The awarding of the Nobel Prize in Physics to pioneers in neural networks highlights their substantial influence across diverse disciplines, including soil science. This article explores the evolution and transformative impact of machine learning and artificial intelligence (AI) in soil science. These technologies have revolutionised the modelling of complex soil processes, enhancing our ability to predict and map soil properties, simulate water movement and assess global soil carbon dynamics. The article discusses future directions for AI in soil science, such as developing new mathematical soil matrices and integrating AI with soil science knowledge to improve the precision and efficiency of soil assessments. As AI evolves, its potential in soil science includes generating new hypotheses, optimising soil carbon–mineral associations for better sequestration and enhancing soil phenotyping with high‐throughput data analysis. Integrating AI with physical models could lead to more precise, data‐driven soil management practices that support net‐zero, nature‐positive stewardship for improved soil security.

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An improved understanding of soil can enable more sustainable land-use practices. Nevertheless, soil is called a complex, living medium due to the complex interaction of different soil processes that limit our understanding of soil. Process-based models and analyzing observed data provide two avenues for improving our understanding of soil processes. Collecting observed data is cost-prohibitive but reflects real-world behavior, while process-based models can be used to generate ample synthetic data which may not be representative of reality. We propose a framework, knowledge-guided representation learning, and causal structure learning (KGRCL), to accelerate scientific discoveries in soil science. The framework improves representation learning for simulated soil processes via conditional distribution matching with observed soil processes. Simultaneously, the framework leverages both observed and simulated data to learn a causal structure among the soil processes. The learned causal graph is more representative of ground truth than other graphs generated from other causal discovery methods. Furthermore, the learned causal graph is leveraged in a supervised learning setup to predict the impact of fertilizer use and changing weather on soil carbon. We present the results in five different locations to show the improvement in the prediction performance in out-of-sample and few-shots setting.

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.

The most complete, nonpartisan source of information on this hot agronomic topic available today, this book brings together a diverse group of papers and data to resolve the debate between sedimentologists and soil scientists and agronomists over whether the effects of soil erosion on carbon and atmospheric CO2 is beneficial or destructive. Divided into four sections, it offers data on how soil erosion affects soil, water, and air quality. Topics include mineralization rate, inundation, sediment deposition, and global warming potential, as well as carbon dioxide, methane, and nitrous oxide emissions, and the implications of soil erosion on the global carbon cycle and carbon budget

In the modern agricultural industry, technology plays a crucial role in the advancement of cultivation. To increase crop productivity, soil require some specific characteristics. For watermelon cultivation, soil needs to be sandy and of high temperature with proper irrigation. This research aims to design and implement an intelligent IoT-based soil characterization system for the watermelon field to measure the soil characteristics. IoT based developed system measures moisture, temperature, and pH of soil using different sensors, and the sensor data is uploaded to the cloud via Arduino and Raspberry Pi, from where users can obtain the data using mobile application and webpage developed for this system. To ensure the precision of the framework, this study includes the comparison between the readings of the soil parameters by the existing field soil meters, the values obtained from the sensors integrated IoT system, and data obtained from soil science laboratory. Excessive salinity in soil affects the watermelon yield. This paper proposes a model for the measurement of soil salinity based on soil resistivity. It establishes a relationship between soil salinity and soil resistivity from the data obtained in the laboratory using artificial neural network (ANN).

The term “soil health” has captured the interest of government, and land managers, whilst the academic community has struggled to rationalise its use and wider benefit. It has proved a powerful tool in conveying best practice to a lay audience. However, the widespread adoption of the “metaphor” has resulted in calls for tools that facilitate the measurement of soil health, preferably quantitatively, and often as a single figure, for ease of use/communication and cost of monitoring. The insurmountable problem is that soil health is neither a readily quantifiable nor measurable object. Only organisms can have ‘health’, which manifests as characteristics of a living system—true of complex systems exhibiting “emergent” properties such as resilience in the face of perturbation. We pose the key question: is soil really a system capable of exhibiting “health”, or any other property emerging from a complex, connected, self-regulating system? We argue that if you cannot detect emergent properties, you are: (i) looking at the wrong dynamic parameter; (ii) not considering the entire system; or (iii) not evaluating at a system at all. We suggest that our focus should instead be on the relationships between components, complexity, and function. Using this as a basis for a new framework will allow us to assemble and align disparate threads of soil science into a cogent and coherent “new theory of soil health”, which is an essential and practical step forward for the sustainable management of global soil resources, across all land uses. Highlights: The term “soil health” is widely used, but understood and used in different ways. Health is a characteristic of an identifiable entity – usually an organism or discrete system. To identify and measure soil health we need to discover its system properties and their dependence on complexity and connectivity. The scale at which system properties emerge is currently unclear. We suggest a broad programme of research to identify dynamic emergent properties, especially resilience, in order to determine soil health

During past decades a marked manifestation of widespread erosion phenomena was studied worldwide. Global conservation community has launched campaigns at local, regional and continental level in developing countries for preservation of soil resources in order not only to stop or mitigate human impact on nature but also to improve life in rural areas introducing new approaches for soil cultivation. After the adoption of Sustainable Development Goals of UNs and launching several world initiatives such as the Land Degradation Neutrality (LDN) the world came to realize the very importance of the soil resources on which the biosphere relies for its existence. The main goal of the chapter is to review different types and structures erosion models as well as their applications. Several methods using spatial analysis capabilities of geographic information systems (GIS) are in operation for soil erosion risk assessment, such as Universal Soil Loss Equation (USLE), Revised Universal Soil Loss Equation (RUSLE) in operation worldwide and in the USA and MESALES model. These and more models are being discussed in the present work alongside more experimental models and methods for assessing soil erosion risk such as Artificial Intelligence (AI), Machine and Deep Learning, etc. At the end of this work, a prospectus for the future development of soil erosion risk assessment is drawn.

The United Nations (UN) Sustainable Development Goals (SDGs) offer an opportunity to improve soil science education on sustainability because they provide specific context to educate faculty and students from various disciplines, including Science, Technology, Engineering, and Mathematics (STEM) about SDGs. Soil science is a STEM discipline with a wide range of applications in the SDGs. The objectives of this study were to use a matrix approach (framework for presenting options for discussion and implementation) to integrate SDGs into an existing introductory soil science course taught to undergraduate students from different STEM fields (environmental and natural resources; wildlife biology; and forestry). The course was enriched with a lecture on SDGs and students were asked to link soil properties and class activities to specific SDGs. A post-assessment survey revealed an increase in students’ familiarity with SDGs, and their relevance to soil properties and course activities. Students acknowledged the importance of soils and individual actions for achieving the SDGs. There was an overall increase in student familiarity (+59.4%) with SDGs. Most students agreed (46.7%) and strongly agreed (23.3%) that the course activities were an effective way to learn about SDGs with examples from soil science. Identified learning gaps in subject matter found through the surveys on SDGs were clarified during later classroom discussions. The advantage of this teaching approach is that it seamlessly integrates SDGs with existing course materials while relying on students’ critical thinking skills to effectively analyze soil science information and form a judgement on how it relates to SDGs.

© 2018 The Authors. European Journal of Soil Science published by John Wiley & Sons Ltd on behalf of British Society of Soil Science. Given the costs of soil survey it is necessary to make the best use of available datasets, but data that differ with respect to some aspect of the sampling or analytical protocol cannot be combined simply. In this paper we consider a case where two datasets were available on the concentration of plant-available magnesium in the topsoil. The datasets were the Representative Soil Sampling Scheme (RSSS) and the National Soil Inventory (NSI) of England and Wales. The variable was measured over the same depth interval and with the same laboratory method, but the sample supports were different and so the datasets differ in their variance. We used a multivariate geostatistical model, the linear model of coregionalization (LMCR), to model the joint spatial distribution of the two datasets. The model allowed us to elucidate the effects of the sample support on the two datasets, and to show that there was a strong correlation between the underlying variables. The LMCR allowed us to make spatial predictions of the variable on the RSSS support by cokriging the RSSS data with the NSI data. We used cross-validation to test the validity of the LMCR and showed how incorporating the NSI data restricted the range of prediction error variances relative to univariate ordinary kriging predictions from the RSSS data alone. The standardized squared prediction errors were computed and the coverage of prediction intervals (i.e. the proportion of sites at which the prediction interval included the observed value of the variable). Both these statistics suggested that the prediction error variances were consistent for the cokriging predictions but not for the ordinary kriging predictions from the simple combination of the RSSS and NSI data, which might be proposed on the basis of their very similar mean values. The LMCR is therefore proposed as a general tool for the combined analysis of different datasets on soil properties. Highlights: Differences in sample support mean that two datasets on a soil property cannot be combined simply. We showed how a multivariate geostatistical model can be used to elucidate the relationships between two such datasets. The same model allows soil properties to be mapped jointly from such data. This offers a general basis for combining soil datasets from diverse sources

PCR-based analysis of DNA is utilized in a wide variety of fields, including Forensic Science. Aside from the more common ample sources, material analyzed here can refer to specimen excavated from a soil environment, or a sampling of the soil itself to recover DNA leached into the soil from decomposing human remains or from body fluids intermingled with the soil in an outdoor crime scene. The common problematic of these types of sample is the presence of humic acids, which are a component of any soil environment, and when the co-extracted with the DNA, lead to inhibition of enzyme-based procedures including PCR. While a variety of methods exist for the extraction of DNA from excavated skeletal remains, protocols for extraction of DNA from the soil directly are usually targeting soil microorganism. To address the need for methodology suitable for extraction of human DNA from soil, a selection of three published protocols were adapted for this purpose, to be tested and evaluated using standardized samples. The resulting protocols are presented here.

Attaining sustainable agriculture requires acknowledging the impact of human behavior. Despite the pivotal role of soils in agriculture, our understanding of the psychological drivers that motivate farmers to adopt soil conservation practices remains limited. Our objective was to explore the influence of soil science knowledge and connection to soil on farmers ’ soil conservation behavior. To achieve this goal, we developed scales capable of measuring farmers ’ soil knowledge, connection to soil

Contrasting tillage strategies not only affect the stability and formation of soil aggregates but also modify the concentration and thermostability of soil organic matter associated with soil aggregates. Understanding the thermostability and carbon retention ability of aggregates under different tillage systems is essential to ascertain potential terrestrial carbon storage. We characterised the concentration and thermostability of soil organic carbon within various aggregate size classes under both zero and conventional tillage using novel Rock-Eval pyrolysis. The nature of the pore systems was visualised and quantified by X-ray Computed Tomography to link soil structure to organic carbon preservation and thermostability. Soil samples were collected from experimental fields in Botucatu, Brazil, which had been under zero-tillage for 2, 15 and 31 years, along with adjacent fields under conventional tillage. Soils under zero-tillage significantly increased pore connectivity whilst simultaneously decreasing inter-aggregate porosity (10% compared to 4% in conventional and zero-tilled macroaggregates respectively), providing a potential physical mechanism for protection of soil organic carbon in the 0-20 cm soil layer. Changes in the soil physical characteristics associated with the adoption of zero-tillage resulted in improved aggregate formation compared to conventionally tilled soils, especially when implemented for at least 15 years, with the mean weight diameter of aggregates increasing by 74% in zero-tilled surface soils). In addition, we identified a chemical change in composition of organic carbon to a more recalcitrant fraction following conversion to zero-tillage, suggesting aggregates were accumulating soil organic carbon, rather than being exposed to mineralisation by the soil microbial community. These data reveal profound effects of different tillage systems upon soil structural modification, with important implications for the potential of zero-tillage to increase carbon sequestration compared to conventional tillage

Sustainable Organic Integrated Livelihoods (SOIL) is a research and development organization that aims to increase access to cost-effective household sanitation services in urban communities in Haiti. Each week, SOIL provides over 1,000 households with ecological sanitation toilets, then transports the waste to be transformed into rich compost. However, SOIL faces several challenges regarding the route optimization of their mixed fleet vehicle routing. This paper builds upon the authors' submission to Bloomberg's 2019 Data for Good Exchange (D4GX), presenting preliminary findings from a joint collaboration between DataKind, a data science nonprofit, and SOIL. This research showcases how optimization algorithms and open source tools (i.e., OpenStreetMap and Google OR-Tools) can help improve and reduce the costs of mixed-fleet routing problems, particularly in the context of developing countries. As a result of this work, SOIL is able to make improvement to their collection routes, which account for different road conditions and vehicle types. These improvements reduce operational costs and fuel use, which are essential to the service's expansion in the coming years.

The European Union's soil strategy for 2030 has set the objective of achieving ‘healthy soils’ in Europe by 2050. To achieve this ambitious goal, dedicated soil science skills will be needed in the future. This article presents the results of a survey on soil science skills for the future conducted within the framework of the European Joint Program—EJP SOIL. The survey was distributed online in the 24 countries participating in the EJP SOIL. The skills were expressed as having knowledge in particular topics related to soils, practical know‐hows or abilities or outcomes of knowledge and know‐hows put into action. No significant differences in the importance of soil‐science related skills were found between countries or stakeholder categories. The two groups of skills that ranked at the top of stakeholders' concern were ‘Having a scientific basis of knowledge on soils and their functioning’ and ‘Knowing how to mobilise agronomic drivers to manage and protect soils’. This means that there is a need for people with an in‐depth knowledge in soil science, but also soil scientists with integrated knowledge in agronomy and crop production. Three important findings relate to the knowledge of soil science itself: (i) there is a clear need to develop the knowledge of the biological and ecological functioning of soil; (ii) this should not happen at the expense of a strong general knowledge of the fundamentals of soil science; (iii) future soil scientists should be trained to have a more holistic appreciation of soil rather than be trained as a specialist of a specific aspect of soil science only. Being able to exchange knowledge with farmers, knowing how to assess soil fertility, quality or health and how to improve them are viewed as essential skills for the future. These findings offer a clear path for the evolution of soil science education curricula across Europe.

The paraffin-coated method is a well-used approach to measure the soil bulk density (BD). BD is a physical property of great importance for studies of soil quality and health. Therefore, representative measurements of this property are highly valued. Resin and paraffin wax are utilized to coat soil samples; however, if these materials ingress into the sample it could affect the representativeness of BD evaluation. The advance in three-dimensional (3D) image analysis techniques such as X-ray microtomography (μCT) offers a great opportunity to visualize and quantify the possible penetration of paraffin wax into clod samples. In this paper we investigated porous system morphological properties of soil samples coated with paraffin wax. The morphological properties of the pores filled with paraffin wax inside the samples were also studied. We observed qualitatively that samples with large pores close to their borders were more susceptible to the penetration of paraffin wax. Samples with pores >10 mm3 had the highest amount of paraffin wax into them. Triaxial shaped and complexly pores also offered less resistance to the ingress of paraffin wax. Positive relations between the amount of paraffin wax inside the samples and the volume of pores measured, pore tortuosity and degree of anisotropy were found. Conversely, the soil pore connectivity was not correlated with the penetration of paraffin wax into the samples, at least for the region of interest (≈27.3 cm3) studied. Finally, an analysis of the impact of paraffin wax ingress inside the samples in measured BD showed increments of ≈0.09 and ≈0.11 g cm−3 in this property when the paraffin wax penetrates into the large pores

Soil organic carbon (SOC) is a key indicator of soil health, fertility, and carbon sequestration, making it essential for sustainable land management and climate change mitigation. However, large-scale SOC monitoring remains challenging due to spatial variability, temporal dynamics, and multiple influencing factors. We present WALGREEN, a platform that enhances SOC inference by overcoming limitations of current applications. Leveraging machine learning and diverse soil samples, WALGREEN generates predictive models using historical public and private data. Built on cloud-based technologies, it offers a user-friendly interface for researchers, policymakers, and land managers to access carbon data, analyze trends, and support evidence-based decision-making. Implemented in Python, Java, and JavaScript, WALGREEN integrates Google Earth Engine and Sentinel Copernicus via scripting, OpenLayers, and Thymeleaf in a Model-View-Controller framework. This paper aims to advance soil science, promote sustainable agriculture, and drive critical ecosystem responses to climate change.

The importance of soils to society has gained increasing recognition over the past decade, with the potential to contribute to most of the United Nations’ Sustainable Development Goals (SDGs). With unprecedented and growing demands for food, water and energy, there is an urgent need for a global effort to address the challenges of climate change and land degradation, whilst protecting soil as a natural resource. In this paper, we identify the contribution of soil science over the past decade to addressing gaps in our knowledge regarding major environmental challenges: climate change, food security, water security, urban development, and ecosystem functioning and biodiversity. Continuing to address knowledge gaps in soil science is essential for the achievement of the SDGs. However, with limited time and budget, it is also pertinent to identify effective methods of working that ensure the research carried out leads to real‐world impact. Here, we suggest three strategies for the next decade of soil science, comprising a greater implementation of research into policy, interdisciplinary partnerships to evaluate function trade‐offs and synergies between soils and other environmental domains, and integrating monitoring and modelling methods to ensure soil‐based policies can withstand the uncertainties of the future.

Digital soil maps describe the spatial variation of soil and provide important information on spatial variation of soil properties which provides policy makers with a synoptic view of the state of the soil. This paper presents a study to tackle the task of how to map the spatial variation of soil pH across Zambia. This was part of a project to assess suitability for rice production across the country. Legacy data on the target variable were available along with additional exhaustive environmental covariates as potential predictor variables. We had the option of undertaking spatial prediction by geostatistical or machine learning methods. We set out to compare the approaches from the selection of predictor variables through to model validation, and to test the predictors on a set of validation observations. We also addressed the problem of how to robustly validate models from legacy data when these have, as is often the case, a strongly clustered spatial distribution. The validation statistics results showed that the empirical best linear unbiased predictor (EBLUP) with the only fixed effect a constant mean (ordinary kriging) performed better than the other methods. Random forests had the largest model-based estimates of the expected squared errors. We also noticed that the random forest algorithm was prone to select as “important” spatially correlated random variables which we had simulated

This work leverages the recent advancements of deep learning in image processing to find optimal locations that present the important characteristics of a field. The data for training are collected at different fields in local farms with five features: aspect, flow accumulation, slope, NDVI (normalized difference vegetation index), and yield. The soil sampling dataset is challenging because the ground truth is highly imbalanced binary images. Therefore, we approached the problem with two methods, the first approach involves utilizing a state-of-the-art model with the convolutional neural network (CNN) backbone, while the second is to innovate a deep-learning design grounded in the concepts of transformer and self-attention. Our framework is constructed with an encoder-decoder architecture with the self-attention mechanism as the backbone. In the encoder, the self-attention mechanism is the key feature extractor, which produces feature maps. In the decoder, we introduce atrous convolution networks to concatenate, fuse the extracted features, and then export the optimal locations for soil sampling. Currently, the model has achieved impressive results on the testing dataset, with a mean accuracy of 99.52%, a mean Intersection over Union (IoU) of 57.35%, and a mean Dice Coefficient of 71.47%, while the performance metrics of the state-of-the-art CNN-based model are 66.08%, 3.85%, and 1.98%, respectively. This indicates that our proposed model outperforms the CNN-based method on the soil-sampling dataset. To the best of our knowledge, our work is the first to provide a soil-sampling dataset with multiple attributes and leverage deep learning techniques to enable the automatic selection of soil-sampling sites. This work lays a foundation for novel applications of data science and machine-learning technologies to solve other emerging agricultural problems.

Macroscopic models of soil organic matter (SOM) turnover have faced difficulties in reproducing SOM dynamics or in predicting the spatial distribution of carbon stocks. These models are based on a largely inadequate linear response of soil microorganisms to bulk concentrations of nutrients and it is clear that a new approach to SOM modelling is required. Introducing explicit microbial activity and organic matter (OM) reactivity in macroscopic models represents a challenge because of the fine spatial scales at which the processes occur. To get a better grasp on interactions that take place at the microscale, a new generation of SOM models have been developed at the spatial scale of the soil microenvironments where microorganisms evolve. These models are well adapted to challenge traditional hypotheses about the influence of soil architecture on soil microbial activity. Soil architecture provides the stage for a dynamic spatial accessibility of resources to microbes and the emergence of interactions between the actors in SOM decomposition. In this context, we review microscale models of microbial activity that have been designed for soils and soil analogues. To understand how these models account for spatial accessibility, we look in detail at how soil microenvironments are described in the different approaches and how microbial colonies are spatialized in these microenvironments. We present the advantages and disadvantages of the developed strategies and we discuss their limits.

Green roofs are a key to providing nature-based solutions in cities. However, most green roofs installed in the Northern hemisphere are shallow, stonecrop planted systems (“extensive” green roofs), which have been shown to support limited biodiversity and could be more effective at providing ecosystem services. One issue with this type of extensive green roof is that rootzones are almost sterile on construction, relying on natural colonisation to provide a soil food web. This is a slow process, meaning plant growth can also be slow. Our aim was to determine if a soil food web could be introduced when the green roof is built. We applied microbial inoculants (mycorrhizal fungi and bacteria (Bacillus spp.)) to a new green roof and monitored plant growth and the soil food web (bacteria, mycorrhizal fungi and microarthropods). Different inoculants altered the composition of microarthropod communities, potentially impacting later succession. In particular, bacterial inoculants increased microarthropod populations. This is one of the first studies to demonstrate that the addition of microbial inoculants impacts not only plant growth, but also faunal components of the soil food web, which could have implications for long-term resilience. Bacteria were effective at aiding mycorrhizal colonisation of plants roots, but this colonisation had no impact on the growth of our selected stonecrops, Sedum album, Petrosedum reflexum and Phedimus spurius. We suggest that if a beneficial mycorrhiza could be found to promote the growth of these specific species on green roofs, bacteria could be effective “helper” species to aid colonisation. This study enables green roof researchers and the industry to justify further exploration of the impact of microbial inoculants on green roofs

This paper aims to comprehensively grasp the research status and development trends of soil microplastics (MPs). It collects studies from the Web of Science Core Collection covering the period from 2013 to 2024. Employing CiteSpace and VOSviewer, the paper conducts in - depth analyses of literature regarding the environmental impacts of microplastics. These analyses involve keyword co - occurrence, clustering, burst term identification, as well as co - occurrence analysis of authors and institutions. Microplastics can accumulate in soil, transfer through food chains, and ultimately affect human health, making the research on them essential for effective pollution control. Focusing on the international research on the impacts of microplastics on soil and ecosystems, the study reveals a steadily increasing trend in the number of publications each year, reaching a peak of 956 articles in 2024. A small number of highly productive authors contribute significantly to the overall research output. The keyword clustering analysis results in ten major clusters, including topics such as plastic pollution and microbial communities. The research on soil microplastics has evolved through three distinct stages: the preliminary exploration phase from 2013 to 2016, the expansion phase from 2017 to 2020, and the integration phase from 2021 to 2024. For future research, multi - level assessments of the impacts of microplastics on soil ecosystems and organisms should be emphasized, in order to fully uncover the associated hazards and develop practical solutions.

Previous studies have indicated that diversity is a concern in soil science, including how this relates to gender equity. However, gender equity data for soil science are extremely limited, with only the USA having a current relatively extensive study published in the scientific literature. Therefore, this study was undertaken to gain a preliminary understanding of gender equity issues in a range of countries worldwide using the gender data of membership of national soil science societies, international conferences and journals. Data for 2020 membership was supplied by 44 soil science societies, providing a broad geographic representation. Thirty‐seven of the 44 societies had more male members than female. Only 32% of the soil science society members were women, although the highest percentage for an individual society was 69% female membership. Regionally, Asia had the lowest female membership at 22%, whereas Africa had the highest at 40%. Only 20% of the national soil science societies who are members of the International Union of Soil Sciences had female presidents, indicating that women are under‐represented in the top leadership roles in our professional societies. Women were under‐represented as keynote speakers at conferences and on many editorial boards. This initial scoping study demonstrates that there is an urgent global need to address gender equity within the soil science profession. However, gender equity extends beyond just the number of women in our profession: it also includes equal access to wages, leadership roles and decision making. We conclude that it is critical that individual countries undertake detailed studies of gender equity so we have a more complete picture of the national and international scale of this issue.

© 2020 Elsevier B.V. Previous biofortification studies have established that the residual effect of added selenium (Se) fertilisers on second-season crops is minimal. To explore the fate of exogenous Se in soil, chemical and biological methods were employed to assess the change in Se bioavailability with time. Eight soils varying in physicochemical properties were spiked with sodium selenate (0.5 mg kg−1 Se) and incubated at 25 °C for different periods (1, 30, 60, 90 and 300 d). At the end of the incubation, soil Se was fractionated by a sequential extraction procedure into ‘soluble’, ‘adsorbed’ and ‘organically-bound’ Se fractions. Simultaneously, wheat was grown in the Se-aged soils, under controlled conditions for six weeks, and uptake was determined. A general decrease in Se solubility over time was observed, but the rate of decrease varied depending on soil type. A reversible first order model fitted the Se ageing kinetics well, except in an Oxisol. The most pronounced ageing was observed in calcareous soils. Concentrations of Se in the shoots of wheat grown in freshly spiked soils ranged from 71.8 ± 17.5 mg kg−1 in calcareous soils to 110 ± 31.6 mg kg−1 in non-calcareous, low-OM soils. With ageing, shoot Se concentrations decreased t

The movement of metals through the environment links together a wide range of scientific fields: from earth sciences and geology as weathering releases minerals; to environmental sciences as metals are mobilized and transformed, cycling through soil and water; to biology as living things take up metals from their surroundings. Studies of these fundamental processes all require quantitative analysis of metal concentrations, locations, and chemical states. Synchrotron x-ray tools can address these requirements with high sensitivity, high spatial resolution, and minimal sample preparation. This perspective describes the state of fundamental scientific questions in the lifecycle of metals, from rocks to ecosystems, from soils to plants, and from environment to animals. Key x-ray capabilities and facility infrastructure for future synchrotron-based analytical resources serving these areas are summarized, and potential opportunities for future experiments are explored.

Soils are a fundamental natural resource but intensifying demands and increasing soil degradation necessitate focussed research into the sustainable use of soils. Since soil functioning is critical for the operations and performance of multiple industries, businesses and municipalities, soil scientists need to actively engage with these bodies to orientate research goals towards stakeholder needs. To achieve this, stakeholder views about the current and potential contributions of soil science to different sectors need to be taken into account when setting the future research agenda. Here, we assessed whether the current and future research priorities of soil science match the needs of four major industrial and environmental sectors: agriculture, ecosystem services and natural resources, waste management, and water management. We used an online questionnaire, distributed to 192 organisations and via social media, to compare stakeholders' and scientists' perceptions of (a) the contributions of soil science to date, (b) the areas not currently served by soil science and (c) future research needs in soil science. Stakeholders generally rated the contributions of soil science to date as ‘great’ or ‘fundamental’, but scientists rated the contributions more highly. Respondents identified numerous areas that soil research has not yet sufficiently addressed, which were mostly sector‐specific and often overlapped with perceived future research needs. Importantly, stakeholders' and scientists' views of future research priorities differed strongly within sectors, with the notable exception of agriculture, where views were generally consistent. We conclude that soil science may hold unexplored potential in several industrial and environmental sectors. We call for improved research communication and greater stakeholder involvement to shape the future soils research agenda and ensure the sustainable use of soils across multiple areas of society.

Soil information is essential for agricultural research and development. In developing countries like Zambia, the availability of soil information is limited by the cost of new soil surveys. Legacy soil information could be useful to fill this gap. This study evaluated the availability of legacy soil information in Southern and Eastern Provinces of Zambia. We also examined the survey scales, legends, boundary density and map texture. We examined a number of exemplar transects undertaken by C.G. Trapnell from 1932 to 1942 in the region, evaluating the information provided on soil, vegetation, landscape and farming systems. An overview of this legacy was presented to a workshop of varied stakeholders in Lusaka, and their opinions on its utility were elicited. More than 60% of large-scale surveys known to have been conducted in the two provinces could not be traced and among those traced some map-sheets were not available. Most of the survey reports exist as hard copies and some are in poor condition, while only 7 are available online on the ISRIC website as scanned copies. The map scale and texture were not entirely consistent with general expectations, with map texture consistent with a smaller publication scale in assessed map-sheets. Stakeholders regarded the legacy information as a valuable resource, despite some limitations, suggesting that some of its features could usefully be reproduced in modern soil information. The holistic assessment provided for soil map units may be more accessible than single-property maps. Accessibility and understanding of soil information is seen as a limitation. There is an urgent need to preserve the legacy soil survey reports and maps that are still available in Zambia, and in other countries. This entails physical preservation of material, making it available, and studying it closely with current and retired soil surveyors to preserve their recollection of how it was produced and their understanding of how it should be interpreted. The preservation should include scanning of hardcopy reports and storage in national, regional, and global soil survey data hubs with links provided for easy access. Stakeholder enthusiasm for the format of legacy soil surveys suggests that renewed systematic soils surveys would be valuable

This paper presents a method for mathematical modelling of surfaces conditioned on empirical data. It is based on solving a discrete biharmonic equation over a domain with given inner point and inner curve data. The inner curve data is used to model boundary values while the inner point data is used for modeling a load vector with the goal to generate a smooth surface. The construction of boundary data is an ill-posed problem, for which a special regularization approach is suggested. The method is designed for surface construction problems with a very limited amount of measured data. In the paper we apply the method by using empirical data of soil thickness and geological maps indicating exposed bedrock regions.