Exploring the potential of mapped soil properties, rhizobium inoculation, and phosphorus supplementation for predicting soybean yield in the savanna areas of Nigeria.

Rapid and accurate soybean yield prediction at an on-farm scale is important for ensuring sustainable yield increases and contributing to food security maintenance in Nigeria. We used multiple approaches to assess the benefits of rhizobium (Rh) inoculation and phosphorus (P) fertilization on soybean yield increase and profitability from large-scale conducted trials in the savanna areas of Nigeria [i.e., the Sudan Savanna (SS), Northern Guinea Savanna (NGS), and Southern Guinea Savanna (SGS)]. Soybean yield results from the established trials managed by farmers with four treatments (i.e., the control without inoculation and P fertilizer, Rh inoculation, P fertilizer, and Rh + P combination treatments) were predicted using mapped soil properties and weather variables in ensemble machine-learning techniques, specifically the conditional inference regression random forest (RF) model. Using the IMPACT model, scenario analyses were employed to simulate long-term adoption impacts on national soybean trade and currency. Our study found that yields of the Rh + P combination were consistently higher than the control in the three agroecological zones. Average yield increases were 128%, 111%, and 162% higher in the Rh + P combination compared to the control treatment in the SS, NGS, and SGS agroecological zones, respectively. The NGS agroecological zone showed a higher yield than SS and SGS. The highest training coefficient of determination (R2 = 0.75) for yield prediction was from the NGS dataset, and the lowest coefficient (R2 = 0.46) was from the SS samples. The results from the IMPACT model showed a reduction of 10% and 22% for the low (35% adoption scenario) and high (75% adoption scenario) soybean imports from 2029 in Nigeria, respectively. A significant reduction in soybean imports is feasible if the Rh + P inputs are large-scaled implemented at the on-farm field and massively adopted by farmers in Nigeria.

African soil properties and nutrients mapped at 30 m spatial resolution using two-scale ensemble machine learning.

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 ([Formula: see text]) 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.