Abstract. Spatial soil databases can help model complex phenomena in which soils are decisive, for example, evaluating agricultural potential or estimating carbon storage capacity. The Soil Information System for Latin America and the Caribbean, SISLAC, is a regional initiative promoted by the FAO's South American Soil Partnership to contribute to the sustainable management of soil. SISLAC includes data coming from 49,084 soil profiles distributed unevenly across the continent, making it the region's largest soil database. However, some problems hinder its usages, such as the quality of the data and its high dimensionality. The objective of this research is twofold. First, to evaluate the quality of SISLAC and its data values and generate a new, improved version that meets the minimum quality requirements to be used by different interests or practical applications. Second, to demonstrate the potential of improved soil profile databases to generate more accurate information on soil properties, by conducting a case study to estimate the spatial variability of the percentage of soil organic carbon using 192 profiles in a 1473 km² region located in the department of Valle del Cauca, Colombia. The findings show that 15 percent of the existing soil profiles had an inaccurate description of the diagnostic horizons. Further correction of an 4.5 additional percent of existing inconsistencies improved overall data quality. The improved database consists of 41,691 profiles and is available for public use at https://doi.org/10.5281/zenodo.6540710 (Díaz-Guadarrama, S. & Guevara, M., 2022). The updated profiles were segmented using algorithms for quantitative pedology to estimate the spatial variability. We generated segments one centimeter thick along with each soil profile data, then the values of these segments were adjusted using a spline-type function to enhance vertical continuity and reliability. Vertical variability was estimated up to 150 cm in-depth, while ordinary kriging predicts horizontal variability at three depth intervals, 0 to 5, 5 to 15, and 15 to 30 cm, at 250 m-spatial resolution, following the standards of the GlobalSoilMap project. Finally, the leave-one-out cross-validation provides information for evaluating the kriging model performance, obtaining values for the RMSE index between 1.77 % and 1.79 % and the R² index greater than 0.5. The results show the usability of SISLAC database to generate spatial information on soil properties and suggest further efforts to collect a more significant amount of data to guide sustainable soil management.

中文翻译：

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改进拉丁美洲土壤信息数据库以进行数字土壤测绘提高了其可用性和可扩展性

摘要。空间土壤数据库可以帮助对土壤起决定性作用的复杂现象进行建模，例如，评估农业潜力或估算碳储存能力。拉丁美洲和加勒比土壤信息系统 SISLAC 是粮农组织南美土壤伙伴关系推动的一项区域倡议，旨在促进土壤的可持续管理。SISLAC 包括来自 49,084 个土壤剖面的数据，这些土壤分布不均匀，分布在整个大陆，使其成为该地区最大的土壤数据库。然而，一些问题阻碍了它的使用，例如数据的质量和它的高维度。这项研究的目的是双重的。首先，评估 SISLAC 的质量及其数据值并生成新的，满足不同兴趣或实际应用程序使用的最低质量要求的改进版本。其次，通过进行案例研究，使用 1473 公里的 192 个剖面估计土壤有机碳百分比的空间变异性，展示改进的土壤剖面数据库生成更准确的土壤特性信息的潜力²位于哥伦比亚考卡山谷省的地区。研究结果表明，15% 的现有土壤剖面对诊断范围的描述不准确。进一步纠正 4.5% 的现有不一致性提高了整体数据质量。改进后的数据库包含 41,691 个配置文件，可在 https://doi.org/10.5281/zenodo.6540710（Díaz-Guadarrama, S. & Guevara, M., 2022）上公开使用。使用定量土壤学算法对更新的剖面进行分割，以估计空间变异性。我们生成了一厘米厚的片段以及每个土壤剖面数据，然后使用样条函数调整这些片段的值，以增强垂直连续性和可靠性。估计垂直变化深度可达 150 厘米，而普通克里金法则按照 GlobalSoilMap 项目的标准，以 250 m 的空间分辨率预测三个深度间隔（0 到 5、5 到 15 和 15 到 30 cm）的水平变化。最后，留一法交叉验证提供了评估克里金模型性能的信息，获得了介于 1.77% 和 1.79% 之间的 RMSE 指数和 R²指数大于0.5。结果表明 SISLAC 数据库可用于生成有关土壤特性的空间信息，并建议进一步努力收集更多数据以指导可持续土壤管理。