Site-to-site and year-to-year variation inTriticum aestivum–Aegilops cylindricainterference relationships

Abstract

Crop yield loss–weed density relationships critically influence calculation of economic thresholds and the resulting management recommendations made by a bioeconomic model. To examine site-to-site and year-to-year variation in winterTriticum aestivumL. (winter wheat)–Aegilops cylindricaHost. (jointed goatgrass) interference relationships, the rectangular hyperbolic yield loss function was fit to data sets from multiyear field experiments conducted at Colorado, Idaho, Kansas, Montana, Nebraska, Utah, Washington, and Wyoming. The model was fit to three measures ofA. cylindricadensity: fall seedling, spring seedling, and reproductive tiller densities. Two parameters:i, the slope of the yield loss curve asA. cylindricadensity approaches zero, anda, the maximum percentage yield loss asA. cylindricadensity becomes very large, were estimated for each data set using nonlinear regression. Fit of the model to the data was better using spring seedling densities than fall seedling densities, but it was similar for spring seedling and reproductive tiller densities based on the residual mean square (RMS) values. Yield loss functions were less variable among years within a site than among sites for all measures of weed density. For the one site where year-to-year variation was observed (Archer, WY), parameteravaried significantly among years, but parameteridid not. Yield loss functions differed significantly among sites for 7 of 10 comparisons. Site-to-site statistical differences were generally due to variation in estimates of parameteri.Site-to-site and year-to-year variation in winterT. aestivum–A. cylindricayield loss parameter estimates indicated that management recommendations made by a bioeconomic model cannot be based on a single yield loss function with the same parameter values for the winterT. aestivum-producing region. The predictive ability of a bioeconomic model is likely to be improved when yield loss functions incorporating time of emergence and crop density are built into the model's structure.