Precision Ag for Corn - Joe Lauer - 15

Lauer © 1994-2017http://corn.agronomy.wisc.edu

The Realities of Precision Farming for Corn:A Case Study of Variable Rate Seeding

Joe LauerUniversity of Wisconsin – Madison

Eastern Ontario Crop ConferenceKemptville, Ontario, Canada

Febraury 14, 2017


• To determine whether a maximum yield plant density (MYPD) and an economic optimum plant density (EOPD) could be determined for one soil type given changing genetics over time.üSpatial variabilityüTemporal variability

• A real world situation:üTo develop and classify site-specific management zones based on past grain yield

üTo determine if managed inputs (i.e. seeding rate) should be adjusted based upon management zone classification

2

Overview


• Farmers are increasing plant density (PD)

• Farmer questions include:üWhat is the maximum yield PD?üWhat is the economic optimum PD?ü Is the MYPD and EOPD the same for silage and grain?

üDo hybrids differ for MYPD and EOPD?üDo fields differ for MYPD and EOPD?

• How does risk change? üDroughtüLodging

• Precision farming (VRT) and variable plant density in-field

Background

1980 1990 2000 201020,000

22,000

24,000

26,000

28,000

30,000

32,000

34,000

36,000

38,000

WI= 306Linear(MN= 326)Linear(IA= 310)Linear(IL= 305)Linear(WI= 306)Linear(IN= 301)Linear(OH= 262)Linear(NE= 188)

Rate of Change (Plants / A*yr)

Plant density(Plants /A)

Lauer, 2015Source: USDA-NASS


4

Determining Maximum Yield v. Economic Optimum Yield

Input (Effort)

Yiel

d (b

u/A)

Cost

or R

etur

n ($

/A)

Cost

ba

c

c = Equilibrium yield (EQ) where farmers make no profit (potential risk)

Yield

a = Maximum yield (MY)b = Economic optimum yield (EO):

the greatest difference between cost and yield (or return)

d

Nitrogen

FungicideTillageRow spacing

d = Yield (Y0)with no input


Relationship between corn plant density and grain yield, economic optimum, forage yield, Milk/Ton, and Milk/Acre

Harvested plant density (plants/A x 1000)

Rela

tive

mea

sure

(%)

15 20 25 30 35 40 45 50 5565

70

75

80

85

90

95

100

Grain yield (R2=0.76)Grain economics (R2= 0.94)Forage yield (R2=0.67)Milk per ton(R2=0.73)Milk per acre (R2=0.65)

5

40,300 53,900 67,300 80,700 94,200 107,600 121,100 Harvested plants ha -1

Lauer, 2005-2014, ArlingtonPDTs >= 4 and PD >= 40K

GrainMYPD

GrainEOPD

ForageMYPD

MilkPer Ton

MilkPer Acre


6

Precision farming – “Success is proving elusive”Technology is available, but the agronomy is lacking.


Time (Years)

Rate

of A

dopt

ion

(% p

lant

ed a

cres

)

1997 2001 2005 2009 2013

100

90

80

70

60

50

40

30

20

10

0

Corn

Time (Years)1995 2000 2005 20100

10

20

30

40

50

60

70

80

90

100Soybean

Precision Agriculture Adoption“Technology is available, agronomy is lacking.”

USDA-ERS, 2010


• Engineered PFaka “The Hammer”üGlobal positioning systems*

Ø Planter *Ø Sprayer *Ø Combine *

üYield mapping *üVariable rate technologies*

Ø Fertilizer *Ø Seeding *Ø Hybrid/Variety *

üAuto-steer *üAutomatic row shut-offs*üRecord-keeper *üCloud computing/Big data*

8

The Realities of Precision Farming“A big expensive hammer looking for nails.”

• Responsive PFüVRT N rateü In conjunction with grid soil testing:Ø VRT LimeØ VRT P and K rateØ VRT manure rate

ü In conjunction with scouting (drones):Ø HerbicidesØ InsecticidesØ Fungicides

• Predictive PFüVRT HybridüVRT Plant density üVRT Starter fertilizerüLand rental

* = Success


9

Estimated Market Area Using VRT Technology Over Time

adapted from Erickson and Widmar, 2015


Equipment and Data People• Lack of training and time required for producers and industry to learn, operate and maintain equipment and software

• Lack of local technical assistance for management decisions. Uncertainty with using analyzed data to influence decision making and future recommendations

Ø Is the pressure put on growers and consultants to use PF justified?

Ø Ads: “Place the right _____ in the right field at the right time.” Or “We will help you make your decisions regarding precision farming.”

• Most benefit is to people in the field rather than absentee owners.ü Data requires interpretation (notes)

• The technology is available, but the agronomy is lacking.

• Cost of precision agricultural equipment

• Sensitive equipmentü Requires frequent calibration (“GIGO”)

• Requires both yield monitor AND GPS data.

• Computer resources ü Data managementü Software for analysis is sophisticated and

complicated

10

Farmer frustration because so far little economic benefit seen with yield maps …

OR


Materials and Methods

Field 1Field 2 Field 3

Randomized Complete Block Design with four seeding rates applied to plots within each subfield: 22000, 30000, 38000, 46000 seeds/A (54400, 74100, 93900, and 114000 seeds ha-1)

Fields selected due to availability of past yield maps

Field 1: 10 strips, 12 years of data previous field management 2001-2012 included strip treatments

20 subfields (0.8 A = 0.33 ha)Plot size: 60 by 150 ft = 18 by 46 m

Bunselmeyer, 2014


12

Management Zone Creation Process


Materials and Methods - Planted Map

Costs PriceSeed $3.73 per 1000 kernels

(Dairyland 95-01) Handling $0.78 Mg-1Hauling $1.57 Mg-1Trucking $4.33 Mg-1Storage $0.78 Mg-1Grain drying $0.03 per point of moisture

Partial Budget AnalysisGrower Return = (Commodity Price * Grain Yield ) – Costs2013 Commodity Price = $159.06 Mg-1

Bunselmeyer, 2014

ANCOVA of MYPD and EOPD using SAS PROC MIXED• Utilized spatial exponential covariance structure• Model Fitting Procedure

1. Ho: all slopes were equal to zero2. Ho: covariant slope is equal to zero3. Fit data to a common slope model of significant

coefficient terms where y = a + bx + cx2


2013 Grain Yield Data: Mean and Standard Deviation

Bunselmeyer, 2014

A maximum yield plant density in 22% of the subfields and 25% of the management zonesAn economically optimum plant density in 41% of the subfields and 0% of the management zones


15

Maximum Yield Plant Density at Harvest

Plant Density (plants ha-1 x 1000)

Gra

in Y

ield

(Mg

ha-1

)

140120100806040200

20

18

16

14

12

10

8

6

4

2

0

y (Grain Yield) = a + 0.13x + -0.00077x2 R2 = 0.26

H/L MZ a = 7.5 + 0.14 L/L MZ a = 6.7 + 0.13 HL/H MZ a = 7.6 + 0.10

86 000 plants ha-1 = 35 000 plants/A


16

Economically Optimum Plant Density at Harvest

Plant Density (plants ha-1 x 1000)

Gro

wer

Ret

urn

($ h

a-1)

0 20 40 60 80 100 120 140

2000

1800

1600

1400

1200

1000

800

600

400

200

0

y (Grower Return) = a + 13.40x + -0.10x2 R2 = 0.59

H/L MZ a = 1084.2 + 19.2 L/L MZ a = 1003.1 + 19.1 HL/H MZ a = 1073.0 + 13.5

65 400 plants ha-1 = 27 000 plants/A

Bunselmeyer, 2014


17

How do we study Variable Rate Seeding Technology?

35K

101

30K

102

25K

103

40K

104

35K

101

30K

102

25K

103

40K

10430K

201

40K

202

25K

203

35K

304

25K

301

40K

302

35K

303

30K

30425K

401

30K

402

35K

403

40K

404 35K

101

30K

102

25K

103

40K

10430K

201

40K

202

25K

203

35K

304

25K

301

40K

302

35K

303

30K

30425K

401

30K

402

35K

403

40K

404

35K

101

30K

102

25K

103

40K

10430K

201

40K

202

25K

203

35K

304

25K

301

40K

302

35K

303

30K

30425K

401

30K

402

35K

403

40K

404

35K

101

30K

102

25K

103

40K

104

30K

201

40K

202

25K

203

35K

304

25K

301

40K

302

35K

303

30K

304

25K

401

30K

402

35K

403

40K

404

35K

101

30K

102

25K

103

40K

104

Rep designExperiment design


• Total plant density datasetüGxE Experiments = 986 (1987-2013)üPlots = 12,853

• First data cutü Include experiments with 4 or more plant density treatments and grown on a Plano silt loam at Arlington, WI

üGxE Experiments = 127üPlots = 2090

• Second data cutüUse first data cut AND only include experiments with 2 or more years of the same treatment.

üGxE Experiments = 74; Reps = 230üPlots = 1181

• Model forms evaluated1. Linear and/or Quadratic (covariate)2. Linear:Plateau segmented3. Linear:Linear segmented4. Linear:Linear broken 5. Quadratic:Plateau segmented6. Quadratic:Quadratic segmented7. Quadratic:Quadratic broken line8. Gompertz (Negative Exponential)9. Mitscherlich (Exponential)10. Square root11. Carmer-Jackobs12. Log linear13.Quadratic:Linear segmented14. Linear:Quadratic segmented15. Intercept, Linear, Quadratic (abc),

Quadratic (ac)

18



What Does the Relationship Between Grain Yield And Plant Density Look Like?

-L= 3%

-Q= 1%

-L+Q= 1%

19Lauer, 1995-2014, Arlington, GxE N= 88

PDTs >= 4 and PD >= 40K


20

Corn Grain Maximum Yield Plant Density (x 1000/A) at Harvest

Year GxEs ARL JAN LAN FON GAL HAN CHP MAR SEY VALLast 10 years 91 34 38 37 34 42 45 23 41 33 33

2013 6 43 42 22 43 43 362012 4 33 40 Silage2011 4 29 43 Silage2010 10 33 Silage Silage Silage 282009 14 40 Silage Silage Silage 502008 12 40 Silage Silage Silage 422007 2 362006 4 332005 12 28 37 37 53 16 35 282004 21 34 37 37 46 44 48 27 44 33 332003 42 27 35 28 25 33 39 28 22 37 402002 20 23 45 41 39 34 45 25 44 252001 5 312000 5 311999 10 35 40 37 361998 12 32 35 38 421997 8 26 251996 12 37 28 33 Silage Silage1995 4 35 Silage Silage Silage1994 -- Silage Silage Silage Silage1993 --1992 24 21 261991 64 26 20 29 28 23 23 18 26 371990 46 28 25 18 25 27 27 27 33 22 311989 42 22 22 20 31 35 32 18 36 261988 35 25 18 19 34 29 18 22 231987 8 23

Lauer, 1987-2013PDTs >= 4


21

Frequency of Plant Density (x 1000/A) Model Forms for a Plano silt loam

Lauer, Arlington 1987-2013same treatment grown in two or more years

Experiment level Rep level

Model FrequencyOptimum

PDStandard deviation Frequency

Optimum PD

Standard deviation

Total N= 74 N= 230

NS 39 23 4 67 21 5

+L 20 39 5 15 43 7

+L-Q 39 38 7 16 37 5

+Q --- --- --- 0 36 ---

-L 1 31 --- 0 26 ---

-Q --- --- --- --- --- ---

-L+Q --- --- --- 2 34 4


22

Spatial and Temporal Variability of the Maximum Yield Plant Density (x 1000/A) on a Plano silt loam

Source of Variation Mean

Standard deviation Mean

Standard deviation

Spatial 32 5 28 7Temporal 32 5 28 7

Experiment level Rep level



23

Maximum Yield Plant Density (MYPD) Change Over Time



Adding Data Layers to Delineate Management Zones

Picture from < https://www.pioneer.com/home/site/mobile/plan/corn/on-farm-trials-field360/>)

https://www.pioneer.com/home/site/

Lauer © 1994-2017http://corn.agronomy.wisc.edu Bunselmeyer, 2014 25

Correlation Matrix – Field 1


26

Multiple regression by management zone on 2013 grain yield

(Picture from < https://www.pioneer.com/home/site/mobile/plan/corn/on-farm-trials-field360/>)

† SE: standard error.‡ Management zones = H/L: high-yielding / low yield variance, L/L: low-yielding / low yield variance, HL/: Variable(high- or low-yielding / high yield variance).

Bunselmeyer, 2014


• Data used was 26 years from a corn-soybean rotation x tillage experiment at ArlingtonüConventional tillageüContinuous corn

• Cells measured 30 x35 feet. üThree to four measurements (pixels) in cell per year

üSame site every yearØ Cells: 6, 28, 32, and 53

27



28

Materials and MethodsField Locations at Arlington

Corn-Soybean X Tillage Study1983 - present

Corn-Soybean X Tillage Study2001 - present

Continuous Corn Maximum Yield

Study2001 - present

Prescription Plant Density Experiments


29


6

28

32

53


30

Spatial and Temporal Variation of Corn Yield (bu/A) of Four Subfields (6, 28, 32, and 53)

Temporal variability(1987-2012)

Subfield 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 Mean Range StD

6 176 175 163 215 220 239 232 255 216 201 184 92 40

28 159 185 169 235 210 --- 244 268 191 168 176 109 42

32 122 180 134 223 222 230 213 268 193 160 175 146 43

53 134 186 142 212 219 204 207 250 172 157 169 116 43

Spatial variability (4 Sites)176 116 42

Mean 148 181 152 221 218 224 224 260 193 172 176

Range 54 11 35 23 12 35 37 18 44 43 32

StD 24 5 17 10 6 18 17 9 18 20 12

Bunselmeyer and Lauer, 2015 AJ 107:558 (CC, CT, Arlington, WI)


31

Spatial and Temporal Variation of Corn Yield Subfields 6, 28, 32, and 53

Lauer, 1983-2012(CC, CT, Arlington, WI)

1982 1986 1990 1994 1998 2002 2006 2010 20140

50

100

150

200

250

6 28 32 53

Grain yield (bu/A)

Stan

dard

dev

iatio

n (b

u/A)

1982 1986 1990 1994 1998 2002 2006 2010 20140

10

20

30

40

50Spatial variation = +12 bu/A

Stan

dard

dev

iatio

n (b

u/A)

6 28 32 530

10

20

30

40

50Temporal variation = +42 bu/A


32

Spatial and temporal variability for each tillage by rotation treatment combination for corn

Rotation TillageMean Yield

Spatial Variability

Temporal Variability

Years to first

significant subfield ranking

Years to consistent

significantly different sub-field patterns

Grain yield difference

between high- and low- subfields

Experiment 1

CC CT 176 +12 +42 4 4 +16

CC NT 162 +13 +44 2 20 +11

CS CT 196 +15 +44 18 20 +29

CS NT 197 +11 +42 14 20 +13

Experiment 2

CC CT 179 +21 +30 6 6 +41

CC NT 160 +21 +30 10 10 +32

CS CT 182 +22 +33 6 6 +63

CS NT 181 +27 +31 8 8 +68

Bunselmeyer and Lauer, 2015 AJ 107:558 (CC, CT, Arlington, WI)


33

Conclusions

• Temporal variability is greater than spatial variability.ü Starter fertilizerüHybrid performanceüPlant density

• There is often an overall response to a management decision, but not by management zone.

• The maximum yield plant density was 35,000 plants/A, while the economically optimum plant density was 27,000 plants/A.

• Site-specific management of seeding rate was not more profitable than whole field management for grain yield classified management zones.

• Future directions:üTo study variable seeding rate, use more plant density treatments and fewer reps.üWhat is the impact of legacy variability?


34

Future Directions

• What do we do with all these yield maps?üKeep collecting themüAssociate GIS data with yield and moisture measurementsüCollect other agronomic notesü Invest in storing and managing data until you have enough years

• QuestionsüShould a field always get variable rates of inputs?

Ø Variance criteria? Spatial > Temporal

ü If a management change is made, how long does it take for the management change to equilibrate? Do you start the clock over again?

üCould land value increase if you have a yield map history?

• Future crop yield gains will likely occur with overall agronomic management decisions.üPrecision farming gains will be relatively small gains at great expense.


• Fancy “Record Keeper”• Land rental agreementsüShort-term P & K corrections

Ø May require grid soil sampling expense

• Land salesüAdded value of annual yield maps üMay allow some prediction for future management decisions.

• Provides some “numbers” to backup visual observationsüDon’t under estimate the power of observation. Make decisions using data.

• DronesüMid-Season Crop Health Monitoring (aka Scouting)

ü Irrigation Equipment MonitoringüMid-Field Weed IdentificationüVariable-Rate FertilityüCattle Herd Monitoring

35

Where does Precision Farming fit in agriculture?


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Precision Ag for Corn - Joe Lauer - 15