Knowledge of soil physical and chemical properties is vital to the optimal growing performance of agricultural crops, including plantation forest trees. Near infrared (NIR) spectroscopy has been shown to be a tool that enables rapid and low-cost assessment of soils, however its use in forest plantations has been slow to develop. This study shows the development of calibrations for total organic carbon, total nitrogen and soil pH using a handheld NIR spectrometer for soils at three sites in Sabah, Malaysia. Soil samples were collected, dried, milled and scanned after which they were analysed using standard chemical methods to obtain total organic carbon (TOC) and total nitrogen (TN). Partial least squares regression was used to develop calibrations between reference data and NIR spectra and validated using an independent sample set. The calibration of soil pH is made using a subset of samples across A- and B-horizons for samples from two of the three sites. The most effective spectral pre-treatment was the standard normal variate for TOC and TN while the Savitzky-Golay first derivative was the best pre-treatment for predicting soil pH. Principal component analysis was performed on the raw NIR spectra of all samples to confirm that the samples from different sites were able to be used in a single regression analysis. Kennard-Stone selection was used to create calibration sets and validation sets from the combined spectra from all sites and both soil horizons. Calibrations were also developed independently on the A- and B-horizon samples, but there were insufficient sample numbers to utilize an independent validation set. The coefficients of determination for the validation set (r²_p) were 0.77 and 53 for TOC and TN respectively while the root mean square error of prediction (RMSEP) was 0.44 g 100 g⁻¹ for TOC and 0.051 g 100 g⁻¹ for TN. In addition, it showcases the application of these calibrations to provide spatial assessment of two differing micro-sites within a single Eucalyptus pellita progeny breeding trial. Combined with the potential to monitor foliar nutrients, the ability to obtain high spatial details of soil composition will assist tree plantation growers and also other agricultural producers, such as oil palm plantation managers, to better manage their soil and fertiliser regimes.

了解土壤的物理和化学性质对于包括种植林木在内的农作物的最佳生长性能至关重要。近红外（NIR）光谱已被证明是一种能够快速，低成本评估土壤的工具，但是其在人工林中的应用发展缓慢。这项研究显示了使用手持式近红外光谱仪对马来西亚沙巴州三个地点的土壤中的总有机碳，总氮和土壤pH进行校准的过程。收集，干燥，研磨和扫描土壤样品，然后使用标准化学方法对其进行分析，以获得总有机碳（TOC）和总氮（TN）。偏最小二乘回归用于建立参考数据和NIR光谱之间的校准，并使用独立的样本集进行验证。使用来自三个地点中两个地点的跨A和B地平线的样本子集对土壤pH进行校准。最有效的光谱预处理是TOC和TN的标准正态变量，而Savitzky-Golay一阶导数是预测土壤pH值的最佳预处理。对所有样品的原始NIR光谱进行主成分分析，以确认来自不同位置的样品能够用于一次回归分析。Kennard-Stone选择用于从所有站点和两个土壤层的组合光谱中创建校准集和验证集。还针对A和B地平线样本独立开发了校准，但是样本数量不足以利用独立的验证集。TOC和TN的² _p）分别为0.77和53，而TOC的预测均方根误差（RMSEP）为0.44 g 100 g ^-1和TN为0.051 g 100 g ^-1。此外，它展示了这些校准方法的应用，以在单个桉木后代繁殖试验中提供两个不同微位点的空间评估。结合监测叶面养分的潜力，获得土壤成分的高空间细节的能力将有助于树木种植者和其他农业生产者，例如油棕种植者，更好地管理其土壤和肥料状况。