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With changing climatic and market conditions, Indian agriculture sector needs appropriate transition in cropping patterns to ensure sustainability in growth of the sector and inclusiveness of land-constrained farmers in the intensification process. The need for transition in cropping patterns has been accentuated further following the COVID-19 pandemic that has led to loss of non-farm livelihood opportunities for millions and thus created further pressure on agriculture. Nevertheless, promoting transition in the sector would require deeper understanding of the underlying factors and addressing them through appropriate intervention strategies. This paper attempts for the same in context of West Bengal, India that houses significant number of land-constrained farmers. Using panel data of 18 districts for the period from 2004 to 05 to 2013–14, the paper finds crop diversification, irrigation, fertilizer use, road connectivity and market and storage facilities as the major drivers of agrarian transition in favour of non-foodgrains. In addition, the climatic factors such as relative humidity and variations in temperature also influence such transition. While the findings apparently suggest for further development of infrastructure facilities, sustaining the transition would also require agricultural education, extension services and development of human and social capitals in a decentralized manner to incorporate various local level dynamics.

Given that urbanization is considered as one of the most significant anthropogenic alterations of the environmental framework, the present study attempts to understand spatiotemporal characteristics of urban growth and its implications for the hydro-meteorological parameters in the Howrah Municipal Corporation (HMC) of the Indian state of West Bengal. The empirical approach of the paper is based on land use/land cover (LULC) changes and normalized difference built-up index (NDBI) computed using remote sensing and GIS techniques. Spatiotemporal satellite images and conventional data are used to characterize the urban growth process, whereas K-Means based unsupervised classification technique is used for LULC changes. Inverse distance weighting (IDW) interpolation method is applied for the spatial distribution of rainfall, temperature and groundwater level analysis. In order to examine whether hydro-meteorological (e.g., rainfall, temperature) parameters have any relationship with hydrological components (e.g., groundwater level) the Kendalls Tau test was performed. It is found that the maximum urban built-up area has increased during the last two decades with fluctuations in depth to groundwater level in northern, north-western and south-western side of the city. Notably, built-up expansions have taken place from the north-eastern to the south-eastern part. There are evidences of urban sprawl or shrinkage indicating expansion of built-up area and thus causing environmental degradation in the city area. While the methodology used in the paper has the potential for understanding the urbanization process, the findings have important implications for designing necessary policies and regulations. Keywords: Urbanization, LULC change, Groundwater, Kendalls Tau, Geographically Weighted Regression, Remote Sensing, GIS