Funding information Deutsche Forschungsgemeinschaft, Grant/Award Numbers: LU 583/19-1, VO 1509/7-1 Abstract The robustness of soil organic carbon (SOC) and total nitrogen (TN) content prediction accuracy by visible near-infrared spectroscopy (visNIRS) and mid-infrared spectroscopy (MIRS) models after a change in the quantity or quality of crop residues requires investigation. Arable soils (0–20 cm) from 20 locations across Germany were collected, and 0, 2, 4, or 8 g C kg soil of wheat straw (C/N ratio, 54) or clover (C/N ratio, 13) were added. Before and after a 56-d incubation, dried and ground samples were measured for SOC and TN content and by visNIRS and MIRS. The complete dataset (n = 280) was subdivided into calibration and validation datasets to test the robustness of partial least squares regression models to changes in crop residue quantity and quality in soil. Noise-reducing data pretreatments included region selection, moving averages, resampling every second data point, and the SavitzkyGolay algorithm. The MIRS estimates for SOC (7.4–33 g kg) had lower root mean squared error of validation (RMSEV = 0.9–2.9 g kg ) compared with visNIRS (RMSEV = 1.6–7.1 g kg ). Total N estimates (0.7–2.8 g kg) were more comparable for MIRS (RMSEV = 0.1–0.3 g kg ) and visNIRS (RMSEV = 0.1–1.0 g kg ). Loadings of partial least squares regression components suggested the predictive mechanisms for SOC and TN were more similar for visNIRS than for MIRS. Differing crop residue quantity or quality in calibration versus validation resulted in biased SOC and TN estimates by visNIRS and MIRS models. However, calibration with a global residue model containing all soils and crop residue quantities and qualities lowered RMSEV for SOC and TN prediction with visNIRS and MIRS, demonstrating the usefulness of this approach.

中文翻译：

---

可见/近红外和中红外光谱模型对土壤中作物残留物数量和质量变化的稳健性

资金信息 Deutsche Forschungsgemeinschaft，资助/奖项编号：LU 583/19-1，VO 1509/7-1 摘要 可见近红外光谱 (visNIRS) 对土壤有机碳 (SOC) 和总氮 (TN) 含量预测准确性的稳健性) 和中红外光谱 (MIRS) 模型在作物残留物的数量或质量发生变化后需要调查。收集了德国 20 个地点的耕地土壤（0-20 厘米），以及 0、2、4 或 8 g C kg 小麦秸秆（C/N 比，54）或三叶草（C/N 比，13）的土壤添加。在培养 56 天之前和之后，通过 visNIRS 和 MIRS 测量干燥和研磨样品的 SOC 和 TN 含量。完整的数据集 (n = 280) 被细分为校准和验证数据集，以测试偏最小二乘回归模型对土壤中作物残留量和质量变化的稳健性。降噪数据预处理包括区域选择、移动平均、每隔一个数据点重新采样和 SavitzkyGolay 算法。与 visNIRS (RMSEV = 1.6-7.1 g kg ) 相比，MIRS 对 SOC (7.4-33 g kg) 的估计具有更低的验证均方根误差 (RMSEV = 0.9-2.9 g kg )。MIRS（RMSEV = 0.1-0.3 g kg ）和visNIRS（RMSEV = 0.1-1.0 g kg ）的总氮估计值（0.7-2.8 g kg ）更具可比性。偏最小二乘回归分量的加载表明 visNIRS 的 SOC 和 TN 的预测机制比 MIRS 更相似。校准与验证中不同的作物残留量或质量导致 visNIRS 和 MIRS 模型对 SOC 和 TN 的估计有偏差。然而，使用包含所有土壤和作物残留量和质量的全球残留模型校准降低了使用 visNIRS 和 MIRS 进行 SOC 和 TN 预测的 RMSEV，证明了这种方法的有用性。