What works and what doesn’t! The latest on precision agriculture research and practical findings in Ontario.

1. Ben Rosser
Corn Specialist, OMAFRA
Nicole Rabe
Land Resource Specialist, OMAFRA
How Do You Evaluate Precision
Ag Strategies On‐Farm?
Lessons Learned from the GFO
Precision Ag Project

2. Co-operator yield
data submitted
+
collect other base
data layers to fill gaps
Goals: wireless
data transfer &
analyze data layers
with transparent
mathematics for
teaching farmers
Rx maps:
implemented with
validation built in
& industry
support
Project Scope:
This project was funded in part through Growing Forward 2, a federal-
provincial-territorial initiative.
The Agricultural Adaptation Council assists in the delivery of Growing
Forward 2 in Ontario.

3. • ~50 acres committed to a full rotation (corn, soybeans, wheat)
• good drainage
• average to medium base levels P & K
• Manure history: project would have to document & monitor for impacts
• Farmer had to have VR equipment for at least 1 project operation (seed or fertilizer )
Total of 20-25 fields (constant), 3 year study (2015-2017)

4. Precision Ag in a nutshell:
• Yield (y) results from natural processes described by f:
• The function is made up of :
– things that the farmer does control = x (e.g. seed / fertilizer type, source, rate etc)
– field characteristics = c that a farmer does not control and they vary spatially (e.g.
soil type, topography – slope)
– vector z - the farmer does not control & this varies temporally (principally weather
variables)
Y=f(x,c,z)
So far the case studies explored here are missing a couple of
field characteristics (C) (e.g. soil chemistry, landforms) &
weather (z) was not incorporated into variable rate prescriptions
Conceptual formula courtesy: Dr. David Bullock, Ag Economist, Ohio State University

5. Historical Yield based
Management Zones
• 2008 Wheat
• 2009 Corn
• 2011 Wheat
• 2012 Corn
• 2014 Wheat
• 2015 Corn • Project started with yield data
acknowledging most farmers would
have some sitting on a drive in office
somewhere
• Research Crop Portal:
– includes fully and semi automated cleaning
tools for yield data
– transparent math to relay the message that
maps aren’t pretty pictures!
• Yield Potential Index (YPI): best to work
with single crops over time (e.g. 3yrs
corn, 3yrs wheat, 3yrs of soybeans)
– pairing corn and wheat maintains consistent
zone geometry
– soybeans do not have same yield response
distribution (likely to due to disease)
http://cropportal.niagararesearch.ca/

6. Research Crop Portal- 2017 additions:
- Delta cleaning tool
- Elevation & Topographic analysis tools to create
landform classes
4 Landforms
Red = Tops of knolls
Green = depressions

7. -0.15
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0-20%
0-40%
0-70%
0-100%
DifferenceBetweenOverandUnderPerformingCells
Percentage of Yield Points
Over and Under Performing Gaps For 4 Landform Classes
Knolls
Upper Sideslopes
Lower Sideslopes
Depressions
Yield performance is
consistent across the full
distribution of yield.
Landform 3 always
outperforms (in the data
we have collected so far)
$
$
$
$
Yield Performance per Landform
Slide courtesy of: Dr. Mike Duncan, NSERC Prec Ag Research Chair, Niagara College

8. 8
Elevation: Topographic Wetness Potential7 Year - Yield Potential Index (YPI)
UAV Natural Colour
Image
July 2016
Electrical
Conductivity
Proxy for Soil
Texture
Highest producing areas
Middle
Lowest producing areas
Baseline Soil Chemistry
Directed 1 ac grid
Other spatial data layers collected on each field…

9. Validating Precision
Ag Strategies

10. 2016
Strip Trial
Examples
Variable Rate Nitrogen
VR Soybean Population
Validating Precision Ag Strategies

11. 2016 “Learning Stamp” Example
11
Prescription Maps
Yield Potential Index
based so far…
As-Applied
Verification of Equipment
Cleaned Yield Data

12. The dilemma of incorporating
as-applied data and learning stamps or blocks…
Smart Rectangles
Points
Data
representation ,
block orientation,
delays, offsets, and
equipment
footprint?

13. Size of blocks v.s. replication
180x180 ft blocks = 170-200yld points
Simple Block
Fully automated
randomized and replicated
60ft aligned grid
5 acre blocks

14. Did the YPI based management zones show up
in both 2015 and 2016?
• Seed & Nitrogen Corn trials: on 5 fields zones no SD, 6 fields showed
only two distinct zones, and 4 fields showed all three zones were distinct
(Type 1 Error: 10%)
• VR Soybean Population Trials: on 2 fields zones no SD, 4 fields
showed only two distinct zones, and 3 fields showed all three zones
were distinct (Type 1 Error: 10%)
• Potential Reasons:
–not enough historical yield data for reliable zone creation
–medium zone stability not well defined in the YPI algorithm
–extreme seasonal conditions (dry or wet)
–good soil health/type
–genetics masks crop response
YPI = Yield potential Index SD = statistical difference

15. Relationships
to other data
layers?
Elevation: Topographic Wetness Potential
Yield Potential Index (YPI)
Electrical Conductivity

16. Soil Sensing & Conductivity Readings
Low conductivity
High conductivity
- Often correlated to yield
- Sometimes positive
- Sometimes negative

17. What is the value of the other spatial data layers in
explaining yield variability?
If a farmer doesn’t have good repository of historical yield data
then could they start with elevation or soil sensing to develop
management zones?
• Table below shows 2015 snapshot of nitrogen corn strips trials &
the % improvement in explaining yield variability by adding YPI,
elevation or electrical conductivity (EC) to the regression model
Data Layer Field 1
(Vernon)
Field 2
(Ottawa)
Field 3
(Hensall)
Field 4
(Exeter)
Field 5
(Tillsonburg)
Notes:
YPI 20% 12% 10% 4% 60% Yield increases as YPI increases
Elevation 22% 12% 1% n/a 43% Highest yields associated with
mid-regions
EC
(shallow)
n/a 7% n/a n/a 70% As EC decreases across all N
rates - yield decreases
EC (deep)
Related to
parent
material
21% 7% n/a n/a 70% As EC decreases across all N
rates - yield decreases
Clay loams Clay loam / silt loams Loamy sands
/ sand

18. Population
Case Studies

19. 2015 Corn Population
Case Study
Kenmore, ON
• Soil Survey: Bainsville
very fine sandy loam
(Poor)
• Rotation: corn,
soybeans
• Topography: flat
topography, gentle slope

20. Corn Population Validation:
Corn Population Trial:
Kenmore
- Blocks
- 30, 34 and 38K/ac
Lack of
Replication
in All Zones

21. Corn Population Validation:
Corn Population Trial:
Kenmore
- Blocks
- 30, 34 and 38K/ac
Average Yield by Zone (2015):
Low: 203 bu/ac
Medium: 205 bu/ac
High: 217 bu/ac
0
50
100
150
200
Low Medium High
Yield(bu/ac)
YPI Yield Zone
30
34
38
Lack of
Replication
in All Zones
Lack of
Replication in All
Zones
Lack of replication…
uncertain if these are
true treatment effects
0
200
400
600
800
1000
Low Medium High
PartialBudget($/ac)
YPI Yield Zone
30
34
38
Seed: $300/80K
Corn: $4.50/bu

22. Corn Population Validation:
Corn Population Trial:
Port Perry
- Strip Test Strips
- 28, 32 and 36K/ac

23. Corn Population Validation:
Corn Population Trial:
Port Perry
- Strip Test Strips
- 28, 32 and 36K/ac
1 Rep of High Yield Zone Response
1 Rep of Med Yield Zone Response
1 Rep of Low Yield Zone Response

24. Corn Population Validation:
- Sufficient separation in rates important!
- 32K, 34K, 36K vs. 25K, 30K, 35K

25. Corn Population Validation:
- Sufficient separation in rates important!
- 32K, 34K, 36K vs. 25K, 30K, 35K
Hooker and Stewart, 2009

26. Corn Population Validation:
- Sufficient separation in rates important!
- 32K, 34K, 36K vs. 25K, 30K, 35K
- Enough rates to make a conclusion
- 25K and 35K vs. 25K, 30K, 35K

27. Corn Population Validation:
- Sufficient separation in rates important!
- 32K, 34K, 36K vs. 25K, 30K, 35K
- Enough rates to make a conclusion
- 25K and 35K vs. 25K, 30K, 35K
- Consistency of rates across all zones of
the field
- Shouldn’t prejudge expected optimum
rate in each zone

28. Soybean Population
Case Studies

29. Soil: Fox sandy loam, Honeywood silt loam
Rotation: corn & soybeans, some wheat history
Tillage: vertical tillage / 1 fall / 1 spring pass
Topography: gentle to very strong slopes
Yield history: 7 years (80 acres)
22
48
8 0
OM by Texture Score
1 2 3 4
Case Study #2 (Ayr, ON)
Case Study #1 (Hensall, ON)Soil: Brookston clay loam, Harriston silt loam
Rotation: wheat, corn, soybeans
Tillage: no-till
Manure History: poultry after wheat
Topography: gentle slopes
Yield History: 8 yrs (80 acres)
3
37
30
10
OM by Texture Score
1 2 3 4
OM%
Min: 2.2
Max: 6.1
Avg: 3.6
OM%
Min: 0.6
Max: 3.3
Avg: 2.3
Variability? Management history & soil quality matter!

30. Variable why?
Management history & soil matter
Case Study #1 (Ayr, ON)
2015 Soybean population block
trial:
• High yield zones: average 5bu
(120,000 sds/ac) and 25bu (210,000
sds/ac) higher than medium & low zones
• BUT most profitable was 120,000 sds/ac
(gained $25/ac)
• Low yield zones: very light textured
soil, yield increased by 11bu/ac
($110/ac) for 190,000sds/ac rate v.s.
120,000 sds/ac
(Caution: due to lack of replication – less confidence in statistical differences)
Rx Soybean Theory:
High yield corn zones get
low population due to
disease pressure in wetter
years.
Low yield zones get higher
population.
(Type 1 Error 10%)
Zone Yield
(bu/ac)
Return ($)
High 53.3 $626a
Med 28.3 $555b
Low 48.1 $288c
Does the prescription theory work across regions and years?

31. 2016 Soybeans:
• Low Yield Zone: prescription
assumption of increasing seeding
rate was incorrect
• Profit decreased linearly at a rate
of $0.97/ac per 1000 seeds/ac
(i.e. $97/ac loss from 100 to 200
thousand seeds/ac)
Case Study #2 (Hensall, ON)
Zone Yield Return
($)
High 65.2 $794a
Med 60.5 $730b
Low 58.6 $705b
(Type 1 Error 10%)
- Soybean Price: $13.50/bu
- Soybean seed Cost $0.57/1000 seeds

32. Variable Corn
Nitrogen
Case Study

33. 2015 Corn Variable Rate
N - Case Study
Chesterville, ON
• All 3 mng’t zones were present in 2015 (Type 1 Error 5%)
• Soil: North Gower
(Poor)/ Morrisburg
(Well) / Wolford
(Imperfect) - clay loams,
• Tillage: no-till
• Manure: none
• Topography: nearly level
• Yield History: 3 yrs (2
yrs soybeans, 1 yr corn)
• Acres: 84
Zone Avg. Yield
(bu/ac)
Return
($)
High 184.8a $ 767.98a
Medium 179.5b $ 744.05b
Low 169.4c $ 698.85c

34. Corn Nitrogen Validation:
Low Yield
High Yield
5 rates
Replicated twice
Every yield point matched
with corresponding layers
(i.e. EC, elevation, YPI etc)

35. Relationship to other data layers
• YPI Zones Grouped: YPI of less than 2
(low) YPI between 2 and 2.7 (Medium)
and YPI over 2.7 (high) increased the
variability explained by the statistical
model from 63% to 75%.
• Elevation: increased corn yield variability
explained by the statistical model from
63% to 75%
• Soil Sensing: EC (both shallow and
deep) increased corn yield variability
explained from 63% to 70%.
– Corn grain yields tended to decrease
as electrical conductivity (shallow and
deep) decreased
– larger decreases occurring where N
rates were 67 or 97 lb-N/ac and
electrical conductivity (shallow and
deep) values were less than 21
(ms/m)
Veris – Electrical Conductivity

36. Corn Nitrogen Results
• Corn yields at this trial for all N rates generally increased as YPI increased, and yields
decreased as elevation increased (especially over 248 ft) and electrical conductivity decreased
(especially below 21 units(ms/m)).
• In all cases the corn yield responses to YPI, elevation and electrical conductivity were greatest
with the lowest N rate (37 lb-N/ac).
• Delta Yield Recommendations:
– Based on actual regression curves this site required 26 to 55 lb-N/ac over the base rate of 37
lb-N/ac as YPI decreased from 3.2 to below 1.5
100
120
140
160
180
200
67 97 127 157 187
Yield(bu/ac)
Nitrogen (lbs/ac)
Yield x Zone x N Rate
Low Med High
Yields zones statistically
distinct, but small
differences in optimum N
rates by zone

37. Common Grower Comments With
Validation
- Zero nitrogen rate prescriptions
- Validation blocks are lined up with
equipment passes
- Rate transitions
- Be familiar with prescription setup and
loading
- Equipment setup for wide range of rates, or
adjust speed

38. Future Work 2018
• Include baseline soil chemistry (directed 1 ac grid) – best interpolation method?
• Add topographic derivatives: potential wetness index, landform classes etc.
• In-season imagery: include 2017 UAV imagery into the analysis as additional layer of
information to explain yield variability
• Determine best statistical approach to comparing field trial areas to growers normal
practice within a growing season
• Relationship to soil health parameters – subset of 10 fields
NDVI
Red Edge
NDVI
Green NDVI
Acknowledge UAV Partner:

39. Acknowledgements
Ian McDonald (Crop Innovation Specialist)
Ken Janovicek (UofG – Research Assistant)
Thank-you!
nicole.rabe@ontario.ca
ben.rosser@ontario.ca
More information on the project:
http://gfo.ca/Research/Understanding-Precision-Ag