Inorganic fertilizer use and its association with rice yield gaps in sub-Saharan Africa.

Where and which countries should receive higher priority for improving inorganic fertilizer use in rice fields in sub-Saharan Africa (SSA)? This study addressed this question by assessing the spatial variation in fertilizer use and its association with rice yield and yield gap in 24 SSA countries through a systematic literature review of peer-reviewed papers, theses, and grey literature published between 1995 and 2021. The results showed a large variation in N, P, and K fertilizer application rates and rice yield and an opportunity for narrowing the yield gap by increasing N and P rates, especially in irrigated rice systems. We identified clusters of sites/countries based on nutrient input and yield and suggested research and development strategies for improving yields and optimizing nutrient use efficiencies. Further research is essential to identify the factors causing low fertilizer use and the poor association between its use and yield in rainfed systems.

Estimating nutrient concentrations and uptake in rice grain in sub-Saharan Africa using linear mixed-effects regression.

Context or problem: Quantification of nutrient concentrations in rice grain is essential for evaluating nutrient uptake, use efficiency, and balance to develop fertilizer recommendation guidelines. Accurate estimation of nutrient concentrations without relying on plant laboratory analysis is needed in sub-Saharan Africa (SSA), where farmers do not generally have access to laboratories. Objective or research question: The objectives are to 1) examine if the concentrations of macro- (N, P, K, Ca, Mg, S) and micronutrients (Fe, Mn, B, Cu) in rice grain can be estimated using agro-ecological zones (AEZ), production systems, soil properties, and mineral fertilizer application (N, P, and K) rates as predictor variables, and 2) to identify if nutrient uptakes estimated by best-fitted models with above variables provide improved prediction of actual nutrient uptakes (predicted nutrient concentrations x grain yield) compared to average-based uptakes (average nutrient concentrations in SSA x grain yield). Methods: Cross-sectional data from 998 farmers' fields across 20 countries across 4 AEZs (arid/semi-arid, humid, sub-humid, and highlands) in SSA and 3 different production systems: irrigated lowland, rainfed lowland, and rainfed upland were used to test hypotheses of nutrient concentration being estimable with a set of predictor variables among above-cited factors using linear mixed-effects regression models. Results: All 10 nutrients were reasonably predicted [Nakagawa's R2 ranging from 0.27 (Ca) to 0.79 (B), and modeling efficiency ranging from 0.178 (Ca) to 0.584 (B)]. However, only the estimation of K and B concentrations was satisfactory with a modeling efficiency superior to 0.5. The country variable contributed more to the variation of concentrations of these nutrients than AEZ and production systems in our best predictive models. There were greater positive relationships (up to 0.18 of difference in correlation coefficient R) between actual nutrient uptakes and model estimation-based uptakes than those between actual nutrient uptakes and average-based uptakes. Nevertheless, only the estimation of B uptake had significant improvement among all nutrients investigated. Conclusions: Our findings suggest that with the exception of B associated with high model EF and an improved uptake over the average-based uptake, estimates of the macronutrient and micronutrient uptakes in rice grain can be obtained simply by using average concentrations of each nutrient at the regional scale for SSA. Implications: Further investigation of other factors such as the timing of fertilizer applications, rice variety, occurrence of drought periods, and atmospheric CO2 concentration is warranted for improved prediction accuracy of nutrient concentrations.

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.

Arbuscular mycorrhizal symbiosis elicits proteome responses opposite of P-starvation in SO4 grapevine rootstock upon root colonisation with two Glomus species.

Although plant biotisation with arbuscular mycorrhizal fungi (AMF) is a promising strategy for improving plant health, a better knowledge regarding the molecular mechanisms involved is required. In this context, we sought to analyse the root proteome of grapevine rootstock Selection Oppenheim 4 (SO4) upon colonisation with two AMF. As expected, AMF colonisation stimulates plant biomass. At the proteome level, changes in protein amounts due to AMF colonisation resulted in 39 differentially accumulated two-dimensional electrophoresis spots in AMF roots relative to control. Out of them, 25 were co-identified in SO4 roots upon colonisation by Glomus irregulare and Glomus mosseae supporting the existence of conserved plant responses to AM symbiosis in a woody perennial species. Among the 18 proteins whose amount was reduced in AMF-colonised roots were proteins involved in glycolysis, protein synthesis and fate, defence and cell rescue, ethylene biosynthesis and purine and pyrimidine salvage degradation. The six co-identified proteins whose amount was increased had functions in energy production, signalling, protein synthesis and fate including proteases. Altogether these data confirmed that a part of the accommodation program of AMF previously characterized in annual plants is maintained within roots of the SO4 rootstock cuttings. Nonetheless, particular responses also occurred involving proteins of carbon metabolism, development and root architecture, defence and cell rescue, anthocyanin biosynthesis and P remobilization, previously reported as induced upon P-starvation. This suggests the occurrence of P reprioritization upon AMF colonization in a woody perennial plant species with agronomical interest.