Crop modelling for stress situation

Mukesh Bishnoi
PhD Horticulture (Fruit Science)
CCS Haryana Agricultural University, Hisar
Crop : Aggregation of individual plant species
grown in a unit area for economic
purpose.

Modeling : It is an act of mimicry or a set of

equations, which represents the
behaviour of a system.

Stress : A phenomenon that limits crop

productivity or destroys biomass.
 Stress
• Any external constraint that limits productivity
below genetic potential of the plant may be
considered as stress.
• It is usually defined as an external factor that
exerts a disadvantageous influence on the plant.
• It is measured in relation to plant survival, crop
yield, growth or the assimilation process, which
are related to overall growth.
 Types of stress:-
Abiotic Stress
• The negative impact caused by environmental
factors or non-biological factor.
• Losses due to Abiotic stress: 50%

 Biotic stress
• This is caused by biological agents or factors such
as diseases, insects and parasitic weeds.
• Losses due to Biotic stress: 25-30%

(ICAR-NIASM)
PLANTS RESPONSE TO STRESS
 DEFINATION

Crop- weather modelling, firstly used by “BAIER”

in 1979, refers to the techniques that can be used to
determine the likely effects of weather on crop, its
growth & production.

According to USDA (2007), crop models are

computer programmes that mimic the growth and
development of crops.
 NEED FOR CROP-WEATHER MODELLING

The main purpose of developing the crop-weather

models are:
• To understand crop weather interactions, processes
involved and their limitations.
• To assess the effect of environment, crop genotype
and management of input resources on crop yields,
and to quantify the yield gaps with existing
knowledge.
• To undertake strategic and policy decisions to
increase the productivity of resource based efficient
cropping systems.
 TYPES OF MODELS
 Depending upon the purpose for which it is designed the models
are classified into different groups or types. Of them a few are :
i. Statistical Model
ii. Phenological Model
iii. Mechanistic Model
iv. Deterministic Model
v. Stochastic Model
vi. Dynamic Model
vii. Static Model
viii. Crop Simulation Models
ix. Descriptive Model
x. Explanatory Model
 Models: Brief Description
Statistical or Emperical Model:
 These are direct descriptions of observed data and are
generally expressed as regression equations (with one or a
few factors) and are used to estimate the final yield. This
approach is primarily one of examining the data, deciding on
an equation or set of equations and fitting them to data. These
models rely on Statistical techniques such as Correlation and
Regression of the appropriate plant and environment variable.

 Example: Response of crop yield to fertilizers application or

the relationship between leaf area and leaf size in a given
plant species.
 Phenological models:
 These models predict the crop development from one
crop growth stage to another. The Prediction is
generally based on accumulated Heat units.

Mechanistic Model:
 These models explains not only the relationship
between weather parameters and yield, but also the
mechanism of these models (explains the
relationship of influencing independent variable)
 Deterministic Model:
 These models estimate the exact value of yield. It make
definite predictions for quantities without any
probability, variance or random element.

Stochastic Model:
 When Variation and Uncertainty reaches a high level, it
becomes advisable to develop a Stochastic Model.
 For each set of Inputs ,different outputs are given along
with probabilities. It Defines status of dependent
variable at a given rate
 Dynamic Model:
 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.

Static Model:
 Time is not included as a variable. The dependent and
independent variable having values remain constant.
 Crop Simulation Model:
These models predict the final yield and also provide
quantitative information on intermediates steps like
daily weight of plant parts.
It estimate agriculture production as a function of
weather and soil conditions as well as crop
management.
This model uses one or more differential equation over
time normally from planting until harvest.
 Descriptive Model:
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. But,
consists of one or more mathematical equations.

Explanatory Model:
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.
 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.
 IMPACT OF MODELING ON 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 & excessof nutrients.
• Yield prediction and forecasting.
• These are resource conserving tools.
• Solve various practical problems in agriculture.
• Helps to prepare adaptation strategies to minimize the negative
impacts of climate change.
• Identification of the precise reasons for yield gap at farmer's field.
• Evaluate cultivar stability under long term weather condition.
 POPULAR CROP MODELS USED EXTENSIVELY IN INDIA AND
WORLD-WIDE

 Developed by IARI in India

i. WTGROWS
ii. ORYZA1N
iii. InfoCrop
iv. InfoSoil
 Acquired by India
i. DSSAT
ii. ORYZA1
iii. ORYZAW
iv. WOFOST
v. DNDC
 A few successfully used models in agrometeorology
1. The de Wit school of models:
• In the sixties, the first attempt to model photosynthetic rates of
crop canopies was made (de Wit, 1965).
• The results obtained from this model were used among others,
to estimate potential food production for some areas of the
world and to provide indications for crop management and
breeding (Wit, 1967; Linneman et al., 1979).
• This was followed by the construction of an Elementary CROp
growth Simulator (ELCROS) by de Wit et al. (1970).
• This model included the static photosynthesis model and crop
respiration was taken as a fixed fraction per day of the biomass,
plus an amount proportional to the growth rate.
2. IBSNAT and DSSAT Models (International
Benchmark Sites Network for Agrotechnology
Transfer and Decision Support System for Agro-
Technology Transfer)
• The goal is to obtain higher yields from the crops
that they have been growing for a long time. Also,
while sustaining the yield levels they want to :
i. Substantially improve the income.
ii. Reduce soil degradation.
iii. Reduce dependence on off-farm inputs.
iv. Exploit local market opportunities.
3. ALOHA-Pineapple model:
• Existing pineapple production models predict fruit
development based on heat-units (Fleisch and Bartholomew,
1987;Fournier et al., 2010).
• A more comprehensive model was developed, the ALOHA-
Pineapple model (Malezieux et al., 1994; Zhang,1992; Zhang
et al., 1997) based on the CERES-Maize model (Jonesand
Kiniry, 1986), which simulates the growth, development, and
yield of the ‘Smooth Cayenne’ cultivar.
• However, this model was calibrated only in locations with
low thermal variability and did not test low input scenarios.
4. SIMPINA model:
• The SIMPINA model which simulates, the development and
growth of the ‘Queen Victoria’ pineapple cultivar under
various climatic conditions and N and water management
practices on Reunion Island.
• The new model simulates water and nitrogen balances and
estimates stress coefficients that affect pineapple growth and
development.
 OTHER CROP MODELS
 EPIC, ALMANAC, CROPSYST, WOFOST, ADEL and CERES-
Maize models for maize crop management.
 SORKAM, SorModel, and SORGF models for sorghum crop
management.
 CERES – pearl millet, CROPSYST, PmModels for Pearl millet
crop management.
 GOSSYM and COTONS models for cotton crop management.
 PNUTGRO model for groundnut crop management.
 CHIKPGRO model for chick pea crop management.
 WTGROWS model for wheat crop management.
 SOYGRO for soybean, BEANGRO for beans and QSUN for
sunflower crop management.
 APSIM, GROWIT added with several modules are being used in
crop rotation, crop sequence and simulation studies involving
perennial crops.
 LIMITATIONS OF CROP-WEATHER MODELLING
 Inaccurate projections of natural processes.
 Unreliable and unrealistic projections of changes in climate
variability.
 Crop models are not universal (site specificity).
 Misuse of models.
 Inappropriate for Heterogeneous plot.
 Inherent soil heterogeneity over relatively small distances.
 Model performance is limited ta the quality and input data.
 Sampling errors also contribute to inaccuracies in the observed
data.
 Rudimentary model validation methodology.
 Plant, soil and meteorological data are rarely precise and come
from nearby sites.
 An ideal crop model can’t be developed because of complex
biological system.
 Remote sensing
 It is a technique used to collect information about an
object or area without actually being in contact with
that object or area.
 Remote Sensing can be done through Aerial
photography or by satellite imaging.
 It may be of two types i.e. active and passive remote
sensing:
• Passive remote sensing (i.e., when the reflection of
sunlight is detected by the sensor).
• Active remote sensing (i.e., when a reflection by the
object is detected by the sensor).
 Principle of Remote Sensing
Every material on the earth absorbs and
reflect the solar energy. In addition they emit
certain amount of Internal energy.
The absorbed, reflected and emitted energy is
detected by remote sensing instruments or
sensors which are carried by Aircraft or
Satellites.
The detection are made by the characteristics
term called “Spectral Signature” and
“Images”
 ASSESSING THE STRESS THROUGH REMOTE SENSING

Spectral indicators of plant chlorophyll content :

 Chlorophyll pigment content, in particular, is directly associated with
photosynthetic capacity and productivity. Reduced concentrations of
chlorophyll are indicative of plant stress.
 In stressed vegetation, leaf chlorophyll content decreases, thereby changing
the proportion of light-absorbing pigments, leading to a reduction in the
overall absorption of light.
 These changes affect the spectral reflectance signatures of plants through a
reduction in green reflection and an increase in red and blue reflections,
resulting in changes in the normal spectral reflectance patterns of plants
 Thus, detecting changes from the normal (unstressed) spectral reflectance
patterns is the key to interpreting plant stress
 Crop modelling: A tool for agricultural research – A review
Patricia Oteng-Darko, S. Yeboah, S. N. T. Addy, S. Amponsah and E. Owusu
Danquah

 The Earth’s land resources are finite, whereas the number of people that the
land must support continues to grow rapidly. This creates a major problem for
agriculture.
 Production (productivity) must be increased to meet rapidly growing demands
while natural resources must be protected.
 New agricultural research is needed to supply information to farmers, policy
makers and other decision makers on how to accomplish sustainable
agriculture over the wide variations in climate around the world.
 In this direction the use of crop models in research is being encouraged.

Oteng-Darko, P., Yeboah, S., Addy, S.N.T., Amponsah, S. and Danquah, E.O., 2013. Crop modeling: a tool for
agricultural research–A. J Agric Res Dev, 2(1), pp.001-006.
Quantification of plant stress using remote sensing observations
and crop models: the case of nitrogen management
F. Baret, V. Houle`s and M. Guerif
 Remote sensing techniques offer a unique solution for mapping stress and
monitoring its time-course.
 This article reviews the main issues to be addressed for quantifying stress
level from remote sensing observations, and to mitigate its impact on crop
production by managing cultural practices.
 The case of nitrogen fertilization is used here as a paradigm.
 It is used for nitrogen stress evaluation by comparison with a reference
unstressed situation.
 Advances in remote sensing data and interpretation methods, more realistic
crop models with large improvement in computation performance can be
prepared. These models, calibrated using remote sensing observations could
be used in prognostic mode to select the best strategies for crop
management.
Baret, F., Houlès, V. and Guerif, M., 2007. Quantification of plant stress using remote sensing observations
and crop models: the case of nitrogen management. Journal of Experimental Botany, 58(4), pp.869-880.
 CONCLUSION
Crop growth model is a very effective tool for
predicting possible impacts of climatic change on
crop growth and yield.

Remote sensing data has become an important

tool for yield modeling as the satellites are taking
continuous images which give an prediction of a
crop situation and status for yield estimation and
for adopting suitable management practices.
SYMBOL OF TRUST