Reliable crop type maps from satellite data are an essential prerequisite for quantifying crop growth, health, and yields. However, such maps do not exist for most parts of Africa, where smallholder farming is the dominant system. Prevalent cloud cover, small farm sizes, and mixed cropping systems pose substantial challenges when creating crop type maps for sub-Saharan Africa. In this study, we provide a mapping scheme based on freely available Sentinel-2A/B (S2) time series and very high-resolution SkySat data to map the main crops—maize and potato—and intercropping systems including these two crops on the Jos Plateau, Nigeria. We analyzed the spectral-temporal behavior of mixed crop classes to improve our understanding of inter-class spectral mixing. Building on the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE), we preprocessed S2 time series and derived spectral-temporal metrics from S2 spectral bands for the main temporal cropping windows. These STMs were used as input features in a hierarchical random forest classification. Our results provide the first wall-to-wall crop type map for this key agricultural region of Nigeria. Our cropland identification had an overall accuracy of 84%, while the crop type map achieved an average accuracy of 72% for the five relevant crop classes. Our crop type map shows distinctive regional variations in the distribution of crop types. Maize is the dominant crop, followed by mixed cropping systems, including maize–cereals and potato–maize cropping; potato was found to be the least prevalent class. Plot analyses based on a sample of 1166 fields revealed largely homogeneous mapping patterns, demonstrating the effectiveness of our classification system also for intercropped classes, which are temporally and spatially highly heterogeneous. Moreover, we found that small field sizes were dominant in all crop types, regardless of whether or not intercropping was used. Maize–legume and maize exhibited the largest plots, with an area of up to 3 ha and slightly more than 10 ha, respectively; potato was mainly cultivated on fields smaller than 0.5 ha and only a few plots were larger than 1 ha. Besides providing the first spatially explicit map of cropping practices in the core production area of the Jos Plateau, Nigeria, the study also offers guidance for the creation of crop type maps for smallholder-dominated systems with intercropping. Critical temporal windows for crop type differentiation will enable the creation of mapping approaches in support of future smart agricultural practices for aspects such as food security, early warning systems, policies, and extension services.

Desertification has become one of the most pronounced ecological disasters, affecting arid and semi-arid areas of Nigeria. This phenomenon is more pronounced in the northern region, particularly the eleven frontline states of Nigeria, sharing borders with the Niger Republic. This has been attributed to a range of natural and anthropogenic factors. Rampant felling of trees for fuelwood, unsustainable agriculture, overgrazing, coupled with unfavourable climatic conditions are among the key factors that aggravate the desertification phenomenon. This study applied geospatial analysis to explore land use/land cover changes and detect major conversions from ecologically active land covers to sand dunes. Results indicate that areas covered by sand dunes (a major indicator of desertification) have doubled over the 25 years under consideration (1990 to 2015). Even though 0.71 km² of dunes was converted to vegetation, indicative of the success of various international, national, local and individual afforestation efforts, conversely about 10.1 km² of vegetation were converted to sand dunes, implying around 14 times more deforestation compared to afforestation. On average, our results revealed that the sand dune in the study area is progressing at a mean annual rate of 15.2 km² annually. The land cover conversion within the 25-year study period was from vegetated land to farmlands. Comparing the progression of a sand dune with climate records of the study area and examining the relationship between indicators of climate change and desertification suggested a mismatch between both processes, as increasing rainfall and lower temperatures observed in 1994, 2005, 2012, and 2014 did not translate into positive feedbacks for desertification in the study area. Likewise, the mean annual Normalized Difference Vegetation Index (NDVI) from 2000 to 2015 shows a deviation between vegetation peaks, mean temperatures and rainfall. On average, our results reveal that the sand dune is progressing at a mean annual rate of about 15.2 km² in the study area. Based on this study’s land cover change, trend and conversion assessment, visual reconciliation of climate records of land cover data, statistical analysis, observations from ground-truthing, as well as previous literature, it can be inferred that desertification in Nigeria is less a function of climate change, but more a product of human activities driven by poverty, population growth and failed government policies. Further projections by this study also reveal a high probability of more farmlands being converted to sand dunes by the years 2030 and 2045 if current practices prevail.

Soil loss caused by erosion has destroyed landscapes, as well as depositing sterile material on fertile lands and rivers, clogged waterways and accelerated flash floods, declined the populations of fish and other species, and diminish soil fertility. In some places, erosion has also destroyed buildings, caused mudflow, create new landforms, displaced people, and slowed down the economy of the affected community by destroying roads and homes. Erosion is aggravated by climate change and anthropogenic factors such as deforestation, overgrazing, inappropriate methods of tillage, and unsustainable agricultural practices. In this study, remote sensing (RS) and geographic information (GIS) data and tools were used to model erosion and estimate soil loss in the catchment area of Koromi-Federe in Jos East, Plateau State Nigeria which is our study area. Soil loss estimation was performed using the revised universal soil loss equation (RUSLE) model and was computed by substituting the corresponding values of each factor inherent in the equation (rainfall erosivity, soil erodibility, slope steepness and slope length, cover management, and conservation practices) using RS and GIS tools. Soil data was obtained from the study area and analyzed in the laboratory, rainfall data, land cover, digital elevation model (DEM), as well as the management practice of the study area were the parameters computed in spatial analyst tool using map algebra based on RUSLE. The soil loss generated was classified into four classes and the results revealed 95.27% of the catchment with a tolerable loss of less than 10 t/h⁻¹/y⁻¹. At 3.6%, a low or minimal loss of 10–20 t/h⁻¹/y⁻¹, at 1.03% there exists a moderate loss of 20–50 t/h⁻¹/y⁻¹, while there was and critical or high loss of >50 t/h⁻¹/y⁻¹ at 0.12% of the catchment. The result showed that critical soil loss in the catchment area is exacerbated by the influence of the slope length and steepness, and the amount of rainfall received. This poses great concern with annual rainfall projected to increase up to 12% in West Africa. However, our sensitivity analysis revealed that it can be reduced with the effect of vegetated cover and management practices. This is an important finding as it can guide sustainability practices to control erosion and the loss of valuable lands in the region, especially now under climate change.

Crop disease management is crucial for sustainable food production. Although farmers in Nigeria continue to apply broad-spectrum fungicides to potato, potato diseases are still on the rise. Machine-learning methods have recently become more common as part of epidemiological early warning systems. They provide vital information on data–disease relations that may be useful for predisease management. In this study, we build on machine-learning methods to develop spatial early warning tools for Nigeria, using the Jos Plateau as a test case. Both remote sensing meteorological and field data were used to (i) predict disease incidence using field reference data and a random forest (RF) classifier and to (ii) identify local conditions conducive to potato diseases using multi-criteria classification (MCC) based on machine-learning results. The results of the RF for 2019, 2020, and 2021 showed similar spatial characteristics, whereas the MCC varied significantly. Both models predicted that between 72 and 96% of potato fields would be infested. The MCC model further revealed that spatiotemporal frequencies of vulnerability in June can serve as the indicator that informs degrees of infestation. A 5-day vulnerability window used in the context of the MCC proved to be the most useful tool for developing an efficient spraying regime, based on a combination of temperature, rainfall, and relative humidity thresholds. As a result, we were able to develop an operational early-warning system for potato disease in the tropical highlands of Africa. In particular, we introduced spatial risks, creating a more sustainable early-warning approach. [ ] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

The development of water resources in Nigeria has not risen alongside population increase. Although groundwater is the available source of water at all locations in Nigeria, groundwater aquifer is becoming deeper due to increased groundwater harvest, especially in urban areas. There is a need for continuous groundwater modelling using geological, climatic, environmental data, and spatial tools especially under climate change and intense landcover conversions. The Spatial Multi-Criteria Analysis (SMCA) was adopted in this study to model the current groundwater potential in Nigeria. The multi-criteria analysis tool in ArcGIS was explored to overlay the nine factor maps to model historic and futuristic groundwater potentials. Our groundwater factor maps show an interesting pattern across Nigeria, the southern parts show more potential considering suitability factors like; surface water density, rainfall, temperature, soil, land cover and elevation in the region. Whereas the upland regions even with higher rainfall and lower temperatures suitable for groundwater recharge, are disadvantaged by geomorphological factors. Northern part of Nigeria shows high potentials, considering the geology, soil, lineament density and slope, but disadvantaged by other factors like lower rainfall and higher temperatures. This informed the final groundwater potential maps; results for the historic potential revealed that, no location in Nigeria has optimal (9–10), very poor or no (1–2) groundwater potential. The results further revealed that areas with higher groundwater potentials are largely within the corridors of major rivers in Nigeria (Niger and Benue), covering about 17.6% of the Nigerian landmass, while 2.6%, 33.7, 44% and 2.2% are occupy areas with very low, low, moderate and very high groundwater potentials respectively. Further analysis highlighs locations of concern due to climatic and environmental changes. Interestingly, our groundwater projection results show a persistent increase in groundwater potential from 2021 to 2100 if current landuses and environmental factors are maintained, and if the projected increase in rainfall is true. Despite these groundwater potentials, the recent issues of shallow aquifers have been cautioned by scientists, especially due to groundwater uncertainties in Nigeria especially with intense landcover conversions, combined with accelerated water demands due to increase in population, and incessant groundwater extractions. Therefore, there is a need to seek caution in the pattern of unregulated and incessant groundwater harvest in Nigeria and we recommend frequent updates of the groundwater potential using geospatial tools to inform governing policies on a centralised consolidated sustainable water supply in Nigeria.