|Canner, S - FORMER ARS POST DOC|
Submitted to: Weed Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: October 24, 2008
Publication Date: March 1, 2009
Citation: Stephen R. Canner, L. J. Wiles, Robert H. Erskine, Gregory S. McMaster, Gale H. Dunn, and James C. Ascough, II*: Modeling with Limited Data: The Influence of Crop Rotation and Management on Weed Communities and Crop Yield Loss: Weed Science 2009 57:175–186 Interpretive Summary: Growers rotate crops in a field for many reasons including managing weeds. Some weeds are easier to control in certain crops so some sequences of crops can minimize both weed problems and reduce control costs. However, the best rotation depends on the weeds present in a field, and weed management has to be considered against the many other reasons for choosing a crop rotation. Scientists have developed models that predict the control of weeds and yield loss from uncontrolled weeds – the information growers need to plan crop rotations considering weed management. These models, however, are available for just a few combinations of a single weed in single crop because the required information about weed biology is limited and expensive. We developed a new method for this type of model that uses only data that is already available supplemented with expert knowledge. This model can be used for any combination of 15 weeds in rotations of 6 different crops and has been incorporated into a decision tool for whole-farm strategic planning. Now growers in the Central Great Plains can compare how different crop rotations influences farm income, herbicide use and control of weeds in their fields.
Technical Abstract: Theory and models of crop yield loss from weed competition have lead to decision models to help growers with cost-effective tactical weed management. Weed management decision models are available for multiple-species populations in a single season of several crops. Growers also rely on crop rotation for weed control, however, theory and models of weed population dynamics have not lead to similar tools to help growers with planning of crop rotations for cost-effective weed management. Obstacles have been the complexity of modeling the dynamics of populations of multiple species of weeds compared to a single species and lack of data. We developed a method to use limited, readily observed data to simulate population dynamics and crop yield loss of multiple-species weed populations in response to crop rotation, tillage system, and specific weed management tactics. Our method is based on the general theory of density dependence of plant productivity and extensive use of rectangular hyperbolic equations for describing crop yield loss as a function of weed density. Only two density-independent parameters are required for each species to represent differences in seed bank mortality, emergence and maximum seed production. One equation is used to model crop yield loss and density-dependent weed seed production as a function of crop and weed density, relative time of weed and crop emergence and differences among species in competitive ability. The model has been parameterized for six crops and 15 weeds and limited evaluation indicates predictions are accurate enough to highlight potential weed problems and solutions when comparing alternative crop rotations for a field. The model has been incorporated into a decision support tool for whole-farm management so growers in the Central Great Plains of the US can compare alterative crop rotations and how their choice influences farm income, herbicide use and control of weeds in their fields.