A novel platform for modeling and simulation of seedling emergence under various sowing depths and water regimes in direct-seeded rice

Abstract Direct-seeding of rice by sowing dry seeds on dry soils often results in poor seedling emergence due to erratic rainfall. Adjusting the sowing depth to a given rainfall pattern may improve rice emergence. To assess risks of crop failure in direct-seeded rice, we developed a platform for modeling and simulation of rice emergence at different sowing depths. We combined the HYDRUS-1D soil simulation model, which simulates the surface soil’s moisture dynamics, with two rice emergence models recently developed by our research group. The platform used 48 years of daily weather data (1977–2024) for the study site as inputs for the soil model to simulate soil moisture and temperature at designated depths. We then input the simulated values and sowing depths into the emergence models to simulate final emergence and the emergence date. The simulated soil water tension at a depth of 1 cm showed huge interannual variation, reaching 10 MPa in dry years. The simulation showed that relative to a 1-cm sowing depth, depths of 4 and 6 cm greatly reduce the probability of crop failure under rainfed conditions (from 8% to between 1% and 2%). Our novel platform for risk assessment should therefore facilitate the use of direct-seeded rice in suboptimal environments. The platform also fills a knowledge gap for simulation of crop establishment in direct-seeded rice under future climate scenarios.