Alexandre M.J.-C. Wadoux

Few would disagree that artificial intelligence (AI) holds potential for advancing knowledge and innovation. Over the past decades, substantial research has been devoted to the development and application of AI in soil science. While most of today’s AI applications in soil science are related to machine learning (ML), AI also encompasses other fields such as digital image analysis, natural language processing (NLP), expert systems, and knowledge representation. This review aims to provide a comprehensive overview of AI in soil science. A definition of AI that equates intelligence with rationality is provided, followed by a typical classification of AI into the three main domains of sensing and interacting, reasoning and decision-making, and learning and predicting. From this framework, a taxonomy of AI in soil research is derived and serves as a basis for a literature review. The major findings are as follows: i) AI in soil science is diverse, with applications in decision support systems, image classification, prediction with ML and expert systems; ii) AI in soil science is currently almost exclusively characterized by ML; iii) applications of ML are predominantly found in the field of digital soil mapping and for the development of pedotransfer functions; and iv) most AI applications are used for prediction purposes. A few notable exceptions stand apart from mainstream applications, particularly in the realms of NLP, the development of soil cognitive models, and interpretable ML. Based on these findings, I discuss attention points, such as using AI almost exclusively for prediction at the expense of explanation and the lack of integration of soil knowledge in algorithmic AI solutions. I envision that future developments could include the use of AI for text recognition of legacy soil profile data, providing a new source of soil information. Another promising line of research is the language processing of soil texts to build meta-analyses that summarize the growing body of soil science literature. These new applications could foster substantial new contributions to soil science research.

In 2023, the European Commission released a legislative proposal for a Directive on Soil Monitoring and Resilience, which aims to define a legal framework to achieve healthy soils across the European Union (EU) by 2050. A key component of the initial Directive is the mandate for Member States to establish basic geographic soil-governance units, referred to as soil districts, and appoint a district-specific authority to oversee the implementation of soil-health assessments. This paper proposes an operational definition of the districts following the conditions outlined in the proposed Directive and discusses various points requiring attention for their implementation. Tentative districts were developed for seven EU countries, considering soil type, climate, topography, and land-cover factors, starting from the smallest existing administrative unit (i.e. municipalities). Experts were asked to report on the applicability of the proposed districts within well-known pedo-ecological regions and discuss the relevance of the districts for establishing an EU-wide monitoring network and reporting on soil health and degradation. The outcomes highlight the need for detailed soil maps to account for specific soil types when stratifying countries into soil districts. The soilscape approach allows for a consistent method to define soil districts across Member States. This enables contrasting soils within a district to be managed in a similar manner, with soil-degradation and soil-health thresholds applied to each district based on land cover. However, it is unclear whether soil districts as currently formulated in the Directive are the right tool to support local soil management and monitoring of soil health. Districts can help ensure that all soil conditions are covered in a monitoring system, but they may not support soil management or monitoring at a local scale because of short-range soil variability and threats affecting soil management within the same soilscape. Beyond the use of districts for designing a European- or national-scale monitoring system, districts can help create animations and other educational tools to promote soil literacy and local connectivity to soils.

For decades scientists have produced maps of biological, ecological and environmental variables. These studies commonly evaluate the map accuracy through cross-validation with the data used for calibrating the underlying mapping model. Recent studies, however, have argued that cross-validation statistics of most mapping studies are optimistically biased. They attribute these overoptimistic results to a supposed serious methodological flaw in standard cross-validation methods, namely that these methods ignore spatial autocorrelation in the data. They argue that spatial cross-validation should be used instead, and contend that standard cross-validation methods are inherently invalid in a geospatial context because of the autocorrelation present in most spatial data. Here we argue that these studies propagate a widespread misconception of statistical validation of maps. We explain that unbiased estimates of map accuracy indices can be obtained by probability sampling and design-based inference and illustrate this with a numerical experiment on large-scale above-ground biomass mapping. In our experiment, standard cross-validation (i.e., ignoring autocorrelation) led to smaller bias than spatial cross-validation. Standard cross-validation was deficient in case of a strongly clustered dataset that had large differences in sampling density, but less so than spatial cross-validation. We conclude that spatial cross-validation methods have no theoretical underpinning and should not be used for assessing map accuracy, while standard cross-validation is deficient in case of clustered data. Model-free, design-unbiased and valid accuracy assessment is achieved with probability sampling and design-based inference. It is valid without the need to explicitly incorporate or adjust for spatial autocorrelation and perfectly suited for the validation of large scale biological, ecological and environmental maps.

Pedometrics, the application of mathematical and statistical methods to the study of the distribution and genesis of soils, has broadened its scope over the past two decades. The primary focus of pedometricians has traditionally been on spatial and spatio-temporal soil inventories with numerical soil classification, geostatistical modelling of spatial variation and mapping. The rapid development of remote and proximal soil sensing as well as data-driven statistical modelling techniques have had a major impact on pedometrics over the past decades. During this time, a general demand for quantitative digital soil information for environmental modelling and management has compelled pedometricians to address other soil-related questions from a quantitative point of view: soil genesis and utility and quality of soil. While scientific progress is largely an autonomous process that is difficult to steer, research efforts could benefit from an agenda with pressing pedometric research topics. This paper defines and discusses ten recent or longstanding pedometrics challenges, with the attempt to identify knowledge gaps and suggest new concepts and methods to overcome them. The ten challenges were selected through a collaborative effort and may serve as a guidance for future pedometrics research and to foster collaboration among soil scientists. The challenges discussed in this paper are also indicators of the current understanding and state of knowledge from which progress can be measured in the future.

This book provides a didactic overview of techniques for inferring information from soil spectroscopic data, and the codes in the R programming language for performing such analyses. It is intended for students, researchers and practitioners looking to infer soil information from spectroscopic data, focusing mainly on, but not restricted to, the infrared range of the electromagnetic spectrum. Little prior knowledge of the R programming language or digital soil spectra is required. We work through the steps to process spectroscopic data systematically.