首页|Estimating soil mineral nitrogen from data-sparse field experiments using crop m odel-guided machine learning approach
Estimating soil mineral nitrogen from data-sparse field experiments using crop m odel-guided machine learning approach
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News – According to news reporting based on a preprint a bstract, our journalists obtained the following quote sourced from biorxiv.org: “Sandy soils are susceptible to excessive nitrogen (N) leaching under intensive crop production which is linked with the soil\’s low n utrient holding capacity and high-water infiltration rate. Estimating soil miner al nitrogen (SMN) at the daily time-step is crucial in providing fertilizer reco mmendations balancing plant nitrogen use efficiency (NUE) and N losses to the en vironment. Crop models [e.g., Decision Support System for Agr otechnology Transfer (DSSAT)] can simulate the trend of SMN i n varied fertilizer rates and timing of application but are unable to replicate its magnitude due to the inability to capture high-water table conditions in a s ub-irrigated soil.
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