A stochastic simulation modeling framework was developed for measuring the impact of weed management technologies in terms of their risk and efficacy. The framework explicitly accounted for the variability of environmental conditions, which underpins risk, and its effect upon the weed population dynamics and crop yields. It was applied to wild oat and wild radish in spring wheat as case studies. Technologies considered included a postemergence herbicide, preseeding tillage, increased crop density, and a selective spray-topping (seed-head sterilizing) herbicide. All stages of the weed life cycle were specified as random variables on the basis of triangular probability distributions, which either were derived from daily environmental conditions or specified as a subjective probability distribution. By using probability density functions the study identified the risks to changes in the weed seed bank and weed density associated with various integrated weed management strategies. This approach not only quantified the probabilities associated with the different outcomes, but also identified how the probability distributions of outcomes were changed as a result of different technology combinations used in weed management strategies. For instance, strategies involving a selective spray-topping herbicide to control seed rain not only resulted in lower seed banks, but the range in possible values was considerably reduced, implying lower likelihood of a population increase. The use of such a risk framework not only benefits weed scientists in terms of designing more effective weed management technologies, but can also assist farmer adoption by being able to quantify the probabilities of success and failure of a technology.
Nomenclature: Wild oat, Avena spp.; wild radish, Raphanus raphanistrum L.; wheat, Triticum aestivum L.