This study aimed to simultaneously evaluate the effects and identify key mechanisms of combined applications of microbial organic.
When NH3 is released into the atmosphere, it reacts with acid gases to form secondary aerosols, which has a critical impact on air quality. Modest prediction improvements resulted from ML ensembles. Ammonia (NH3) volatilization is one of the significant causes of nitrogen (N) loss in farmland.ML models also differed in their sensitivities to input variables (weather, soil properties, management, initial conditions), thus depending on the data availability researchers may use a different ML model. Across all ML models, yield prediction error decreased by 10%-40% as the training dataset increased from 0.5 to 1.8 million data points, whereas N loss prediction error showed no consistent pattern. Biol compared DNDC-Rice, CERES-Rice and APSIM-Oryza with respect Fertil Soils 9:3136 (1990). They also differed in their sensitivities to the size of the training dataset. Meanwhile, Li et al.65 interdependence of ammonia volatilization and denitrication as nitrogen loss processes in ooded rice elds in the Philippines. ML meta-models reasonably reproduced simulated maize yields using the information available at planting, but not N loss. XGBoost was the most accurate ML model in predicting yields with a relative mean square error (RRMSE) of 13.5%, and Random forests most accurately predicted N loss at planting time, with a RRMSE of 54%. Ammonia (NH3) losses from swine manure contribute to odor problems, decrease animal productivity, and increase the risk of acid rain deposition. We asked: (1) How well do ML meta-models predict maize yield and N losses using pre-season information? (2) How many data are needed to train ML algorithms to achieve acceptable predictions? (3) Which input data variables are most important for accurate prediction? And (4) do ensembles of ML meta-models improve prediction? The simulated dataset included more than three million data including genotype, environment and management scenarios.
#Nitrogen volatilization losses using apsim simulator#
We evaluated the potential of four machine learning (ML) algorithms (LASSO Regression, Ridge Regression, random forests, Extreme Gradient Boosting, and their ensembles) as meta-models for a cropping systems simulator (APSIM) to inform future decision support tool development. After evaluating how much N remains in the soil and if that will be enough. Thus, there is a need for more computationally expedient approaches to scale up predictions. Wet conditions in May and June can delay planed side-dress applications and promote loss of previously applied nitrogen. Simulation crop models can assist in scenario planning, but their use is limited because of data requirements and long runtimes. Pre-growing season prediction of crop production outcomes such as grain yields and nitrogen (N) losses can provide insights to farmers and agronomists to make decisions. Nitrogen Use Efficiency,Nitrogen Fertilizers,RAMP,N Rich Strip,N deficiency,nitrogen algorithms,Nitrogen Management,Wheat Nitrogen Fertilization,Maize,Corn Nitrogen Fertilization, pocket sensor.