Weed Population and Crop Yield Response to Recommendations from a Weed Control Decision Aid

Abstract

AbstractSelecting among the diverse options available to manage weeds requires varied knowledge. This study field‐tested the performance of a decision aid model that integrates biological and economic factors to make weed control recommendations. The model was parameterized using data from the literature and subjected to sensitivity analysis. Tactics generated from the resulting model were compared with standard‐herbicide weed control in continuous corn (Zea mays L.) having a long‐term history of various tillage and herbicide application regimes. The model recommended preemergent and postemergent weed control based on weed seed and weed seedling densities, respectively. Using standard‐herbicide weed control resulted in fewer weed seeds in the soil each year compared with model‐based weed control. Variation among model‐based weed control treatments applied to plots with different management histories was eliminated over the course of the study as weed populations shifted in response to weed control practices. The result was a 98% relative abundance of foxtail (Setaria spp.) seedlings after 3 yr of model‐based treatments, compared with 70% in standard‐herbicide treatments. In 1994, the model reduced herbicide use and weed control treatment costs, but often reduced corn yields and net economic returns. In 1995, model‐based treatments maintained or increased corn yields and net returns. In 1996, however, model‐based treatments consistently reduced corn yields and net returns. Variability in control treatment effectiveness and weed‐crop interaction greatly affected the performance of the model. The use of decision‐aid models in weed management is still a developing technology. Interactions of the weed seed bank, weed emergence, treatment effectiveness, weed‐crop interference, and environmental conditions are very complex. The model could become a valuable tool for producers if consistency of weed control were improved by better understanding and modeling of threshold levels and accounting for weed seed production and by adding data on weed emergence, seed production, yield loss, and control efficacy.