Replication Data for: Application of infrared spectroscopy for estimation of concentrations of macro- and micronutrients in rice in sub-Saharan Africa

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.

植物养分浓度的测定，是评估作物养分携出量、养分利用效率、肥料推荐方案的核心信息，进而可为提升粮食安全、降低作物生产的环境足迹提供支撑。漫反射红外（IR）光谱法是一种高效、快速、低成本且低污染的分析工具，可替代传统实验室方法开展植物养分浓度的测定工作。然而，其用于预测水稻植株养分浓度的准确性尚不明晰。本研究旨在达成三个目标：① 明确可通过近红外（NIR，7498–4000 cm⁻¹）、中红外（MIR，4000–600 cm⁻¹）或二者联合（NIR-MIR，7498–600 cm⁻¹）光谱实现精准预测的大量元素与微量元素浓度；② 筛选出可同时精准分析水稻植株（秸秆与稻谷）养分浓度的最优光谱范围；③ 评估农业生态区与生产系统对秸秆及稻谷（未脱壳籽粒）样品养分浓度的影响。本研究采用偏最小二乘回归（PLSR）将二阶导数光谱与植物实验室参考数据进行拟合，以估算采集自撒哈拉以南非洲（SSA）20个国家1628个农户田块的收获期稻谷与水稻秸秆样品中的6种大量元素（N、P、K、Ca、Mg、S）及7种微量元素（Na、Fe、Mn、B、Cu、Mo、Zn）浓度。模型的预测性能通过决定系数（R²）、均方根误差（RMSE）、平均绝对误差（MAE）以及性能与四分位距之比（RPIQ）进行评估。针对N、P、K、Ca、Mg、Mn及Cu这7种养分浓度，构建得到了性能优良的预测模型（0.75 < R² ≤ 0.95）；硫（S）、铁（Fe）与硼（B）的预测结果则较为理想（0.62 ≤ R² ≤ 0.75）。近红外、中红外以及联合NIR-MIR漫反射红外光谱法，分别可对这10种可精准预测养分中的3种、1种和6种实现最优的预测效果。秸秆与稻谷中的所有养分浓度均呈现中等至高度变异（变异系数CV=15%~111%）。农业生态区与生产系统对水稻秸秆及稻谷中的绝大多数养分浓度均具有显著影响。灌溉低地（IL）田块水稻的N、P、K浓度均高于雨养低地（RL）与旱地（RU）田块。在所研究的田块中，分别有2%、16%和16%的秸秆样品存在N、P、K养分缺乏的情况；钾缺乏现象在三种生产系统中均有出现，而磷缺乏则主要集中于雨养旱地系统。总体而言，联合NIR-MIR漫反射红外光谱法具备良好的应用潜力，可作为测定水稻植株大量元素浓度的替代方法。后续需针对本研究测定的土壤属性、水稻生产力与养分浓度数据之间的关联开展深入研究，以期为撒哈拉以南非洲地区的精准土壤管理与肥料推荐提供基础依据。