Soil property and class maps for the continent of Africa were so far only available at very generalised scales, with many countries not mapped at all. Thanks to an increasing quantity and availability of soil samples collected at field point locations by various government and/or NGO funded projects, it is now possible to produce detailed pan-African maps of soil nutrients, including micro-nutrients at fine spatial resolutions. In this paper we describe production of a 30 m resolution Soil Information System of the African continent using, to date, the most comprehensive compilation of soil samples ( $$N \approx 150,000$$N≈150,000 ) and Earth Observation data. We produced predictions for soil pH, organic carbon (C) and total nitrogen (N), total carbon, effective Cation Exchange Capacity (eCEC), extractable—phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), zinc (Zn)—silt, clay and sand, stone content, bulk density and depth to bedrock, at three depths (0, 20 and 50 cm) and using 2-scale 3D Ensemble Machine Learning framework implemented in the (Machine Learning in ) package. As covariate layers we used 250 m resolution (MODIS, PROBA-V and SM2RAIN products), and 30 m resolution (Sentinel-2, Landsat and DTM derivatives) images. Our fivefold spatial Cross-Validation results showed varying accuracy levels ranging from the best performing soil pH (CCC = 0.900) to more poorly predictable extractable phosphorus (CCC = 0.654) and sulphur (CCC = 0.708) and depth to bedrock. Sentinel-2 bands SWIR (B11, B12), NIR (B09, B8A), Landsat SWIR bands, and vertical depth derived from 30 m resolution DTM, were the overall most important 30 m resolution covariates. Climatic data images—SM2RAIN, bioclimatic variables and MODIS Land Surface Temperature—however, remained as the overall most important variables for predicting soil chemical variables at continental scale. This publicly available 30-m Soil Information System of Africa aims at supporting numerous applications, including soil and fertilizer policies and investments, agronomic advice to close yield gaps, environmental programs, or targeting of nutrition interventions.

The effect of rice-husk char (potentially biochar) application on the growth of transplanted lettuce (Lactuca sativa) and Chinese cabbage (Brassica chinensis) was assessed in a pot experiment over a three crop (lettuce-cabbage-lettuce) cycle in Cambodia. The biochar was the by-product of a rice-husk gasification unit and consisted of 28.7% carbon (C) by mass. Biochar application rates to potting medium of 25, 50 and 150 g kg⁻¹ were used with and without locally available fertilizers (a mixture of compost, liquid compost and lake sediment). The rice-husk biochar used was slightly alkaline (pH 7.79), increased the pH of the soil, and contained elevated levels of some trace metals and exchangeable cations (K, Ca and Mg) in comparison to the soil. The biochar treatments were found to increase the final biomass, root biomass, plant height and number of leaves in all the cropping cycles in comparison to no biochar treatments. The greatest biomass increase due to biochar additions (903%) was found in the soils without fertilization, rather than fertilized soils (483% with the same biochar application as in the “without fertilization” case). Over the cropping cycles the impact was reduced; a 363% increase in biomass was observed in the third lettuce cycle.

Drought is the major constraint to rice production in rainfed areas across Asia and sub-Saharan Africa. In the context of current and predicted water scarcity, increasing irrigation is generally not a viable option for alleviating drought problems in rainfed rice-growing systems. It is therefore critical that genetic management strategies for drought focus on maximum extraction of available soil moisture and its efficient use in crop establishment and growth to maximize biomass and yield. Extensive genetic variation for drought resistance exists in rice germplasm. However, the current challenge is to decipher the complexities of drought resistance in rice and exploit all available genetic resources to produce rice varieties combining drought adaptation with high yield potential, quality, and resistance to biotic stresses. The strategy described here aims at developing a pipeline for elite breeding lines and hybrids that can be integrated with efficient management practices and delivered to rice farmers. This involves the development of high-throughput, high-precision phenotyping systems to allow genes for yield components under stress to be efficiently mapped and their effects assessed on a range of drought-related traits, and then moving the most promising genes into widely grown rice mega-varieties, while scaling up gene detection and delivery for use in marker-aided breeding.Rachid Serraj, Kenneth L. McNally, Inez Slamet-Loedin, Ajay Kohli, Stephan M. Haefele, Gary Atlin and Arvind Kuma