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18. Precision to Digital Agriculture - John Fulton

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Evolving from Precision to Digital Agriculture with farm data and ability to provide value and information.

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18. Precision to Digital Agriculture - John Fulton

  1. 1. Precision to Digital Agriculture Dr. John Fulton 2018 Eastern Ontario Crop Conference
  2. 2. Air Conditioning in the Cab Smart phones / iPads / Tablets Guidance technology Yield monitors Food, Agricultural and Biological Engineering
  3. 3. Planter Displays By-Row Seed Monitoring 3 Will improve planting quality Food, Agricultural and Biological Engineering
  4. 4. Section / Row Control for Planters - SAVINGS: 4.3% - YIELD: 17% less - HARVET LOSS: >6 times higher Corn Automatic Section Control Technology for Row Crop Planters (Auburn Extension Publication) www.aces.edu/pubs/docs/A/ANR-2217/ANR-2217.pdf Food, Agricultural and Biological Engineering
  5. 5. Average Overlap Reduction PA Technology Percent Savings GPS-based Guidance 10% Variable-Rate Application 7% Automatic Section Control (ASC) 5% TOTAL AVERAGE 22% NOTE: Data based upon Auburn studies. Values could be higher or lower depending upon many production factors. Food, Agricultural and Biological Engineering
  6. 6. Variable-rate Seeding in Corn • Average 5% yield gain • Fields may exist where VR Seeding does not work so maintain fixed rate. • 10% to 15% gain in some fields using VR Food, Agricultural and Biological Engineering
  7. 7. Field-by-field basis Prescriptive Agriculture Food, Agricultural and Biological Engineering
  8. 8. Today’s Technology Food, Agricultural and Biological Engineering
  9. 9. By-row Prescription (Rx) • Hybrid • Population • Starter & pop-up fertilizer • Downforce • Row-cleaner Food, Agricultural and Biological Engineering Rx Management
  10. 10. Planter Technology Precision Planting Food, Agricultural and Biological Engineering
  11. 11. Multi-Hybrid Planting Technology – Data – People - Evaluation Food, Agricultural and Biological Engineering
  12. 12. Today’s focus on Ag IoT Image courtesy of New Holland Food, Agricultural and Biological Engineering
  13. 13. Digital Agriculture Precision Agriculture Prescriptive Agriculture Enterprise Agriculture Big Data in Agriculture Information adapted from an Iowa AgState / Hale Group report. Food, Agricultural and Biological Engineering
  14. 14. Food, Agricultural and Biological Engineering Prescriptive Agriculture │data driven recommendations and information; fastest growing area.
  15. 15. • Preseason Fertility Management – Prescription P and K application (Precision Crop Services) • Tillage Management – Prescription tillage maps (AGCO; CNH) • Multi-Hybrids – Prescription seeding of multi-hybrids (Beck’s; Pioneer) • SCN Management – Prescription application/use of nematicides (FMC) • In-Season Fertility Management – Prescription N application (DuPont Pioneer; Climate Corp) • Irrigation Management – Prescription Irrigation (AgSmart) • Disease Management – Prescription fungicide application (BASF) Producer Data Exchange for Growers Recommendations Food, Agricultural and Biological Engineering
  16. 16. Why collect data? Collecting and archiving data enables its use on-farm and participation in data services (Prescriptive Agriculture). Food, Agricultural and Biological Engineering
  17. 17. Precision / Digital Ag Evolution - Electronic drives for metering inputs (planter drives, PWM nozzles, etc.) - Automating machinery - M2M, M2I… - Prescriptive agriculture (data driven) - Online viewing dashboards (operational centers) - Integration of agronomic, machine and imagery data - Sustainability and Environmental Stewardship Food, Agricultural and Biological Engineering
  18. 18. Types of Data 1) Agronomic – yield, as-applied, as-planted, etc. 2) Machine – engine parameters, tractor status variables, implement mode & functions 3) Production - Information within home office, weather, notes, etc. 4) Remote Sensed Imagery – satellite, aerial, drones 5) Public Data – SSURGO, imagery, DEMs, etc. 6) Business Food, Agricultural and Biological Engineering Field-by-Field Database
  19. 19. Food, Agricultural and Biological Engineering In-Cab Display Feedback Producer Value 1) Identify and correct equipment issues immediately.; 2) Execute prescriptions; 3) Identify soil characteristics (e.g. clods, trafficked areas); 4) Verification of seed placement Agronomic Data: As- Planted Data Verification of seed placement
  20. 20. As-Planted Data: Row-Unit Ride Quality Map In-Cab Display Feedback Producer Value 1) Identify and correct equipment issues immediately.; 2) Execute prescriptions; 3) Identify soil characteristics (e.g. clods, trafficked areas) Food, Agricultural and Biological Engineering
  21. 21. Hidden variables impacting crop development and yield… COMPACTION (soil health component) Question: How do we identify and quantify? Tractor tire paths visible after field cultivator Food, Agricultural and Biological Engineering
  22. 22. As-Planted Data Downforce Map Producer Value 1) Identify and correct equipment issues immediately.; 2) Execute prescriptions; 3) Identify soil characteristics (e.g. clods, trafficked areas) Food, Agricultural and Biological Engineering
  23. 23. Food, Agricultural and Biological Engineering Agronomic Data Yield Maps Requires cleaning before creating Rx’s. 4 to 8 years of good yield data for prescriptive services. Producer Value Quality data leads to accurate analyses and information. Historical data provides value to RX creation.
  24. 24. General Use of Yield Maps for Nutrient Prescriptions Published Research - Good for identifying management zones by production levels (placement). - Good for using within P and K management (removal map for helping establish rate) - Cautiously use yield maps to drive variable-rate N. Food, Agricultural and Biological Engineering
  25. 25. Machine Data CAN messages, Health, etc. Effective tool to evaluate operating costs and capacity --- FUEL USAGE, UPTIME vs. DOWNTIME, ENGINE LOAD. Food, Agricultural and Biological Engineering
  26. 26. Telematics (Farmobile) – Harvest Operation Paths Food, Agricultural and Biological Engineering Producer Value: 1) Identify potential zones of soil compaction / structure issues; 2) Data for future analyses.
  27. 27. 0 1000 2000 3000 4000 5000 6000 0 3 6 9 12 15 18 21 24 27 30 33 36 39 42 Frequency Fuel Use Rate (L/h) Field 1C Planting 0 500 1000 1500 2000 2500 3000 3500 4000 4500 0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 Frequency Fuel Use Rate (L/h) Field 4A NH3 Application Fuel Use Rate Distributions Fuel Use Summary based on CANbus data Food, Agricultural and Biological Engineering Source: Shearer and Klopfenstein; Ohio State University
  28. 28. Identifying Man- / Machine-made Vs. Natural variability NDVI Image Early July Corn Food, Agricultural and Biological Engineering
  29. 29. Identifying Man- / Machine-made Vs. Natural variability Food, Agricultural and Biological Engineering
  30. 30. Individual data streams are valuable but the merging these data streams provides powerful insights.
  31. 31. Food, Agricultural and Biological Engineering Agronomic Data Yield Maps, As-applied…
  32. 32. Bridging Agronomic and Machine Data Moisture Content (%) Ground Speed (mph) Fuel Usage (gallons per acre) Mean % Engine Load Mean Field Capacity (ac/hr) Hybrid A 14.8 2.8 1.71 86 10.2 Hybrid B 14.3 5.2 0.86 44 18.9 Food, Agricultural and Biological Engineering
  33. 33. Best Practices to Managing Farm Data 1) What are you key data layers? 2) Maintain Copies & Backups of Display Data 3) Data storage (on and off-farm) 4) Organization of stored data 5) Who do I plan to share data with? Food, Agricultural and Biological Engineering
  34. 34. “You can't manage what you don't measure!“ (W. Edwards Deming) & You can’t utilize what you don’t collect. Food, Agricultural and Biological Engineering
  35. 35. Digital Agriculture Providing solutions to meet world demand John Fulton Fulton.20@osu.edu 334-740-1329 @fultojp Ohio State Precision Ag Program www.OhioStatePrecisionAg.com Twitter: @OhioStatePA Facebook: Ohio State Precision Ag Food, Agricultural and Biological Engineering

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