Agricultural Big Data & Ag Input Retailers Iowa Institute for Cooperatives Summer Workshop June 19, 2014 800.229.4253 hale@halegroup.com www.halegroup.com Mapping Success in the Food System Discover. Analyze. Strategize. Implement. Execute.
Major Sections of Our Presentation Overview of Big Data The AgState Project Implications for Ag Input Retailers Page 2
Overview of Big Data Page 3
Big Data: 3V s BIG Data? Complexity VOLUME Large amounts of data VELOCITY Needs to be analyzed quickly VARIETY Different types of structures and unstructured data Speed BIG Data Volume Source: Professor M. Eltabakh: Worcester Polytechnic Institute Page 4
What is Generating Big Data? Scientific Instruments (collecting all sorts of data) Sensor Technology and Networks (measuring all kinds of data) Source: Professor M. Eltabakh: Worcester Polytechnic Institute Page 5 Mobile Devices (tracking all objects all the time)
The Importance of This Issue Why is Big Data in agriculture emerging now? Components of Precision Agriculture have been used for years with modest benefit Computer capacity has now expanded to process vast volumes of data Why is Big Data an important issue? Enables the promise of Precision Agriculture to be realized Makes critical agronomic decisions on small areas within each field The most important change for row crop farming since the 1940s What is the level of urgency? Several major ag companies have just launched Big Data Service offerings that will define the market. What is the timing of this technology? Crop year 2014 will be critical in competitor positioning. Page 6
Value Generated by Ag Big Data 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 7
Enabling Farmers to Make 40 Critical Decisions Critical Decision Sets: Planning Data Seed Selection Weed- Control Program Insect- Control Program Pre-Planting Data Fertility Program Tillage Program Planting Data Plant Population Dynamics Seed Depth In-Season Data Post- Emergence Pest Control Crop Diagnostics Harvest Data Equipment Crop Marketing Goal: Maximize Net Return Per Acre Productivity Tools: Seed Factors Seed is lynchpin decision key to establishing yield potential Source: Monsanto Investor Presentation, October 2, 2013 Page 8 Planting Factors Focus on best field configuration, preparation and planting elements Pest-Control Factors Focus on insect and weed control regimes In-Season Decisions Diseases, nutrient, etc. approaches based on in-field environment
Current and Potential Players in Agricultural Big Data Major Players Monsanto Pioneer John Deere Public Sector Purdue University Iowa State University of Illinois Page 9 Startup Companies Farmer Organizations MyFarms 640 Labs Farmlogs Ag Input Distribution CPS / AgJunction Growmark / AgJunction LOL / Winfield National Organizations State Organizations IBM Hitachi IT Companies
Vendor-Driven or Farmer-Driven Big Data Who owns and controls the data? Vendor-Driven Services provided to farmers by Ag Input or Equipment company, e.g., Monsanto or Deere Vendor obtains full control over farmers data via the user / license agreement Sells a range of products and services to the farmer based on the data collected Farmer-Driven Big Data services are provided to farmers by an independent IT company, e.g., IBM or Hitachi Farmers retain control over the data and allow the IT companies to develop specific services based on the data Farmers retain right to monetize the use of their data Page 10
Monsanto Roll-Out 2012 Crop Year On-farm locations at 56 locations 5 to 10 bushel per acre performance advantage Fees Currently $10 per acre Climate Corp charges $15 per acre Distribute via Ag Input Retailers. 2013 Crop Year 150 farmers on 40,000+ acres 2014 Plans 4 states Hundreds of thousands of acres MN IA IL IN Source: Monsanto Investor Presentation, October 2, 2013 Page 11
MyJohnDeere Platform John Deere and Partners Provide near real-time field level data to growers Links Partner and Deere software and farmer services To be widely available to growers in 2014 Each company markets through its local distribution channels The platform is non-exclusive Integrated wireless technology linking the equipment, managers, operators, dealers and agricultural consultants to provide more productivity and efficiency to a farm or business. Pricing is based on multiple options and number of machines. Annual cost in the range of $5-15,000 per farm Distribution via John Deere Dealers. Page 12
DuPont Pioneer Encirca Services Encirca View: Mobile app for crop observations: Free of charge Encirca View Premium : News, Market Info., Local weather, all provided by DTN: $150 per month per farm Encirca Yield: Personalized management solutions via Certified Service Agents to maximize yield and control costs: $10-20 per acre. To be launched later this year. Direct distribution. Page 13
MyFarm s Business Model Seed Company pays $150/customer per year Source: MyFarms private communication January 6, 2014 Page 14
Nearly 1 Million Acres Served by MyFarms Source: MyFarms private communication January 6, 2014 Page 15
Business Functions The Co-op Model The co-op model is flexible, and the boundary between regional and local functions shifts under changing industry conditions. The Regional Cooperative Local Co-op Local Co-op Super Local Co-op Local Co-op Local Co-op Local Co-op Super Local Co-op Local Co-op Local Co-op Local Co-op Local Co-op Page 16
Thoughts on the Business Model The farmer takes the risk: the per acre fee is paid in advance. Monsanto s estimate of industry revenue is based on $20 - $25 per acre. A per acre business model for a service to farmers, similar to chemicals or seed, might not be readily accepted: Can Big Data improve yields 5 10 bushel per acre year after year? Can Big Data improve comparable yields on ALL acres? Won t larger operators insist on a lower fee for volume? Integrating all of the pieces of precision agriculture data to support farmer decision-making will take time. The roll-out of Big Data in agriculture will occur gradually. Page 17
Implementation Hurdles for Big Data Access to data from 10,000s of farmers Data ownership and privacy issues Convincing farmers the benefits are real this time Fast broadband / wireless access in rural areas Confusing complex options for farmer choice Uncertainty over new technology roll-out Some farmers not comfortable with IT technology Getting diverse systems to communicate with each other Source: The Hale Group and International Page 18
The introduction of Big Data is likely to be bumpy and uneven, but it is occurring. However, Big Data will create the next major technological sea change. It ranks with other sea changes such as the tractor, CPCs, and biotech. It should not be assumed that the business model for selling seed and chemicals will work for Big Data services. Technology will change the balance of power in the agri-food value chain. There will be winners and losers with the new technology. A solution needs to be found for the ownership and control of farmer data. All participants in the current agri-food chain must assess how the technology will affect their future business. Source: The Hale Group and International Page 19 Summary
Big Data in Agriculture Opportunities and Threats Which Kind of Future...... for Row Crop Farmers? Page 20
The AgState Project on Big Data
Purpose To develop the most appropriate strategy and tactics for row crop farmers to utilize Agricultural BIG Data to enhance the productivity, efficiency, and choices of American farmers while also protecting their farm data and intellectual property. Page 22
Approach for Phase 1 Phase 1. Strategic Issues Regarding Agricultural BIG Data Task 1. Technology Task 2. Business Model Task 3. Policy Task 4. Farmer Education How it will evolve? How will it be applied to row crop farming? What are alternative models for providers? How do they protect farmer interests? What is being considered to protect data and intellectual property rights? What is the level of understanding of big data within the row crop community? Task 5. Big Data Strategy for Farmers and Farmer-Driven Organizations Initiatives Goals Required Resources Metrics Page 23
Approach for Phase 2 Phase 2. Implementation Plan for Agricultural BIG Data Task 6. Technology Task 7. Business Model Task 8. Policy Task 9. Farmer Education What can farmer organizations do to influence technology development in agriculture? What actions can farmers take to encourage fairness and competition? What is need to support development of policy? What should guide policy formation? What information or materials do constituents need to make informed decisions on use of Big Data? Task 10. Implementation Plan for Farmers and Farmer-Driven Organizations Objectives Action Steps Accomplishments Accountability Timing Resources Page 24
Sources of Information Information Sources Desk-Based Research In-Person Interviews Focus Groups Electronic Survey Telephone Interviews Page 25
Focus Groups and Electronic Survey Interviews Estimated 150 200 interviews Target of 3-4 Focus Groups Farmers using Precision Ag tools and equipment Farmers not using Precision Ag tools and equipment Advisors to farmers Web-based survey farmers and businesses serving farmers A benchmark of farmer attitudes and usage Identification of farmer education needs Perspectives of businesses serving farmers Page 26
Categories of Interviewees Agricultural input suppliers offering Big Data services Other companies offering Big Data services to ag Farmers owners, renters, operators Absentee land owners Trusted advisors, e.g., Certified Crop Advisors Farm Management Companies Land Grant Universities Iowa farmer organizations National farmer organizations Farmer cooperatives IT companies who understand business models in other industries Grower Information Services Cooperative Independent technical experts State and Federal government agencies Stock market analysts Page 27
Project Schedule Interviews intensive in June, July, and August Focus Groups tentatively scheduled for last half of July Web-based Electronic Survey mid July to early August Draft Strategic Direction, AgState Meeting early September Final Strategic Plan mid-october Draft Implementation Plan mid-december Final Implementation Plan early January 2015 Page 28
Implications for Ag Input Retailers
Caveat The following are some of our initial thoughts on the implications of Big Data for retailers. At the end of the AgState project, we will have more specific concepts. Page 30
Will Big Data Shift the Competitive Balance? Disruptive Technology New Entrants Suppliers Intra- Industry Competition Buyers Substitutes Source: Michael E. Porter, the Five Forces of Competition Page 31
Where Will Value-Creation Occur in Vendor-Driven Big Data? Ag Input Companies Ag Input Retailers Farmers Commodity Processors Present Future Present Future Present Future Present Future Source: The Hale Group and International Page 32
Where Will Value-Creation Occur in Farmer-Driven Big Data? Ag Input Companies Ag Input Retailers Farmers Commodity Processors Present Future Present Future Present Future Present Future Source: The Hale Group and International Page 33
Big Data Risks and Benefits: Farmers Key Benefits Significant yield increases with reduced costs Management of complex decisions Scale of farming operations can increase Farm generated-data has significant value and might become new revenue stream Key Risks Ag input suppliers have effective control of farming decisions Complex systems will make it difficult for a farmer to switch input suppliers Complexity will make it difficult to compare offers from competitors Source: The Hale Group and International Page 34
Big Data Risks and Benefits: Input Retailers Key Benefits Improved efficiency and logistics to reduce costs and inventory levels New agronomic services that can be offered using Big Analytics New service opportunities such as the operation of drones for crop inspection. Farmer customers are larger and stronger financially Key Risks Reduced margins with different business model The input companies serve the larger farmers directly Customer concentration Reduction in traditional advisory services which reduces competitive differentiation Source: The Hale Group and International Page 35
Percent of Revenue from Services, Top 10 Ag Retailers Company Rev. CPS Fert Seed Serv. Crop Protection Services $1 Bil+ 31% 52% 13% 4% Helena Chemical Co. $1 Bil+ 37% 43% 17% 3% Growmark, Inc. $1 Bil+ 17% 62% 16% 5% Wilbur-Ellis Company $1 Bil+ 40% 49% 6% 5% CHS $1 Bil+ 20% 61% 15% 4% J.R. Simplot $1 Bil+ 35% 59% 4% 2% Southern States $0.5-1.0 Bil 16% 63% 16% 4% Jimmy Sanders, Inc. $0.5-1.0 Bil 31% 36% 30% 3% MFA Incorporated $0.5-1.0 Bil 18% 62% 16% 4% Tennessee Farmers $0.1-0.5 Bil 20% 58% 19% 3% Source: CropLife website, http://croplife.com/top100 Page 36
Revenue versus Profits from Services Context Network estimates that services represented 6% of revenue but 17% of profits for the top 20 retailers in 2012. Source: ASTA presentation by Context Network, December 13, 2013 Page 37
Your Business Model BIG Data will change the way you do business. Page 38
Technological Complexity Decision-Making in Agriculture Page 39 1950 1975 2000 2020 Common sense Minimal scientific education Human judgment Common sense Good scientific education Technical advice from many sources Human judgment Common sense Good scientific education Technical input Expert advisors Precision farming Human judgment Strong scientific education Precision farming Huge data volume Predictive analytics Limited judgment
How will Big Data change your business? Huge companies will squeeze you out of the advice business. A computer will give advice the Amazon effect Margins for commodity products will be squeezed. Not where value is added Technology will drive further consolidation Much larger farming operations Price transparency will increase Will provide even more incentive to bypass retailers. Much larger distributors / retailers Will require a much stronger knowledge base than in the past. Will shift the skills required. Competition for employees with high level computer skills will intensify. Page 40
Initial Assessment of Ag Retailers Strengths Trust of farmers Little conflict of interest Boots on the ground Local presence and large scale through regional co-op Weaknesses Less capital for investment than the major players Less bargaining power than major service providers Less experience in Big Data and Data Analytics Opportunities New, emerging services Threats Large farms bypass retailers Page 41
Market Growth Anaylze Your Lines of Business Unless you want to be only a logistics business, you must invest in Big Data. Make the other business lines feed Big Data. High Question Marks Strategy: Grow or Sell Stars Strategy: Invest Low Dogs Strategy: Divest Cash Cows Strategy: Reap Source: Boston Consulting Group Page 42 Low High Market Share
This is the time to invest The tug-of-war for cash in a co-op. Maximize Patronage Refunds Strengthen Balance Sheet Invest for the Future Page 43 Whatever goes into one bucket cannot go into the other buckets!
Strategic Questions for You as Retailers What is an honest appraisal of your current business? What is your competitive advantage today? How strong will your competitive advantage be 3 years hence? Do you have a good estimate of the profitability of each business line? Are all business lines profitable? Should you divest unprofitable businesses? What should you do to strengthen your relationship with members? How vulnerable is the co-op to bypassing by large farmers? What can the regional cooperatives do for you in Big Data? Page 44
Strategic Questions for You as Retailers How can we remain well informed about Big Data developments? This is changing weekly; someone must track it This could be through your regional co-op or jointly with several co-ops What are our strategic options? No involvement in Big Data Align with Monsanto as a service provider Align with one or several smaller service providers Join forces with other cooperatives Alliance with your regional cooperative Alliance with a group of nearby local cooperatives Merger with a group of nearby local cooperatives What are the pros and cons of each option? What are the investments, costs, benefits, and risks of each option? Page 45
Recommendations Conduct a tough self-assessment Prepare for a major shift in your value proposition and business model Gain access to Big Data, high tech skill sets As an individual cooperative In collaboration with other local cooperatives Through a regional cooperative Seek alliances with other companies Capitalize on your independence as a trusted advisor Page 46
What Questions Do You Have? Page 47