Climate change and ensuring food security for an exponentially growing global human population are the greatest challenges for future agriculture. Improved soil management practices are crucial to tackle these problems by enhancing agro-ecosystem productivity, soil fertility, and carbon sequestration. To meet Paris climate treaty pledges, soil management must address validated approaches for carbon sequestration and stabilization. The present synthesis assesses a range of current and potential future agricultural management practices (AMP) that have an effect on soil organic carbon (SOC) storage and sequestration. Through two strategies—increasing carbon inputs (e.g., enhanced primary production, organic fertilizers) and reducing SOC losses (e.g., reducing soil erosion, managing soil respiration)—AMP can either sequester, up to 714 ± 404 (compost) kg C ha⁻¹ y⁻¹, having no distinct impact (mineral fertilization), or even reduce SOC stocks in the topsoil (bare fallow). Overall, the carbon sequestration potential of the subsoil (>40 cm) requires further investigation. Moreover, climate change, permanent soil sealing, consumer behavior in dietary habits and waste production, as well as the socio-economic constraints of farmers (e.g., information exchange, long-term economic profitability) are important factors for implementing new AMPs. This calls for life-cycle assessments of those practices.

In‐field soil spectroscopy represents a promising opportunity for fast soil analysis, allowing the prediction of several soil properties from one spectral reading representing one soil sample. This facilitates data acquisition from large amounts of samples through its rapidity and the absence of required chemical processing. This is of particular interest in agriculture, where the chance to retrieve information from soils directly in the field is very appealing. This review is focused on in‐field visible to near infrared (Vis–NIR) spectroscopy (350–2500 nm), aimed at analysing soils directly in the field through proximal sensing. The main scope was to explore the available knowledge to identify existing gaps limiting the reliability and robustness of in‐field measurement, to foster future research and help transition towards the practical application of this technology. For this purpose, a literature review was performed, and surveyed information encompassed sensor range, carrier platforms in use, sensor type, distance to the soil sample, measurement methodology, measured soil properties and soil management, among many others. From this, we derived a list of tools in use with their spectral measurement properties, including the potential cross‐calibration with soil spectral libraries from laboratory spectroscopy of soil samples and potential measured target soil properties. Different instruments and sensors used to measure at varying wavelength ranges and with different spectral qualities are available for a large range of prices. The most frequently analysed soil properties included soil carbon contents (soil organic carbon, soil organic matter, total carbon), texture (clay, silt, sand), total nitrogen, pH and cation exchange capacity. Future perspectives comprise the implementation of larger databases, including different instruments and cropping systems as well as methodologies combining existing knowledge regarding laboratory spectroscopy with in‐field methods. The authors highlight the need for a broadly accepted measurement protocol for in‐field soil spectroscopy, fostering harmonization and standardization and consequently a more robust application in practice.

Soil microbial physiology controls large fluxes of C to the atmosphere, thus, improving our ability to accurately quantify microbial physiology in soil is essential. However, current methods to determine microbial C metabolism require liquid water addition, which makes it practically impossible to measure microbial physiology in dry soil samples without stimulating microbial growth and respiration (namely, the “Birch effect”). We developed a new method based on in vivo ¹⁸O‐water vapor equilibration to minimize soil rewetting effects. This method allows the isotopic labeling of soil water without direct liquid water addition. This was compared to the main current method (direct ¹⁸O‐liquid water addition) in moist and air‐dry soils. We determined the time kinetics and calculated the average ¹⁸O enrichment of soil water over incubation time, which is necessary to calculate microbial growth from ¹⁸O incorporation in genomic DNA. We tested isotopic equilibration patterns in three natural and six artificially constructed soils covering a wide range of soil texture and soil organic matter content. We then measured microbial growth, respiration and carbon use efficiency (CUE) in three natural soils (either air‐dry or moist). The proposed ¹⁸O‐vapor equilibration method provided similar results as the current method of liquid ¹⁸O‐water addition when used for moist soils. However, when applied to air‐dry soils the liquid ¹⁸O‐water addition method overestimated growth by up to 250%, respiration by up to 500%, and underestimated CUE by up to 40%. We finally describe the new insights into biogeochemical cycling of C that the new method can help uncover, and we consider a range of questions regarding microbial physiology and its response to global change that can now be addressed.

Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield using a re-calibration data assimilation approach. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields. LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The assimilation of LAI in EPIC provided an improvement in yield estimation in both years even though in 2017 strong underestimations were observed. The diverging results obtained in the two years indicated that the assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.