Find the ROI of different precision ag tools and management strategies. John Fulton, Ohio State University

1. Value of Digital Ag
Dr. John Fulton
2020 Eastern Ontario Crop Conference, Kemptville, Ontario
@OhioStatePA

2. DigitalAgriculture(IoTin Ag)
Image courtesy of NewHolland
Food, Agricultural and Biological Engineering

3. Food, Agricultural and Biological Engineering
#AgTech that enables higher planting speeds…

4. Food, Agricultural and Biological Engineering@OhioStatePA
#PrecisionAg

5. As-Planted
Food, Agricultural and Biological Engineering

6. Machine Data
FuelUse,Engineload,Speed,Torque
Field Operations --- planting, spraying, fertilizer, harvest
Food, Agricultural and Biological Engineering

7. Platforms
Food, Agricultural and Biological Engineering
PlatformFunctionality
• Precision Ag Services including imagery
• Enterprise Resource Planning (ERP)
• Benchmarking
• IoT integration

8. Food, Agricultural and Biological Engineering
Digital Ag Tech – DATA SHARING
92% Sharing Data today
o 66% sharing data with 2 or more people
o Seed Rep and Agronomic Consultant (>60% sharing with both)
Nearly 70% have high or very high expectations for their data and
that sharing data is valuable.
90% of farmers prefer to manage their own data.
(2017 USB farmer survey on Digital Technologies)

9. Food, Agricultural and Biological Engineering
Farmers actively using digital technologies…
(2017 USB farmer survey on Digital Technologies)
• 77% view variety results online (67% with a smartphone or tablet).
• 96% are using data collected as a direct input for management decisions.
• 91% are using some type of digital tool or service.
• 88% use prescription maps for managing inputs such as seeding or fertilizers.

10. Farm Data - Currently
Terra Project (Case Study)
Food, Agricultural and Biological Engineering

11. VALUE
Non-
$
$
Food, Agricultural and Biological Engineering

12. “Terra” Project
Food, Agricultural and Biological Engineering
@OhioStatePA@OhiostatePA
39 different file types
2475 different files
60.2 Petabytes for the field
@OhioStatePA
− *.TXT
− Shapefile (*.shp)
− *.XML
− *.DAT
− *.agdata
− *.yld
− *.gsd
− *.rbin
− *.log
− Many others…
18.4 GB per plant

13. CPU Productivity Zone
Delineation
Soil Zone
Analysis
Guidance Line
Generation
Post-Emerge
Herbicide
Aerial-applied
Fungicide
Airscout
Fertilizer Rx
Generation
Fert Spreader Rx
Execution Starter Sidedress
Seeding Rx Generation
Planting
Pre-plant Compaction
Readings
Growth Stage,
Compaction
Readings
Root Analysis
Tassel
Disease
Evaluation
Yield
Estimate Pre-HarvestScout
Timelapse
Camera
Weather Data
"Terra-Byte" Harvest
Harvest
Post-Harvest
Analysis
3/1 3/31 4/30 5/30 6/29 7/29 8/28 9/27 10/27 11/26
"Terra's Data Timeline"
Timeline Airscout Fertilizer Planting Scouting Timelapse Camera Weather Data Harvest
Food, Agricultural and Biological Engineering@OhioStatePA

14. When planter hits the field, we’ve used 14 different data layers
Food, Agricultural and Biological Engineering

15. As-planted
These data layers bring quantifiable
value to the farm.
Food, Agricultural and Biological Engineering

16. Pre-plant
• Weather
• CPU’s
• Bare Soil
• Base Scouting
Emergence
• As-planted
• Weather (GDUs)
• Seeding Rx’s
• Base Scouting
Side-dress
• Weather
• CPU’s
• Historical Yield
• Imagery
• Base Scouting
Tassel
• Imagery
• Base Scouting
(Disease)
• Rescue N
• Population
In-season Focus
Weather Data
CPU’s
Bare Soil Image Seeding Rx Historical Yield
Imagery
Disease Scouting
Base Layer
Key Data Layer
Food, Agricultural and Biological Engineering@OhioStatePA

17. Tier 1 Data:
• As-planted Data (4.7)
• Soil Sampling Data (4.7)
• Yield Data (4.0)
• Seeding Rx
• Base Scouting
• Aerial Imagery
Tier 2 Data:
• CPU Zones
• Weather Data
• As-applied Fertilizer
• Historical Yield Data
• Scouting Plus
Food, Agricultural and Biological Engineering@OhioStatePA

18. 0.04
0.33
0.31
1.60
2.89
15.62
Data Collection by Value (Gb)
Basic Data
Operational Data
Field Mangagement Data
Imagery
Scouting Data
Unactionable data
25% of collected data able to be used in
making on-farm decisions today
Food, Agricultural and Biological Engineering@OhioStatePA

19. 0.04 0.33
0.31
1.60
2.89
Valuable Data by Category (Gb)
Basic Data
Operational Data
Field Mangagement Data
Imagery
Scouting Data
Food, Agricultural and Biological Engineering@OhioStatePA

20. Additional value to the farm
operation…

21. VRT - Value
• Improve input planning and application(more info)
- Fertilizer and lime savings
- Seed savingsor yield gains
• Better information for records and learning
• Better informed in-season (investor not)
• Improved efficiencies
- Equipmentmanagement– identify inefficiencies
- Defined actual VC’s for operating equipmentand technology (by field)
- Increased nutrientuseefficiency (NUE) - N & P
• Profit mapping or more in-depth analysesby field
• Long-termdata for analysis
Food, Agricultural and Biological Engineering

22. Costs Straight Rate Variable Rate
MAP $4,223 (210 lb/ac) $2,788 (140 lb/ac)
Potash $2,936 (210 lbs/ac) $2,765 (199 lbs/ac)
Spread
Charge
$489 ($5/ac) $537 ($5.5/ac)
Total $7,648 $6,090
VR Fertilizer ROI:
$1,558 ($15.95/ac)
P & K Application
1. Data 2. Information 3. Knowledge
Food, Agricultural and Biological Engineering

23. Making the correct decision….
Food, Agricultural and Biological Engineering
“Raw” Yield Map Cleaned Yield Map P2O5 Removal Map

24. Making the correct decision….
Food, Agricultural and Biological Engineering
Ohio State#eFields- https://digitalag.osu.edu/efields

25. eFields represents an Ohio State University
program dedicated to advancing production
agriculture through the use of field-scaleresearch.
https://digitalag.osu.edu/efields
Food, Agricultural and Biological Engineering@OhioStatePA

26. Digital Agriculture
Providing solutions to meet world demand
John Fulton
Fulton.20@osu.edu
334-740-1329
@fultojp
Ohio State PrecisionAg Program
www.OhioStatePrecisionAg.com
Twitter: @OhioStatePA
Facebook: Ohio State Precision Ag
Food, Agricultural and Biological Engineering