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

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 R ² 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 CO ₂ concentration is warranted for improved prediction accuracy of nutrient concentrations.

Highlights

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  Concentrations of 10 nutrients in rice grain in sub-Sahara Africa were estimated using LMER model.

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  Predictors used were agro-ecological zone, production system, fertilizer rate, and soil properties.

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  10 nutrients were reasonably estimated with Nakagawa’s R ² ranging from 0.27 (Ca) to 0.79 (B).

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  The estimation of K and B concentrations was satisfactory with a modeling efficiency > 0.5.

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  Use of the above models did not improve the estimation of nutrient uptake except for B.