CROP GROWTH MODELLING

1. TERM PAPER
SWE-606
Crop Growth Simulation Models; A
research tool
PRESENTED BY:
MEHRAJ-U-DIN-DAR
L-2K16-AE-123-D
COURSE INSTRUCTOR:
Dr.P.P.S LUBANA

2. Model
 A model is a set of mathematical equations describing/mimic
behaviour of a system
 Model simulates or imitates the behavior of a real crop by
predicting the growth of its components
Modeling
 Modeling is based on the assumption that any given process
can be expressed in a formal mathematical statement or set
of statements.

3.  Simulation:
 It is the process of building models and analyzing the system.
 The art of building mathematical models and study their
properties in reference to those of the systems (de Wit,
1982)
 CROP MODEL:
Simple representation of a crop.
 SYSTEM:
Limited part of reality that contains inter related components

4. Types of models (purpose for which it is
designed )
 Statistical & Empirical models
 Mechanistic models
 Deterministic models
 Stochastic models
 Static models
 Dynamic models

5. Statistical & Empirical models
 Direct descriptions of observed data, generally
expressed as regression equations
 These models give no information on the
mechanisms that give rise to the response
 Eg: Step down regressions, correlation, etc.

6. Mechanistic models
 These attempt to use fundamental mechanisms
of plant and soil processes to simulate specific
outcomes
 These models are based on physical selection.
 Eg. photosynthesis based model.

7. Deterministic models
 These models estimate the exact value of the yield
or dependent variable.
 These models also have defined coefficients.
 Eg: NPK doses are applied and the definite yields
are given out.

8. Stochasticmodels
 The models are based on the probability of
occurrence of some event or external variable
 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.

9. Static models
 Time is not included as a variables.
 Dependent andindependent variables having
values remain constant over a given period of
time.

10. Dynamic simulation models
 These models predict changes in crop status with
time.
 Bothdependent and independent variables are
having values which remain constant over a
given period of time.

11. Chronology of crop simulation modeling
1960 Simple water-balance models
1965 Model photosynthetic rates of crop canopies (De Wit )
1970 Elementary Crop growth Simulator construction (De Wit )
1977 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
Agrotechnology Transfer) began the development of a
model (University of Hawaii)
Decision Support System for Agro- Technology Transfer (DSSAT)
1994 ORYZA1 (Kropff et al., 1994)

12. Steps in modelling
1.Define goals: Agricultural system
2.Define system and its boundaries: Crop model
3.Define key variables in system:
 State variables are those which can be measured. e.g. soil
moisture content, crop yield etc
 Rate variables are the rates of different processes operating
in a system. e.g. phosynthesis rate, transpiration rate.
 Driving variables are the variables which are not part of the
system but they affect the system. e.g. sunshine, rainfall.
 Auxiliary variables are the intermediated products . e.g. dry
matter partitioning, water stress etc
4. Quantify relationships (evaluation):

13. 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 (out-put) for a
given set of assumed conditions with observed data for the
same conditions.
6.Validation:
Testing of 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.

14. Crop Simulation Models

15. Experimental
Data File
Cultivar data
Previous
crop data
Crop Data during
season
Output Depending on Option Setting and Simulation Application
Weather DataSoil Data
Crop
Models
INPUTS

16. Major & popular crop simulation models
 DSSAT (Decision Support System for
Agrotechnology Transfer)
 Aqua Crop
 Info Crop
 APSIM (Agricultural Production System
Simulator)

17. Overview of the components and sub-modular
structure of DSSAT-CSM
J.W. Jones et al., 2003. Europ. J. Agronomy 18; 235-265

18. Components of AquaCrop, FAO model
http://www.fao.org/nr/water/infores_databases_aquacrop.htm

20. Sub-modular structure of APSIM
model

21. Who Uses simulation model Tools?
 Agronomic Researchers and Extension
Specialists
 Policy Makers
 Farmers and their Advisors
 Private Sector
 Educators

22. Understanding of research
 Testing scientific hypothesis.
 Yield prediction and forecasting.
 Evaluation of climate change.
 Useful for solving various practical problems in agriculture.
 ‰Helps in adaptation strategies, by which the negative
impacts due to climate change can be minimized.
 Crop growth models identifies the precise reasons for yield
gap at farmer’s field and forecasting crop yields.
 Evaluate cultivar stability under long term weather

23. Crop simulation models; a
research tool

24. Journal of Agrometeorology. 16 (1) : 38-43
Observed and CERES-Rice model simulated panicle initiation
(PI), flowering date and physiological maturity date (in
days) of rice for different dates of planting
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

25. Simulated and observed total N uptake by rice
(cultivar Pusa 44) in Delhi in 1999 and 2000 (all N
and irrigated treatments using DSSAT
Jagadish Timsina et al., 2014
http://www.regional.org.au/au/asa/2004/poster/2/3/659_timsinaj.htm

26. Simulated and observed losses of N (kg ha-1) from
rice in northwest India using CERES model
Pathak et al., 2004
http://www.clw.csiro.au/publications/technical2004/tr23-04.pdf
Losses Observed a Simulated b
Denitrification 30 (10-40) 48
Leaching 15 (10-18) 14
Volatilization 25 (20-35) 4
a = Mean for saturated and AWD treatments with recommended N (120 kg ha-1)
b = Mean for saturated and AWD treatments with recommended N (120 kg ha-1)

27. Climatic potential, on-station and on-farm yields of rice (Mg
ha-1) and yield gaps in rice for three sites in India using
validated CERES 4.1 model
http://www.clw.csiro.au/publications/technical2004/tr23-04.pdf
a = Average yield of the whole district with different water and nitrogen management
Pathak et al., 2004
Station name
Potential
yield
Actual yield
On station On farm
Yield gap (%)
(A-B) (A-C)
(A) (B) a (C) A*100 A*100
Ludhiana 10.8 7.8 5.6 28 48
Delhi 10.3 7.1 3.3 31 68
Modipuram 10.0 6.2 3.5 38 65

28. Observed and simulated yield(CERES RICE) of
rice cultivar N2 with statistical analyses
results. 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), 19-26

29. Main growth and development variables of CNT1 variety,
obtained from observation, and simulation of ORYZA 2000
and CERES Rice
Thai Journal of Agricultural Science. 43(1): 17-29
Wikarmpapraharn & Kositsakulchai, 2010
Observed ORYZA CERES
Variables
Panicle initiation
Anthesis
Physiological Maturity
Yield at harvest
maturity kg/ha
LAI maximum
53 48 45
72 64 77
124 120 103
5275 5319 5202
2.57 3.45 2.33

30. Validation of dry tuber yield (t ha-1) and cultivars of potato
cultivars Rosara and Karin using SUBSTOR model
Agriculturae Conspectus Scientificus; 73 (4), 227-234.
Sastana and Dufkova, 2008
Rosara Karin
limited limited
Year Water
Potential Observed
Water
Potential Observed
1994
yield
06.1
yield
19.4
yield
06.5
yield
05.6
yield
18.5
yield
07.6
1995 04.3 19.9 10.3 04.2 19.8 10.7
1996 08.4 22.0 11.4 08.3 22.0 10.8
1997 11.7 15.8 06.4 12.0 15.9 07.3
1998 09.5 26.6 05.5 09.7 26.7 09.2
1999 13.0 24.1 08.5 13.0 24.2 11.7
2000 07.1 21.6 08.4 06.94 21.7 09.5
2001 08.3 23.3 07.8 08.6 23.2 08.8
2002 17.3 23.6 11.7 17.6 24.0 12.0

31. 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)
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
Statistical summary comparing observed data with
simulated values for DS rice–wheat cropping system using
Crop Syst simulation model

32. Estimated changes in rice yield predicted by the
ORYZA1 model for each observation site in eastern
India under the three GCM scenarios
Agriculture, Ecosystems and Environment 122; 233–242
Sites
Rice
yields
(t/ha)
Krishnan et al., 2007
GFDL GISS UKMO
Bhubaneswar
Chinsurah
Cuttack
Faizabad
Jabalpur
Jorhat
Kalyani
Pusa
Raipur
Ranchi
Average
Change (%)
4.46
5.18
4.93
4.72
7.54
3.83
3.55
3.82
3.75
4.50
4.63
Predicted
change
(%)
-17.33
-8.03
-19.67
-9.02
-11.05
12.13
-7.75
-4.93
-2.79
-7.87
-7.63
Predicted
yield
(t/ha)
3.69
4.76
3.96
4.29
6.71
4.29
3.27
3.63
3.65
4.15
4.24
Predicted
change
(%)
-20.36
-8.72
-20.32
-11.27
-14.08
12.64
-9.76
-6.31
-5.22
-10.35
-9.38
Predicted
yield
(t/ha)
3.55
4.73
3.93
4.19
6.48
4.31
3.20
3.58
3.55
4.03
4.16
Predicted
change
(%)
-27.53
-9.59
-30.75
-18.82
-21.05
8.31
-16.51
-6.58
-10.09
-25.98
-15.86
Predicted
yield
(t/ha)
3.23
4.68
3.41
3.83
5.95
4.15
2.96
3.57
3.37
3.33
3.85

33. Changes in yield (%) under the GCMs scenarios of the rice
variety IR 36 under different sowing dates grown during the
kharif season at Cuttack using InfoCrop model. Krishnan et al., 2007
Agriculture, Ecosystems and Environment 122; 233–242

34. Modeling the sensitivity of CERES-Rice under future
scenarios Lamsal et al., 2013
Agronomy Journal of Nepal. 3; 11-22

35. Predicted yield of BR3 variety of boro rice (kg ha-1) at 12
selected locations for the years 2008, 2030, 2050 and
2070 Jayanta Kumar Basak et al., 2010
Journal of Civil Engineering. 38 (2); 95-108

36. Integrating growth stage deficit irrigation into a
process based crop model Jose R et al., 2017.

37. Conclusions
 A new modeling framework for the evaluation of
irrigation strategies in water limited areas was
described.
 This approach links water availability to crop
yield using a crop model, weather data, and soil
information.
 The new irrigation algorithm will be available to
a broad audience in the next release of the DSSAT
cropping system model (DSSAT v4.7).

38. Modeling soil and plant phosphorus withinDSSAT
Dzotsi et al 2010
In this study, a soil–plant P model integrated to
DSSAT was described, and results showing the
ability of the model to mimic wide differences in
maize responses to P in Ghana are presented as
preliminary attempts to testing the model on
highly weathered soils.
The model simulates P transformations between
soil inorganic labile, active and stable pools and
soil organic microbial and stable pools.

39. Summary of processes in the integrated soil–plant phosphorus model in DSSAT. Arrows indicate
the directions of flows
Summary of processes in the integrated soil–plant phosphorus model in
DSSAT. Arrows indicate the directions of flows

40. Measured and simulated responses of cumulative biomass (a–i) and grain yield (j–i) to
different combinations of nitrogen and phosphorus levels in theWa experiment

41. Study Conclusions
 As the complex soil P chemistry makes the availability of P
to plants extremely variable, testing under a wider range
of agro-ecological conditions is needed to complement the
initial evaluation of maize growth and yield is presented
here which had led to extended use of the DSSAT-P model
to other P-deficient environments.
 Furthermore, detailed measurements on soil inorganic and
organic P are needed to evaluate the soil modules.
 In addition to model testing, a global sensitivity analysis is
needed to evaluate the uncertainty and variability
associated with model structure, inputs and parameters,
and determine an appropriate choice of model complexity.

42. Estimating wheat yield and quality by coupling the DSSAT-CERESmodel and proximal
remote sensing
Li et al 2015
In this study, a data assimilation approach using a particle swarm
optimization algorithm was developed to integrate remotely sensed data
into the DSSAT-CERES model for estimating the grain yield and protein
content of winter wheat.
The field experiments were
conducted during the 2008–2009,2009–
2010, 2010–2011, and 2012–2013
growing seasons at the Xiaotangshan
experimental site), near Beijing, PR
China. The soil and crop management
practices at the site were
representative of those in this region.

43. Flowchart of the PSO assimilation scheme, integrating remote-sensing data into the DSSAT-CERES model.

44. Table:

45. Relationship between simulated and measured harvest variables for grain yield
(a) and GPC (b).

46.  Simulated and measured grain yields showed a good linear
relationship, with R2and RMSE values of 0.711 and 0.63 t
ha−1,respectively.
 Grain protein content estimation by gluten type could
improve estimation accuracy, with R2and RMSE values of
0.519 and1.53%, respectively. Integrating remote sensing
with the DSSAT-CERES model is a potential approach for
grain protein content estimation.
Study Conclusions

47. Assimilation of canopy cover and biomass measurements in the crop model Aqua
Crop
Linker and Ioslovich 2017
This work presents procedures for
performing data assimilation, using the
crop model AquaCrop as specific example.
The procedures are based on Extended
Kalman Filter, with some heuristic
adjustments, and enable re-initialisation
of state variables and/or adjustments of
selected parameters of the model
The uncertainties of the measurements
are taken into account explicitly in the
proposed assimilation scheme. The
procedures were tested with data
obtained from experiments conducted
with potato in Denmark and cotton in
Greece.

48. DATASET 1

49. DATASET 2

50. RESULTS

51. Results for cotton (left frames: Plot #1, right frames: Plot #2). Symbols and
colours as in FIG.

52. Study Conclusions
 A framework for data assimilation and parameter adjustments has been
developed.
 This framework, which could be applied to any situation in which the
user has (1) few samples at irregular time intervals and (2) a dynamic
model which can be re-parametrised or re-initialised and “hot-started”,
was illustrated with the crop model AquaCrop.
 The proposed procedures followed a simplified (extended) Kalman filter
approach with some heuristic adjustments.
 These procedures were applied to two case studies corresponding to
very different crops and climates.
 Both case studies demonstrated the soundness of the procedures, but
also emphasised the inherent limitations associated with data
assimilation.

53. References
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http://www.fao.org/nr/water/aquacrop.html.

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