Soil carbon stocks are commonly quantified at fixed depths as the product of soil bulk density, depth and organic carbon (OC) concentration. However, this method systematically overestimates OC stocks in treatments with greater bulk densities such as minimum tillage, exaggerating their benefits. Its use has compromised estimates of OC change where bulk densities differed between treatments or over time periods. We argue that its use should be discontinued and a considerable body of past research re‐evaluated. Accurate OC estimations must be based on quantification in equivalent soil masses (ESMs). The objective of this publication is to encourage accurate quantification of changes in OC stocks and other soil properties using ESM procedures by developing a simple procedure to quantify OC in multiple soil layers. We explain errors inherent in fixed depth procedures and show how these errors are eliminated using ESM methods. We describe a new ESM procedure for calculating OC stocks in multiple soil layers and show that it can be implemented without bulk density sampling, which reduces sampling time and facilitates evaluations at greater depths, where bulk density sampling is difficult. A spreadsheet has been developed to facilitate calculations. A sample adjustment procedure is described to facilitate OC quantification in a single equivalent soil mass layer from the surface, when multiple‐layer quantification is not necessary.

Abstract. Intensification of smallholder agriculture in sub-Saharan Africa is necessary to address rural poverty and natural resource degradation. Integrated Soil Fertility Management (ISFM) is a means to enhance crop productivity while maximizing the agronomic efficiency (AE) of applied inputs, and can thus contribute to sustainable intensification. ISFM consists of a set of best practices, preferably used in combination, including the use of appropriate germplasm, the appropriate use of fertilizer and of organic resources, and good agronomic practices. The large variability in soil fertility conditions within smallholder farms is also recognised within ISFM, including soils with constraints beyond those addressed by fertilizer and organic inputs. The variable biophysical environments that characterize smallholder farming systems have profound effects on crop productivity and AE and targeted application of limited agro-inputs and management practices is necessary to enhance AE. Further, management decisions depend on the farmer's resource endowments and production objectives. In this paper we discuss the "local adaptation" component of ISFM and how this can be conceptualized within an ISFM framework, backstopped by analysis of AE at plot and farm level. At plot level, a set of four constraints to maximum AE is discussed in relation to "local adaptation": soil acidity, secondary nutrient and micro-nutrient (SMN) deficiencies, physical constraints, and drought stress. In each of these cases, examples are presented whereby amendments and/or practices addressing these have a significantly positive impact on fertilizer AE, including mechanistic principles underlying these effects. While the impact of such amendments and/or practices is easily understood for some practices (e.g., the application of SMNs where these are limiting), for others, more complex interactions with fertilizer AE can be identified (e.g., water harvesting under varying rainfall conditions). At farm scale, adjusting fertilizer applications within-farm soil fertility gradients has the potential to increase AE compared with blanket recommendations, in particular where fertility gradients are strong. In the final section, "local adaption" is discussed in relation to scale issues and decision support tools are evaluated as a means to create a better understanding of complexity at farm level and to communicate best scenarios for allocating agro-inputs and management practices within heterogeneous farming environments.

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Abstract. Intensification of smallholder agriculture in sub-Saharan Africa is necessary to address rural poverty and natural resource degradation. Integrated soil fertility management (ISFM) is a means to enhance crop productivity while maximizing the agronomic efficiency (AE) of applied inputs, and can thus contribute to sustainable intensification. ISFM consists of a set of best practices, preferably used in combination, including the use of appropriate germplasm, the appropriate use of fertilizer and of organic resources, and good agronomic practices. The large variability in soil fertility conditions within smallholder farms is also recognized within ISFM, including soils with constraints beyond those addressed by fertilizer and organic inputs. The variable biophysical environments that characterize smallholder farming systems have profound effects on crop productivity and AE, and targeted application of agro-inputs and management practices is necessary to enhance AE. Further, management decisions depend on the farmer's resource endowments and production objectives. In this paper we discuss the "local adaptation" component of ISFM and how this can be conceptualized within an ISFM framework, backstopped by analysis of AE at plot and farm level. At plot level, a set of four constraints to maximum AE is discussed in relation to "local adaptation": soil acidity, secondary nutrient and micronutrient (SMN) deficiencies, physical constraints, and drought stress. In each of these cases, examples are presented whereby amendments and/or practices addressing these have a significantly positive impact on fertilizer AE, including mechanistic principles underlying these effects. While the impact of such amendments and/or practices is easily understood for some practices (e.g. the application of SMNs where these are limiting), for others, more complex processes influence AE (e.g. water harvesting under varying rainfall conditions). At farm scale, adjusting fertilizer applications to within-farm soil fertility gradients has the potential to increase AE compared with blanket recommendations, in particular where fertility gradients are strong. In the final section, "local adaption" is discussed in relation to scale issues and decision support tools are evaluated as a means to create a better understanding of complexity at farm level and to communicate appropriate scenarios for allocating agro-inputs and management practices within heterogeneous farming environments.

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.