The document discusses crop growth simulation models, emphasizing their role in understanding interactions between soil, plants, and weather for optimizing crop productivity. It outlines the history, types, and applications of crop models, highlighting their importance in agricultural management, yield prediction, and adaptation to climate change. The conclusion stresses the need for properly validated models to maximize their effectiveness in supporting sustainable agriculture.

1. SUBMITTED TO
Prof. A.K. Mohanty
Professor, Dept. of Agronomy
Dr. Rajeswari Das
Asst. Professor, Dept. of Soil Science
GIET UNIVERSITY
Gunupur - 765022

2. INTRODUCTION
 Crop growth is a very complex phenomenon and a product of a series of complicated interactions of
soil, plant and weather.
 Crop growth simulation is a relatively recent technique that facilitates quantitative understanding of the
effects of these factors and agronomic management factors on crop growth and productivity.
 These models are quantitative description of the mechanisms and processes that result in growth of crop.
The processes could be physiological, physical and chemical processes of crop.

3. MODEL
• A model is a set of mathematical equation describing/mimic behaviour
of a system . Model imitates or simulates the behaviour of a real crop by
predicting the growth of its components.
MODELLING
• Modelling is based on the assumption that any given process can be
expressed in a formal mathematical statement or set of statements.
SIMULATION
• It is the process of building models and analysing the system.
• The art of building mathematical models and study their properties in
reference to those of the systems. (De Wit,1982)
CROP MODEL:
• It is the simple representation of a crop.
CROP SIMULATION MODEL:
• It is a simulation model that describes the processes of crop growth &
development as a function of weather conditions, soil conditions & crop
management .

4. HISTORY OF CSM
 Crop simulation models were first developed to run on
mainframe computers in the 1960s. Such models were
used to estimate light interception and photosynthesis by
crops.
 In 1970s the complexity of CSMs increased giving
comprehensive models requiring large quantities of input
data. However, such complexities don’t always lead to
better models.
 In 1980s summary models were developed, although this
was often with the aid of more complex models.
 Customized models also became important with the
realisation that user-requirements often determined which
should be used.
 Awareness of the limitations of CSMs has been increased
since their introduction.
 Despite difficulties, there is evidence that CSMs can play
an important role in scientific research, decision support,
and education.

5. CROP GROWTH MODELS
 These models are computer software programmes
that can simulate daily growth (e.g. biomass,
yield) and development (e.g. emergence,
flowering, harvest) of crops such as wheat, maize
or potato.
 These models have been developed over a long
period of time so that they can be now applied to
support agricultural management practices (e.g.
fertiliser recommendation, irrigation, crop rotating
planning).

6. HOW DO CROP GROWTH MODELS WORK?
 Depending on soil characteristics, weather
conditions, and crop species, crop models calculate
the daily growth of biomass in individual plant
organs (stem, root, leaves, grains/tubers etc.) as well
as the progress of plant development from sowing to
maturity.
 In addition, crop models account for important
processes in the soil (water and nitrogen availability)
in order to simulate crop growth during a whole
growing season.

7. STEPS IN MODELLING
1. Define goals: Agriculture system.
2. Define system and its boundaries: Crop model
3. Define key variables in system:
o State variables are those which can be measured.
 e.g. soil moisture content, crop yield etc.
o Rate variables are the rates of different processes operating in a system. e.g. photosynthesis rate,
transpiration rate.
o Driving variables are those variables which are not part of the system but affect the system. e.g.
sunshine, rainfall.
o Auxiliary variables are the intermediated products. e.g. dry matter partitioning, water stress.
4. Quantify relationships (evaluation)
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(output) for a given set of
assumed conditions with observed data for the same conditions.
6. Validation: testing 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.

8. MAJOR & POPULAR CROP SIMULATION
MODELS
DSSAT (Decision Support System for Agrotechnology Transfer)
Aqua Crop
Info Crop
APSIM (Agricultural Production System Simulator

9. TYPES OF MODELS
A. STATISTIC MODEL: This model represents the relationship between yield components and weather
parameters. Statistical techniques are used to measure relationship.
B. STOCHASTIC MODEL: These models calculate output at a given rate. When variation and uncertainty reach
to high level, it becomes advisable to develop a stochastic model that gives an expected mean value as well as the
associated variance.
C. DYNAMIC MODELS: Time is included as a variable and output is a function of time. Both dependent and
independent variables are having values which remain constant over a given period of time.
D. DETERMINISTIC MODELS: In these the input and output remains same. These models estimate the exact
value of yield. E.g. NPK doses are applied and the definite yields are given out.
E. MECHANISTIC MODELS: These models explain not only the relationship between weather parameters and
yield, but also the mechanism. These models are based on physical selection. Examples of this model is the
accumulation of dry matter of plant, photosynthesis based model.

10. F. STATIC MODELS: In this model, time is not included as a variable. Dependent and independent variable
having values remain constant over a given period of time.
G. SIMULATION MODELS: Computer models, in general, are a mathematical representation of a real-world
system. One of the main goals of crop simulation models is to estimate agricultural production as a function of
weather and soil conditions as well as crop management. These models use one or more sets of differential
equations, and calculate both rate and state variables over time, normally from planting until harvest maturity or
final harvest.
H. DESCRIPTIVE MODEL: It defines the behaviour of a system in a simple manner. The model reflects little or
none of the mechanisms that causes of phenomena but, consists of one or more mathematical equations.
Example of such an equation is the one derived from successively measured weights of a crop. This equation is
helpful to determine quickly the weight of the crop where no observation was made.
I. EXPLANATORY MODEL: This consists of quantitative description of the mechanisms and processes that
cause the behaviour of the system. To create this model, a system is analysed and its processes and mechanisms are
quantified separately. It consists descriptions of distinct processes such as leaf area expansion, tiller production,
etc.

11. IMPACT OF MODELLING IN AGRICULTURE
 Evaluation of optimum management for cultural practice in crop production.
 Evaluate weather risk via weather forecasting.
 Proper crop surveillance with respect to pests, diseases and deficiency & excess of nutrients.
 Yield prediction and forecasting.
 These are resource conserving tools.
 Solve various practical problems in agriculture.
 To prepare adaption strategies to minimize the negative impacts of climate change.
 Identification of the precise reasons for yield gap at farmer’s field.
 Forecasting crop yields.
• Evaluate cultivar stability under long term weather conditions

12. USES OF CROP MODEL
 Crop system management: to evaluate optimum management production for cultural practice.
i. Seed Rate: optimum seed rate can be found out with the help of these models.
ii. Irrigation: optimum amount and time of application can be simulated.
iii. Fertiliser: optimum amount of fertiliser and time of application of the fertiliser can be simulated.
 Yield gap analysis: Potential yield can be simulated using these models and the difference between potential yield
and actual yield is the yield gap.
 Yield prediction and forecasting.
 Evaluation of climate change.
 Useful for solving various practical problems in agriculture.
 Are resource conserving tools.
 Can be used in precision farming studies.
 Are very effective tool for predicting possible impacts of climatic change on crop growth and yield.
 Helps in adaptation strategies, by which the negative impacts due to climatic change can be minimised.
•

13. APPLICATIONS OF CROP MODELS
 Based on understanding of plants, soil, weather and management interactions.
 Predict crop growth, yield, timing(outputs).
 Optimize management using climate predictions.
 Diagnose yield gaps, Actual vs. Potential.
 Optimize irrigation management.
 Greenhouse climate control.
 Quantify pest damage.
 Precision farming.
 Climate change effects on crop production.
 Can be used to perform “what if” experiments on the computer to optimise management.

14. CONCLUSION
o Models that are based on sound physiological data are capable of supporting extrapolation to alternative
cropping cycles and locations, thus permitting the quantification of temporary and spatial variability.
o Most models are virtually untested or poorly tested, and hence their usefulness is unproven. Indeed, it is
easier to formulate models than to validate them.
o Because most agronomists understand the concept of crop growth modelling and system approach
research, training in this area is required.
o An intensely calibrated and evaluated model can be used to effectively conduct research that would in
the end save time and money and significantly contribute to developing sustainable agriculture that
meets the world’s needs for food.

15. CONTRIBUTION
1. 20Bag041- Sujit Patnaik - Information collection (steps in modelling)
2. 20Bag042- Monalisha Sahoo - Prepared the word file and image collection.
3. 20Bag043- Yashaswini Maharana - Prepared the Power point presentation and types of crop modelling
4. 20Bag045- Susmita Lenka - Information on popular crop models used and types of model in agriculture
5. 20Bag046- Sarthak Moharana - Information collection (crop growth model) and printing work
6. 20Bag047- Anish Subudhi - Information on advantage of crop simulation model
7. 20Bag048- Swagatika Hota - Information on chronology of crop simulation model
8. 20Bag049- Amrusha Debata - Prepared the Power point presentation and history of CSM
9. 20Bag050- Champati Chapadia - Information on impact of modelling in agriculture
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16. REFERENCE
 https://www.slideshare.net/sreenivasReddy131/crop-modeling-types-of-crop-growth-models-in-
agriculture
 https://www.slideshare.net/SHIVAJISURYAVANSHI2/crop-simulation-model-219537954
 https://slideplayer.com/amp/7731906/
 https://www.semanticscholar.org/paper/A-Review-on%3A-Performance-Evaluation-of-Crop-Model-
Wolday-Hruy/0ca7418d8b59e2df6ecc3ad394eea52af56fc2fd
 https://www.slideshare.net/MEHRAJDAR1/term-ppt
 https://www.google.com/imgres?imgurl=https://image.slidesharecdn.com/cropmodelling-
220915085650-a00ea19b/85/crop-modellingpptx-17-320.jpg?cb%3D1669989384&tbnid=--
UXXspBUI93dM&vet=1&imgrefurl=https://www.slideshare.net/kalpeshvaghela8/crop-
modellingpptx&docid=ONlzvOqTn4M_dM&w=320&h=240&hl=en-US&source=sh/x/im

17. THANK YOU