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

1. Precision to Digital Agriculture
Dr. John Fulton
2018 Eastern Ontario Crop Conference

2. Air Conditioning in the Cab
Smart phones / iPads / Tablets
Guidance technology
Yield monitors
Food, Agricultural and Biological Engineering

3. Planter Displays
By-Row Seed Monitoring
3
Will improve planting quality
Food, Agricultural and Biological Engineering

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. 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. 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. Field-by-field basis
Prescriptive Agriculture
Food, Agricultural and Biological Engineering

8. Today’s Technology
Food, Agricultural and Biological Engineering

9. By-row Prescription (Rx)
• Hybrid
• Population
• Starter & pop-up fertilizer
• Downforce
• Row-cleaner
Food, Agricultural and Biological Engineering
Rx Management

10. Planter Technology
Precision Planting
Food, Agricultural and Biological Engineering

11. Multi-Hybrid Planting
Technology – Data – People - Evaluation
Food, Agricultural and Biological Engineering

12. Today’s focus on Ag IoT
Image courtesy of New Holland
Food, Agricultural and Biological Engineering

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. Food, Agricultural and Biological Engineering
Prescriptive Agriculture │data driven
recommendations and information; fastest
growing area.

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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.

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Frequency
Fuel Use Rate (L/h)
Field 1C Planting
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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. Identifying Man- / Machine-made Vs. Natural variability
NDVI Image
Early July Corn
Food, Agricultural and Biological Engineering

29. Identifying Man- / Machine-made Vs. Natural variability
Food, Agricultural and Biological Engineering

30. Individual data streams are valuable but
the merging these data streams
provides powerful insights.

31. Food, Agricultural and Biological Engineering
Agronomic Data
Yield Maps, As-applied…

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. 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. “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. 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