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Cassava ( Manihot esculenta Crantz ) is a vital staple crop in sub-Saharan Africa, underpinning food security, rural livelihoods, and agro-industrial development. Despite its resilience to marginal soils and drought, cassava productivity is severely constrained by viral and bacterial diseases, notably cassava mosaic disease (CMD), cassava brown streak disease (CBSD), and cassava bacterial light (CBB). Conventional breeding approaches for disease resistance remain limited by long phenotyping cycles, genotype-by-environment interactions, and inadequate spatial integration. This review highlights the emerging role of geospatial genomics, an interdisciplinary framework that integrates Geographic Information Systems (GIS), remote sensing, and genomic tools to accelerate cassava improvement. GIS enables spatial mapping of disease incidence and environmental gradients, while remote sensing technologies such as satellite indices and drone-based hyperspectral imaging provide real-time crop health monitoring. Advances in genomics, including SNP genotyping, genotyping-by-sequencing, and marker-assisted selection, facilitate the identification of resistance loci and predictive breeding. Integrating spatial datasets with genomic information through environmental association analyses enhances understanding of genotype-environment interactions and supports climate-resilient breeding strategies. Applications of geospatial genomics in cassava include disease surveillance, predictive modeling of outbreaks, targeted germplasm deployment, and improved selection efficiency across diverse agro-ecological zones. Through linking environmental variability, disease dynamics, and genetic diversity, geospatial genomics offers a comprehensive pathway to develop disease-resistant cassava varieties, thereby strengthening food security and sustainable agriculture in Africa.
Amara et al. (Tue,) studied this question.