifles competition and customer choice in an already 

dramatically shrinking landscape of agricultural giants. 

I believe we need to properly align incentive structures and drive 

standardization across the industry. Every farmer has a right to 

access and use their data regardless of where it came from or what 

system contains it. We should encourage the flow of information 

that could help farmers and their trusted advisers to make the best 

decisions for their farms and the food industry at large. 

The power of data can go way beyond the field. When farm data 

is available in real time in a standardized portable format, like 

Farmobile’s electronic field records, there’s a huge potential to reduce the volatility in commodity markets. 

Agricultural markets are volatile for good reason: there’s a massive lag time in getting the information. The USDA is the gold 

standard, but even the USDA sometimes takes 3 weeks to get out 

information in the right form for release, but most row crops are 

only alive for about 90 days. Faster information will dramatically 

reduce the volatility in the markets and enable traditional risk 

management strategies, like hedging, to work again for farmers 

and the agricultural businesses that rely on these commodities to 

produce their products. 

In 1960, John F. Kennedy said, ‘‘The farmer is the only man in 

our economy who has to buy everything at retail, sell everything 

at wholesale, and pay the freight both ways.’’ It’s unfortunate that 

this statement is still very true today. 

But disruption is coming in the form of technology. We need to 

make sure that our legal system keeps up with the technology 

available and is informed on the formative debates that will define 

the future in my industry. Most farmers I talk with believe we 

have 2 to 3 years to figure this out, or they will lose. Farmers need 

policies that safeguard data rights, are interoperable, and improve 

11 

data access to drive efficiencies in innovation and food production. 

As you consider these issues further, ask yourselves, ‘‘Why is it 

ever okay for others to own or control farmers’ data? And how do 

we enact policies that create true data interoperability?’’ 

I firmly believe that done right, data is the answer to advancing 

agriculture and the entire food industry while protecting America’s 

farmers. 

I appreciate your openness to ideas and action from the private 

sector as well as administrative and legislative change. I look forward to working with the industry as well as the members of the 

Committee to advance this vision. Thank you. 

[The prepared statement of Mr. Tatge follows:] 

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

FARMOBILE

Chairman and Members of the Subcommittee: 

Thank you for the invitation, today, to share my ideas about how to improve the 

collection, standardization and interoperability of agricultural data for the benefit 

of every American. 

My name is Jason Tatge, co-founder, president and CEO of Farmobile—a relatively small agtech startup company, from Kansas, with a creative business model 

that turns our farmer customers data into a monetizable commodity and shares the 

revenue with the farmers. We recently celebrated our four-year anniversary and employ over 40 people with plans to add at least 20 more over the next 12 months. 

Farmobile offers a ‘‘data as a service’’ subscription that properly aligns our company’s future success with our farmers’ success. Practically speaking, we help farmers to collect and organize their data so they can use it to better manage their own 

operations, share their collected data with their trusted partners and/or sell their 

data to interested third parties, the same way musicians can sell their music. 

I’ve been on over 200 farms in the Midwest in the last few years and am completely amazed by how ridiculously awesome these people are at growing our food. 

These folks are the ‘‘rock stars’’ of global agriculture. 

One of the most common topics we discuss when on the farm is data ownership. 

Many are confused over how we’ve gotten to this current place or when data ownership even become a question They are confused about who has access to their data 

and what they are doing with it. All have an expressed interest in being able to 

establish a value for the data they generate. As one farmer, David Seba from Cleveland, Missouri told me, ‘‘Big ag has been collecting our data for so long, that there’s 

this attitude that the way we farm carries no value. Well, it does. For farmers, the 

field is our business and the way we manage it is our formula for success. So, why 

is it okay for these companies to claim the data as theirs and then sell it without 

our knowledge?’’ 

At Farmobile we are proud to be working alongside some of the most innovative 

farmers in the world, and we are passionate about providing these farmers the opportunity to establish ownership and directly profit from the data generated from 

their field activities, if they choose to sell licenses to their data. 

Introduction 

Whether you represent the 2 percent of the U.S. population who farm or the nonfarm constituents who—like all of us—eat, farm data will become a digital currency 

that impacts both farmers and food buyers. Today, I’ll share my thoughts about the 

state of the industry, and the needs, risks and opportunities we have. 

Data and analytics are disrupting and changing most industries. From grocery 

shopping to political campaigns, the world is forever changed by data. Farming is 

no different, although I’d suggest we are a few years behind other industries when 

it comes to data collection. That’s changing fast, and we have a lot to learn from 

other industries that have already made the move from analog to digital, like 

healthcare. 

A big part of farming today is being able to manage a large mixed fleet of equipment. Real-time data connectivity empowers farmers to remotely manage their logistics like never before by using any Internet connected device. While adoption of precision agriculture technologies has been on the rise for years, now that it’s available 

12 

in real-time, adoption has accelerated because farmers quickly ‘‘see’’ the value of 

data. 

Real-time data is the ‘‘game changer’’ for the future of farming because of the ability to gain insights and react ‘‘right now’’ during the season. This is the foundational 

driver to improve yields, lower input costs, strengthen stewardship and pave the 

way for cutting-edge programs like yield guarantees that enable seed and chemical 

companies to ‘‘share the performance risks’’ associated with their recommended 

products. 

Big ag companies certainly agree that the industry is going digital. Look no further than public statements made by ag business giants framing this opportunity 

for their shareholders. They absolutely know it will benefit them to own the digital 

content coming out of the farmers’ fields. 

But this comes with a cost to the farmers, not only do these big companies expect 

to get the data for free, but they also create ‘‘silos’’ for the data and make it very 

difficult to get that data back out from their systems. This stifles competition and 

customer choice in an already dramatically shrinking landscape of agricultural giants, whose recent mergers have reduced the big six to the big four. 

Farmers are just beginning to understand that their data has value outside the 

perimeter of their operations, and that data ownership and a neutral digital strategy is necessary to be competitive today. Because of this, some farmers are starting 

to ask tough questions about data: Who owns it? How will it be used? How do I 

extract maximum value from it? And, most importantly, how do I put a ‘‘fence’’ 

around my data so that it’s protected for future generations?1

This brings me to the first opportunity and risk for farmers—data ownership. 

Farmers and Data Ownership 

As business owners, farmers face a very real risk from many ag companies with 

whom they do business because: 1) companies gain access, control and sometimes 

ownership over the farmer’s private data; and 2) these companies can lock farmers 

into their data policies. 

At the center of this growing concern is the method in which ag companies typically collect and use a farmer’s data. To understand it, let me provide a consumerfacing illustration that everyone understands—Google. 

When I choose to use Google to search the web (for free), I understand that Google 

is collecting information about me through my interaction with their technology. I 

know that Google turns this collected data into information by combining it with 

other datasets. Further, I realize Google makes money from selling this information 

to marketers that want to learn more about me. In spite of this, I choose to use 

Google search because it is of value to me, and it’s free.2

On the other hand, when I purchase a license to use Microsoft Office, I gain access to tools like Microsoft Word, Microsoft Excel and Microsoft PowerPoint. These 

tools provide value to me. When I use these tools, Microsoft does not get rights to 

the content I create. Could you imagine the types of congressional hearings we’d be 

having on that topic—if Microsoft treated its customers the way big ag treats their 

customers? 

When a U.S. farmer spends hundreds of thousands of dollars on a new piece of 

equipment, the largest manufacturers profit from the initial equipment sale PLUS 

they profit from the data generated from the farmer using that equipment. The collection of this data often happens without the farmer’s knowledge due to complex 

and heavy-handed user agreements. 

While the fact that Google is collecting search data doesn’t bother me as a consumer, the stakes are much higher and far different in the farmer example. The 

data being collected by many big ag companies is the farmer’s Intellectual Property—the special and unique formula or ‘‘secret recipe’’ for operating their successful 

business. 

Imagine if we, as a user of Google, asked for its search engine algorithms. Or, 

as a customer of Microsoft, if we asked for its source code to the Microsoft Office 

Suite? Asking a farmer for their ‘‘secret recipe’’ would be bad enough, but tricking 

them into signing away that unique formula with complicated legal agreements is 

appalling to most and the main reason I am here today. 

We believe farming practices represent Intellectual Property that could be copyright protectable. Yet, today, it is difficult to establish who owns this information 

because farmers are caught in the habit of unknowingly giving this data for free 

when they sign complicated legal agreements pertaining to an entirely different subject. It is my personal motivation to help farmers by providing alternatives with upside potential. 

I’ve been working for the better part of three years with the American Farm Bureau Federation (AFBF) to address these issues. The AFBF has shown great leaderVerDate Nov 24 2008 08:08 Aug 05, 2019 Jkt 000000 PO 00000 Frm 00016 Fmt 6633 Sfmt 6621 S:\GPO\DOCS\37228.TXT JACKIE
13 

ship in trying to bring transparency to these confusing legal contracts farmers are 

required to sign. Working with commodity groups, farm organizations and agriculture technology providers, the AFBF established the Privacy and Security Principles for Farm Data in November 2014. Thirty-seven different organizations participated in drafting the ‘‘Core Principles’’ document. Many of these organizations were 

very opinionated around the wording of the Ag Data Transparency Evaluator’s ten 

questions, but only nine of these companies have agreed to become Ag Data Transparent! The ones who haven’t signed are challenging the very need for ‘‘ownership’’ 

of farm data to be defined in the ‘‘Core Principals.’’ 

Make no mistake about it, these companies are intentionally delaying participation because they hope this issue will blow over and farmers will continue to operate 

the way they have in the past—by unknowingly checking a box in a legal contract 

in order to take delivery of their product. 

Missouri Farm Bureau President Blake Hurst of Tarkio, Missouri describes the 

situation like this: ‘‘So much of what we do is done by habit. As soon as we get in 

the habit of giving that data away, no company is going to remark on the fact that 

it is a heck of a good deal for them. If we don’t start out doing it the right way, 

it will be very harmful to farmers in the future.’’ 

In agriculture, we are at a point in time where there is a great opportunity to 

‘‘do the right things for the right reasons’’ on behalf of the people who produce our 

food. 

Data Interoperability 

We know the genetic yield potential for corn in the U.S. is over 500 bushels per 

acre, yet the national average for yield is about 170 bushels per acre. Having farmer-controlled digital records (such as Electronic Field Records) to document farming 

practices will help U.S. agriculture better determine best practices farmers. Those 

records enable farmers to make better decisions, identify efficiencies, boost productivity and mitigate risks, as well as aid the industry in streamlining the manual 

processes required to participate in Federal programs and crop insurance. 

These kinds of ag data benefits, however, require agriculture to get past roadblocks to data interoperability and over the ‘‘not-invented-here’’ syndrome. Farmers 

need a uniform standard that allows data to be portable and enables them and their 

trusted service providers to make real use of the information. 

The need for data interoperability is not a new issue. My written remarks contain 

an excerpt from the testimony of the late Neal Patterson, who spoke before the Senate Committee on Health, Education, Labor and Pensions in June of 2015.3 Neal 

was a personal friend and mentor of mine as well as co-founder and CEO for 

Cerner, a leading health information technology company. 

Neal believed, as I do, in the parallels between Electronic Health Records and 

Electronic Field Records. His testimony stated: ‘‘The intersection of healthcare and 

IT is one of the most important in modern society. Every citizen touches and depends on both.’’ 3

I absolutely believe the same is true for agriculture, everyone eats. Every farmer 

has a right to access and use their data, regardless of where it came from or what 

system contains it. We should encourage the flow of information that could help 

farmers—and their trusted advisors—to make better-informed decisions about their 

businesses and food production. 

In agriculture, sensor technology and communication protocols exist for data to 

move quickly across different systems; however, many existing companies are not 

interested in building tools that would allow standard data to move efficiently. At 

Farmobile, we build technology that supports interoperability; we are a neutral provider that enables farmers to compare ‘‘apples to apples’’ when looking at products 

and services offered to them. 

It is not by accident that big ag companies use their war chests of cash to hold 

farmer data hostage in their platform. They make it very labor intensive to move 

the data from one system to another. I believe in properly aligning incentive structures to drive standardization and financially benefit farmers—who are the creators 

of Electronic Field Records. The Electronic Field Record is a universal commodity 

in support of digitizing agriculture, and both farmers and consumers benefit. 

Farmobile is the first company to build a business model around the monetization 

of standardized farm data whereby farmers share in the revenue, and data buyers 

can drive further innovation as the consumers of this valuable information. This is 

a powerful new revenue opportunity—a true win-win for farmers and the industry. 

(Figure 1) 4

The idea of farmers harvesting their data and selling it as a new ‘‘crop’’ is a gamechanger. It adds economic strength to rural communities, and also contributes to 

food safety—which is in the national security conversation. 

14 

Real-time Data and the Impact on Commodity Markets 

After graduating with a Bachelor of Arts in Financial Economics from Gustavus 

Adolphus College in St. Peter, Minnesota, I spent the next 20 years trading agricultural commodities—the pure economic theory of supply and demand fascinated me 

and still does today. I first traded for the Pillsbury Company and then for a large 

regional player, The Scoular Company. When my career began, commodity trading 

was done ‘‘in the pits’’ using an open outcry system. There was an inherent time 

delay to disseminate pricing data—first from the pits in Chicago to the local grain 

buyers, then from the grain buyers to the farmers. This created an unfair advantage 

for those, who could afford to pay for the real time pricing feeds. For years, this 

opportunity was used to take advantage of additional margin—and the farmers paid 

the freight for decades. 

The last 10 years of my trading career were all about challenging the status quo 

in the commodity trading world and changing sides from being the buyer to helping 

the seller. My company helped farmers become better grain marketers by utilizing 

new technology, which enabled them to take advantage of real-time data feeds in 

their marketing plans. This opportunity was fueled by the Chicago Mercantile Exchange acquisition of the Chicago Board of Trade which rapidly accelerated the use 

of electronic trading and hedging. 

Today a similar opportunity exists to ramp up the creation of farm data into 

‘‘tradeable’’ information in the form of Electronic Field Records. To work, the data 

must be interoperable and available in real-time to those who desire to purchase 

it. This data liquidity will dramatically accelerate the foundational science to help 

solve the looming global food challenge and identify best practices, minimize environmental impact and maximize nutritional content of food being produced. Every 

time this information is ‘‘sold,’’ it is with the explicit permission of the farmer, and 

the farmer who created it shares in the revenue. The same digital information can 

be sold multiple times with an opportunity to create an estimated $1billion annually 

of new revenue returned to rural agricultural communities of America. 

Once you get something faster, you rarely go back. The commodity markets are 

no different. I will challenge anyone to debate the notion that real-time data, data 

ownership and interoperability would not be good for the farmer. 

The reason that there is so much volatility in the agricultural markets is because 

of the massive time lag in getting the information. The USDA is the gold standard 

in historical commodity information. However, this information is released three 

weeks after it is observed due to the process required to get that information in the 

right place. The delay causes much of the volatility given the fact that most row 

crops are alive about ninety days and it takes about 21 days to get the data from 

the county offices to the markets in the form of USDA reports. 

The technology exists today to get that information to the market daily. Faster 

information will dramatically reduce volatility in the markets and enable traditional 

risk management strategies, like hedging, to work again for farmers and the agricultural businesses that rely on these commodities to produce their products. 

Many large commercial grain trading companies have reported significant losses 

in the markets recently as traditional hedging practices are introducing more risk 

than they are reducing. Faster access to better information will help normalize markets and monetarily benefit the farmers who choose to sell licenses to their information. 

Conclusion 

I’d like to conclude by revisiting history. It is 1960 and John Fitzgerald Kennedy 

is running for president when he visits a group of farmers in Senator Thune’s home 

state of South Dakota and he says: ‘‘The farmer is the only man in our economy 

who has to buy everything at retail, sell everything he sells at wholesale, and pay 

the freight both ways.’’ 

It’s pretty incredible to think that—with all the change we’ve seen in the last 57 

years—this statement is, unfortunately, as true today as it was then. 

But disruption is coming and it’s coming in the form of technology. We need to 

make sure that our legal system keeps up with the technology available. Most farmers I talk with think we have probably two to five years to figure this out, or they 

will lose. 

Thank you for your time today. I hope my testimony sheds some light on what 

is happening in the industry and I look forward to continued conversations about 

the many ways we can help the farmer finally stop paying the freight both ways. 

I firmly believe that, done right, data is the answer. 

1. Farmers need policies that safeguard their data rights, and allow interoperability and accessibility to drive efficiencies and innovation in food production. 

15 

2. As you review this topic, ask yourselves: 

» Why is it ever ‘‘o.k.’’ for others to own or control a farmer’s data? 

» How do we affect policies for true data interoperability? 

I appreciate your openness to ideas and action from the private sector, as well 

as administrative and legislative change. I look forward to working with the industry, as well as members of the Committee, to advance this vision. 

Thank you. 

cc: Addendum 

ADDENDUM

Additional References 

1The Problem of Vendor Lock-In for Ag, http://bit.ly/2xHeie5 2Farmobile: Changing the Game in Ag Data, http://bit.ly/2oObquw 3Testimony of Neal L. Patterson, Co-founder, Chairman and CEO of Cerner Corporation, U.S. 

Senate Committee on Health, Education, Labor and Pensions, Hearing: Health Information Exchange: A Path Towards Improving the Quality and Value of Health Care for Patients, June 10, 

2015, http://bit.ly/2zLI1YB 4Farmobile’s Business Model (Figure 1) 

(Figure 1, Farmobile’s Business Model) 

Senator MORAN. Thank you, Jason. 

Dr. Ferrell. 

STATEMENT OF SHANNON L. FERRELL, J.D., M.S., ASSOCIATE 

PROFESSOR, OKLAHOMA STATE UNIVERSITY DEPARTMENT 

OF AGRICULTURAL ECONOMICS 

Dr. FERRELL. Subcommittee Chairman Moran, Ranking Member 

Blumenthal, and members of the Subcommittee, thank you for the 

opportunity to present my observations in the collection and utilization of data in agriculture, the opportunities and challenges that 

presents, and the legal issues surrounding agricultural data collection, transmission, and use. 


1114TATG1.eps
16 

The new frontier in agriculture presents a fascinating and sometimes paradoxical mix of cutting-edge technology, recent legal 

changes, and centuries-old common law. 

Farm equipment rolls off the assembly line with a suite of sensors and transmitters enabling it to share unprecedented amounts 

of farm-level or ‘‘Small Data,’’ that gives farmers the ability to 

proactively manage risks that heretofore they may have found unmanageable and, in some cases, even unknowable. 

At the same time, this perfusion of Small Data can now be aggregated by many means into what we call agricultural ‘‘Big Data.’’ 

Analysis of big data in agriculture holds many potential advantages for producers, who can apply Big Data insights to their individual operations, and at the same time, we now have the opportunity for better market analysis, as Mr. Tatge was just saying, 

and Mr. Knopf as well, and we can now manage agricultural risk 

at a national and potentially even global scale in a few years. 

Within the policy and academic realms, Big Data holds the potential for us to provide more timely responses to industry crises 

and have better evaluation of farm policy impacts and food programs. So Big Data may eventually be able to predict many crises 

before they even emerge. 

However, agricultural data faces a peculiar chicken-and-the-egg 

problem in that the development of datasets sufficiently large to 

take full advantage of all the opportunities ag data opposes requires participation by a large number of producers. At the same 

time, farmers are often reluctant to participate in those agricultural data systems if they’re concerned about the share of the value 

that they’re going to receive for the contribution of their data. 

Further, despite a little potential shown by agricultural data, the 

current technological, economic, and legal environment raises some 

issues about how the value of agricultural data will be shared between data aggregators and producers, as Mr. Tatge just mentioned, so producers receiving what they deem to be sufficient value 

is going to be a gateway issue for us having a critical mass of producers making data contributions to really truly understand the 

value that agricultural data could pose for our industry. 

So addressing the concerns of producers with respect to their 

rights and data, the value it creates, and their privacy if they 

choose to share their information is vital. Farmers often express 

concerns like this collectively under the question of, ‘‘Who owns 

their data?’’ And that may not be the question that has a clear answer in our current intellectual property framework, although, arguably, there is a colorable argument to be made that they do own 

their data. The question is, ‘‘What does that ownership actually 

mean?’’ 

So that question of ownership may not be as important as ensuring farmers always have access to their data once it has been 

shared, that they can receive value from its use, and they can feel 

comfortable with the level privacy or that they’re, conversely, comfortable with the lack of privacy that they’re going to experience as 

a result of sharing those agricultural data platforms. 

Well, agricultural technology making use of data grows at an exponential rate, but the technology and policies for protecting data 

has not. For example, opting out of data collection is going to grow 

17 

increasingly difficult as more and more of even the used equipment 

machinery fleet has embedded technologies that make data sharing 

something you must opt out of rather than opting into. And, indeed, many producers may not even know that they have an option 

of opting out of data sharing if they so choose. 

Further, in some circumstances, it may be almost impossible to 

truly anonymize data once it has been shared, because with the addition of some publicly available data, we could almost interpolate 

everything that you would want to know about that operation from 

what data has been shared, even though, ostensibly, it was supposed to be amalgamated with others and rendered anonymous. 

The resolution of these issues may depend on the relative bargaining power of those at the table when data use agreements are 

negotiated, and historically farmers have been at something of a 

disadvantage in that regard. However, significant steps are already 

underway to facilitate consensus among industry stakeholders regarding those issues, and you’ll hear about some of those steps 

today, and you already have heard some of those in passing from 

the previous witnesses. 

There are a number of ways I think the Subcommittee and Congress, as a whole, can facilitate the realization of agricultural 

data’s true potential. 

First, Congress can support continuing efforts to build industry 

consensus between farmers, equipment manufacturers, and data 

service providers. Whether through consensus or with legislation, 

Congress could also consider support of a clear framework of rightto-know issues with respect to how your data is being used, the 

right to opt out of data collection if you so choose, guidelines for 

the disclosure of agricultural data uses by service providers, and 

protections against the disclosure of data. It can also fund research 

and educational efforts to help agricultural producers make informed decisions about how to engage agricultural data systems 

and how to develop protections for agricultural data shared with 

service providers and the government. 

Finally, if the agricultural data revolution is to realize its true 

potential, sustained efforts to build and maintain a robust 

broadband Internet infrastructure for rural America must be sustained, as you mentioned, Chairman. The current support of rural 

broadband access from a supply perspective is having a positive impact, but we also need to support demand-side drivers for rural 

Internet access as well. Widely available wireless and hardwired 

broadband connectivity both are crucial to realizing the potential of 

agricultural data as well as maintaining the economic opportunities 

that can revitalize rural America. 

Chairman Moran, Ranking Member Blumenthal, and members of 

the Committee, thank you for the opportunity to share, and I look 

forward to helping you explore this issue further. 

[The prepared statement of Dr. Ferrell follows:] 

18 

PREPARED STATEMENT OF SHANNON L. FERRELL, J.D., M.S., ASSOCIATE PROFESSOR— 

OKLAHOMA STATE UNIVERSITY DEPT. OF AGRICULTURAL ECONOMICS; JOINTLY

PREPARED BY THE WITNESS AND DR. TERRY GRIFFIN, ASSISTANT PROFESSOR, 

KANSAS STATE UNIVERSITY DEPT. OF AGRICULTURAL ECONOMICS

Executive Summary 

Today’s technology affords farmers the ability to instantaneously collect data 

about almost every facet of their cropping (and increasingly, their livestock operations) year-round. As a result, there has been unprecedented growth in the amount 

of data collected at the farm level. This farm-level ‘‘Small Data’’ increasingly provides management insights to agricultural producers allowing them to manage more 

risk factors than ever before. At the same time, this profusion of Small Data can 

now be aggregated by many means to create agricultural ‘‘Big Data.’’ Analysis of Big 

Data in agriculture holds many potential advantages for producers and creates the 

opportunity for better macroeconomic analysis of farm policy tools, food programs, 

and management of agricultural risk at a national scale. 

The current technological, economic, and legal environments raise issues about 

how the value of agricultural data will be captured among the agricultural producers generating the data and the agricultural technology providers (ATPs) aggregating it. Producers receiving what they deem to be sufficient value for their data 

contributions is critical as a potential gateway issue for making those contributions; 

without large, robust participation in agricultural data systems, such systems will 

fail to reach their full potential. 

Thus, addressing the concerns of agricultural producers with respect to their 

rights in data, the value it creates, and their privacy if they choose to share their 

information is vital to see that the agricultural industry collectively maximizes the 

value of these data technologies. Farmers often express these concerns collectively 

as a concern about who ‘‘owns’’ their data, and there are no clear answers in the 

current intellectual property framework. However, the question of agricultural data 

ownership may not be as important as ensuring farmers always have access to their 

data can receive value from its use, and can feel comfortable with the level of privacy—or lack thereof—that can be afforded to those participating in Big Data platforms. 

Significant steps are already underway to facilitate consensus among industry 

stakeholders regarding these issues. This Committee and Congress as a whole may 

best be able to facilitate the realization of Big Data’s potential advantages to U.S. 

agriculture through support of this consensus effort, support of educational efforts 

to help agricultural producers make informed decisions about how to engage with 

Big Data systems, continued development of more robust protections for agricultural 

data shared with the government, and continued support of improved broadband access in rural areas. 

Acknowledgements 

Dr. Terry Griffin of Kansas State University’s Department of Agricultural Economics was instrumental in the preparation of this testimony, and his assistance 

in the creation of this document is gratefully acknowledged. Dr. John Fulton of Ohio 

State University’s Department of Food, Agricultural, and Biological Engineering, 

Ms. Maureen Kelly Moseman, Adjunct Professor of Law at the University of Nebraska College of Law, Mr. Todd Janzen of the Janzen Agricultural Law firm, Mr. 

Ryan Jenlink of the Harness, Dickey & Pierce law firm, Dr. Keith Coble of the Mississippi State University Department of Agricultural Economics, Dr. Ashok Mishra 

of the Arizona State University Morrison School of Agribusiness, and Mr. Matthew 

Steinert of Steinert Farms, LLC also provided vital input in the development of this 

testimony. 

Perhaps the greatest contribution to this testimony and my understanding of agricultural data systems, though, was made by Dr. Marvin Stone. Dr. Stone was a 

giant in the agricultural data field, contributing tremendously to the development 

of the Green Seeker technology that significantly advanced machine-sensing of plant 

health. He was also instrumental in the development of the SAE J1939 standard 

that forms the foundation for many of the machine data technologies at the heart 

of this discussion. Beyond being a giant in the field we examine here today, Dr. 

Stone was a mentor to myself and hundreds of other students at Oklahoma State 

University. He and his wife were both killed in the tragic Oklahoma State University homecoming parade accident of October 24, 2015. I hope this testimony honors 

his memory, the contributions he made to this field, to the U.S. agriculture industry, 

and to all his students. 

19 

1T. Griffin, et al., ‘‘Big Data Considerations for Rural Property Professionals.’’ JOURNAL OF

THE AMERICAN SOCIETY OF FARM MANAGERS AND RURAL APPRAISERS, 2016:167, 168. 

Issue Analysis 

1. Introduction 

I would like to thank Subcommittee Chairman Moran, Ranking Member 

Blumenthal, and the Members of the Committee for the opportunity to present my 

observations on the collection and utilization of data in agriculture and the legal 

issues surrounding the concept of Big Data and its application to U.S. farmers and 

ranchers. This new frontier in agriculture presents a fascinating and sometimes 

paradoxical mix of cutting edge technology, recent legal changes, and centuries-old 

doctrines of common law. In my testimony today, I will discuss how both ‘‘Small 

Data’’ and ‘‘Big Data’’ in agriculture are being utilized by agricultural producers and 

what lies just over the horizon for those technologies. I will also discuss some of the 

opportunities and challenges posed by the advancements in agricultural data technology. Then, I lay a framework for discussing the legal issues surrounding Big 

Data in agriculture, discuss how the current U.S. legal environment addresses ownership and privacy rights in agricultural data, and suggest some potential avenues 

for policy responses that may facilitate the economic advantages to be gained from 

the application of Big Data principles to agricultural data while dealing with the 

concerns associated with such applications. 

2. The growth of Small Data and Big Data in production agriculture 

The concept of Big Data has exploded in a relatively short period of time. However, there would be no Big Data in agriculture were it not for Small Data. Since 

these definitions and the issues surrounding data use in agriculture continue to 

evolve, my testimony today will provide some framing for both. 

2.1 Defining core terms in the Data-Driven Farming discussion 

Three terms immediately rise to the top in an examination of the agricultural 

data discussion: agricultural data, Small Data, and Big Data. Taken together, the 

use of Small Data and Big Data in agriculture is increasingly referred to as ‘‘digital 

agriculture.’’ 

The concept of agricultural data is almost too broad to define, but looking at research in the field and conversations surrounding agricultural data indicates the 

term centers around two more specific concepts: ‘‘telematics’’ or ‘‘machine’’ data and 

‘‘agronomic’’ data. Telematics data (sometimes called ‘‘machine data’’) refers to the 

information an agricultural implement (such as a planter) or self-propelled vehicle 

(such as a tractor or combine) collects about itself. Almost by definition, telematics 

data comes from agricultural equipment owned, operated, or hired under contract 

by the agricultural producer. Agronomic data refers to information about a crop or 

its environment, such as ‘‘as-planted’’ information from a seed planter, ‘‘as-applied’’ 

information from a fertilizer sprayer, yield data from a grain combine, and so on. 

While agronomic data resembles telematics data in that much of it is gleaned directly from agricultural implements, agronomic data can also be obtained from 

many other sources such as hand-held sensors, aerial platforms such as manned 

survey flights or flights by unmanned aerial systems (UAS, commonly called 

‘‘drones’’), and even satellite imagery. 

Another piece of the agricultural data puzzle is so-called ‘‘metadata,’’ which includes management information such as seeding depth, seed placement, cultivar, 

machinery diagnostics, time and motion, dates of tillage, planting, scouting, spraying, and input application. In addition to data on the products and how those products are applied, information on external environmental circumstances such as 

weather including precipitation events, evapotranspiration, and heat unit accumulation help to round out the complete agricultural data package.1

Beyond these data sources, numerous other data sources continue to emerge in 

the agricultural data space. Work continues to build data collection technology in 

the livestock industries, ranging from GPS-enabled cattle ear tags to ‘‘bolus’’ sensors 

that can be swallowed by animals to provide health data. Some would argue that 

vendor-generated data about producers might also fit into this category; such data 

could include everything from payment history data to customer relationship management (CRM) information (does the producer try to negotiate input prices, have 

preferences for some products over others, typically buy inputs from one salesperson 

versus others, etc.). 

Agricultural data is the foundation for Small Data systems. In simplest terms, 

farms use ‘‘Small Data’’ when data are isolated to the fields where the data originated. Farmers who use information technology to conduct their own on-farm exVerDate Nov 24 2008 08:08 Aug 05, 2019 Jkt 000000 PO 00000 Frm 00023 Fmt 6633 Sfmt 6621 S:\GPO\DOCS\37228.TXT JACKIE
20 

2K. Coble, T. Griffin, A. Misrha, and S. Ferrell, ‘‘Big Data in Agriculture: A Challenge for 

the Future,’’ forthcoming in APPLIED ECONOMICS AND POLICY PERSPECTIVES (accepted for publication October 20, 2017). 3T. Griffin, Miller, N.J., Bergtold, J., Shanoyan, A., Sharda, A., and Ciampitti, I.A. 2017. 

Farm’s Sequence of Adoption of Information-Intensive Precision Agricultural Technology. AP- PLIED ENGINEERING IN AGRICULTURE 33(4):521–527 DOI: 10.13031/AEA.12228. 4 B. Erickson, and D. Widmar. 2015. Precision Agricultural Services Dealership Survey Results. 

West Lafayette, Indiana, Purdue University, August. Accessed June 21, 2016: http://agribusiness.purdue.edu/files/resources/2015-crop-life-purdue-precision-dealer-survey.pdf 5Griffin, supra note 1. 6L. Arthur. 2013. What is big data? FORBES, CMO Network blog entry. Available at http:// 

www.forbes.com/sites/lisaarthur/2013/08/15/what-is-big-data/, last accessed November 15, 

2014. 

periments, document yield penalties from poor drainage, or negotiate crop share 

agreements are using data that is considered ‘‘small.’’ 

Perhaps ironically, the evolution and revolution in agricultural Big Data comes 

from the expansion of ‘‘Small Data’’ in agriculture.2 There has been remarkable 

growth in producers’ ability to collect data pertaining only to their own operation 

through the growth of techniques and technologies such as grid soil sampling, 

telematics systems for farm equipment, Global Positioning Systems (GPS)/Global 

Navigation Satellite Systems (GNSS), farm aerial imagery acquired via small unmanned aerial systems (sUAS) and the like. Producer adoption of these information 

technologies has increased dramatically in recent years,3 giving rise to a profusion 

of agricultural data heretofore unseen.4

The new abundance of field-level information provided by these technologies could 

improve the ability of producers to make profit-maximizing decisions benefitting the 

producer operating the field, i.e. Small Data.5 However, pooling the datasets of hundreds or thousands of fields could hold a much greater potential value both to individual producers and the agricultural industry as a whole. Agricultural Big Data— 

farm data that has been combined into an aggregate form—has the potential to reveal undiscovered insights. Currently, only limited quantitative evidence exists regarding the value of assembling data from precision agriculture technology into a 

community; however, indirect evidence suggests farm data has economic value. 

While the term Big Data is relatively new, it refers to a concept that is not. There 

are many definitions for the term, but a straight-forward one might be ‘‘a collection 

of data from traditional and digital sources inside and outside your company that 

represents a source for ongoing discovery and analysis.’’ 6 While this definition 

sounds much like traditional data analysis (and it is), recent advances in both data 

collection and transmission increase the analytical power of data analysis procedures by orders of magnitude. The ‘‘big’’ in Big Data comes from the fact data sets 

continue to grow exponentially both in breadth (with more and more firms collecting 

data) and depth (with data from more and more sources long the food supply chain 

being aggregated by more firms). Conceptually, Big Data is defined as the analysis 

of datasets requiring advanced tools to manage the data due to four factors: volume, 

velocity, variety, and veracity. 

Table 1: Big Data defining factors 

Factor Definition 

Volume The sheer amount of data precludes its storage on a single computer system; 

analytic software must aggregate the data from multiple systems 

Velocity New data enters the analysis continuously at high rates of transmission. 

Variety Data is aggregated from a variety of sources, many of which may use different 

data formats. 

Veracity Accuracy of the data is vital to correct analysis, while the data source may apply 

varying (or no) methods of data validation. Thus the Big Data system may 

have to independently validate the data or make assumptions about its accuracy. 

Agricultural data has arguably already crossed over into the realm of Big Data 

as measured by these factors. 

Existing technologies can already generate over 10 MB of data per acre, and when 

extrapolated over the 90 million acres of corn ground in the U.S., this means 900 

terabytes (TB, 1 TB being equal to 1,000,000 MB) of data could be generated on corn 

21 

7T. Griffin, ‘‘Can Agricultural or Farm Data Be Considered Big Data?’’ Kansas State University, https://www.agmanager.info/machinery/precision-agriculture/precision-ag-farm-data-blog 

/can%C2%A0agricultural%C2%A0or%C2%A0farm%C2%A0data%C2%A0be (last visited November 8, 2017). 8M. Brookhart and M. Reese. ‘‘World Record for Data Collection Set by OSU Precision Ag 

Team.’’ Ohio Country Journal, October 11, 2017, http://ocj.com/2017/10/world-record-for-datacollection-set-by-osu-precision-ag-team/#.Wd45GMwR0Qo.twitter (last visited November 8, 2017). 9Grifin, supra note 7. 10 Id. 11 Id. 12 Id. 13See, e.g., M. Darr, ‘‘Big Data and Big Opportunities,’’ paper presented at PrecisionAg Big 

Data Conference, August 21, 2014 (Ames, Iowa). 

acres alone.7 A student at Ohio State University recently completed the ‘‘Terra 

Byte’’ project to determine how much data could be garnered from one corn plant, 

with a resulting 18.4 gigabytes) of data; over a 100 acre corn field, this would be 

the equivalent of 60 petabytes (PB, 1 PB being equal to 1,000,000,000 MB).8 Already, the commodity dataset has grown too large to be transported via broadband 

connections, or even physically via external hard drives, meaning analytical software must go to the data 9 Thus, the volume requirement for Big Data is satisfied. 

Looking only at as-planted data collected from planters via telematics, 5.5 MB of 

data on location, speed, cultivar, and other geo-spatial and meta-data are collected 

for each acre planted. During planting seasons, the size of the aggregated farm data 

community becomes much larger every day. Although agricultural operations are 

seasonal, it should be recognized that even for commodity crops like corn, cotton, 

soybean, rice, and wheat that peak planting times differ for each such that as-planted data are collected during several months of the year rather than all at once. In 

addition to planting, other field operations such as tillage, spray applications, and 

harvest occur at other times during the season; each operation adding to the community of data. Thus, Griffin observes, planting data alone would satisfy the ‘‘velocity’’ component of Big Data.10 By the same token, each of these data points are 

being collected by different brands of equipment using different file formats and 

supplemented using manually-collected data such as soil samples, all of which may 

be reported in non-standard formats, satisfying the ‘‘variety component.’’ 11

That leaves the ‘‘veracity’’ component and agricultural data can certainly pose veracity challenges. Such challenges arise from the problems inherent in trying to 

measure biological processes by mechanical means. Data quality has been a contentious topic in precision agriculture for decades; especially regarding raw yield monitor data and other farm data collected by on-the-go sensors. A part of the debate 

on the veracity of yield data involves whether the farmer or combine operator properly calibrates the yield monitor. Therefore, both sensors and human error influence 

farm data quality. Given this, agricultural data appears to more than satisfy the 

Big Data test.12

Although not as prominent to the discussion as Big Data and agricultural data, 

another important term to define is service provider. Service provider (sometimes 

called an ‘‘Agricultural Technology Provider’’ or ‘‘ATP’’) is the term frequently used 

to describe a party external to the farm providing some service regarding either crop 

production or management of the crop enterprise. Crop production services could include fertilizer or chemical applicators, custom operators, or harvest contractors 

whose equipment generate agricultural data regarding the farm. Management services include traditional services such as crop consulting and scouting, but increasingly include services targeted specifically at data collection and analysis. 

2.2 Opportunities and Challenges arising from Small and Big Data use in 

Agriculture 

It is important to note this discussion would not occur were it not for the tremendous potential the nascent farm data revolution promises. Existing technologies 

such as real-time kinematics (RTK) and ‘‘auto-steer’’ (sometimes referred to as 

‘‘GNSS-enabled navigation technology) have already provided substantial economic 

returns to farmers.13 Improved sensing of soil conditions, crop health, and yields has 

led to significantly improved management information for agricultural producers. As 

mentioned above, this represents Small Data, with data generated—and decisions 

made—at the farm level. 

To date, much of the gains from improved sensing technologies and their sharing 

with service providers have come from eliminating inefficiencies in the utilization 

of agronomic and machinery inputs. Put another way, we have seen significant increases in the use of Small Data. 

22 

14 Note that secondary uses of data will be discussed later in this testimony. 15T. Griffin., and Taylor, M.R. (2015). Precision Agriculture Data Impact on Farmland Values: 

Big Data in Ag. K-State Department of Agricultural Economics AgManagerInfo AM–TWG– 

PRAG–4.2015 Online: http://www.agmanager.info/crops/prodecon/precision/PrecisionAgData _FarmlandValues.pdf., last visited November 8, 2017. 16T. Griffin, ‘‘Farmers’ Use of Yield Monitors,’’ University of Arkansas Fact Sheet FSA36, 

available at https://www.uaex.edu/publications/pdf/FSA-36.pdf (last visited November 8, 

2017). 17Table 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). 

Small Data sees a variety of farm-level uses. Data kept isolated to the originating 

farm has value, but the value of that data is limited to just that farm or potentially 

to farms in relative proximity. The primary uses of farm data are those for which 

the data were initially generated such as documenting within-field site-specific 

yields with a yield monitor.14 Typically, primary uses of data are restricted to the 

field that the data originated; consider the analogy of using a computer when that 

computer is not connected to the Internet. Primary data uses are ‘‘local’’ to the field 

or operation from which they originate and are not connected to data from other 

areas. Considerable effort has been made by farmers, researchers, and others from within and external to the agricultural industry to profitably utilize data generated from 

precision agricultural technologies. The majority of these efforts have historically focused on one-field-at-a-time or maybe even at the whole farm level but for only that 

one farm. The value of farm data when isolated to a specific farm has been limited 

and only of value to that particular farm (or some value for the next farmer of the 

land). At the very least, the value of that data decays very quickly with distance 

from the field. 

Indeed, it is possible that the site-specific value of farm data might actually play 

a role in farmland values themselves. Griffin and Taylor 15 explored how big data 

could impact farmland values and rental rates, stating ‘‘It remains unclear whether 

the ‘data premium’ [for farmland conveyed with a significant farm-specific dataset] 

will be a true premium (an amount added to the market price of land) or a penalty 

(an amount deducted from the market price of land). In the short-run, early movers 

who choose to provide data to land buyers may see a premium. However, as the 

transfer of data with a land sale becomes more common, a penalty to land parcels 

without data may become more common.’’ They also describe how biophysical data, 

such as historical yield, soil test results, and other production data have been included in farmland sales and/or rental agreements, but they suggest these data have 

not substantially influenced farmland values nor are sufficient to be considered 

‘‘big.’’ These historical data could be annual whole-field yield written on paper or 

site-specific geospatial data including GPS yield monitor data or grid soil samples 

in either electronic form or printed maps. Although the above mentioned data may 

provide evidence of historical productivity and soil amendment utilization, they do 

not impact farmland values directly. Farmland values and rental rates will likely 

be a function of both quantity and quality of geospatial metadata once the big data 

sector of the agriculture industry matures. 

Farmers have made use of precision agriculture technology and farm data in a 

variety of ways, and oftentimes in ways that the manufacturers had not anticipated. 

An early report on how farmers used yield monitors indicated the primary uses of 

yield data include but not limited to: 1) conduct on-farm experiments, 2) tile drainage decisions, and 3) split crop share rents.16 To estimate the value of farm data 

for each of these examples, the alternative decision making process must be evaluated. However, back of the napkin extreme examples make the point that the value 

of the above scenarios are finite and limited to a single farm. 

Perhaps the most dramatic gains lie ahead, though, as agriculture puts the ‘‘Big’’ 

in Big Data by compiling datasets of sufficient size to enable much more robust statistical analyses of multiple factors influencing commodity production. Examples of 

how the aggregation of farm data across large datasets can significantly increase 

value to farmers are illustrated in Table 2 below.17

Table 2: Comparison of Primary and Secondary Agricultural Data Uses 

Data Primary Use ‘‘Small Data’’ Secondary Use ‘‘Big Data’’ 

Yield monitor 

data 

Documenting yields; onfarm seed trials Genetic, environmental, management effect 

(G x E x M) analyses 

23 

18 V. Mayer-Scho ̈nberger, and K. Cukier, Big Data: A Revolution That Will Transform How 

We Live, Work, and Think, Kindle Edition. Houghton Mifflin Harcourt Publishing Company, 

New York, NY. 257 pp. 2014. 19This discussion of agricultural data and competitive issues is taken from Griffin, et al., 

supra note 1. 20 J. Manyika, Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. 

(2011). ‘‘Big data: The next frontier for innovation, competition, and productivity.’’ McKinsey 

Global Group report, available at https://www.mckinsey.com/business-functions/digitalmckinsey/our-insights/big-data-the-next-frontier-for-innovation, last visited November 8, 2017). 21S. Sonka. (2014). Big Data and the Ag Sector: More than Lots of Numbers. International 

Food and Agribusiness Management Review, 17(1), 1–20. Available online at http:// 

www.ifama.org/files/IFAMR/Vol%2017/Issue%201/(1)%2020130114.pdf, last visited November 

8, 2017. 

Table 2: Comparison of Primary and Secondary Agricultural Data Uses—Continued 

Data Primary Use ‘‘Small Data’’ Secondary Use ‘‘Big Data’’ 

Soil sample 

data 

Fertilizer decisions Regional environmental compliance 

Scouting Spray decisions Regional analytics of pest patterns 

As an example of initial or primary use of farm data, yield monitor data on one 

farm can help document the farm’s productivity on a field-by-field basis and can illustrate how a seed hybrid performed on that farm in one year, given the environment of that farm for that year and the management practices employed during 

that year. Interesting opportunities arise when that data is ‘‘re-used’’ in Big Data 

aggregation with similar data across hundreds or even thousands of farms, and this 

aggregation creates the bridge linking Small and Big Data. 

Such aggregation allows for the evaluation of that cultivar across tens of thousands of permutations of factors such as management practices, soil type, and climate. This enables both seed companies and agricultural producers to learn via observational data in one or two years what would take decades of collections by use 

of traditional seed trials via experimentation. Soil sample data coupled with yield 

data can inform an agricultural producer about the nutrient uptake of the crop on 

his or her farm, but Big Data could allow all the agricultural producers in a region 

to effectively tackle nutrient loading to impaired water bodies through voluntary 

management of non-point pollution. Crop scouting can help an individual agricultural producer make decisions about the application of a specific pesticide, but Big 

Data could allow a crop industry to spot trends in plant pathogens that could be 

used to head off the spread of potentially devastating plant health threats. The true 

maturity of Big Data in agriculture may come when the value of secondary uses realize greater aggregate economic value than the primary uses of the data.18

The integration of Small Data and Big Data at the farm level could hold important implications for farms competitiveness.19 Early adopters of big data in other 

industries (such as healthcare, transportation, and retail) are shown to have gained 

a competitive advantage within their industries and have realized significant increases in operating margins.20 There is an emerging discussion in the agribusiness 

industry and its literature about the potential of big data and its capacity to change 

the basis of competition in agriculture.21 This belief is based on the previous trends 

in the history of innovations powering productivity and enhancing competitiveness 

in the agri-food supply chain, enabled by information and communication technology 

(ICT). Among such examples is precision agriculture powered by GPS, remote sensing, and variable rate technology (VRT) technologies in crop farming. While the 

adopters of ICT-based applications in agricultural production were primarily motivated by the efficiency gains, they also have laid the foundation for the big data 

infrastructure within agriculture. As a result, modern farms are generating, or have 

a capacity to generate, a substantial amount of agricultural production data. This 

data becomes an important intangible resource alongside the physical and human 

resources, which if managed effectively, can produce substantial value for the farming operation. The important question to ask is under which circumstances the data, 

as an intangible resource, can become a source of competitive advantage? 

Beyond the benefits of Big Data to production agriculture, it also presents the agricultural economics community with numerous opportunities to enhance and expand the analysis of numerous microeconomic, macroeconomic, and agricultural polVerDate Nov 24 2008 08:08 Aug 05, 2019 Jkt 000000 PO 00000 Frm 00027 Fmt 6633 Sfmt 6621 S:\GPO\DOCS\37228.TXT JACKIE
24 

22The following examples are taken from K. Coble, T. Griffin, A. Misrha, and S. Ferrell, ‘‘Big 

Data in Agriculture: A Challenge for the Future,’’ forthcoming in APPLIED ECONOMICS AND POLICY PERSPECTIVES (accepted for publication October 20, 2017). 

icy issues.22 For example, microeconomic farm management issues could now be 

analyzed by aggregating data across thousands of farms using management decisions as variables instead of using a farm-by-farm case study approach. Food program evaluations, regulatory impact analysis, and demand estimation could be accomplished by rapid aggregation and analysis of grocery store UPC scanner data. 

Geospatial analysis of crop yields could lead to improved precision in the pricing of 

crop insurance products. Broad environmental sensor networks coupled with farm 

data could significantly enhance the ability to manage crop fertilizer applications to 

minimize nutrient runoff impacts. 

To understand the potential policy implications of Big Data’s growth in agriculture, one must recall that one of the defining characteristics of agricultural Big 

Data is combining data from multiple farms into a community. A leading reason for 

this is that each farmer becomes a variable (rather than a constant) once a critical 

mass of farms is in the community. When farm data were isolated to a single farm, 

then there was no opportunity to evaluate the management practices specific to that 

farmer, i.e. the management was held constant. 

Farm data must be aggregated to perform community analysis. A leading example 

of community analysis is evaluating how a product (G for ‘‘genetics,’’ from classic 

varietal or hybrid tests) in a given location (E for ‘‘environment,’’ including soils, 

weather, and other uncontrolled factors) under the farm’s production practices (M 

for ‘‘management,’’ including controlled factors such as planting dates, seeding rates, 

timing of operations, tillage practices and many others). When farm data are not 

aggregated across numerous farms, then the data remain ‘small’ and the value is 

limited since the M in analysis known as GxExM, is not a viable variable (only the 

traditional GxE). When data are aggregated such that M is a variable to the analysis GxExM, insights can be discovered for a majority of participants. Examples of 

previously unknown discoveries may include which products or bundle of products 

(seed, fungicides, planting dates) maximize profitability for a given region under 

specific farm production practices. 

Each player (and each group of players) benefit differently with respect to the big 

data system. One must consider how these different players benefit to comprehend 

how the value of Big Data systems may be captured relative to the data contributors 

(farmers) and aggregators (ATPs). The economics of networks are important to fully 

understand the value gained from the big data community. The value of the data 

community depends not only on the quality of the data but on how many others participate in the system. Data from numerous farms aggregated into a community are 

more valuable than data from any one individual farm. In the long run, the 

aggregator controlling the flow of data enjoys the majority of the value. Other 

groups, such as those offering analytic services of the aggregated data, enjoy their 

value capture especially in the short run. Once a critical mass of farms are in the 

data community, i.e. the long run, farmers’ bargaining power with the data 

aggregator likely will be greatly reduced. 

In the long-run the majority of the value will be enjoyed by the one controlling 

the data community, i.e. the data service provider. Other players such as input manufacturers, retailers, and advisors may enjoy their own levels of varying value capture. The important part to be cognizant is that 1) the farmer is not the only player 

at the big data table and 2) the farmer is not likely to receive the vast majority 

of the value from participating in the big data system. However, that is not to say 

that farmers will not still see potentially important benefits from the analyses provided by Big Data systems. Such systems pose the opportunity of providing potentially unprecedented insights to inform farm management decisions, decreasing production risk, and potentially reducing financial and market risks as well. 

While there are countless potentially positive uses of Big Data tools, any tool can 

also be misused. Farmers, ranchers, and other participants in the agricultural industry have expressed concerns about several potential misuses of agricultural data 

beyond the mere disclosure of confidential information (discussed below). Some producers worry that the ability of equipment manufacturers to access a significant 

amount of data about their operations, giving the manufacturers the ability to interpolate the farmer’s financial condition and use such information to an unfair advantage in transactions with the farmer or to alter the balance of negotiating power 

in the manufacturer’s favor for any number of transactions. Others worry about government agencies taking advantage of aggregated datasets to acquire information 

that the producer could not be compelled to produce without a formal legal process. 

Yet another concern is that falsified data could be introduced into individual or agVerDate Nov 24 2008 08:08 Aug 05, 2019 Jkt 000000 PO 00000 Frm 00028 Fmt 6633 Sfmt 6621 S:\GPO\DOCS\37228.TXT JACKIE
25 

23Supra, note 4. 24See generally GEORGE G. JUDGE, ET AL, INTRODUCTION TO THE THEORY AND PRACTICE OF

ECONOMETRICS (2nd ed, 1988), 96. 25T. Griffin and A. Shanoyan, ‘‘Is Going Off the Grid Possible in the Age of Farm Data?’’ Kansas State University, https://www.agmanager.info/machinery/precision-agriculture/precisionag-farm-data-blog/going-grid-possible-age-farm-data (last visited November 7, 2017). 

gregated agricultural datasets to skew environmental assessments of farm performance. 

One additional Big Data challenge worries both producers and economists. As 

stated in Coble, et. al: 23

The Holy Grail for market participants is to get perfect information as soon as 

it is knowable, and preferably before it is knowable to others. While Big Data 

has a long, long way to go before achieving this, bigger steps toward that goal 

are being taken faster than ever before. Thus, a significant concern with aggregating agricultural data is whether—either legitimately or not—a small number 

of market participants (or a single actor) could get access to information sufficient to move (or even manipulate) markets faster than, or to the exclusion of, 

other market participants. While there are numerous rules in place to deal with 

a broad range of market-manipulating activities, none of these current rules 

contemplate the type of actions that could take place with a sufficiently large 

aggregated dataset. Currently, there are various rules restricting insider trading (see 17 C.F.R. § 1.59(a), 17 C.F.R. § 1.3(ee)), and government employees are 

prohibited from using data for financial gain that has not been disseminated to 

the public (7 U.S.C. § 6c(a)(3)). However, there are no rules governing ‘‘very 

good market information’’ such as that which could be obtained through completely legal means by aggregating sufficient telematics data (as an example). 

As a result, research on the potential market effects of growing market 

asymmetries that could be triggered by growing Big Data aggregations and the 

implications of policies restricting the use of aggregated data in commodity market transactions could do much to inform the development of law in the arena. 

Only time and experience will tell whether these concerns are well-founded, but 

the fact they exist may well impact producers’ willingness to participate in Big Data 

systems, and thus impact the future of the industry. Most industry observers believe 

the benefits to individual producers and the agricultural industry as a whole far 

outweigh the potential risks. However, bringing about the full economic benefits of 

Big Data in agriculture requires a robust system by which large numbers of agricultural producers can share their data since the predictive power of statistical analysis increases with the number of observations available for each variable examined.24 The agricultural data industry is working tirelessly to create those systems. 

The issue is one of trust—farmers must feel they can trust Big Data systems before 

they will participate. Thus, the issue of most concern to this hearing may not be 

whether we will have systems that can accept and analyze that data; it is perhaps 

how Congress can facilitate the development of an environment in which farmers 

will share their data. Metcalfe’s Law states that the value of a network is proportionate to the number of its members. Put another way, Facebook has little value 

if you are its only member, but it has tremendous value when populated by millions 

of members. Thus, agricultural producers can only harness the value of Big Data 

if we can foster an environment in which they are comfortable sharing their data. 

However, that participation might be inevitable given the increasing prevalence of 

data-collection technologies. As Griffin and Shanoyan observe, going ‘‘off the grid’’ 

with respect to agricultural data may be possible in the near term, but eventually 

will require farmers to use then-antiquated technology, placing them at further competitive disadvantage.25

Given this potential inevitability of data sharing, one must turn to questions of 

what rights farmers can retain in their shared data. Do they retain ownership of 

their information? Is there any hope of retaining their privacy in that information 

once it is shared? 

2.3 Framing the legal issues surrounding data in agriculture 

The issues involved in the discussion of data in agriculture are almost innumerable, but many can be brought under the umbrella of two over-arching concepts: agricultural data ownership, and protections against its unauthorized disclosure. Although each of these issues is discussed in greater detail later in this testimony, 

a brief framing of each issue is provided here. 

26 

26L. Smith. 2006. ‘‘RFID and other embedded technologies: who owns the data?’’ SANTA CLARA

COMPUTER AND HIGH TECHNOLOGY LAW JOURNAL 27 R. Peterson. 2013. ‘‘Can data governance address the conundrum of who owns data?’’ 

Educause blog, http://www.educause.edu/blogs/rodney/can-data-governance-address-conundrum-who-owns-data, last accessed November 8, 2017. 28 U.S. Constitution, Article I, § 8, clause 8. 29The 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 

2.3.1. Ownership of agricultural data 

As agricultural producers began to realize the information they were generating 

(and, in some cases, sharing with service providers) had potential economic value, 

questions began to arise regarding who had the superior ‘‘ownership’’ right to that 

information, given that multiple parties had a hand in its creation. Further, the realization of agricultural data’s value changed the relative negotiation power between 

parties. This is an important concept; if their data is shared by someone other than 

them with a third party, that sharing may cause the farmer to lose negotiation 

power with vendors, landlords, and the like as a result. Thus, farmers may wish 

to assert ‘‘ownership’’ of data so as to exercise one of the rights of property ownership, namely, to exclude others from its use. Thus, this issue might be framed as 

‘‘Who owns data generated about an agricultural producer’s operation?’’ 

2.3.2. Privacy rights for agricultural data 

As discussed in more detail below, it is possible—and even likely –the greatest 

economic value of agricultural data to the farm owner comes not from his or her 

own analysis of the data but from its aggregation with data from hundreds or even 

thousands of other farms (in a true Big Data model) to provide management information and trend identification that could not be derived from any smaller dataset. 

For example, one of the most common analyses provided by ATPs to farmers are 

‘‘comparative analytics’’ (for example, benchmarking performance relative to similarly-situated operations). While that might have some economic value for the producer, much greater benefits await via advanced analysis. The balance of negotiating power between the farmer and the aggregator will eventually determine what 

proportion of the analyses conducted benefit each party. While aggregation may in 

some ways reduce the disclosure or discovery of information about any one farm 

(through the anonymization of individual farm data by aggregation with many other 

farms), it naturally also raises fears about the release of that information (whether 

the result of intentional activity such as database hacking or an accidental disclosure). This leads to the second question: ‘‘What protections prevent the disclosure of 

agricultural data to outside parties?’’ 

3. Current Legal Framework for Ownership of Agricultural Data 

The United States has one of the most robust systems of property rights in the 

world, empowered by a legal system making it easy (relatively speaking) to enforce 

those rights. Thus, the first place many look for a means of protecting one’s data 

from misappropriation and/or misuse is the property right system. This requires one 

to examine who ‘‘owns’’ agricultural data. The answer to the question is not simple, 

though, as traditional notions of property ownership find challenge in their application to pure information. 

The notion of property ownership typically involves some form of six interests, including the right to possess (occupy or hold), use (interact with, alter, or manipulate), enjoy (in this context, profit from), exclude others from, transfer, and consume 

or destroy. Some of these interests do not fit, or at least do not fit well, with data 

ownership. Excluding others from data, for example, is difficult, particularly when 

it is possible for many people to ‘‘possess’’ the property without diminishing its value 

to the others, just as the value of a book to one person may not be diminished by 

the fact other people own the same book.26 Thus, the better question may be ‘‘What 

are the rights and responsibilities of the parties in a data disclosure relationship 

with respect to that data?’’ 27

Data is difficult to define as a form of property, but it most closely resembles intellectual property. As a result, the intellectual property framework serves as a useful starting point to define what rights a farmer might have to their agricultural 

data. Intellectual property can be divided into four categories: (1) trademark, (2) 

patent, (3) copyright, and (4) trade secret. The first three areas compose the realm 

of Federal intellectual property law as they are defined by the Constitution as areas 

in which Congress has legislative authority.28 Since trademark is not relevant to a 

discussion about data,29 the analysis will focus on patent, copyright, and trade secret. 

27 

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. 30 35 U.S.C. §§ 102, 103. 31 17 U.S.C. § 102(a). 32 499 U.S. 340 (1991). 33See id. 

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’’).30 Patent serves as a poor fit for a model 

of agricultural data ownership since it protects ‘‘inventions.’’ Raw data, such as agricultural data, would not satisfy the definition of invention. 

It should be noted patentable inventions could be derived from the analysis of agricultural data. While this does not mean the data itself is patentable, it does suggest that any agreement governing the disclosure of agricultural data by the agricultural producer should address who holds the rights to inventions so derived. 

3.2 Application of copyright law to agricultural data 

The Federal Copyright Act states the following: 

Copyright protection subsists, in accordance with this title, in original works of 

authorship fixed in any tangible medium of expression, now known or later developed, from which they can be perceived, reproduced, or otherwise communicated, either directly or with the aid of a machine or device. Works of authorship include 

the following categories: 

literary works; 

musical works, including any accompanying words; 

dramatic works, including any accompanying music; 

pantomimes and choreographic works; 

pictorial, graphic, and sculptural works; 

motion pictures and other audiovisual works; 

sound recordings; and 

architectural works.31

More so than trademark and patent, the copyright model at least resembles a 

model applicable to agricultural data. At the same time, however, the model also 

has numerous problems in addressing agricultural data. First, the list of ‘‘works of 

authorship’’ provided in the statute strongly suggests a creative component is important to the copyrightable material. Second, the term ‘‘original works of authorship’’ 

also has been interpreted to require some element of creative input by the author 

of the copyrighted material. This requirement was highlighted in the case of Fiest 

Publications Inc. v. Rural Telephone Service Company,32 where the U.S. Supreme 

Court held the Copyright Act does not protect individual facts. In Fiest, the question 

was whether a pure telephone directory (consisting solely of a list of telephone numbers, organized alphabetically by the holder’s last name) was copyrightable. Since 

the directory consisted solely of pure data and was organized in the only practical 

way to organize such data, the Supreme Court held the work did not satisfy the 

creative requirements of the Copyright Act.33 This ruling affirmed the principle that 

raw facts and data, in and of themselves, are not copyrightable. Put another way, 

the fact that ABC Plumbing’s telephone number is 555–1234 is not copyrightable. 

However, an author can add creative components to facts and data such as illustrations, commentary, or alternative organization systems and can copyright the creative components even if they cannot copyright the underlying facts and data. Continuing the analogy, ABC’s phone number alone is not copyrightable, but a Yellow 

Pages® ad with ABC Plumbing’s number accompanied by a logo and a description 

of the company’s services would be copyrightable. 

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, 

28 

34See generally M. Leaffer, UNDERSTANDING COPYRIGHT LAW, 109–110 (5th ed. 2011). 35 17 U.S.C. §§ 512, 1201–1205, 1301–1332; 28 U.S.C. § 4001 36See Deere & Company, ‘‘Long Comment Regarding a Proposed Exemption Under 17 U.S.C. 

1201’’ (2015). Available at http://copyright.gov/1201/2015/comments-032715/class%2022/ 

John_Deere_Class22_1201_2014.pdf (last visited November 8, 2017). Compare K. Weins, WIRED

(Business Blog Section, online edition) (editorial) ‘‘We Can’t Let John Deere Destroy the Very 

Idea of Ownership,’’ April 21, 2015. http://www.wired.com/2015/04/dmca-ownership-johndeere/ (last visited November 8, 2017). 37 An agricultural producer could, hypothetically, use such data to bid rented agricultural land 

away from another tenant if they could somehow demonstrate they could provide the landowner 

with evidence they could increase the landowner’s returns. However, this seems a tenuous argument for the economic value element of the UTSA test and has no application at all in a scenario with owned agricultural land. 

then, it is incumbent upon those disclosing agricultural data to include language in 

their agreements with the receiving party to define the rights to such works derived 

from the data. 

A separate issue regarding copyrights deriving from agricultural data also continues to emerge. Increasingly, the original agricultural data is never even disclosed 

to the agricultural producer; rather, the data has been processed into a report or 

a new form through use of a computer algorithm. Quite simply, agricultural producers may often receive a completely computer-generated report with no human 

author. This requires moving into the realm of copyrights in computer generated 

works—an area that is far from settled.34 The evolution of understanding who holds 

the rights to computer-generated works with regard to agricultural data played out 

recently in the discussions surrounding comments by Deere & Company on proposed 

exemptions to the Digital Millennium Copyright Act 35 regarding copyright protection systems in vehicle software.36

3.3 Application of trade secret law to agricultural data 

While trademark, patent, and copyright do not appear to fit as models for farm 

data ownership, trade secret has the potential to serve the agriculture industry’s 

concerns regarding rights in data shared with Big Data service providers. Importantly, trade secret is a function of state law (unlike trademark, patent, and copyright, which are all creatures of Federal law). At the time of this testimony, all but 

three states have adopted the Uniform Trade Secrets Act, providing a degree of consistency in trade secret law across most states. 

Under the Uniform Trade Secrets Act (‘‘UTSA’’), a ‘‘trade secret’’ is defined as: 

. . . information, including a formula, pattern, compilation, program, device, 

method, technique, or process, that: 

(i) derives independent economic value, actual or potential, from not being generally known to, and not being readily ascertainable by proper means by, other 

persons who can obtain economic value from its disclosure or use, and 

(ii) is the subject of efforts that are reasonable under the circumstances to 

maintain its secrecy. 

Importantly, this definition makes clear ‘‘information . . . pattern[s], [and] 

compilation[s]’’ can be protected as trade secret. This, at last, affords hope of a protective model for farm data. This is not to say that trade secret is a perfect model 

for protecting farm data, however. Note the two additional requirements of trade secret: first, the information has actual or potential economic value from not being 

known to other parties, and second, it is the subject of reasonable efforts to maintain the secret. 

The first provision requires that to be protected as a trade secret, farm data such 

as planting rates, harvest yields, or outlines of fields and machinery paths must 

have economic value because such information is not generally known. While a 

farmer may (or may not) have a privacy interest in this information, the question 

remains as to whether the economic value of that information derives, at least in 

part, from being a secret. The counterargument to that point is the economic value 

of the information comes from the farmer’s analysis of that information and the application of that analysis to his or her own operation—a value completely independent of what anyone else does with the information—and that the information 

for that farm, standing alone, has no economic value to anyone else since that information is useless to anyone not farming that particular farm.37 One can see this 

first element poses problems for the trade secret model. It should be noted here 

there is a clear economic benefit to the collection of farm data; otherwise companies 

would not be investing billions of dollars to position themselves in the agricultural 

29 

38See B. Upbin, FORBES (Tech business blog), ‘‘Monsanto Buys Climate Corp for $930 Million,’’ 

October 2, 2013. http://www.forbes.com/sites/bruceupbin/2013/10/02/monsanto-buys-climatecorp-for-930-million/. 39Smith, supra note 4, citing Restatement of Unfair Competition (Third) § 757 (1995). 40Griffin and Shanoyan, supra note 20. 41The 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 enContinued 

data industry.38 This represents a question yet to be answered clearly by the body 

of trade secret law: whether one can have trade secret protection in information that 

standing alone has no economic value to other parties, but does have such value 

when aggregated with similar data from other parties. 

The second provision—the data be subject to reasonable efforts to maintain its secrecy—also finds problems in an environment where the data is continuously 

uploaded to another party without the intervention of the disclosing party. The fact 

that data are disclosed to another party does not mean it cannot be protected as 

a trade secret; if that were the case, there would be little need for much of trade 

secret law. Rather, the question is how and to whom the information is disclosed. 

As noted in the Restatement (Third) of Unfair Competition’s comments on the Uniform Trade Secret Act, ‘‘. . . the owner is not required to go to extraordinary 

lengths to maintain secrecy; all that is needed is that he or she takes reasonable 

steps to ensure that the information does not become generally known.’’ 39

The question becomes what constitutes ‘‘reasonable steps’’ to keep continuously 

uploaded data protected, or data that is voluntarily shared with a Big Data ATP. 

Almost certainly this means there must be some form of agreement in place between 

the disclosing party and the receiving party regarding how the receiving party must 

treat the received information, including to whom (if anyone) the receiving party 

may disclose that information. Such agreements are discussed in greater detail 

below. However, there is some question as to whether any agreement could protect 

the trade secret claim for data that was disclosed to an ATP. When one discusses 

farm data privacy, one often consider the concept of remaining anonymous. However, in the Big Data world anonymity is no longer achievable, at least in the same 

manner as it once was. Mayer-Scho ̈nberger and Cukier describe how even sanitized 

data can reveal the identity of individuals by combining additional layers of (probably publicly available) data. Given the prevalence of public geospatial data, data 

from USDA, and plat maps, it is possible in many circumstances to use those data 

layers with a sanitized community of farm data to reveal all the data that were intended to remain anonymous. As a result, one could argue sharing data with an 

aggregator essentially renders it ineligible as a trade secret (regardless of a nondisclosure agreement with the aggregator) since the receiver cannot make a reasonable guarantee that the data can be kept secret.40 This concept has implications not 

only for the potential application of trade secret principles to agricultural data, but 

to broader privacy policy concerns as well. 

Assuming for the moment that trade secret protection can be obtained for agricultural data, one should consider the use of a ‘‘non-disclosure agreement’’ when sharing data with an ATP. While an explicit written ‘‘non-disclosure agreement’’ (or 

‘‘NDA’’) is not necessary to claim trade secret protection, such an agreement is almost certainly a good idea if an agricultural producer wishes to retain a protectable 

ownership interest in their data if such an interest exists. Not only can such an 

agreement clarify a number of issues unique to the relationship between the disclosing and receiving parties, but also can address numerous novel issues in the current information environment that trade secret law have not yet reached. 

The concept of NDAs as separate agreements may be practicable for one-on-one 

relationships, such as those between agricultural producers and smaller consulting 

firms, negotiating separate agreements with multiple entities poses significant 

transaction costs. This problem is particularly magnified when one considers larger 

corporate service providers who would face the issue of negotiating tens of thousands of NDAs. Unsurprisingly, such entities choose to create standard agreements 

in their form contracts. While certainly understandable, this in turn creates the 

‘‘opt-out problem’’ wherein a farmer who believes the form contract does not adequately protect his or her interests is forced to either agree to the form or do without the product or service—which may be the only product or service compatible 

with a significant portion of the very expensive equipment he or she already owns 

or uses. This then provokes the discussion of whether such contracts are enforceable 

or are, instead, adhesion contracts. There is yet to be found consistency among Federal courts as to the enforceability of such software use agreements.41

30 

forceable: 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). 42 J. Pepitone, ‘‘5 of the Biggest-ever Credit Card Hacks,’’ (2013) CNN Money, available at 

http://money.cnn.com/gallery/technology/security/2013/12/19/biggest-credit-card-hacks/ (last 

visited November 8, 2017). 43Federal Trade Commission, ‘‘The Equifax Data Breach: What to Do.’’ https:// 

www.consumer.ftc.gov/blog/2017/09/equifax-data-breach-what-do (last visited November 8, 

2017). 44 Interview with Dr. John Fulton, Ohio State University Department of Food, Agricultural, 

and Biological Engineering, July 6, 2015. 

To conclude the trade secret analysis, colorable arguments exist both for and 

against the proposition farm data poses an ‘‘ownable’’ and protectable trade secret. 

That said, this option provides the best doctrinal fit among the traditional intellectual property forms, and farmers wishing to preserve whatever rights they do indeed have in that data seem best advised to use the trade secret model to inform 

the their protective measures. Even so, use of trade secret doctrine as a protective 

measure for agricultural data has drawbacks in the lack of consistency among states 

in trade secret law (although the UTSA has done much to add consistency to the 

field) and the fact it is often a ‘‘backward looking’’ and costly solution since trade 

secret must frequently be used to seek damages (which are often difficult to both 

prove and quantify) through litigation after a disclosure has already been made. 

4. Current Legal Framework for Privacy Rights in Agricultural Data 

Those concerned about the disclosure of personal data can certainly cite a number 

of damaging data breach examples. Recent history suggests many of the real threats 

in data transfers come from insufficient controls to prevent the disclosure of personally identifiable information (‘‘PII’’) to outside parties and inadequate agreements on 

the uses of data by parties to whom it is disclosed. 

To the extent producers regard agricultural data as proprietary, their concerns 

about its disclosure naturally invite a review of the release or theft of proprietary 

information in other sectors. One need not look far into the past to find numerous 

examples of the disclosure of PII, whether merely inadvertent or the result of targeted hacks. Attacks on companies’ payment systems have resulted in the credit 

card information of hundreds of millions of customers from Adobe Systems (150 million customers), Heartland Payment Systems (130 million customers), TJX (parent 

company of TJ Maxx and Marshalls, 94 million customers), TRW Information Systems (credit reporting company, 90 million customers), Sony (70 million customers) 

each of which dwarf breaches attracting more media attention such as Home Depot 

(56 million customers) and Target (40 million customers).42 Perhaps the most troubling data breach in recent history, though, was the 2017 Equifax data breach, 

which exposed a large array of personal and financial data for over 143 million.43

The Equifax breach is especially troubling for many consumers, as Equifax was entrusted with the most sensitive personal information consumers could provide, and 

was supposed to serve as a secure repository for that information. It is reasonable 

to surmise that particular breach was a significant setback for the trust of agricultural producers in systems that could collect their financial data. 

To some extent, there may be a very limited reasonable ‘‘expectation of privacy’’ 

in agricultural data since a significant segment of such data is available from public 

sources or sources obtainable from public vantage points (such as aerial or satellite 

imagery). Nevertheless, there remains an also-significant segment of data for which 

an argument could be made that a privacy interest exists. The challenge may be 

figuring out who has the best ability to protect that data from disclosure. 

The greatest risk of data breaches for agricultural producers may be attacks 

against aggregators, since attacks against individual farm systems pose very high 

barriers relative to the amount of data such an attack could obtain. Theoretically, 

a hacker could tap into the tractor/implement network (also called the tractor/implement bus) using a number of commercially-available technologies allow farmers to 

plug into the network and access Controller Area Network (‘‘CAN’’) messages directly; for example, one could purchase a CAN message reader (‘‘CAN sniffer’’) to 

read machine diagnostic codes for repairs.44 Someone wishing to ‘‘steal’’ data would 

likely not want to be present to retrieve the data from the device, though, and would 

likely prefer to use a CAN data logger coupled with a device to wirelessly transmit 

the data. Many data loggers are available to the public as well; for example, the 

‘‘Snapshot®’’ device used by Progressive Insurance for some insurance programs is 

31 

45See Progressive Corporation, ‘‘Snapshot® Terms and Conditions,’’ https://www.pro 

gressive.com/auto/snapshot-terms-conditions/ (last visited November 8, 2017). 46See interview with Dr. Marvin Stone (June 10, 2015). 47 Interview with Dr. John Fulton, Ohio State University Department of Food, Agricultural, 

and Biological Engineering, July 6, 2015. 48 A relatively quick search of Google will yield many GPS receiver units for less than $50. 49SAE International, ‘‘The SAE J1939 Communications Network: An Overview of the J 1939 

Family of Standards and How they are Used,’’ 5 (white paper), available at http://www.sae.org/ 

misc/pdfs/J1939.pdf (last visited November 8, 2017). See also INTERNATIONAL ORGANIZATION

FOR STANDARDIZATION, ISO DRAFT INTERNATIONAL STANDARD ISO/DIS 11783: TRACTORS AND MACHINERY FOR AGRICULTURE AND FORESTRY—SERIAL CONTROL AND COMMUNICATIONS DATA NETWORK (2012). The ISO 11783 standard is often referred to as the ‘‘ISOBUS standard’’ and defines 

how the on-board computer networks on most agricultural equipment works and how their individual components work together. Combined, SAE J1939 and ISO 11783 govern much of how 

the data-collection network on any agricultural equipment works. 50M. Miettien, ‘‘Implementation of ISO 11783 Compatible Task Controller,’’ XVI CIGR (International Commission of Agricultural and Biosystems Engineering) World Congress, Bonn, Germany (2006), available at http://users.aalto.fi/∼ttoksane/pub/2006_CIGR20062.pdf (last visited 

November 8, 2017). 51For a primer on the process of encoding and decoding cellular signals, see How Stuff Works, 

‘‘How Cell Phones Work,’’ http://electronics.howstuffworks.com/cell-phone.htm (last visited November 8, 2017). 52See C. Timberg & A. Soltani, By Cracking Cellphone Code, NSA Has Ability to Decode Private Conversations, THE WASHINGTON POST, December 13, 2013. Online edition, available at 

http://www.washingtonpost.com/business/technology/by-cracking-cellphone-code-nsa-has-capacity-for-decoding-private-conversations/2013/12/13/e119b598-612f-11e3-bf45- 

61f69f54fc5f_story.html (last visited November 8, 2017). 

simply a CAN data logger plugged into a vehicle’s On-Board Diagnostic (OBD–II) 

port.45 Alternatively, of course, if one wanted to steal large amounts of agricultural 

data at once, one could attempt to hack a cellular network provider used by an 

equipment manufacturer to carry their data signals. Further, it should be noted the 

equipment manufacturer likely has no ability to specify or enforce the security protocols used to safeguard such cellular transmissions. 

While such an approach would work for standard messages transmitted over the 

bus, it would not work for proprietary messages. To decode such messages, the prospective hacker would have to develop a system for decoding the information being 

provided from the task controller for the implement, and that task would take almost as much work (if not more) than the work in developing the task controller 

system in the first place.46 Note, that several companies now provide means for reverse-engineering proprietary CAN messages (such as those related to crop yield) so 

farmers can automatically transfer yield data to the cloud. Such technology could 

also be used to decode other proprietary information.47 Perhaps ironically, the 

growth of proprietary data network protocols that lead to complaints about the lack 

of interoperability of farm equipment systems could also provide greater protection 

against data breaches. 

Additionally, the Global Positioning System ‘‘GPS’’ receiver in most systems connects directly to the implement’s task controller. As a result, a ‘‘bug’’ might receive 

information about the commands sent to the implement but without the associated 

location data, rendering it meaningless. The bug would require its own GPS receiver 

along with implement data (the configuration and dimensions of the implement), 

which today could be done for a modest equipment cost.48 Obtaining agronomic data 

via a physical connection to an implement poses a task manageable for someone 

knowledgeable in SAE J1939 and ISO 11783 49 technology.50 However, building and 

deploying such a device poses a significant amount of effort (to say nothing of the 

potentially-criminal trespass involved in deploying it) in relation to the prospect of 

collecting data on only one farm. 

As illustrated from this discussion, a number of factors in the configuration and 

operation of farm data networks limit the opportunities for hackers to take agricultural data directly from the agricultural producer. Admittedly, most producers put 

little thought into their systems being physically hacked but worry instead about 

their data being accessed through an intercepted cellular signal. They might also 

worry about a bad actor hacking the system to implant false data. First, virtually 

all cellular signals are encrypted when transmitted and decrypted at the cellular 

tower;51 without the decryption key, interpreting any data transmitted would be difficult (although not impossible for a sophisticated hacker; recent news has highlighted the ability of some groups to do so 52). The use of data encryption through 

a secure sockets layer (‘‘SSL’’) protocol by the farmer and his or her service provider 

32 

53See C. Heinrich, Secure Socket Layer (SSL), in ENCYLOPEDIA OF CRYPTOGRAPHY AND SECU- RITY 1135 (Henck C.A. van Tilborg, Sushil Jajodia, eds., 2011) 54S. Sammataro, ‘‘Cybersecurity for Small or Regional Law Firms,’’ paper presented at American Agricultural Law Association Annual Symposium, Charleston, South Carolina (October 23, 

2015). 55 18 U.S.C. §§ 1030 et seq. 56 18 U.S.C. §§ 2510 et seq. 57S. 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-stopEPA-release-of-farmers-personal-data-07082013.asp (last visited November 8, 2017). 58T. Janzen, ‘‘Legal Issues Surrounding Farm Data Ownership, Transfer, and Control,’’ paper 

presented at American Agricultural Law Association Annual Symposium, Charleston, South 

Carolina (October 23, 2015). 59 42 U.S.C. § 300gg, 29 U.S.C. §§ 1181 et seq. and 42 U.S.C. §§ 1320d et seq. 60 15 U.S.C § 6803. 61 15 U.S.C. §§ 1681 et seq. 62 5 U.S.C. § 552a. 

in data transfers adds another difficult-to-break security barrier to interception of 

the data.53

Most agricultural data disclosed to a service provider is likely in the form of 

telematics data, raw data regarding crop production, GIS information about the 

farm, and other similar types. Conversely, hackers frequently go after large concentrations of data with easily-converted financial value, such as credit card information. Thus, it may be difficult for hackers to make a ‘‘quick buck’’ from agricultural data making it a less-appealing target of attack. Nevertheless, an adage in 

computer security is ‘‘where there is value, there will be a hacker.’’ 54 As a result, 

systems storing agricultural data are less likely to be directly attacked, but farmers 

are understandably concerned that PII may be stolen if, for example, their vendor 

account information is somehow linked to their agricultural data or if their account 

information is stored with a third party that is a more appealing target. Depending 

on the type of computer at issue and its common use, the Federal Computer Fraud 

and Abuse Act (‘‘CFAA’’) 55 may provide a means of prosecuting unauthorized access 

of the computer in the event agricultural data linked to PII is compromised. Discussed below, the Federal Electronic Communications Privacy Act (ECPA) 56 could 

also be used as a potential prosecutorial tool for those attempting to intercept agricultural data during the data transmission process. 

The theft of PII by criminals is one threat posed by data transfers, but so too is 

the inadvertent, or perhaps intentional but misinformed, disclosure of data by the 

party receiving that data. Take, for example, the disclosure of thousands of farmers’ 

and ranchers’ names, home addresses, GPS coordinates and personal contact information’’ by EPA in response to a Freedom of Information Act (FOIA) request regarding concentrated animal feeding operations (CAFOs) which prompted a lawsuit from 

the American Farm Bureau Federation and National Pork Producers Council alleging the agency overstepped its authority in doing so.57 While this event represents 

the disclosure of information by an enforcement agency, many farmers fear the converse—that an enforcement agency could compel a data-receiving party to disclose 

information even if such disclosure were not legally required. Another concern is 

whether an adverse party in litigation (or even a party contemplating litigation) 

could persuade a party holding a farmer’s data to disclose the data as an aid to their 

case, again even if such disclosure was not legally required. 

Much work remains to be done on defining governmental safeguards against disclosures, and even more work remains to be done in defining how the government 

can obtain electronic data. Although laws such as the ECPA (heavily modified by 

the USA Patriot Act) govern the acquisition of information through intercepted communications, there is little law to prevent a government agency from simply requesting data from a service provider. Anecdotal evidence suggests service providers 

and their legal counsel continue to struggle in defining parameters for how to respond to non-subpoenaed requests for data by government agencies. 

All these issues surround restrictions on the taking of information by some unauthorized (or at least questionable) means. While there are at least some laws potentially applicable in these circumstances, there are no laws defining an inherent privacy right in agricultural data.58 For example, the Federal Health Insurance Portability and Accountability Act (‘‘HIPAA’’) 59 provides privacy rights and restrictions 

against disclosure of health information; the Gramm-Leach Bliley Act (also known 

as the Financial Modernization Act of 1999) 60 and Fair Credit Reporting Act 61 protect financial information from disclosure; the Privacy Act of 1974 62 restricts disclosures of personal information by held by the Federal government. As of now, 

though, there are large categories of agricultural data that may fall between the 

33 

63 American Farm Bureau Federation, ‘‘Privacy and Security Principles for Farm Data,’’ November 13, 2014 (revised April 1, 2016). Available at https://www.fb.org/issues/technology/ 

data-privacy/privacy-and-security-principles-for-farm-data (last visited November 8, 2017). 64Open Ag Data Alliance, ‘‘Principals and Use Cases,’’ http://openag.io/about-us/principalsuse-cases/ (last visited November 8, 2017). 65See www.agdatatransparent.com (last visited November 8, 2017). 66 5 U.S.C. § 552. 

cracks of these laws with no Federal (and in most cases, no state) protections 

against its disclosure. 

5. Potential Policy Responses to Address Agricultural Data Issues 

Having reviewed the current legal environment surrounding the ownership rights 

and privacy protections relevant to agricultural data, what can this Committee and 

Congress do to enable U.S. farmers and ranchers to take maximum economic advantage of Big Data tools? As referenced above, Big Data cannot be Big Data without 

‘‘buy-in’’ to the system from large numbers of agricultural producers. In these beginning years of agricultural data systems, there are many ATPs vying for farmers and 

their acreages to enroll in their systems. As the system matures, this relationship 

will likely shift, and there will be few (or perhaps only one) ATP and the vast majority of farms may be participating. Nevertheless, for the maturation process to begin, 

agricultural producers must ‘‘buy in’’ to the system. At a fundamental level, that 

buy-in requires trust in the system from those producers. That trust, in turn, likely 

requires answers to the questions of ownership and privacy in agricultural data. 

None of the Federal intellectual property laws directly address who holds a 

protectable intellectual property right in agricultural data. Arguably, the most appropriate fit may be found in state law under the UTSA, although the applicability 

of that law is questionable as well. The UTSA may provide a useful map to any Congressional efforts to help define ownership rights in agricultural data. Passage of 

statutory law defining ownership of ‘‘agricultural data’’ may be a daunting task 

given the complexity of the current Federal and state intellectual property framework (which also draws from centuries of common law). Thus, it may be advisable 

instead to use a consensus-driven approach among agricultural producers and service providers to define agricultural data rights. The coalition led by the American 

Farm Bureau Federation and its ‘‘Privacy and Security Principles for Farm Data’’ 63

represents a tremendous step forward on this issue. Other groups, such as the Open 

Ag Data Alliance, continue to build coalitions on the technical side of the Big Data 

issue to develop systems and standards embodying the principles of interoperability, 

security and privacy.64 The next step is to see continued cooperation among groups 

such as these in integrating their principles in legally-binding service agreements. 

Another collaborative effort to help agricultural producers evaluate the data policies and protections of data service providers has been the Ag Data Transparency 

Evaluator, coordinated by the American Farm Bureau Federation, which requires 

service providers to undergo a ten-factor review (based in part on the Privacy and 

Security Principles, with the review self-reported by the service provider) with a satisfactory review resulting in the ‘‘Ag Data Transparent’’ seal.65 Congressional support of this and other efforts to equip farmers and ranchers in evaluating the data 

tools available can help foster trust, encourage Big Data participation, and drive 

many of the potential advantages Big Data services have to offer. 

Modern agricultural producers are expected to be proficient in a broad array of 

the disciplines of science and business, but few have a background in intellectual 

property law. Support of educational programs to help these producers understand 

the legal issues at play in Big Data service agreements could do much to help increase trust, advance the consensus process, and empower producers to make informed decisions about the cost-benefit analysis of sharing their data under those 

service agreements. The consensus process may also provide a vehicle for developing 

an understanding among all stakeholders as to the privacy protections necessary 

and appropriate to protect agricultural data, which occupies a unique space between 

purely personal and business information. Such information does not readily fit into 

the existing framework of Federal privacy laws, and as business information, may 

not belong in such a framework. 

One matter in which Congressional action may be directly applied is the development of clearer guidelines regarding the production of agricultural data held by private data aggregators, more robust safeguards against inadvertent disclosure or intentional hacking by outside parties, and clear guidance on when disclosure of government-held data is, and is not, required under the Freedom of Information Act 66

or other circumstances. 

Finally, although outside the direct scope of a discussion of legal issues in agricultural use of agricultural data tools, rural access to wireless broadband services is 

34 

67Griffin, T.W., and Mark, T.B. (2014). ‘‘Value of Connectivity in Rural Areas: Case of Precision Agriculture Data.’’ International Conference on Precision Agriculture. July 20–23, 2014. 

Sacramento, CA. 68Whitacre, B.E., Mark, T.B., and Griffin, T.W. (2014). How Connected are Our Farms? 

Choices. Online: http://www.choicesmagazine.org/choices-magazine/submitted-articles/howconnected-are-our-farms. 

crucial to fully utilizing the potential of agricultural data systems. Before the rapid 

adoption and usage of agricultural data technologies will occur, the lack of this enabling technology must be addressed. The expansion of connectivity across the U.S. 

has been a priority, but access has grown slowly. This is especially true in the major 

crop producing regions. The majority of data transfer occurs over cellular systems, 

but there are worldwide initiatives to provide wireless connectivity via satellite, balloons, and other platforms. Regardless of platform, the agricultural industry relies 

upon wireless connectivity to support big data systems. 

Telematics allows data to be wirelessly uploaded and downloaded between farm 

machinery and online servers. However, limited connectivity is a barrier to adoption 

leading to potential economic losses.67 Whitacre et al. addressed the current connectedness of agricultural production areas.68 It was these areas that were impacted 

by the United States Federal Communications Commission (FCC) updated definition 

of connectivity that could be considered broadband in January 2015. The definition 

changed from 4 Megabits per second (Mbps) download and 1 Mbps upload to 25 

Mbps download and 3 Mbps upload. Although broadband speeds did not instantly 

change, the level of connectivity that service providers could advertise as 

‘broadband’ changed. The faster speeds required to be considered broadband brought 

light to connectivity barriers, especially with respect to connectivity gaps in rural 

areas where agricultural production occurs. Specifically, the 25 Mbps download 

speed requirement negates the majority of United States wireless connections from 

being classified as broadband. 

However, the vast majority of data being passed between farm equipment and online servers is uploaded rather than downloaded; and upload speeds are typically 

only a fraction of download speeds. For some types of data such as machine 

diagnostics and prescriptions, current speeds may be adequate. However, yield data 

and specifically imagery data may require connectivity speeds in excess of what is 

currently available. In summary, a concerted national policy effort must be made 

to expand broadband access in rural areas for a number of important rural development purposes, not the least of which is to facilitate the potential economic advantages to be gained by integration of agricultural data technologies on farms and 

ranches. 

Concluding Remarks 

The application of Big Data to agricultural production holds the potential to improve the profitability of U.S. agriculture and to better prepare its farmers and 

ranchers to handle the inherent risks of the industry. Additionally, Big Data could 

play a vital role in the further development of tools and techniques necessary to feed 

an ever-growing, hungry world. I commend this Subcommittee for its foresight in 

addressing these issues, and sincerely thank the Subcommittee, Chairman Moran, 

and Ranking Member Blumenthal for the opportunity to address you today. 

Senator MORAN. Thank you very much, Doctor. 

Mr. Janzen, welcome. 

STATEMENT OF TODD J. JANZEN, PRESIDENT, 

JANZEN AGRICULTURAL LAW LLC 

Mr. JANZEN. Thank you, Mr. Chairman, Ranking Member 

Blumenthal, and members of the Subcommittee. My name is Todd 

Janzen. I’m an attorney, a private practice attorney, in Indianapolis, Indiana. And the firm that I work at, Janzen Agricultural Law 

LLC, specializes in helping farmers, ag technology providers, and 

also agribusinesses. 

But I speak here today not only as an attorney, but also as somebody who grew up on a farm in south-central Kansas, a grain and 

livestock farm. And so I’m particularly attuned to the issues that 

farmers are facing on a legal front. 

35 

Let me start, though, by talking about the types of data that are 

being collected by the various ag data platforms that are out there 

because I think that’s helpful in understanding the framework 

here. 

So there are various streams of data I like to say that come off 

of fields and farms, and this can include land data, agronomic data, 

weather data, management data, machine data, and also livestock 

data, which would be information about genetics or feed consumption by animals. 

But what’s really changed in the last 5 years is not just that 

farmers are generating this information, but now farmers are taking this information and storing it in cloud-based servers that are 

not located on the farm. And so this transfer of information off the 

farm I think is a pretty monumental transition in history for U.S. 

agriculture. 

But with this transfer of information, there have also been some 

groups that have started to raise concerns, you know, about farmers losing out on the benefit of knowing all this and having all this 

information on their farm. 

A poll by American Farm Bureau in 2016 identified a number of 

issues that farmers faced, but I classify them into three big categories. One is a lack of trust with a lot of these ag technology providers that are on the market today. Second would be a loss of control to these companies. And then the third would be something 

that some other panelists have already mentioned, which is just 

the overall complexity of the agreements that farmers are being 

asked to sign. I heard a farmer yesterday describe this as the aggressive fine print. 

So to address these problems, American Farm Bureau, National 

Farmers Union, and the commodity groups from a number of national organizations came together and said, ‘‘Let’s create a set of 

core principles that we can all agree on.’’ So in 2014, they introduced a document called the ‘‘Privacy and Security Principles for 

Farm Data,’’ which really outlined some core principles that they 

wanted to see adopted by the industry. 

And 37 different companies and organizations signed onto these 

core principles by pledging that they would incorporate them into 

their contracts, but here we are today, and we don’t yet have all 

37 of those who have really met the challenge that they agreed to 

take on years ago. 

So as a follow up to this effort, American Farm Bureau, Farmers 

Union, and others came together and said, ‘‘Let’s create some sort 

of certification process where we can recognize companies that are 

adhering to these core principles for agricultural data.’’ And so out 

of that came this Ag Data Transparency Evaluator process, which 

I’ve been fortunate enough to be a part of. And I want to just briefly describe how that works so that it’s clear to the Subcommittee. 

Companies that want to be certified as ‘‘Ag Data Transparent’’ 

can submit their contracts and answer a 10-question form about 

how they use farmers’ ag data. That is then reviewed by an independent third-party administrator, and currently Janzen Agricultural Law is that administrator. So we take that information and 

we review it, and we ultimately determine if companies are being 

transparent with how they are using agricultural data or if they’re 

36 

not being transparent, in which case, we send them back and ask 

them to do it over. 

But if they are transparent, then they are awarded the ‘‘Ag Data 

Transparent’’ seal of approval. And you may see this on some companies’ marketing materials. And I’ll mention Farmobile, one of the 

very first companies to go through this process and has been 

awarded the ‘‘Ag Data Transparent’’ seal of approval. 

So we review these questions, such as, ‘‘What data is being collected?’’ Does the company obtain consent before the data is transferred in or out of that platform? And then also, ‘‘Can a farmer delete their data if they’re finished using that platform?’’ And then 

we post the results of this question-and-answer to the AgData 

Transparent.com website so that farmers can review this and make 

informed decisions before they go down the road of sending their 

data to one of these companies. 

So here we are today. We’ve had nine companies go through this 

evaluation process and be certified ‘‘Ag Data Transparent.’’ There 

is still a lot of work to do because there are a lot of companies that 

have said they want to do this process or maybe they’ve committed 

to it, but yet they haven’t followed through and become certified. 

So, Mr. Chairman and members of the Subcommittee, I’m honored to be here today, and I look forward to your questions. I think 

this is a very important issue for farmers, and I hope that we can 

address them. 

Thank you. 

[The prepared statement of Mr. Janzen follows:] 

PREPARED STATEMENT OF TODD J. JANZEN, PRESIDENT, 

JANZEN AGRICULTURAL LAW LLC 

Good afternoon Chairman Moran, Ranking Member Blumenthal, and members of 

the Subcommittee. My name is Todd J. Janzen, I am the president and attorney 

with Janzen Agricultural Law, LLC, a law firm based in Indianapolis, Indiana that 

serves the needs of America’s farmers, ag technology providers, and agribusinesses. 

One of the reasons we founded Janzen Ag Law in 2015 was that we wanted to 

be at the forefront of the changes that have been occurring on the farm for the past 

few years. Farms are becoming more digital every day, and together with that digitalization is a movement of agricultural data stored on computers in the farm office 

to cloud-based data storage devices. Agricultural data (ag data) can be many things, 

including yield data, soil data, planting information, weather data, financial data, 

etc. This marks the first time in history that the majority of the information that 

farmers generate and use on their farms has been moved into the hands of companies outside the farm. 

As a result, we are seeing a digital land-rush occurring across the United States. 

The past few years have seen millions of dollars pour into ag data startups from 

Silicon Valley, to Kansas City, to North Carolina. Historic legacy agricultural companies, such as John Deere, are also at the forefront of this movement by expanding 

their product offerings to include cloud-based data storage platforms. All of these 

companies are scrambling to get the most acres of data into their platforms so that 

when consolidation of ag technology providers (ATPs) begins, they are in the strongest position. 

In the race to the cloud, we must also be cautious so that the American farmer 

is not left behind. Today I will address the issues facing farmers as digitalization 

occurs and how the industry has begun to address these issues. 

Issues Facing Farmers as Ag Data Moves into the Cloud 

American Farm Bureau Federation (Farm Bureau) conducted a poll of over 400 

farmers in 2016 to understand their issues concerning ag data privacy, security, and 

control. The poll highlighted what are essentially three issues that continue to come 

up when asking farmers about ag data concerns: 

37 

1. Lack of Trust 

Seventy-seven percent (77 percent) of farmers expressed concern about which entities can access their farm data after the data is uploaded to cloud-based servers. 

The same percentage expressed concern about whether uploading the data could 

cause it to be used for regulatory purposes. 

Sixty-seven percent (67 percent) of farmers said they consider how outside parties 

will use their ag data when deciding whether to entrust their data with a certain 

ATP. 

A farmer’s lack of trust can come from many sources, but I speculate it originates 

in two places. Many ag data companies are new. Ag data startups lack the goodwill 

that older agricultural companies have spent years building. They have new sales 

associates who are strangers to the farm, or in some instances, strangers to agriculture. They are viewed as outsiders. 

Older, long-established agricultural companies do not suffer from a general lack 

of trust with the farmer, since they have spent years building that relationship. But 

when a seed company, equipment manufacturer, or ag retailer begins offering an ag 

data platform to store the farmer’s ag data, farmers often are skeptical about 

whether the storage provider is trying to help the farmer raise a better crop or 

using the ag data to sell the farmer more or higher-priced goods and services. This 

skepticism may erode a farmer’s trust. 

2. Concern with Losing Control 

Farmers are also concerned that uploading their ag data to cloud-based platforms 

means they will lose control over downstream uses. Sixty-six percent (66 percent) 

of respondents in the Farm Bureau poll believe farmers should share in the potential financial benefits from the use of their data beyond the direct value they may 

realize on their farm. 

Farmers raised concerns that ATPs could use their ag data to gain an unfair advantage in the marketplace. Sixty-one percent (61 percent) of farmers expressed 

worry that ATPs could use their data to influence market decisions. 

These concerns arise from a fundamental legal truth about ag data—there are no 

laws that specifically protect farmers’ privacy and security concerns. Ag data is not 

typically ‘‘personally identifiable information,’’ such that it would be protected by 

state laws which prevent misuse of personal information like name, address, and 

phone number. Nor does ag data fit into a class of data that Congress has chosen 

to protect legally, such as medical information (HIPAA). Finally, ag data does not 

neatly fit into existing legal protections for intellectual property, such as patents, 

trademarks, or copyrights. Ag data ultimately may be deemed a trade secret under 

existing state and Federal trade secret laws, but that will depend upon whether 

courts interpret existing statutes to include information such as agronomic data. 

These uncertainties mean that the contracts between farmers and ag tech providers are very important. These contracts will determine farmers’ rights in the ag 

data their farms create. 

3. Frustration with Complexity of Current Legal Agreements 

Fifty-nine (59 percent) percent of farmers were confused about whether current 

legal agreements allowed ATPs to use their ag data to market other services, equipment, or inputs back to them. Zippy Duvall, president of Farm Bureau, said: ‘‘This 

indicates a higher level of clarity and transparency is needed to secure grower confidence. One of the topics I hear most about from farmers on the data issue is having a clear understanding about the details of ‘Terms and Conditions’ and ‘Privacy 

Policy’ documents we all sign when buying new electronics. You should not have to 

hire an attorney before you are comfortable signing a contract with an ag technology 

provider.’’ 

Our experience as a law firm working in this area confirms that this is a real 

problem for farmers and ATPs. There is no standard agreement that governs ag 

data transfer, use, and control by ATPs. Instead, technology companies have adapted other forms of legal agreements to try to address the issues associated with moving ag data into cloud-based platforms, but with limited success. A farmer seeking 

to compare two similar products today might find that they are governed by two 

very different sets of contracts. 

This only adds to a farmer’s confusion. If we want to make technology easy to embrace and use—and we do—then we need to simplify the contracts farmers sign 

when implementing new ag data technology on the farm. Contracts that no one 

reads and understands set the stage for problems down the road. 

38 

How the Ag Industry is Addressing Farmers’ Concerns 

1. The Privacy and Security Principles for Farm Data 

Farm Bureau, National Farmer’s Union, and national commodity organizations 

for corn, soybeans, wheat, and sorghum, led an effort in 2014 to establish fundamental principles for companies working in the ag data space. These organizations 

held a series of meetings where roundtable discussions occurred among industry 

stakeholders, such as John Deere, CNH Industrial, AGCO, Monsanto, DuPont Pioneer, Beck’s Hybrids, Dow Agrosciences, Farmobile, and other ag technology providers. The culmination of these efforts was the drafting of the ‘‘Privacy and Security Principles for Farm Data,’’ also known today as ag data’s ‘‘Core Principles.’’ 

The Core Principles address thirteen key elements related to ag data. These include: 

• Education 

• Ownership 

• Collection, Access and Control 

• Notice 

• Transparency and Consistency 

• Choice 

• Portability 

• Terms and Definitions 

• Disclosure, Use, and Sale Limitation 

• Data Retention and Availability 

• Contract Termination 

• Unlawful or Anti-Competitive Activities 

• Liability & Security Safeguards 

After releasing the Core Principles in 2014, Farm Bureau asked companies to voluntarily ‘‘sign on’’ to the document. As of October 2017, the following organizations 

and companies have agreed to implement the Core Principles into their contracts 

with farmers. 

AGCO 

Ag Connections, Inc. 

Agrible, Inc.* 

AgSense 

AgWorks 

Ag Leader Technology 

American Farm Bureau Fed. 

American Soybean Assoc. 

Beck’s Hybrids* 

CNH Industrial 

Conservis* 

Crop IMS 

CropMetrics 

Dow AgroSciences LLC 

DuPont Pioneer 

Farm Dog 

Farmobile LLC* 

Granular* 

Grower Information Services Cooperative 

GROWMARK, Inc.* 

Independent Data Management LLC* 

ohn Deere 

Mapshots, Inc. 

National Assoc. of Wheat Growers 

National Barley Growers Assoc. 

National Corn Growers Assoc. 

National Cotton Council 

National Farmers Union 

National Potato Council 

National Sorghum Producers 

North American Equipment Dealers 

Assoc. 

OnFarm 

Raven Industries 

Reinke Manufacturing Co., Inc. 

Syngenta 

The Climate Corporation—a division of 

Monsanto 

USA Rice Federation 

Valley Irrigation 

ZedX Inc. 

*Company certified to be Ag Data Transparent. For more information, visit 

www.agdatatransparent.com 

A copy of the Core Principles is attached as Exhibit A. 

39 

2. The Ag Data Transparent Effort 

Having the Core Principles in place was a great starting point for the ag data 

industry to address farmers’ concerns with ag data privacy, use, and control. However, the Core Principles are only guidelines, and only valuable if companies incorporate the Core Principles into their contracts with farmers. Therefore, following the 

release of the Core Principles, several farm groups and industry stakeholders 

worked together to create an independent verification tool that could help farmers 

determine if ag tech providers are abiding by the Core Principles. This tool is called 

the Ag Data Transparency Evaluator. It is a simple three-step process: 

• Participating companies must answer 10 questions about how they store, use, 

and transfer ag data. 

• The 10 question answer form is reviewed by an independent third party for 

transparency and completeness. 

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

Participation is voluntary, but all companies that signed onto the Core Principles 

have been asked to participate in the Ag Data Transparent effort as well. 

a. The 10 Question Evaluation. Here is a list of the 10 questions that each participant is asked to answer as part of the evaluation: 


1114JANZ1.eps
40 

Answers to all questions except for question 1 are ‘‘yes’’ or ‘‘no,’’ but companies 

are also given space to explain their answer. 

b. Reviewing the 10 Question Evaluation. 

After an ag tech company completes the 10 question evaluation form, the company 

submits its answers to an independent third party evaluator to determine compliance. Janzen Agricultural Law LLC is the law firm that has been selected to conduct the evaluations. After reviewing a company’s answers, we typically go back to 

that company with suggestions for improving its contracts and policies to bring into 

compliance with Core Principles. Companies then make those revisions to their contracts and policies and resubmit their 10 question form. Once a company’s answers 

align with the Core Principles, we send an official letter designating the company 

as ‘‘Ag Data Transparent’’ and authorizing use of the seal of approval. 

The final, approved 10 question answer forms are posted on the Ag Data Transparent website at www.AgDataTransparent.com Farmers can research and review 

companies’ answers online. The website requires no log in and is free to use. An 

example of the home page is attached as Exhibit B. 

c. The Ag Data Transparent Seal of Approval 

Companies that undergo evaluation and are approved as ‘‘Ag Data Transparent’’ 

may then use the seal of approval on their websites and marketing materials. To 

date, nine companies have completed the evaluation and been approved as ‘‘Ag Data 

Transparent.’’ These nine companies are: 

• AgDNA 

• AgIntegrated, Inc. 

• Agrible, Inc. 

• Beck’s Hybrids 

• Conservis Corporation 

• Farmobile 

• Granular 

• GROWMARK 


1114JANZ2.eps
41 

• Independent Data Management LLC 

The participants are diverse, from a Silicon Valley ag tech startup, to a Midwestern seed company, to one of the Nation’s largest farm cooperatives and ag retailers. These companies may use the Ag Data Transparent seal on their websites, 

denoting their compliance with the Core Principles. Farmers who see the seal of approval will know the company went through the time and effort to certify its contract. 

The Ag Data Transparent process addresses farmers’ three main concerns with 

ag data. First, the process instills trust. No company submits its contracts to a voluntary evaluation unless the company is willing to revise its contracts, as necessary, 

to bring them into compliance with the Core Principles. Second, loss of control is 

addressed by requiring tech providers to obtain farmer consent before transferring 

data to third parties. Finally, farmers’ complexity frustration is addressed by condensing all of a tech provider’s contracts into a 10 question form that answers the 

questions farmers want to know. The Ag Data Transparent process makes contracts 

better. 

d. Who is behind the Ag Data Transparent effort? 

The Ag Data Transparent effort is governed by a non-profit corporation, the Ag 

Data Transparency Evaluator Inc. The corporate bylaws create two classes of directors: (1) farm organizations that are made up of farmer-member organizations; and 

(2) diverse ag technology providers, referred to as ‘‘industry partners.’’ The farm organizations are American Farm Bureau Federation, American Soybean Association, 

National Corn Growers Association, National Farmers Union, National Sorghum 

Producers, National Association of Wheat Growers and National Potato Council. The 

industry partner board members are ag technology providers ranging from large corporations, medium-sized companies, and ag tech startup organizations. 


1114JANZ1.eps
42 

Janzen Agricultural Law LLC, which serves as the administrator of the program 

and conducts the evaluation reviews, is not a board member. 

3. The Ag Data Use Policy 

Our law firm also drafts terms of service, license agreements, privacy policies, and 

other contracts for ag technology providers. This work has confirmed many concerns 

facing farmers today when it comes to ag data. We see how companies struggle to 

communicate clearly how they intend to store, use, and transfer ag data. 

For these reasons, we have encouraged companies to draft ‘‘data use policies’’ or 

‘‘data use agreements’’ for their farmers. In a typical data use contract, the technology provider addresses all of the issues raised by the 10 questions and the Core 

Principles. For example, a data use policy will explain what information the provider collects and what permission is required before the provider transfers that 

data to another party. 

From our standpoint, the Ag Data Transparent effort has helped drive more technology providers into creating data use policies. Thus, the effort has paid dividends 

even for some companies that have not participated in evaluations because it has 

caused them to rethink how they are contracting with farmers. 

Conclusion 

The Ag Data Transparent effort is great step towards bringing transparency to 

ag data contracts between farmers and their technology providers. Wider participation would certainly help the effort, but that is up to the industry. Out of the dozens 

of ag tech providers with cloud-based platforms on the market today, only nine have 

completed the certification process. A few companies are in the process of certifying, 

but uptake could be better. 


1114JANZ3.eps
43 

Farmers should ask their technology providers why they have not earned that Ag 

Data Transparent seal. This Subcommittee should ask technology providers this 

question as well when they come before you to testify in future hearings. 

Thank you, Mr. Chairman and Ranking Member for your time and attention to 

this important issue. I look forward to answering any questions you may have for 

me. 

EXHIBIT A 

PRIVACY AND SECURITY PRINCIPLES FOR FARM DATA

(AG DATA’S CORE PRINCIPLES) 

November 2014 

The recent evolution of precision agriculture and farm data is providing farmers 

with tools, which can help to increase productivity and profitability. 

As that technology continues to evolve, the undersigned organizations and companies believe the following data principles should be adopted by each Agriculture 

Technology Provider (ATP). 

It is imperative that an ATP’s principles, policies and practices be consistent with 

each company’s contracts with farmers. The undersigned organizations are committed to ongoing engagement and dialogue regarding this rapidly developing technology. 

Education: Grower education is valuable to ensure clarity between all parties and 

stakeholders. Grower organizations and industry should work to develop programs, 

which help to create educated customers who understand their rights and responsibilities. ATPs should strive to draft contracts using simple, easy to understand 

language. 

Ownership: We believe farmers own information generated on their farming operations. However, it is the responsibility of the farmer to agree upon data use and 

sharing with the other stakeholders with an economic interest, such as the tenant, 

landowner, cooperative, owner of the precision agriculture system hardware, and/or 

ATP etc. The farmer contracting with the ATP is responsible for ensuring that only 

the data they own or have permission to use is included in the account with the 

ATP. 

Collection, Access and Control: An ATP’s collection, access and use of farm data 

should be granted only with the affirmative and explicit consent of the farmer. This 

will be by contract agreements, whether signed or digital. 

Notice: Farmers must be notified that their data is being collected and about how 

the farm data will be disclosed and used. This notice must be provided in an easily 

located and readily accessible format. 

Transparency and Consistency: ATPs shall notify farmers about the purposes for 

which they collect and use farm data. They should provide information about how 

farmers can contact the ATP with any inquiries or complaints, the types of third 

parties to which they disclose the data and the choices the ATP offers for limiting 

its use and disclosure. 

An ATP’s principles, policies and practices should be transparent and fully consistent with the terms and conditions in their legal contracts. An ATP will not 

change the customer’s contract without his or her agreement. 

Choice: ATPs should explain the effects and abilities of a farmer’s decision to opt 

in, opt out or disable the availability of services and features offered by the ATP. 

If multiple options are offered, farmers should be able to choose some, all, or none 

of the options offered. ATPs should provide farmers with a clear understanding of 

what services and features may or may not be enabled when they make certain 

choices. 

Portability: Within the context of the agreement and retention policy, farmers 

should be able to retrieve their data for storage or use in other systems, with the 

exception of the data that has been made anonymous or aggregated and is no longer 

specifically identifiable. Non-anonymized or non-aggregated data should be easy for 

farmers to receive their data back at their discretion. 

Terms and Definitions: Farmers should know with whom they are contracting if 

the ATP contract involves sharing with third parties, partners, business partners, 

ATP partners, or affiliates. ATPs should clearly explain the following definitions in 

a consistent manner in all of their respective agreements: (1) farm data; (2) third 

party; (3) partner; (4) business partner; (5) ATP partners; (6) affiliate; (7) data account holder; (8) original customer data. If these definitions are not used, ATPs 

44 

should define each alternative term in the contract and privacy policy. ATPs should 

strive to use clear language for their terms, conditions and agreements. 

Disclosure, Use and Sale Limitation: An ATP will not sell and/or disclose non-aggregated farm data to a third party without first securing a legally binding commitment to be bound by the same terms and conditions as the ATP has with the farmer. Farmers must be notified if such a sale is going to take place and have the option to opt out or have their data removed prior to that sale. An ATP will not share 

or disclose original farm data with a third party in any manner that is inconsistent 

with the contract with the farmer. If the agreement with the third party is not the 

same as the agreement with the ATP, farmers must be presented with the third 

party’s terms for agreement or rejection. 

Data Retention and Availability: Each ATP should provide for the removal, secure 

destruction and return of original farm data from the farmer’s account upon the request of the farmer or after a pre-agreed period of time. The ATP should include 

a requirement that farmers have access to the data that an ATP holds during that 

data retention period. ATPs should document personally identifiable data retention 

and availability policies and disposal procedures, and specify requirements of data 

under policies and procedures. 

Contract Termination: Farmers should be allowed to discontinue a service or halt 

the collection of data at any time subject to appropriate ongoing obligations. Procedures for termination of services should be clearly defined in the contract. 

Unlawful or Anti-Competitive Activities: ATPs should not use the data for unlawful or anti-competitive activities, such as a prohibition on the use of farm data by 

the ATP to speculate in commodity markets. 

Liability & Security Safeguards: The ATP should clearly define terms of liability. 

Farm data should be protected with reasonable security safeguards against risks 

such as loss or unauthorized access, destruction, use, modification or disclosure. Polices for notification and response in the event of a breach should be established. 

The undersigned organizations for the Privacy and Security Principles of Farm 

Data as of April 1, 2016. 

AGCO 

Ag Connections, Inc. 

Agrible, Inc.* 

AgSense 

AgWorks 

Ag Leader Technology 

American Farm Bureau Federation 

American Soybean Association 

Beck’s Hybrids* 

CNH Industrial 

Conservis* 

Crop IMS 

CropMetrics 

Dow AgroSciences LLC 

DuPont Pioneer 

Farm Dog 

Farmobile LLC* 

Granular* 

Grower Information Services Cooperative 

GROWMARK, Inc.* 

Independent Data Management LLC* 

John Deere 

Mapshots, Inc. 

National Association of Wheat Growers 

National Barley Growers Association 

National Corn Growers Association 

National Cotton Council 

National Farmers Union 

National Potato Council 

National Sorghum Producers 

North American Equipment Dealers 

Association 

OnFarm 

Raven Industries 

Reinke Manufacturing Co., INC. 

Syngenta 

The Climate Corporation—a division of 

Monsanto 

USA Rice Federation 

Valley Irrigation 

ZedX Inc. 

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

45 

EXHIBIT B 

AG DATA TRANSPARENT HOMEPAGE

www.AgDataTransparent.com 

Senator MORAN. Thank you very much. 

Dr. Haman. 

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

AND CHAIR, AGRICULTURAL AND BIOLOGICAL ENGINEERING, 

INSTITUTE OF FOOD AND AGRICULTURAL SCIENCES, 

UNIVERSITY OF FLORIDA (UF/IFAS) 

Dr. HAMAN. Chairman Moran, Ranking Member Blumenthal, 

and members of the Committee, thank you very much for this invitation to talk about technology in agriculture and data-driven farming. 

My name is Dorota Haman, and I am Professor and Chair of Agricultural Engineering at the University of Florida, and I have 

been working there since 1985. Agriculture is a major economic 

driver in Florida. Florida agriculture is very diverse and focused on 

specialty crops with most farms smaller and complex, and they are 

much more complex than large Midwest farms dedicated to crops 

like soybean, corn, or wheat. 

This diversity makes introduction of new technologies more complicated, and data collection is also more complicated, and analytics 

are more complicated. Many Agricultural Technology Providers are 


1114JANZ4.eps
46 

focused on agronomic crops, not on specialty crops, such as citrus, 

tomatoes, strawberries, blueberries, and other fruit and vegetables 

produced in Florida. 

Data-driven farming is the future of agriculture. Furthermore, it 

is becoming clear that the future of agricultural operation will embrace the concept of Internet of Things, a system of interrelated 

computing devices, machines, robots, sensors, actuators, and network connectivity. Many technologies are needed to bring agricultural operation to this new level, and many of them have been 

available for some time, but they are now becoming economical to 

introduce in agriculture. Farmers will need to accept these technologies to be competitive. 

New farm technologies and monitoring equipment are already 

producing enormous amount of data at a wide variety of spatial 

and temporal scales. Raw data are not very useful, but become very 

valuable if appropriate algorithms are developed and applied. Process data used by farmers are also valuable to others, including insurance companies and commodity markets. 

A level of data standardization will be necessary for optimal 

sharing and utilization, including a common pool infrastructure to 

facilitate transfer and integration of data from different sources. 

Common pool infrastructure would also facilitate collection of information for the USDA National Statistics Service. 

There is no doubt that standardization and openness of platforms 

would accelerate solution development and innovation. However, 

data ownership and privacy and security of information in relation 

to agricultural Big Data analytics must be addressed. 

It can be argued that access to high-quality farm data gives large 

agribusiness advantage over small farmers with limited resources. 

Open source analytics developed by public institutions, such as universities and Cooperative Extension Service using public funds, for 

example, USDA funds, NSF funds, would provide the solution for 

those farmers. 

These are exciting times for agriculture. Scientists and engineers 

have been focusing on research in the area of robotics, remote sensing, machine vision, machine learning, for many years. Progress 

has been made on early estimation of yields for specialty crops, 

such as citrus, strawberries. This was done using autonomous vehicles and machine vision. These techniques need to be adapted for 

other specialty crops. 

Extensive weather data and crop models help growers evaluate 

climate change adaptation strategies. We have developed technology to remotely diagnose citrus ‘‘greening,’’ a devastating citrus 

disease. Data-driven technologies can also improve farm safety 

through use of alerts and wearable sensors, and save lives. 

I want to thank you for your time. I’m happy to answer any 

questions you might have. 

[The prepared statement of Dr. Haman follows:] 

PREPARED STATEMENT OF DR. DOROTA HAMAN, PH.D., PROFESSOR AND CHAIR, AGRICULTURAL AND BIOLOGICAL ENGINEERING, INSTITUTE OF FOOD AND

AGRICULTURAL SCIENCES, UNIVERSITY OF FLORIDA (UF/IFAS) 

Chairman Moran, Ranking Member Blumenthal and Members of the Committee, 

thank you for the invitation to talk about technology in agriculture and data-driven 

farming. My name is Dorota Haman. I am a Professor and Chair of Agricultural and 

47 

Biological Engineering Department (ABE) at the University of Florida (UF) in 

Gainesville, Florida. 

I was educated as an agricultural engineer at Michigan State University and I 

have been living in Florida and working at the University of Florida since 1985. My 

research, teaching and extension work has been focused on water management in 

irrigated agriculture. Since 2007, I have been in a leadership position in Agricultural and Biological Engineering at UF and have been working with an interdisciplinary group of scientists and engineers. I have also been involved in the American 

Society of Agricultural and Biological Engineers (ASABE) for over 30 years, serving 

on various technical committees and on the Board of Trustees and I am Fellow of 

this organization. This testimony represents my view on the emerging, and rapidly 

growing area of data-driven agriculture. 

Technology is rapidly changing the way we live, work and interact. This is also 

true in agriculture. The way we farm and produce food and fiber is experiencing a 

rapid change and it will only undergo more dramatic change in the near future. Agricultural operations and machinery are becoming more automated, computerized 

and data-driven. The future of agricultural operations will include the Internet of 

Things (IoT), defined as a system of interrelated computing devices, machines, robots, sensors, actuators and network connectivity. The transfer of data in this system does not require human-to-human or human-to-computer interaction. 

More and more sensors are introduced every day to agricultural operations and 

monitoring systems. Increasingly, these sensors are connected through wireless communications to the internet, making data available in farm databases and mapping 

systems, and, more importantly, for enabling analysis of what affects what, why, 

when and how. 

Many technologies that are needed to bring agricultural operations to this new 

level have been available for some time, but they are only now becoming economical 

for introduction into agriculture. The convergence of technologies in other fields is 

rapidly bringing down the cost of devices and sensors, and therefore making agricultural applications economically feasible. For example, sensors built as components 

of costly medical devices are now economical for farmers. The medical sensor may 

cost several hundred (if not thousands) of dollars but a simplified, maybe less accurate form, but totally adequate for the agricultural application, is becoming available for a small fraction of the price. The use of unmanned aerial vehicles (UAVs 

or Drones) in agricultural operations is another good example of technology becoming affordable. A drone that costs a few hundred dollars today, was sold for thousands of dollars a few years ago. 

Agriculture is a major economic driver in Florida. Agricultural research and extension at the University of Florida (UF) provides knowledge, innovation and technology transfer that supports 2.2 million agriculture-related jobs and direct industry 

output of $148 billion in Florida (2014). Florida agriculture is very diverse and focused on specialty crops with most farms smaller and more complex than large Midwest farms dedicated to crops like soybean, corn or wheat. This diversity of production often makes introduction of new technologies more complicated for data collection and analytics. Many Agricultural Technology Providers (ATP) are focusing on 

agronomic crops not on specialty crops such as citrus, tomatoes, strawberries, blueberries and many other fruits and vegetables produced in Florida. 

Florida is also a significant milk producer. Many modern dairy farms are highly 

computerized and data-driven to optimize their operation. Last week, I visited one 

of the dairies in North Florida. Each calf is tagged at birth and monitored throughout its entire life on the farm. Calves are fed by robots that adjust the formula of 

their feed based on need at their individual stage of life. The feed, health, milk production, milk quality, location etc. of every cow are monitored and available when 

needed. Solid waste is converted to high quality compost sold to the ornamental industry and the liquid waste is recycled through field irrigation to produce more feed 

for the cows. This is just a glimpse where modern agriculture is now and where it 

is going. 

A successful peanut grower in Levy County, remotely controls center-pivot irrigation systems in response to soil moisture probe data, from anywhere in the world 

where there is internet, saving time and labor. Remote video systems on center-pivots allow visual monitoring for security and system operation. Enhanced GPS controlled auto-steering systems on tractors on this farm are accurate to less than an 

inch and virtually hands-off. They precisely map the location of planting, for precise 

and efficient harvesting several months later. Such precise operations eliminate 

over-application and overlap of pesticides and fertilizers. Soil can be sampled automatically and indexed to GPS coordinates to regulate a variable fertilizer spreader. 

These efficiencies save water, fertilizer and money and reduce adverse impacts on 

the environment. 

48 

Responding to Florida growers’ needs, scientists and engineers have been focusing 

on research in the area of robotics, remote sensing and machine vision for many 

years. Early yield estimation is critical for harvest planning and marketing. Great 

progress has been made on estimating the yields of various specialty crops such as 

citrus and strawberries using autonomous vehicles and machine vision. These techniques need to be adapted to other specialty crops. 

Huanglongbing (HLB)—commonly called ‘‘greening’’ is a devastating disease that 

has had significant impact on citrus yield and quality in Florida. Unfortunately, so 

far, no cure has been reported for HLB. The first critical step for successful control 

of HLB is its detection and diagnosis. It has been demonstrated that high-resolution 

aerial imaging using a low-cost UAV or drones can detect the disease. Recently, a 

vision sensor was introduced and successfully tested at UF for early detection of 

HLB. Since the majority of Florida citrus is already showing symptoms of the disease, research emphasis is shifting to genetic solutions to the HLB problem. 

Sensor-based management of water delivery through irrigation has been implemented on many farms across the U.S. and in Florida. Precision irrigation offers the 

potential for improving irrigation efficiency through localized water delivery based 

on plant needs, weather predictions and soil moisture sensors. Reported water savings due to sensor control of irrigation and precise application of water are on average 60 percent. This results in money savings to a farmer and reduced use of precious water resources. 

Data-driven technologies can also improve farm safety. Wearable sensors, now 

under development, can save lives and reduce harm to farm workers. Alert systems 

that can detect personal overheating, or inform about approaching thunderstorms 

and lightening (typical in Florida), will also increase safety in the fields. Nine out 

of the 10 hottest years on record have occurred in the past decade and, according 

to the Centers for Disease Control and Prevention, farmworkers are more than 20 

times at risk for heat-related deaths compared to other occupational groups. Wearable sensors can also alert workers to chemical exposure through direct contact during application, contact with residue on plants, or through drift from nearby applications. The wearable sensors can be also used on various machines, drones and robots to provide immediate safety intervention. 

Weather data systems have been essential for efficient management of agricultural systems. A system of automatic weather stations and satellite data provide excellent management tools for Florida growers. The cost of a sophisticated blue-toothenabled weather station is now less than $1000. This monitoring will become more 

critical as climate change leads to increased changes in seasonal temperatures, rainfall patterns, and the frequency and severity of storms. For example, in the last few 

winters, Florida growers have been affected by insufficient chill-hours to optimize 

production of temperate fruits such as blueberries, peaches, and strawberries. 

Adapting to these changes, Florida blueberry growers are experimenting with ‘‘evergreen’’ production of blueberries that does not require chilling. Agriculture in Florida is not only sensitive to the manifestations of climate change mentioned above, 

but also to the salt water intrusions in coastal irrigation wells as a result of sealevel rise. 

Data quality has always been a key issue in farm management information systems, and is more challenging in an era of Big real-time data. Intelligent processing 

and analytics of Big Data is challenging because of the large amount of often 

unstructured, heterogeneous data which requires a smart interplay between skilled 

data-scientists and domain experts. At present, new farm technologies and monitoring equipment are producing enormous amounts of data at a wide variety of spatial and temporal scales. Raw data sets are cumbersome and not directly useful, but 

become very valuable if appropriate algorithms are developed and applied. Data 

analytics, frequently provided by ATPs, are being developed for agricultural applications and are a necessary step to make these data valuable to growers. These processed data are also very valuable to others including insurance companies and commodity markets. A level of data standardization will be necessary for optimal sharing and utilization, including a common pool infrastructure to facilitate transfer and 

integration of data from different sources/companies. 

New technologies are introducing new problems and issues that need careful consideration. There is no question that openness of platforms would accelerate solution 

development and innovation. However, data ownership, and related privacy and security issues are problems that are frequently discussed in relation to Big Data and 

analytics. These concerns need to be addressed, realizing that enforcement may slow 

down innovation. 

It can be argued that access to high quality data, that allows for predictive business modeling of every aspect of farming, gives large agribusinesses advantage over 

49 

farmers, especially small farmers, who do not have sufficient resources to pay for 

data analytics. 

Leveling the playing field for smaller farmers, such as Florida farmers who 

produce specialty crops, through the use of open source analytics developed by public institutions such as universities and Cooperative Extension Service using public 

funds (e.g., USDA, NSF) is an option, providing data processing though utilities 

(apps), interactive models and maps. 

In summary: 

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

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

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

to make data valuable to growers. 

• The processing and analytics of agricultural Big Data is in its infancy, and is 

challenging because of the large amount of often unstructured, heterogeneous 

data which requires a smart interplay between skilled data-scientists and domain experts. A common pool infrastructure should be developed to enhance 

sharing and integration. 

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

maps. 

• Data ownership, and related privacy and security issues, are problems that are 

frequently discussed in relation to Big Data and analytics. These concerns need 

to be addressed. 

I want to thank you for taking the time to focus on technological innovations in 

agriculture. Thank you for inviting me to testify today. I would be happy to answer 

any questions that you might have. 

Senator MORAN. I want to thank you for your time. Thank you 

very much. And there will be questions. 

I think I’ll turn now to the Ranking Member for purposes of his 

questions, and we’ll then deal with the other Members of the Senate who are here, and I’ll go toward the end. 

So, Senator Blumenthal. 

Senator BLUMENTHAL. Thanks, Mr. Chairman. 

And thank all of you for your testimony. And I think that you 

have all identified one of the key issues here, ‘‘How do we enable 

farmers to maintain control, either through some kind of opt-in 

consent or some other means?’’ 

And I just want to call your attention to the fact and ask your 

comment on the vote that the Senate took not so long ago on a resolution to overturn the Federal Communication broadband privacy 

rules, which I found extraordinarily regrettable. These rules were 

repealed, which constituted, in my view, an attack on consumer 

rights to privacy, but also these rules underlay the potential for 

protection of privacy in your industry as well. 

So I want to ask each of you whether you’ve considered that all 

of these efforts may be undermined by the repeal of the FCC’s 

broadband privacy rules because they leave no baseline protection, 

privacy protection, in place, and broadband providers can sell and 

share farmer data just as they can other consumer data, and 

whether you might recommend that we and principally the FCC reinstate those rules? And I’ll ask that question of all of you. 

You look like you’re ready to answer, Dr. Ferrell. 

50 

Dr. FERRELL. Well, and the lens through which I’ve examined a 

lot of the protections for agricultural data is interesting because 

there is not really a specific existing statute that would address the 

type of data that we’re discussing here. 

If my tractor uses a cellular signal and basically makes a phone 

call to a service provider, there are Federal rules governing telephone communications that would protect that transmission. If I email a file to my crop consultant, there are protections for that as 

well. 

But if we’re just talking about the transmission of telematics 

data, that really doesn’t fall comfortably into any realm, especially 

when we start to combine and package that agricultural data with 

other things. 

One example I sometimes give is that if my data service provider 

has raw agronomic data about my farm, but they also have a customer relationship management software program that pairs with 

that data, well, now there might be protectable, you know, personally identifiable information that is now coupled with my farm, 

that’s a very different privacy issue than if we’re just talking about 

agronomic data. 

And so I think one thing that we may have to do is—I kind of 

agree that we need more robust consumer protections for data generally, and I think specifically when we talk about agricultural 

data, we’ve got a lot of work to do definitionally in defining what 

this kind of data looks like. It’s not health data under HIPAA, it’s 

not financial data under the Fair Credit Reporting Act, it’s something unique unto itself that I think may deserve some efforts to 

actually define individually and define what those protections 

should be. 

Senator BLUMENTHAL. You are certainly correct that there are 

lots of different kinds of data, even within farming, but the principles I think have to be framed in a way that they’re applicable 

to all of these kinds of data. And you’re right also, there may not 

be a specific statute here, but the principles have to be embodied 

at some point in regulations or rules or statutes, and the authority 

is there for the FCC to adopt protections. 

So I would just invite you to think about what those protections 

should be, because right now a provider could paint an extraordinarily detailed picture of a farmer’s business practices, from the 

type of feed he may use, the type of crops he grows, the type of fuel 

he uses, how many workers, how many livestock. 

And I recall well a lunch I had, referring back to my early experience, with my grandfather, who had a farm—by the way, Senator 

Fischer, just south of Omaha—and the lunch was at the stockyards, and I was asking someone having lunch with us, one of my 

grandfather’s friends, how many cattle he was feeding. And my 

grandfather nudged me and gave me that look of disapproval that 

grandfathers do when they are not enamored of the line of questioning, and he explained afterward that’s like asking somebody 

how many dollars he has in his bank account. And that sense of 

privacy is broken, I think, by many of these practices, and farmers 

are very, very respectful about their privacy, with good reason. 

Thank you. 

Senator MORAN. Senator Blumenthal, thank you. 

51 

The Senator from Nebraska, Senator Fischer. 

STATEMENT OF HON. DEB FISCHER, 

U.S. SENATOR FROM NEBRASKA 

Senator FISCHER. Thank you, Mr. Chairman. 

And thank you, Ranking Member Blumenthal. 

As a cattle rancher, I thank you for your respect to my privacy. 

I would like to thank both of you for calling the hearing today 

so we can focus on managing that Big Data with new technologies 

for our Nation’s farmers and ranchers. Not only will this help boost 

productivity, but advancements in digital analytics can improve 

how we feed our communities and conserve our natural resources. 

In fact, Tim Hassinger of Lindsay Corporation, from Omaha, Nebraska, noted at last week’s Internet of Things hearing that the 

combined yield enhancement and resource savings from new technologies can increase the American farmers’ profits by an average 

of $40 per acre. 

And I would like to please submit for the record follow-up materials that Lindsay has provided regarding its research on data analytics and irrigation management, Mr. Chairman. 

Senator MORAN. Without objection. 

Senator FISCHER. Thank you. 

[The information referred to follows:] 

FOR IMMEDIATE RELEASE—November 8, 2017 

LINDSAY CORP PRESIDENT AND CEO SPEAKS TO U.S. SENATE COMMITTEE

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

(OMAHA, Neb.)—November 2017—Tim Hassinger, president and CEO of Nebraska-based Lindsay Corporation, offered testimony during a hearing yesterday 

conducted by the U.S. Senate Committee on Commerce, Science and Transportation. 

The hearing, titled Advancing the Internet of Things in Rural America, focused on 

the benefits of the Internet of Things (IoT) in rural communities and the infrastructure needs necessary to advance the IoT market to ensure rural America has access 

to products and devices that are driving the digital economy. 

‘‘Like all business owners, farmers in rural communities need the ability to go online,’’ Hassinger said. ‘‘The Internet fuels the innovative, advanced technology that 

will help America’s farmers meet the food, fuel and fiber needs of our rapidly growing global population.’’ 

Hassinger’s testimony contends that with reliable, high-speed Internet access, 

farmers can take advantage of tools that deliver hyper-local weather forecasts, real 

time data on soil moisture conditions and GPS for planting and irrigation management. They can also take advantage of a myriad of emerging technologies available 

from Lindsay Corporation and other American manufacturers. The testimony further explains that these innovations enable efficiencies through remote data collection, transfer and analysis from connected devices like soil moisture sensors, weather stations and cloud-based tools. 

‘‘At Lindsay Corporation, we are developing and deploying technologies to help 

growers produce more with less. For example, our FieldNET® and FieldNET Advi- sorTM remote irrigation management and decision support tools help farmers decide 

precisely when, where and how much to irrigate—to help them maximize yields 

while reducing overwatering and related input costs and nutrient losses,’’ Hassinger 

said. ‘‘But we know the technology is only as good as the farmer’s ability to access 

it.’’ 

According to the Federal Communications Commission Broadband Access Report, 

an estimated 39 percent of the rural population (23.4 million Americans) lack access 

to broadband that meets today’s benchmark speeds of 25 Mbps for downloads and 

3 Mbps for uploads. By contrast, only 4 percent of urban Americans lack access to 

25/3 Mbps broadband. 

‘‘While cities and municipalities typically have access to several high-speed Internet service providers, that access often ends at the city limits. Those living in rural 

52 

communities must depend on radio networks, satellite or cell service—all of which 

typically operate at lower speeds, limiting connectivity,’’ Hassinger said. ‘‘All farmers are faced with the pressure to increase yields while conserving resources. The 

lack of reliable broadband hinders their ability to adopt the new technologies that 

will help them optimize their operations and compete in the global marketplace.’’ 

Hassinger’s full testimony can viewed at https://commerce.senate.gov. (https://commerce.senate.gov.) 

REPORT


1114FIEL1.eps
53 


1114FIEL2.eps

OPTIMIZING 

IRRIGATION THROUGH BETTER 

DECISION-MAKING 

In a single map or list vtew, you can see the most critical information across 

all your fields, including current soil water depletion, recommended next 

irrigation start date and application depth rec:ommended to avoid crop stress. 

Improves yield output and crop performance- Tools to help avoid crop water 

stress and nutrient leaching 

Increases profits and sustafnabllity-Toolstoreduceoverwatering, saving related 

input cosls and wasted resources 

For growers who have relied on v1sual inspect•on of the1r crops or less sctentific methods for irngatJon management, 

FieldNET Adv1sor is the revolutionary tool that provides highly accurate information for better decision-making 
54 


1114FIEL3.eps

SCIENCE+ DATA+ TECHNOLOGY= 

SIMPLE RECOMMENDATIONS 

CONTINUOUSLY UPDATED'\.. 

AS·APPliEO IRRIGATION ~ 

CROP TYPES, 

HYBRJOS. PlANTING DATES 

CANOPY 

ROOT ZONE 

f . 

PERCOLATION 

AND DRAINAGE 

HOURLY CROP 

EVAPOTRANSPIRATION 

MASSIVE AMOUNT'S OF DATA HARNESSED- -CLOUD COMP\ITING CAPABllllY 

WHEN, WHERE AND HOW MUCH 

Using technology to simplify irrigation science 

FieldNET Advisor combines more than 40 years of crop and irrigation research into Field NET-by Undsay's 

proven technology platform to simplify your irrigation management decisions and equipment operation. 

You'll no longer have to rely exclusively on visual inspections of your field, use complex calculations or 

juggle multiple tools to track your crops' water needs. 
55 


1114FIEL4.eps

FOUR POWERFUL 

TOOLS IN ONE 

FIELDNET IRRIGATION ADVISOR'" 

The core of FieldNET Advisor, this tool compiles critical 

Inputs related to the so1l water balance (e.g. crop water 

usage. effective rainfall, actual as-applied irrigation 

history. and past deep percolation) to track the current 

soil water depletion and forecast the upcoming crop 

water requirements across your entire field. It's like 

having thousands of virtual soil moisture sensors 

placed throughout the field. 

View current soil water deplebon map and current pivot 

operational status. plus a summary of key acbonable 

mfonnahon With avadablhty to d•rectly select manual or 

VRI mode and control tmgatton equ1pment 

FIELDNET CROP ADVISOR'" 

Dynamic, proprietary crop growth models track the 

development of each hybrid, including development 

stages and root growth, and continuously update the 

crop's forec;uted maturity date. This tool also provides an 

estimate of any yield loss to date due to water stress- plus 

a projection of potential yield loss through crop maturity 

if no additional irrigation is applied. 

Crop Advisor conhnuously tracks the allowable soil water 

depletion to help avo1d crop stress Wlth customtzable 

buffers throughout the season, plus the crop's forecasted 

1mgahon needs through matunty 
56 


1114FIEL5.eps

FieldNET Advisor streamlines irrigation management and features all of your key information in one place. 

Fore xam pie, the dashboard view provides as urn mary of the most critical detai Is on one page, including 

current maximum field depletion, date next irrigation must be started. and amount of irrigation required. 

Fully integrated remote irrigation monitoring and control means that you can stay informed with customized 

alerts and take immediate action from anywhere. 

(If/// 

FIELD NET WEATHER ADVISOR'" 

A critical input is weather data. which is why the 

Weather Advisor uses hyper-k>c.al. hyper-accurate 

field-specific weather data. includ1ng hourly data for 

the current season-to-date plus a IS-day hourly forecast 

and historical norms to project the remainder of 

the season. This tool also provides the ability to set 

customized wcatheraleru. so you're aware of changing 

conditions and arc able to react quickly. For fields where 

an optional Growsmart9weather station is 1nstalled, the 

data can be seamlessly fed into FieldNET Advisor as well. 

Weather Adv1sor prOVIdes the valuable weather 

info you really need. includmg a~speofic daily 

ET (ratherthan reference crop ET), dally grow~ngdegree 

umts (GDUs), and da1ly plus hourly wind speed. peak 

w.nd gust speed. forecasted hkehhood ofprecip!tallon 

and amount. and more 

FIELDNET VARIABLE RATE 

IRRIGATION (VRI) ADVISOR'" 

Gone are the days of using the same plan throughout the 

season, as VRI Advisor provides continuously updated 

water prescriptions based on the dynamically changing 

field conditions. This is accomplished by leveraging 

the high resolution soil water depletion data across the 

field to generate VRI prescriptions that are dynamically 

optimized in real time to apply the required amount of 

water across every zone in the field. VRJ Advisor can 

create both Precision VRI and basic sector VRI plans. 

VRI Adv1sor allows you to rev1ew the current 

recommended dynamic VRI plan and make mod1fica!Jons 

tf desired, or create and save new or mod1fied plans 
57 


1114FIEL6.eps
58 


1114FIEL7.eps

VRI OPTIONS WITH FIELDNET ADVISOR 

DYNAMICMODEL 

CURRENT SOIL --

WATER DEPlETION 

VRI PR£SCRIPTlONS 

SECTORVRI 

Onndes the field into 360 "pie 

sltees" aod adJUSts the prvot speed 

over these sectors to change 

the application depth_ (Does not 

control IndiVIdual spnnlder1 ) 

Wtn.aonbrwNit.ePM:Q...,BOS$Ot 

VISION~ Alto-orUonz-.epovco 

~·8A$1C---OtOII .... ..-..cP'f'(lt 

Drrdlby-"*'QFolllciHETP'NoiCOI'*OI-Of 

,_.,..... .... 

PRECISION VRI 

A vinually unlimited number of 

flexible applicatiOn areas prOVIdes 

exactly the right amount of water 

by controllmg Individual sprinklers 

-----------------

; ·~ 3/A '~· :-:- ~~ . 

What is FieldNETremote 

irrigation management? 

Irrigation with industry-leading 

FieldNET is like having a remote 

control for all your irrigation related 

equipment. FieldNET hardware can 

be retrofitted on virtually any brand of 

irrigation equipment. Once installed, 

the FleldNET website and mobile apps 

allow you lo monitor and control your 

systems from virtually anywhere. 

With FieldNET, you get advanced 

feaiUres. such as variable rate 

irrigation, real-time shut-down alerts, 

and mud'l more. It all adds up to less 

time in the field, less spent on valuab~ 

resources and more confidence. 

FteldNET Advtsor is a cloud· based 

irrigauon management tool w•thm 

FieldNITs remote momtorJng and 

comrol platform .lt'san add·onoptlon 

that requires no additional hardware 

and prov•des seamless I ntegr au on so 

you can Immediately put re<:ommended 

plans lmoac.tlon 

. -· .·.~~~·. 

Field NET Advtsoralsosends real·ume 

text or email alerts tO noufyyou when 

I rrlgatlon Is required, prOVidmgyou 

peace of mind 
59 


1114FIEL8.eps

INDUSTRY-LEADING CAPABILITIES 

Talk to your local Zlmmatic• dealer orvisitmyfieldnetadvisor.com to see which crops are 

currently available and to see if Field NET Advisor Is available in your region. 

THE LINDSAY ADVANTAGE LINDSAY DURABLE • RUGGED • EASY TO USE • INTEGRATED TECHNOLOGIES • 

BROADEST LINE OF SOLUTIONS 

2222 N. 111 th St., Omaha, NE 68164 · 1·800-829-5300 · 1-402 -829-6800 · www.lindsay.com 

FN ADVISOR SRO 

ENG-20(725000<117 
60 

Senator FISCHER. Mr. Knopf, in your testimony, you describe 

your farm management operations. And what benefit do you see on 

your farm from implementing Big Data technology? How do you 

analyze it? Do you have input from your nutritionists, your equipment dealer, local co-op? How do you get it all put together? 

Mr. KNOPF. That’s a good question. There’s a lot of information 

to try to analyze. And so in our farm—that answer is going to look 

different for each individual farm, of course, on the strengths and 

talents and the passions of that individual farm operator. Some 

farmers are very passionate about their data, very passionate 

about precision agriculture, and they want to be very hands-on in 

that process. And those are also I think the guys that are very passionate about the ownership and transparency of that data. They 

want it on a server in their office. 

There’s the other spectrum of farmers—and we’re kind of in the 

middle, let me say that—there’s the other spectrum of farmers, and 

sometimes it changes based on age demographics as well. I’ve noticed in my community, in my experience, and thinking about our 

own farm, the older generation is much more private about their 

data, very unwilling to share it. The younger generation is—you 

know, they grew up with smartphones, and Google is tracking 

them all the time and telling them things about themselves that 

they didn’t know already, and so they’re more used to that. So 

there’s a generational change there as well. 

For us, we’re kind of in the middle of—I have an agronomic background from Kansas State. I have a young man that works—we 

have one full-time employee that also is an agronomist. And we do 

most of our data analysis in-house. But our data is stored in a software system that is web-based, and it’s subscription-based. And 

that was a big change for us about 2 years ago, 3 to 4 years ago, 

is transitioning our data from being stored in-house to web-based, 

which is somewhat of an uncomfortable step because you feel like 

you’re losing control, and that’s why the transparency is so important. But we are still processing a lot of the data and analyzing a 

lot of it in-house. But there is a growing amount of service providers dealing with that. We collaborate a lot with our land grant 

with Kansas State University, and a lot of their research we collaborate with and host on our farm. So that’s a very important collaboration. And I appreciate two representatives here from universities as well. 

Senator FISCHER. And as you noted in your testimony, the ag 

economy is in a downward cycle right now, it’s hurting. I also know 

that there’s a lack of access to broadband, and we have a lot of 

slow downloading speeds. And how does that affect your bottom 

line—— 

Mr. KNOPF. That has a—— 

Senator FISCHER.—when you have this slow access? 

Mr. KNOPF. Yes. Excuse me. That has a very significant impact. 

In fact, the county that we live in is just now receiving fiber optics 

ran to farms. In fact, it’s not even hooked up yet, it’s just being ran 

right—right now, being buried, because a lot of my peers that live 

in more—we have a community of about 40- to 50,000 within our 

county, but yet there is still a lot of rural part of the county. So 

a lot of my colleagues that live in rural counties in Kansas qualiVerDate Nov 24 2008 08:08 Aug 05, 2019 Jkt 000000 PO 00000 Frm 00064 Fmt 6633 Sfmt 6601 S:\GPO\DOCS\37228.TXT JACKIE
61 

fied for grants from the government to help build that initial infrastructure. 

Well, our county happened to not qualify because of the urban 

center that we had within that county, and we have some 

broadband antennas on top of elevators and towers and so forth, 

but it is very unreliable and fairly slow. So that has limited our 

utilization and access of data, particularly in web-based software 

platforms, and been an economic disadvantage in utilization of data 

because we’re kind of caught in the middle of that process. 

Senator FISCHER. Thank you. 

Thank you, Mr. Chairman. 

Senator MORAN. You’re welcome. 

Senator Klobuchar. 

STATEMENT OF HON. AMY KLOBUCHAR, 

U.S. SENATOR FROM MINNESOTA 

Senator KLOBUCHAR. Thank you, Mr. Chairman. Thanks for allowing me to go, too. And just to follow up with Senator Fischer, 

and we’ve worked on some of these broadband issues, I, like Senator Fischer, have heard many stories from my state about the 

need for better broadband access. And I always remember a precision ag company out of Willmar, Minnesota, that actually told 

farmers to go to the restaurant parking lot because they would get 

better Internet. 

And when you think about it, you know, way back about 10 years 

ago people were just trying to get Internet so they could write their 

grandkid an e-mail or something, and it has changed so much that 

if they don’t have that ability, they’re just not going to be able to 

do their work basically, and that’s everything from Jennie-O in 

Minnesota measuring the temperature in barns for turkeys—I 

mention turkeys, we are number one in turkeys in Minnesota, and 

Thanksgiving is upon us—and other things that we’ve seen that it’s 

just really a must-do for these farmers. 

Mr. Ferrell, most broadband connections provide faster download 

than upload speed. Why is upload speed important for ag data? 

And how are reliability and bandwidth needs of farmers different 

than the average consumer? 

Dr. FERRELL. Well, you’re very right, Senator, in that we’ve seen 

tremendous gains in terms of what rural broadband access has 

been able to offer in terms of download speeds, and that’s been 

good for farmers acquiring information. But as the witnesses have 

mentioned today, we’re gathering tremendously more data that has 

to be uploaded. And in certain cases, in some applications, we need 

that data to be uploaded in real time. 

And so even if you’re in a broadband-connected accessible area, 

your farm equipment needs to be able to access wireless broadband 

speeds as well to upload that data and receive real-time updates 

about things like cropping prescriptions, like Mr. Knopf was mentioning. 

As you’re doing intensive management of a specific field, you 

need to have advanced broadband upload speeds to be collecting 

that information, getting real-time recommendations back, so you 

can make on-the-fly adjustments to things like seed population, fertilizer application, pesticide application, and things of that sort. 

62 

So the download speeds are good, but as the ag data revolution 

continues, upload speeds I think will be even more important because of the amount of data that we’re pushing to the cloud rather 

than receiving from it. 

Senator KLOBUCHAR. Thank you. 

Dr. Haman, I just recently visited, in fact, just a week ago, Aker 

Technologies in Winnebago, Minnesota. You may not have been 

there before, but they’re developing this innovative drone technology to help farmers be more efficient when applying fertilizer, 

pesticides, and water. It saves money. It leads to more sustainable 

farming because they’re going to be able to see where they need the 

water, where they need the pesticide, and not do one-size-fits-all for 

their whole fields, nor do they have the resources, especially small 

farmers, to be able to walk through every row. 

The FAA recently introduced a pilot program to explore the use 

of drones far beyond the line-of-sight operations in limited cases. 

For farmers managing large tracts of land, this could be a valuable 

option in the future. And in your testimony, you mentioned the use 

of drones to combat citrus diseases in Florida. Could additional 

drone monitoring help prevent the spread of diseases? 

Dr. HAMAN. Absolutely, yes. They absolutely. We already are 

using drones, for example, in forestry. And in forestry, the drones 

can detect the areas where the disease is likely, where the plants 

are stressed. Using different spectra, you can detect stress which 

is from water or stress which is due to disease, and separate that. 

And we are right now doing research to detect specific stresses due 

to specific deficiencies of different nutrients and different—so possibilities are enormous, and as the cameras are getting smaller, it 

makes also big difference because they can be carried on small 

drones. 

Senator KLOBUCHAR. Exactly. And that’s what this concept is 

going on at this company. 

Dr. HAMAN. Yes. 

Senator KLOBUCHAR. Last question, Mr. Knopf. The Minnesota 

Corn Research & Promotion Council has been working with local 

farmers to test innovative conservation approaches, pilot new technology applications, by promoting the adoption of precision ag technology, like drone monitoring, smart irrigation, that I was just 

talking to the doctor about how we can help farmers with conservation. 

Could you talk about how Federal research initiatives or landgrant universities partner with industry to further advance innovation? Quickly. 

Mr. KNOPF. I really appreciate that question, and it is invaluable 

and becoming increasingly invaluable to both economic sustainability and environmental stewardship. We host yearly on our farm 

between four to a dozen experiments in collaboration with Kansas 

State. Their resources are limited more as time goes on because of 

budgets, so farmer-private university collaboration is increasingly 

important. 

Senator KLOBUCHAR. Thank you very much. 

Thank you, Mr. Chairman and Senator Blumenthal. 

Senator MORAN. You’re very welcome. Thank you, Senator 

Klobuchar. 

63 

Senator Lee. 

STATEMENT OF HON. MIKE LEE, 

U.S. SENATOR FROM UTAH 

Senator LEE. Thank you very much. Thanks to all of you for 

being here. We’re witnessing a technological revolution. It’s very 

significant. I think it’s going to soon be the case we’ll look back at 

what we’re experiencing today and realize that this was just the tip 

of the iceberg. And it’s one that I think has the ability to allow 

farmers and ranchers enjoy cutting-edge technology and make their 

operations more effective, more productive, so that they can feed 

more people and do what they do. 

I want to focus today a little bit on the application of unmanned 

aerial vehicle technology, drone technology, as we sometimes refer 

to it, to agriculture. 

Mr. Knopf, I want to follow up on some questions Senator 

Klobuchar asked you about. Tell us what—how this has potential 

application in your field? 

Mr. KNOPF. We happen to be 15 miles from the Polytechnic campus of Kansas State University, which is in Salina, and they are 

one of the leading institutions for UAS and UAV programming. For 

the last couple years, they have been carrying out a pilot project 

alongside the FAA with a grant they received on some extended 

line-of-sight operations for drones, which was already brought up, 

and looking at some of those regulations and how that can work. 

So specifically what that means to our farm, other collaboration 

that we’re doing with Kansas State from the Agronomy Department is looking at things such as being able to sense NDVI ratings, 

which is a plant health greenness index, and across the field in 

real time to very quickly pick up any problem areas in the field, 

to quickly address issues that need to be addressed before they 

make more significant impact. And it’s just a better, more efficient, 

precise way to collect data across a wide scale. So there are a lot 

of implications in that, and I think satellite imagery is also an increasing area of data that is going to be easily accessible as well 

that will have a lot of applications. 

Senator LEE. Thank you. And following up on that, Dr. Ferrell, 

you mentioned in your testimony that drones are one technology, 

one useful piece of technology, that can be used to gather agronomic data. Can you explain how important this is for farmers, 

why it’s important? 

Dr. FERRELL. Yes. I think it’s incredibly important because it’s 

constantly being called upon for farmers to do more with less, and 

that’s not just in terms of input, sometimes it’s in terms of labor 

as well. You know, we’ve talked about the use of UAS and drones 

to scout crop conditions, and certainly that enables one farmer to 

cover potentially thousands of acres in a day whereas if they were 

trying to do that on the ground by scouting, that might take days 

out of their schedule, and they just don’t have the manpower to 

handle that. 

But another important aspect of UAS use that we haven’t talked 

about much is actually in livestock operations, especially if you’re 

talking about large, you know, pasture-based operations, sometimes using drones to check cattle can save a tremendous amount 

64 

of time and resources, and enable us to identify cattle that are in 

poor health, get them treated quickly, and get them back to health. 

So I think there are unprecedented opportunities there for us in 

the agricultural sector. 

Senator LEE. It could help a lot of people in their operations. 

Now, Mr. Janzen, your law firm represents people in regulatory 

compliance and registration. What can you tell us about what the 

largest regulatory barriers are to farmers and ranchers using drone 

technology, or for drone technology more broadly being implemented in the United States? 

Mr. JANZEN. Thank you, Senator. I think right now probably 

some of the largest barriers for farmers would be the cost of the 

drones and also the ability to use the data and get real results back 

that they can put into practice on their particular farms. And I 

think that there was a lot of hype when drones first started to become very popular, and, you know, now here we are today, and 

we’re trying to figure out how to best use that information today. 

So I don’t know currently that there is an enormous regulatory 

hurdle for use of those drones on the farm. 

Senator LEE. What about the FAA’s restriction on the use of 

drone technology beyond line of sight? 

Mr. JANZEN. Oh, yes. 

Senator LEE. Has that had an impact on people’s adoption of this 

technological platform? 

Mr. JANZEN. That’s a very good point. Yes, it probably is creating 

some restrictions on people wanting to use the technology because 

that is a barrier to, you know, a single farmer who may want to 

use this technology on their farm. 

Senator LEE. OK. It’s one of the reasons why I’ve been a big believer in and an advocate for legislation that would help provide 

clarity on drone regulation. This is a technology that has tremendous promise, not just for the U.S. economy, but for humanity, but 

because the technology is developing in the way that it is here in 

the United States, it can offer some real benefits to the United 

States specifically. 

As we’ve seen in other areas, one of the things that can hold 

back technological innovation is stifling government regulation, 

and so that’s why I’ve been an advocate on that. 

Thank you, Mr. Chairman. I see my time has expired. 

Senator MORAN. Thank you, Senator Lee. 

Let me start I think with Mr. Tatge, although I’m happy to have 

a response from any of you. The promise, or a promise, from data 

collection and its usage is its increasing value, and, therefore, 

added profitability to production agriculture. 

So in your testimony, Mr. Tatge, you estimated that a billion dollars of annual revenue could be returned to rural communities from 

multiple sales of that information. 

Mr. TATGE. Yes, sir. 

Senator MORAN. So how do you envision Mr. Knopf earning additional income as a result of collecting data on his farm? 

Mr. TATGE. So we create what is called an electronic field record. 

An electronic field record is a map basically of what happened on 

what part of the field, whether it be seeding rates that are being 

planted, whether it be application rates from sprays that may have 

65 

been applied to the crop, and then from harvest as well. And once 

you get those—that data in a state where it’s in a standard, and 

we call this electronic field record, we can make that available for 

sale. 

So when we make that available for sale, companies come to us, 

and we’ve already sold data for farmers, will come to us and say, 

‘‘I would like to buy that layer of data, and this is what I’m willing 

to pay.’’ And then we go back to the farmers and we tell them who 

the buyer is of that data and how much they’re willing to pay, and 

the farmer chooses if they want to—let’s think of it as licensing a 

copy of that data to that company. And if they do, we split the revenue with the farmers. 

So we’ve turned it into a profit center for basically taking digital 

exhaust and packaging it to get it to go to the people who have a 

desire to acquire it. 

Senator MORAN. So you’re the market. You’re the intermediary 

between those who have something to sell and those who have 

something they want to buy? 

Mr. TATGE. Yes, sir. We’re also helping with the collection of the 

data in the first place to get it standardized in a way that makes 

it easily portable or interoperable. Because our job is not to do any 

analytics on data, our job is to help farmers get the best dataset 

that they can get in one place and in a format that it can move 

wherever they direct it to go. 

So when our focus is exclusively on being able to focus on how 

can we get the best dataset into a database, and then how can we 

get that data to flow into whatever system the farmer directs us 

to send it? If they’re working with their local agronomist, we’ve got 

several examples where a farmer is automatically sending data to 

their agronomist without having to do anything. The day they get 

done spraying the field, this file is going directly to their agronomist, and there’s no charge for that. 

The farmer can choose to share that data with anyone they want. 

It’s when you get enough of this data packaged together, though, 

that’s when you bring the opportunities for monetization of the 

data, just like a brand-new commodity. 

Senator MORAN. That’s not much value to individual data in and 

of itself, an individual farmer’s data, but if you can combine it with 

broad data, bigger data—— 

Mr. TATGE. Yes, there are values to it on the farm itself for your 

operations, making sure you’re running your machines as efficiently as you can, fuel usage, idle time. It’s a different value prop 

to the farmers. Then there’s a value to sharing that data with 

trusted advisers, and it adds increased service opportunities and 

increased ability to react during the in-season mode to make some 

changes to hopefully put down any threats that come up or minimize their impact. 

And then to the market, there’s a greater opportunity to really 

start to identify and make these datasets available for us to understand best practices. Are we planning—how come when the farmer 

rips open the bag, we are—we lose all this genetic yield potential? 

Is it because they’re planting the seeds too deep? Are they planting 

them too shallow? Are they planting them at the wrong time? Are 

they planting them too early? Are they planting them too late? 

66 

There are all of these things that get opened up with the data 

for us to identify what the best practices are and what the best 

pieces of equipment are to help drive efficiencies. It’s very exciting. 

But that is how you can create a revenue stream going back there 

because the major ag companies that you see today, they could easily change their marketing budgets. Instead of buying commercials 

and hats and jackets and boots—right?—we could change that into 

let’s be open and honest with our customers, let’s buy their data 

from them so that we have the opportunity to service them better. 

Senator MORAN. Who are the purchasers of data today? And who 

do you expect them to be in the future? 

Mr. TATGE. They range from a wide range of data analytics companies as well as insurance and reinsurance companies trying to 

identify risk levels and some of that. They’re also equipment manufacturers. So there are a bunch of people that are trying to figure 

out and benchmark data against each other, and they’re getting access to data that they’ve never had before. 

Senator MORAN. Is this function that you’re helping create, is it 

similar to what USDA does as they collect data and then make it 

public for purposes of farmer decisionmaking—— 

Mr. TATGE. Yes, it can be. You know, the USDA has got the— 

I mean, they are the gold standard of data, and they are the ones— 

when you see the markets move a lot, a lot of times it’s because 

a USDA report came out, and it wasn’t what the market was anticipating, and that causes a lot of the volatility there. 

Traders love the volatility, and I was one for several years. But 

farmers traditionally, and the companies that use that raw commodity as the base for their product, like a flour mill, does not care 

for the volatility. So getting that volatility out of the market and 

getting that information to the market quicker is a huge opportunity to help reduce, I think, overall volatility in the markets. 

Senator MORAN. In the billion dollars that you estimated, does 

that include all three—the value of all three components of that 

data that you described? 

Mr. TATGE. That includes—no, that is the value of the marketplace side of the data only. 

Senator MORAN. And you base that estimate on what? 

Mr. TATGE. We base that estimate on 25 percent of the acreage 

in the four major row crops—which would be corn, beans, wheat, 

and cotton—and being able to monetize that data four to five times 

on a turn. Once one person buys the data, the others want to buy 

it as well. 

Our problem right now is the fact that we don’t have enough 

data. And when you go into a new market like this, when you’re 

creating a new marketplace, supply brings demand. So that’s one 

of the things that we’re working very hard with right now to address. 

Senator MORAN. And this is a question for again maybe you, Mr. 

Tatge. So when a farmer buys a piece of equipment, at what point 

in time—what year, what model year, of equipment collects the 

data? Where did we start? How recent of a development is this? 

Mr. TATGE. So I think in 2011 is when most of the new equipment started coming with a modem in it. I think it was a 2G 

modem at that time. So I would say from 2011 and newer, most 

67 

of the equipment that is sold today has a modem in it. But we have 

an aftermarket solution that allows you go to back to roughly 2002 

on most of the pieces of equipment. So as long as there are sensors 

on it, we have a piece of hardware that can allow us to make that 

older piece of equipment more modern in its form of collecting data. 

Senator MORAN. So when you say there’s a shortage of data, is 

it because farmers are using equipment that’s older than that, the 

technology isn’t utilized on their farm, or they don’t know or utilize 

the information, the data, that they have? 

Mr. TATGE. It’s the second one more so. I would estimate that 80 

percent of the data generated in cabs of equipment today does not 

make it out of the cab ever, and that’s a roadblock. The industry 

lacks—I’m going to use a navigation app for me. So Google Maps 

is something that I use just about everywhere. I used it to get here. 

And when I went to buy my new pickup, I couldn’t get it without 

the electronics in it, without the navigation system. I wanted to because it was expensive and I didn’t need it. But I couldn’t buy that 

truck without it. My wife has a car as well that we’ve had the same 

challenge on not buying the navigation system. But we ended up 

buying two of them that we don’t use. And the reason we use 

Google Maps is because of the interface and it is portable and it 

can move with me. 

The ag industry lacks a common interface of that nature to be 

able to get this data out of the cab. So when you only plant once 

a year, I mean, you kind of forget how the technology works until 

next year. And that’s one of the challenges that we have, and we 

have the opportunity with the technology available today to make 

those tools smarter and easier for digital immigrants. 

Senator MORAN. Thank you very much. 

Do any of you have anything to add in regard to how this use 

of data, the monetization of information, means money returning to 

rural America? 

Well said, Mr. Janzen? 

Mr. JANZEN. Yes. I would add a little bit to that. I always tell 

people, because we get asked this question a lot, ‘‘What is the real 

value of the farm data?’’ Right? 

And I always say imagine that you had two 80-acre parcels of 

land that you had grown corn and soybeans and wheat on for the 

last 20 years, and, you know, one of those you have accurate data 

records for how things were planted, when they were fertilized, 

when you applied pesticides, and one you did not. You know, how 

much more would somebody be willing to pay for that information 

at the time of sale than the parcel that’s identical that doesn’t have 

any of this that goes along with it? 

And so I think the answer is certainly something, right? I mean, 

it’s certainly worth something. Is it worth $20 an acre? $50? $100? 

I don’t think we really know that yet because it’s such a young industry. And I also think we haven’t done a good enough job of collecting that information year after year after year to really see the 

value. But we will get there and there will be a time when that 

is a valuable commodity and certainly people are willing to pay 

more for it. 

Senator MORAN. Mr. Knopf? 

68 

Mr. KNOPF. Mr. Chairman, I may quickly mention that I think 

it’s important to keep in mind that the value may not always be 

just monetary. I think one of the things we’re seeing in private industry right now is there is tremendous competition to grasp and 

collect that digital acre. Sometimes the value going back to the 

farmer is in benchmarking against other farms on our performance 

from a yield standpoint or a sustainability standpoint. Other times, 

the value coming back to the farmer by sharing his data is access 

to a model that that data can be implemented to that has been designed by Big Data that a private industry has paid to capture. 

And so it may be access to a model or access to something other 

than monetary value, it may also be the nugget given back to farmers for sharing data. 

Senator MORAN. When Mr. Tatge says that 80 percent of the information collected from a piece of farm equipment is not being utilized, is that by the farmer? Is the information still being utilized 

by the manufacturer, the seller of the farm equipment? 

Mr. KNOPF. From my perspective, in most of our equipment, it 

is not automatically, as far as I know, automatically being sent 

somewhere even though there are modems in the machine. And, 

yes, I think most of it is that it’s not being utilized by the farmer. 

You know, we only have so much time. I think a lot of this data 

utilization is going to have to be, with the exception of very large 

farms, more of it’s going to be outsourced to service providers, 

which is a new economic opportunity for rural America, and the 

drone industry as well. 

Because the farmer just literally doesn’t have the time to manage 

all of this data to take it out of the cab and, you know, I can’t— 

my dad, we know the age of farmers, and this is going to be an 

important transition, as that generational change happens, there is 

going to be a lot of change as we see that transition happen, but 

the current generation of farmers, it’s a big technological hurdle to 

cross for most farmers. 

Senator MORAN. Because of the access to this information, farmers can be more efficient and, therefore, more profitable is the theory, and I think the facts I think will demonstrate that. Does that 

increase the chances that farms get larger, or does it make it more 

likely that the medium-sized farm becomes more efficient and, 

therefore, more sustainable economically? 

Dr. Ferrell, you look interested. 

Dr. FERRELL. We’ve actually speculated about that in some of the 

academic research. And there’s definitely the argument to be made 

that says there are efficiencies to be had, and those efficiencies 

might be magnified at the larger scale of farms. And so from one 

perspective, that could actually accelerate the growth and the scale 

of farming, and that’s one thing that’s true. 

It depends on what we talked about earlier, which was, ‘‘How do 

we go about sharing the value and the gains from those technological advances between the aggregators and the individual farmers?’’ It might actually be that this technology might enable our 

medium and smaller producers to capture some of the efficiencies 

that heretofore were only available to those larger producers and 

might actually keep them more viable. I think there are probably 

more macroeconomic trends that are going to still push toward 

69 

larger operations, but I think this could be an important source of 

invigoration for those small and medium producers as well. 

Senator MORAN. That’s encouraging. 

Senator Blumenthal has some additional questions. We’ll have 

more one more round. Senator Blumenthal will ask his question, 

I’ll ask mine, and we’ll try to wrap this up. 

Senator BLUMENTHAL. Thank you. 

I think, Dr. Ferrell, you were the one who used the term ‘‘aggressive fine print.’’ Or Mr. Janzen, sorry. 

[Laughter.] 

Senator BLUMENTHAL. And let me address this question to the 

panel, but first to you. What do you worry about in those contracts, 

performers in the fine print, and is one of those worries mandatory 

arbitration clauses, which, as you know, deprive any potential 

party and litigant of the right to go to court under some circumstances, often very broad circumstances, and often contained in 

the fine print? 

Mr. JANZEN. Thank you. Yes. Well, as an attorney, I worry a lot 

about this, and I get asked to draft these types of contracts by ag 

technology providers that hire our services. And so we think about 

this a lot. And, you know, what I always tell my clients is you want 

the contract to be something that the farmer can understand when 

they read it. And that’s very different than I think the traditional 

route that we’ve gone in this country. 

I mean, we all get the updates on our cell phones all the time, 

and nobody reads those, you know, and that’s a problem, and it’s 

a real problem when the information is not just, you know, the 

stuff that you’re clicking on your cell phone, but it’s actually proprietary information that belongs to a farmer. 

So, you know, I worry a lot about the complexity of these agreements, and I worry a lot that the industry adopts standard form 

contracts for these new agricultural technologies when it’s like putting a square peg in a round hole and it really doesn’t work. 

Regarding arbitration provisions, I haven’t seen that as widely 

used as probably in some other areas, but they are definitely in 

there. And, yes, that certainly could be a problem. You know, as 

somebody whose Equifax data was breached, I can certainly relate 

to that issue. 

I would say the other thing that I concern myself with is a choice 

of forum provision in a contract because if anyone has to go to New 

York City to adjudicate a dispute, and they live in Wyoming, you 

already know the outcome of that, right? So that’s the other thing 

that I worry about in these standard form contracts. 

Senator BLUMENTHAL. Any other responses? 

Dr. Ferrell, you’re nodding. 

Dr. FERRELL. Oh, I would just agree. We’ve seen those in lots of 

contracts. I’ve seen a lot of those in some of the ag data contracts. 

And interestingly enough, I’ve seen those a lot in oil and gas contracts, wind energy contracts, solar contracts, and there is some 

limited empirical evidence that shows that those arbitration 

clauses really work to disfavor the landowner in those circumstances because not only is the choice of forum usually a remote urban location as opposed to the home court of the producer, 

but also the arbitration process usually kind of tends to stack the 

70 

deck in favor of the more—and I don’t want to call these unsophisticated, that’s not what I intend to say, but corporate entities tend 

to see arbitration more, are very familiar with that process, and 

use it to their advantage I would say generally. 

Senator BLUMENTHAL. They’re more experienced in it. 

Dr. FERRELL. Yes, sir. 

Senator BLUMENTHAL. Especially if it’s mandatory. 

Dr. FERRELL. Yes, sir. 

Senator BLUMENTHAL. Thank you. 

Senator MORAN. Mr. Knopf, does K-State Extension, for example, 

have any ability to educate you and other farmers in regard to 

data? Is that happening? 

Mr. KNOPF. K-State Research and Extension has data specialists. 

I’m very impressed with Dr. Ferrell’s knowledge. Come north a little bit if you—— 

[Laughter.] 

Mr. KNOPF. So, yes, we have precision agricultural specialists, 

but, again, it’s—again, it’s another—it’s a whole other realm that 

they have historically not dealt within, and the amount of capital 

and things to just fund positions is I think limited in how extensively they can take on that roll. 

Senator MORAN. Is there anyone who is vying for your data? 

Does somebody want to buy it? 

Mr. KNOPF. Nobody has offered me any money for it yet. 

[Laughter.] 

Mr. KNOPF. But, yes, certainly private companies are not as 

much directly, but very indirectly competing for access to my data, 

and they don’t really know, I think in many instances, yet what 

they’re going to do with it because it’s the amount that they can 

collect. The bigger the amount is, the more value. Whatever that 

value is, it’s about scale. And so I think my perception as a farmer 

is a lot of times they don’t know or aren’t letting on what they’re 

going to do with it, but once they have a bigger scale of it, there’s 

inherently going to be an incredible amount of value there. And the 

competition for me to just hand over my data card is very high. 

Senator MORAN. Is it possible that a monopoly could arise owning the data, that the information is available only to a select few? 

Could an exchange end up being the purchaser of data and know 

more than others who are interested in the markets? 

Dr. Ferrell. 

Dr. FERRELL. That’s a perfect opportunity for me to loop in the 

fact that Dr. Terry Griffin at K-State, their precision cropping systems economist, actually taught me a lot about this issue specifically. And if you look at the trends that we’ve seen in other technology providers, take, for example, operating systems for personal 

computers, you start with lots of those systems out there, and over 

time, through acquisitions, market share, and the fact that, as Mr. 

Tatge said, we want to see interoperability, that’s why so many of 

us either have a Mac or a PC, is because eventually those two operating systems kind of boil down, and I can create a Microsoft Office 

document and share it with everybody who’s on the Committee 

with me here fairly easily. 

71 

And so lots of factors, economic and practical, tend to drive us 

toward that, either monopoly or monopsony, that small group of 

providers that are eventually going to take over. 

There are lots of reasons to suspect you would probably see that 

in the data space as well, and that’s why I think that the dialogue 

between the producers and the aggregators is so important, is to 

make sure that as that consolidation occurs, that we maintain 

some parity in the bargaining position between the users and those 

aggregators. 

Senator MORAN. That could be a potential difference between 

USDA information and data that’s collected through this process 

we’re talking about. The access could be much more limited in this 

setting? 

Dr. FERRELL. I think so. And the interesting hypothetical situation is, you know, we have USDA data trying to provide us information about crop conditions, acres planted, things of that sort, 

and, you know, as the other witnesses mentioned, that’s a huge 

driver of both market information and volatility as well. What happens if someday USDA is, for lack of a better word, outmoded by 

private aggregators of that information that have much better, 

much more rapid market intelligence? How would that influence 

markets? 

Senator MORAN. Right. I mean, that’s not a question I thought 

of until I was hearing the panel today. Information is valuable, but 

there can be a corner on the market, which then changes the nature of the market. 

Mr. TATGE. So I talk about this and debate this a lot. I traded 

commodities for several years. The interesting thing is that if a 

farmer opts-in to sharing their data or selling their data, let’s say 

licensing their data, to a commodity brokerage house, they’re getting their profit, they’re taking the risk off the table right away because they’re getting a piece of it, regardless of where the market 

goes. 

So I would argue that the data flowing to the market quicker is 

going to reduce volatility and give the farmers additional reasons 

and ways to monetize that data that they haven’t probably thought 

of in the past. 

The farmer is the first consumer of that data, if you will. When 

you’re driving through your combine, you’re the first consumer of 

that data. OK? So you might think, ‘‘Oh, my yields are much better 

than I thought. I should hurry up and sell some.’’ Right? He—that’s 

a good use of that first—that first, you know, consumer of that 

data. The problem is you’re not quite certain enough if it’s just that 

field or if it’s just your area. But if we can have a larger pool of 

that data, suddenly that becomes really relevant for you in your 

marketing decisions as well as the market at a whole. 

I absolutely have no—I have no doubt that getting information— 

once you get something faster, you never go back. OK? And I believe that will be the same in the markets and the information that 

we’re collecting as well. 

Mr. KNOPF. Mr. Chairman, I may—you’ve been a—you understand regulation and how that impacts farming systems, and I 

think that’s one important note that hasn’t been perhaps mentioned, is we also need to recognize the risk of—as access of data 

72 

increases, I think we need to be cognizant of the risk that that— 

the risk that that presents in utilizing that data down to the farm 

level perhaps to regulate a farmer on specific practices even though 

we’re working within a biological system. 

There are going to be large swings across years based on rainfall, 

on how much nitrogen we use per bushel of wheat per se. So this 

year is going to look very different than last year. And when folks 

don’t understand a biological system and weather variation and 

just look at a dataset, it can be an easy thing to immediately think, 

well, how can we get them to always just use this much nitrogen 

per bushel of wheat? So I think that’s an important thing to think 

about as well. 

Senator MORAN. These markets, of course, are international. And 

is the market for data international? Is the same thing we’re talking about here happening in Brazil and Argentina and Europe? 

Mr. TATGE. I think hypothetically, absolutely. There is going to 

be a lot of different regulations and rules, and it’s going to take a 

while to understand how each country wants to deal with their 

data that’s collected. But I absolutely think this is a global opportunity for sure. 

Senator MORAN. Let me just ask a few questions that I think can 

be quick. Is my understanding correct that there really is not an 

agency of the Federal Government that protects farmers or regulates the collection of data? Is that the conclusion of what I heard 

you all say? 

Dr. FERRELL. That’s correct, sir. 

Senator MORAN. And I guess I should ask the next question: 

Should there be? 

Dr. Haman? 

Dr. HAMAN. I think that it is very important, one thing is very 

important, that the data is standardized because the closer the formats of the data, the possibility of comparing that is extremely important. Right now, we are getting a lot of data which is really not 

very good. And even farmers would admit that the data, raw data, 

is something which they cannot process. 

I have mentioned that we are moving toward Internet of Things 

or Internet of agriculture. At that point, we remove the factor of 

human being in a lot of situations because machines talk to each 

other and the computers talk to each other. Sensors are talking directly to the computers. 

So some kind of standardization will become reality anyway. 

That’s also true of the data which is coming from the satellites because all of that has to be downscaled because it’s on a scale which 

is not really practical for farmers. But there’s a lot of work going 

on, on downscaling the data which is coming from satellites. 

But all these databases have to talk to each other, and that’s 

very difficult to do if there are a lot of small—it seems to me if 

there are a lot of small entities, small companies, which are organizing the data differently, then there is a problem. Of course, 

sooner or later, the Microsoft or an Apple is going to emerge and 

just take over. This is probably what’s going to happen, but the 

sooner we make this data comparable, the easier it will be for everybody. 

73 

That also creates kind of a positive situation for very small farmers and for farmers with specialty crops because the big companies 

are not interested in data on specialty crops, and this is where 

farmers don’t have such large farms, but there is a lot of very good 

data going on there. 

And I just recently heard that in Brazil they are tagging individual trees in the orchard and collect data on individual trees. We 

are not doing that right now, but maybe somebody else is doing. 

So the interest is all over the world. Germany is investing a lot of 

money into data as well. 

Senator MORAN. Thank you very much. 

Dr. HAMAN. Thank you. 

Senator MORAN. I can see USDA is the agency or the department 

that would be involved in the standardization of data. 

Dr. HAMAN. Probably. 

Senator MORAN. That’s a slightly different question than where 

I was going, but an important one. And ultimately that could be 

of great value to FSA and to RMA, risk management and to farm 

programs, if the Federal Government actually had the information 

in which they’re basing payments, crop losses, or crop production, 

standardized. My question, slightly different than that, is no one 

is out there protecting the farmer. 

And, Mr. Janzen, my understanding of what you do is related to 

transparency, not related to privacy, not related to standardization. 

You’ve got a—I mean, ‘‘niche’’ makes it sound less important, but 

you have an area that you’re dealing with related to transparency, 

which is important, but nothing broader than that, and no one else 

is doing what you’re doing in those other areas, right? 

Mr. JANZEN. That’s essentially right. I would say we do deal with 

privacy issues because the Transparency Evaluator does ask questions that really get at privacy as well and protecting that. 

Senator MORAN. OK. 

Mr. JANZEN. And going back to your question about regulation, 

I think that the Transparency Evaluator really is sort of an attempt at the industry to police itself so that Congress doesn’t have 

to step in. Now, for that to work, we need full buy-in from the industry, and right now I would say we have a good first step, but 

we need wider buy-in from other players. 

Senator MORAN. When you indicated earlier that there is uncertainty as to who owns the data, whose job, whose responsibility is 

it to clear up this issue of data ownership? 

Dr. FERRELL. Well, it’s interesting, we’ve actually tried to explore 

this. There is not really a way to trademark it, patent it. You really 

can’t copyright data because copyright requires an element of creativity, and data is raw facts, which we have a Supreme Court and 

President that says you cannot copyright true facts. 

So that leaves us in the realm of trade secret, and this is something that Mr. Janzen and I have talked about quite a bit. We 

think there’s a colorable argument to be made that farm data is a 

trade secret. If it is a trade secret, there are a lot of strings that 

come with keeping it a trade secret. And trade secret is also a function a state law, not Federal law. So you run into some problems 

with that being consistent across states. 

74 

Now, we do have the Uniform Trade Secret Act, which the last 

time I had checked, 48 states had adopted, and the remaining 2 

states were considering it. But there might be an advantage toward 

a Federal protection there to keep that consistent across states and 

to allow, in a way, more interoperability as well because we would 

have a consistent application of those principles no matter where 

you were. 

Senator MORAN. Senator Blumenthal and witnesses and audience, this is I think my last question, to give you hope. 

[Laughter.] 

Senator MORAN. So my question was going to be about, what are 

the risks and consequences of data breach, data security, insecurity? And this question of about, ‘‘Who owns data?’’ causes me to 

ask, so if someone steals your data, is there a question about 

whether that’s a theft because who owns it? So is it that uncertain? 

Dr. FERRELL. There’s a very valid question with that because if 

you are claiming that your ag data is a trade secret, number one, 

that means it has to derive independent economic value from not 

being known. In other words, the data actually has to be more valuable because people don’t know it rather than people are—you get 

value from it, but so does someone else as well. 

And the argument there is to say, you know, if I have data about 

Mr. Knopf’s farm, that doesn’t do me any good on my farm because 

I don’t have his farm, I have mine. And so the one side of that argument is that, well, so what if ag data gets disclosed because 

that’s data that’s incredibly location-specific to your farm, and it 

gets out there? 

But the other argument is, well, if you know more about my 

farm, you might be able to bid resources away from me. You might 

go to my landlord and say, ‘‘Wow, Ferrell is doing a terrible job of 

farming your place, I could do a much better job, and I’ve got the 

data to back it up.’’ 

So there’s an argument there that disclosing the data might actually reduce its economic value. But the point of that is it’s really 

nebulous, and you would spend a lot of time trying to make a very 

legal argument about whether or not you even had that ownership 

in the absence of something more carefully defining what ag data 

ownership really meant. 

Senator MORAN. So in today’s world of cyber breaches, cyber attacks, and data breaches, is there something we should worry 

about in the realm of agricultural production data? 

Mr. JANZEN. It seems to me—oh, go ahead. I was going to say 

it seems to me like the most likely candidate for hacking into and 

obtaining disclosure of agricultural data from someone that 

shouldn’t have it would probably be some form of corporate espionage or, you know, a competitor of one company thinking that if 

they could get their hands on that information, it may give them 

a leg-up in the marketplace. And, you know, I don’t think we would 

see that from a United States company, but it’s very possible that 

it could be from a foreign company that wants to get their hands 

on information to give them a leg-up. 

Senator MORAN. I can see some production facilities who would 

love to keep their information private. 

75 

Mr. TATGE. I think one of the big questions that’s yet unanswered is the companies that have data, and they’ve had it, collected it for years, what happens to that data when those companies are sold? That’s the question that is looming out there as far 

as if we’ve collected data for a long time, and we go to sell our company, part of the value of that company is based upon the data that 

you’ve collected. And did you obtain it in ways that were described 

and really covered under some form of agreement? And I would say 

for the most part today that doesn’t exist. 

Senator MORAN. Justin, Mr. Knopf, if you sold your farm in the 

future, I don’t imagine you have enough data at the moment to 

make the data itself valuable, does the data go with the farm, or 

that’s a separate commodity? 

Mr. KNOPF. Well, that’s a fair question. I haven’t really conceived 

that question. 

Senator BLUMENTHAL. Why is it not like any other property—— 

Mr. KNOPF. I think—— 

Senator BLUMENTHAL.—the grain that remains in the silo or the 

gas in your tractor or—— 

Mr. KNOPF. Yes. 

Senator BLUMENTHAL. Now, the interesting question, though, 

is—and we haven’t really talked about it—mentioning the word 

‘‘copyright,’’ what about the software in your tractor? When you sell 

your farm and you sell that equipment, you’re also selling the tractor, but the software belongs to GM or John Deere or whoever 

made the machine, and you can get in trouble if you start tinkering 

with the software even on your car, let alone your tractor. So this 

is a common problem in terms of who owns what when we’re talking about Big Data. 

Mr. KNOPF. Yes, two questions there. Chairman Moran’s question, I think a lot of it is the value is still very much being defined, 

and it’s rapidly changing every day in the marketplace because I 

think as we’re—right now, farmers are still struggling to know 

what the value is in that data and how to use it, and there’s a lot 

of personal intuition about looking at that data from your farm 

that has to—that another person from outside your farm that 

doesn’t have the understanding that you do of your farm is not 

going to see the same value that you do in your data. But as models develop and software develops to better automate that process, 

that data will become more valuable and an independent value 

proposition. 

And, Mr. Blumenthal, that’s a very appropriate question, and 

perhaps you all talked about this as the right—the right to repair, 

and that’s a whole other realm of, you know, when our tractors 

break down with some—a sensor or a piece of software that’s running something mechanical, we get a code, the tractor stops moving. We have to call out the service repairman from the implement 

company that has the only access to that software to be able to 

reset that code. There is no third-party competition within the access of that right to repair that specific code. 

Now, I understand protecting intellectual property rights and innovation and being able to collect and based on your—recoup value 

and innovation and protecting that, but there is some—there is 

also a balance there between at some point in that machinery alVerDate Nov 24 2008 08:08 Aug 05, 2019 Jkt 000000 PO 00000 Frm 00079 Fmt 6633 Sfmt 6601 S:\GPO\DOCS\37228.TXT JACKIE
76 

lowing third parties to enter that software and to be able to access 

it for repairs as well. 

Mr. TATGE. One example, just to add on to that, that I’ve heard 

quite a bit is if you were to buy a piece of land in your general 

area, how many years does it take you to get it to perform the way 

it was performing for the individual before? On average, I hear it’s 

probably 3 or 4 years for you to really get to know that piece of 

dirt. 

When you think about that, when you go to resell or when you 

go to market a piece of land, having that history and giving the formula as to what was used before I think could greatly allow the 

new acquirer to be able to bring that up to the same production 

standards much faster than it would be on their own. 

Mr. KNOPF. And we’ve always had that, you know, we’ve always 

had soil samples and more kind of raw non-digital data on our 

farms in the past, and that typically, depending on the transaction, 

if it’s a friendly transaction, sometimes it will just be given over, 

but as that value increases, that becomes more of a very appropriate question. 

Senator MORAN. Senator Blumenthal, I take no offense at you indicating now this is the more interesting question, I guess because 

truth is a defense. 

[Laughter.] 

Senator MORAN. Thank you for an interesting question. 

Senator BLUMENTHAL. Thank you all for your participation 

today. And I want to thank the Chairman for having this hearing. 

Senator MORAN. Mr. Blumenthal, thank you very much. Thanks 

for your participation. 

The hearing record will remain open—let me—I always ask this 

question, Does anyone, any witness, have anything they want to 

say before we close today’s hearing? Anything we missed? 

[No audible response.] 

Senator MORAN. Great. The hearing record will remain open for 

2 weeks. During this time, Senators are asked to submit any questions for the record. Upon receipt, the witnesses are requested to 

submit their written answers to the Committee as soon as possible. 

That concludes today’s hearing. I thank you all for your presence. 

[Whereupon, at 4:07 p.m., the hearing was adjourned.] 

(77) 

A P P E N D I X 

PREPARED STATEMENT OF HON. BILL NELSON, U.S. SENATOR FROM FLORIDA

Thank you, Mr. Chairman, and thank you for holding this hearing to discuss a 

topic that is certainly of paramount importance to the State of Florida. 

Mr. Chairman, agriculture is one of the most important pillars of Florida’s economy, with an annual economic impact of over $120 billion dollars. We have over 

47,000 farms and ranches, which employ over 2 million people. Many people know 

about Florida Oranges, which by the way make up 60 percent of the total U.S. 

value, but in addition to oranges, we are also ranked #1 in value of production for 

snap beans, cucumbers, grapefruit, sugarcane, fresh market tomatoes, and watermelons. 

So I am very pleased to be holding a hearing that discusses how we can make 

farming more productive, more efficient, and more sustainable through the use of 

data. Human activity is drastically changing our world’s climate, and not for the 

better, and we will have to use data and analysis to adapt agricultural practices and 

protect workers. 

In Florida, farmers use precision agriculture to monitor the health and development of beef cattle and dairy cows. They use it to assess soil health in peanut and 

cotton fields. 

And if a fascinating experiment continues to show promise, citrus growers might 

soon be using lasers to inject pesticides directly into the leaves of citrus trees. Last 

January I visited with researchers from the University of Florida Citrus Research 

and Education Center in Lake Alfred, to see this incredible technology in person. 

A laser makes a tiny incision in the leaf of an infected citrus tree, and then a machine sprays a bactericide directly into the laser cut to get it into the tree’s system 

more effectively. 

Research like this couldn’t come at a more crucial moment for the citrus industry. 

Last week, the U.S. Department of Agriculture updated its estimate for the 2017– 

2018 season, lowering the expected harvest to 50 million boxes. The initial prediction for this season was already a decades-low 54 million boxes. 

And that was before Hurricane Irma swept through the state in early September. 

Farmers and ranchers are still trying to calculate total losses and figure out how 

to salvage the season. The initial reports are devastating: $760 million in losses for 

citrus growers, and $2.5 billion in total agriculture losses in Florida. I’m working 

very closely with Senator Rubio and others to make sure that the next supplemental 

package includes disaster aid for these farmers so that they don’t go out of business. 

In the meantime, this hearing can explore how precision agriculture is playing a 

role in our fight to find a cure for citrus greening, canker, laurel wilt, and other 

pests and diseases. 

Lastly, this committee has a long history of examining the issues of ‘‘big data’’ 

and how it affects consumers and our economy. Whether it’s a consumer shopping 

online, or a farmer tending to his or her fields, companies are collecting information 

and monetizing that information for their purposes. Just as I have long advocated 

for consumers when they are using their smartphone or shopping at the mall, they 

should have a say in if and how their information is collected and used. 

Lastly, I’d like to welcome our distinguished witness panel today, but I want to 

particularly recognize Dr. Dorota Haman (Door-OH-tah HAH-man). Dr. Haman is 

Professor and Chair of the Department of Agriculture and Biological Engineering at 

the University of Florida. She is a renowned expert as an agricultural engineer in 

advanced irrigation management and technologies. I look forward to hearing Dr. 

Haman and the testimony from all of our witnesses today. 

Mr. Chairman, I am excited to see the new ways in which technological advances 

can increase agricultural productivity and efficiency in my state and in the rest of 

the country. Thank you for holding this important hearing. 

78 

PREPARED STATEMENT OF TIMOTHY HASSINGER, PRESIDENT AND CEO, 

LINDSAY CORPORATION

Thank you Subcommittee Chairman Moran, Ranking Member Blumenthal, Senator Deb Fischer and all of the members of the subcommittee for this opportunity 

to submit this written testimony. 

My name is Tim Hassinger. I am the president and chief executive officer of Nebraska-based Lindsay Corporation—a leading manufacturer of center pivot and lateral move agricultural irrigation systems. For more than 50 years, Lindsay Corporation has been at the forefront of research and the development of products and services designed to meet the world’s rapidly growing agriculture and transportation 

needs. 

As you may know, it’s estimated that by 2050, the global demand for food will 

be 60 percent higher than it is today. To meet this daunting challenge, it’s imperative that we develop and deploy technologies that will help growers produce more 

with less, while preserving water and other natural resources. 

Data-driven farming is the key to allowing growers to meet the food, fuel and 

fiber needs of the rapidly growing global population. With the touch of a button or 

swipe of a finger, farmers who have broadband access can: 

• Receive commodity price information; 

• Monitor and respond to changing weather conditions; 

• Use GPS for planting and irrigation management; 

• Get real time data on soil and moisture conditions; 

• Connect with other farmers and agriculture experts, and 

• Store and analyze data to increase sustainability and productivity. 

They can also take advantage of a myriad of new technologies now available from 

Lindsay Corporation and other American manufacturers. Among other things, these 

innovations enable remote data collection, transfer and analysis from connected devices like soil moisture sensors, weather stations and cloud-based support tools. 

Farmers are using this information to streamline their operations, maximize efficiency and increase productivity. 

We work with farmers every day, so we know the power that comes with the ability to leverage big data. We now offer technology that helps farmers decide precisely 

when, where and how much to irrigate—maximizing yields while reducing overwatering and related input costs and nutrient losses. 

In recently conducted field studies, our researchers found that remote telemetry 

streamlined growing operations in several key ways, including: 

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

• 17 percent reduction in water usage (saving more than 9.25 million gallons on 

a 130 acre field); 

• $10/acre reduction in energy costs; and 

• 75 percent reduction in time spent going back and forth to the fields (another 

$5/acre saved). 

This combination of yield enhancement and resource savings can increase American farmers’ profits by an average of $40 per acre—profits that can be reinvested 

in their operation and in their local economy. 

Data-driven technologies allow growers to increase yields while conserving water 

and other natural resources. These technologies are no longer luxuries. Rather, they 

are critical tools needed to increase the overall operational efficiency and productivity needed to complete in the global marketplace. 

PREPARED STATEMENT OF DEERE & COMPANY

Deere & Company (‘‘John Deere’’) respectfully submits these comments for the 

record as part of the Subcommittee’s November 14, 2017 hearing on the subject of 

‘‘Technology in Agriculture: Data-Driven Farming.’’ 

John Deere is a global leader in the manufacture of agricultural, construction, turf 

and forestry equipment. Deere provides advanced agricultural and other equipment 

and services to customers that cultivate, harvest, transform, enrich and build upon 

the land to meet the world’s dramatically increasing need for food, fuel, fiber and 

infrastructure. Deere has been providing innovative equipment and services to customers since 1837, and today is pioneering state-of-the-art data and information solutions designed to greatly enhance productivity and sustainability. 

79 

1See Kurt Marko, Forbes, Precision Agriculture Eats Data, CPUC Cycles: It’s a Perfect Fit 

for Cloud Services (Aug. 25, 2015), available at: http://www.forbes.com/sites/kurtmarko/2015/ 

08/25/precision-ag-cloud/. 

The Value of Data-Enabled Agriculture 

John Deere believes that the growth of data-enabled agriculture is as transformational today as was the introduction of mechanization to the farm almost one 

hundred years ago. Insights generated from producer data will be critical to meeting 

the goal to produce enough food and build the infrastructure required to sustain a 

growing global population. Properly used, producer data has the potential to greatly 

improve precision, productivity, profitability, and sustainability on the farm. 

American farmers face constant pressure to improve efficiency, environmental 

stewardship, and output. For this purpose, farmers look to advanced smart farming 

technology solutions, including solutions that take advantage of mobile and fixed 

broadband access. Today, producers are able to farm to within a few centimeters of 

accuracy thanks to innovative GPS-enabled positioning systems that are now standard on virtually all modern farming equipment, as supplemented with data available from satellite signals. Using these high precision techniques, advanced agricultural equipment and services now include technology that provides real-time agronomic data that can be analyzed to optimize the precise amount of seed, fertilizer 

and pesticides needed, reduce costs for fuel, labor, water, and identify best practices 

for fields in a given location. (Deere’s Precision Ag Technologies, for instance, gives 

farmers access to detailed agronomic information in the field essential for improved 

decision-making with respect to managing costs and recourses.) 

Where possible, producers use data and communication technologies to interact 

with customers and vendors, follow commodity markets, obtain real-time information on field conditions, weather and other environmental factors, and manage fleets 

and regulatory compliance. Farmers can also employ innovative machine-to-machine 

(‘‘M2M’’) operations in the field and machine-to-farm (‘‘M2F’’) from the field that enable producers to make significant improvements in real-time productivity and cost 

management. 

Today, these technologies are making an enormous contribution to improved use 

of limited resources, regulatory compliance and ag sustainability. Precision technologies are enabling more efficient, prescriptive use of soils, water, fertilizer, herbicides and fuel by allowing producers to tailor farming practices and applications to 

the specific conditions of an individual field. 

For example, when the farmer leaves his field in the fall, he is able to share harvest yields directly and immediately with trusted agronomist advisors. This helps 

the advisor to prescribe the appropriate amount of nutrients to be added back to 

the soil, based only on what the farmer took off at harvest, and ensure those nutrients are added and incorporated before winter. The farmer can also make decisions 

on which seeds to buy for next year, taking advantage of early order price discounts. 

By reducing inputs, improving resource management, minimizing land impacts and 

lowering costs, these technologies are delivering the promise of sustainability on the 

farm. 

The economic impact of these technologies is significant. According to recent reports, data-driven decisions about irrigation, fertilization and harvesting can increase corn farm profitability by $5 to $100 per acre, and a recent 6-month pilot 

study found precision agriculture improved overall crop productivity by 15 percent.1

The Importance of Data Privacy 

In addition to offering a full line of innovative, high-quality agricultural equipment to producer customers worldwide, John Deere provides data and data application services that support customer business needs and the optimal utilization of 

Deere machines. These services are provided through Deere’s proprietary data management platform, the John Deere Operations Center. 

John Deere believes that all involved in the generation and use of data and data 

services should have effective processes in place to ensure privacy, security and ultimate control for the producer. Deere has been actively engaged with individual customers, grower organizations, ag service providers, agronomists and many others to 

develop practices and processes that ensure producer privacy and control, while 

making data processing, analysis, and use as seamless as possible. Deere believes 

that the market participants across this value chain—through collaboration, private 

agreement and mutual trust—are best able to develop and implement the necessary 

practices and protocols that protect producers and serve commercial needs. To this 

end, Deere has developed a set of business data principles that govern its use of 

production data, machine data and administrative, and are incorporated into every 

customer’s John Deere Operations Center services contract. These principles are deVerDate Nov 24 2008 08:08 Aug 05, 2019 Jkt 000000 PO 00000 Frm 00083 Fmt 6601 Sfmt 6621 S:\GPO\DOCS\37228.TXT JACKIE
80 

signed to give the customer the ability to control whether and how his/her data can 

be used, by whom, and for how long. 

These principles are: 

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

equipment and technologies. 

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

these important distinctions. 

3. Deere utilizes customer data only with the customer’s consent, in order to improve grower productivity and profitability, and to optimize the utilization of 

John Deere products and services in the customer’s farming operations. 

4. The producer customer retains control of his business data including whether, 

what and how his data is used and shared. The customer may withdraw this 

consent or request that data be deleted from his account at any time. 

5. Any disclosure of explicit customer business data is determined solely by the 

customer’s designated account preferences and through contractual agreements 

with John Deere. 

Farming is a complex, dynamic industry and this makes the notion of farm data 

ownership complex as well. Farmers use Deere’s tools and offerings in many different ways, which further complicates the issue of ownership. Expectations, relationships, contracts and laws regarding data control and ownership vary from place 

to place, operation to operation and even on a single farm. Companies that assert 

that farmers own their data are not being transparent—it is not as simple as that, 

which is why John Deere is focused on efforts to ensure that farmers control their 

data. 

Real-world circumstances that make data ownership complicated and uncertain 

include: 

• Custom harvesters or equipment operators who may have the right to share 

production data. 

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

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

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

This is why Deere believes that customer control of the data is the most important 

issue. Deere’s data management services and applications are designed to ensure 

customer control of their farm’s data. 

There are important distinctions between the types of data that are generated 

through integrated ag technologies, and Deere and its customers agree to manage 

these differentiated data sets accordingly. John Deere segregates customer data into 

three subsets—Machine Data, Production Data, and Administrative Data. 

• Machine Data are data that generally relate to how equipment is functioning 

(fuel consumption, vehicle diagnostic, engine performance). This data may be 

utilized, with the customer’s consent, in original or aggregated, anonymized 

form to proactively address equipment issues and improve the customer’s experience with the machine. 

• Production Data relate to the work being performed by the customer, and enable Deere to administer services the customer has opted into, such as field 

tasks, location history or wireless data transfer. By using our systems, customers agree to allow Deere to create aggregated and anonymized Production 

Data sets. These anonymized data sets are proprietary to John Deere. John 

Deere is free to use and disclose the anonymized data, and John Deere may promote information and services derived from anonymized data. Anonymized data 

is never traceable back to a specific customer. 

• Administrative Data is information that helps Deere support a customer’s account and activities in our system. Examples of administrative data are: data 

sharing permissions, account users, machines and licenses connected to accounts, acres or file sizes. 

These distinctions are a critical part of the data management process. They preserve customer control while distinguishing the sensitivities associated with certain 

data sets. They are reflected in the contractual agreements between John Deere and 

its customers. 

81 

It should be noted that the marketplace for technology around data collection, 

transmission, storage and use is evolving rapidly and will continue to evolve in the 

years to come. Producers will continue to be presented with new options and product 

offerings that can deliver even greater value, while rewarding the most innovative 

technology and service providers at the same time. This can best happen through 

the collaborative private sector efforts of market participants, without the specter 

of more rigid standards or codes imposed from outside that could stifle innovation. 

The Ag Data Transparency Evaluator, created and managed in conjunction with the 

American Farm Bureau Federation, is a good example of this private sector effort. 

John Deere played an integral part in the creation and implementation of this tool. 

We are actively working with this group to achieve its ‘‘seal of approval.’’ Initially, 

discussions around the requirement to recognize farmers’ ownership, rather than 

control, of data slowed our progress. After much discussion, Deere believes we have 

greater alignment with this group and look forward to adding our name to the list 

of companies that have gone through the transparency process. 

Finally, it should also be noted that, without essential broadband connectivity in 

croplands, many of the potential benefits of data-driven agriculture can never be realized. Real-time ag services using data generated on the farm are dependent on reliable, high-speed wired and wireless connections to the Internet—connections that 

in turn depend on a robust rural broadband infrastructure that is currently lacking 

in many parts of the country. More attention must be given at the Federal level to 

ensure that the build-out of wireless broadband infrastructure, including 

connectivity in the fields where farmers and equipment operate, is achieved. 

At the heart of John Deere’s efforts and principles around data-enabled agriculture lies our history of, and commitment to, helping those linked to the land. 

Since 1837, John Deere has been building lasting relationships with agricultural 

producers based on our core values of integrity, quality, innovation, and commitment. Deere believes that the trust we have established with producers, built up 

over these 180 years, is exponentially more important than the value we might derive from producer data. 

Deere & Company appreciates this opportunity to share its views, and looks forward to working with the Subcommittee on these important issues. 

RESPONSE TO WRITTEN QUESTIONS SUBMITTED BY HON. JERRY MORAN TO

JUSTIN KNOPF

Question 1. Your testimony described the usage of data as a ‘‘driver of economical 

sustainability and environmental stewardship.’’ As a Kansas farmer focused on both 

being a good steward of the land and making a living to provide for your family, 

could you please further describe your efforts to balance sustainable farming practices with improving efficiencies to increase profits? Can the two goals go hand in 

hand? 

Answer. Yes, often the two goals can go hand in hand. I believe most ‘‘sustainable 

farming practices’’ will have positive impacts on profit in the long term. The challenge is ‘‘the long term’’ might be ten years, or twenty, or a lifetime. There are numerous examples of sustainable farming practices which evidence would point to 

having a long term positive impact on profit, yet be a possible net cost in the short 

term. These types of practices will tend to have slower adoption curves. An example 

of one of these practices on our farm would be cover crops. They are crops planted 

between our main grain crops solely intended to provide environmental benefits to 

soil protection and health. The seed and investment in time and machinery to plant 

them is a significant cost and we have yet to document a yield or economic benefit 

to the subsequent grain crop. However, evidence and agronomic principles predict 

that across time, the environmental and biological benefits from cover crops in our 

climate and soils will improve the resiliency of our farm and perhaps the productivity, and therefore profit. In the meantime, we utilize data from both on farm research trials and field scale to evaluate what cover crops in which part of our cropping sequence will have the most impact with the least amount of cost. We then 

start with limited acreage and hopefully scale the practice to broader implementation as we learn and begin to reap some benefits over time. 

There are other examples of sustainable farming practices that will also improve 

efficiencies to increase profit as well in the short term. An example from our farm 

is zone management. We utilize various sources of data, typically multi-year yield 

maps, satellite imagery, and soil maps, to divide a field into zones based on productivity. Then, the more productive zones of the field receive the right amount of fertilizer to sustain that productivity while fertilizer rates are cut on less productive 

zones. Right away, we have improved the efficiency of our fertilizer which increases 

82 

our return on that investment, plus reduces fertilizer carryover and loss into the 

environment. 

Question 2. How does data collection and sharing specifically assist farmers in 

striking the appropriate balance, including innovations in live-time monitoring of 

crops and measurements of surrounding conditions? 

Answer. Data collection and sharing improves our understanding of how crops are 

impacted by certain factors such as weather events, management decisions, soil 

types, etc. Obviously some of these factors are outside of the farmer’s control, but 

access to improved live-time monitoring of crops and measurement of surrounding 

conditions can help farmers be more proactive in predicting crop response to these 

factors or events and lead to more timely and improved decision making. One quick 

example, this past summer we had a summer hail storm that significantly reduced 

soybean stands in its path. We utilized satellite images of the impacted fields that 

were available several days after the storm to fine tune our scouting and decision 

making about where to replant and where we could salvage the stand of soybeans. 

Question 3. Your testimony divides the data that your farm specifically uses into 

three categories: microdata, service provider data, and macrodata. Will you please 

describe how farmers and their operations benefit from each category of data? 

Answer. Microdata-this is data a farmer collects from his own operation and is 

specific to his operation. This likely helps better characterize specific aspects or 

management factors unique to his farm, leading to improved decision making. 

Service provider data-this is data that is produced by a service provider outside 

of the farm. Likely, there will be data from the farm shared with or collected by 

the service provider, but then the service provider will utilize data from that farm 

and perhaps integrate it with data from other farms and/or a proprietary algorithm 

or internal data set to provide analyzed data back to the farm in order to help with 

improved decision making by the farmer. 

Macrodata-this is ‘‘big data’’ collected from many farms likely across a broad geography. The farmer may or may not have contributed data from his farm, however, 

there are insights gained from the sheer volume of data that may not be possible 

if the data set was not so large. The insights may be more universal in nature yet 

still applied by many farmers to improve decision making. 

Question 4. In a 2016 poll conducted by the American Farm Bureau Federation, 

regarding the loss of control over downstream uses of data, sixty-six percent of the 

farmers polled expressed concern about not being compensated for the potential benefits from the use of their data beyond the direct value they may realize on their 

farm. Meanwhile, sixty-one percent of the farmers were concerned that agricultural 

technology providers (ATPs) could use their data to influence market decisions. 

Which of the two concerns do you believe is the greatest threat to farmer profitability and well-being, and what should be done to alleviate these concerns? 

Answer. I don’t know which of these two concerns is the greatest threat to the 

farmer. As the statistics indicate, both are of significant concern to many farmers. 

Farmers are accustomed to dealing with concrete things we can put our hands ontractors, soil, grain. Data is very abstract and therefore more difficult for farmers 

to quantify the value of, although most of us certainly recognize it does have value. 

As was mentioned in the hearing, I believe one of the most important steps to reducing the threat of non-compensation is transparency and understandable communication up front before data transactions and agreements take place. It is important 

the farmer can quickly and easily understand what is happening with his data and 

the parameters of any data agreement he is considering. I also believe it’s important 

to recognize that farmers may be compensated for their data in forms other than 

money. Compensation may be access to insights gleaned from the larger data set 

they are contributing to, or access to a proprietary decision making tool. 

I believe most would agree that there is power in data and recognize the consolidation in agricultural companies, which is likely why farmers feel concern about 

their data being used to influence market decisions. Consolidation and concentration 

of data is perhaps something that should be monitored. 

Question 5. With connectivity being crucial to the successful implementation of 

the technology we have discussed today and almost 30 percent of farms not receiving adequate broadband connection according to the USDA’s Farm Computer Usage 

and Ownership August 2017 report, what role can this Committee play in closing 

the gap to make sure all of our farms are able to benefit from broadband and innovative technologies? Do you see a role for advanced wireless networks in achieving 

that goal? 

Answer. Our farm is one of the 30 percent not receiving adequate broadband connection. We do have a broadband connection, but to date it is slow, not very reliable, 

and with only one provider choice, there is no competition to drive improvement. 

83 

Nearly all data management software has now become web based so as the amount 

of data on the farm needed to be uploaded and downloaded exponentially increases, 

effective utilization of data and implementation of technology becomes impossible 

without reliable and high speed data transfer technology. My time as a farmer is 

very limited as it is, especially during the growing season, so I cannot afford to sit 

and wait on a slow data connection. There have been numerous times I’ve had to 

abandon a project because of slow data transfer. However, I am hopeful as a local 

communications company has undertaken the project of running new fiber optics to 

rural residences and farms in our community. This will help with effective data 

transfer from our farm office, but it will take advanced wireless networks to achieve 

this goal in the field from mobile devices. It is my hope this committee would have 

a renewed commitment to learning where these gaps still exist and assisting small 

local companies, such as Home Communications, Inc. in Galva, Kansas, along with 

wireless network providers in closing those access gaps to fast and reliable data networks. 

RESPONSE TO WRITTEN QUESTIONS SUBMITTED BY HON. CATHERINE CORTEZ MASTO

TO JUSTIN KNOPF

Question 1. Obviously in the west we have some greater agriculture challenges 

than other regions, and wildfires are one of them that we’ve seen have detrimental 

effects over and over again. In many cases, these fires have a multiplier effect on 

production and for things like future flooding. And, in general, water is also another 

constant challenge. Are there technologies that can help measure and account for 

drought conditions, or measure the volatility of fires within parched forests or grasslands before we have fires that potentially get out of control? Are there any associations with improving broader weather prediction or forecasts? And are there other 

specifics where you foresee this data and technologies helping us get a better handle 

on our climate change crisis? 

Answer. It has and continues to be heart breaking to see and hear reports from 

the west on the devastation caused by these wildfires. We’ve had devastation in 

Kansas as well from wildfires recently. Thousands upon thousands of acres of grassland have been burned, countless cattle and miles of fencing lost, and homes and 

barns that have been passed down through families on the prairie for generations 

destroyed. There are certainly technologies that can provide data to help measure 

and account for drought and predict risk of fire. While I’m certainly not the best 

person to speak to these technologies, I can give you an example from Kansas that 

is helping. Since 1986, Kansas State University has managed a network of high tech 

weather stations, called the Kansas Mesonet. Through recent efforts to grow the 

number of stations in the network, they now have coverage across the state. These 

stations are not only monitoring and recording typical weather data, but also soil 

moisture, which is of course helpful in characterizing drought, and when used in 

conjunction with relative humidity and wind speed, has been a helpful proactive 

warning for high risk of grassland fires. I believe increased number of weather stations such as these that are available for farmers, ranchers, emergency preparedness personnel, and the public to access will hopefully help quantify and characterize more extreme weather, in turn leading to increased preparedness. 

There are technologies being implemented on farms that are scalable enough to 

help mitigate climate change. Two specific examples are improved fertilizer management through using the right source, rate, time, and place for plant nutrient applications, which significantly reduces Nitrous Oxide emissions, and also no-till farming practices which sequesters Carbon Dioxide from the atmosphere in the soil. 

With the help of Dr. Charles Rice, soil microbiologist at Kansas State University 

and world-renowned soil Carbon expert, I recently calculated that through building 

soil organic matter by implementing a no-till based cropping system, our family 

farm has offset the average Carbon emissions for roughly 4000 Americans. The soil 

is one of the biggest Carbon sinks on the planet and practices such as no-till that 

increase the organic matter of the soil not only offsets Carbon Dioxide, but also improves the ability of the soil to capture and hold water, reduces erosion, and allows 

soil biology to thrive, all leading to a more productive and resilient system. 

Question 2. There were many references to the environmental benefits of agricultural data. Are we in a position yet where we can authoritatively quantify the environmental benefits experienced by the use and attention to these technologies and 

data analysis? For example, is this science proven to the point that we should be 

creating incentives in the farm bill conservation title for their utilization to keep 

pristine watersheds like Lake Tahoe, or water quantity in drought areas, solidified 

84 

for the decades to come? Or is there a place for this use in connection with the Federal crop insurance program? 

Answer. Yes, we are now able to much better quantify environmental benefits experienced through utilization of technology, data, improved decision making, and effective conservation practices. However, the challenge becomes as geography, climate, soils, and numerous other things change from state to state, region to region, 

and even farm to farm, the outcomes and impacts of practices will vary widely. 

Therefore, the ‘‘right’’ decision for meaningful and long lasting environmental impact on my farm may be the ‘‘wrong’’ decision for a farm 100 miles west of me. 

Farmers are operating within an incredibly complex biological system with an infinite number of relationships all impacting each other. This is why it becomes so difficult to legislate effective change to a biological system. That being said, I do believe incentives can be incredibly effective at driving long lasting and meaningful 

change if they are flexible and tailored to a local level by local experts and advisors 

whom farmers trust. I have personally utilized the Conservation Stewardship Program in the farm bill to help offset some of the risk and short term costs in utilizing 

cover crops, which has allowed me to implement the practice on more acres. And 

I do believe crop insurance could be an effective avenue to offer incentives for implementing conservation practices, but they must be practices that are proven to be effective, economic, and increase reliance at a local level. As you know, the devil is 

always in the details. 

RESPONSE TO WRITTEN QUESTIONS SUBMITTED BY HON. JERRY MORAN TO

DR. SHANNON FERRELL

Question 1. In your testimony, you state that as participation in the data community increases to a critical mass, farmers’ bargaining power with the data service 

providers likely will be greatly reduced and a majority of the value will be enjoyed 

by the providers. You then state that for farmers to take maximum economic advantage of Big Data tools, large numbers of farmers must ‘‘buy-in’’ and participate in 

the data community. Where is most of this value enjoyed by the data service providers derived from? What can be done to mitigate the disparate levels of value received, especially for producers? 

Answer. Witness response to Question 1, part A: Where is most of this value enjoyed by the data service providers derived from? 

Before discussing the source of the value enjoyed by data service providers 

through data compilation and deployment of Big Data tools, it is important to discuss the value of data at the farm level in the form of Small Data. At the individual 

farm level, farmers may take advantage of today’s data collection and analysis tools 

to run on-farm experiments with respect to seed varieties, fertilizer applications, 

moisture management, and so on. They can also use data to calculate crop shares 

for rent, look for improved efficiencies in equipment, management, conservation 

practices, and so on. In these uses, farmers capture all of the data’s value on-farm 

in the form of increased returns or reduced costs. 

While these ‘‘micro’’ level benefits can be considerable for the individual producer, 

there can also be significant value derived through the use of Big Data systems and 

analytical techniques at the ‘‘macro’’ level when data from hundreds or even thousands of operations are aggregated. As mentioned in the written testimony, Big 

Data analytics can be used to much more rapidly develop hybrids by running trials 

across multiple soils and environmental conditions, developing more accurate and 

robust models for predicting risk factors such as weather patterns and production 

numbers, and development of improved agricultural equipment. All of these items 

can be derived from the aggregation of data from farmers and provide value back 

to those farmers. 

When one examines the potential economic values of data for the data service providers, there are five primary sources to consider: 

(1) Data service providers can derive revenue from the services they provide directly to the farmer that provided the data. This could be in the form of service fees for things like data collection and validation, creating a repository of 

the farmer’s data that can be easily shared with other parties to whom the 

farmer would like to provide access (such as a crop consultant, landlord, etc.), 

or in the case of a data service provider that also serves another role such 

as a crop consultant, providing reports, prescriptions, or recommendations to 

the farmer based upon the data. 

(2) The data service provider could derive revenue from using the data to market 

goods or services to the farmer. For example, if the data service provider is 

85 

a subsidiary of a seed company, the farmer’s data could help the seed company make seed recommendations that are a good fit for the farmer’s operation. This form of focused marketing can sometimes benefit the farmer as 

well. For example, consider purchasing a mixing bowl for your kitchen on 

Amazon. Based on that purchase, Amazon suggests a whisk and baking sheet 

that are commonly purchased with your mixing bowl that have gotten favorable reviews from people who purchased all three items simultaneously. If you 

also make the suggested purchases and enjoy them, it is a ‘‘win-win’’ transaction for both you and Amazon. Similarly, purchasing a seed variety that performs well for your farm and increases profits creates a win-win for you and 

the seed company. However, companies can also use a farmer’s data to provide 

recommendations while extracting more profit from the producer. As another 

example, seed companies already use information about producers to know 

which seed varieties are better suited to the farmer’s land and charge them 

more for that variety than they would charge another producer. With increasing access to producer data, input suppliers could continue to derive more precise information about a producers willingness to pay (or ability to pay) for 

their inputs and adjust their pricing accordingly. 

(3) Data service providers might provide data products or services based on farmers’ data to other companies. For example, data service providers might sell 

reports or predictive models to insurance underwriters might help them price 

crop insurance products. These reports or models might be derived from farmers’ data but their sale would not necessarily involve the transfer of the farmers’ data. 

(4) Data service providers can function as data aggregators and then sell the 

farmer’s data to third parties, deriving revenue from those sales. In some 

cases, such data service providers may pay the farmer for their data, but in 

other cases they may charge the farmer a fee for their data collection services 

(while providing some analytical services or reports back to the farmer) and 

also derive a fee from the sale of the data to other entities. 

(5) Eventually, if and when enough farms join data networks, a fifth use could 

come of that data—use of that information for significant transactions in commodities markets. A hypothetical example would be a data service provider 

who had access to a sufficiently large sample of farms to make accurate predictions of eventual U.S. crop yield who then takes positions in the commodities markets well before anyone else would be able to access that information. 

A potential sixth value source is from the data service provider positioning itself 

as an acquisition target with the purchasing company getting either the data it 

holds and/or the subscription relationships with the farmers it serves. Economic theory and historical precedent both suggest that we will see an evolution in the agricultural data industry starting with a large number of service providers vying to 

engage farmers because, as Metcalfe’s law suggests, the value of their data networks 

will increase as a square of the number of their network participants. Better-capitalized firms or firms with another competitive advantage will acquire other firms 

until eventually only a handful of dominant service providers—or even a singular 

monopolistic provider—emerges. In the course of this evolution, the more farmers 

and data a company can acquire, the more attractive they become as an acquisition 

target. While some firms are certainly pursing the strategy of becoming one of the 

dominant providers, it is equally certain that other firms are seeking simply to be 

acquired. 

Witness response to Question 1, part B: What can be done to mitigate the disparate levels of value received, especially for producers? 

Answer. Research continues to determine both the value of agricultural data in 

the aggregate and what proportion of that value is captured by the farmer relative 

to others in the value chain. As with the current USDA estimate that farmers capture 15.6¢ of the food dollar, it is likely farmers will not capture a large proportion 

of data values since they are relatively small, ‘‘atomistic’’ players in the market with 

little bargaining power and face significant barriers to the kind of collective action 

necessary to increase that bargaining power. 

Having said that, farmers’ bargaining power may be at a maximum right now. 

As mentioned in the response to Part A of Question 1, most data service providers 

recognize they are in a race to acquire access to the data of as many farmers and 

their acres as they can, as quickly as possible. Some are approaching this with the 

strategy of a telemarketer telling a prospect ‘‘sign up today, because this offer will 

be gone tomorrow!’’ However, farmers should be thinking like economists, and carefully weighing the benefits presented by any particular service provider with the 

86 

value they can receive from that provider’s services (or, indeed, the payment the 

provider is offering to secure the farmer’s data). To that end, farmers should ask 

five questions of any prospective data service provider: 

(1) How many growers/farms/fields/acres are in the data service provider’s data 

community? The higher the number, the greater the value it can potentially 

provide to the individual farmer. 

(2) What analytics conducted on the community will benefit my farm? This aims 

at the direct ability of the data service provider to increase the farmer’s ability 

to make profitable decisions, regardless of any external benefits. 

(3) What data quality control standards are being used? If the data service provider is not taking strong measures to ensure the quality of the data in their 

community, it cannot provide reliable insights to the producer. To quote the 

age-old computer principle: ‘‘garbage in, garbage out.’’ 

(4) What uses will be made of my data? This question has a number of implications discussed in the responses to other questions below, but here its purpose 

is to help the producer gauge what potential values the data service provider 

may be trying to capture that will not directly benefit the producer (or may 

actually increase costs for the producer). 

(5) What assurances can the data service provider make that the farmer’s data 

will not be provided to third parties without the ability of the producer to 

share in the revenues from that transaction? Consider the analogy of a farmer 

giving an unrestricted easement to an oil pipeline company. The farmer may 

get a payment for the easement, but the oil pipeline company may be able to 

sell co-located easements to a natural gas company, a telephone company, a 

fiber optic company, and so on, without the farmer having any ability to capture the value realized from those transactions. Similarly, if a farmer does not 

have an agreement restricting ‘‘downstream’’ uses of his or her data, they only 

have one opportunity to capture the value of that data. 

While farmer’s negotiating power may be at a peak, it may also be a matter of 

timing to make sure farmers do not completely miss out on the ability to capture 

the value of their data. As the industry continues to evolve, we will likely see a progression going from a) farmers paying data service providers for their services, b) 

data service providers realizing they need more and more farmers in their network 

and thus reducing their costs, potentially to nothing, to join, c) companies actually 

paying farmers for their data, but then d) companies securing a critical mass of 

farms or acres to have sufficiently robust data networks that they no longer need 

additional farmers to join. 

In the end, the most effective means of helping farmers secure the maximum 

amount of their data’s value may be educational efforts to help them determine the 

value of their data, evaluate data service providers and their agreements, and make 

informed choices about their data sharing relationships. 

Question 2. You note the irony in the growth of proprietary data network protocols 

that lead to complaints about the lack of interoperability of farm equipment systems 

also providing greater protection against data breaches. What measures can be 

taken to continue improving the interoperability of data collection without sacrificing security? 

Answer. Witness response to Question 2: There are two primary reasons computer 

viruses are far more problematic for computers using a Windows operating system 

than an Apple OS system: first, far more machines use Windows, and second, the 

nature of Windows architecture permits more access points to its code than Apple 

OS. At the same time, one could also make the argument that all computer users 

have benefitted from consolidation in the computer industry in that so many programs are now interoperable and data can be shared among what is now billions 

of other users with relatively little friction. By analogy, farmers and data service 

providers may both benefit from consolidation in the industry and the increased 

interoperability it provides. 

Such consolidation may also ‘‘consolidate’’ security concerns as more eggs are kept 

in fewer baskets. This is the Windows problem again—since there are far more Windows-based computers in the world, virus writers devote more resources to viruses 

that target them. Thus, as consolidation continues in the agricultural data sector, 

increased research and development efforts will be needed to make sure that these 

‘‘fewer baskets’’ are guarded by increasingly robust security tools. Congressional 

support of these research efforts would benefit not only agricultural producers but 

a vast number of Americans who rely on data security to keep their personal information secure. 

87 

The increasing automation of agricultural data collection and transmission tasks 

might actually serve as a means of increasing data security. The second reason Windows computers suffer from so many security issues is the Windows operating system was not built with security as one of its primary concerns, since its foundations 

were built before networked computing was a primary use of PCs. As a result, there 

are far more access points to its code that virus writers can use to insert malicious 

instructions. Conversely, there are fewer access points in Apple OS—a system built 

from the ground up for networked, multi-user applications that thus requires explicit user permission for code to be activated. The analogy in agricultural data is 

that the tighter integration of data networks across agricultural equipment creates 

fewer intervention opportunities for third parties. The market will likely continue 

to drive this integration. With that said, the issue of user’s data security needs to 

be continuously brought to the attention of hardware and software developers so 

they can keep security as a foundational principle in their designs. 

Another tool that might aid the security of farm data networks may not seem to 

be directly connected to the security issue, but certainly is: the expanded development of broadband cellular networks in rural areas. Cellular transmissions are 

encrypted by the nature of the processes that make cellular communication work; 

this has the most beneficial side effect of adding to the security of the transmission. 

However, in many rural areas, sufficient cellular signal or bandwidth is not available to make use of many current agricultural data technologies, requiring farmers 

to manually download and transmit their data using much less secure methods. Expansion of rural broadband cellular access would have the dual benefit of making 

agricultural data tools more accessible and profitable for farmers while also reducing the security risks associated with collecting and transmitting agricultural data. 

Question 3. In your testimony, you note that the current legal framework for ownership of agricultural data is inadequate for the transfer and aggregation of agricultural data. Is the agricultural data space unique enough to require specific legislation regarding ownership and property rights, or is a novel combination of existing 

property ownership laws more appropriate and adequate? 

Answer. Witness response to Question 3: One of the first questions farmers always ask about their data is ‘‘do I own it?’’ It is a natural question to ask, since 

farmers depend on access to land whether it is owned or leased, and thus are closely 

attuned to property rights. However, traditional notions of ownership break down 

to some extent with agricultural data. Thus, the better question for the farmer to 

ask may be ‘‘what rights do I have with respect to my data, and what rights to others have with respect to it. The most critical element of this may be what rights 

(and abilities) does the farmer have to exclude others from access to the data. 

If one thinks about it, the notion of ‘‘privacy’’ is really a function of one’s ability 

to exclude others from access to information. For example, HIPAA’s provisions regarding health information naturally couples with the notion of privacy in one’s 

health matters, as they pertain to matters of one’s own body. Thus, HIPAA provides 

a legal right to exclude others from access to health information without explicit 

consent for the disclosure of that data. The Fair Credit Reporting Act (FCRA) covers 

matters of financial information, which also couples with traditional notions of privacy and the right to exclude others from access to one’s financial records. 

However, the barriers start to blur a bit with the FCRA in that financial transactions mean reaching out to and communicating with another party; we recognize 

that others may have some limited right to access our financial information if that 

information is relevant to their financial risk in some respect, such as loaning us 

money. Therefore, we allow credit bureaus to collect financial information and to 

disclose that information to others so they may make credit decisions about us. 

The credit reporting analogy is important to understanding whether we need a 

specific legal framework for agricultural data. Whenever someone requests credit, 

they are required to ask if the potential borrower will give consent to the lender 

to access a credit report. Thus, for a third party to make use of the financial data, 

they must have the consent of the person about which the data is collected. Conversely, though, in many farm data agreements, the farmer may not have the right 

to approve or deny access to their data to ‘‘downstream’’ users. 

One could argue that agricultural data is significantly different from HIPAA-protected health information (about the workings and condition of one’s own body) or 

financial data, but one should also consider the fact that farmers lack the ability 

to protect their information from disclosure. Put another way, it would be impossible 

for farmers to try and keep confidential much of their production information and 

practices because they can literally be seen from aerial or satellite imagery. With 

relatively little effort one can tell how many acres a particular farmer owns and 

what proportion of it is in what crop for a given year (compare this to a bicycle 

maker or a coffee shop—it would be extraordinarily difficult to determine the volVerDate Nov 24 2008 08:08 Aug 05, 2019 Jkt 000000 PO 00000 Frm 00091 Fmt 6601 Sfmt 6621 S:\GPO\DOCS\37228.TXT JACKIE
88 

ume or nature of their business, or to even tell one business from the other without 

continuous monitoring of the business at high image resolutions, simply because 

their businesses have roofs and farms cannot). It could be argued then that perhaps 

farmers should be given more protections than other businesses because one could 

derive a significant amount of financial information about them from publically 

available resources, to say nothing of the improved ability to do so if one coupled 

data sources from a data service provider that transferred that information without 

the farmer’s consent. 

However, if one did desire to provide enhanced protections for agricultural data 

and allow farmers to exclude others from their data without explicit consent, two 

significant barriers loom. First, one would have to define the type of agricultural 

data subject to the protection. This would be challenging, given the broad diversity 

of data that can be collected and transmitted on farms today. Second, it would be 

difficult to define when such consent would be needed. Would the farmer have to 

give consent to any data transfer to another party? There would be significant 

transactional costs in such an approach. Further, there are doubtlessly data uses 

that will be available in the near future that might not even be conceivable today; 

it would be quite challenging to give informed consent in an up-front data use agreement when one doesn’t know what data uses might be possible in the future. 

The current legal framework might be serviceable as an interim tool to help provide farmers some grounds for the excludability of agricultural data, and enhancements to that framework may be possible. In the near term, though, perhaps one 

way to help farmers maintain control of their data is additional research into 

encryption algorithms that give farmers a key that would be required to access the 

information—this would put more control over downstream uses back in the hands 

of the farmers, and also give them an increased ability to participate in the value 

received for data transactions. 

Question 4. As more and more firms enter the agriculture-technology space and 

interact with data used by and/or generated by farmers, the need for clarity and 

consistency on privacy principles is growing. For these new entrants, can you suggest any best practices these firms should engage upon to ensure their data privacy 

procedures properly convey the data’s expected use? 

Answer. Witness response to Question 4: Much use is made in the agricultural 

data industry of the word ‘‘transparency’’ but there can often be much ambiguity 

in what that term means. The greatest value of that term, in the witness’ opinion, 

is to err on the side of disclosure to the farmer when discussing the internal and 

external uses that the data service provider will be making of the farmer’s data. 

Those uses should be disclosed clearly in language that is understandable by farmers with a wide range of experiences and educational backgrounds. One such example may be a Truth in Lending Act (TILA) disclosure. Though an agricultural data 

use agreement might not bear a clear analogy to a lending transaction, TILA makes 

clear the potential impacts of the lending transaction and the borrower’s rights and 

remedies. Data service providers could benefit from making sure their data use 

agreements have similar levels of clarity. 

Another principle beyond the clarity of the disclosure is its frequency. Using another financial analogy, individuals can use credit monitoring services to receive notifications when someone makes a credit inquiry about them. Data service providers 

could also provide notices when an external entity has made a request to access the 

farmer’s data, or when a new internal use is made of the data. Robust notification 

procedures can also help farmers take protective actions in the event of a data 

breach. 

As mentioned in the response to Question 3, there arises the issue of informed 

consent when a new data use arises that was not contemplated by the original data 

use agreement. Though it might increase transactional costs, the simple answer to 

this problem is to require disclosure of a potential new use and secure the farmer’s 

consent to the use before it is implemented. The counterpoint to this approach, however, is that its increased transactional costs might make companies implementing 

it less competitive than those who do not. 

Finally, as new companies enter the agricultural data sector, they would do well 

to avail themselves of the efforts of farm groups, existing data service providers, and 

equipment manufacturers to develop consensus on the principles that should govern 

agricultural data management. The Privacy and Security Principles Farm Data developed by the American Farm Bureau Federation and the Ag Data Transparency 

Evaluator are two good starts for companies to use in developing their operating 

policies and procedures. Both of these tools continue to develop, and the dialogue 

can provide greater benefits to the agricultural industry with increased participation 

from more farmers and data firms. 

89 

Question 5. While much of the data we discussed in the hearing is generated on 

farm and captured by farmers or their equipment, significant quantities of data is 

publicly available and critically important to inform risk modeling, yield prediction, 

etc. in both the public and private sector. How can we encourage the continued use 

of this type of data, and even grow our sources, while ensuring that farmers understand their role in this process? 

Answer. Witness response to Question 5: Perhaps the best steps that can be made 

toward this goal are to continue funding of research and extension efforts through 

our Land Grant universities to help producers understand the value of the data resources to their decision making processes. For example, the witness is currently the 

principal investigator on a Southern Risk Management Education Center grant 

funded through USDA–NIFA to develop a handbook and decision tools that can help 

producers understand the value of agricultural data tools and help them make informed choices about their uses (SRMEC Agreement 21667–19). 

Additional research on how agricultural data systems can be made more robust, 

reliable, and accurate can also add to the volume and quality of publically-available 

data. For example, the most commonly-logged seed variety on planter data systems 

is the variety that comes first alphabetically on the system’s drop down list. This 

means that producers sometimes inadvertently (although potentially carelessly or 

intentionally) select data inputs that are inaccurate, which in turn affects all downstream uses of their data. Research of tools to help improve data accuracy will not 

only increase profitability for producers, but will also improve the data and decision 

tools available to the industry. 

RESPONSE TO WRITTEN QUESTIONS SUBMITTED BY HON. CATHERINE CORTEZ MASTO

TO DR. SHANNON FERRELL

Question 1. Ranching is a part of the fabric of rural Nevada’s frontier. We have 

operations under generations of family management. While we heard many of the 

virtues of row crop farming and big data during the hearing, can you outline the 

advancements and prospects for animal agriculture? Is there also an advantage of 

agricultural data in ensuring a higher bar of food safety as well, for farmers, ranchers and consumers? 

Answer. While most of the discussion around agricultural data and Big Data in 

agriculture has revolved around applications for crop systems, the data revolution 

holds tremendous implications for livestock producers and the consumers of their 

products. Space does not permit a full exploration of all the potential avenues by 

which new data technologies could impact the livestock industry, but below are a 

few examples. 

Animal traceability: One example of how data technologies are being used right 

now in other countries is to provide robust animal traceability from farm to fork. 

For example, the bovine spongiform encephalopathy (BSE, sometimes called ‘‘mad 

cow disease’’) outbreak in the U.K. during the late 1990s spurred the implementation of an animal identification system that can trace an animal throughout its life 

from birth to the retail packaging in which its cuts are sold. Put another way, a 

steak could be traced back to the retailer, wholesaler, meat processor, feedlot, and 

cow-calf operation at which the calf was born. That level of traceability has the potential to prevent losses that could reach billions of dollars in the event of a BSE 

discovery in the United States by quickly isolating every other animal with which 

a diseased animal came into contact and drastically reducing the number of animals 

that would have to be quarantined or destroyed to prevent spread of the disease. 

It could simultaneously determine the wholesaler and retailer location of any potentially dangerous food products. Quick and accurate disease traceability thus has important implications for both livestock producers and consumers. 

Disease traceability is an important example of the loss-prevention capability of 

data technologies, but there are also important value-added applications as well. 

The same technology can enhance the ability of livestock producers to provide age 

and source verification of beef products, enhancing their ability to market their beef 

as a branded product rather than a commodity. Traceability also allows ranchers 

to show compliance with certifications and standards to demonstrate to consumers 

that given affinity traits have been maintained throughout the beef supply chain. 

Keeping of such traceability information is possible with manual technologies, but 

is made faster, easier, and much more accurate and reliable by automation technologies such as radio frequency identification (RFID) tags for animals coupled with 

automated readers and loggers. Those technologies, working together with an already robust supply chain data system in the wholesaler and retailer sector, have 

significant potential to rapidly improve traceability within the U.S. beef system. 

90 

Managing production inputs: One of the reasons the use of data technologies has 

received so much attention in crop production is that it is relatively easier (though 

one hates to apply the term ‘‘easy’’ to the significant work in technological research 

and application that has occurred there) to deploy sensors and data collection/transmission systems on tractors, planters, sprayers, and combines than it is to cattle. 

However, advancements in sensors and computers continue to make it easier to use 

these advanced data technologies in livestock applications. 

In addition to the traceability systems mentioned above, we continue to see advances in sensor systems allowing ranchers to learn more and more about their production input use. The corn farmer cares about seed variety, water uptake, fertilizer 

inputs, and disease pressure as variables in determining their crop yield. A robust 

genetic database and traceability system coupled with wearable sensors could help 

a rancher track a cow’s water and feed intake and health. We already have technologies allowing for feed and water tracking in closed environments like a feed 

yard or dairy, but advancements continue toward wearable sensor technologies that 

would allow similar tracking in open pasture environments. 

Beyond tracking these factors, such sensors could also be coupled with other data 

systems to provide significantly enhanced management information for ranchers to 

see what production variables yield the greatest financial returns. An example of 

this is a program here in Oklahoma called the Oklahoma Quality Beef Network 

(OQBN). Through a number of manual protocols, producers participating in the 

OQBN have the information needed to show compliance with a number of valueadded programs giving them access to premiums in the marketplace for their cattle. 

Participation in the program also gives ranchers the ability to analyze the costs and 

returns of their production practices. OQBN represents another system that could 

be enhanced by the deployment of data technologies and a strong animal 

traceability system. For example, as an animal is given the OQBN vaccine regimen, 

each vaccine could be scanned as administered to the cow after the cow’s RFID scan; 

those vaccines would then be part of the cow’s OQBN record to show compliance 

with its vaccination requirements, and that information could be displayed at the 

auction as the cow comes into the ring. 

Managing range cattle production: At the risk of stating the obvious, managing 

range cattle production is hard, and those challenges are multiplied in Nevada by 

the significant size and/or rugged topography of ranches. Many of the sensor technologies referenced herein require cattle to be close to a collection point such as a 

watering point or feed area, but those points are difficult to come by in range production. Even if a rancher could put a transceiver near common watering points or 

feeding areas on a range, those transceivers depend on cellular data networks to collect and transmit data. The expansion of rural broadband cellular coverage could 

increase the opportunities for producers to take advantage of these technologies, 

however. Broader and stronger networks would allow many more opportunities for 

range cattle producers to take advantage of these data technologies. 

Another technology that could be of great benefit to range cattle producers is unmanned aerial systems (UAS or ‘‘drones’’). In their most straightforward application, 

UAS could be used to help ranchers check on cattle much more quickly than they 

could by ground vehicle or horseback. This could include everything from taking pictures or video to count cattle, inspect fences, or check on water availability. This 

is particularly true in rugged, mountainous terrain that is difficult to access. 

If the UAS were equipped with data transceivers backed by a strong rural 

broadband network, the UAS could even collect and relay the sensor data mentioned 

above, or could alert a rancher to a cow that was ill and in need of immediate treatment. While some current UAS technologies could already help in this regard, current FAA regulations only allow most UAS to be operated by ‘‘direct line of sight’’ 

which means the pilot must have direct visual contact with the UAS. That rule 

makes some of these applications impractical, particularly in mountainous terrain. 

Continued improvements in camera and transmitter technologies could soon make 

a ‘‘point of view’’ camera mounted on the UAS an acceptable substitute for direct 

visual contact, though, permitting amendment of the FAA regulations to allow 

longer flight ranges. 

Consumer-side data: Much has been discussed in terms of how Big Data technologies can create models to help crop production, but there is also significant potential for those technologies to impact beef production, and indeed, consumption. 

We already have large volumes of universal product code (UPC) scanner data from 

retail outlets that can tell us much about where and when certain food products are 

consumed. However, if the beef industry were to couple that data and supply chain 

information with the traceability elements mentioned above, the amount of data 

available for analysis would grow exponentially, and tremendous insights could be 

derived about beef demand, consumption patterns, and other factors. Big Data could 

91 

also provide significant insights into the effectiveness of food policy such as the use 

of SNAP EBT benefits for certain types of foods, the nutrient profile of those purchases, and consumer incentives. 

Question 2. There were many references to the environmental benefits of agricultural data. Are we in a position yet where we can authoritatively quantify the environmental benefits experienced by the use and attention to these technologies and 

data analysis? For example, is this science proven to the point that we should be 

creating incentives in the farm bill conservation title for their utilization to keep 

pristine watersheds like Lake Tahoe, or water quantity in drought areas, solidified 

for the decades to come? Or is there a place for this use in connection with the Federal crop insurance program? 

Answer. The most concise answer is ‘‘we’re not there yet, but we’re getting closer 

rapidly.’’ 

At the ‘‘micro’’ or farm scale, we may indeed be getting close to having the technology in place to help producers demonstrate compliance with environmental requirements. For example, say a producer has a feedlot that meets the definition of 

a Concentrated Animal Feeding Operation (CAFO) under the Clean Water Act and 

wants to show their compliance with the CAFO’s nutrient management plan (NMP). 

A grid-sampled soil test of the field to receive animal waste could create a precise 

prescription for how much of each macronutrient should be applied to that field. 

Sensors on the tractor used to inject liquefied animal waste to crop ground, coupled 

with a nutrient analysis of the waste, could show the amount of nitrogen, phosphorous, and potassium applied using variable-rate technologies to comply with the 

prescription. Later, the crop harvested from the ground where the wastes were applied is reported using a combine’s yield monitor data, which can show the nutrient 

uptake of the crop and thus the nutrient balance for the soil. 

This scenario demonstrates the technologies available to manage agricultural environmental factors on the input side, but if we want to reliably automate environmental compliance efforts, we must also have strong sensor networks in and around 

environmental receptors such as streams and lakes, particularly at points where 

watersheds connect. This will require continued development of environmental sensors and again, strong rural broadband networks over which to transmit the data. 

The discussion to this point has focused on farm-level compliance, but what about 

compliance at a ‘‘macro’’ or regional level, such as demonstrating compliance of a 

watershed with an agriculturally-based total maximum daily load (TMDL)? We may 

have a bit to go before we can reliability show compliance on a regional level, simply 

because doing so would require 1) the continued penetration of sensor and transmission technology into more agricultural implements and 2) the interoperability of 

those systems to ‘‘speak the same language’’ so insights could be derived across multiple farms, rather than on a farm-by-farm basis. However, with that said, these factors continue to advance rapidly, and we may cross that threshold soon. Such technologies could soon facilitate the ability of farmers to manage nutrients in impaired 

watersheds. For example, in a TMDL-limited watershed with a ‘‘cap and trade’’ system, producers could validate how much of each nutrient they have applied to show 

their compliance with the nutrient limitations they received or traded. 

In any of these scenarios, it is important to note that the accuracy of any compliance system depends on the proper calibration and operation of the sensors used. 

While automation may make compliance much easier, if automation is ever used to 

demonstrate compliance with a regulatory program, it should also be paired with 

the opportunity for a producer to review their submissions and explain anomalies. 

If a sensor suffers a malfunction, it could show a producer applied far more than 

their allocation of a nutrient, even though the proper amount was actually applied. 

Conversely, a third party might try to ‘‘hack’’ a tractor system or the data collection 

to implant false data. Any compliance system created in the future should make allowances for how to handle these anomalies. 

With respect to the crop insurance program, there are tremendous opportunities 

to hone the actuarial models for the system and to facilitate produce’s demonstration of compliance with insurance program requirements afforded by both Small 

Data and Big Data systems. Big Data analytics can continue to drive gains in accuracy for models in setting premiums and cost management, while Small Data can 

help producers accurately report yields (again, presupposing proper calibration and 

operation of the equipment sensors). 

Question 3. Obviously in the west we have some greater agriculture challenges 

than other regions, and wildfires are one of them that we’ve seen have detrimental 

effects over and over again. In many cases, these fires have a multiplier effect on 

production and for things like future flooding. And, in general, water is also another 

constant challenge. Are there technologies that can help measure and account for 

92 

drought conditions, or measure the volatility of fires within parched forests or grasslands before we have fires that potentially get out of control? Are there any associations with improving broader weather prediction or forecasts? And are there other 

specifics where you foresee this data and technologies helping us get a better handle 

on our climate change crisis? 

Answer. There are indeed a number of technologies that can help address these 

concerns. One present example of a weather and climate monitoring system that has 

provided tremendous benefits for my home state is the Oklahoma Mesonet. The 

Mesonet is a system of weather stations spread throughout the state, with at least 

one (if not more) such stations placed in every county. These stations fill in the significant geographic gaps between NOAA weather monitoring stations, and allow us 

to collect data on dozens of weather and climate parameters. These observations are 

fed into a number of models that help us keep close tabs on a number of factors 

for everything from fire weather hazards to drought monitoring and evaporative 

losses. The Oklahoma Mesonet has already helped our meteorologists and climatologists refine their predictive models and led to a tremendous output of research into 

Oklahoma weather and climate issues. The application of similar technologies in 

Nevada could provide significant improvements to prediction of fire weather conditions with the potential to issue advisories and reduce the risk of ignition sources. 

Similarly, the evaporation, soil moisture, and mesoscale models could help refine the 

use of scarce water resources. To help illustrate the applications of the Mesonet and 

our models, such as the fire-weather model, I have attached a summary prepared 

for you by Mr. Al Sutherland, coordinator of Mesonet Agricultural Data and Products, with the assistance of Dr. J.D. Carlson, lead investigator on the OK–FIRE 

model. 

Further, in the future, the reduction in cost of reliable weather sensors could 

mean individual landowners could have their own weather stations (as an increasing number of Oklahoma farmers and ranchers do) that could be interoperable with 

Mesonet stations. This would have the effect of filling in even more gaps in the network, increasing the precision of its observations and providing even better information for predictive models. Indeed, weather monitoring is an arena in which there 

is tremendous opportunity for improved sensor systems and Big Data to have a positive impact in climate management. 

RESPONSE TO WRITTEN QUESTIONS SUBMITTED BY HON. JERRY MORAN TO

TODD J. JANZEN

Question 1. As administrator of the Ag Data Transparency Evaluator, you are familiar with the lack of trust and confusion that many farmers experience in identifying what exactly is done with data collected from their land. Can you please describe considerations among industry stakeholders that led to publishing the ‘‘Core 

Principles’’ that are incorporated into the Evaluator? 

Answer. The main drivers behind the ‘‘Core Principles’’ for ag data were the concerns from farmer-members of national farm organizations, such as American Farm 

Bureau Federation, National Farmers Union, National Corn Growers, National Association of Wheat Growers, American Soybean Association, and National Sorghum 

Producers. These organization spearheaded the effort to develop the Core Principles 

because their members wanted a basic framework around how ag data is collected 

and shared. 

Question 2. Your testimony states that only nine companies (including Farmobile) 

are currently approved as ‘‘Ag Data Transparent’’ according to the Ag Data Transparency Evaluator’s formal process. Why have not more companies voluntarily completed the evaluation, especially given the fact that nearly 40 companies participated in drafting the ‘‘Core Principles?’’ 

Answer. This is a question best addressed to those companies that have not participated. I can only speculate as to their delay in participation. My belief is that 

these companies want more control over farmers’ data than they are willing to publicly admit. Therefore, it is easier to remain quiet and say nothing than subject 

themselves to the Ag Data Transparent process. 

Question 3. How can we incentivize more active participation by industry stakeholders to complete this evaluation? 

Answer. I think the fear that Congress might step in and regulate the privacy 

and collection of ag data is something that will drive more companies to participate. 

As the value of the Ag Data Transparent brand increases over time, that will drive 

more participation as well. 

93 

Question 4. As agricultural data becomes more valuable to entities outside of the 

farmers that collect it, data security concerns are likely to grow exponentially while 

criminals with all types of motives seek to illegally gain access to and capture privately-owned data. How do you foresee data security practices in the agricultural 

industry evolving as a result? 

Answer. Data security in the ag data space must progress at the same rate as 

data security in the non-agricultural space. Ag tech companies should not think that 

they are immune to security challenges. 

Question 5. Are there any specific security traits to agricultural data that need 

to be accounted for steps going forward? 

Answer. Ag data can contain proprietary information, which makes it different 

than other types of consumer-type data that may not be proprietary. 

Question 6. In a 2016 poll conducted by the American Farm Bureau Federation, 

regarding the loss of control over downstream uses of data, sixty-six percent of the 

farmers polled expressed concern about not being compensated for the potential benefits from the use of their data beyond the direct value they may realize on their 

farm. Meanwhile, sixty-one percent of the farmers were concerned that agricultural 

technology providers (ATPs) could use their data to influence market decisions. 

Which of the two concerns do you believe is the greatest threat to farmer profitability and well-being, and what should be done to alleviate these concerns? 

Answer. I believe the greatest threat to the farmer is that ATPs will be able to 

influence the ag markets by using ag data, but without making that same g data 

available to farmers. That would put certain holders of information in a superior 

position to the average farmer. 

Question 7. As more and more firms enter the agriculture-technology space and 

interact with data used by and/or generated by farmers, the need for clarity and 

consistency on privacy principles is growing. For these new entrants, can you suggest any best practices these firms should engage upon to ensure their data privacy 

procedures properly convey the data’s expected use? 

Answer. New firms in the ag data space should do two things when they begin 

to collect data. First, they should determine their guiding principles for how they 

intent to treat ag data. Second, they should develop easy to understand data use 

policies that they can share with farmers that explain how the firm intends to use 

the farmer’s data. 

Question 8. As more and more firms enter the agriculture-technology space and 

interact with data used by and/or generated by farmers, the need for clarity and 

consistency on privacy principles is growing. For these new entrants, can you suggest any best practices these firms should engage upon to ensure their data privacy 

procedures properly convey the data’s expected use? 

Answer. New firms in the ag data space should do two things when they begin 

to collect data. First, they should determine their guiding principles for how they 

intent to treat ag data. Second, they should develop easy to understand data use 

policies that they can share with farmers that explain how the firm intends to use 

the farmer’s data. 

Question 9. While much of the data we discussed in the hearing is generated on 

farm and captured by farmers or their equipment, significant quantities of data is 

publicly available and critically important to inform risk modeling, yield prediction, 

etc. in both the public and private sector. How can we encourage the continued use 

of this type of data, and even grow our sources, while ensuring that farmers understand their role in this process? 

Answer. Witness did not respond. 

RESPONSE TO WRITTEN QUESTIONS SUBMITTED BY HON. CATHERINE CORTEZ MASTO

TO TODD J. JANZEN

Question 1. Given the dramatic benefits of agricultural data that were outlined 

in the hearing, are we addressing these advancement in our high school agricultural 

education and training efforts? Or at the technical or community college education 

level? And much of what we heard about, and what we think about in the innovation sector, requires more students and a workforce who can work with the technology, data analytics, and various other computer science skill sets. Is that accurate from your perspectives? Are there ways in your mind we can better incentivize 

and be developing the workforce we’ll need to see the great promise of what we’re 

talking about today? Including the cyber security needed to protect agricultural and 

ag-related business in this sector? 

94 

Answer. I am not involved with high school or secondary education and cannot 

therefore directly speak to this question. From my personal experience, more could 

be done to drive more students into science and technology focused careers, including agriculture. National and state FFA organizations do a lot to help foster growth 

in this area, but these are non-profit organizations that rely on donations and volunteers. 

Question 2. There were many references to the environmental benefits of agricultural data. Are we in a position yet where we can authoritatively quantify the environmental benefits experienced by the use and attention to these technologies and 

data analysis? For example, is this science proven to the point that we should be 

creating incentives in the farm bill conservation title for their utilization to keep 

pristine watersheds like Lake Tahoe, or water quantity in drought areas, solidified 

for the decades to come? Or is there a place for this use in connection with the Federal crop insurance program? 

Answer. The data collection and analytic tools today are already smart enough to 

make meaningful differences in environmental protection. For example, we could 

measure fertilizer use on farmland and fertilizer run-off from field tiles and other 

pathways to determine proper fertilizer application rates, assuring that as little as 

possible is lost. Likewise, we can compare fertilizer usage and yield across fields to 

determine proper application rates and timing to maximize fertilizer and soil resources. 

There could certainly be more use of data in the Federal crop insurance program. 

Deb Casurella at Independent Data Management LLC is an expert on this subject 

and would be the right person to testify on this topic. 

Question 3. So if we acknowledge the virtues of agricultural data, and that small 

data can even be used to market the sale of land or an operation, what safeguards 

in place to verify this information so a potential land buyer isn’t defrauded by 

skewed land performance data or analytics that inflated the profitability of the 

land? 

Answer. The best way to verify that ag data is not fraudulent would be to insist 

on receiving the original raw data from a seller, or obtain a copy of the same data 

from a third-party source. Even then, there would still be a question whether the 

machine that produced the raw data was properly calibrated. One way to verify that 

would be benchmark the seller’s data with other data in the same area. Any dramatic difference could be due to data manipulation. 

Question 4. Have agribusinesses been utilizing varying strategies to collect big 

data in agriculture? What was the old typical procedure for companies to obtain information from farmers, or their customers, on the progress or performance of their 

product? And are there situations where the producers are forced into a data collection program by seed, fertilizer or equipment companies? Or can they opt-out? 

Answer. Many of the larger agribusiness companies have been collecting agricultural data from customers for years. Often, they collected this information because 

they were providing services to farmers, such as fertilizer of pesticide application. 

They collected data during such activities and then retained it for their own records. 

I am not aware of any specific situations where companies require ag data submission as a condition of using a machine or device, although I think that will inevitably happen. 

RESPONSE TO WRITTEN QUESTIONS SUBMITTED BY HON. JERRY MORAN TO

DR. DOROTA HAMAN

Question 1. One of the aspects of your background I found interesting is the work 

you’ve done with farmers in developing nations, specifically as it relates to irrigation. I chair the Senate Hunger Caucus and have worked for many years on the 

issue of reducing food insecurity in the world. I believe that agriculture development 

initiatives that help countries feed themselves is a key part of the long-term strategy to end global hunger. Can you elaborate on your work with farmers in developing countries, specifically as it relates to using water more efficiently, and how 

that work has reduced food insecurity? 

Answer. I would like to take this opportunity to thank Senator Moran for asking 

this question and for his bipartisan leadership on the Senate Hunger Caucus. Food 

assistance, and other support provided by the U.S. all over the world, leads to reduction of global food insecurity. 

My expertise is in agricultural engineering with a focus on water management 

and irrigation. As reported by the Food and Agriculture Organization (FAO) of the 

United Nations and by the World Bank, approximately 70 percent of fresh water 

95 

usage is for agriculture. Most of agricultural irrigations systems are poorly designed 

and poorly managed. Even the best irrigation systems, if not maintained and carefully managed, are inefficient. Most of the irrigation systems over-apply water and 

there is a potential to improve efficiencies through technology and education. 

My major effort has been focused on Florida growers and specialty crop production 

in Florida. I have also been a university teacher working with the next generation 

of farmers, academics and irrigation specialists. I teach people how to design, manage and maintain irrigation systems. My work has been focusing on efficient systems such as microirrigation and sprinklers. These systems are usually used for 

higher value, specialty crops such as fruits and vegetables but can also be adapted 

for small farmers in developing countries. 

My international work has been largely in education. I have worked for FAO in 

Zimbabwe designing curriculum and lab experiments for a six-months intensive 

course focused on planning, design, maintenance and management of irrigation for 

smallholder farmers. I have taught two 2-week courses in Egypt. I have spent 3 

months in Mexico and 4 months in Chile investigating and teaching efficient methods of irrigation. In addition, my students have worked with farmers in India, Ecuador, Columbia and Poland. 

As an example, one of my students, working in Jamaica on his Masters project, 

implemented a simple drip system for calaloo (Jamaican spinach) and cucumbers. 

The increases in yield and reduction in water usage were significant. After the experiment was finished, the farmer adopted the system on the entire farm. 

Question 2. What opportunities exist, if any, to take the technology being used 

today by large-scale U.S. farmers and use it to help smallholder farmers in developing nations be more productive and sustainable? 

Answer. New technologies can eliminate many maintenance mistakes through automation and sensor control. New technologies can provide inexpensive alerts, and 

in the future, automatic intervention. 

Technology leapfrogging is likely in agriculture in developing countries. Use of cell 

phones in developing countries is often cited as an example of leapfrogging. Apps 

and advisory programs, built and available from the open sources, can be made 

available on the smartphones. Solar phone chargers are becoming available even in 

very remote locations without an electrical grid. Access to quality data is critical for 

development of Apps and tools that can be available to poor farmers. One of the examples of an open platform is the BioSense Institute in Serbia. This project was 

funded from the European program Horizon 2020. 

RESPONSE TO WRITTEN QUESTIONS SUBMITTED BY HON. BILL NELSON TO

DR. DOROTA HAMAN

Question 1. In addition to improving crop yields, how can big-data be used to protect farm workers from heat-related injuries? Is the farming industry using technology and data to improve worker safety? 

Answer. In collaboration with the Farmworker Association of Florida, Dr. Linda 

McCauley, Emory University in Atlanta (GA), has established the Los Girasoles 

heat stress study. Los Girasoles, funded by the National Institute for Occupational 

Safety and Health (NIOSH), is a four-year project (now in its third year) that aims 

to better understand how agricultural workers respond to heat stress and collect 

better data on the magnitude of heat-related illnesses like heat stroke in agricultural work. 

The data have been used to develop the Heat Related Prevention Illness training 

for the PISCA (FSU–FWAF) project and the Farm Labor Supervisor Training 

(FLST) in IFAS/UF. 

An APHA policy statement have been drafted and approved. A local representative is sponsoring local legislation to promote a regulation of heat stress. A pilot 

study has been conducted with FSU looking at alternative clothing. Data on kidney 

functions as related to heat stress have been collected by researchers last year and 

will be continued next year. 

Large amounts of physiological data collected by Los Girasoles was presented at 

several conferences and meetings. This is a list of current publications on the topic: 

1. Flocks, J., Mac, V., Runkle, J., Tovar-Aguilar, A., Economos, J., & McCauley, 

L. (2013). Female farmworkers’ perceptions of heat-related illness and pregnancy health. Journal of agromedicine, 18(4), 350–358. 

2. Mutic, A., Mix, M., Elon, L., Mutic, J., Economos, J., Flocks, J. Tovar-Aguilar, 

A., & McCauley, L. (2017). Classification of Heat-Related Illness Symptoms 

96 

among Florida Farmworkers. Journal of nursing scholarship.doi:10.1111/ 

jnu.12355 

3. Mac, V., Tovar-Aguilar, A., Flocks, J., Economos, E., Hertzberg, V., & 

McCauley, L. (2017). Heat exposure in Central Florida fernery workers: results 

of a feasibility study. Journal of agromedicine, 22(2), 89–99. 

4. Hertzberg, V., Mac, V., Elon, L., Mutic, N., Mutic, A., Peterman, K., TovarAguilar, A., Economos, E., Flocks, J., & McCauley, L. (2017). Novel Analytic 

Methods Needed for Real-Time Continuous Core Body Temperature Data. 

Western journal of nursing research, 39(1), 95–111. 

Wearable sensors similar to Fitbit can measure temperature and heart beat (or 

other biometrics) to alert workers to dangerous conditions. Sensors that are built 

into the clothing are also under development. CorTemp http://www.hqinc.net/ has 

been used for body core temperature, which also allows capture of heart rate signals, in addition to button temperature sensors in and outside workers’ clothing, including ActiGraphs (medical-grade wearable activity and sleep monitoring solutions 

for the research community). 

In addition to Florida, there are teams in California, Tennessee, and Sinaloa 

(Mexico) who are using similar equipment. To improve diagnostics, researchers are 

collecting blood, urine, BMI, HBP, body fat percentage, and temperature at workers’ 

homes. 

Question 2. Florida has faced both record droughts and record rainfall in the past 

few years. Can you describe how technology can help farmers prepare for and adapt 

to wild swings in weather? 

Answer. Technology can help farmers prepare and adapt to wild swings of weather in several ways. First it can help inform farmers of upcoming weather conditions 

in a timely and user-friendly way. It can also help farmers monitor conditions on 

a farm to better track the risk of pests and diseases, soil moisture conditions, and 

other potential yield-reduction factors that may require control measures. However, 

collecting data in an efficient and cost-effective way is just the first step in the process. Data must be translated into information through the use of mathematical models, machine learning, artificial intelligence and other data analytics to infer information from this new ‘‘data rich’’ environment. Data and information only have 

value if used to drive a farmers’ decisions. That is why we need to provide farmers 

with tools that are customized to their farming environment and conditions. 

It is also important to highlight that we need to develop and promote technologies 

and management practices that help farmers become more resilient to climate variability and change: http://agroclimate.org/fact-sheets/climate/ 

To adapt to these extremes, systems should be designed to a higher standard than 

is currently normal. For example, drainage systems should be designed to handle 

extreme rainfall events. At the same time, to cope with extreme drought events, efficient irrigation systems and additional water supplies should be identified. 

Question 3. How can big data help us prepare for and adapt to the effects of climate change? 

Answer. Large-scale geo-reference data can be very helpful in identification of the 

long-term trends in weather patterns and climate change. With new ways of data 

collection through satellites, drones, robots and cheap sensors, there will be improved data collection and analysis. 

Reliable and early prediction of both drought and rainfall based on climate models 

and global and local weather data (that are becoming more dense and precise) are 

critical for dealing with extreme weather events. Forecasting weather for the next 

few days or a week, the next rain front, the next heat wave, the path of the next 

hurricane and trends in climate change over the next decades use the same climate 

models which are build, tested and updated with billions of weather/climate data 

from tens of thousands of weather stations and sensors from land, ships, airplanes, 

ocean floats at various depths, research stations, and satellites. These are examples 

of big data already used and applied in daily life. 

Increasing accuracy of weather event predictions is critical to the timeliness of 

preparations, decisions and interventions. For example, expectation of unusual rainfall may require lowering of the water table in the field or draining of water reservoir to create more space for upcoming rain. Timing of this operation is critical. 

For example, draining water storage in preparation for extreme rain cannot be done 

too early or the plants may be stressed to the point of yield reduction. The accuracy 

of prediction is critical to successful preparation and big data from numerous sensors, as well as remote sensing, can increase the accuracy of prediction. 

Technology can also be helpful in managing scarce resources during drought and 

regulating water application to optimize incomes under drought conditions. SuffiVerDate Nov 24 2008 08:08 Aug 05, 2019 Jkt 000000 PO 00000 Frm 00100 Fmt 6601 Sfmt 6621 S:\GPO\DOCS\37228.TXT JACKIE
97 

cient lead-time is important for implementation of water storage to minimize the 

impact of drought on production. 

It is important to remember that improved short-term forecasts (on the scale of 

days up to the entire season) can be very useful for management decisions and can 

mitigate the impact of an extreme event. 

Big data may be able to help in analyzing reports of the impacts of extreme 

events. Currently, state climatologists and the National Drought Mitigation Center 

rely on somewhat subjective reports of drought impacts. Big data can help in analyzing diverse reports of impacts in a more objective manner. 

Question 4. Can big data help us reduce greenhouse gas emissions from agriculture? 

Answer. Big data allow us to see the patterns and fine-tune systems to make 

them more efficient. Well-known examples of the agricultural sources of greenhouse 

gas emissions are paddy-rice production and animal production. Another is oxidation 

of the organic material in cultivated soils. 

To shift from the paddy-rice, scientists are working on increasing production of 

upland rice that is not flooded and this growing system contributes much less to 

atmospheric greenhouse gasses. Increased efficiency in animal production and waste 

management may be beneficial to reduction of methane emissions. Efficient management of these agricultural systems is critical and the information provided by big 

data can help us optimize the systems through mathematical modeling. For example, by increasing the land-surface area covered with plants and reducing deforestation we can increase removal of CO2 from the atmosphere. New technologies, including robotics and low-energy LED lights for protected (indoor) plant production, 

are opening possibilities for vertical growth (in multistore buildings) of high value 

vegetables and fruits in urban areas. This production may benefit from CO2 enrichment (using industrially produced CO2) at the same time preventing its release into 

the atmosphere. 

To reduce CO2 in the atmosphere, numerous management strategies have been 

discussed including using plants to draw carbon out of the atmosphere and developing techniques to hold it in the soil. These strategies vary in effectiveness across 

different climates, soils and geographies. Sequestration of carbon is achieved 

through transferring atmospheric carbon into the soil via plant photosynthesis. Soil 

carbon must then be protected as effectively as possible from microbial activity that 

will release the carbon back to the air. Most, if not all, of the management techniques (operations) that promote carbon sequestration also improve soil aggregation, 

water retention, soil fertility, and food security. 

Question 5. Should we be thinking about ‘‘big data’’ and how farmers control their 

information the same way we think about consumers using the Internet or conducting financial transactions? 

Answer. I believe that these factors need to be carefully analyzed and a set of acceptable rules should be developed and established. I believe that it is important 

to make sure that sufficient high-quality data are available as an open source to 

allow for creating free access to critical information (or at very reasonable cost) especially for small, family farms and for farmers in developing countries. For example, in Europe, all the data created under the Horizon 2020 program are open access 

and the program includes a lot of agricultural big data. Another example of this 

type of platform is the BioSense Institute in Serbia. 

Question 6. Can you describe how extension services like the one at the University 

of Florida help famers—particularly small farmers, new farmers, and specialty crop 

farmers—access the same type of information that corporate farmers have? 

Answer. The University of Florida Institute of Food and Agricultural Sciences 

(IFAS) has been engaged in developing services and decision aids to help Florida 

farmers, including small farmers, improve resource use efficiency and reduce risk 

associated with climate variability and change. AgroClimate.org (http:// 

www.agroclimate.org) for example provides climate related information and dynamic 

application tools that interact with a climate database system for the Southeastern 

U.S.A. Information includes climate forecasts combined with risk management tools 

for a range of crops, forestry, pasture, and livestock. It has been quite successful 

and adopted by farmers in Florida to decide when to apply fungicide to strawberry 

fields based on weather conditions or to track the accumulation of chill hours during 

the winter in farms growing temperate fruits such as blueberries and peaches. 

AgroClimate integrates weather and climate data from public sources such as the 

Florida Automated Weather Network (FAWN) and gridded weather data from 

NOAA that covers the entire U.S.A. and the globe. Several mobile phone apps have 

been developed based on the weather and climate database services provided by 

AgroClimate including the Smart Irrigation Apps http://www.smartirriga 

98 

tionapps.org) that help farmers in Florida and Georgia schedule irrigation of crops 

such as citrus, strawberries, cotton and turf; thereby saving water and reducing 

leaching of nutrients into the groundwater. 

At UF, we are planning to create a similar portal for safety information and safety practices in agriculture. We are in the process of hiring a new faculty member 

to work on this project. 

Question 7. Do you think that consumer concerns about genetic engineering and 

GMO crops might create a backlash against precision technology, particularly as it 

relates to our food supply? 

Answer. I believe that this is unlikely. Precision agriculture is not directly linked 

to genetic engineering or GMO. In fact, due to precise management of resources 

(water, nutrients, pesticides and other chemicals, etc.), available technologies benefit 

the environment and reduce input costs. Precision technologies have been used for 

years in agriculture. Data availability, especially Big Data, makes these systems 

more precise, allows for faster and better decisions, and increases operation and production efficiency. These technologies do not introduce anything new or foreign into 

food chain. New genetic technologies, such as CRISPR (gene editing) seems to be 

less controversial and more likely to be accepted by the general public. 

RESPONSE TO WRITTEN QUESTION SUBMITTED BY HON. CATHERINE CORTEZ MASTO

TO DR. DOROTA HAMAN

Question. There were many references to the environmental benefits of agricultural data. Are we in a position yet where we can authoritatively quantify the environmental benefits experienced by the use and attention to these technologies and 

data analysis? For example, is this science proven to the point that we should be 

creating incentives in the farm bill conservation title for their utilization to keep 

pristine watersheds like Lake Tahoe, or water quantity in drought areas, solidified 

for the decades to come? Or is there a place for this use in connection with the Federal crop insurance program? 

Answer. I do not believe that we are yet ready for inclusion of specific incentives, 

policies and regulations. Scientists are working on information that can improve 

crop insurance program but we are not ready to implement it yet. The new technologies and sensors are providing abundant and potentially high quality agricultural data that can be very beneficial (in the future) to evaluate the environmental 

impact of agriculture and hopefully optimize the entire system to reduce the impact 

on the environment. Massive data require the development of adequate models, and 

these are not yet mature but are rapidly evolving. At this point, model development 

is one of the major challenges in big data analytics. The concept of analyzing big 

data relies excessively on ‘‘blind’’ machine learning, where ‘‘black boxes’’ of data are 

‘‘mined’’ in the hope that the process will ‘‘tell us what it contains’’. The problem 

is that these large data sets are of highly dimensional (complex) and there are many 

possible combinations of the drivers that could potentially produce similar results. 

Identifying the correct relationships requires experts with mature and relevant conceptual models to drive the search process. In addition, ‘‘dimension reduction’’ (selecting a smaller set of really important factors suitable for management) is a critical 

step. 

Interdisciplinary approach is necessary to assure that big data are appropriately 

analyzed. Teams of subject-matter experts need to team up with machine-learning 

experts (from statistics and informatics) to identify the correct solutions to the problems. The challenge today is that because of high potential economic gains, sensors, 

data, and machine learning has progressed rapidly but has not teamed up with content experts for specific problems. This is particularly true in Agriculture and Environmental Sciences, which are particularly complex as they are based on open and 

largely uncontrolled systems (as opposed to other artificial or human-controlled settings). 

Æ 

99 

This page intentionally left blank.