ABOUT THE COURSE: For development and dissemination of agricultural technologies, experiments are conducted in research stations for long period. Conducting these experiments while involving all the factors i.e., nutrients, water, tillage, pest control, cultural practices, genotype, environment, etc. is very complex, expensive and time consuming. Coupling the modeling exercises with the field experiments can generate data, information, knowledge, etc. for technology development in a lesser time and expenses. This course will enrich the knowledge of students on use of DSS for crop yield prediction under varying environments and agronomic management options. The information generated in the modeling and simulation process through the DSS will assist in better planning and management of agricultural operations in farmers’ field to avoid the risk of crop failure and economic loss due to inadequate management technology, climate change, weather variability, etc.INTENDED AUDIENCE: Students, researchers, and scientists of agricultural engineering and agricultural science disciplinePREREQUISITES: B. Tech (Agril Engg.) or B. Sc. (Agri.) in final yearINDUSTRY SUPPORT: IBM Research India Pvt Ltd., Skymet Weather Services Pvt. Ltd., FrontalRain Technologies Pvt. Ltd, RML AgTech Pvt. Ltd., Agricultural Insurance Company of India, International Agriculture Consulting Group Monsanto Farm Agvisory Services

Syllabus

Week 1: Introduction to crop production system, Crop classification, Crop growth and development, Crop ecosystem and agroclimatic zones
Week 2:Crop growth factors and growth laws, Effect of climatic factors, Solar radiation interception concept for biomass accumulation and yield prediction, Growing Degree Days concept for growth duration and yield prediction
Week 3:Introduction to crop modeling and simulation, Decision support system (DSS), Types of crop model, Experiment and data requirement in crop model
Week 4:Crop simulation: Terminologies, Simulation approach, Objectives in modeling and simulation
Week 5:Concepts of crop modeling: Compartment model symbols, State variables working principle, Crop systems model for different production situations
Week 6:Potential production simulation: Input factors, duration and growth rate simulation, Yield formation
Week 7:DSSAT model: Introduction, application, crops involved, Modules, Biomass yield simulation in CERES model
Week 8:File system of DSSAT: Input file, File naming, Input data for model simulation, statistical technique for calibration and validation
Week 9:Application of models as risk management tools, Uncertainty and risk analysis, Climate Risk management (CRM), Support for crop insurance scheme
Week 10:Hands on DSSAT: Genotype coefficient estimation in CERES and other models, experimental data inputs, soil data inputs, weather data inputs, management data inputs
Week 11:Hands on Exercises: Evaluation of production management inputs (water, nutrients, soil, weather, etc.) and discussion of the results
Week 12:Group assignment presentation and discussion (Focus: Why crop modeling, Yield response, technology recommendation, CRM tools, etc.)