ArticleField crops research2023
Estimating nutrient concentrations and uptake in rice grain in sub-Saharan Africa using linear mixed-effects regression.
Article in Field crops research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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 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 CO
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