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Model-based evaluation in terms of weed management and overall sustainability of cropping systems designed with three different approaches

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  • Cavan, Nicolas
  • Omon, Bertrand
  • Dubois, Sophie
  • Toqué, Clotilde
  • Van Inghelandt, Bastien
  • Queyrel, Wilfried
  • Colbach, Nathalie
  • Angevin, Frédérique

Abstract

For arable crops, weeds are the most harmful pests among those targeted by pesticides. They are also crucial sources of plant biodiversity in agricultural landscapes, and host and feed wild fauna. As there is, to date, no curative weed control method as effective as herbicides, reducing herbicide use while limiting the damage to crop production requires combining many partially-efficient cultural practices at the cropping-system scale.

Suggested Citation

  • Cavan, Nicolas & Omon, Bertrand & Dubois, Sophie & Toqué, Clotilde & Van Inghelandt, Bastien & Queyrel, Wilfried & Colbach, Nathalie & Angevin, Frédérique, 2023. "Model-based evaluation in terms of weed management and overall sustainability of cropping systems designed with three different approaches," Agricultural Systems, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:agisys:v:208:y:2023:i:c:s0308521x23000422
    DOI: 10.1016/j.agsy.2023.103637
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    References listed on IDEAS

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    1. Chloé Salembier & Blanche Segrestin & Elsa Berthet & Benoit Weil & Jean-Marc Meynard, 2018. "Genealogy of design reasoning in agronomy: Lessons for supporting the design of agricultural systems," Post-Print hal-01810426, HAL.
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    6. Chloé Salembier & Blanche Segrestin & Elsa Berthet & Benoit Weil & Jean-Marc Meynard, 2018. "Genealogy of design reasoning in agronomy: Lessons for supporting the design of agricultural systems," Post-Print hal-01823313, HAL.
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    1. Asadollah, Seyed Babak Haji Seyed & Jodar-Abellan, Antonio & Pardo, Miguel Ángel, 2024. "Optimizing machine learning for agricultural productivity: A novel approach with RScv and remote sensing data over Europe," Agricultural Systems, Elsevier, vol. 218(C).

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