Abstract. In this forum paper we discuss how soil scientists can help to reach the recently adopted UN Sustainable Development Goals (SDGs) in the most effective manner. Soil science, as a land-related discipline, has important links to several of the SDGs, which are demonstrated through the functions of soils and the ecosystem services that are linked to those functions (see graphical abstract in the Supplement). We explore and discuss how soil scientists can rise to the challenge both internally, in terms of our procedures and practices, and externally, in terms of our relations with colleague scientists in other disciplines, diverse groups of stakeholders and the policy arena. To meet these goals we recommend the following steps to be taken by the soil science community as a whole: (i) embrace the UN SDGs, as they provide a platform that allows soil science to demonstrate its relevance for realizing a sustainable society by 2030; (ii) show the specific value of soil science: research should explicitly show how using modern soil information can improve the results of inter- and transdisciplinary studies on SDGs related to food security, water scarcity, climate change, biodiversity loss and health threats; (iii) take leadership in overarching system analysis of ecosystems, as soils and soil scientists have an integrated nature and this places soil scientists in a unique position; (iii) raise awareness of soil organic matter as a key attribute of soils to illustrate its importance for soil functions and ecosystem services; (iv) improve the transfer of knowledge through knowledge brokers with a soil background; (v) start at the basis: educational programmes are needed at all levels, starting in primary schools, and emphasizing practical, down-to-earth examples; (vi) facilitate communication with the policy arena by framing research in terms that resonate with politicians in terms of the policy cycle or by considering drivers, pressures and responses affecting impacts of land use change; and finally (vii) all this is only possible if researchers, with soil scientists in the front lines, look over the hedge towards other disciplines, to the world at large and to the policy arena, reaching over to listen first, as a basis for genuine collaboration.

Achieving household and national food security is the core focus for policy makers in sub-Saharan Africa (SSA). However, land degradation and dearth of effective soil nutrient management strategies for spatially variable soils is significantly contributing to food insecurity in SSA. The aim of this study was to establish the spatial variability of soil secondary and micronutrients (SMN) under smallholder maize production system in sub-humid condition of Zimbabwe. Geo-referenced samples collected in all maize fields in seven of the eleven villages in ward 10 of Hurungwe district using purposive sampling were analyzed for Ca, Mg, Fe, Mn, Cu and Zn using standard methods. Pearson's correlation analysis was conducted to reveal the relationships among the nutrient status. The linear mixed model (LMM) was used to analyse spatial variation of the data with evidence for spatial dependence in the random component of the model assessed by calculating Akaike's information criterion (AIC). The results indicate that only Ca was significantly (p < 0.05) influenced by field type with larger concentrations in home fields compared to outfields. Soil texture had a significant (p < 0.05) effect on Ca, Mg and Zn with the largest values for Ca and Mg found in the sandy clay loams (with the largest clay content) compared to sand, loamy sand and sandy loam. Loamy sand had the largest Zn concentration with sandy clay loams having the least. There was evidence for spatial dependence in Ca and Mg with field type, latitude and longitude as fixed effects; hence these nutrients were mapped in the GIS environment. It can be concluded that SMN tend to be deficient in some areas and vary spatially as influenced by soil intrinsic conditions and management, even at ward level

The current availability of soil moisture data over large areas comes from satellite remote sensing technologies (i.e., radar-based systems), but these data have coarse resolution and often exhibit large spatial information gaps. Where data are too coarse or sparse for a given need (e.g., precision agriculture), one can leverage machine-learning techniques coupled with other sources of environmental information (e.g., topography) to generate gap-free information and at a finer spatial resolution (i.e., increased granularity). To this end, we develop a spatial inference engine consisting of modular stages for processing spatial environmental data, generating predictions with machine-learning techniques, and analyzing these predictions. We demonstrate the functionality of this approach and the effects of data processing choices via multiple prediction maps over a United States ecological region with a highly diverse soil moisture profile (i.e., the Middle Atlantic Coastal Plains). The relevance of our work derives from a pressing need to improve the spatial representation of soil moisture for applications in environmental sciences (e.g., ecological niche modeling, carbon monitoring systems, and other Earth system models) and precision agriculture (e.g., optimizing irrigation practices and other land management decisions).

Abstract Global soil science research collaboration is essential to understand soil and its role in global ecosystem functioning. In particular, collaboration between developed and less-developed countries can generate new knowledge and provide capacity building. However, this collaboration is not always equal. ‘Helicopter research’ in soil science describes the situation where scientists from wealthier nations collect soil samples from less-developed countries, take the samples back to their country for analysis and publish the results with little involvement of local researchers. This article briefly reviews colonial science and helicopter research from different fields including soil science, and highlights the negative effects. The argument that local scientists do not fulfil the criteria of being an author is often used as an excuse for not establishing true collaboration. Finally, this paper offers suggestions to achieve equal research partnerships and ground helicopter research. Soil science can provide leadership in this issue which is less-discussed in cognate fields.

© 2020, Czech Academy of Agricultural Sciences. All rights reserved. Agronomic zinc (Zn) biofortification of crops could help to alleviate dietary Zn deficiency, which is likely to affect more than one billion people worldwide. To evaluate the efficiency of agronomic Zn biofortification of broc-coli, four application treatments were tested: no Zn application (control); soil application of 5 mg/kg ZnSO4·7 H2O (soil); two sprays (15 mL/pot each) of 0.25% (w/v) ZnSO4·7 H2O (foliar); and soil + foliar combination. Soil Zn application increased Zn-DTPA (diethylenetriamine pentaacetic acid) concentration by 3.7-times but did not affect plant growth or plant Zn concentration. Foliar Zn application increased stem + leaves and floret Zn concentration by 78 and 23 mg Zn/kg, respectively, with good bioavailability based on phytic acid concentration. Boiling decreased mineral concentration by 19%, but increased bioavailability by decreasing the phytic acid concentration. The entire broccoli could constitute a good nutritional source for animals and humans. An intake of 100 g boiled florets treated with the foliar treatment will cover about 36% of recommended dietary intake (RDI) of Zn, together with 30% of Ca, 94% of K, 32% of Mg, 6% of Na, 55% of P, 60% of S, 10% of Cu, 22% of Fe, 43% of Mn, and 35% of Se RDIs

The Rapid Carbon Assessment (RaCA) project was conducted by the US Department of Agriculture's National Resources Conservation Service between 2010-2012 in order to provide contemporaneous measurements of soil organic carbon (SOC) across the US. Despite the broad extent of the RaCA data collection effort, direct observations of SOC are not available at the high spatial resolution needed for studying carbon storage in soil and its implications for important problems in climate science and agriculture. As a result, there is a need for predicting SOC at spatial locations not included as part of the RaCA project. In this paper, we compare spatial prediction of SOC using a subset of the RaCA data for a variety of statistical methods. We investigate the performance of methods with off-the-shelf software available (both stationary and nonstationary) as well as a novel nonstationary approach based on partitioning relevant spatially-varying covariate processes. Our new method addresses open questions regarding (1) how to partition the spatial domain for segmentation-based nonstationary methods, (2) incorporating partially observed covariates into a spatial model, and (3) accounting for uncertainty in the partitioning. In applying the various statistical methods we find that there are minimal differences in out-of-sample criteria for this particular data set, however, there are major differences in maps of uncertainty in SOC predictions. We argue that the spatially-varying measures of prediction uncertainty produced by our new approach are valuable to decision makers, as they can be used to better benchmark mechanistic models, identify target areas for soil restoration projects, and inform carbon sequestration projects.

The value of physical archives of soil material from field sampling activities has been widely recognized. If we want to use archive material for new destructive analyses to support a task, such as spatial mapping, then an efficient sub-sampling strategy is needed, both to manage analytical costs and to conserve the archive material. In this paper we present an approach to this problem when the objective is spatial mapping by ordinary kriging. Our objective was to subsample the physical archive from the Ethiopia Soil Information System (EthioSIS) survey of the Southern Nations, Nationalities and Peoples Region (SNNPR) for spatial mapping of two variables, concentrations of particular fractions of selenium and iodine in the soil, which had not been measured there. We used data from cognate parts of surrounding regions of Ethiopia to estimate variograms of these properties, and then computed prediction error variances for maps in SNNPR based on proposed subsets of the archive of different size, selected to optimize a spatial coverage criterion (with some close sample pairs included). On this basis a subsample was selected. This is a preregistered experiment in that we have proposed criteria for evaluating the success of our approach, and are publishing that in advance of receiving analytical data on the subsampled material from the laboratories where they are being processed. A subsequent short report will publish the outcome. The use of preregistered trials is widely recommended and used in areas of science including public health, and we believe that it is a sound strategy to promote reproducible research in soil science

X-ray micro-computed tomography (X-ray microCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray microCT images, using low-cost resources in Googles Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.

Spaceborne radar remote sensing of the earth system is essential to study natural and man-made changes in the ecosystem, water and energy cycles, weather and air quality, sea level, and surface dynamics. A major challenge with current approaches is the lack of broad spectrum tunability due to narrow band microwave electronics, that limit systems to specific science variable retrievals. This results in a significant limitation in studying dynamic coupled earth system processes such as surface and subsurface hydrology, where broad spectrum radar remote sensing is needed to sense multiple variables simultaneously. Rydberg atomic sensors are highly sensitive broad-spectrum quantum detectors that can be dynamically tuned to cover micro-to-millimeter waves with no requirement for band-specific electronics. Rydberg atomic sensors can use existing transmitted signals such as navigation and communication satellites to enable remote sensing. We demonstrate remote sensing of soil moisture, an important earth system variable, via ground-based radar reflectometry with Rydberg atomic systems. To do this, we sensitize the atoms to XM satellite radio signals and use signal correlations to demonstrate use of these satellite signals for remote sensing of soil moisture. Our approach provides a step towards satellite-based broad-spectrum Rydberg atomic remote sensing.

Transdisciplinary approaches that provide holistic views are essential to properly understand soil processes and the importance of soil to society and will be crucial in the future to integrate distinct disciplines into soil studies. A myriad of challenges faces soil science at the beginning of the 2020s. The main aim of this overview is to assess past achievements and current challenges regarding soil threats such as erosion and soil contamination related to different United Nations sustainable development goals (SDGs) including (1) sustainable food production, (2) ensure healthy lives and reduce environmental risks (SDG3), (3) ensure water availability (SDG6), and (4) enhanced soil carbon sequestration because of climate change (SDG13). Twenty experts from different disciplines related to soil sciences offer perspectives on important research directions. Special attention must be paid to some concerns such as (1) effective soil conservation strategies; (2) new computational technologies, models, and in situ measurements that will bring new insights to in-soil process at spatiotemporal scales, their relationships, dynamics, and thresholds; (3) impacts of human activities, wildfires, and climate change on soil microorganisms and thereby on biogeochemical cycles and water relationships; (4) microplastics as a new potential pollutant; (5) the development of green technologies for soil rehabilitation; and (6) the reduction of greenhouse gas emissions by simultaneous soil carbon sequestration and reduction in nitrous oxide emission. Manuscripts on topics such as these are particularly welcomed in Air, Soil and Water Research.

One of the main challenges in Environmental sciences is the identification and chemical evolution of polluting traces (e.g, cadmium or antimony) in soil, which requires long acquistion times for accurate measurements at synchrotron facilities. In this context, the potential of a new generation multi-element germanium detectors to identify traces at 0.1-1~ppm in a reasonable time has been studied using Allpix Squared framework. This code has been customized to include the three dimensional electric and weighting field maps generated by COMSOL Multiphysics software, and several features to model the sample environment at SOLEIL synchrotron and the signal response of a germanium detector equipped with a Digital Pulse Processor (DPP). The full simulation chain has been validated by experimental data from SAMBA beamline of SOLEIL synchrotron. This work presents a first estimation of the detection limit to cadmium traces in a soil sample for a future multi-element germanium detector, using this simulation chain.

Seventeen Sustainable Development Goals (SDGs) were adopted by 193 Governments at the General Assembly of the United Nations in 2015 for achievement by 2030. These SDGs present a roadmap to a sustainable future and a challenge to the science community. To guide activities and check progress, targets and indicators have been and are still being defined. The soil science community has published documents that describe the primary importance of soil for SDGs addressing hunger, water quality, climate mitigation and biodiversity preservation, and secondary relevance of soil for addressing several other SDGs. Soil scientists only marginally participated in the SDG discussions and are currently only peripherally engaged in discussions on targets or indicators. Agreement on several soil‐related indicators has still not been achieved. Involvement of soil scientists in SDG‐based studies is desirable for both developing solutions and increasing the visibility of the soil profession. Inputs into policy decisions should be improved as SDG committee members are appointed by Governments. Possible contributions of soil science in defining indicators for the SDGs are explored in this paper. We advocate the pragmatic use of soil–water–atmosphere–plant simulation models and available soil surveys and soil databases where “representative” soil profiles for mapping units (genetically defined genoforms) are functionally expressed in terms of several phenoforms reflecting effects of different types of soil use and management that strongly affect functionality.

Understanding of the formation and evolution of the Solar System requires understanding key and common materials found on and in planetary bodies. Mineral mixing and its implications on planetary body formation is a topic of high interest to the planetary science community. Previous work establishes a case for the use of Optical PhotoThermal InfraRed (O-PTIR) in planetary science and introduces and demonstrates the technique's capability to study planetary materials. In this paper, we performed a measurement campaign on granular materials relevant to planetary science, such as minerals found in lunar and martian soils. These laboratory measurements serve to start a database of O-PTIR measurements. We also present FTIR absorption measurements of the materials we observed in O-PTIR for comparison purposes. We find that the O-PTIR technique suffers from granular orientation effects similar to other IR techniques, but in most cases, is is directly comparable to commonly used absorption spectroscopy techniques. We conclude that O-PTIR would be an excellent tool for the purpose of planetary material identification during in-situ investigations on regolith and bedrock surfaces.

Abstract Intermittent rivers and ephemeral streams (IRES) encompass fluvial ecosystems that eventually stop flowing and run dry at some point in space and time. During the dry phase, channels of IRES consist mainly of dry riverbeds (DRBs), prevalent yet widely unexplored ecotones between dry and wet phases that can strongly influence the biogeochemistry of fluvial networks. DRBs are often overlooked because they do not strictly belong to either domain of soil or freshwater science. Due to this dual character of DRBs, we suggest that concepts and knowledge from soil science can be used to expand the understanding of IRES biogeochemistry. Based on this idea, we propose that DRBs can be conceptually understood as early stage soils exhibiting many similarities with soils through two main forces: i) time since last sediment transport event, and ii) the development status of stabilizing structures (e.g. soil crusts and/or vascular plants). Our analysis suggests that while DRBs and soils may differ in master physical attributes (e.g. soil horizons vs fluvial sedimentary facies), they become rapidly comparable in terms of microbial communities and biogeochemical processes. We further propose that drivers of DRBs biogeochemistry are similar to those of soils and, hence, concepts and methods used in soil science are transferable to DRBs research. Finally, our paper presents future research directions to advance the knowledge of DRBs and to understand their role in the biogeochemistry of intermittent fluvial networks.

Non-destructive methods of measuring water content in soils have been extensively developed in the last decades, especially in soil science. Among these methods, the measurements based on the electrical resistivity are simple and reliable thanks to the clear relationship between the water content and the electrical resistivity of soils. In this work, a new electrical resistivity probe was developed to monitor the change in local water content in the triaxial apparatus. The probe is composed of two-pair of electrodes, and an electrical current is induced through the soil at the vicinity of the contact between the probe and the specimen. Some experimental data on the changes in resistivity with the degree of saturation were obtained in specimens of a natural unsaturated loess from Northern France. Two theoretical models of resistivity were also used to analyze the obtained data. Results are finally discussed with respect to the loess's water retention properties.

Abstract The soil, at the interface between geosphere, biosphere, and atmosphere, is a particularly challenging object to study. The ecological and environmental impacts of soil functioning encourage soil scientists to exploit new technologies to generate new data or develop new research protocols. Among these technologies, 3D printing is a promising technology for improving the understanding of soils. The general principle of 3D printing is to build parts by adding materials layer by layer following a three-dimensional solid model. The 3D printing technology makes it possible to produce almost any geometrically complex shape or feature in a wide range of materials. Its interest lies in its ability to produce customized objects easily and rapidly in an endlessly reproducible 3D-arrangement. A large number of techniques and materials are available, which differ in their principles, advantages, and shortcomings. In this article, we present the main challenges and opportunities of using 3D printing in soil science. To this end, we present a review of the literature from 2000 to 2019 to identify and understand recent applications of 3D printing in this field. We first describe applications such as the manufacturing of agricultural equipment and laboratory devices in soil science, the development of new construction materials, or the geotechnical characterization of soil as a construction base. However, none of these applications requires replication of the soil functional properties as opposed to applications that would be dedicated to improving the understanding of soil functioning. We detail here the challenges and opportunities of building soil models that reproduce its physical, chemical, biological properties, and its dynamics in contact with living organisms. Despite the remarkable and rapid progress made in the development of 3D printing in recent years, this technology is still underused in the field of soil science. In particular, very few applications focus on the functioning of the soil itself as an ecological compartment. Indeed, several technical limitations have still to be overcome. 3D printed objects must be biocompatible, chemically and mechanically stable, and must be spatially resolved on the microscale. Many efforts are being made by the 3D printing community to push these boundaries. This paves the way for the wider use of 3D printing in soil science. In the near future, the availability of additive manufactured soil models, with strict and controlled composition and structure, will provide researchers with an irreplaceable opportunity to conduct reproducible experiments and better understand soil functioning factors.

Our view of the microbial world and its impact on human health is changing radically with the ability to sequence uncultured or unculturable microbes sampled directly from their habitats, ability made possible by fast and cheap next generation sequencing technologies. Such recent developments represents a paradigmatic shift in the analysis of habitat biodiversity, be it the human, soil or ocean microbiome. We review here some research examples and results that indicate the importance of the microbiome in our lives and then discus some of the challenges faced by metagenomic experiments and the subsequent analysis of the generated data. We then analyze the economic and social impact on genomic-medicine and research in both developing and developed countries. We support the idea that there are significant benefits in building capacities for developing high-level scientific research in metagenomics in developing countries. Indeed, the notion that developing countries should wait for developed countries to make advances in science and technology that they later import at great cost has recently been challenged.

Mechanical characterizations of plant roots are of primary importance in geophysics and engineering science for implementing mechanical models for the stability of root reinforced-soils, as well as in agronomy and soil science for understanding the penetration of roots in soils and optimizing crop. Yet the mechanical properties of plant roots depend on their water content, which can drastically evolve with drying or flooding of the external soil. The present work deals with the determination of the longitudinal Young's modulus of single non-lignified plant roots, chick-peas (Cicer arietinum L.), tested in compression along their root axis for different external environments: in controlled conditions of natural drying in air or in a osmotic solution of mannitol at the isotonic concentration where no water exchange occurs between the root and the external solution. We submitted the chick-pea radicles to successive mechanical compression cycles separated by rest periods to follow the time evolution of the root mechanical properties in drying and non-drying environments. Control experiments on non-drying roots placed in isotonic osmotic solutions showed no evolution of the root's Young's modulus whose value was around 2 MPa. On the contrary, the experiments performed in air exhibited a dramatic increase of the root's Young's modulus with the drying time, sometimes by a factor of 35. Moreover, the Young's modulus in these cases was observed to scale as a decaying power-law with the root's cross section measured at diffrent times of drying. We interpreted our results in the framework of the mechanics of cellular foams.

Soil science is one of the least diverse fields within science, technology, engineering and mathematics (STEM). Because demographics of groups and institutions provide a window into the culture, climate, equity and inclusion of minoritized scholars, we discuss how lack of diversity continues to affect our science and the scientific community, and its implications for the welfare of the global population. We highlight the role of antiracist practices and policies for improving workplace climate and thereby developing a diverse and inclusive scientific community. We present this article as a starting point for discussions on issues of race and racism in our scientific community and institutions.

In this article, I will present some figures and milestones of the written production of the Instituto Argentino de Radioastronomia (IAR), as well as a personal review of the scientific achievements carried out in recent years by the researchers working at the IAR. I will also briefly describe the scientific objectives of the IAR's flagship project, the Multipurpose Interferometric Array (MIA), in the context of the instrumental projects that have lately been or are being installed on Argentine soil.