Given the high costs of soil sampling, low and extra-low sampling densities are still being used. Low-density soil sampling usually does not allow the computation of experimental variograms reliable enough to fit models and perform interpolation. In the absence of geostatistical tools, deterministic methods such as inverse distance weighting (IDW) are recommended but they are susceptible to the “bull’s eye” effect, which creates non-smooth surfaces. This study aims to develop and assess interpolation methods or approaches to produce soil test maps that are robust and maximize the information value contained in sparse soil sampling data. Eleven interpolation procedures, including traditional methods, a newly proposed methodology, and a kriging-based approach, were evaluated using grid soil samples from four fields located in Central Alberta, Canada. In addition to the original 0.4 ha⋅sample⁻¹ sampling scheme, two sampling design densities of 0.8 and 3.5 ha⋅sample⁻¹ were considered. Among the many outcomes of this study, it was found that the field average never emerged as the basis for the best approach. Also, none of the evaluated interpolation procedures appeared to be the best across all fields, soil properties, and sampling densities. In terms of robustness, the proposed kriging-based approach, in which the nugget effect estimate is set to the value of the semi-variance at the smallest sampling distance, and the sill estimate to the sample variance, and the IDW with the power parameter value of 1.0 provided the best approaches as they rarely yielded errors worse than those obtained with the field average.

鉴于土壤采样成本高昂，低采样密度和超低采样密度仍在使用。低密度土壤采样通常不允许计算足够可靠的实验变异函数来拟合模型和执行插值。在缺乏地质统计工具的情况下，建议使用反距离加权 (IDW) 等确定性方法，但它们容易受到“牛眼”效应的影响，从而产生不光滑的表面。本研究旨在开发和评估插值方法或方法来生成稳健的土壤测试图，并最大化稀疏土壤采样数据中包含的信息价值。使用来自加拿大艾伯塔省中部四个田地的网格土壤样本评估了 11 种插值程序，包括传统方法、新提出的方法和基于克里格的方法。除了原来的0.4 ha⋅sample ⁻¹采样方案外，还考虑了0.8 和3.5 ha⋅sample ⁻¹两种采样设计密度。在这项研究的众多结果中，我们发现现场平均值从未成为最佳方法的基础。此外，所评估的插值程序似乎在所有田地、土壤特性和采样密度上都不是最好的。在鲁棒性方面，提出了基于克里金法的方法，其中块金效应估计设置为最小采样距离处的半方差值，基台估计设置为样本方差，IDW具有幂参数值 1.0 提供了最佳方法，因为它们很少产生比使用现场平均值获得的错误更严重的错误。