Spatial predictions of soil macro and micro-nutrient content across Sub-Saharan Africa at 250 m spatial resolution and for 0-30 cm depth interval are presented. Predictions were produced for 15 target nutrients: organic carbon (C) and total (organic) nitrogen (N), total phosphorus (P), and extractable-phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S), sodium (Na), iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), aluminum (Al) and boron (B). Model training was performed using soil samples from ca. 59,000 locations (a compilation of soil samples from the AfSIS, EthioSIS, One Acre Fund, VitalSigns and legacy soil data) and an extensive stack of remote sensing covariates in addition to landform, lithologic and land cover maps. An ensemble model was then created for each nutrient from two machine learning algorithms- random forest and gradient boosting, as implemented in R packages ranger and xgboost-and then used to generate predictions in a fully-optimized computing system. Cross-validation revealed that apart from S, P and B, significant models can be produced for most targeted nutrients (R-square between 40-85%). Further comparison with OFRA field trial database shows that soil nutrients are indeed critical for agricultural development, with Mn, Zn, Al, B and Na, appearing as the most important nutrients for predicting crop yield. A limiting factor for mapping nutrients using the existing point data in Africa appears to be (1) the high spatial clustering of sampling locations, and (2) missing more detailed parent material/geological maps. Logical steps towards improving prediction accuracies include: further collection of input (training) point samples, further harmonization of measurement methods, addition of more detailed covariates specific to Africa, and implementation of a full spatiotemporal statistical modeling framework.

Household characteristics are commonly used to explain variation in smallholder efficiency levels. The underlying assumption is that differences in intended behavior are well described by such variables, while there is no "a priori" reason that this is the case. Moreover, heterogeneity in farmer goals and preferences, in relation to the role of the farm enterprise, are not well documented in developing countries. This article makes a contribution to fill this gap, by empirically determining heterogeneity in farmer goals and attitudes in Nigeria through a pair-wise ranking, supplemented with Likert scales. Factor analysis is used to reduce these data into behavioral factors. We estimate technical and allocative efficiency levels and analyze how these are related to farm characteristics and the identified behavioral factors. The models in which both intended behavior and farmer characteristics are included give a significantly better fit over models in which only household characteristics are included. These regression results suggest that the socioeconomic environment affects efficiency levels both directly and indirectly, through changes in goals and attitudes. Copyright (c) 2010 International Association of Agricultural Economists.

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We conduct a structured search of the academic literature that assesses the impact of development interventions that aim to build and strengthen local-level institutions to facilitate Inclusive Green Growth. Inclusive Green Growth extends the standard growth perspective to include welfare enhancements both the poor ('inclusive') and for future ('green') generations. We restrict our search to studies in the domain of agriculture and poverty alleviation in the developing world. We access ten online databases and various working paper series and focus on summarising evidence from quantitative studies that use rigorous evaluation methods. Together, this yields 158 studies. We then retain 66 studies that contain a credible counterfactual. We visualize the interventions and outcomes in an Evidence Gap Map, highlighting both the available evidence and remaining knowledge gaps. Most studies suggest that strengthening local institutions can improve the delivery and targeting of public services and overall satisfaction with local governance. There are however, clear limitations and knowledge gaps highlighting priorities for future work. Few studies assess impacts on final outcomes such as household income or agricultural productivity and no studies assess inclusive and green outcomes jointly. We discuss the key benefits of a structured literature search and Evidence Gap Map for policy-makers and development practitioners and illustrate how it serves as a knowledge repository and identifies where evidence is lacking, thus setting the agenda for future work.