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Evaluation of models predicting winter wheat yield as a function of winter wheat and jointed goatgrass densities

Published online by Cambridge University Press:  20 January 2017

Marie Jasieniuk ,
Bruce D. Maxwell ,
Randy L. Anderson ,
John O. Evans ,
Drew J. Lyon ,
Stephen D. Miller ,
Don W. Morishita ,
Alex G. Ogg Jr. ,
Steven S. Seefeldt  and
Phillip W. Stahlman
...Show all authors
Marie Jasieniuk*
Affiliation:
Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717
Bruce D. Maxwell
Affiliation:
Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717
Randy L. Anderson
Affiliation:
Central Plains Research Center, USDA-ARS, Akron, CO 80720
John O. Evans
Affiliation:
Department of Plant, Soils, and Biometeorology, Utah State University, Logan, UT 84322
Drew J. Lyon
Affiliation:
Panhandle Research and Extension Center, University of Nebraska, Scottsbluff, NE 69361
Stephen D. Miller
Affiliation:
Department of Plant, Soil, and Insect Sciences, University of Wyoming, Laramie, WY 82071
Don W. Morishita
Affiliation:
Twin Falls Research and Extension Center, University of Idaho, Twin Falls, ID 83303
Alex G. Ogg Jr.
Affiliation:
National A. cylindrica Research Program, P.O. Box 53, Ten Sleep, WY 82442
Steven S. Seefeldt
Affiliation:
AgResearch, Ruakura Agricultural Research Centre, PB 3123, Hamilton, New Zealand
Phillip W. Stahlman
Affiliation:
Agricultural Research Center, Kansas State University, Hays, KS 67601
Francis E. Northam
Affiliation:
Agricultural Research Center, Kansas State University, Hays, KS 67601
Philip Westra
Affiliation:
Department of Bioagricultural Science and Pest Management, Colorado State University, Fort Collins, CO 80523
Zewdu Kebede
Affiliation:
Department of Bioagricultural Science and Pest Management, Colorado State University, Fort Collins, CO 80523
Gail A. Wicks
Affiliation:
West Central Research and Extension Center, University of Nebraska, North Platte, NE 69101
*
Corresponding author. [email protected]
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Abstract

Three models that empirically predict crop yield from crop and weed density were evaluated for their fit to 30 data sets from multistate, multiyear winter wheat–jointed goatgrass interference experiments. The purpose of the evaluation was to identify which model would generally perform best for the prediction of yield (damage function) in a bioeconomic model and which model would best fulfill criteria for hypothesis testing with limited amounts of data. Seven criteria were used to assess the fit of the models to the data. Overall, Model 2 provided the best statistical description of the data. Model 2 regressions were most often statistically significant, as indicated by approximate F tests, explained the largest proportion of total variation about the mean, gave the smallest residual sum of squares, and returned residuals with random distribution more often than Models 1 and 3. Model 2 performed less well based on the remaining criteria. Model 3 outperformed Models 1 and 2 in the number of parameters estimated that were statistically significant. Model 1 outperformed Models 2 and 3 in the proportion of regressions that converged on a solution and more readily exhibited an asymptotic relationship between winter wheat yield and both winter wheat and jointed goatgrass density under the constraint of limited data. In contrast, Model 2 exhibited a relatively linear relationship between yield and crop density and little effect of increasing jointed goatgrass density on yield, thus overpredicting yield at high weed densities when data were scarce. Model 2 had statistical properties that made it superior for hypothesis testing; however, Model 1's properties were determined superior for the damage function in the winter wheat–jointed goatgrass bioeconomic model because it was less likely to cause bias in yield predictions based on data sets of minimum size.

Keywords

Jointed goatgrass, Aegilops cylindrica Host. AEGCYwinter wheat, Triticum aestivum L.Crop–weed interferencedecision aideconomic thresholdintegrated weed managementIPMnonlinear regressionparameter estimationAEGCY
Type
Research Article
Information
Weed Science , Volume 49 , Issue 1 , February 2001 , pp. 48 - 60
Copyright
Copyright © Weed Science Society of America 

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