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Performance of WeedSOFT for Predicting Soybean Yield Loss

Published online by Cambridge University Press:  20 January 2017

Shawn M. Hock ,
Stevan Z. Knezevic ,
Alex R. Martin  and
John L. Lindquist
Shawn M. Hock
Affiliation:
University of Nebraska, Lincoln, NE 68583
Stevan Z. Knezevic*
Affiliation:
University of Nebraska, Concord, NE 68728
Alex R. Martin
Affiliation:
University of Nebraska, Lincoln, NE 68583
John L. Lindquist
Affiliation:
University of Nebraska, Lincoln, NE 68583
*
Corresponding author's E-mail: sknezevic2@unl.edu
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Abstract

Decision support systems (DSSs) have been developed to assist producers and consultants with weed management decisions. WeedSOFT is a DSS currently used in several states in the north-central region of the United States. Accurate estimates of crop yield loss due to weed interference are required for cost-effective weed management recommendations. WeedSOFT uses competitive indices (CIs) to predict crop yield loss under multiple weed species, weed densities, and relative times of weed emergence. Performance of several WeedSOFT versions to predict soybean yield loss from weed competition was evaluated using CI values in WeedSOFT version 9.0 compared to new CI values calculated from weed dry matter, weed volume, and soybean yield loss in two soybean row spacings (19 and 76 cm) and two relative weed emergence times (at soybean emergence and first trifoliate leaf stage). Overall, new CI values improved predictions of soybean yield loss by as high as 63%. It was especially true with using new CI values based on yield loss compared to those based on weed dry matter or weed volume. However, there were inconsistencies in predictions for most weed species, suggesting that additional modifications are needed to further improve soybean yield loss predictions.

Keywords

SoybeanGlycine max (L.) MerrCrop modelingcompetitiondecision support systemssimulationAbutilon theophrastiAmaranthus retroflexusAmaranthus rudisAmbrosia trifidaEchinochloa crus-galliHelianthus annuusSetaria faberiSetaria glaucaAE, average errorACI, adjusted competitive indexCI, competitive indexDM, WeedSOFT version based on weed dry matterDSS, decision support systemsRCBD, randomized complete block designTCL, total competitive loadTDM, total dry matterV1, soybean first trifoliate leaf stageVE, soybean emergenceVP, soybean plantingVOL, WeedSOFT version based on weed volumeWS, WeedSOFT version 9.0YL, WeedSOFT version based on yield loss
Type
Research Article
Information
Weed Technology , Volume 20 , Issue 2 , June 2006 , pp. 478 - 484
Copyright
Copyright © Weed Science Society of America 

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Footnotes

1 Published as University of Nebraska Agricultural Research Division Journal Series 14933.

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