Soil acidity remediation in sub-Saharan Africa requires targeted investments.

Acid soils are widespread across sub-Saharan Africa. Agricultural lime can be used to alleviate production constraints associated with soil acidity, but lime is not widely available in the region, and it is unclear if applying it would be profitable. Using lime requirement models and crop yield responses to soil acidity modelled as plateau-linear decay functions, we estimated the profitability of acid soil remediation through liming.

Statistical approaches are inadequate for accurate estimation of yield potential and gaps at regional level.

Accurate spatial information on yield potential and gaps is key to determine crop production potential. Although statistical methods are widely used to estimate these parameters at regional to global levels, a rigorous evaluation of their performance is lacking. Here we compared outcomes derived from four published statistical approaches based on highest average farmer yields over time and space against those derived from a 'bottom-up' approach based on crop modelling and local weather and soil data for major rain-fed crops in the United States.

High-resolution global maps of yield potential with local relevance for targeted crop production improvement.

Identifying untapped opportunities for crop production improvement in current cropland is crucial to guide food availability interventions. Here we integrated an agronomically robust bottom-up approach with machine learning to generate global maps of yield potential of high resolution (ca. 1 km at the Equator) and accuracy for maize, wheat and rice.

Adopting yield-improving practices to meet maize demand in Sub-Saharan Africa without cropland expansion.

Maize demand in Sub-Saharan Africa is expected to increase 2.3 times during the next 30 years driven by demographic and dietary changes. Over the past two decades, the area cropped with maize has expanded by 17 million hectares in the region, with limited yield increase.

Filling the agronomic data gap through a minimum data collection approach.

Context: Agronomic data such as applied inputs, management practices, and crop yields are needed for assessing productivity, nutrient balances, resource use efficiency, as well as other aspects of environmental and economic performance of cropping systems. In many instances, however, these data are only available at a coarse level of aggregation or simply do not exist.

Objectives: Here we developed an approach that identifies sites for agronomic data collection for a given crop and country, seeking a balance between minimizing data collection efforts and proper representation of the main crop producing areas.

Estimating lime requirements for tropical soils: Model comparison and development.

Acid tropical soils may become more productive when treated with agricultural lime, but optimal lime rates have yet to be determined in many tropical regions. In these regions, lime rates can be estimated with lime requirement models based on widely available soil data. We reviewed seven of these models and introduced a new model (LiTAS).

The scale dependency of spatial crop species diversity and its relation to temporal diversity.

Increasing crop species diversity can enhance agricultural sustainability, but the scale dependency of the processes that shape diversity and of the effects of diversity on agroecosystems is insufficiently understood. We used 30 m spatial resolution crop classification data for the conterminous United States to analyze spatial and temporal crop species diversity and their relationship. We found that the US average temporal (crop rotation) diversity is 2.