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An agent-based model for assessing grazing strategies and institutional arrangements in Zeku, China

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  • Yu, Rui
  • Evans, A.J.
  • Malleson, N.

Abstract

The assessment of grassland grazing strategies and institutional arrangements is essential for ensuring the sustainable development of grassland grazing systems. By employing per-pixel grazing information derived from remote sensing data, this paper presents an agent-based model of grassland grazing (ABMGG) for Zeku, China that was designed as a framework for assessing the effects of different combinations of grazing strategies and institutional arrangements on grassland status. By calibrating the parameter values of the ABMGG to the system status values under a policy that has already been implemented, the ABMGG can help us to understand grassland degradation in response to management interventions for each patch of land. In the Zeku implementation, it was found that although different grazing policy scenarios could not significantly improve or decrease the overall grassland leaf area index, a rotational group grazing scenario with a land market tenure system did produce a smaller number of severely degraded grass patches than other policy scenarios (except regional continuous grazing). This provides a new perspective on the consequences of grassland management practices where past research has concentrated more on overall grassland productivity. The ABMGG can extend the ability of policy assessment tools to a high resolution level with pixel-specific real-time remote sensing data, making the assessment results more accurate and representative.

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  • Yu, Rui & Evans, A.J. & Malleson, N., 2019. "An agent-based model for assessing grazing strategies and institutional arrangements in Zeku, China," Agricultural Systems, Elsevier, vol. 171(C), pages 135-142.
    • Handle: RePEc:eee:agisys:v:171:y:2019:i:c:p:135-142
    DOI: 10.1016/j.agsy.2019.02.004
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    1. DeMartino, George & Grabel, Ilene & Scoones, Ian, 2024. "Economics for an uncertain world," World Development, Elsevier, vol. 173(C).

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