Assessing maize and cassava extent and intercropping in southwest Nigeria

Abstract Smallholder systems contribute to nearly three-quarters of food production in Sub-Saharan Africa and employ half of the continent’s population. At the same time, these primarily rainfed systems are sensitive to climate variability and have limited capital for obtaining buffering inputs. Both their significance and vulnerability require improved agricultural monitoring. However, the small field sizes of smallholder farms (<2 ha), their complex multi- and intercropped systems, and persisting growing season cloud cover in tropical and sub-tropical regions where many smallholder systems occur mean that the fine-scale cropping patterns of smallholder landscapes remain poorly understood. Focusing on the Oyo state in southwest Nigeria, the country with the largest smallholder population in Africa, we seek to overcome these challenges by mapping the extent of maize and cassava cultivation, two of the country’s most widely cultivated staple crops. Using geolocated GoPro images collected as ground-truth data, we trained machine/deep-learning models (i.e., random forest and convolutional neural network) to classify high-resolution (10 m) multispectral satellite imagery. In doing so, we estimated that 175,604 ha of cassava and 150,538 ha of maize would be cultivated across the state in 2022, in close agreement with official statistics and coarser global crop-type products. We then evaluated a suite of dry-season vegetation indices to assess the prevalence of maize-cassava intercropping (in which maize is harvested at the end of the rainy season and cassava persists), finding indicative evidence of the practice in more than a third (45.2%) of maize locations. Thus, our findings challenge conventional remote sensing assumptions (i.e., monocultured cultivation) and provide important advances for scalable and transferable fine-scale crop type mapping in complex smallholder systems.