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. Following this trend could potentially result in further maize cropland expansion and the need for imports to satisfy domestic demand. Here, we use data collected from 14,773 smallholder fields in the region to identify agronomic practices that can improve farm yield gains. We find that agronomic practices related to cultivar selection, and nutrient, pest, and crop management can double on-farm yields and provide an additional 82 million tons of maize within current cropped area. Research and development investments should be oriented towards agricultural practices with proven capacity to raise maize yields in the region.

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. Methods: The developed approach followed a stratified sampling method based on a spatial framework that delineates major climate zones and crop area distribution maps, which guides selection of sampling areas (SA) until half of the national harvested area is covered. We provided proof of concept about the robustness of the approach using three rich databases including data on fertilizer application rates for maize, wheat, and soybean in Argentina, soybean in the USA, and maize in Kenya, which were collected via local experts (Argentina) and field surveys (USA and Kenya). For validation purposes, fertilizer rates per crop and nutrient derived at (sub-) national level following our approach were compared against those derived using all data collected from the whole country. Results: Application of the approach in Argentina, USA, and Kenya resulted in selection of 12, 28, and 10 SAs, respectively. For each SA, three experts or 20 fields were sufficient to give a robust estimate of average fertilizer rates applied by farmers. Average rates at national level derived from our approach compared well with those derived using the whole database ( ± 10 kg N, ± 2 kg P, ± 1 kg S, and ± 5 kg K per ha) requiring less than one third of the observations. Conclusions: The developed minimum crop data collection approach can fill the agronomic data gaps in a cost-effective way for major crop systems both in large- and small-scale systems. Significance: The proposed approach is generic enough to be applied to any crop-country combination to guide collection of key agricultural data at national and subnational levels with modest investment especially for countries that do not currently collect data.