Climate-State-Dependent Mortality Risk in Smallholder Cattle and Buffalo Systems: An Environmental Systems Model of Livestock Loss, Insurance, and Land Carrying Capacity in Thailand

Abstract

Mortality in smallholder cattle and buffalo systems is driven by climate, and the climate signal is not uniform. Heat and cold stress, flooding, and climate-sensitive disease emergence act through different physiological and ecological pathways, yet livestock-loss models usually compress them into a single elevated-mortality state. This paper builds a climate-state-dependent mortality model for the Thai national herd, separating an endemic baseline from a temperature-extreme regime and a moisture- and disease-driven regime. A 100,000-iteration Monte Carlo, calibrated to the 2024 national herd and a 2017 primary farmer survey updated to 2026 prices, generates the annual loss distribution and decomposes it by climate driver. The average year is governed by endemic mortality, which accounts for about 81 percent of expected loss but none of the extreme tail. The tail belongs entirely to the two climate regimes: the moisture- and disease-driven regime carries roughly 69 percent of losses beyond the 95th percentile and the temperature regime about 31 percent. The driver of the typical year is therefore not the driver of the catastrophe. A second result concerns the ecological footprint of insurance. Using a reduced-form behavioral layer, mortality-payout design is shown to suppress adaptive destocking and lift stocking pressure 10 to 16 percent above a sustainable land-carrying-capacity benchmark, so that an instrument promoted for climate adaptation can degrade the rangeland it is meant to protect. The findings argue for regime-specific risk financing, for pairing insurance with heat-adaptation and animal-health investment, and for treating the carrying-capacity externality as a design parameter rather than a side effect. The paper closes with a research agenda for climate-state livestock-loss modeling, intended to give the field a transparent and reproducible starting point.