Performance of WeedSOFT for Predicting Soybean Yield Loss

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 northcentral region of the United States. Accurate estimates of crop yield loss due to weed interference are required for...

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Bibliographic Details
Published in:Weed technology 2006-04, Vol.20 (2), p.478-484
Main Authors: Hock, S.M, Knezevic, S.Z, Martin, A.R, Lindquist, J.L
Format: Article
Language:English
Subjects:
Biological and medical sciences
competitive index
computer software
crop models
crop yield
crop-weed competition
Crops
Datasets
Decision support systems
Density
Fundamental and applied biological sciences. Psychology
Glycine max
Herbicides
Parasitic plants. Weeds
Phytopathology. Animal pests. Plant and forest protection
plant density
prediction
Row spacing
seedling emergence
Soybeans
Sunflowers
Weed control
Weeds
WeedSOFT
yield losses
yield prediction
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Summary: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 northcentral 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.
ISSN:0890-037X
1550-2740
DOI:10.1614/WT-05-069R1.1