The Digital Transformation of Row Crop Agriculture

Transcription

1 The Digital Transformation of Row Crop Agriculture Winter Workshop Iowa Institute for Cooperatives January 14-15, Mapping Success in the Food System Discover. Analyze. Strategize. Implement. Execute.

2 Consulting Expertise of the THG / Partnership Page 2

3 Sample Client List Page 3

4 Sections of this Presentation Introduction The Situation Analysis Company Positioning Strategic Implications Farmer and Retailer Strategies Page 4

5 Background of this Presentation Many slides of this presentation were developed for the Iowa AgState project on Big Data in agriculture. New material has also been added subsequently. Page 5 Agribusiness Association of Iowa Dairy Iowa Iowa Cattlemen's Association Iowa Corn Growers Association Iowa Corn Promotion Board Iowa Department of Agriculture and Land Stewardship Iowa Department of Economic Development AGSTATE MEMBER ORGANIZATIONS Iowa Farm Bureau Federation Iowa Institute for Cooperatives Iowa Pork Producers Association Iowa Poultry Association Iowa Soybean Association Iowa State University College of Agriculture and Life Sciences Iowa Turkey Federation Midwest Dairy Association

6 Introduction

7 Big Data: A Problem of Definition Big Data in Agriculture: differently by different people. A very broad term that is used 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. We prefer the term Digital Agriculture which is broader and more comprehensive than the term Big Data. Page 7

8 Major Data Sets Used in Row Crop Agriculture Agriculture will not totally convert to Big Data. Some Not Big Data will always be important. Big Data by Definition Weather data sets -- historical, current and forecasts. Satellite imagery of large farming areas. Aggregated farm level data from hundreds of thousands of acres. Not Big Data, Farm Level Yield maps for fields and management zones. As planted maps for fields and management zones. Soil samples on a grid basis. Farm financial, marketing, & risk management records. Page 8

9 The Major Components of Digital Agriculture Definition Use of new tools that give farmers better operational control. Examples Auto-steer Yield monitors Variable rate machinery Weather stations Page 9 Precision Agriculture Many services in-between Prescription Agriculture Definition Detailed prescription of agronomic practices to maximize yield or profit per acre using computer algorithms. Examples FieldScripts Encirca Answer Tech Plus many other data components Enterprise Agriculture Definition Integrated, computer platform including: planning; agronomy; labor; work orders; purchasing; risk; inventory; logistics; machinery; marketing; profit per acre. Examples Granular, Conservis

10 Preferred Term Digital Agriculture Digital Agriculture: A family of activities related to farming that includes Precision Agriculture, Prescription Agriculture, Enterprise Agriculture and depends on the collection, use, coordination, and analysis of data from a multiplicity of sources. Digital Agriculture uses Big Data and some data that is NOT Big Data. Digital Agriculture holds the promise of being a very powerful tool that will assist farmers in increasing their profitability. Page 10

11 The Situation Analysis

12 Technological Eras Expanded use of mechanized power 1954 Tractor numbers exceeded horses and mules 1921 Commercial hybrid corn 1935 Hybrid demand exceeds supply % of corn acreage planted to hybrids 1959 Applied to 47% of croplands by 75% of farms Commercial fertilizer use doubled 1996 Herbicidetolerant and insectresistant crops M acres, half of the US cropland, sown to GE crops 1994 Satellite guidance begins Mechanization Hybridization Chemization GMOs Data Directed Ag Source: Dr. J. B. Penn, Deere & Co., The Snyder Memorial Lecture at Purdue University Page 12

13 Digital Transformation of Agriculture Past-Present-Future 5. System of Systems 1. Product 2. Smart Product Smart, Connected Product Product System Smart Tractors + Planters Farm Equipment System Cultivation Combine Harvesters + Rain, Humidity, Temperature Sensors Farm Equipment System Weather Maps Weather Data System Farm Management System Irrigation System Weather Forecasts Weather Data Application Seed Optimization System Farm Performance Database Seed Database Seed optimization Application Field Sensors Irrigation Nodes Irrigation Application Source: Harvard Business Review, Michael E. Porter and James E. Heppelmann, November 2014 Page 13

14 The Goal is to Link Data and Decisions Making the link between On-Farm Optimization Data-based decision-making for many more decisions Early problem identification for management response Custom solutions to minimize inputs and maximize yields Input Product Innovation for Unique Conditions Biotech / seed research Equipment R&D Other input supplies Market Linkage Improves transparency and predictability of markets Page 14

15 Key Challenges to be Resolved There are 3 times as many U.S. farmers over the age of 65 as there are under 35. Tom Vilsack, U.S. Secretary of Agriculture Human Capital: Farmers need to be Tech-Savvy and to have access to IT skills for use of data in decision making. Quality Data: The majority of Data generated currently is not useable due to poor quality, e.g., lack of calibration. Data Access: Much of the Data is on cards, sticks, hard drives or in binders of printed documents, and is very hard to access. Page 15

16 Important Enablers of Digital Agriculture More Embedded Knowledge in the equipment, devices or systems required. Easy to use, intuitive, meeting expectations, e.g., iphone. More Standardization across the vast range of products, services and systems being offered. Validation Processes for services offered and business models. Technology Pull process that empowers farmers to define problems and influence innovation. User Training that works for the broad population of farmers. Page 16

17 Attitudes of Iowa Farmers, Summer 2014 Based on an electronic survey. Skeptical and/or Fearful of the New Technology 65% The biggest concern is misuse of farm data by: The ATPs Activist groups Grain traders 16% 19% N = 95 The government Computer hackers Skepticism that it doesn t work agriculture is a complex biological system. 65% Neutral or Nuanced in Attitudes 19% It has potential, but must be implemented carefully. Embracing the New Technology 16% No one that is highly profitable today is doing it with only their own ideas and Page 17 crop data.

18 Iowa AgState Agronomist Survey A web-based survey was conducted among agronomists and managers of ag retailers in Iowa. Participants were recruited by two organizations: A total of 215 people answered most of the questions in the survey. Page 18

19 Agronomy Services Provided Agronomists were asked if they analyze farm-specific data from precision ag and help farmers improve yields or profitability using farm-specific data? N = 215 Page 19

20 Agronomy Services Provided (continued) Agronomists were asked if they provide Prescription Ag services that recommend hybrids/varieties to plant, population, row spacing, etc., based on aggregated farmer data? N = 211 Page 20

21 Impacts of Widespread Adoption of Prescription Ag Agronomists were asked the impacts good and bad of widespread adoption of Prescription Ag N = 198 Page 21

22 Open-Ended Comments The last question of the survey asked, What other ways might prescription agriculture change row crop farming or businesses closely related to farming over the long-term? There were 65 responses to this question. The following slides summarize the write-in comments into four categories: Potential opportunities for farmers Potential threats to farmers Potential opportunities for ag retailers Potential threats to ag retailers Page 22

23 Opportunities and Threats for Farmers Page 23 Opportunities Decisions based more on data and less on personal relationships and emotion. Will enable reduced use of fertilizers and CPC achieving cost savings and reduced environmental impact. Enable larger scale of farming operations. Increased profitability. Threats The potential for more government regulation or mandates using the data. Accelerated consolidation of farms: Farmers less tech savvy will likely exit Large progressive farmers will get bigger Could lead to integration of row crop farming. High switching costs and fewer choices.

24 Potential Opportunities for Ag Retailers Will enable retailers to build stronger relationships with farmers Creates new business opportunities UAVs Software / data management Will increase the demand for agronomists who understand all of the interactions in growing a crop The trusted advisor will be the most important person for each farmer Will create stronger bonds between farmer and ag retailer Will enable better inventory management Page 24

25 Potential Threats to Ag Retailers Creates the potential for more government regulation Will accelerate consolidation of ag retailers Will shift some of the services farmers receive from local ag retailers to distant ATPs Will require more investment to provide needed services and some retailers may provide these services for no fees to gain market share Page 25

26 Company Positioning

27 Technology Drivers Technology Map The Technology Map is designed to present companies in the appropriate cells based on their offerings and position in the market. Products and Services Precision Ag Equipment Data Warehouse Ag Retailer Software Smart Data Deterministic Models Probabilistic Models Farm Enterprise System Data Generation & Capture Cloud & Computer Processing Capacity Delivery Systems Source: The Hale Group, LLC and International, Inc. Page 27

28 Ability to Execute Assessment of Key Players Completeness of Vision Challengers AGCO CaseIH Mapshots Raven Trimble Leaders Deere & Co. Dupont / Pioneer Monsanto / TCC SST Software WinField / LOL AgJunction AgLeader Conservis GeoVantage MyFarms Niche Players AGSOLVER Beck s Hybrids Farm Link / True Harvest Visionaries Source: The Hale Group, LLC and International, Inc. Page 28

29 Deere & Company: Backend and Frontend Software Cos AgGateway 85+ Cos JD Link IT Staff of API Developers John Deere & Co Dupont Pioneer John Deere Dealers MyJohnDeere. com Growmark Dow Agro BASF BCS Ag Retailers Page 29 Farmers

30 Dupont Pioneer: Backend and Frontend Farmer data via Deere DTN USDA & Universities Pioneer Agronomy & Encirca Tech AGCO & Raven Dupont / Pioneer Encirca Sales Agents Encirca. pioneer.com Pioneer Seed Agents Page 30 Farmers

31 Monsanto-Climate: Backend and Frontend Climate Corp IT staff Precision Planting Monsanto Seed Group AgGateway & OADA CaseIH Monsanto / Climate Corp. Helena GrowMark CPS Climate Agronomy Reps Climate.com WinField LOL Crop Insurance Agents Ag Retailers Page 31 Farmers

32 SST Software: Backend and Frontend SST Software staff Data Warehouse AgX Platform Raven Slingshot AgGateway SST Software Ag Retailer A Crop Ag Retailer B Helena Co-op A Consultants Co-op B Page 32 Farmers

33 WinField : Backend and Frontend Climate Corp Geosys Answer Plot R7 Tool Answer Tech Winfield Data Silo AgGateway OADA WinField / Land O Lakes Co-op A Co-op B WinField.com Ag Retailer A Ag Retailer B Page 33 Farmers

34 Unclear Value Clear Value Establishing Value is a Challenge Farmers need solutions with clear value. Tools Solutions Auto steer Variable rate seeding/fertilizer Aerial imagery Telematics Yield maps UAVs Local weather Scouting apps Multi-hybrid planting Current prescription models Nitrogen management Farm Enterprise System Benchmarking Source: The Hale Group, LLC and International, Inc. Page 34

35 High Level Summary Business Model For many companies, their business model is in a state of flux. Some farmers find it difficult to establish the value of the services being offered. Potential sources of value: Agronomic Value Logistics Information Sharing Operational Management Regulatory Compliance Crop Budgeting at sub-field level Page 35

36 Strategic Issues

37 Overall Assessment This technology will continue to improve rapidly. Tech savvy farmers are already adopting it with enthusiasm. There is likely to be a major turnover in farm operators due to: The current age of farmers. The prospect of low crop prices for the next several years. The farmers that adopt this technology may have an advantage in renting land and will expand. There is a significant gap between those farmers who are prepared to adopt this technology and those who are skeptical and/or fearful. Page 37

38 The Problems to be Addressed in the Strategy Unequal Market Power Farmers do not have equal negotiating power with major ATPs Information asymmetry puts farmers at a major disadvantage There are few places a farmer can turn for detailed information Complexity Farmers find the hardware, software, and business models hard to understand Hardware and software is not fully compatible across ATPs Many companies are marketing tools rather than solutions Legal Obstacles Some user agreements limit farmers choices Some legal documents are hard to understand Unclear Benefits There is a mixture of fact and hype in the marketplace The economic benefit of some products is not quantified Page 38

39 Five Strategic Questions Page Will all of the components of Digital Agriculture combine to create a major inflection point similar to the introduction of hybrid corn many decades ago? 2. Will Digital Agriculture Technology cause the row crop sector to become integrated, i.e., coordinated through contracts with farm operators by: A few large ATPs A handful of large corn and soybean processors Branded food manufacturers and foodservice chains 3. How rapidly will consolidation occur within the row crop sector? 4. Will the sophisticated agronomy models allow computers to provide agronomic advice with little local agronomic input? 5. What does this technology mean for profitability within the row crop value chain?

40 Incremental Change Inflection Point 1 Incremental Change or Inflection Point Not likely at this time since the value gain has not been established. Most likely for the next 4 years since neither a dramatic breakthrough nor clear value have been developed as yet. Given rapid changes in Digital Technology and the age profile of farmers an inflection is likely in the period. Most likely if the value created is at the current modest level of +/- 5%. Years Page 40

41 2-a The Case for Integration by Major ATPs Likely Major ATPs already have a substantial integrated seed production system. The exit of older farmers will create opportunity for integration. Not Likely Improving pork quality and consistency was part of the motivation for swine industry integration. ATPs do not have this motivation. Volatility of crop prices would create huge risks for ATPs. At this time we believe full-scale integration by large ATPs providing agronomic prescriptions is not likely in the next 8 to 10 years. Page 41

42 2-b The Case for Integration by Major Processors Likely Some farmers may need help using the new technology. Large corn and soybean users might fill the gap. Large feed, ethanol, soy processors, and other industrial users could control genetics and quality for greater product consistency and quality. Not Likely Large independent farmers will resist such arrangements. Weather and the volatility of crop prices creates huge risks for integrators. If Prescription Agriculture produces only modest benefit. Integration by large customers cannot be ruled out if Prescription Agriculture is proven to be successful. Page 42

43 2-c The Case for Integration by Food Companies Likely Branded food companies need to protect their brand equity. Sustainability and traceability are becoming very important to a segment of consumers. In some respects, corporate office suites have more power than legislatures. Not Likely Not all farm practices are of interest to food companies. Consumer product food companies are unlikely to invest backward. Page 43 Standards compliance by food companies protecting their brand will almost certainly use this technology to monitor adherence to their specs after the technology is perfected.

44 3 What Will Be the Rate of Farm Consolidation? Current Speed If adoption of Digital Agriculture is slow due to complexity human constraints. If the ATPs can t demonstrate value. Caps on total farm payments under Farm Bill may limit consolidation in low price environment. Accelerated Speed Aging farmers retire and high tech, low cost producers capture land rentals. This technology is simplifying operations for large-scale farmers. Large farmers can hire people with specialty skills, e.g., IT and agronomy. Page 44 We believe that because of Moore s Law, the technology will develop quickly and farm consolidation will accelerate.

45 4 The Digitization of Agronomic Advice How far will this trend go? Past Present Future Obviously the actual trend is not a straight line the graphic is directional only. It s too early to tell how far this technology will go. Page 45

46 How will margins change? If the ATP s gain more control of decision-making via data access Ag Input Companies Ag Input Retailers Farmers Commodity Processors Present Future Present Future Present Future Present Future Source: The Hale Group and International Page 46

47 How will margins change? If farmers and ag retailers retain significant control of decision-making Ag Input Companies Ag Input Retailers Farmers Commodity Processors Present Future Present Future Present Future Present Future Source: The Hale Group and International Page 47

48 Strategic Principles: Competing in the Digital Economy Seven Forces at Work: New Pressures on prices and margins Competitors emerge from unexpected places Winner-takes-all dynamics Plug and Play business models Growing talent mismatches Converging global supply and demand Relentlessly evolving business models-at higher velocity Source: McKinsey & Co., Strategic Principles for Competing in the Digital Age, McKinsey Quarterly, May 2014 Page 48

49 Digital Transformation: Key Strategy Questions Which set of smart, connected products and service capabilities should be pursued? What data must the retailer capture, secure, and analyze to maximize the value of its offering? How does the retailer manage ownership and access rights to its product and service data? What are the new competitive threats from established competitors and new entrants? Should the retailer change its business model for example in terms of scope or monetizing its data? Source: Michael E. Porter & James E. Heppelmann, How Smart Connected Products are Transforming Competition, Harvard Business Review, November 2014 Page 49

50 Farmer and Retailer Strategies

51 Five Components of the Iowa AgState Strategy A Farmer-Centric Strategy Education Data Warehouse Assessment Technology Pull Research Page 51

52 Iowa AgState: Five Strategic Initiatives for Ag Organizations 1. Education: Provide continuous, on-going education for farmers, ag retailers, other local businesses, and policy makers that will enable them to make informed decisions. 2. Data Warehouse: Create an independent, farmer-controlled data warehouse for farm level data and aggregated agronomic data which can be used to better serve farmer participants. 3. Assessment: Create mechanisms to provide an assessment of the many products, services, and business models in the market while promoting uniform, agreed-upon industry standards and guidelines. 4. Technology Pull: Drive a technology pull strategy focused on products and services that provide solutions to farmer problems rather than just complicated tools. 5. Research: Create a center for inter-disciplinary research that will position Iowa farmers to be at the cutting edge of digital technology for generations to come. Page 52

53 Digital Agriculture s Implications for Farmers Good, smart farmers are asking for help. What should my next steps be? an Ontario farmer What advice should I give my neighbor? an AgState board member Digital Agriculture is making the farmer s job more complex. But his only options are: Adapt or Exit The AgState recommendations to ag organizations will help farmers. But many farmers will need individualized help. What should I do next on my farm? Page 53

54 What Some Farmers Are Asking For To date, Digital Agriculture has been driven by the introduction of new tools. That s a bad strategy. Digital Agriculture should be driven by improving decision making using more and better data to make better decisions. Some farmers don t need help they are doing it on their own. Some farmers are resisting it. But some want to learn and adopt the new technology. We believe this is a major business opportunity. Can your cooperative fill this need? Page 54

55 Helping Farmers Develop a Digital Plan A. What s your long-term plan for the farm? 1. Retire within 5-7 years? Maybe you don t need a plan. 2. Continue operation within the family long-term. a. Goals for: size, crop diversity, location, risk management, succession plan? B. What s your current portfolio of Digital Agriculture tools? 1. Farm machinery 2. Computers/software 3. Human skills C. Decision Improvement Analysis 1. Which decisions, if improved, would increase profitability the most? 2. Which one should you work on first? Financial analysis Nutrient management Hybrid selection Machinery logistics Page 55 Marketing Other

56 Helping Farmers Develop a Digital Plan Page 56 D. What options are there to improve your decision making? 1. Equipment 2. Computers / software 3. Human skills E. What is an appropriate plan from getting from where you are to where you want to be? 1. Equipment 2. Computers / software 3. Human skills 4. Business relationships 5. Time line 6. Estimated investment requirement 7. Implications of plan for operation a. New employees b. Expanded operation to justify investment

57 Potential Business Opportunities for Ag Retailers Machine retrofitting Drone scouting after legal issues resolved Prescription service: FieldScripts WinField Your own, with software support, e.g., SST Recordkeeping / IT services Farmer consulting: Answer the questions: Where do I start? What should I do next? How can an ERP type system be implemented on my farm? Other opportunities Page 57

58 Implications for Ag Retailers Page 58 Digital Agriculture poses major opportunities and major threats. You cannot afford to ignore this issue. Your basic options: Merge Is this the time to merge to increase size and / or gain access to additional skills? Intensify add new skills and new service offerings and compete to be one of the survivors in a consolidated retailer sector. Specialize in Logistics become the UPS of the agricultural community. Would involve huge scale and major investment. Specialize in Services be the best in providing Digital Agriculture services for farmers and quit selling products. Exit sell the business for cash. Other options????

59 Strategic Principles: Competing in the Digital Economy Six big decisions: Decision 1: Decision 2: Decision 3: Decision 4: Decision 5: Decision 6: Buy or sell businesses in your portfolio? Lead your customers or follow them? Cooperate or compete with new attackers? Diversify or double down on digital initiatives? Keep digital businesses separate or integrate with current non-digital ones? Delegate or own the digital agenda? Source: McKinsey & Co., Strategic Principles for Competing in the Digital Age, McKinsey Quarterly, May 2014 Page 59

60 Major Take-Aways Digital Agriculture will create structural change in global agriculture. We re in the 2 nd inning. Farmers should be pro-active in shaping their destiny. Farmer-centric organizations should collaborate to exert influence. Cooperatives and ag retailers need to understand the strategic implications of Digital Agriculture both for their customers/members and for their own organization. Now this is not the end. It is not even the beginning of the end. But it is, perhaps, the end of the beginning. Winston Churchill, Nov 1942 Page 60

61 Thank you Mapping Success in the Food System Discover. Analyze. Strategize. Implement. Execute.