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In sub-Saharan Africa there is increasing focus on identifying women's trait preferences within crop breeding to enable gender-responsive product development. In the case of maize, breeding programs are ready to incorporate specific traits to increase gender-responsiveness but lack guidance on what these specific traits might be. We propose an inductive approach to determine a pathway towards increasing gender-responsiveness within maize breeding. A survey of 306 farmers was conducted to determine gender differences in maize varieties used together with key agronomic practices. Variety was a significant predictor of the gender of the plot manager and of the household head in contrast to previous surveys conducted in researcher-led on-farm trials. On-farm trials are conducted using pre-defined agronomic management practices and preferences identified at harvest are likely to centre around yield. This study highlighted significant differences in several agronomic practices used by female plot managers and female household heads. Although further studies are required to understand preferences associated with varietal choice, our results suggest that current researcher-led on-farm trials may not identify gender-specific trait preferences driving varietal choice. Furthermore, a trait-specific approach is not the only avenue towards increasing gender-responsiveness in maize breeding in southern Africa. The scope for increasing gender-intentionality in maize breeding could be expanded to incorporate selection environments more relevant to agronomic management practices used by female plot managers and households at advanced stages of the breeding pipeline. This approach could provide an immediate entry point to increase gender-intentional maize breeding in southern Africa. [ABSTRACT FROM AUTHOR]

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 ≈ 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 mlr (Machine Learning in R) 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. [ABSTRACT FROM AUTHOR]