precision ag for corn - joe lauer - 15
TRANSCRIPT
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
Lauer © 1994-2017http://corn.agronomy.wisc.edu
• 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
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Overview
Lauer © 1994-2017http://corn.agronomy.wisc.edu
• 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
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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
Lauer © 1994-2017http://corn.agronomy.wisc.edu
6
Precision farming – “Success is proving elusive”Technology is available, but the agronomy is lacking.
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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
Lauer © 1994-2017http://corn.agronomy.wisc.edu
• 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*
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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
Lauer © 1994-2017http://corn.agronomy.wisc.edu
9
Estimated Market Area Using VRT Technology Over Time
adapted from Erickson and Widmar, 2015
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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Management Zone Creation Process
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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 = $159.06 Mg-1
Bunselmeyer, 2014
ANCOVA of MYPD and EOPD using SAS PROC MIXED• Utilized spatial exponential covariance structure• Model Fitting Procedure
1. Ho: all slopes were equal to zero2. Ho: covariant slope is equal to zero3. Fit data to a common slope model of significant
coefficient terms where y = a + bx + cx2
Lauer © 1994-2017http://corn.agronomy.wisc.edu
2013 Grain Yield Data: Mean and Standard Deviation
Bunselmeyer, 2014
A maximum yield plant density in 22% of the subfields and 25% of the management zonesAn economically optimum plant density in 41% of the subfields and 0% of the management zones
Lauer © 1994-2017http://corn.agronomy.wisc.edu
15
Maximum Yield Plant Density at Harvest
Plant Density (plants ha-1 x 1000)
Gra
in Y
ield
(Mg
ha-1
)
140120100806040200
20
18
16
14
12
10
8
6
4
2
0
y (Grain Yield) = a + 0.13x + -0.00077x2 R2 = 0.26
H/L MZ a = 7.5 + 0.14 L/L MZ a = 6.7 + 0.13 HL/H MZ a = 7.6 + 0.10
86 000 plants ha-1 = 35 000 plants/A
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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Economically Optimum Plant Density at Harvest
Plant Density (plants ha-1 x 1000)
Gro
wer
Ret
urn
($ 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
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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How do we study Variable Rate Seeding Technology?
35K
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Rep designExperiment design
Lauer © 1994-2017http://corn.agronomy.wisc.edu
• 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)
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Materials and Methods
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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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
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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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
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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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
Lauer, Arlington 1987-2013same treatment grown in two or more years
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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Maximum Yield Plant Density (MYPD) Change Over Time
Lauer, Arlington 1987-2013same treatment grown in two or more years
Lauer © 1994-2017http://corn.agronomy.wisc.edu
Adding Data Layers to Delineate Management Zones
Picture from < https://www.pioneer.com/home/site/mobile/plan/corn/on-farm-trials-field360/>)
Lauer © 1994-2017http://corn.agronomy.wisc.edu Bunselmeyer, 2014 25
Correlation Matrix – Field 1
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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
Lauer © 1994-2017http://corn.agronomy.wisc.edu
• 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
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Materials and Methods
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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Materials and Methods
6
28
32
53
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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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)
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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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
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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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)
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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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?
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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.
Lauer © 1994-2017http://corn.agronomy.wisc.edu
• 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
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Where does Precision Farming fit in agriculture?
Lauer © 1994-2017http://corn.agronomy.wisc.edu
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Wisconsin Corn Conferences
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