Francis Kamau Muthoni

Changes in the amount and distribution of rainfall highly impact agricultural production in predominantly rainfed farming systems in Africa. Reliable rainfall forecasts on a daily timescale are vital for in-season decision-making. This study evaluated the relative prediction abilities of the European Centre for Medium-Range Weather Forecasts Season 5.1 (ECMWFSv5.1) and the Climate Forecast System version 2 (CFSv2) gridded rainfall models across Africa and three sub-regions from 2012–2022. The results indicate that the performance of both models declines with increasing lead times and improves with aggregated or coarser temporal resolutions. ECMWFv5.1 consistently represented observed daily rainfall better than CFSv2 at all lead times, particularly in West Africa. On dekadal timescales, ECMWFv5.1 outperformed CFSv2 across all sub-regions. CFSv2 tended to overestimate low- and high-intensity rainfall events, whereas ECMWFv5.1 slightly underestimated low-intensity rainfall but accurately captured high-intensity events. While ECMWFv5.1 showed superior skill overall, model reliability was generally limited to West Africa; in contrast, both models performed poorly in East Africa. The high probability of detection (POD) indicates that the models are generally effective at identifying rainy days. However, their overall accuracy in forecasting rainfall across Africa varies depending on lead time, region, rainfall intensity, and elevation. While we did not apply bias-correction methods in this study, we recommend that such techniques be used in future work to improve the reliability of forecasts for operational and sectoral applications. This study therefore highlights both the strengths and the limitations of CFSv2 and ECMWFv5.1 for climate impact assessments, particularly in West Africa and low-elevation regions.

Mycotoxin contamination in staple cereals like maize poses significant health risks to humans and livestock worldwide. The fungus Aspergillus flavus, the primary aflatoxin producer, is influenced by climate, soil type, nutrients, and crop management practices. This study mapped aflatoxin risk in maize and its drivers using ensemble (gradient boosting, adaptive boosting, random forest) and non-ensemble (support vector machine, neural networks, naïve bayes, K-nearest neighbours) machine learning methods. We analysed 907 pre-harvest samples from Kenya, Uganda, Malawi, and Tanzania collected between 2009 and 2022. Aflatoxin levels were categorized into low (5–20 ppb), and high (>20 ppb) risk classes. Models were trained on biophysical variables – temperature, moisture, topography, and soil – using space-time cross-validation (80:20 training-to-testing split). The trained random forest ranger model showed consistent performance with a balanced accuracy of 51%, overall accuracy of 48%, and F1-scores of 32%. In the test set, gradient boosting achieved the highest balanced accuracy of 62% and class-based F1-scores of 67% (low), 45% (medium), and 41% (high). External validation on 2020 data exhibited model generalizability, with an F1-score of 71% for the low-risk class, while the medium- and high-risk classes had lower F1-scores (5% and 14%, respectively) due to small sample sizes. The gradient boosting model that performed best on unseen data was used for variable importance and spatial predictions. Key risk factors were precipitation (higher risk <200 mm), elevation (higher risk <1000 m asl), and soil temperature (18–24 °C for low-moderate risk; 24–27 °C for higher risk). Spatial predictions indicated major risks along the coast, with projections suggesting rising temperatures could expand high-risk areas inland. Despite model limitations, findings can guide better sampling strategies and early action. Future research should incorporate crop management practices and finer-resolution environmental and socio-economic data to improve model reliability.

Timely monitoring of land degradation (LD) is essential to guide targeting the sustainable land management (SLM) practices to the suitable context that assists in achieving an LD neutral world. This study applied a 40‐year time series of remote sensing data representing vegetation indices and rainfall to identify the hotspots for the climatic and human‐induced LD or improvements in the East and Southern Africa (ESA) region. This was complemented by a field assessment of LD and SLM practices applied by farmers in Tanzania. Remote sensing analysis at the regional scale identified hotspots in the ESA region that experienced statistically significant LD and improvement trends primarily driven by human and climatic factors during two temporal segments from 1983 to 2005 (T1) and 2006–2022 (T2). The Normalized Difference Vegetation Index (NDVI) trends exhibited a browning‐to‐greening trend reversal between T1 and T2 in northern Zambia and Tanzania, contrasting with persistent browning in Central Malawi and southern Zambia. At the local scale, severe LD in Kongwa district of Tanzania was primarily caused by erosion by water, wind, and unsustainable exploitation of natural vegetation, although their magnitude varied over different landscape gradients. These collaborated with the remotely sensed browning trend observed in the Kongwa district, but the greening plots were largely smoothened out by coarse‐resolution NDVI data. The regional scale identification of factors driving the greening or browning trends provides a first‐instance evidence‐based sampling frame for future studies to identify the actual practices applied in the greening zone that can be used to rehabilitate the degraded land.

This paper assesses evidence-based applications of Remote Sensing for Sustainable and Precision Agriculture in the Northern Savanna Regions of Ghana for three decades (1990–2023). During this period, there have been several government policy intervention schemes and pragmatic support actions from development agencies towards improving agriculture in this area with differing level of success. Over the same period, there have been dramatic advances in remote sensing (RS) technologies with tailored applications to sustainable agriculture globally. However, the extent to which intervention schemes have harnessed the incipient potential of RS for achieving sustainable agriculture in the study area is unknown. To the best of our knowledge, no previous study has investigated the synergy between agriculture policy interventions and applications of RS towards optimizing results. Thus, this study used systematic literature review and desk analysis to identify previous and current projects and studies that have applied RS tools and techniques to all aspects of agriculture in the study area. Databases searched include Web of Science, Google Scholar, Scopus, AoJ, and PubMed. To consolidate the gaps identified in the literature, ground-truthing was carried out. From the 26 focused publications found on the subject, only 13 (54%) were found employing RS in various aspects of agriculture observations in the study area. Out of the 13, 5 studies focused on mapping the extents of irrigation areas; 2 mapped the size of crop and pasturelands; 1 focused on soil water and nutrient retention; 1 study focused on crop health monitoring; and another focused on weeds/pest infestations and yield estimation in the study area. On the type of data, only 1 (7%) study used MODIS, 2 (15%) used ASTER image, 1 used Sentinel-2 data, 1 used Planetscope, 1 used IKONOS, 5 used Landsat images, 1 used Unmanned Aerial Vehicles (UAVs) and another 1 used RADAR for mapping and monitoring agriculture activities in the study area. There is no evidence of the use of LiDAR data in the area. These results validate the hypothesis that failing agriculture in the study area is due to a paucity of high-quality spatial data and monitoring to support informed farm decision-making.

Land suitability assessment matches crop requirements with available resources to promote sustainable production. Scaling out of sustainable agricultural intensification practices to suitable biophysical and socio-economic conditions reduces the risk of failure and increases their adoption rate. This study applies a geospatial framework to identify potentially suitable sites for extrapolating two improved vegetable cultivars grown under integrated management practices (IMP's) in Babati District of Tanzania. On-farm trial data on the yield and income of two cultivars were used as a reference. Extrapolation was based on the gridded biophysical and socio-economic layers that limit the production of the two improved vegetable cultivars'. The extrapolation suitability index (ESI) showed the areas where cultivation of the two improved vegetable cultivars under IMP's can be scaled-out with a potentially low risk of failure. We generate maps of the most important limiting factor for each cultivar in every pixel to guide the spatial targeting of appropriate remedial measures. Application of these maps will promote evidence-based scaling out of improved vegetable technologies by the extension and development agencies.

REVIEW REPORT ON PRECIPITATION AND TEMPERATURE TRENDS OVER THE LAKE TANA BASIN, ETHIOPIA This study applies a systematic literature review to describe trends and variabilities of Precipitation and temperature in the Lake Tana basin in Ethiopia. The...

The Lake Naivasha basin (Kenya) simultaneously supports a wetland of international importance for biodiversity conservation and agricultural activities of national economic relevance. Water demands have increased substantially over time, while land-use changes have also affected the water balance and put the ecosystem under pressure. Consequently, a wicked-problem situation has developed, with conflicting interests between stakeholders. The human influence on the water balance of Lake Naivasha and its downstream lake has increased over time which has particularly become evident during periods of drought. Several known sociohydrological phenomena have emerged in the Lake Naivasha basin. We have identified and discuss four sociohydrological phenomena: the Supply-demand Cycle, the Rebound effect, the Sequence effect, and the Reservoir effect.The Supply-demand cycle relates to the exploitation of Lake Naivasha and its connected aquifer using pumps. With investments in groundwater pumping it has become possible to supply an increasing water demand, largely serving the flower industry (with logistics and transport). This triggered more investment, labour migration, and thus more water demand. With increasing demands and strong variations in water availability, the risk of economic loss has increased, as became clear during a recent period of drought.The Rebound effect is seen in the context of investments in technologies for increasing irrigation efficiency. By installing drip technology and hydroponics farmers have become able to produce more with the same amount of water withdrawn. The result is an increase in total evaporation and a reduction of return flow into the lake and its connected aquifer. The resulting increased water consumption thereby offsets the water saving potential of the improved technologies. Investments are not limited to the direct surroundings of Lake Naivasha but also take place in upstream parts of the Lake Naivasha basin.The Sequence effect has been observed when during a relatively long period with low water availability a situation had developed in which water users moved their activities closer to Lake Naivasha. This allowed them to make better use of the fertile lands and have access to the scarce water. Placing investments at lower elevations also increased their vulnerability to flood.The Reservoir effect has been observed in relation to the growing flower industry which largely relies on lake water reserves and it’s connected aquifer. Following drought, economic losses occurred due to limited water abstraction for production of flowers. The overreliance on water infrastructures escalated society’s vulnerability and led to considerable economic losses during  a period.This study involved integrated assessment which has yielded relevant insights into the effects of human activities on the hydrology of the area resulting in more pronounced hydrological extremes and related ecological and societal impacts. This knowledge base is useful to inform the sustainable management of the Lake Naivasha basin.

Rainfall onset and cessation date greatly influence cropping calendar decisions in rain-fed agricultural systems. This paper examined trends of onsets, cessation, and the length of growing season over Northern Ghana using CHIRPS-v2, gauge, and farmers’ perceptions data between 1981 and 2019. Results from CHIRPS-v2 revealed that the three seasonal rainfall indices have substantial latitudinal variability. Significant late and early onsets were observed at the West and East of 1.5° W longitude, respectively. Significant late cessations and longer growing periods occurred across Northern Ghana. The ability of farmers’ perceptions and CHIRPS-v2 to capture rainfall onsets are time and location-dependent. A total of 71% of farmers rely on traditional knowledge to forecast rainfall onsets. Adaptation measures applied were not always consistent with the rainfall seasonality. More investment in modern climate information services is required to complement the existing local knowledge of forecasting rainfall seasonality.

Monitoring land degradation (LD) to improve the measurement of the sustainable development goal (SDG) 15.3.1 indicator (“proportion of land that is degraded over a total land area”) is key to ensure a more sustainable future. Current frameworks rely on default medium-resolution remote sensing datasets available to assess LD and cannot identify subtle changes at the sub-national scale. This study is the first to adapt local datasets in interplay with high-resolution imagery to monitor the extent of LD in the semiarid Kiteto and Kongwa (KK) districts of Tanzania from 2000–2019. It incorporates freely available datasets such as Landsat time series and customized land cover and uses open-source software and cloud-computing. Further, we compared our results of the LD assessment based on the adopted high-resolution data and methodology (AM) with the default medium-resolution data and methodology (DM) suggested by the United Nations Convention to Combat Desertification. According to AM, 16% of the area in KK districts was degraded during 2000–2015, whereas DM revealed total LD on 70% of the area. Furthermore, based on the AM, overall, 27% of the land was degraded from 2000–2019. To achieve LD neutrality until 2030, spatial planning should focus on hotspot areas and implement sustainable land management practices based on these fine resolution results.