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.

No abstract

With increasing concerns raised by climate change, understanding biological processes within cocoa (Theobroma cacao L.) agroforest (CAF) and fallow systems is a prerequisite for developing actions related to emission reduction in the shifting agricultural landscape of Cameroon. Carbon (C) stocks and accretion were assessed and modeled in various C components (large trees, small trees, dead wood, litter, roots, soil, and total C) of fallow and CAF systems along a 50-year chronosequence. Several functions were empirically fitted to a time series of C stocks. Large tree, soil, and total C stocks were best described by a logistic growth function while that for small trees by a rational quadratic function. The best-fitted functions explained 72-96 % of C stock accumulation over time. Two metrics describing C stock accretion were derived from these functions: the point of maximum C growth and the C growth coefficient (GC). The rate of maximum growth of total C stock was reached after 12-13 years in both fallow and CAF, with maximum GCs of 6.9 and 6.3 Mg C ha -1 year -1 , respectively. Over the 50-year period, the GCs of total C stocks varied between 0.2 and 6.9 Mg C ha -1 year -1 , with quick accumulation within the first decade that then slowed until it levelled off after 45 years. Over a period of about 30 years, both systems sequestered a total of *200 Mg C ha -1 . This indicates that cocoa agroforests, a main source of income for local populations, can also provide significant climate change mitigation services.

No abstract