MONITORING AND FORECASTING OF SEASONAL RICE CROP PRODUCTIVITY FOR PADDY DOMINATED REGIONS OF INDIA
Keywords: Crop Modeling, Hydrologic Modeling, Rice Yield, Seasonal Forecasting, Remote Sensing
Abstract. The relevance of this study is immense for India. The Indian economy largely depends on agriculture, which is impacted by weather extremes and variability in monsoon. India is more vulnerable to disruption from drought than countries like the United States. While agriculture accounts for just 16 percent of India’s economy, half of its 1.3 billion people work on farms, thus, making agriculture the backbone of the Indian economy. However, in agriculture, rice is India’s most important food crop with nearly 1 billion Indian people reliant on it as their major food source. Most important constraint to rice production is water stress which affects nearly ~40 million ha of rainfed system from the total ~45 million ha area under rice cultivation. Future climate change effects on rainfall timing and amount, and projected increases in temperature are expected to exacerbate existing water stresses and will have a direct impact on agriculture in India, especially rice cultivation. We have developed an integrated system that is successfully implemented in many countries. The integrated system RHEAS (Regional Hydrological Extreme and Assessment System) coupled with M-DSSAT (modified DSSAT crop model) ingests various NASA Earth science data to produce a set of relevant hydrologic products (e.g., drought indices, water excess/stress information) and rice yields nowcasts (current conditions), forecasts, and seasonal projections, and then feed them into the operational agency. The overarching goal of this study is to provide this integrated system to stakeholder to improve decision-making process and mitigate the plights of rice farmers and prepare the country to deal with ground realities based on the forecast of rice production.