Phosphorus (P) fertilisation recommendations rely primarily on soil content of plant available P (P avl ) that vary spatially within farm fields. Spatially optimized P fertilisation for precision farming requires reliable, rapid and non-invasive P avl determination. This laboratory study aimed to test and to compare visible-near infrared (Vis–NIR) and mid-infrared (MIR) spectroscopy for P avl prediction with emphasis on future application in precision agriculture. After calibration with the conventional calcium acetate lactate (CAL) extraction method, limitations of Vis–NIRS and MIRS to predict P avl were evaluated in loess topsoil samples from different fields at six localities. Overall calibration with 477 (Vis–NIRS) and 586 (MIRS) samples yielded satisfactory model performance (R 2 0.70 and 0.72; RPD 1.8 and 1.9, respectively). Local Vis–NIRS models yielded better results with R 2 up to 0.93 and RPD up to 3.8. For MIRS, results were comparable. However, an overall model to predict P avl on independent test data partly failed. Sampling date, pre-crop harvest residues and fertilising regime affected model transferability. Varying transferability could partly be explained after deriving the cellulose absorption index from the Vis–NIR spectra. In 62 (Vis–NIRS) and 67% (MIRS) of all samples, prediction matched the correct P avl content class. Rapid discrimination between high, optimal and low P classes could be carried out on many samples from single fields thus marking an improvement over the common practice. However, P avl determination by means of IR spectroscopy is not yet satisfactory for determination of precision fertilizer dosage. For introduction into agricultural practice, a standardized sampling protocol is recommended to help achieve reliable spectroscopic P avl prediction.

中文翻译：

---

使用红外光谱预测植物可用磷，并考虑未来精准农业中的移动传感应用

磷 (P) 施肥建议主要依赖于在农田内随空间变化的植物有效磷 (P avl ) 的土壤含量。用于精准农业的空间优化磷肥需要可靠、快速和非侵入性的磷 avl 测定。本实验室研究旨在测试和比较用于 P avl 预测的可见-近红外 (Vis-NIR) 和中红外 (MIR) 光谱，重点是未来在精准农业中的应用。在使用传统的醋酸乳酸钙 (CAL) 提取方法校准后，评估了 Vis-NIRS 和 MIRS 对来自六个地点不同田地的黄土表土样品预测 P avl 的局限性。使用 477 个 (Vis-NIRS) 和 586 个 (MIRS) 样本进行的总体校准产生了令人满意的模型性能（R 2 0.70 和 0.72；RPD 分别为 1.8 和 1.9）。局部 Vis-NIRS 模型产生了更好的结果，R 2 高达 0.93，RPD 高达 3.8。对于 MIRS，结果具有可比性。然而，在独立测试数据上预测 P avl 的整体模型部分失败。采样日期、作物前收获残留物和施肥制度影响模型的可移植性。从 Vis-NIR 光谱得出纤维素吸收指数后，可以部分解释不同的可转移性。在所有样本的 62 (Vis–NIRS) 和 67% (MIRS) 中，预测与正确的 P avl 内容类别匹配。可以对来自单个领域的许多样本进行高、最佳和低 P 类之间的快速区分，从而标志着对常见做法的改进。然而，通过红外光谱法测定 P avl 还不能满足精确肥料用量的测定。