Search

Agriculture faces the dual challenge of sustainably increasing productivity to meet the food demand of a rapidly growing population and adapting to climate change. Despite significant efforts to develop more adaptive and productive crop cultivars and to improve water and nutrient management practices, the potential of crops to tackle this challenge by optimizing soil resource utilization remains underexplored. Here, we propose that optimizing root systems to promote the efficient acquisition of soil resources can increase yield, improve resilience to climate variability, and reduce environmental impacts. This optimization can be achieved through genetic manipulation at the crop species level and effective management of cropping systems at the field level (e.g., intercropping, rotation, and agroforestry). Advances in three-dimensional soil data collection, linking root traits to plant performance, and modelling of climate-soil–plant-management interactions are paving the way for soil-smart cropping. Effective communication and knowledge exchange with stakeholders beyond the scientific community are vital for accelerating the development and adoption of soil-smart practices for climate-smart and sustainable agricultural production.

Soil organic carbon (SOC) is crucial for soil health and quality, and its sequestration has been suggested as a natural solution to climate change. Accurate and cost-efficient determination of SOC and its functional fractions is essential for effective SOC management. Visible near-infrared spectroscopy (vis-NIR) has emerged as a cost-efficient approach. However, its ability to predict whole-profile SOC content and its fractions has rarely been assessed. Here, we measured SOC and its two functional fractions, particulate (POC) and mineral-associated organic carbon (MAOC), down to a depth of 200 ​cm in seven sequential layers across 183 dryland cropping fields in northwest, southwest, and south China. Then, vis-NIR spectra of the soil samples were collected to train a machine learning model (partial least squares regression) to predict SOC, POC, MAOC, and the ratio of MAOC to SOC (MAOC/SOC – an index of carbon vulnerability). We found that the accuracy of the model indicated by the determination coefficient of validation (Rval2) is 0.39, 0.30, 0.49, and 0.48 for SOC, POC, MAOC, and MAOC/SOC, respectively. Incorporating mean annual temperature improved model performance, and Rval2 was increased to 0.64, 0.31, 0.63, and 0.51 for the four carbon variables, respectively. Further incorporating SOC into the model increased Rval2 to 0.82, 0.64, and 0.59, respectively. These results suggest that combining vis-NIR spectroscopy with readily-available climate data and total SOC measurements enables fast and accurate estimation of whole-profile POC and MAOC across diverse environmental conditions, facilitating reliable prediction of whole-profile SOC dynamics over large spatial extents.

Soil microbes drive soil organic carbon (SOC) mineralization. Because microbial groups differ in metabolic efficiency and respond differently to temperature variation, it is reasonable to expect a close association of SOC mineralization and its temperature sensitivity (Q10 which is defined as the factor of the change of soil carbon mineralization induced by 10 °C temperature increase) with microbial community diversity and composition. However, these relations have rarely been tested. Here, we conducted an incubation experiment to assess the temperature responses of microbial α diversity and the relative abundance of microbial r- and K-strategists in soils from a wide range of ecosystems across a climate gradient in the southeast Tibet. The results indicated that the instantaneous α diversity and the relative abundance of r- and K-strategists are significantly (P