Probabilistic forecasting of remotely sensed cropland vegetation health and its relevance for food security

18029 6 97875 2 301271 document 97875 1-s2.0-S0048969722032545-main.pdf application/pdf 73e17e8fba5262bdd412c1ad98bcaa15 MD5 2392495 2022-05-30 07:50:01 https://pure.iiasa.ac.at/id/eprint/18029/1/1-s2.0-S0048969722032545-main.pdf 18029 1 1 application/pdf text en public cc_by_4

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published 97877 1 301282 document 97877 indexcodes.txt text/x-c fcca9271dbd25a808f6f895a0d3f35ac MD5 18059 2022-05-30 07:59:50 https://pure.iiasa.ac.at/id/eprint/18029/2/indexcodes.txt 18029 2 2 text/x-c other Generate index codes conversion from text to indexcodes en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/97875 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/97875 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/97875 archive 353 disk0/00/01/80/29 2022-05-30 07:52:06 2022-05-30 07:52:06 2022-05-30 07:52:06 article show Hammad A.T. Falchetta G. 9187 0000-0003-2607-2195 Probabilistic forecasting of remotely sensed cropland vegetation health and its relevance for food security pub ECE IACC Food security; Probabilistic machine learning; Remote sensing of the environment; Vegetation health; Vulnerability analysis In a world where climate change, population growth, and global diseases threaten economic access to food, policies and contingency plans can strongly benefit from reliable forecasts of agricultural vegetation health. To inform decisions, it is also crucial to quantify the forecasting uncertainty and prove its relevance for food security. Yet, in previous studies both these aspects have been largely overlooked. This paper develops a methodology to anticipate the agricultural Vegetation Health Index (VHI) while making the underlying prediction uncertainty explicit. To achieve this aim, a probabilistic machine learning framework modelling weather and climate determinants is introduced and implemented through Quantile Random Forests. In a second step, a statistical link between VHI forecasts and monthly food price variations is established. As a pilot implementation, the framework is applied to nine countries of South-East Asia (SEA) with consideration of national monthly rice prices. Model benchmarks show satisfactory accuracy metrics, suggesting that the probabilistic VHI predictions can provide decision-makers with reliable information about future cropland health and its impact on food price variation weeks or even months ahead, albeit with increasing uncertainty as the forecasting horizon grows. These results - ultimately allowing to anticipate the impact of weather shocks on household food expenditure - contribute to advancing the multidisciplinary literature linking vegetation health, probabilistic forecasting models, and food security policy. 2022-09-10 published Elsevier 10.1016/j.scitotenv.2022.156157 http://dx.doi.org/10.1016/j.scitotenv.2022.156157 3119 public Science of the Total Environment 838 e156157 TRUE 1879-1026 no info:eu-repo/semantics/article