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Clarifying Grazing Management Methods: A Data-Driven Review

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  • Robin Russias

    (INRAE, Université Clermont Auvergne, VetAgro Sup, UMRH, 89 Avenue de l’Europe, 63370 Lempdes, France
    Moy Park Beef Orléans, Rue des Pins, 45400 Fleury-les-Aubrais, France)

  • Rémy Delagarde

    (PEGASE, INRAE Institut Agro, 16 Le Clos, 35590 Saint-Gilles, France)

  • Katja Klumpp

    (INRAE, Université Clermont Auvergne, VetAgro Sup, UMREP, 5 Chemin de Beaulieu, 63000 Clermont-Ferrand, France)

  • Audrey Michaud

    (INRAE, Université Clermont Auvergne, VetAgro Sup, UMRH, 89 Avenue de l’Europe, 63370 Lempdes, France)

Abstract

Grasslands, particularly permanent grasslands, provide vital ecosystem services and, therefore, focus a number of management challenges. Grassland management revolves around organizing how livestock graze in both space and time, using various grazing methods. However, international research describes these grazing methods using diverse and sometimes inconsistent terminologies. This lack of standardization may create ambiguity and hinder comparative research on grazing methods. Here, to address this issue, we conducted a literature review aiming to identify common patterns of grazing methods based on shared grazing management criteria. Through multivariate analysis, we analyzed 249 experimental datapoints derived from 102 studies. We ran principal component analysis followed by hierarchical clustering on principal components on seven management criteria. This review identified 4 broad families of grazing methods: continuous grazing, conventional rotational grazing, deferred rotational grazing, adaptative multi-paddock grazing. This work distinguishes rotational from continuous grazing methods, as commonly described in the literature. Furthermore, it identifies adaptative multi-paddock grazing as a distinct and innovative group of rotational grazing. The approach developed here could serve as support to characterize and compare different grazing methods.

Suggested Citation

  • Robin Russias & Rémy Delagarde & Katja Klumpp & Audrey Michaud, 2025. "Clarifying Grazing Management Methods: A Data-Driven Review," Sustainability, MDPI, vol. 17(11), pages 1-20, June.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:5200-:d:1672560
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    References listed on IDEAS

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    1. Lê, Sébastien & Josse, Julie & Husson, François, 2008. "FactoMineR: An R Package for Multivariate Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i01).
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