Perspectives on data-driven soil research

Soil is a complex system in which biological, chemical and physical interactions take place. The behaviour of these interactions changes in spatial scale from the atomic to the global, and in time. To understand how this system works, soil scientists usually rely on incremental improvements in the knowledge by refinement of theories through hypothesis testing and development using carefully designed experiments. In the last two decades, the primacy of this knowledge construction process has been challenged by the development of large soil databases and algorithms such as machine learning. The data-driven research approach to soil science, the inference of soil knowledge directly from data by using computational tools and modelling techniques, is becoming more popular. Despite the wide adoption of a data-driven research approach to soil science, there has been little discussion on how a research driven by data instead of hypotheses affects scientific progress. In this paper, we provide an introductory perspective on data-driven soil research by discussing some of the issues and opportunities of knowledge discovery from soil data. We show that while data-driven soil research may seem revolutionary for some, soil science has a long history of exploratory efforts to generate knowledge from data. Empirical and factual soil classifications, for example, were data driven. We further discuss, with examples, (i) data, databases and the logic of data storage for data-driven soil research, (ii) the issues of extreme empiricist claims that arise corollary to the increase in the use of computational tools, and (iii) the challenge of formulating a scientific explanation based on patterns observed in the data and data analysis tools. By considering the epistemic challenges of the data-driven scientific research in the light of the historical literature, we found that there is a continuity of practices, some being certainly amplified by recent technological changes, but that the core methods of scientific enquiry from data remain essentially unchanged. Highlights Historical account of data-driven soil science research. Describe data to be used for data-driven soil science. Discuss conceptual issues and opportunities for data-driven soil science. Investigate the challenge of formulating an explanation from soil data.

Automated summary

Soil is a complex system where interactions occur on various scales and over time. Data-driven research in soil science, utilizing computational tools and modeling techniques, is gaining popularity in recent years. This approach presents both challenges and opportunities for knowledge discovery in soil science.