Seasonal variability of nitrate concentrations below the root zone: A monthly predictive modelling approach

Le résumé fourni par la source

NLES5 is an empirical model extensively used for estimating annual nitrate leaching from the root zone. The model is based on leaching data obtained by multiplying the measured nitrate concentration below the root zone depth by the percolation calculated using a hydrological model, which together provides estimates of annual nitrate leaching from the root zone. However, this approach has some limitations, including redundancy and unclear error propagation in the relationship between nitrate concentration and percolation without considering seasonal variability. This study presents an approach to estimate the monthly distribution of nitrate concentration based on measurements of soil water samples taken with suction cells installed below the root zone. Our workflow includes screening algorithms to identify the most relevant predictors, testing the predictive performance, simplifying the number of predictions for practical implementation, and evaluating the impact on the final nitrate leaching calculations. The workflow was applied to the suction cup measurement dataset in the NLES5 support database of experimental sites. The results show that the regression tree-based XGBoost (Extreme Gradient Boosting) algorithm effectively estimates monthly variations in nitrate concentrations without relying on percolation data, by using temporal, management, soil, and weather covariates such as month, spring mineral fertilization, main crop, winter crop, clay content, mean monthly temperature, and accumulated precipitation in the harvest year. A cross-validated error of 34% was achieved for nitrate concentration, and a correlation of 0.8 with N leaching calculated from observed concentrations demonstrates a consistent description of the seasonal distribution of nitrate concentrations below the root zone.

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