Digital Agriculture: Leveraging Technology and Information into Profitable Decisions

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1 Digital Agriculture: Leveraging Technology and Information into Profitable Decisions Dr. Matt Darr, Ag & Biosystems Engineering Advancing Agricultural Performance and Environmental Stewardship Some material adapted from The Digital Transformation of Row Crop Agriculture Authors: The Hale Group, Ltd & LSC International, Inc. Funded in part by the soybean checkoff

2 Presentation Guiding Principles 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.

3 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.

4 Is Big Data New? Big Data in 1910s

5 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

6 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

7 Where s the Value in Digital Agriculture 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. 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?

8 Segments of the Digital Agriculture Industry Which ones are you in? 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

9 Data Generation: What Role Will High Resolution Imagery Play? 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

10 UAV Legal Update FAA proposed commercial UAV rules on Feb 15 th, 2015

11 Remote Sensing Basics

12 High Resolution Imagery in Agriculture Red-Green-Blue (RGB) Imagery

13 High Resolution Imagery in Agriculture Near Infrared (NIR) Imagery

14 High Resolution Imagery in Agriculture Normalized Difference Vegetation Index (NDVI) Imagery

15 High Resolution Imagery in Agriculture

16 Can you rank the image resolution of these three fields?

17 Can you rank the image resolution of these three fields?

18 Eliminate Status Quo Crop Production Farming by the Foot means we will no longer accept individual plant or row failures.

19 Comparison of Imagery Resolution 25 cm 1 m 6 m 8 Row 12 m 16 Row

20 Drone Mapping Options

21 Data Analytics: Field Example 170 Acre Field, Continuous Corn

22 Data Analytics: Field Example Hybrid A Hybrid B 170 Acre Field, Continuous Corn

23 Grain Yield (bu/ac) Data Analytics: Field Example 200 Hybrid A Yield Comparison of Two Hybrids in a Side-by-Side Test 176 Hybrid B Hybrid A Hybrid B 170 Acre Field, Continuous Corn

24 Data Analytics: Field Example

25 Compaction from previous machine operations Variety A Variety B 25

26 Compaction from previous machine operations Variety A Variety B 26

27 Compaction from previous machine operations Variety A Variety B 27

28 Highly Big productive Data Field zone Example

29 Highly Big productive Data Field zone Example 195 bu/ac

30 Big Data Field Example Healthy Plants in Compacted Area

31 Big Healthy Data Plants Field in Example Compacted Area 160 bu/ac

32 Big Data Field Example Weak Plants in Compacted Area

33 Weak Big Plants Data in Compacted Field Example 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 34

35 When is the Right Time to Fly? 6/24/2014 NDVI 7/17/2014 NDVI

36 When is the Right Time to Fly? 7/17/2014 NIR 9/02/2014 NIR

37

38 Data Analytics: 1995 High Definition

39 Data Analytics: 2014 High Definition

40 Data Analytics: 2014 High Definition

41 Data Analytics: 2015 High Definition

42 Data Analytics: 2015 High Definition

43 Profit Benchmarking

44 Profit Benchmarking If we stop farming the lowest yielding 20% of this field we have the potential to double of per acre field profit.

45 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 Compatibility of systems Protection of GPS Regulation of UAVs Use of aggregated data Consistency of agreements Simple language Transparency and consistency Farmers express concern about all of these issues.

46 Producer Surveys on Big Data Skeptical of the New Technology 65% The biggest concern is misuse of farm data by: The ATPs Activist groups Grain traders The government Computer hackers 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.

47 American Farm Bureau Big Data Resources 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; The right of a farmer to have access to their own data, regardless of when it was shared with a company; and 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.

48 Data Warehouse Crop Consultant Seed/Fert Supplier Machinery Supplier Insurance Agent Grower Driven Entity Landlord Internal Mng Team Pooled Analysis

49 Will Digital Agriculture Change Production Ag? Will it Create an Inflection Point in Ag Production? How Will it Impact Integration of Row Crop Agriculture? Will it Change the Source of Agronomic Advice?

50 Will Digital Agriculture Change Production Ag? 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 onfarm 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.

51 Understanding the Value of Precision Data 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.

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