Determination of the concentration of nutrients in the plant is key information for evaluating crop nutrient removal, nutrient use efficiency, fertilizer recommendations guidelines, and in turn for improving food security and reducing environmental footprints of crop production. Diffuse infrared (IR) reflectance spectroscopy is a powerful, rapid, cheap, and less pollutant analytical tool that could be substituted for traditional laboratory methods for the determination of the concentration of nutrients in plants. However, its accuracy for predicting the concentration of nutrients in rice plants is poorly known. This study aimed i) to determine macro- and micronutrients concentration that can be accurately predicted by near-infrared (NIR, 7498–4000 cm⁻¹), mid-infrared (MIR, 4000–600 cm⁻¹), or their combination (NIR-MIR, 7498–600 cm⁻¹) spectra, ii) to identify the most suitable spectral range with the best prediction potential for the simultaneous analysis of nutrients concentrations in rice plants (straw and paddy) and iii) to assess the influence of agro-ecological zone and production system on nutrients concentrations in straw and paddy (unhulled grains) samples. Second-derivative spectra were fitted against plant laboratory reference data using partial least-squares regression (PLSR) to estimate six macronutrients (N, P, K, Ca, Mg, and S) and seven micronutrients (Na, Fe, Mn, B, Cu, Mo, and Zn) concentration in paddy and rice straw samples collected at harvest from 1628 farmers’ fields in 20 sub-Saharan African (SSA) countries. The modeling prediction potential was assessed by coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and ratio of performance to interquartile distance (RPIQ). Good prediction models (0.75 < R² ≤ 0.95) were obtained for 7 nutrients concentrations consisting of N, P, K, Ca, Mg, Mn, and Cu. Satisfactory predictions (0.62 ≤ R² ≤ 0.75) were obtained for S, Fe, and B. NIR, MIR, and combined NIR-MIR diffuse reflectance spectroscopy demonstrated the best prediction potential for 3, 1, and 6 of these 10 well-predicted nutrients concentrations, respectively. All nutrients concentrations both in straw and paddy were moderate to highly variable (CV = 15–111%). Agro-ecological zone and production system had a significant impact on most nutrients concentrations both in rice straw and paddy. N, P, and K concentrations in rice in irrigated lowland (IL) fields were higher than in rainfed lowland (RL) and upland (RU). From the studied fields, 2%, 16%, and 16% of straw samples were deficient in N, P, and K, respectively. K deficiency occurred in all three production systems, whereas P deficiency mainly occurred in the rainfed upland systems. Overall, the combined NIR-MIR diffuse reflectance spectroscopy has a good potential to be applied as an alternative method in the determination of macronutrient concentrations in rice plants. Further investigation on the relationships between soil attributes, rice productivity, and nutrients concentration data determined in this study is needed for providing fundamental information for site-specific soil management and fertilizer recommendations in SSA.

确定植物中的养分浓度是评估作物养分去除、养分利用效率、肥料推荐指南的关键信息，进而用于改善粮食安全和减少作物生产的环境足迹。漫红外 (IR) 反射光谱是一种强大、快速、廉价且污染物较少的分析工具，可以替代传统的实验室方法来确定植物中的营养物质浓度。然而，其预测水稻植株中养分浓度的准确性却鲜为人知。本研究旨在 i) 确定可通过近红外（NIR，7498-4000 cm ^-1）、中红外（MIR，4000-600 cm ^-1）准确预测的常量和微量营养素浓度) 或它们的组合 (NIR-MIR, 7498–600 cm ^-1) 光谱，ii) 确定具有最佳预测潜力的最合适光谱范围，用于同时分析水稻（稻草和稻谷）中的养分浓度，以及 iii) 评估农业生态区和生产系统对养分浓度的影响在稻草和稻谷（未脱壳谷物）样品中。使用偏最小二乘回归 (PLSR) 将二阶导数光谱与植物实验室参考数据进行拟合，以估计六种常量营养素（N、P、K、Ca、Mg 和 S）和七种微量营养素（Na、Fe、Mn、B、收获时从 20 个撒哈拉以南非洲 (SSA) 国家的 1628 个农田收集的稻草和稻草样品中的 Cu、Mo 和 Zn) 浓度。建模预测潜力通过决定系数 (R ²)、均方根误差 (RMSE)、平均绝对误差 (MAE) 和性能与四分位距的比值 (RPIQ)。对于 7 种营养物质浓度，包括 N、P、K、Ca、Mg、Mn 和 Cu，获得了良好的预测模型 (0.75 < R ² ≤ 0.95)。令人满意的预测 (0.62 ≤ R ²≤ 0.75) 获得了 S、Fe 和 B。NIR、MIR 和组合 NIR-MIR 漫反射光谱分别证明了这 10 种良好预测的营养素浓度中的 3、1 和 6 种的最佳预测潜力。秸秆和稻谷中的所有营养物质浓度均中等至高度可变（CV = 15-111%）。农业生态区和生产系统对稻草和稻谷中的大多数养分浓度都有显着影响。灌溉低地 (IL) 稻田中水稻的 N、P 和 K 浓度高于雨养低地 (RL) 和高地 (RU)。在所研究的田地中，分别有 2%、16% 和 16% 的秸秆样品缺乏 N、P 和 K。三个生产系统都缺钾，而缺磷主要发生在雨养旱地系统。全面的，组合 NIR-MIR 漫反射光谱具有良好的潜力，可用作测定水稻植物中常量营养素浓度的替代方法。需要进一步研究土壤属性、水稻生产力和本研究中确定的养分浓度数据之间的关系，以便为 SSA 中特定地点的土壤管理和施肥建议提供基本信息。