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Weed Interference Models

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Decision Support Systems for Weed Management

Abstract

Weeds are a major biotic constraint in agricultural systems. Farmers need to quantify the damage that weeds cause to crops, and many models have been developed to predict yield loss. Empirical functions are the most commonly used models, which additionally provide information for weed threshold values. The limitations of such models are that they are based on statistical functions and usually do not consider biological insights for crop-weed interference. Conversely, mechanistic models take into account various underlying processes but are rather complex in nature; thus, their major utility lies in generating information for weed studies under different locations/conditions. Mechanistic models are based on simulation models that mingle both explanatory and descriptive features, with well-known plant processes studied in a mechanistic fashion, and poorly understood processes considered as a descriptive approach. Weed interference models are an important part of the decision support systems to establish recommendations based on the economic quantification of different weed management strategies. Thus, these models are very useful tools for the development of integrated weed management. In this chapter, we present empirical and mechanistic models that are currently in use for studying crop-weed interference.

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Authors and Affiliations

  1. Punjab Agricultural University, Ludhiana, Punjab, India

    Manpreet Singh & Simerjeet Kaur

  2. The Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation (QAAFI) and School of Agriculture and Food Sciences (SAFS), The University of Queensland, Gatton, QLD, Australia

    Bhagirath S. Chauhan

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  1. Manpreet Singh
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  2. Simerjeet Kaur
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  3. Bhagirath S. Chauhan
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Correspondence to Manpreet Singh .

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Editors and Affiliations

  1. Departamento de Agronomía, Universidad Nacional del Sur - CERZOS/CONICET, Bahía Blanca, Buenos Aires, Argentina

    Guillermo R. Chantre

  2. Department of Crop Protection, Institute for Sustainable Agriculture, Córdoba, Córdoba, Spain

    José L. González-Andújar

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Singh, M., Kaur, S., Chauhan, B.S. (2020). Weed Interference Models. In: Chantre, G., González-Andújar, J. (eds) Decision Support Systems for Weed Management. Springer, Cham. https://doi.org/10.1007/978-3-030-44402-0_6

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