Abstract Hypotheses are of major importance in scientific research. In current applications of machine learning algorithms for soil mapping the hypotheses being tested or developed are often ambiguous or undefined. Mapping soil properties or classes, however, does not tell much about the dynamics and processes that underly soil genesis and evolution. When the interest in the soil map is for applications in a context different than soil science, such as for policy making or baseline production of quantitative soil information, the interpretation should be made in light of this application. If otherwise, we recommend soil scientists to provide hypotheses to accompany their research. The hypothesis is formulated at the beginning of the research and, in some cases, motivates data collection. Here we argue that when applying data-driven techniques such as machine learning, developing hypotheses can be a useful end point of the research. The spatial pattern predicted by the machine learning model and the correlation found among the covariates are an opportunity to develop hypotheses which are likely to require additional analyses and datasets to be tested. Systematically providing scientific hypotheses in digital soil mapping studies will enable the soil science community to build on previous work, and to increase the credibility of data-driven algorithms as a means to accelerate discovery on soil processes.

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假设、机器学习和土壤测绘

摘要 假设在科学研究中非常重要。在用于土壤测绘的机器学习算法的当前应用中，正在测试或开发的假设通常是模棱两可的或未定义的。然而，绘制土壤特性或类别并不能说明土壤发生和进化背后的动力学和过程。当对土壤图的兴趣是用于不同于土壤科学的应用时，例如用于政策制定或定量土壤信息的基线生成，应根据此应用进行解释。否则，我们建议土壤科学家提供假设来配合他们的研究。该假设是在研究开始时制定的，在某些情况下，会激发数据收集。在这里，我们认为，在应用机器学习等数据驱动技术时，提出假设可能是研究的有用终点。机器学习模型预测的空间模式和协变量之间的相关性是提出假设的机会，这些假设可能需要额外的分析和数据集进行测试。在数字土壤制图研究中系统地提供科学假设将使土壤科学界能够在以前的工作的基础上发展，并提高数据驱动算法作为加速发现土壤过程的一种手段的可信度。机器学习模型预测的空间模式和协变量之间的相关性是提出假设的机会，这些假设可能需要额外的分析和数据集进行测试。在数字土壤制图研究中系统地提供科学假设将使土壤科学界能够在以前的工作的基础上发展，并提高数据驱动算法作为加速发现土壤过程的一种手段的可信度。机器学习模型预测的空间模式和协变量之间的相关性是提出假设的机会，这些假设可能需要额外的分析和数据集进行测试。在数字土壤制图研究中系统地提供科学假设将使土壤科学界能够在以前的工作的基础上发展，并提高数据驱动算法作为加速发现土壤过程的一种手段的可信度。