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Adaptive decision-making on stocking rates improves the resilience of a livestock system exposed to climate shocks

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  • Joly, Frédéric
  • Sabatier, Rodolphe
  • Tatin, Laurent
  • Mosnier, Claire
  • Ahearn, Ariell
  • Benoit, Marc
  • Hubert, Bernard
  • Deffuant, Guillaume

Abstract

Risks of drought complicate decision-making in grass-based livestock systems. Here, we assessed the pertinence of the stochastic viability framework (SV) for making relevant decisions in a system exposed to climate shocks. SV involves maximizing the probability of satisfying predefined constraints over time through adapted decision-making. We applied the approach to the case of Mongolia where climate hazards, combined with high animal densities, regularly cause massive livestock die-offs. We used a livestock system model on which we made preliminary simplifications, based on a thorough understanding of its behaviour, to allow for SV use. Then, we used SV to iteratively identify, based on herd size and plant biomass, the most adapted management decisions. Decisions involve selling/purchasing a certain number of heads of the five local species. We obtained 100-year trajectories satisfying herders’ constraints of income and subsistence consumption at a 94% rate. This results from (i) cautious stocking rates reducing die-off frequency and (ii) sales of heads of resistant species to buy heads of fragile species after die-offs to compensate losses of fragile species. These management actions generate resilience, as they mitigate the effects of climate variability and offer reorganization mechanisms after a crisis. We thereby confirm the potential of SV for adaptive decision-making when resilience is at stake.

Suggested Citation

  • Joly, Frédéric & Sabatier, Rodolphe & Tatin, Laurent & Mosnier, Claire & Ahearn, Ariell & Benoit, Marc & Hubert, Bernard & Deffuant, Guillaume, 2022. "Adaptive decision-making on stocking rates improves the resilience of a livestock system exposed to climate shocks," Ecological Modelling, Elsevier, vol. 464(C).
  • Handle: RePEc:eee:ecomod:v:464:y:2022:i:c:s0304380021003446
    DOI: 10.1016/j.ecolmodel.2021.109799
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