application of the cropgro-soybean model to predict
TRANSCRIPT
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Application of the CROPGRO-SoybeanModel to Predict Soybean Yield
P. Pedersen and J.G. Lauer
Department of AgronomyUniversity of Wisconsin-Madison
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Background
• Soybean production has increased tremendously in WI the last 20 years
• Yield goals are increasing year after year
• Few published studies have measured growth processes during the season to understand how high yields are achieved
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• To determine current yield potential of soybean under various management systems
• To determine if growth pattern differs between older and newer cultivars under various management systems
• Analyze yield improvements among older and newer cultivars using the CROPGRO-soybean model
Objectives
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Materials and MethodsStudies in 1997-2000 for five different management systems
Irrigation
1 2 3 4 5
No-till Conv. tillage
Irrigation Irrigation
Silt Loam Soil Sandy Loam
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Materials and Methods
• Each management systems consisted of a RCBD in a split plot arrangement with 4 replications – Main plots
• Two planting dates (early and late May)
– Split plots• Three different cultivars (two new cultivars-
DeKalb CX232 and Spansoy 250, and one old cultivar – Hardin)
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Growth Measurements• Triweekly
– Plant density – Leaf area index– Lodging– Height– Biomass (leaf, stem,
pod, and seed proportions)
– Pod and seed counts– Growth and
reproductive stages (V & R stage)
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Environmental Measurements
• Triweekly– Soil moisture
• (0-15, 15-30, 30-60, and 60-90 cm)
• Daily– Solar radiation– Precipitation– Soil temperature– Air temperature
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CROPGRO – Soybean Model
“A mechanistic crop growth model that predicts daily photosynthesis, growth, and partitioning in response to daily
weather inputs, soil traits, crop management, and genetic traits”
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CROPGRO-Model
Observed Data& Site Description
SoilDescription
Weather
Management
Database of Grid-Search Results
Crop Model
Data Conversion
Grid-Search Driver
Obs. Yield &Phenology
M.G. CultivarCoefficients
Cultivar-Specific Coefficients
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Observed vs. Simulated Phenology
113.94112.74
78.4477.79
70.2269.83
50.4449.94
2.5Spansoy 250
111.15111.38
78.3378.02
72.0172.33
49.1849.27
2.3CX232
107.68107.88
72.0273.00
66.1365.91
48.7948.77
1.9Hardin
--------------Days--------------R7R5R3R1MGCultivar
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Simulated Yields Across 34 Management Systems
0.53
7121
5.7
2.6
2979
0.13
3799
5036
Spansoy
41268766398Biomass at R8 (kg ha-1)
0.020.560.60Seed HI (kg kg-1)
0.35.85.3Max LAI (m2 m-2)
0.22.52.3Seeds per pod
28126552805Seed no. (per m2)
0.20.150.14Weight per seed (g;dry)
NS38873818Seed yield (kg ha-1)
13451764991Pod yield (kg ha-1)
LSD (0.05)CX232Hardin
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1998 CX232 (Arlington, Late, CT)
0
2000
4000
6000
8000
10000
0 20 40 60 80 100 120 140
Days After Planting
Bio
mas
s (k
g h
a-1)
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Biomass
PodGrain
StemLeaf
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Spansoy 250, 2000 - Biomass and Grain Yield (Early vs. Late Planting)
0
2000
4000
6000
8000
10000
0 20 40 60 80 100 120 140 160
Days After Planting
Bio
mas
s (k
g h
a-1)
Grain
BiomassEarly
Late
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Seed HI - 2000 (Late, CT, Irrigated)
00.10.20.30.40.50.60.7
0 20 40 60 80 100 120 140
Days After Planting
See
d H
arve
st In
dex
Hardin
Spansoy 250
CX232
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LAI 1998 (Early, CT)
0
1
2
3
4
5
6
0 20 40 60 80 100 120 140
Days After Planting
LAI (
m2 m
-2)
Hardin
CX232Spansoy 250
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Biomass and Grain Yield, Sandy 1998
0
2000
4000
6000
8000
10000
12000
0 20 40 60 80 100 120 140
Days After Planting
Bio
mas
s (k
g h
a-1)
HardinCX232
Spansoy 250
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Biomass
Grain
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CX232 Biomass and Grain Yield, 1999(CT vs. NT)
02000400060008000
10000
0 20 40 60 80 100 120 140
Days After Planting
Bio
mas
s (k
g h
a-1)
NT
CT
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Biomass
Grain
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Summary
• CROPGRO-Soybean model could be used to quantify attainable soybean yields for the three cultivars in different management systems
• Simulated yield responses for the three cultivars were variable and ranged from 39 to 85 bu acre-1 across years and management systemsUW MADISON AGRONOMY
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Summary
• New soybean cultivars for the Upper Midwest are associated with traits of:– Higher total canopy biomass and LAI– Lower seed harvest index– More seeds per pod– Higher leaf photosynthesis– Longer seed fill duration and pod
addition
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• Iowa and Illinois Soybean Promotion Boards
• Wisconsin Soybean Marketing Board• College of Agricultural Life Sciences,
University of Wisconsin-Madison
Acknowledgements