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An accurate assessment of nitrate leaching is important for efficient fertiliser utilisation and groundwater pollution reduction. However, past studies could not efficiently model nitrate leaching due to utilisation of conventional algorithms. To address the issue, the current research employed advanced machine learning algorithms, viz., Support Vector Machine, Artificial Neural Network, Random Forest, M5 Tree (M5P), Reduced Error Pruning Tree (REPTree) and Response Surface Methodology (RSM) to predict and optimize nitrate leaching. In this study, Urea Super Granules (USG) with three different coatings were used for the experiment in the soil columns, containing 1 kg soil with fertiliser placed in between. Statistical parameters, namely correlation coefficient, Mean Absolute Error, Willmott index, Root Mean Square Error and Nash–Sutcliffe efficiency were used to evaluate the performance of the ML techniques. In addition, a comparison was made in the test set among the machine learning models in which, RSM outperformed the rest of the models irrespective of coating type. Neem oil/ Acacia oil(ml): clay/sulfer (g): age (days) for minimum nitrate leaching was found to be 2.61: 1.67: 2.4 for coating of USG with bentonite clay and neem oil without heating, 2.18: 2: 1 for bentonite clay and neem oil with heating and 1.69: 1.64: 2.18 for coating USG with sulfer and acacia oil. The research would provide guidelines to researchers and policymakers to select the appropriate tool for precise prediction of nitrate leaching, which would optimise the yield and the benefit–cost ratio. [ABSTRACT FROM AUTHOR]

Urea, a nitrogenous fertilizer which plays an important role in enhancing yield, but has a drawback of low nitrogen use efficiency (NUE). Use of urea super granule (USG) and coated urea have advantages over prilled urea in terms of higher NUE and eventually improved yield. Depth of placement (DOP) of USG plays a major role in achieving higher NUE and yield. However, appropriate depth of placement needs detailed knowledge about water and nitrogen (N) movements to provide maximum nutrient to plants. Therefore, an experiment was conducted in the paddy field using USG coated with three different types of coating (USG with nano bentonite and neem oil (T1), USG with heated nano bentonite and neem oil (T2) and USG with sulfer and acacia gum solution (T3)) and placed at the centre of four hills of paddy as basal dose at ICAR-IARI, New Delhi. Spatial and temporal data of soil moisture and Nwere collected for the initial 32 days in paddy field after crops establishment. The HYDRUS-2D model was calibrated and validated using the observed data. Calibrated model was used for simulation of N movement in different soil types and for different depths of USG placement. Results revealed that permissible soil like sandy loam, the optimum DOP irrespective of coating was 5 cm. For sandy clay loam soil, optimum depth of placement was found to be 10 cm, 6 cm and 10 cm for T1, T2 and T3 type of coatings. Similarly, for clay loam soil, depth of USG placement for T1, T2 and T3 type coating was found to be 8 cm, 10 cm and 8 cm, respectively. Using the optimum DOP in sandy clay loam soil, results showed that both i.e., DOP and coating type has significant effect on number of tillers m−2 and yield (t ha−1). In 2020 and 2021, the highest number of tillers per square meter at 30 days after transplanting (DAT) was observed in the T3 treatment at DOP of 10 cm, with counts of 453.66 and 454.88, respectively. Following closely were T2 at DOP of 10 cm (447.33 and 119.55) and T1 at DOP of 6 cm (443.44 and 444.77). Similarly, the best yield in 2020 and 2021 were observed from T3 treatment at DOP of 10 cm (5.41 t/ha and 5.44 t/ha), followed by T2 at DOP of 10 cm (5.33 t/ha and 5.42 t/ha), and T1 at DOP of 6 cm (5.29 t/ha and 5.31 t/ha). This study may assist the farmers, researchers, manufacturers and policy makers to make selection of the optimum depth of placement for higher NUE and yield in different soil types. • USGs were coated with different coating materials. • HYDRUS model was calibrated and validated for moisture and nitrogen concentration. • Root growth characteristics of paddy was observed by using root scanner. • The optimized placement depth was determined based on the maximum nutrient supply. • Agronomic evaluation result was also observed to verify the simulation result. [ABSTRACT FROM AUTHOR]