How do you evaluate precision ag strategies on-farm

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

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.

• ~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)

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

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/

Research Crop Portal - 2017 additions:

- Delta cleaning tool

- Elevation & Topographic analysis tools to create

landform classes

4 Landforms

Red = Tops of knolls

Green = depressions

-0.15

-0.10

-0.05

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0-2

0%

0-4

0%

0-7

0%

0-1

00

%

Dif

fere

nce

Bet

we

en

Ove

r an

d U

nd

er

Pe

rfo

rmin

g C

ells

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

Elevation: Topographic Wetness Potential 7 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…

Validating Precision Ag Strategies

2016

Strip Trial

Examples Variable Rate Nitrogen

VR Soybean Population

Validating Precision Ag Strategies

2016 “Learning Stamp” Example

11

Prescription Maps

Yield Potential Index

based so far…

As-Applied

Verification of Equipment

Cleaned Yield Data

The dilemma of incorporating as-applied data and learning stamps or blocks…

Smart Rectangles

Points

Data representation ,

block orientation, delays, offsets, and

equipment footprint?

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

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

Corn Population Validation:

Corn Population Trial:

Port Perry

- Strip Test Strips

- 28, 32 and 36K/ac



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



Port Perry

- Strip Test Strips

- 28, 32 and 36K/ac



Guelph

- Rate Blocks

- 28, 32 and 36K/ac



Guelph

- Rate Blocks

- 28, 32 and 36K/ac


Soil Conductivity Readings

Low conductivity

High conductivity

- Often correlated to yield

- Sometimes positive

- Sometimes negative



- Sufficient separation in rates important!

- 32K, 34K, 36K vs. 25K, 30K, 35K



- 32K, 34K, 36K vs. 25K, 30K, 35K

Hooker and Stewart, 2009



- 32K, 34K, 36K vs. 25K, 30K, 35K

- Enough rates to make a conclusion

- 25K and 35K vs. 25K, 30K, 35K



- 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

Corn Nitrogen Validation:

Low Yield

High Yield


Rep 1

Rep 2

Rep 3


Rep 1

Rep 2

Rep 3


Rep 1

Rep 2

Rep 3

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

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

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:

Acknowledgements

Ian McDonald (Crop Innovation Specialist)

Ken Janovicek (UofG – Research Assistant)

Thank-you!

[email protected]

[email protected]

More information on the project:

http://gfo.ca/Research/Understanding-Precision-Ag

mailto:[email protected]

mailto:[email protected]

Documents

How do you evaluate precision ag strategies on-farm