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.

• In upto 25% of fields, maize is non-responsive to fertilizer and amendments. • Multiple factors that vary by site explain poor crop response to fertilizers. • Low Mn, Cu and B contents are the most striking differences between the poor non-responsive cluster and others. • Site specific management recommendations are needed to improve the efficiency of fertilizer application.

The multiple ecosystem services provided by healthy soil are well known and include soil carbon sequestration to mitigate climate change, a medium for plant and agricultural production and regulating the hydrologic cycle. Despite the wide recognition of the importance of these services, drivers of soil organic carbon (SOC) dynamics across various land uses in East Africa are poorly understood. The objectives of this study were threefold: to quantify SOC stocks across Tanzania; assess the effect of land cover and erosion on SOC; and investigate the relationship between inherent and dynamic soil properties under diverse land uses. The Land Degradation Surveillance Framework (LDSF) was used to assess the variability of ecological metrics at different spatial scales. SOC was quantified within and between different land cover types (forest, woodland, shrubland, grassland and cropland) in Tanzania. A total of 2052 soil samples from 1082–1000m2 plots were collected from seven 100-km2 sentinel sites in 2010. Composite soil samples were collected at each plot from two depths (0–20 and 20–50cm) and cumulative soil mass samples were collected to 100cm. Soil samples were analyzed using a combination of traditional analytical laboratory methods and mid-infrared spectroscopy (MIR). Model performance of MIR spectral predictions for carbon was good, with an R2 of >0.95 and RMSEP of 4.3gkg−1, when using an independent validation datasets. Woodland and cropland were the most frequently occurring vegetation structure types in the sampled sites, with 388 and 246 plots, respectively. Average topsoil OC (and range) was 12.4 (1.5–81.4) gCkg−1 (n=1082) and average subsoil OC (and range) was 7.3 (0.64–53.8) gCkg−1 (n=970) for the seven sites. Forested plots had the highest mean topsoil organic carbon concentrations (17.3gCkg−1) followed by cropland (13.3gCkg−1), for all sites included in the study, but with high levels of variability between sites. Soil mass at 30cm was measured and these data were used to calculate carbon stocks for the different land cover types. An approach based on remote sensing was explored for the mapping of SOC stocks at 30cm for Tanzania using Moderate Resolution Imaging Spectroradiometer (MODIS) imagery from 2012. Results indicate that the use of image reflectance for the mapping of SOC stocks has promising potential, with R2 values ranging from 0.77 to 0.81 and RMSEP values from 0.90 to 1.03kgm−2 for the three validation datasets. There is high utility of these maps for strategic land management interventions that prioritize ecosystem services

Site-specific nutrient management can reduce soil degradation and crop production risks related to undesirable timing, amount, and type of fertilizer application. This study was conducted to understand the spatial variability of soil properties and delineate spatially homogenous nutrient management zones (MZs) in the maize belt region of Nigeria. Soil samples (n = 3387) were collected across the area using multistage and random sampling techniques, and samples were analyzed for pH, soil organic carbon (SOC), macronutrients (N, P, K, S, Ca and Mg), micronutrients (S, B, Zn, Mn and Fe) content, and effective cation exchange capacity (ECEC). Spatial distribution and variability of these parameters were assessed using geostatistics and ordinary kriging, while principal component analysis (PCA) and multivariate K-means cluster analysis were used to delineate nutrient management zones. Results show that spatial variation of macronutrients (total N, available P, and K) was largely influenced by intrinsic factors, while that of S, Ca, ECEC, and most micronutrients was influenced by both intrinsic and extrinsic factors with moderate to high spatial variability. Four distinct management zones, namely, MZ1, MZ2, MZ3, and MZ4, were identified and delineated in the area. MZ1 and MZ4 have the highest contents of most soil fertility indicators. MZ4 has a higher content of available P, Zn, and pH than MZ1. MZ2 and MZ3, which constitute the larger part of the area, have smaller contents of the soil fertility indicators. The delineated MZs offer a more feasible option for developing and implementing site-specific nutrient management in the maize belt region of Nigeria.

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.

Absence of site-specific nutrient recommendation and high spatial variability of soil fertility are major factors affecting maize response to applied nutrients in Nigeria. In this study, we assessed maize response to applied nutrients and nutrient use efficiency in different management zones (MZs), for designing site-specific nutrient management recommendations for maize in the maize belt of Nigeria. The maize belt in Nigeria was earlier delineated into four MZsMZs (MZ1 to MZ4) based on soil properties. In the current study, data from two different trials, nutrient omission trials (N = 293) and fertilizer response trial (N = 705), conducted in the years 2015–2017, were extracted for MZ1 to MZ3; to analyze maize yield responses to application of N, P and K, and secondary and micro-nutrients. Maize yield response to K application was only positive in MZ1. Responses to N and P application were positive for all MZs. However, the magnitude of maize response to P varied between the MZs, indicating a differentiation in the degree to which P is limiting maize production in the study area. Average nitrogen requirement was higher for MZ3 (138 kg ha⁻¹), than for MZ2 and MZ1 (121 and 83 kg ha⁻¹, respectively). Average P requirement was higher for MZ3 (45 kg ha⁻¹) than for the other zones. Potassium requirement was 26% and 28% higher in MZ2 and MZ3 compared with MZ1 (∼15 kg ha⁻¹). The use of the specific nutrient rates for the MZs may reduce risks and uncertainties in crop production. The delineated MZs of the maize belt of Nigeria that incorporates spatial variability in soil fertility conditions are useful for nutrient management for larger areas.