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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.
9.
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
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