Successful modelling of visible and near-infrared (vis-NIR) spectra for on-line prediction of key soil quality indicators is crucial for accurate variable rate applications of farm input resources. The aim of this paper is to optimize modelling of on-line collected spectra for the prediction of soil pH, organic carbon (OC), extractable phosphorous (P) and potassium (K) by means of spiking, combined with clustering and/or extra-weighting. A mobile fiber-type vis-NIR spectrophotometer (CompactSpec from Tec5 Technology, Germany), with spectral range of 305−1700 nm was calibrated using 100 samples collected from five different fields, which were merged with 28 samples collected from a target field. The resulting dataset was subjected to spectral pretreatments followed by k-means clustering and 95 % confidence ellipsoid, resulting in three optimal datasets. Partial least squares regression (PLSR) analyses were carried out on the calibration set (75 % of samples) for four calibration strategies: (i) non-clustered and non-weighted (NCNW), (ii) clustered and non-weighted (CNW), (iii) non-clustered but extra-weighted (NCW), and (iv) clustered and extra-weighted (CW). Results showed that the quality of on-line predictions was the best after clustering combined with extra-weighting. Modelling based with CW significantly improved model prediction accuracy to be very good for pH (ratio of prediction deviation (RPD) = 2.32) and P (RPD = 2.05), and good for OC (RPD = 1.90) and K (RPD = 1.80), whereas results of NCNW (standard calibration approach) were the poorest to be fair for P (RPD = 1.74) and OC (RPD = 1.50), and poor for K (RPD = 1.1) and pH (RPD = 1.39). It can be concluded that optimal sample selection with k-mean clustering when combined with extra-weighting will result in accurate PLSR calibration models for on-line prediction of soil pH, P, OC and K using a multi-field diverse dataset.

对于主要土壤质量指标的在线预测，成功建立可见和近红外（vis-NIR）光谱模型对于准确输入农场输入资源的可变速率至关重要。本文的目的是通过加标，结合聚类和/或额外的方法来优化在线收集光谱的模型，以预测土壤的pH，有机碳（OC），可提取的磷（P）和钾（K）。 -加权。使用从五个不同场采集的100个样品校准了光谱范围为305-1700 nm的移动纤维型vis-NIR分光光度计（德国Tec5 Technology的CompactSpec），将其与从目标场采集的28个样品合并。对所得数据集进行光谱预处理，然后进行k均值聚类和95％置信度椭球，得出三个最佳数据集。在四种校准策略的校准集上进行了偏最小二乘回归（PLSR）分析：（i）非聚类和非加权（NCNW），（ii）聚类和非加权（CNW） ），（iii）非群集但超重（NCW），以及（iv）群集和超重（CW）。结果表明，在结合了额外加权之后，在线预测的质量是最好的。基于CW的建模显着提高了模型的预测精度，对于pH值（预测偏差比（RPD）= 2.32）和P（RPD = 2.05）非常好，对于OC（RPD = 1.90）和K（RPD = 1.80）很好。 ，而对于P（RPD = 1.74）和OC（RPD = 1.50）而言，NCNW（标准校准方法）的结果最差，不能令人满意，而K（RPD = 1.1）和pH（RPD = 1.39）较差。可以得出结论：k-均值聚类与额外权重结合使用时，将得出准确的PLSR校准模型，以便使用多字段多样的数据集在线预测土壤的pH，P，OC和K。