PDF(Pubmed)
Abstract：
African pastoralists suffer recurrent droughts that cause high livestock mortality and vulnerability to climate change. The index-based livestock insurance (IBLI) program offers protection against drought impacts. However, the current IBLI design relying on the normalized difference vegetation index (NDVI) may pose limitation because it does not consider the mixed composition of rangelands (including herbaceous and woody plants) and the diverse feeding habits of grazers and browsers. To enhance IBLI, we assessed the efficacy of utilizing distinct browse and grazing forage estimates from woody LAI (LAIW) and herbaceous LAI (LAIH), respectively, derived from aggregate leaf area index (LAIA), as an alternative to NDVI for refined IBLI design. Using historical livestock mortality data from northern Kenya as reference ground dataset, our analysis compared two competing models for (1) aggregate forage estimates including sub-models for NDVI, LAI (LAIA); and (2) partitioned biomass model (LAIP) comprising LAIH and LAIW. By integrating forage estimates with ancillary environmental variables, we found that LAIP, with separate forage estimates, outperformed the aggregate models. For total livestock mortality, LAIP yielded the lowest RMSE (5.9 TLUs) and higher R2 (0.83), surpassing NDVI and LAIA models RMSE (9.3 TLUs) and R2 (0.6). A similar pattern was observed for species-specific livestock mortality. The influence of environmental variables across the models varied, depending on level of mortality aggregation or separation. Overall, forage availability was consistently the most influential variable, with species-specific models showing the different forage preferences in various animal types. These results suggest that deriving distinct browse and grazing forage estimates from LAIP has the potential to reduce basis risk by enhancing IBLI index accuracy.
摘要：
非洲牧民遭受经常性干旱，导致牲畜死亡率高和对气候变化的脆弱性。基于指数的牲畜保险（IBLI）计划可防止干旱影响。然而,当前基于归一化植被指数（NDVI）的IBLI设计可能会造成限制，因为它没有考虑牧场（包括草本植物和木本植物）的混合组成以及放牧者和浏览器的不同摄食习惯。为了增强IBLI，我们评估了利用木本LAI（LAIW）和草本LAI（LAIH）的不同浏览和放牧草料估计的功效，分别,从总叶面积指数(LAIA)得出，作为改良IBLI设计的NDVI的替代方案。使用肯尼亚北部的历史牲畜死亡率数据作为参考地面数据集，我们的分析比较了两个竞争模型（1）总饲料估计，包括NDVI的子模型，LAI(LAIA);和(2)包含LAIH和LAIW的分区生物量模型(LAIP)。通过将饲料估计与辅助环境变量相结合，我们发现LAIP,用单独的饲料估计，性能优于聚合模型。牲畜总死亡率，LAIP产生了最低的RMSE(5.9TLU)和更高的R2(0.83)，超越NDVI和LAIA型号RMSE（9.3TLU）和R2（0.6）。对于特定物种的牲畜死亡率也观察到了类似的模式。环境变量在模型中的影响各不相同，取决于死亡率聚集或分离的水平。总的来说,牧草的可获得性一直是最具影响力的变量，物种特异性模型显示了各种动物类型的不同牧草偏好。这些结果表明，从LAIP得出不同的浏览和放牧草料估计有可能通过提高IBLI指数的准确性来降低基础风险。
