Crop Modeling types of models, crop yield models, advantages 7 limitations in crop growth models

1. CROP WEATHER MODELING- ADVANTAGE ,
TYPE OF MODEL , CROP YIELD
MODEL,
APPLICATION OF MODELINGSUBMITTED BY
SREENIVAS REDDY .K
BAM17-08
1SREENIVAS REDDY.K

2. CROP-WEATHERMODELING
“Growing the crop on the computer” 2

3. It is a simplified description
(often, a mathematical representation)
of a system to assist
calculations and predictions.
In the present context, ‘model’ is expressed as a computer program that
can be repeatedly run several times for computing several designed
mathematical or statistical expressions (equations) governing crop
growth-environment relations, given appropriate input data.
3SREENIVAS REDDY.K

4. A model is a 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. Also, a model is “A representation of an object, system or idea in
some form other than that of the entity itself”. Its purpose is usually to aid
in explaining, understanding or improving performance of a system.
A model is, by definition“ A simplified version of a part of reality, not a
one to one copy”. This simplification makes models useful because it offers
a comprehensive description of a problem situation.
 It is a difficult task to produce a comprehensible, operational
representation of a part of reality, which grasps the essential elements and
mechanisms of that real world system and even more demanding, when the
complex systems encountered in environmental management (Murthy,
2002).
OTHER DEFINATIONS OF MODEL
4SREENIVAS REDDY.K

5. *Type of Models
1. Mathematical Model
2. Growth Model
3. Crop Weather Model
TYPES OF MODELS IN AGRICULTURE
5SREENIVAS REDDY.K

6. 1. Mathematical Model :- Physical relationship of natural
phenomenon by Means of a mathematical equation are
called mathematical Model .
2. Growth Model :- If the phenomenon is expressed in the
growth define it is define as growth model
3. Crop Weather Model:- Crop weather model is basedon
the principle that govern the development of crop and its
growing period based on temperature and day length .
46SREENIVAS REDDY.K

7. There are three kinds of mathematical model used in agriculture:
Linear programming models,Empirical curve-fitting, and Mechanistic or
dynamic models.
Linear programming is used in economic planning or rationing of resources
involved in farm management, such as deciding on the amount and composition
of fertilizer applied to a field.
 Empirical curve-fitting quantifies in a few parameters a series of measurements
made of plants or animals, such as fitting the logistic curve to samples of a crop
taken at several times to find the specific growth rate.
Mechanistic models are used to examine hypotheses relating physiological
processes, such as photosynthesis and respiration, to the behavior of whole
plants, such as the gain in weight of a plant.
These kinds of models are not related mathematically or conceptually, so it is
difficult to cover each kind to the same degree or survey the literature with the
same breadth.
(ByJ. France andJ. H. M. Thornley. Butterworths, London and Boston 1984.)
MATHEMATICAL MODELS IN AGRICULTURE
7

8. Examples of models of the Linear programming, Empirical
curvefitting, and Mechanistic sorts are illustrated, showing how
each type is used and how particular models are developed from
each general method.
 Linear programming, Empirical curvefitting, and Mechanistic
models are then introduced, in separate chapters. Straightforward
linear programming problems applied to farm management are
described in detail. A variety of exponential functions suitable for
empirical fitting of weight accumulation in plants and animals are
described but they are not applied to specific examples.
8SREENIVAS REDDY.K

9. 9SREENIVAS REDDY.K

10. Crop growth models are computer software programs 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 by scientists
worldwide over the last 40 years. As a result, they have now
reached a high degree of maturity, so that they can be confidently
applied to support agricultural management practices (e.g.
fertiliser recommendation, irrigation, crop rotation planning).
What are crop growth models ?
10SREENIVAS REDDY.K

11. Depending on soil characteristics, weather conditions and crop
species, crop models calculate the daily growth of biomass in the
individual plant organs (stems, leaves, roots, 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 nutrient availability) in order to simulate crop growth
during a whole growing season.
Moreover, an advanced crop model, as the one used in FARM/IT,
calculates nitrogen mineralisation, leaching and volatilisation
depending on precipitation and soil moisture content. Nitrogen
deficiency and drought stress reduce crop biomass growth and
yield.
How do crop growth models work ?
11
SREENIVAS REDDY.K

12. 12
SREENIVAS REDDY.K

13. YEAR DEVELOPMENTS
1960 Simple water-balance models
1965 Model photosynthetic rates of crop canopies (De Wit )
1970
1977
Elementary Crop growth Simulator construction(ELCROS) by de Wit et al.
Introduction of micrometeorology in the models & quantification of canopy
resistance (Goudriaan)
1978 Basic Crop growth Simulator (BACROS) [de Wit and Goudriaan]
1982 International Benchmark Sites Network for Agro-technology Transfer(IBSNAT)
began the development of a model (University of Hawaii) Decision Support System for
Agro- Technology Transfer (DSSAT)
1992 James reviewed the history of attempts to quantify the relationships between crop
yield and water use from the early work on simple water-balance models in the 1960s
to the development of crop growth simulation models in the 1980s.
1994 ORYZA1 (Kropff et al., 1994)
1994 India’s Ist crop model WTGROWS followed by the construction of ORYZA1N
1995 INFOCROP model developed that can control 16 crops
13

14. EFFECT OF VARIOUS WEATHER CHANGES ONCROP
GROWTH (reflects the need of crop weathermodeling)
14SREENIVAS REDDY.K

15. Define goals
Define system and its boundaries
Define key variables in system
Preparation of flowchart
Evaluation
Calibration
Validation
Sensitivity analysis
iii.
Key variables in system :
i. State variables are those which can be
measured. e.g. soil moisture content,
crop yield etc
ii. Rate variables are the rates of different
processes operating in a system. e.g.
photosynthetic rate, transpiration rate.
Driving variables are the variables
which are not part of the system but they
affect the system. e.g. sunshine, rainfall.
iv. Auxiliary variables are the intermediated
products. e.g. dry matter partitioning, water
stress etc
STEPS IN CROP MODELLING
15
SREENIVAS REDDY.K

16. BASED ON CLIMATE UNDERSTANDING
Climatological
model
Water-stress model
Dynamic crop-weather
model
BASED ON PURPOSE
Statistical model
Mechanistic model
Deterministic model
Stochastic model
Dynamic model
Static model
Simulation model
Descriptive model
Explanatory model
16SREENIVAS REDDY.K

17. Application: Optimising nitrogen fertilisation in winter wheat
In winter wheat grown in Austria, the question arises in the spring time, as to
how much nitrogen fertiliser should be applied. To answer this question, the
iCrop model can be applied to simulate biomass growth and nitrogen uptake of
crops by the time of scheduled fertilisation.
A Spectral sensor is then used to measure the nitrogen content of the crop in the
field. Using these information, iCrop can make a season- and site-specific
nitrogen fertiliser recommendation.
Application: Crop yield forecasting
Predicting crop yield a few weeks before the harvest is extremely important for
the agricultural processing industry. The FARM/IT project will develop an
advanced software based on iCrop and satellite data to make accurate regional
yield forecasts for potato, sugar beet and wheat.
17
SREENIVAS REDDY.K

18. Source : Short course on Crop weather modeling 2011 by CRIDA, Hyderabad
Fig. - Crop-weather Model Approach for different processesand parameters
18

19. Fig. : Relational diagram of a simulation model at production level (crop-weather
interaction)
Source : Short course on Crop weather modeling 2011 by CRIDA, Hyderabad
19

20. Crop Growth Simulation Model – Input & Output
Inputs Process Output
Weather (Temperature, Rainfall,
solar radiation)
Soil Parameters (Texture, depth,
soil moisture, soil fertility)
Phenological Development
CO2 Assimilation
Transpiration
Biomass, LAI, Yield
Water Use
Nitrogen Uptake
Crop Parameters (Phenology,
physiology, morphology)
Respiration
Partitioning
Management (DOS, irrigation,
fertilizer)
Dry matter Format
20
SREENIVAS REDDY.K

21. ExperimentalData
File
Cultivar
Code
Crop Data
at Harvest Crop Data
during season
Output Depending on Option Setting and Simulation Application
Weather
Data
Soil
Data
Crop
Models
INPUTS
21
SREENIVAS REDDY.K

22. DEVELOPED BY THE IARI IN INDIA
ACQUIRED BY INDIA
22
SREENIVAS REDDY.K

23. *
 Reduces cumbersome field experiment considerably.
 Identify crop production constrains.
 Model could be substitute to multi-location field trails for introducing a
variety in different agro-climatic zones saving time and money .
.
Useful for maximizing the agricultural production through better crop
management practices.
 Help to evaluate expected returns of soil and management practices.
 Help in evaluating the risk associate with management practices.
Help in understanding of biological and physical system and their
interaction.
5
Advantages of Crop Weather Modeling
23
SREENIVAS REDDY.K

24. *
1 Statistic model
2 Mechanistic model
3 Deterministic model
4 Stochastic model
5 Dynamic model
6 Static model
7 Simulation model
8 Descriptive model
9 Explanatory model 624
Types of crop weather modelling

25. Types of crop weather modeling
1. Statistical models: These models represent the relationship
between yield components and weather parameters. Statistical
techniques are used to measure relationship.
25

26. SREENIVAS REDDY.K 26
2. Stochastic models: These models calculate output at a given rate. A
probability element is attached to each output. For each set of inputs different
outputs are given along with probabilities.
 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. However, stochastic models tend to be technically difficult to
handle and can quickly become complex.
 Hence, it is advisable to attempt to solve the problem with a deterministic
approach initially and to attempt the stochastic approach only if the results are not
adequate and satisfactory.
These models are convenient and computationally fast, and are useful in a
number of applications where the observed climate record is inadequate with
respect to length, completeness, or spatial coverage.

27. 3. 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.
4.Deterministic models: In these models the input and output remains same. These
models estimate the exact value of the yield. These models also have defined
coefficients. A deterministic model is one that makes definite predictions for quantities
(e.g. crop yield or rainfall) without any associated probability distribution, variance, or
random element. However, variations due to inaccuracies in recorded data and to
heterogeneity in the material being dealt with are inherent to biological and
agricultural systems (Brockington, 1979).
In certain cases, deterministic models may be adequate despite these inherent
variations but in others they might prove to be unsatisfactory e.g. in rainfall
prediction. The greater the uncertainties in the system, the more inadequate
deterministic models become.
27

28. SREENIVAS REDDY.K 28
5.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.
A mechanistic model is one that describes the behaviour of the system in terms of
lowerlevel attributes. Hence, there is some mechanism, understanding or
explanation at the lower levels (eg. Cell division).
 These models have the ability to mimic relevant physical, chemical or biological
processes and to describe how and why a particular response occurs. The modeler
usually starts with some empirism and as knowledge is gained additional
parameters and variables are introduced to explain crop yield. The system is
therefore broken down into components and assigned processes.
Examples of mechanistic models of the accumulation of plant dry matter are
described; for the most part, these concern whole-plant models of photosynthesis
and respiration.

29. 6.Static model : In this model, time is not included as a variable. Dependent and
independent variable having values remain constant over a given period of time.
7.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.
 These form a group of models that is designed for the purpose of imitating the behaviour of a
system. Since they are designed to mimic the system at short time intervals (daily time-step), the
aspect of variability related to daily change in weather and soil conditions is integrated.
 The short simulation time-step demands that a large amount of input data (climate parameters,
soil characteristics and crop parameters) be available for the model to run. These models usually
offer the possibility of specifying management options and they can be used to investigate a wide
range of management strategies at low costs.
29

30. 8. 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. 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.
9.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
analyzed and its processes and mechanisms are quantified separately. 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.
30SREENIVAS REDDY.K

31.  Evaluation of optimum management for cultural practice in crop production.
 Evaluate weather risk via weatherforecasting
 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 adaptation strategies to minimize the negative impacts of climate
change
 Identification of the precise reasons for yield gap at farmer’sfield
 Forecasting crop yields.
 Evaluate cultivar stability under long term weather conditions
IMPACT OF MODELING IN AGRICULTURE
31

32. Uses of crop weather model
 Crop system management: to evaluate optimum management production for
cultural practice.
1. Seed rate: Optimum seed rate can be found out with the help of these
models.
2. Irrigation: Optimum amount and time of application can be simulated.
3. Fertilizer: Optimum amount of fertilizer and time of application of the
fertilizer 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. 32

33.  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 climate change can be minimized.
33
SREENIVAS REDDY.K

34. 34SREENIVAS REDDY.K

35. 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 Effects on Production
35SREENIVAS REDDY.K

36. Precision Farming
Climate Change Effects on Crop Production
Can be used to perform “what-if” experiments on the computer
to optimize management.
36SREENIVAS REDDY.K

37. Inaccurate projections of natural processes
Unreliable and unrealistic projections of changes in climate variability
Crop models are not universal ( no site specificity).
Misuse of models
Inappropriate for Heterogeneous plot
Inherent soil heterogeneity over relatively small distances
 Model performance is limited to the quality of 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 cannot be developed because of complex biological system37SREENIVAS REDDY.K

38. Journal of Agrometeorology. 16 (1) : 38-43
Bhuvaneswari et al., 2014
Date of planting
Days to flowering Days to physiological maturity
O S O-S O S O-S
D1-1st June 66 68 2 110 108 -2
D2-15th June 65 65 0 104 106 2
D3-1st July 62 64 2 99 105 6
D4-15th July 61 62 1 101 104 3
RMSE 1.5 3.6
NRMSE 2.34 3.5
38SREENIVAS REDDY.K

39. *
treatments) Jagadish Timsina et al., 2014
http://www.regional.org.au/au/asa/2004/poster/2/3/659_timsinaj.htm
39SREENIVAS REDDY.K

40. *
Akinbile, 2013
R2Crop parameter SI OB SI-OB RMSE BIAS
Grain yield 2.63 2.41 0.22 0.99 0.16 0.06
13.74 8.17 5.77 0.99 2.78 1.39
16.47 10.58 5.89 0.99 2.68 1.34
Leaves & stem
biomass
Total above
ground biomass
Note: SI-Simulated Value (t/ha); OB-Observed value (t/ha); R2-Coefficient of determination;
RMSE-Root mean square error;
Agric Eng Int: CIGR Journal Open access at http://www.cigrjournal.org Vol. 15 (1), 19-2640

41. Statistical summary comparing observed data with
simulated values for DS rice–wheat cropping system using
CropSyst simulation model
N= No. of observations;
RMSE= Root mean square error;
MBE= Mean biased error;
R2= Pearson’s correlation coefficient;
MAE= Mean absolute error; MBE
ME= Modelling efficiency.
Singh et al., 2013
Current Science, 104 (10); 1324- 1331
Crop Parameter N
Observed Predicted MAE
R2
RMSE ME MBE
Rice
Wheat
Biomass
(Mg ha-1)
Grain yield
(Mg ha-1)
Biomass
(Mg ha-1)
Grain yield
(Mg ha-1)
N mean mean (Mg ha-1) (Mg ha-1) (%) (Mg ha-1)
24 7.20 6.55 0.65 0.97 0.70 53.0 0.65
24 2.41 2.29 0.27 0.90 0.33 80.2 0.12
24 7.82 7.11 0.71 0.95 0.80 76.2 0.70
24 3.46 3.55 0.26 0.84 0.33 89.5 -0.10
41SREENIVAS REDDY.K

42. 42
As a research tool, model development and application can contribute
to identify gaps in our knowledge, thus enabling more efficient and
targeted research planning.
Models that are based on sound physiological data are capable of
supporting extrapolation to alternative cropping cycles and locations,
thus permitting the quantification of temporal and spatial variability.
 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.
 Because most agronomists understand the concept of crop growth
modeling and systems-approach research, training in this area is
required. 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

43. THANK YOU
43SREENIVAS REDDY.K