This document provides an introduction to crop simulation models. It defines a model as a set of mathematical equations that mimic the behavior of a real crop system. Modeling involves analyzing complex problems to make predictions about outcomes. Simulation is the process of building models and analyzing systems. Crop models provide simple representations of crops. The document outlines different types of models and their purposes. It describes the key components and steps involved in building crop simulation models, including defining goals and variables, quantifying relationships, calibration, and validation. Finally, it discusses several popular crop models and their uses in farm management, research, and experimental applications.

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.
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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
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 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:
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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.

15. Crop Simulation Models

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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