Climate change and agriculture: A review of crop models

Abstract

The clear evidence of climate change impact demands proactive role by scientists, agronomist and meteorologist for upscaling agricultural production, precision forecast and food safety, especially in the tropical region. The crop simulation model suggests probable growth, development and crop yield for soil-plant-atmosphere dynamics assessment. Decision Support System for Agro-Technology Transfer Model (DSSAT) is an application-based model that gives the best-suited recommendations to achieve sustainability in the agriculture by means of simulation of users’ minimum experimental data that includes weather data pertaining to site, crop growth period, and data concerning the soil, crop management practices, etc. Identification of the weather and climate-sensitive problems due to extreme weather events on agriculture in any region can be achieved by crop model. Validation and calibration of crop simulation model is necessary with the help of field experimental data which will contain sensitive analysis, impact of epochal (temperature time period), various temperature ranges, different levels of radiation and CO2, different dates of sowing and various nitrogen and water treatments. Extreme climate change impacts on phenological stages, the growth of a plant, dry matter partitioning to different plant organs for all seasons also need to be studied. Validation, linking and analysis of climate change data for different Representative Concentration Pathway (RCP) with bias-corrected climate change data and crop model data using Decision Support System for Agro-Technology Transfer Model (DSSAT) and probability distribution model will be required to investigate climate change impacts on crops. Based on these results, the formulation of: (a) A multi-pronged plan of using local coping machinery, wider adoption of the existing technologies and/or concerted research and development efforts for evolving new technologies needed for adaptation and mitigation in rainfed and irrigated areas. (b) More precise weather-based agromet advisories for soil, crop yield, crop condition on a spatial and temporal basis to minimize losses and increase the economy of farmers and country. Optimized inputs like land preparation, selection of crop and cultivars, date of sowing, date of harvesting, irrigation scheduling, pesticide and fertilizer application, crop growth, extreme weather events, adaptation and development of flexible and dynamic Farm Management Information System (FMIS) strategies and other value-added services, etc. can be provided for farming community. The research can also be extended by doing a detailed analysis of estimation of soil moisture, evapotranspiration, insolation, vegetation index, growing degree days, standard precipitation index, and land surface temperature. The statistical study of estimated values of above said parameters using probability distribution model, root means square error and bias value of simulated data will be helpful for the development of a hydro-meteorological model, agriculture applications, irrigation planning over arid/semi-arid zones and forecasting systems.