Rakotoson Tovohery, Senthilkumar Kalimuthu, Johnson Jean-Martial, Ibrahim Ali, Kihara Job, Sila Andrew, Saito Kazuki
Laboratoire des RadioIsotopes (LRI), Université d'Antananarivo, BP 3383, Route d'Andraisoro, 101, Antananarivo, Madagascar.
Africa Rice Center (AfricaRice), P.O.Box 1690 Ampandrianomby, Antananarivo, Madagascar.
Field Crops Res. 2023 Aug 1;299:108987. doi: 10.1016/j.fcr.2023.108987.
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.
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).
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.
All 10 nutrients were reasonably predicted [Nakagawa's 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 ) 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.
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.
Further investigation of other factors such as the timing of fertilizer applications, rice variety, occurrence of drought periods, and atmospheric CO concentration is warranted for improved prediction accuracy of nutrient concentrations.
对水稻籽粒中的养分浓度进行量化，对于评估养分吸收、利用效率和平衡，以制定肥料推荐指南至关重要。在撒哈拉以南非洲（SSA），农民通常无法使用实验室，因此需要在不依赖植物实验室分析的情况下准确估算养分浓度。
目标是：1）研究是否可以使用农业生态区（AEZ）、生产系统、土壤特性和矿物肥料施用量（氮、磷和钾）作为预测变量来估算水稻籽粒中的大量元素（氮、磷、钾、钙、镁、硫）和微量元素（铁、锰、硼、铜）的浓度；2）确定与基于平均值的养分吸收量（SSA中的平均养分浓度×籽粒产量）相比，由上述变量的最佳拟合模型估算的养分吸收量是否能更好地预测实际养分吸收量（预测养分浓度×籽粒产量）。
使用来自SSA的4个农业生态区（干旱/半干旱、湿润、亚湿润和高地）的20个国家的998个农民田地的横截面数据，以及3种不同的生产系统：灌溉低地、雨养低地和雨养高地，通过线性混合效应回归模型，检验上述因素中一组预测变量能否估算养分浓度的假设。
所有10种养分均得到合理预测【中川系数范围为0.27（钙）至0.79（硼），建模效率范围为0.178（钙）至0.584（硼）】。然而，只有钾和硼浓度的估算结果令人满意，建模效率高于0.5。在我们的最佳预测模型中，国家变量对这些养分浓度变化的贡献大于农业生态区和生产系统。实际养分吸收量与基于模型估算的吸收量之间的正相关关系（相关系数差异高达0.18）大于实际养分吸收量与基于平均值的吸收量之间的关系。尽管如此，在所有研究的养分中，只有硼吸收量的估算有显著改善。
我们的研究结果表明，除了硼具有较高的模型效率因子且其吸收量相对于基于平均值的吸收量有所改善外，通过使用SSA区域尺度上每种养分的平均浓度，就可以简单地获得水稻籽粒中大量元素和微量元素吸收量的估算值。
为了提高养分浓度预测的准确性，有必要进一步研究其他因素，如施肥时间节点、水稻品种、干旱期的发生情况以及大气二氧化碳浓度。
