Machine Learning approach to Predict net radiation over crop surfaces from global solar radiation and canopy temperature data.

As the ground-based instruments for measuring net radiation are costly and need to be handled skillfully, the net radiation data at spatial and temporal scales over Indian subcontinent are scanty. Sometimes, it is necessary to use other meteorological parameters to estimate the value of net radiation, although the prediction may vary based on season, ground cover and estimation method. In this context, artificial intelligence can be used as a powerful tool for predicting the data considering past observed data.

Evaluating the long-term impact of projected climate on rice-lentil-groundnut cropping system in Lower Gangetic Plain of India using crop simulation modelling.

Most simulations of food production in response to various climates to date have used simulations of the same crop over multiple years. This study evaluated the impact of projected climate on performance of rice-lentil-groundnut cropping sequence in New Alluvial Zone of West Bengal, India, using DSSAT model. The study period consisted of baseline (1980-2010), mid-century (2040-2069) and end-century (2070-2099).