Due to high rate of nutrient removal by cotton plants, the productive cotton-growing soils of Australia are becoming depleted of exchangeable (exch.) cations. For long-term development, data on exch. calcium (Ca), magnesium (Mg), potassium (K) and sodium (Na) throughout the soil profile is required. However, traditional laboratory analysis is tedious. The visible-near-infrared (Vis-NIR) spectroscopy is an alternative; whereby, spectral libraries are built which couple soil data and Vis-NIR spectra using models. While various models have been used to predict exch. cations, their performance was seldom systematically compared. Moreover, most previous studies have focused on prediction of topsoil (0–0.3 m) exch. cations while the effects of depth on applicability of topsoil spectral libraries are rarely investigated. Our first aim was to determine which model (i.e. partial least squares regression (PLSR), Cubist, random forest (RF), or support vector machine regression (SVMR)) produces the best prediction of topsoil exch. Ca, Mg, K and Na. The second aim was to evaluate if the best topsoil model can be used to predict subsurface (0.3–0.6 m) and subsoil (0.9–1.2 m) exch. cations. The third aim was to explore the effect of spiking on the prediction in subsurface and subsoil using the topsoil spectral library. The fourth aim was to see if combining all depths to build a profile spectral library improved prediction. Based on independent validation, PLSR was superior for topsoil exch. cations prediction, while Cubist outperformed PLSR in some cases when spiking was applied, and the profile spectral library was considered. Topsoil PLSR could be applied to predict exch. Ca and Mg in the subsurface and subsoil, while spiking improved prediction. Moreover, a profile spectral library achieved equivalent results with when topsoil samples coupled with spiking were considered. We, therefore, recommended to predict exch. Ca and Mg throughout the profile using topsoil spectral library coupled with spiking approach.

由于棉花植株去除养分的速率很高，澳大利亚的棉花种植土壤正在消耗可交换（交换）阳离子。对于长期发展，exch上的数据。在整个土壤剖面中都需要钙（Ca），镁（Mg），钾（K）和钠（Na）。然而，传统的实验室分析是乏味的。可见-近红外（Vis-NIR）光谱是一种替代方法。因此，建立了光谱库，使用模型将土壤数据和Vis-NIR光谱耦合在一起。虽然已经使用各种模型来预测交换。阳离子，其性能很少进行系统比较。此外，大多数以前的研究都集中在表土（0–0.3 m）排泄量的预测上。尽管很少研究阳离子对深度对表土光谱库适用性的影响。我们的第一个目标是确定哪种模型（表述偏最小二乘回归（PLSR），立体主义，随机森林（RF）或支持向量机回归（SVMR））产生最佳的表层土壤排泄量预测。钙，镁，钾和钠。第二个目的是评估是否可以使用最佳的表土模型来预测地下（0.3–0.6 m）和地下（0.9–1.2 m）的排泄。阳离子。第三个目标是使用表层土波谱库探索峰值对地下和地下土层预测的影响。第四个目标是查看是否结合所有深度以建立轮廓谱库可以改善预测。基于独立验证，PLSR在表土交换方面表现优异。阳离子预测，而在应用加标的情况下，Cubist在某些情况下优于PLSR，并考虑了轮廓光谱库。表土PLSR可用于预测河床排涝。地下和下层土壤中的Ca和Mg增强了预测能力。此外，当考虑到表层土壤样品与尖峰相结合时，剖面谱库获得了等效的结果。因此，我们建议您预测预测。钙和镁在整个剖面中均采用表土光谱库和尖峰法相结合。