In this paper, we present an analysis of summertime atmospheric simulation (June–July 2016) for southern West Africa (sWA) using the RegCM 4.7.1 regional climate model to describe the atmospheric behaviour over the region, and also engage comparisons between the modelled data and observed upper air data acquired during the DACCIWA (Dynamics–Aerosol–Chemistry– Cloud Interactions in West Africa) field campaign period. First, assessments of relative humidity and zonal wind profiles were made for selected coastal and inland stations, to infer the relative vertical and temporal atmospheric differences for both locations. Thereafter, the model’s performance was evaluated, capturing an excessive wet bias in RH profiles of the model with accompanying reduced zonal winds at the Tropical Easterly Jet (TEJ) region and thus produces excessive upper tropospheric cloud liquid water content. Also, in the lower troposphere (particularly, the monsoon layer), RegCM 4.7.1 model captures adequate spatial differences in both RH and zonal wind profiles along the coast and inland. We judge this outcome to be a valuable contribution on the path to rendering RegCM4 a good tool for simulating atmospheric and climate dynamics in sWA.

Smallholder farms are major contributors to agricultural production, food security, and socioeconomic growth in many developing countries. However, they generally lack the resources to fully maximize their potential. Subsequently they require innovative, evidence-based and lowercost solutions to optimize their productivity. Recently, precision agricultural practices facilitated by unmanned aerial vehicles (UAVs) have gained traction in the agricultural sector and have great potential for smallholder farm applications. Furthermore, advances in geospatial cloud computing have opened new and exciting possibilities in the remote sensing arena. In light of these recent developments, the focus of this study was to explore and demonstrate the utility of using the advanced image processing capabilities of the Google Earth Engine (GEE) geospatial cloud computing platform to process and analyse a very high spatial resolution multispectral UAV image for mapping land use land cover (LULC) within smallholder farms. The results showed that LULC could be mapped at a 0.50 m spatial resolution with an overall accuracy of 91%. Overall, we found GEE to be an extremely useful platform for conducting advanced image analysis on UAV imagery and rapid communication of results. Notwithstanding the limitations of the study, the findings presented herein are quite promising and clearly demonstrate how modern agricultural practices can be implemented to facilitate improved agricultural management in smallholder farmers.

Introduction: Land degradation is a significant environmental challenge across sub-Saharan Africa. In recent decades, efforts have been undertaken, with varying successes, to rehabilitate degraded rural landscapes. However, there needs to be more evidence on the outcomes regarding enhanced productivity, environmental management, and socio-economic benefits. Methods: This study uses a case study approach, using contrasting sites from Ethiopia and Mali to appraise restoration innovations implemented through various programs. Two distinct sites were chosen from each of the study cases, and an extensive literature search was conducted to document the evidence, focusing on the sustainability gains derived from integrated landscape management (ILM). For this, the sustainable intensification assessment framework (SIAF) was used, encompassing five domains, namely productivity, economic, environmental, social, and human condition, and featuring scales from plot to landscape, all facilitated by simplified yet robust indicators such as yield, soil loss, net income, land access, and food availability. Results: Results highlighted a higher productivity gain (35% to 55%) and an improved socio-economic benefit (gt;20%). The ILM in the Ethiopian highlands enabled a significant improvement in wheat and barley yield (p lt; 0.01). Introducing new crop varieties integrated with the in-situ and ex-situ practices enabled diversifying crops across the landscape and significantly reduced runoff and soil loss (p lt; 0.05). By increasing the cultivable land by 44%, household income was increased by selling potatoes and agroforestry products. In Mali, ILM practices reduced soil loss to 4.97t/ha from 12.1t/ha. In addition to the improvements in the yield of sorghum and maize (33% and 63%, respectively), rehabilitating the once marginal and abandoned landscape in Mali enabled landless and female-headed households to work together, improving the social cohesion among the groups. The introduction of irrigation facilities enabled widowed women to increase household vegetable consumption by 55% and increase their income by 24%. Discussion: The study showed positive evidence from ILM practices in the two contrasting landscapes. However, there is a need to address challenges related to the absence of timely data monitoring and documentation of successful practices. For this, the generation of evidence-based data and the use of advanced geo-spatial tools such as Remote Sensing and GPS-installed drones are recommended.