Soil cation exchange capacity (CEC) and pH affect the condition of soil. To improve soil capability in sugarcane growing areas, Sugar Research Australia introduced the Six-Easy-Steps Nutrient Guidelines based on CEC and pH of topsoil (0–0.3 m). A three-dimensional digital soil mapping (DSM) framework has been used to predict CEC and pH by fitting equal-area splines to four depth intervals (i.e., topsoil, subsurface [0.3–0.6 m], shallow [0.6–0.9 m], and deep subsoil [0.9–1.2 m]) to resample soil data at 0.01 m increments. A single quantile regression forest (QRF) was calibrated to model the relationship between spline-fitted soil data and individual digital data. These included proximal soil sensing (PSS) data such as electromagnetic (EM) induction and gamma-ray (γ-ray) spectrometry, remote sensing (RS) Sentinel-2 imagery, a light detection and ranging (LiDAR) based digital elevation model (DEM), and soil depth. Various data fusion methods and minimum calibration size have been evaluated, including concatenation and model averaging approaches, namely, simple averaging (SA), Bates-Granger averaging (BGA), Granger-Ramanathan averaging (GRA), and bias-corrected eigenvector averaging (BC-EA). In all cases, an independent validation was used to assess prediction agreement (Lin's concordance correlation coefficient—LCCC) and accuracy (ratio of performance to deviation—RPD). For CEC, γ-ray (LCCC = 0.82) was the best, with EM (0.78) and Sentinel-2 (0.77) producing similar agreement, whereas DEM (0.64) had worst performance. For pH, EM, γ-ray, and Sentinel-2 were similar (0.69, 0.73, and 0.77, respectively), and DEM poor (0.48). Optimum results were achieved when PSS, Sentinel-2, and DEM were fused using GRA; CEC agreement (0.88) and accuracy (RPD = 2.14) were strong, while for pH, concatenation had good agreement (0.79) and accuracy (1.59). Neither agreement nor accuracy varied among sample size, with a minimum of 30 (CEC) and 80 (pH) sites necessary (0.4 and 1.1 sampling sites ha⁻¹, respectively). The final DSM for topsoil CEC and pH, were useful for lime application; the northern fields required 2.5 t ha⁻¹ of lime, whereas the southern fields required variable rates (4 and 5 t ha⁻¹).

土壤阳离子交换容量（CEC）和pH值影响土壤状况。为了提高甘蔗种植区的土壤能力，澳大利亚糖业研究中心推出了六步简单的营养指南基于 CEC 和表土的 pH 值（0-0.3 m）。通过将等面积样条拟合到四个深度间隔（即表土、地下 [0.3-0.6 m]、浅层 [0.6-0.9 m]、和深层底土 [0.9–1.2 m]）以 0.01 m 的增量重新采样土壤数据。校准单个分位数回归森林 (QRF) 以模拟样条拟合土壤数据和单个数字数据之间的关系。其中包括近端土壤传感 (PSS) 数据，例如电磁 (EM) 感应和伽马射线 (γ 射线) 光谱法、遥感 (RS) Sentinel-2 图像、基于光探测和测距 (LiDAR) 的数字高程模型 ( DEM) 和土壤深度。已经评估了各种数据融合方法和最小校准大小，包括连接和模型平均方法，即简单平均（SA）、Bates-Granger 平均（BGA）、Granger-Ramanathan 平均（GRA）和偏差校正特征向量平均（BC-EA）。在所有情况下，使用独立验证来评估预测一致性（Lin 的一致性相关系数 - LCCC）和准确性（性能与偏差的比率 - RPD）。对于 CEC，γ 射线 (LCCC = 0.82) 是最好的，EM (0.78) 和 Sentinel-2 (0.77) 产生相似的一致性，而 DEM (0.64) 的性能最差。对于 pH 值，EM、γ 射线和 Sentinel-2 相似（分别为 0.69、0.73 和 0.77），而 DEM 较差（0.48）。使用 GRA 融合 PSS、Sentinel-2 和 DEM 时获得了最佳结果；CEC 一致性 (0.88) 和准确度 (RPD = 2.14) 很强，而对于 pH 值，串联具有良好的一致性 (0.79) 和准确度 (1.59)。^-1，分别）。表土 CEC 和 pH 值的最终 DSM 可用于石灰应用；北部地区需要 2.5 t ha ^-1的石灰，而南部地区则需要可变比率（4 和 5 t ha ^-1）。