‘Crop Modeling for Stress Situation , Assessing Stress through Remote Sensing’

DOCTORAL SEMINAR–II
ON
‘Crop Modeling for Stress Situation , Assessing Stress through Remote Sensing’
COURSE NO.- 692
CREDIT HOURS- 1(0+1)
Seminar In charge:
Dr. Prabhakar Singh
Professor & Head
Department of Fruit Science
CoA , IGKV, Raipur
Presented by:
Swati Shukla
Ph.D. (Hort.) 1st Year / 2nd Semester
Department of Fruit Science
CoA, IGKV, Raipur

CONTENT
• What is Crop modeling
• Crop-Weather modeling
• Chronology of crop
simulation modeling
• Steps in crop modeling
• Types of Model
• Impact of modeling in
agriculture/horticulture
• Application of crop modeling
• Important crop-simulation
model & limitation of
modeling
• Remote sensing- principle &
process
• Different type of remote
sensing system
• Techniques of remote sensing
and it’s application in
assessing stress
• Case study
• Conclusion
• References

WHAT IS….?
Crop: is defined as an “Aggregation of individual plant
species grown in a unit area for economic purpose”.
Modeling: is an act of mimicry or a set of equations, which
represents the behaviour of a system.
Growth: is defined as an “Irreversible increase in size
and volume and is the consequence of differentiation
and distribution occurring in the plant”.
Simulation: is defined as “Reproducing the essence of a
system without reproducing the system itself”. In simulation
the essential characteristics of the system are reproduced in a
model, which is then studied in an abbreviated time scale.

Types of Models in agriculture/horticulture
1. Mathematical Model
2. Growth Model
3. Crop Weather Model
Model: schematic representation of the conception of a system or
an act of mimicry or a set of equations, which represents the behaviour of
a system. Its purpose is usually to aid in explaining, understanding or improving
performance of a system.
Crop models: are computer programmes that mimic the
growth & development of crops (USDA, 2007).
Crop Weather Model is based on the principle that govern the development of
crop and its growing period based on temperature and day length.
Crop- Weather Modeling , 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.

P. De Reffye & M. Jaeger, CIRAD

Cournede P.H. et al., 2006 (France)

Chronology of crop simulation modeling
YEAR DEVELOPMENTS
1960 Simple Water-balance models
1965 Models for photosynthetic rate of crop canopies (de Wit)
1970 ELCROS (Elementary Crop Growth Simulator construction) de Wit et al.
1977 Introduction of micrometeorology in the models & quantification of crop canopy
resistance (Goudriaan)
1978 BACROS (Basic Crop Growth Simulator) (de Wit and Goudriaan)
1982 DSSAT (Decision Support System for Agro-Technology Transfer) at University of
Hawaii by IBSNAT
1987 SIMRIW (Simulation Model for Rice-Weather relations) by T. Horie (IRRI)
1990 APSIM(Agricultural Production Systems sIMulator) by CSIRO and University of
Queensland
1990 CropSyst by a team at Washington State University's Department of Biological
Systems Engineering
1994 India’s 1st crop model WTGROWS at IARI followed by ORYZA1N by Kropff et
al.

Steps in crop modeling
 Define System & its boundary ,
 Define key variables in system
 Quantification of relationship
Evaluation
Calibration
Validation
Sensitivity analysis

Key Variables in system
State variables are those which can be measured e.g. soil
moisture content, crop yield etc.
Rate variables are those rate of processes being operated
within the system viz; photosynthetic rate,
transpiration rate, ET rate etc.
Driving variable are those which are not part of the
system, but affect it. e.g. sunshine, temperature,
rainfall etc.
Auxillary variables are those which are
intermediated product e.g. dry matter partitioning,
water stress etc.

1. Statistical & Empirical model
2. Mechanistic model
3. Deterministic model
4. Stochastic model
5. Static model
6. Dynamic model
7. Simulation model
8. Descriptive model
9. Explanatory model

Model Types Description about model
Statistical &
Empirical
model
 Direct descriptions of observed data, generally expressed as
regression equations.
 These models express the relationship between yield or yield
components and weather parameters
 These models give no information regarding the mechanisms
that give rise to the response.
 Ex: Step down regressions, correlation etc.
Mechanistic
model
 These attempt to use fundamental mechanisms of plant and
soil processes to stimulate specific processes.
 These models are based on physical selection.
 Ex: photosynthesis based model
Deterministic
Model
 These models have defined coefficients. It helps in estimation
of exact value of yield.
 It makes definite prediction of yield without any associated
probability distribution , variance or random element.
 The greater the uncertainties in the system, the more inadequate
deterministic models become.

Stochastic
Model
 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
Dynamic
Model
 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
Static Model  Time is not included as a variables.
 Dependent and independent variables having values remain constant
over a given period of time.
Simulation
Model
 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.
 models are designed for the purpose of imitating the behaviour of
a system.

Descriptive
model
 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.
 An example of such an equation is the one derived from successively
measured weights of a crop. The equation is helpful to determine
quickly the weight of the crop where no observation was made.
Explanatory
model
 These are consist of quantitative description of the mechanism and
processes that cause the behaviour of the system.
 The model is built by integrating these descriptions for the entire
system.
 It contains descriptions of distinct processes such as leaf area
expansion, tiller production etc.
 Crop growth is a consequence of these processes.
Murthy,2002

Architectural modeling
Architectural analysis was introduced by Hallé and co-workers (Hallé and Oldeman
1970; Hallé et al. 1978).
MODEL
NAME
DESCRIPTION (With example) PICTURE
Holttum’s
model
 It consists only one meristem and it is
not branched.
 The meristem will converts into
inflorescence and plant eventually dies.
Ex: Banana
Corner’s
model
 Trunk is single, monopoidal and
orthotropic in nature.
 Growth is indeterminate and auxiliary
inflorescence is seen. Ex: Papaya,
Datepalm

MODEL
NAME
DESCRIPTION (With example) PICTURE
Rauh model  Trunk is monopoidal, orthotropic
in nature.
 Lateral flowering is seen. Ex:
Apple
Troll’s model Sympodial and plagiotropic in nature.
Ex: Annona squamosa .
Existing models or modeling frameworks for fruit crops-
 The Hi-SAFE model was designed in response to the need for a process-based
model that could simulate tree-crop interaction and management options in a
temperate region (Dupraz et al., 2005).
 The typical agroforestry systems to be simulated by the model are walnut (Juglans
spp.), wild cherry (Prunus avium)or Mediterranean oaks (Quercus spp.) with winter
and summer annual crops, grasses.

IMPACT OF MODELING IN AGRICULTURE /
HORTICULTURE
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.
To prepare adaptation 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

Application of crop modeling
 Research on interaction of plants, soil, weather and management
practices
 Prediction of crop growth as well as limiting factors.
 On farm decision making & agronomic management.
 Optimizing management using climatic predictions.
 Precision farming and site specific experiments.
 Weather based agro-advisory services.
 Yield analysis and forecasting.
 Introduction and breeding of new varieties.
 Policy management.

Some important crop simulation model
Model Developed
by
Feeding Parameters User Nations
DSSAT IBSNAT at
University of
Hawaii
by Climatic parameters (RF, SR, Temp.
etc.), soil data, crop management
information(duration, date of
establishment, LAI, SLA etc.
USA, Canada, India,
Bangladesh, Pakistan, Israel,
Africa, Gulf Nations, S-E
Asian countries
APSIM CSIRO and
University of
Queensland
Climatic parameters (RF, SR, Temp.
etc.), Soil data, Cropping system
information
Australia, India, Nigeria, UK,
France, Brazil
InfoCrop IARI, India Weather (RF, Temp., wind speed,
Frost, humidity), Soil (pH, fertility
data, depth etc.) Crop data (variety,
phenology, morphology),
Management(date of planting,
fertilization, irrigation, residue
management etc.}
India, S-E Asian countries,
Gulf Nations
ORYZA1 Kropff et al.
at IRRI
Climatic parameters, Soil data, Crop
management information
Indonesia, India, Pakistan,
Bangladesh

Component of DSSAT
(Source: Jones et al.,

APSIM
A new model for grapevines (Vitis vinifera) is the first perennial fruit crop
model using the Agricultural Production System sIMulator (APSIM)
Next Generation framework.
It has all the structures and functions to simulate the phenology, radiation
interception, leaf growth, DM production, carbohydrate and nitrogen
allocation and storage, and yield formation of a perennial plant.

InfoCrop Simulation Model
• In 2004, InfoCrop version 1 was launched by IARI.
• This model was developed by Aggarwal and his co-workers
from the Center for Application of Systems Simulation, IARI,
New Delhi(Aggarwal et.al 2004).
• It is a dynamic simulation model for the assessment of crop
yields , losses due to pests and the environmental impact of agro
ecosystem in tropical environment.
• It provides daily & summary outputs on various growth and yield
parameters, nitrogen uptake, green house gas emission, soil water
nitrogen balance.
• InfoCrop version 2.1 released by the IARI in 2015.

WOFOST crop growth model
• The WOFOST model is a mechanism-based crop growth model that simulates
the growth and development processes of most crops.
The objectives of this study were to investigate the potential use of a modified
WOFOST model for predicting jujube yield by introducing tree age as a key
parameter.
The WOFOST model can be implemented in two different ways: potential production,
where crop growth is determined by irradiation, temperature and plant characteristics only;
and water-limited production, where crop growth is limited by the water use.

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 (Jones and Kiniry, 1986), which simulates
the growth, development, and yield of the ‘Smooth
Cayenne’ cultivar.

3. SIMPINA model
 The SIMPINA model which simulates, the development and growth of the
‘Queen Victoria’ pineapple cultivar under various climatic conditions and
Nutrient 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
Some Limitations of Crop Modeling:
• No of inputs requirement is usually very large.
• Most of the crop simulation model (CSM) do not consider plant nutrients
except for Nitrogen .
• Most of the CSM don’t consider pest and disease occurrence.
• Involves complexity and requires expert knowledge .
• Spatial variability in crop management practices has to be considered.
• Finer resolution for soil inputs .
• No crop growth simulation model use remote sensing data directly from serve

Structuring of the simulation model of water use efficiency in citrus species (ESM-Citrus)

Conclusion
• Citrus crop yields will increase up to 600 ppm atmospheric CO2 and 30 °C air
temperature, but they will decrease at higher temperatures and CO2 concentrations.
• Increased atmospheric CO2 concentrations will have a positive effect on CO2
assimilation in orange trees, resulting in increased biomass produced (g) per
millimeter of water transpired.

Remote sensing
Remote sensing" generally refers to the use of satellite- or aircraft-based sensor
technologies to detect and classify objects on Earth, including on the surface and
in the atmosphere and oceans, based on propagated signals (e.g. electromagnetic
radiation).
• It may be split into active and passive remote sensing
• “Active" remote sensing (i.e., when a signal is emitted by a satellite or aircraft and
its reflection by the object is detected by the sensor).
• “Passive" remote sensing (i.e., when the reflection of sunlight is detected by the
sensor)
• Remote sensing techniques offer a unique solution for mapping stress and
monitoring its time-course. (Baret, et al., 2007)
• The combination of remote sensing observations with crop models provides an
elegant solution for stress quantification through assimilation approaches,
• .

Principles and Process of Remote Sensing System
i) Energy Source or Illumination (A)
ii) Radiation and the Atmosphere (B)
iii) Interaction with the Target (C)
iv) Recording of Energy by the Sensor
(D)
v) Transmission, Reception, and
Processing (E)
vi) Interpretation and Analysis (F)
vii) Application (G)

Different types of remote sensing systems and sensors used in fruit crops
Crop Rs Devices/Platforms Purpose Type of Remote
Sensing
Apple Green seeker 505, tetra cam
ADC
Weed biomass
evaluation
Active and passive
remote sensing
IRSAWiFS Orchard
characterization
Satellite remote
sensing
Orange and
pineapple
IRS-P Satellite sensor, LISS-
III
Soil site suitability
analysis
Satellite remote
sensing
Peach Spectra radio diameter Mite damage Aerial remote sensing

Different types of remote sensing systems and sensors used in fruit crops
Crop Rs Devices/Platforms Purpose Type of Remote
Sensing
Nectarines
and peaches
Field spectrometerASD
with reflectance of 400 -
1100nm
Detecting water
stress effects on
fruits
Aerial remote sensing
Mango IRS-P Satellite Yield estimation Satellite remote sensing
Grapes Digital multispectral sensors Zonal vine yard
management
UAV (Rcats/APVS) To know the vigour
of the crop
Kumar L.A. et al., 2021

Techniques of remote sensing and its applications
1. Ultrasound (acoustic) and radar tide gauges
measure sea level, tides and wave direction in coastal.
2. Light imaging detection and ranging (LiDAR)
• It is used to detect and measure the concentration of various
chemicals in the atmosphere,
• while airborne LIDAR can be used to measure heights of objects and
features on the ground more accurately than with radar technology.
• Vegetation remote sensing is a principal application of LIDAR.
3.Radiometers and photometers
• most common instrument in use, collecting reflected and emitted
radiation in a wide range of frequencies.
• The most common are visible and infrared sensors.

4.Remotely Piloted Aircraft Systems (RPAS)
• Based on the images obtained by RPAS many plant physical
characteristics can be derived such as the water content, cellular
structure and plant pigment (e.g. chlorophyll) content.
• it is related to many important plant vital processes such as the
photosynthetic potential, nutrient content, plant stress, maturity
and senescence
5. Vegetation Indices
• These were used as remote sensing indicators
• Different vegetation indices are indicators for different
parameters such as normalised difference vegetation indices
(NDVI) for vegetation cover, structure insensitive pigment
index (SIPI) for stress etc

Detection of plant water stress using remote sensing
• These technologies acquire many hundreds of spectral bands across the
spectrum from 400 nm to 2500 nm, using satellite, airborne or hand-held
devices.
• The spectral characteristics of vegetation are governed primarily by scattering
and absorption characteristics of the leaf internal structure and
biochemical constituents, such as pigments, water, nitrogen, cellulose and
lignin (Asner, 1998; Coops et al., 2002).
• Pigments are the main determinants controlling the spectral responses of
leaves in the visible wavelengths (Gaussman, 1977).
• Chlorophyll pigment content, in particular, is directly associated with
photosynthetic capacity and productivity (Gaussman, 1977; Curran et al.,
1992).
• Reduced concentrations of chlorophyll are indicative of plant stress (Curran et
al., 1992).

Spectral indicators of plant chlorophyll content
• 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 (Murtha, 1982; Zarco-
Tejada et al., 2000).
• 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 (Murtha, 1982; Zarco-Tejada et al., 2000).
• Thus, detecting changes from the normal (unstressed) spectral
reflectance patterns is the key to interpreting plant stress.

Crop water stress (CWS) assessment
• CWS is deﬁned as “an indicator that determines water deﬁcit condition based on the scale
of the leaf and the crop temperature analysis method.”
• RWC can be determined with high accuracy using spectral remote-sensing systems,
whereby spectral data are analyzed to provide simple readable information (Qi et al., 2014).
• Example, successfully used remote-sensing spectral systems to acquire accurate RWC data
in a timely manner.
• The equivalent water thickness (EWT) of a leaf is used to assess RWC. This
remote-sensing technique can precisely quantify crop water stress based on leaf
measurements.

• Models were built in order to predict yield with the help of individual weather
parameter and also by considering all the weather parameters at a time and eliminating non-
significant parameters (step wise regression).
• Maximum temperature along with evaporation (Y = – 45.172 + 1.433 X1 + 2.012X7; R2 =
0.89**) had explained about 89% of variation.
• The parameters maximum temperature, evening relative humidity and evaporation had
explained 99 % of variation (Y = –38.651+ 1.519 X1 – 0.101 X5 +0.975 X7; R2 = 0.99**)
• whereas maximum temperature evening relative humidity, evaporation and bright
sunshine hours (Y= – 38.583 + 1.517 X1 – 0.101 X5 + 0.979 X7 –0.003 X8; R2 = 0.99*)
had also explained 99 % of variation in cashew nut yield.
• It indicated that maximum temperature and evaporation are important weather indices
play a key role for prediction of cashew nut yield.

Conclusion
• Various kinds of models are in use for assessing and predicting crop growth
and yield.
• Crop growth model is a very effective tool for predicting possible impacts of
climatic change on crop growth and yield.
• Crop growth models are useful for solving various practical problems in
agriculture.
• Remote sensing are rapidly proving their potential for applications in crop
biomass detection, soil properties, soil moisture and nutrient content, green
fruit counts, crop yield estimation, damage by biotic and abiotic stresses etc.
• With the launch and continuous availability of multi-spectral (visible, near-
infrared) sensors on polar orbiting earth observation satellites (Landsat,
SPOT, IRS, etc.) remote sensing (RS) data has become an important tool for
yield modeling.

References
• Asner, G.P., 1998. Biophysical and biochemical sources of variability in canopy reflectance. Remote
sensing of Environment, 64(3), pp.234-253.
• Coops, N., Dury, S., Smith, M.L., Martin, M. and Ollinger, S., 2002. Comparison of green leaf eucalypt
spectra using spectral decomposition. Australian Journal of Botany, 50(5), pp.567-576.
• Curran, P.J., Dungan, J.L., Macler, B.A., and Plummer, S.E. 1992. The effect of a red leaf pigment onthe
relationship between red-edge and chlorophyll concentration. Remote Sens. Environ. 35 69-75.
• DeWit, C. T. and Rabbinge. R. 1979. SystemsAnalysis and Dynamic Simulation. EPPO Bull. 9 (3): 149-
153
• Kant, K., Telkar, S.G., Gogoi M., and Kumar, D. 2017.Crop Simulation Models. Biomolecule Reports.
(Retrieved from https://www.researchgate.net/publication/320370783_Crop_Simulation_Models).
• Murtha, P.A. 1982. Detection and analysis of vegetation stress. In: CJ Johannsen and JL Sanders (eds.)
Remote Sensing for Resource Management. Soil Conservation Society of America. Ankeny, Iowa,
USA. pp 141-158.
• Qi, Y., Dennison, P.E., Jolly, W.M., Kropp, R.C. and Brewer, S.C., 2014. Spectroscopic analysis of
seasonal changes in live fuel moisture content and leaf dry mass. Remote Sensing of
Environment, 150, pp.198-206.
• Zarco-Tejada, P.J., Miller, J.R., Mohammed G.H. and Noland T.L. 2000. Chlorophyll fluorescence
effects on vegetation apparent reflectance. I. Leaf-level measurements and model simulation. Remote
Sens. Environ. 74 582-595.

‘Crop Modeling for Stress Situation , Assessing Stress through Remote Sensing’

‘Crop Modeling for Stress Situation , Assessing Stress through Remote Sensing’

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