Evaluating the Economic Risk of Herbicide-Based Weed Management Systems in Corn and Soybean Using Stochastic Dominance Testing

Abstract

Herbicide evaluation trials are typically conducted with the objective of rating herbicide efficacy and assessing crop yield loss. There is little if any attempt to quantify the economic risk associated with each treatment. The objective of this research was to use second-degree stochastic dominance to evaluate the economic stability of corn and soybean weed management systems between two contrasting environments. Weed management systems were evaluated in small-plot replicated trials over a 3-yr time period at two locations in southern Minnesota. One location (Waseca) had a slightly cooler and wetter environment than the second location (Lamberton). The Waseca location also had higher weed density and greater weed species diversity. Adjusted returns from weed management were calculated for each system by measuring economic returns, as determined by deducting weed management costs from the product of crop price and grain yield. Stochastic dominance is a technique that considers the entire distribution of net returns from weed management and compares these cumulative distributions as a basis for analyzing risk. Climate, soils, and weed diversity dictated differences in risk efficiency and effectiveness of the various weed management systems evaluated between the Waseca and Lamberton sites. Stochastic dominance testing is a useful tool for understanding long-term risk across environments. Results can be used to develop effective long-term weed management systems that minimize risk while maximizing profit potential.