Impact of climate change induced future rainfall variation on dynamics of arid-humid zone transition in the western province of India.

The transition of the Earth's climate from one zone to another is one of the major causes behind biodiversity loss, rural-urban migration, and increasing food crises. The rising rate of arid-humid zone transition due to climate change has been substantially visible in the last few decades. However, the precise quantification of the climate change-induced rainfall variation on the climate zone transition still remained a challenge. To solve the issue, the Representative Grid Location-Multivariate Adaptive Regression Spline (RGL-MARS) downscaling algorithm was coupled with the Koppen climate classification scheme to project future changes in various climate zones for the study area. It was observed that the performance of the model was better for the humid clusters compared to the arid clusters. It was noticed that, by the end of the 21st century, the arid region would increase marginally and the humid region would rise by 24.28-36.09% for the western province of India. In contrast, the area of the semi-arid and semi-humid regions would decline for the study area. It was observed that there would be an extensive conversion of semi-humid to humid zone in the peripheral region of the Arabian sea due to the strengthening of land-sea thermal contrast caused by climate change. Similarly, semi-arid to arid zone conversion would also increase due to the inflow of dry air from the Arabian region. The current research would be helpful for the researchers and policymakers to take appropriate measures to reduce the rate of climate zone transition, thereby developing the socioeconomic status of the rural and urban populations.

Proportional impact prediction model of coating material on nitrate leaching of slow-release Urea Super Granules (USG) using machine learning and RSM technique.

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.