Weed Science Beyond the Weeds: The Role of Integrated Weed Management (IWM) in Agroecosystem Health

Author:

Swanton Clarence J.,Murphy Stephen D.

Abstract

Integrated weed management (IWM) research has focused on how crop yields and weed interference are affected by changes in management, e.g., tillage, herbicide application timing and rates, cover crops, and planting patterns. Acceptance of IWM will depend on recommendation of specific strategies that manage weeds and maintain crop productivity; such research will and should continue. However, IWM needs to move from a descriptive to a predictive phase if long-term strategies are to be adopted. Linking management changes with crop-weed modeling that includes such components as weed population dynamics and the ecophysiological basis of competition will help predict future weed problems and solutions and the economic risks and benefits of intervention. Predictive approaches would help incorporate IWM into models of the processes that occur in agricultural systems at wider spatial and temporal scales, i.e., in agroecosystems comprised of the interactions among organisms (including humans) and the environment. It is at these larger scales that decisions about management are initiated and where questions about the long-term consequences and constraints of IWM and agriculture are often asked. These questions can be addressed by agroecosystem health, an approach that integrates biophysical, social, and economic concerns and recognizes that agriculture is part of a world with many complex subsystems and interactions. Indicators are used to examine the status of an agroecosystem, e.g., whether or not it contains all that is necessary to continue functioning. Indicators include soil quality, crop productivity, and water quality; all of these are related to the rationale of IWM, hence IWM can be linked to agroecosystem health. Ancillary effects of using IWM relate to other indicators such as diversity and energy efficiency. Linking IWM to agroecosystem health has at least two benefits: (1) predictive models within IWM can be incorporated into larger agroecosystem models to explore hitherto unforseen problems or benefits of IWM, and (2) the relevance and benefits of IWM should become clearer to the public and government agencies who otherwise might not examine how IWM promotes many of the larger social, economic and environmental goals being promulgated.

Publisher

Cambridge University Press (CUP)

Subject

Plant Science,Agronomy and Crop Science

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