80% of arable land in Africa has low soil fertility and suffers from physical soil problems. Additionally, significant amounts of nutrients are lost every year due to unsustainable soil management practices. This is partially the result of insufficient use of soil management knowledge. To help bridge the soil information gap in Africa, the Africa Soil Information Service (AfSIS) project was established in 2008. Over the period 2008–2014, the AfSIS project compiled two point data sets: the Africa Soil Profiles (legacy) database and the AfSIS Sentinel Site database. These data sets contain over 28 thousand sampling locations and represent the most comprehensive soil sample data sets of the African continent to date. Utilizing these point data sets in combination with a large number of covariates, we have generated a series of spatial predictions of soil properties relevant to the agricultural management—organic carbon, pH, sand, silt and clay fractions, bulk density, cation-exchange capacity, total nitrogen, exchangeable acidity, Al content and exchangeable bases (Ca, K, Mg, Na). We specifically investigate differences between two predictive approaches: random forests and linear regression. Results of 5-fold cross-validation demonstrate that the random forests algorithm consistently outperforms the linear regression algorithm, with average decreases of 15–75% in Root Mean Squared Error (RMSE) across soil properties and depths. Fitting and running random forests models takes an order of magnitude more time and the modelling success is sensitive to artifacts in the input data, but as long as quality-controlled point data are provided, an increase in soil mapping accuracy can be expected. Results also indicate that globally predicted soil classes (USDA Soil Taxonomy, especially Alfisols and Mollisols) help improve continental scale soil property mapping, and are among the most important predictors. This indicates a promising potential for transferring pedological knowledge from data rich countries to countries with limited soil data.

There is a need for up-to-date assessments and maps of soil properties and land health at scales relevant for decision-making and management, including for properties that are dynamic and hence change in response to management. Also, there is a need for approaches to soil mapping that capture the ever increasing effects that humans are having on the environment in general and specifically on soil properties worldwide. In this paper, we develop models for digital soil mapping based on remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) platform for Africa. The article presents maps of soil organic carbon (SOC), pH, sand and sum of exchangeable bases, as well as prevalence of root-depth restrictions in the upper 50cm of the soil profile. Prediction models were developed based on spatially balanced field survey data, representing all major climate zones on the continent. The prediction models for soil property mapping performed well, with overall RMSEP values of 10.6, 0.34, 9.1, and 6.5 for SOC, pH, sand, and sum of bases, respectively. The accuracy of the prediction model for root-depth restrictions was 77%, with an AUC of 0.85 and Cohen's kappa value of 0.52 when averaged across predictions run on independent test data. The methods and maps developed can provide much improved identification of soil and land health constraints, and spatial targeting of land management interventions at various scales, informing both policy and practice

The promotion of land, soil and water conservation measures has been a widespread development in sub-Saharan Africa in a bid to tackle degradation and improve productivity. As a result, several governments have launched various campaigns on soil, land and water conservation measures. The aim of this study is to determine some of the factors that influence farmers’ awareness (knowledge) and adoption of land, soil and water conservation practices. Data for this study was collected from 312 households using a questionnaire survey in the Chinyanja Triangle of Southern Africa. The study sites were sampled from Tete province of Mozambique, central and southern regions of Malawi and eastern Province of Zambia. We used t-tests to classify adopters and non-adopters of soil, land and water conservation measures and binomial logit models to identify the factors that influence farmers’ knowledge of conservation measures and adoption of land productivity practices. The results show that the household head's age, education, agricultural advice reception and farmer group membership are critical in raising awareness. While the household head's age, education, agricultural advice reception, farmer group membership, pieces of land owned or used in production and land-to-man ratio influenced adoption decisions. The study, therefore, concludes that in order to improve land productivity in the Chinyanja Triangle, there is a need to consider farmers’ heterogeneity in terms of household head's age, level of education, extension services outreach, and socio-economic characteristics. This suggests that governments’ policies in the region should be aimed at improving farmers’ level of education, extension delivery that will target the elderly and the youth, land ownership, credit access, and social capital such as group formation

This article is concerned with the adoption of small-scale irrigation farming as a climate-smart agriculture practice and its influence on household income in the Chinyanja Triangle. Chinyanja Triangle is a region that is increasingly experiencing mid-season dry spells and an increase in occurrence of drought, which is attributed largely to climate variability and change. This poses high agricultural production risks, which aggravate poverty and food insecurity. For this region, adoption of small-scale irrigation farming as a climate-smart agriculture practice is very important. Through a binary logistic and ordinary least squares regression, this article determines factors that influence the adoption of small-scale irrigation farming as a climate-smart agriculture practice and its influence on income among smallholder farmers. The results show that off-farm employment, access to irrigation equipment, access to reliable water sources and awareness of water conservation practices, such as rainwater harvesting, have a significant influence on the adoption of small-scale irrigation farming. On the other hand, the farmer’s age, distance travelled to the nearest market and nature of employment negatively influence the adoption of small-scale irrigation farming decisions. Ordinary least squares regression results showed that the adoption of small-scale irrigation farming as a climate-smart agriculture practice has a significant positive influence on agricultural income. We therefore conclude that to empower smallholder farmers to respond quickly to climate variability and change, practices that will enhance the adoption of small-scale irrigation farming in the Chinyanja Triangle are critical, as this will significantly affect agricultural income. In terms of policy, we recommend that the governments of Zambia, Malawi and Mozambique, which cover the Chinyanja Triangle, formulate policies that will enhance the adoption of sustainable small scale-irrigation farming as a climate-smart agriculture practice. This will go a long way in mitigating the adverse effects that accompany climate variability and change in the region.

Soil erosion is a very critical form of land degradation resulting in the loss of soil nutrients and downstream sedimentation of water storages in the highlands of Ethiopia. As it is technically and financially impossible to conserve all landscapes affected by erosion, identification of priority areas of intervention is necessary. Spatially distributed erosion models can help map landscape susceptibility to erosion and identify high erosion risk areas. Integration of erosion models with geographic information systems (GIS) enables assessing evaluate the spatial variability of soil erosion and plan implementing conservation measures at landscape levels. In this study, the Revised Universal Soil Loss Equation adjusted for sediment delivery ratio was used in a GIS system to assess landscape sensitivity to erosion and identify hotspots. The approach was applied in three catchments with size being 10–20 km2 and results were compared against quantitative and semi-quantitative data. The model estimated mean soil loss rates of about 45 t ha−1 y−1 with an average variability of 30% between catchments. The estimated soil loss rate is above the tolerable limit of 10 t ha−1 y−1. The model predicted high soil loss rates at steep slopes and shoulder positions as well as along gullies. The results of the study demonstrate that knowledge of spatial patterns of high soil loss risk areas can help deploy site-specific conservation measures