Dryland cotton farmers need to carefully manage the available water content (AWC) in the root-zone. To reduce cost of mapping, a digital soil mapping (DSM) approach might be useful. The purpose of this study was seeing if we could create DSM of the AWC at the field scale. In the first instance, digital data in the form of proximal sensed electromagnetic (i.e EM) and gamma-ray (γ-ray) spectrometry data were collected along with elevation. Using a grid-based sampling design, 52 soil sample locations were selected and sampled; topsoil (0–0.3 m), subsurface (0.3–0.6 m) and subsoil (0.6–0.9 m). In the laboratory, the field capacity (FC) and permanent wilting point (PWP) were determined using a pressure plate apparatus. The digital and soil (i.e. FC and PWP) data were then calibrated with multiple linear regression (MLR) and stepwise-MLR models compared for each depth (e.g. adjusted coefficient of determination [adj-R²]). The stepwise-MLR were shown to be superior. Subsequently, the DSM of FC and PWP were examined in terms of prediction agreement (Lin’s concordance). Leave-one-out cross validation results showed substantial 1:1 agreement between measured and predicted FC (0.80) and PWP (0.79) at all depths. The final DSMs of FC and PWP, and by difference AWC, were indicative of the two main geomorphological/geological units, with large AWC (> 0.3 m³ m⁻³) associated with the clay rich Vertosols and small AWC (< 0.15 m³ m⁻³) the loamy soil of the Pilliga Sandstone. Owing to the short scale variation and the fact that the local alluvium and Pilliga Sandstone were close together and the fact that the digital data varied in different ways, larger confidence interval (CI) values were evident where these areas were juxtaposed. Nevertheless, the final DSM allows the farmer to better manage AWC.

旱地棉花种植者需要仔细管理根区的可用水分含量（AWC）。为了降低测绘成本，数字土壤测绘（DSM）方法可能会有用。这项研究的目的在于确定是否可以在现场规模上创建AWC的DSM。首先，将以近端感应电磁（即EM）和伽马射线（γ射线）光谱数据的形式收集数字数据以及海拔高度。使用基于网格的采样设计，选择并采样了52个土壤样本位置；表土（0–0.3 m），地下（0.3–0.6 m）和地下（0.6–0.9 m）。在实验室中，使用压板设备确定田间持水量（FC）和永久枯萎点（PWP）。数字和土壤（即² ]）。逐步MLR显示出优越性。随后，FC和PWP的DSM根据预测一致性（Lin的一致性）进行了检查。一劳永逸的交叉验证结果表明，在所有深度处，测得的和预测的FC（0.80）与PWP（0.79）之间的比例基本为1：1。FC和PWP的最终DSM以及AWC的差异表明了两个主要的地貌/地质单元，其中较大的AWC（> 0.3 m ³ m ^-3）与富含粘土的Vertosols和较小的AWC（<0.15 m ³ m ^-3）皮利加砂岩的壤土。由于规模变化小，并且局部冲积层和Pilliga砂岩靠近在一起，并且数字数据以不同的方式变化，因此在这些区域并置的地方，较大的置信区间（CI）值很明显。尽管如此，最终的DSM允许农民更好地管理AWC。