Stress in plants refers to external conditions that adversely affect growth, development or productivity of plants Stresses trigger a wide range of plant responses like altered gene expression, cellular metabolism, changes in growth rates, crop yields, etc. Two type of stress Biotic Stress Biotic stress in plants is caused by living organisms, specially viruses, bacteria, fungi, nematodes, insects, arachnids and weeds. The agents causing biotic stress directly deprive their host of its nutrients can lead to death of plants Abiotic Stress Abiotic stresses such as drought, excessive watering (water logging), extreme temperatures (cold, frost and heat), salinity and mineral toxicity negatively impact growth, development, yield and seed quality of crop and other plants Crop Modelling Crop models are a formal way to present quantitative knowledge about how a crop grows in interaction with its environment Applications of Crop Models Research on Interaction of Plant, Soil, Weather and Management Practices Prediction of Crop Growth as well as Limiting factors On farm decision making and agronomic management Optimizing management using climatic predictions Precision Farming and Site Specific Experimentation Weather Based agro advisory services Yield analysis and Forecasting Introduction and Breeding of New Varieties Policy Management

1. Crop Modelling for Stress Situation
Term Paper Presentation
By: Sanjay Chetry
Ph.D. Scholar
ID: 2020632001
Dept. of Fruit Science
TNAU, Coimbatore
FSC
605:
Biotic
and
Abiotic
Stress
Management
in
Horticultural
Crops
(2+1)

2. Stress
Stress in plants refers to external conditions that adversely affect growth,
development or productivity of plants
Stresses trigger a wide range of plant responses like altered gene expression,
cellular metabolism, changes in growth rates, crop yields, etc.
Abiotic stresses such as drought,
excessive watering (water logging),
extreme temperatures (cold, frost
and heat), salinity and mineral
toxicity negatively impact growth,
development, yield and seed quality
of crop and other plants
Biotic stress in plants is caused by
living organisms, specially viruses,
bacteria, fungi, nematodes, insects,
arachnids and weeds. The agents
causing biotic stress directly
deprive their host of its nutrients
can lead to death of plants

3. High light
High temperature
Drought
Salinity
Cold Stress
Mechanical damage
Air pollution
Plants exposed to a variety of abiotic stresses
These stresses often occurs in combination

4. Abiotic Stress
Stress response
Resistance Susceptibility Avoidance
Acclimation Senescence Survival
Growth Death
Survival

5. Crop Modelling
Crop Crop is defined as an “aggregation of individual plant species grown in
a unit area for economic purpose”
Modeling is the use of equations or sets of equations to represent
the behaviour of a system Modelling
Crop models are a formal way to present quantitative
knowledge about how a crop grows in interaction with its
environment

7. Inputs and system approach of crop growth
models
Site data
Weather data
Crop data
Soil data
Pest data
Management data
M
O
D
E
L
Crop Yield

8. Concepts of Crop Modelling
contains quantitative information
about major processes involved in
the growth and development of the
crop.
imitates the behaviour of a real
crop
In the sixties, the first attempt to
model photosynthetic rates of crop
canopies was made
used to understand the effects of
climate change
Modelling represents a
better way of synthesizing
knowledge about different
components of a system,
summarizing data, and
transferring research
results to users
(France & Thornley,
1984)

9.  Research on Interaction of Plant, Soil, Weather and Management Practices
 Prediction of Crop Growth as well as Limiting factors
 On farm decision making and agronomic management
 Optimizing management using climatic predictions
 Precision Farming and Site Specific Experimentation
 Weather Based agro advisory services
 Yield analysis and Forecasting
 Introduction and Breeding of New Varieties
 Policy Management
Applications of Crop Models

10. Evaluate cultivar stability under long term weather conditions
Impact of Modelling on Agriculture
Evaluation of optimum management for
cultural practices 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
Identification of the precise reasons for yield gap at farmer’s field
Forecasting crop yields

11. Hypothesis
Science is
concerned
with
prediction
Conceptual
model are
hypotheses
Models are logically
and quantitatively
constructed series
of beliefs about how
a system works
Research knowledge
synthesis and
crop system decision
management
Prediction Beliefs
Knowledge
Why Model?

12. Statistical Model
Phonological Model
Mechanistic Model
Deterministic Model
Stochastic Model
Dynamic Model
Static Model
Crop Simulation Models
Descriptive Model
Explanatory Model
Types of Models
1
2
3
4
5
6
7
9
1
0
8

13. Statistical Model
These models express
the relationship
between yield or yield
components and
weather parameters
Relationships are
measured in a system
using statistical
techniques
Statistical models of
crop responses to
climate change, based
on historical datasets
of crop and climate
variables have also
been used to address
impact of climatic
change on food security
in developing countries.

14. Phenological Model
These models predict the crop development from
one crop growth stage to another.
The Prediction is generally based on
accumulated Heat units

15. These models have the ability to mimic relevant
physical, chemical or biological processes and to
describe how and why a particular response occurs
Mechanistic Model
It explain not only the relationship between weather
parameters and yield, but also the mechanism of these
models (explains the relationship of influencing
dependent variables)
These models are based on physical selection
(Murthy, 2002)

17. When variation and uncertainty reaches a high level, it
becomes advisable to develop a stochastic model that gives an
expected mean value as well as the associated variance
In Stochastic models, a probability element is
attached to each output. 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

18. Time is included as a variable
Both dependent and independent variables
are having values which remain constant
over a given period of time
After which these variables changes due to
change in independent variable

19. Time is not included as a variable.
The dependent and independent
variable having values remain
constant
Static Model

20. Simulation models involve
Computer models with a
mathematical representn 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 mgmt.
These form a group of
models that is designed for
the purpose of imitating the
behaviour of a system.
This model uses one or
more differential equation
over time normally from
planting until harvest
1 2
3
4

21. A descriptive model defines the behaviour of a
system in a simple manner.
The model reflects little or none of the
mechanisms that are the causes of
phenomena
It consists of one or more
mathematical equations
This consists of quantitative description of
the mechanisms and processes that cause
the behaviour of the system such as leaf
area expansion, flowering, fruiting etc. as
crop growth is a consequence of these
processes

22. Popular Crop Models Used Extensively in India
and Worldwide
Developed by IARI, India
1. WTGROWS
2. ORYZA1N
3. InfoCrop
4. InfoSoil
Acquired by India
1. DSSAT
2. ORYZA1N, ORYZAW
3. WOFOST
4. DNDC
01
05
04
03
02
Crop
growth
and yield
Key Component of INFOCROP
Weather
Crop/Variety
Agronomic
inputs
Soil
Pest

23. Crop models are not able to give accurate projections because of inadequate
understanding of natural processes and computer power limitation (It is based on
estimations)
Unreliable and unrealistic projection of
changes in climate variability
Crop models are not universal (no site
specific)
A particular limitation for fruit tree crop models is the limited and incomplete
database of good quantitative data for modelling
The best data and knowledge bases generally are for
1. Phenology
2. Leaf photosynthesis (light and temp. responses)
3. Shoot growth and leaf area development
4. Fruit growth and respiration.

24. Major
Gaps
Respiration rates in general-available
data is almost all short term
measurements; responses to
temperature done in short term, not
long-term
Root growth patterns, respiration,
root turnover rates and its
implications
The seasonal demands for carbon
of different organs
Detailed understanding of fruit
abscission processes
Model performance is limited to the quality of
input data

25. Future aspects
Globalization of markets has increased competitiveness, highlighting the need for
products of high quality
(Dimokas et al., 2009)
Fruit breeders must satisfy two requests concurrently: the production of high
quality fruits and the use of sustainable practices
(Li et al., 2009; Quilot et al., 2005; Kropff and Struik, 2002)
Recent advances in genetics and molecular plant biology can play a key role in crop
modelling by improving crop responses to environmental conditions and
management factors
(Bannayan et al., 2007)

26. For the two last decades, crop modelling has become one of the major research tools in
horticulture.
Identifying gaps in our knowledge, thus enabling more efficient and targeted research
planning
Concerning fruit quality, this new generation is really needed to accompany the advances
in fruit genomics
The adoption of standard units, formation of inputs and outputs, selection of variables,
the production of proper documentation, limitations and the use of procedures of
software quality assurance would increase the portability of models and lower the risk of
error or misuse
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
Conclusion