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.

We used field experimental data to evaluate the ability of the agricultural production system model (APSIM) to simulate soil P availability, maize biomass and grain yield in response to P fertilizer applications on a fluvo-aquic soil in the North China Plain. Crop and soil data from a 2-year experiment with three P fertilizer application rates (0, 75 and 300 kg P2O5 ha−1) were used to calibrate the model. Sensitivity analysis was carried out to investigate the influence of APSIM SoilP parameters on the simulated P availability in soil and maize growth. Crop and soil P parameters were then derived by matching or relating the simulation results to observed crop biomass, yield, P uptake and Olsen-P in soil. The re-parameterized model was further validated against 2 years of independent data at the same sites. The re-parameterized model enabled good simulation of the maize leaf area index (LAI), biomass, grain yield, P uptake, and grain P content in response to different levels of P additions against both the calibration and validation datasets. Our results showed that APSIM needs to be re-parameterized for simulation of maize LAI dynamics through modification of leaf size curve and a reduction in the rate of leaf senescence for modern stay-green maize cultivars in China. The P concentration limits (maximum and minimum P concentrations in organs) at different stages also need to be adjusted. Our results further showed a curvilinear relationship between the measured Olsen-P concentration and simulated labile P content, which could facilitate the initialization of APSIM P pools in the NCP with Olsen-P measurements in future studies. It remains difficult to parameterize the APSIM SoilP module due to the conceptual nature of the pools and simplified conceptualization of key P transformation processes. A fundamental understanding still needs to be developed for modelling and predicting the fate of applied P fertilizers in soils with contrasting physical and chemical characteristics.