Agricultural drought is a complex phenomenon with numerous consequences and negative implications for agriculture and food systems. The Sahel is frequently affected by severe droughts, leading to significant losses in agricultural yields. Consequently, assessing vulnerability to agricultural drought is essential for strengthening early warning systems. The aim of this study is to develop a new multivariate agricultural drought vulnerability index (MADVI) that combines static and dynamic factors extracted from satellite data. First, pixel temporal regression from 1981 to 2021 was applied to climatic and biophysical covariates to determine the gradients of trend magnitudes. Second, principal component analysis was applied to groups of factors that indicate the same type of vulnerability to configure the basic equation of vulnerability to agricultural drought. Then, random forest (RF), K-nearest neighbours (KNN), support vector machine (SVM) and naïve Bayes (NB) were used to predict drought vulnerability classes using the 28 factors as inputs and 708 pts of randomly distributed class labels. The results showed statistical agreement between the predicted MADVI spatial variability and the reference model (R=0.86 for RF) and its statistical relationships with the vulnerability subcomponents, with an R=0.73 with exposure to climate risk, R=0.64 with the socioeconomic sensitivity index, R=0.6 with the biophysical sensitivity index and a relatively weak correlation (R=0.21) with the physiographic sensitivity index. The overall vulnerability situation in the watershed is 21.8% extreme, 10% very high, 16.8% high, 27.7% moderate, 22.2% low and 1.5% relatively low considering the cartographic results of the predicted vulnerability classes with SVM having the best performance (accuracy=0.96, Kappa=0.95). The study is the first approach that uses the gradients of magnitudes of satellite covariate anomaly trends in multivariate modelling of vulnerability to agricultural drought. It can be easily scaled up across the Sahel region to improve early warning measures related to the impacts of agricultural drought.

https://doi.org/10.1080/01431161.2023.2234094

Multi standard citation

APAHarvardMLAVancouverChicagoIEEE

Garba Mamanhttps://orcid.org/0000-0002-3377-3064

https://doi.org/10.1080/01431161.2023.2234094

Climate Change; Food Security; Food Systems

Vulnerability; Drought; Climate Change; Machine Learning; Sahel

Africa; West Africa

Niger

Headquarters and Western Africa Hub

International Journal of Remote Sensing