Crop simulation model
- 1. Lecture on Introduction to Crop simulation model By, Prof. S.R. Suryavanshi ProfAsst. Professor of Agronomy, Dr. D.Y. Patil College of Agriculture, Talsande. 1
- 2. Model A model is a set of mathematical equations describing/mimic behaviour of a system Model simulates or imitates the behaviour of a real crop by predicting the growth of its components Modeling Modeling is based on the assumption that any given process can be expressed in a formal mathematical statement or set of statements.
- 3. Modeling : The application of methods to analyse complex, real-world problems in order to make predictions about what might happens various actions.
- 4. Simulation: It is the process of building models and analyzing thesystem. The art of building mathematical models and study their properties in reference to those of the systems (de Wit, 1982) Crop model: Simple representation of a crop. SYSTEM: Limited part of reality that contains inter related
- 5. Types of models (purpose for which it is designed ) Statistical & Empirical models Mechanistic models Deterministic models Stochastic models Static models Dynamic models
- 6. Statistical & Empirical models Direct descriptions of observed data, generally expressed as regression equations These models give no information on the mechanisms that give rise to the response Eg: Step down regressions, correlation, etc.
- 7. Mechanistic models • Theseattempt to usefundamental mechanisms of plant and soil processesto simulate specificoutcomes • These models are based on physical selection. • Eg. photosynthesis based model.
- 8. Deterministic models • These models estimate the exact value of the yield or dependent variable. • These models also have defined coefficients. • Eg: NPK doses are applied and the definite yields are given out.
- 10. Stochastic models • The models are based on the probability of occurrence of some event or external variable • For each set of inputs different outputs are given along with probabilities. • These models define yield or state of dependent variable at a given rate.
- 11. Static models values • Time is not included as a variables. • Dependent and independent variables having remain constant over a given period of time.
- 12. Dynamic simulation models • These models predict changes in crop status with time. • Both dependent and independent variables are having values which remain constant over a given period of time.
- 13. Steps in modelling 1. Define goals: Agricultural system 2. Define system and its boundaries: Crop model 3. Define key variables in system: State variables are those which can be measured. e.g. soil moisture content, crop yield etc Rate variables are the rates of different processes operating in a system. e.g. phosynthesis rate, transpiration rate. Driving variables are the variables which are not part of the system but the affect the system. e.g. sunshine, rainfall. Auxiliary variables are the intermediated products. e.g. dry matter partitioning, water stress etc 4. Quantify relationships (evaluation):
- 14. 5. Calibration: Model calibration is the initial testing of a model and tuning it to reflect a set of field data or process of estimating model parameters by comparing model predictions (out-put) for a given set of assumed conditions with observed data for the same conditions. 6. Validation: Testing of a model with a data set representing "local" field data. This data set represents an independent source different from the data used to develop the relation 7. Sensitivity analysis: Validated model is then tested for its sensitivity to different factors (e.g. temperature, rainfall, N dose). This is done to check whether the model is responding to changes in those factors or not.
- 16. Major & popular crop simulation models DSSAT (Decision Support System for Agrotechnology Transfer) AquaCrop InfoCrop APSIM (Agricultural Production System Simulator)
- 17. Components of AquaCrop, FAO model http://www.fao.org/nr/water/infores_databases_aquacrop.htm
- 18. INFOCROP
- 19. Sub-modular structure of APSIM model
- 20. Input files Weather data Soil data Management data Cultivar data
- 21. Uses On Farm management Crop system management: to evaluate optimum management production for cultural practice. Evaluate weather risk. Investment decisions. These are resource conserving tools
- 22. Understanding of research Testing scientific hypothesis. Yield prediction and forecasting. Evaluation of climate change. Useful for solving various practical problems in agriculture. ‰Helps in adaptation strategies, by which the negative impacts due to climate change can be minimized. Crop growth models identifies the precise reasons for yield gap at farmer’s field and forecasting crop yields. ‰Evaluate cultivar stability under long term weather
- 23. Experimental Data File Cultivar data Previous crop data Crop Data during season Output Depending on Option Setting and Simulation Application Weather DataSoil Data Crop Models INPUTS
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