Performance of WeedSOFT for Predicting Soybean Yield Loss
Autor: | Shawn M. Hock, Stevan Z. Knezevic, John L. Lindquist, Alex Martin |
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Rok vydání: | 2006 |
Předmět: |
0106 biological sciences
Setaria pumila biology Yield (finance) Crop yield fungi food and beverages 04 agricultural and veterinary sciences Plant Science respiratory system biology.organism_classification Weed control 01 natural sciences 010602 entomology Ambrosia trifida Agronomy Setaria faberi parasitic diseases 040103 agronomy & agriculture 0401 agriculture forestry and fisheries Annual plant Weed Agronomy and Crop Science |
Zdroj: | Weed Technology. 20:478-484 |
ISSN: | 1550-2740 0890-037X |
DOI: | 10.1614/wt-05-069r1.1 |
Popis: | 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. |
Databáze: | OpenAIRE |
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