Funded in part by the soybean checkoff

Advancing Agricultural Performance®

and Environmental Stewardship

Digital Agriculture: Leveraging Technology

and Information into Profitable Decisions

Dr. Matt Darr, Ag & Biosystems Engineering

Some material adapted from “The Digital Transformation

of Row Crop Agriculture”

Authors: The Hale Group, Ltd & LSC International, Inc.

• No intent to be critical of, or endorse, specific products/services

to the exclusion of others that fulfill similar functions.

• Mention of product/service name is for information purposes

only.

• This discussion is a snapshot of what is available today, and is

intended to generate positive momentum around the ag data

space.

• We must recognize that everyone along the chain must derive

income from “AG BIG DATA” to be commercially viable.

Presentation Guiding Principles

What is Big Data?

Big Data is data whose scale, diversity, and complexity

require new architecture, techniques, algorithms,

and analytics to manage it

and extract value and hidden knowledge from it.

Digital Agriculture is the new industry which is combining

large data sources with advanced crop and environment

models to provide actionable on-farm decisions.

Is Big Data New?

Big Data in 1910s

• Autosteering and swath control technology have driven strong ROI

which has led to a proliferation of GPS technology on farm.

– This leads to ‘free’ machine data.

– Typical ROI in Iowa:

• 3.3% Planting Overlap Error, $7.89/ac

• 7% Tillage Overlap Error, $0.96/ac

Why the New Emphasis on Data Today?

• Autosteering and swath control technology have driven strong ROI

which has led to a proliferation of GPS technology on farm.

– This leads to ‘free’ machine data.

– Typical ROI in Iowa:

• 3.3% Planting Overlap Error, $7.89/ac

• 7% Tillage Overlap Error, $0.96/ac

Why the New Emphasis on Data Today?

• Our goal in agriculture is to make the best management decisions

each year to meet our economic, social, and environmental goals.

• Every producer enters spring with the best plan for their farm based

on the information they have available.

• The Goal of Digital Agriculture is to help producers accelerate the

natural adoption of new cultural practices and technology to yield

valued added benefits to the farming operation.

Where’s the Value in Digital Agriculture

What if you could pack 50 years of natural cultural practice

change into a 40 year farming career? Would your farm be more

financially stable and better suited for growth?

• Data Generation & Capture

– Yield Maps, Soil Fertility, Aerial Imagery, UAVs

– Wireless Data Transfer

• Data Warehouse

– Cloud Data Storage

• Prescription Agriculture

– VRA, Multi-hybrid Planting

• Probabilistic Decision Management

– Nitrogen Modeling

– Weather & Soil Suitability Modeling

Segments of the Digital Agriculture Industry

Which ones are you in?

• Satellite Delivered:

– 5m Resolution

– Timing can be limiting but more options

are becoming available

• Contracted Flight:

– 1m Resolution

– Typically can schedule images within a +/-

3 day window around target date

• Unmanned Aerial Systems (sUAS)

– ~3 – 10 cm Resolution

– If weather permits scheduling can be

within a few hours of target time

Data Generation: What Role Will High

Resolution Imagery Play?

UAV Legal UpdateFAA proposed commercial UAV rules on Feb 15th, 2015

Remote Sensing Basics

High Resolution Imagery in AgricultureRed-Green-Blue (RGB) Imagery

High Resolution Imagery in AgricultureNear Infrared (NIR) Imagery

High Resolution Imagery in AgricultureNormalized Difference Vegetation Index (NDVI) Imagery

High Resolution Imagery in Agriculture

Can you rank the image resolution of these three fields?

Can you rank the image resolution of these three fields?

Eliminate Status Quo Crop Production

Farming by the Foot means we

will no longer accept individual

plant or row failures.

Comparison of Imagery Resolution

25 cm 1 m

6 m

8 Row 12 m

16 Row

Drone Mapping Options

Data Analytics: Field Example

170 Acre Field, Continuous Corn

Data Analytics: Field Example

Hybrid BHybrid A

170 Acre Field, Continuous Corn

Data Analytics: Field Example

Hybrid BHybrid A

170 Acre Field, Continuous Corn

Hybrid BHybrid A

200

150

100

50

0

Gra

in Y

ield

(b

u/a

c)

131

176

Yield Comparison of Two Hybrids in a Side-by-Side Test

Data Analytics: Field Example

25

Compaction from previous machine

operations

Variety A

Variety B

26

Compaction from previous machine

operations

Variety A

Variety B

27

Compaction from previous machine

operations

Variety A

Variety B

Big Data Field ExampleHighly productive zone

Big Data Field ExampleHighly productive zone

195 bu/ac

Big Data Field ExampleHealthy Plants in Compacted Area

Big Data Field ExampleHealthy Plants in Compacted Area

160 bu/ac

Big Data Field ExampleWeak Plants in Compacted Area

Big Data Field ExampleWeak Plants in Compacted Area

145 bu/ac

34

Producer Value:

Quantify the impact of production practices.

120 acres x 70% compacted x 15 bu/ac yield loss =

1,260 bu yield loss = $5,000+

Cost of imagery = $240

When is the Right Time to Fly?

6/24/2014 NDVI 7/17/2014 NDVI

When is the Right Time to Fly?

7/17/2014 NIR 9/02/2014 NIR

Data Analytics: 1995 High Definition

Data Analytics: 2014 High Definition

Data Analytics: 2014 High Definition

Data Analytics: 2015 High Definition

Data Analytics: 2015 High Definition

Profit Benchmarking

Profit Benchmarking

If we stop farming the lowest yielding

20% of this field we have the potential

to double of per acre field profit.

• Incorporate probability of events occurring, mainly weather related.

• Utilizes extensive historical data and weather forecasting data to drive model predictions.

• As the season progresses real time weather data in integrated into the model to improve robustness.

Probabilistic Decision Management

Deterministic Model:

Outcome is a single value with

no randomness, i.e. soil sample

based fertility recommendations.

Probabilistic Model:

Outcome is a range of potential

values that represent

environmental variability and

can be used to manage risk.

Probabilistic Nitrogen Management

Spatially Specific Probabilistic Modeling

Big Data Policy Issues Receiving Major

National Attention

• Farmer ownership of data

• Farmer control of data

• Disclosure of data usage

• Farmer choice for use of data

• Portability of data

• Security from misuse

• No vulnerability to FOIA

Farmers express concern about all of these issues.

• Compatibility of systems

• Protection of GPS

• Regulation of UAVs

• Use of aggregated data

• Consistency of agreements

• Simple language

• Transparency and consistency

Producer Surveys on Big Data

Skeptical of the New Technology – 65%

The biggest concern is misuse of farm data by:

Fear that it favors the large farmers.

Prescriptions will recommend only some products, i.e., are biased.

It doesn’t work. Agriculture is too complex.

Neutral or Nuanced in Attitudes – 19%

It has potential, but must be implemented carefully.

Embracing the New Technology – 16%

The technology is here to stay. Let’s embrace it and make it work for us.

No one that is highly profitable today is doing it with only their own ideas and crop data.

• The ATPs • Activist groups • Grain traders

• The government • Computer hackers

American Farm Bureau Big Data Resources

http://www.fb.org/index.php?action=issues.bigdata

1. Proprietary data collected from farming and agricultural operations is valuable, should remain the property of the farmer, and warrants

protection.

2. We support:

2.1. Efforts to better educate farmers and ranchers regarding new technology or equipment that may receive, record, and/or transmit

their farming and production data;

2.2. Requiring companies that are collecting, storing, and analyzing proprietary data to provide full disclosure of their intended use of

the data;

2.3. Formation of standardized protocols regarding privacy and terms of conditions to ensure a standard definition of all components

within the contract. We should be an active participant in developing these protocols;

2.4. Compensation to farmers whose proprietary data is shared with third parties that offer products, services or analyses benefitting

from that data;

2.5. Multiple participation options being included in all contracts;

2.6. All proprietary information between the farmer and the company remaining between the two entities. This would not preclude a

farmer from sharing data with whomever he/she chooses (e.g., a consultant);

2.7. Utilizing all safeguards to ensure proprietary data is stored at an entity that is not subject to a Freedom of Information Act (FOIA)

request;

2.8. The farmer’s right to enter into agreement and their rights to sell their proprietary data to another producer (e.g., in a land sale);

2.9. Private companies entering into agreements which would allow for the compatibility/updating of equipment and updating of

software;

2.10. The right of a farmer to have access to their own data, regardless of when it was shared with a company; and

2.11. The right of the producer who no longer wishes to participate in aggregated data sharing with a private company, to remove their

past aggregated data from the company’s database and revoke that company’s ability to sell or use that data in the future.

3. We oppose any federal agency or FOIA-eligible entity from serving as a data clearinghouse for all proprietary data or aggregated data

collected by private companies.

Data Warehouse

Crop Consultant

Seed/Fert Supplier

Machinery Supplier

Insurance Agent

Landlord

Internal Mng Team

Grower Driven Entity

Pooled Analysis

Will Digital Agriculture Change

Production Ag?

How Will it Impact

Integration of Row

Crop Agriculture?

Will it Create an

Inflection Point in Ag

Production?

Will it Change the

Source of Agronomic

Advice?

• Will Digital Ag Create an Inflection Point in Production Ag?– The value of Digital Agriculture is still being defined. Most likely there will be

modest returns from Digital Agriculture in the next 4 – 8 years and a true inflection point in the near future is unlikely.

• Will Digital Ag Lead to Major Integration in Agriculture?– In the next several years we’ll continue to see integration, acquisitions, and

mergers throughout the Digital Agriculture industry as the industry matures.

– If successful, the rapid expansion of services from Digital Agriculture could drive a trend to larger scale farm integration and consolidation just as other technologies have had an impact on this trend.

• Will Digital Ag Change the Way Agronomic Advice is Delivered?– Digital Ag is focused on increasing our ability as growers to make the best on-

farm decisions. It is unlikely that Digital Ag will replace our traditional sources of agronomic advice although it will certainly provide a new and potentially very beneficial tool set to help enhance the advice and provide a broader understand of our farming environment.

Will Digital Agriculture Change

Production Ag?

Big Data and Small Drones: Tools or Toys?

Dr. Matt Darr, Iowa State University

The over-all point is that new technology will not

necessarily replace old technology, but it will date it. By

definition. Eventually, it will replace it. It's like people

who had black-and-white TVs when color came out.

They eventually decided whether or not the new

technology was worth the investment.

~ Steve Jobs, former CEO of Apple Inc.