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Background and aims Despite the widespread co-occurrence of subsoil acidity and compaction, the interaction between these factors and their combined effects on root system development under water-limited conditions is poorly understood. This study aimed to investigate how the removal of soil strength and acidity influenced root development and examine relationships between root system properties and shoot growth under field conditions. Methods Images of root growth were obtained in situ by using rhizotron facilities to assess the temporal effects of soil improvement through loosening and lime incorporation on wheat root development in the 2018 season. Following this, we examined the relationship between enhanced root systems, water and nutrient uptakes, and overall crop performance. Results The results indicated that improved soil conditions significantly enhanced planar root length density (pRLD) through the elongation and proliferation of wheat roots in the subsoil. Regressing tree analysis suggested that subsoil strength had a more dominant influence on pRLD compared to soil pH or aluminium — leading to higher water uptake, wheat head density and yield. In addition to the benefit of loosening, the lime treatment improved soil acidity, promoting continuous root growth with root hairs, allowing plants to access previously unavailable nutrients and improve yield further. This highlights the critical role of managing multiple soil constraints for optimising crop productivity. Conclusions In this study, the integration of root images and soil properties data provided a deeper understanding of root-soil interactions which could be useful for developing sustainable soil management practices to optimise crop productivity under challenging conditions.

Soil organic carbon (SOC) persistence is predominantly governed by mineral protection, consequently, soil mineral-associated (MAOC) and particulate organic carbon (POC) turnovers have different impacts on the vulnerability of SOC to climate change. Here, we generate the global MAOC and POC maps using 8341 observations and then infer the turnover times of MAOC and POC by a data-model integration approach. Global MAOC and POC storages are 975 964 987 Pg C (mean with 5% and 95% quantiles) and 330 323 337 Pg C, while global mean MAOC and POC turnover times are 129 45 383 yr and 23 5 82 yr in the top meter, respectively. Climate warming-induced acceleration of MAOC and POC decomposition is greater in subsoil than that in topsoil. Overall, the global atlas of MAOC and POC turnover, together with the global distributions of MAOC and POC stocks, provide a benchmark for Earth system models to diagnose SOC-climate change feedback. Separating soil organic carbon into mineral-associated and particulate organic carbon enables a more accurate prediction of soil vulnerability to climate change. The authors generate the global atlas of stocks and turnover times of these two fractions.

• Warming-induced desiccation of the fertile topsoil layer could lead to decreased nutrient diffusion, mobility, mineralization and uptake by roots. Increased vertical decoupling between nutrients in topsoil and water availability in subsoil/bedrock layers under warming could thereby reduce cumulative nutrient uptake over the growing season. • We used a Mediterranean semiarid shrubland as model system to assess the impacts of warming-induced topsoil desiccation on plant water- and nutrient-use patterns. A 6 yr manipulative field experiment examined the effects of warming (2.5°C), rainfall reduction (30%) and their combination on soil resource utilization by Helianthemum squamatum shrubs. • A drier fertile topsoil (‘growth pool’) under warming led to greater proportional utilization of water from deeper, wetter, but less fertile subsoil/bedrock layers (‘maintenance pool’) by plants. This was linked to decreased cumulative nutrient uptake, increased nonstomatal (nutritional) limitation of photosynthesis and reduced water-use efficiency, above-ground biomass growth and drought survival. • Whereas a shift to greater utilization of water stored in deep subsoil/bedrock may buffer the negative impact of warming-induced topsoil desiccation on transpiration, this plastic response cannot compensate for the associated reduction in cumulative nutrient uptake and carbon assimilation, which may compromise the capacity of plants to adjust to a warmer and drier climate.

Soil organic carbon (SOC) changes under future climate warming are difficult to quantify in situ. Here we apply an innovative approach combining space-for-time substitution with meta-analysis to SOC measurements in 113,013 soil profiles across the globe to estimate the effect of future climate warming on steady-state SOC stocks. We find that SOC stock will reduce by 6.0 ± 1.6% (mean±95% confidence interval), 4.8 ± 2.3% and 1.3 ± 4.0% at 0–0.3, 0.3–1 and 1–2 m soil depths, respectively, under 1 °C air warming, with additional 4.2%, 2.2% and 1.4% losses per every additional 1 °C warming, respectively. The largest proportional SOC losses occur in boreal forests. Existing SOC level is the predominant determinant of the spatial variability of SOC changes with higher percentage losses in SOC-rich soils. Our work demonstrates that warming induces more proportional SOC losses in topsoil than in subsoil, particularly from high-latitudinal SOC-rich systems. The response of soil organic carbon to climate warming may be soil depth-dependent, but remains unquantified in situ. Here the authors show that warming induces more proportional soil carbon losses in topsoil than in subsoil, particularly from high-latitudinal carbon-rich soils.

Background Even with extensive root growth, plants may fail to access subsoil water and nutrients when root-restricting soil layers are present. Biopores, created from decaying roots or soil fauna, reduce penetration resistance and channel root growth into the deeper soil. Further positive effects on plants result from biopore traits, as the pore walls are enriched in nutrients, microbial abundance, and activity relative to bulk soil. However, negative effects on plant growth have also been observed due to root clumping in biopores, less root-soil contact than in the surrounding bulk soil and leaching of nutrients. Scope We discuss methods for biopore research, properties of biopores and their impact plant performance based on a literature review and own data. We elucidate potential implications of altered root-soil contact for plant growth and the consequences of root growth in pores for the rhizosphere microbiome. Conclusions Biopores play an important but ambiguous role in soils. The effects of biopores on plant growth depend on soil properties such as compaction and moisture in an as-yet-unresolved manner. However, pore properties and root-soil contact are key parameters affecting plant yield. Knowledge gaps exist on signaling pathways controlling root growth in pores and on mechanisms modifying rhizosphere properties inside biopores. The degree to which negative effects of biopores on plant growth are compensated in the bulk soil is also unclear. Answering these questions requires interdisciplinary research efforts and novel imaging methods to improve our dynamic understanding of root growth and rhizosphere processes within biopores and at the rhizosphere-biopore interface.

Soil organic carbon (SOC) in shrublands is an important component of global carbon cycling. However, there is a dearth of large-scale systematic observations of SOC stocks at different soil depths, and it remains uncertain whether and how the relative importance of biotic and abiotic variables in regulating SOC stocks changes with soil depth. Here, we quantified large-scale patterns and controlling factors of SOC storage per area (SOCD, kg m−2) for both topsoils (0–30 cm) and subsoils (30–100 cm) by taking full advantage of a consistent stratified random sampling study of one-meter soil profiles across 1211 sites in Chinese shrublands. We found that subsoils stored about 53.30% of total SOCD, falling into the range of previously reported values for terrestrial ecosystems. SoilGrids250m model-derived assessments overestimated SOCD by 13.72 and 65.49% for topsoils and subsoils, respectively. The effects of climate means and seasonality on SOCD were equally strong in both topsoils and subsoils. The predominant effects of edaphic properties on SOCD were more robust in subsoils than in topsoils. Belowground biomass of shrublands was the only significant predictor of topsoil SOCD, but it did not predict subsoil SOCD accurately. These findings have refined our understanding of the pivotal role of shrublands in SOC storage and sequestration potential and could serve as an ecologically valuable baseline for large-scale improvement and validation of depth-dependent SOC dynamics for multilayer SOC modules in Earth Systems Models.

Sustainability of agricultural production and mitigation of global warming rely on the regeneration of soil organic carbon (SOC), in particulate organic carbon (POC) and mineral-associated organic carbon (MAOC) forms. We conducted a global systematic meta-analysis of the effects of regenerative management practices on SOC, POC, and MAOC in cropland, finding: 1) no-till (NT) and cropping system intensification increase SOC (11.3% and 12.4%, respectively), MAOC (8.5% and 7.1%, respectively), and POC (19.7% and 33.3%, respectively) in topsoil (0 to 20 cm), but not in subsoil (>20 cm); 2) experimental duration, tillage frequency, the intensification type, and rotation diversity moderate the effects of regenerative management; and 3) NT synergized with integrated crop–livestock (ICL) systems to greatly increase POC (38.1%) and cropping intensification synergized with ICL systems to greatly increase MAOC (33.1 to 53.6%). This analysis shows that regenerative agriculture is a key strategy to reduce the soil C deficit inherent to agriculture to promote both soil health and long-term C stabilization.

Aims Grain yields of summer maize are significantly affected by different nitrogen (N) rates and depths through regulating root growth and distribution in soil. Understanding of effects of the deep placement of N on the root and shoot growth, grain yield and N use efficiency in summer maize are limited. Methods In this study, four N rates: 225, 191.25, 157.5 and 0 kg ha −1 applied at four depths: 5, 10, 15, and 20 cm were studied. Soil N content, root dry weight, root length density, biomass, grain yield and N use efficiency of maize were measured. Results Compared to 225 kg N ha −1 applied at a depth of 5 cm, a 15% reduction in the N application rate at a depth of 15 cm induced a larger root length density in the subsoil, as well as a larger rooting depth. It also facilitated maintaining a higher level of biomass and N accumulation during the later growth period, which increased the N assimilation of grain and enhanced grain yield by 3.9%, N recovery efficiency by 66.7%, N agronomic efficiency by 38.5%, and partial factor productivity of N by 22.1%. Conclusions Overall, this study demonstrates that reducing the recommended N application rate of 225 kg ha −1 by 15% but applying it at a depth of 15 cm might be considered an efficient fertilization method that increases agricultural productivity and N use efficiency.

Understanding the geotechnical characteristics of subsoil is important for safety and efficiency in design and construction processes. In particular, the subsoil in the Bangkok Metropolitan area has accumulated soft marine clay over a long period, resulting in a thick layer of soft clay, which poses challenges for engineers. This study presents an approach to modelling the subsoil of the Bangkok Metropolitan region by utilising a large dataset of borehole data, enhanced with a Deep Neural Network (DNN) model, to develop a 3D geotechnical map. The hyperparameters of the DNN were tuned to fit the dataset for classifying the soil layers and the regression models were generated to predict the geotechnical engineering properties of the Bangkok subsoil, including the bulk unit weight, water content, plasticity index, undrained shear strength, and SPT-N values. The DNN model performance has been evaluated to ensure the accuracy and reliability of its predictions. The generated 3D geotechnical map was compared with the map obtained from the traditional kriging method to verify the map accuracy and differences in results between these two approaches. This study demonstrates the potential of machine learning techniques for improving geotechnical mapping and geotechnical engineering information. The outcomes of this research also support Sustainable Development Goals (SDGs), particularly SDG 9, by providing accurate geotechnical data to enhance sustainable infrastructure planning, and SDG 11, by refining the subsoil model in urban areas, which contributes to safer and more sustainable urban development while reducing environmental risks in construction.