Glue Users Guide Version 4.7

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Guidelines for Installing and Running GLUE Program
Jianqiang He, Cheryl Porter, Paul Wilkens, and James W. Jones
Updated by Gerrit Hoogenboom
September 1, 2017
July 7, 2010

1. Overview
The GLUE (Generalized Likelihood Uncertainty Estimation) program is used to
estimate genotype-specific coefficients for the DSSAT crop models. It is a Bayesian
estimation method that uses Monte Carlo sampling from prior distributions of the
coefficients and a Gaussian likelihood function to determine the best coefficients based
on the data that are used in the estimation process. The GLUE program allows users to
select a crop, then a cultivar to be estimated. The program will then identify all
experiments and treatments in the DSSAT data files for the crop that have measurements
for that cultivar. The user then can select one or more experiments and treatments that
will actually be used in the coefficient estimation process. Another option for the user
is to specify whether to estimate only those coefficients that control phenological
development, only those that deal with expansive and dry matter growth, or both sets.
Generally, one would want to estimate all parameters. What happens then is that the
GLUE program will make 3,000 simulation runs for phenology coefficients and another
3,000 runs for growth coefficients. The program randomly generates parameters that
are being estimated (either phenology or growth) from the prior distribution of
parameter values and runs the model for each. The model outputs are used to select the
parameter set with the maximum likelihood value based on comparison of simulated vs.
observed variables, first for phenology parameters, then for growth parameters. The
program also computes the uncertainties of the estimates (variances) for each parameter.
The maximum likelihood coefficients are written to a file in the same format as the
cultivar file for the selected crop. These values can be copied into the CUL file (e.g.,
MZCER045.CUL or SBGRO045.CUL, etc.) to operate for routine DSSAT applications
and further model evaluations.
What measurements are used to estimate the coefficients? For the development
coefficients, measurements of first flower, physiological maturity, and first
reproductive organ appearance dates are all used. For growth coefficients, final grain
yield, above ground biomass, maximum leaf area during the season, final pod weight,
final main stem leaf number, and unit grain weight are used. Thus, the measurements
that go into File – A in DSSAT are used; these are variables measured only one time
during the season, most of which were measured at harvest.
There are several assumptions that may have important effects on the resulting
parameters. First are the prior distributions of coefficients, which are stored in a file
called ParameterProperty.csv. This file has information for all of the DSSAT v4.7 crops.
We assumed that the parameters have uniform distributions with minimum and
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maximum values. This is a conservative assumption, and values are provided in the
files based on previous work with the models. A second assumption is that the final
errors between simulated and observed values are normally distributed and are unbiased.
The assumed values of the variances are given in files named
MeasurementVariance_All.csv and others. This assumption may be a problem,
particularly if the model is not able to describe responses for a particular experiment
very well or if observations are not reliable. Another problem will occur if the
experiment had water, nutrient, or other stresses that are either not in the model or that
the model does not represent well. Users should only use treatments that are near stressfree conditions, if possible, to minimize these problems. Coefficients estimated using
treatments with moderate to severe stress effects will not be reliable. In any case, users
should carefully check results from any estimation process to make sure that results are
realistic and provide good comparisons to observations used in estimation.
There are other cautions that users should be aware of. For example, results from an
estimation process provide conditional estimates of coefficients. That means that the
coefficients are the best set given the measurements that were used, but the coefficients
also depend on the set of observations used in the process. Our aim is for the coefficients
to be robust and useful across environments, but this may not be the case. Another
caution is that coefficients estimated from end of season measurements may not
reproduce observed time series results very well if such measurements were made. We
have seen this occur in various experiments when only end of season measurements are
used, whether using GLUE or other estimation procedures. If users have time series
data, these data can be used manually to refine the coefficients estimated from the
GLUE procedure. It is possible to use in-season measurements and simulations in this
type of Bayesian estimation process, but there are certain complications that make it
difficult to create a robust and reliable automated procedure.
The GLUE program is one of two tools in DSSAT for estimating cultivar coefficients
for the different crops. The first tool, developed by L. A. Hunt and others, evolved from
the GENCALC software available in DSSAT v3.5. There are advantages and
disadvantages of using each. Disadvantages of the GLUE technique is that it may
require a lot of time for the computations, depending on the number of treatments
selected for the estimation process. If there are only 2-5 measurement data sets, one
would expect the GLUE procedure to finish its calculations in less than 2 hours.
However, if there are many measurement data sets, say more than 15, the GLUE method
will likely require several hours of calculations. This is a practical limit. On the other
hand, the GLUE method can be used, without intervention by users, to produce a set of
estimated coefficients. It also provides estimates of the uncertainties of the parameters.
This method does not depend on heuristic rules, making it simple to implement for
additional crops as they are added to DSSAT.

2. Installation of the GLUE Program
The GLUE program was developed using the R statistical programming language.
You should have R installed on your computer before proceeding. The DSSAT
installation disk has an option to install R, as shown below. You should select the
“Install R” button so that R will be available for use in estimating genetic coefficients
using the GLUE program.

3. Use of the GLUE Procedure to Estimate Genetic Coefficients
3.1 Setting conditions for GLUE to estimate coefficients
The GlueSelect program was written by Paul Wilkens (IFDC) as a tool in DSSAT v4.7.
This tool uses much of the code that he and L. A. Hunt developed for GenSelect, which
is a rule-based estimator of cultivar coefficients. Currently, the GLUE program operates
on most crops (except those legacy crops that are not converted to v4.7 standards).
However, we are more confident of the program correctly estimating cultivar
coefficients for the following crops: maize, soybean, peanut, millet, sorghum, chickpea,
cotton, fababean, sweet corn, tomato, green beans, rice, wheat, and drybean. Users
should check the coefficients carefully before using them. This can be done by putting
the estimated parameters in the appropriate CUL file and simulating the crop
interactively for comparison with observed data.
The file that has definitions of genetic coefficients and their ranges of uncertainty for
all crops is “ParameterProperty.csv” (see Appendix A for details), and
“MeasurementVariance_All.csv” (see Appendix B). These files are stored in the
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directory “C:\DSSAT45\Tools\GLUE\”. Advanced model users can modify them to set
other ranges of parameters, change parameters to be estimated, introduce parameters
for new crops, and change the order in which they are estimated.
The GLUE program is integrated into the
DSSAT45 shell, and the user runs the
GlueSelect program from the DSSAT
Tools menu to start the process as shown
at right.

The second GlueSelect screen
shows all of the crops. A user
selects a crop, such as peanut as
shown at left, and then a
particular cultivar that is to be
estimated (“FLORUNNER, std”
in this example). After selecting
“Go” on this screen, a list of
experiments and treatments will
appear as shown below. In this
example, three growing seasons
were selected from three
different years and experiments. These treatments will be simulated, using the GLUE
method to estimate the
coefficients that give the
maximum likelihood for
both phenology and growth
measurements.
The third GlueSelect screen
shows the console for
operating the actual GLUE
calculations and viewing
the results (below). In this
example, there are to be
3,000
runs
for
all
parameters, which means that there will be 3,000 runs for phenology parameters and
another 3,000 for growth parameters. This number can be changed to a few, say 10, to
make sure that the
program is operating ok.
However, results from
runs less than 3,000
would not likely give
reliable results. So, if the
number is changed from
3,000
to
test
the
procedure, then it must be
changed back to 3,000 (or
more) in order to get
reliable results. It is ok to
increase this number to
further refine the results,
but more time will be required if this is done.
3.2. Running GLUE
The figure above shows how users initiate the simulation runs to estimate the
5

coefficients using the “Run GLUE” button. Before running GLUE, you may want to
disable on-access scanning of files by your anti-virus software. Our experience is that
the system will operate much faster when virus scanning is disabled for the DSSATgenerated files. GLUE runs may take some time, possibly from 0.5 to 2 hours for
example, depending on how many seasons are selected for estimating the coefficients.
From this screen, one can view the final estimated coefficients and copy them to put
into the appropriate cultivar file (.CUL in the DSSAT45\Genetics directory) and also to
review statistics (mean, maximum likelihood, and standard deviation of the estimated
coefficients.
3.3 GLUE Results
The main results that users will be interested in can be seen by selecting the “View
Cultivar Coefficient” button on the main screen. This will open an editor with the final
values of the estimated coefficients in it. The format of the file is the same as the CUL
file for the selected crop, so one can copy this new set of cultivar coefficients into the
appropriate CUL file to use in additional simulations. Note that one should use the
DSSAT feature to “Update all Lists” after adding a new cultivar to any CUL file.
All of the outputs of DSSAT and GLUE running are saved in the
“C:\DSSAT45\GLWork\” directory. The contents of main output files are briefly
described as follows:
(a) Optimal Parameters. The optimal parameter set that was chosen through GLUE
procedure was saved as a “CUL” file named according to the name and ID of the
selected cultivar when generating the batch file. For example, if the selected cultivar
was soybean “COBB”, then the “CUL” file is “SBIB0002 COBB.CUL” (Table 1 in
Appendix E).
(b) Statistics of Posterior Distributions (Mean, Standard Deviation, and Maximum
Likelihood Values). The two files identified as “PosteriorDistribution_1.txt” and
“PosteriorDistribution_2.txt” (Table 2 and 3 in Appendix E) store the posterior
distributions for each round of GLUE, including the mean values, standard
deviations, and the parameter set that has the highest likelihood value in that round
of GLUE.
(c) Empirical Distribution of Parameter Tables. The two files identified as
“RandomParameterSetsAndProbability_1.txt” and “RandomParameterSetsAnd
Probability_2.txt” (Table 4 and 5, Appendix E) store the really used parameter sets
and their corresponding probabilities or normalized likelihood values for each
round of GLUE.
(d) Generated Parameter Sets. The two files identified as “RealRandomSets_1.txt”
and “RealRandomSets_2.txt” store the really used parameter set in each round of
GLUE.
(e) Last Model Run Results. “Evaluate_output.txt” stores the content of output file
“Evaluate.OUT” of DSSAT for each model run. Since the “Evaluate_output.txt” is
processed after each model run, only the result of last model run will be available
in the “Evaluate_output.txt” file after the GLUE procedure. This file is not needed
for result analysis, but it is described here because it will be in the directory and
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model users should ignore it.
(f) Results for Computing Likelihood Values. The two files “EvaluateFrame_1.txt”
and “EvaluateFrame_2.txt” store the appended data of the processed
“Evaluate_output.txt” files for the two rounds of GLUE. In each file, the simulated
and measured outputs are saved for each treatment and each model run.
(g) Combined Likelihood Value for Each Parameter Set. The two files identified as
“IntegratedLikelihoodMatrix_Frame_1.txt”, and “IntegratedLikelihoodMatrix_Frame_2.txt”
(not shown) store the combined likelihood values for all treatments in each model
run or for reach parameter set. For example, in the file
“IntegratedLikelihoodMatrix_Frame_1.txt”, it stores the combined likelihood
values for observations “ADAP”, “MDAP”, and “PD1P” for the first round GLUE.
In “IntegratedLikelihoodMatrix_Frame_2.txt”, it stores the combined likelihood
values for observations “PWAM”, “HWAM”, “CWAM”, “LAIX”, and “L#SM” for
the second round GLUE. When the combined likelihood value is “1” in one column,
it means the observation is absent.
(h) Combined Likelihood Value for Each Experiment Treatment. If there are only two
treatments in the experiment for GLUE procedure, then the following files,
“IntegratedLikelihoodTreatment_1_1.txt”,
“IntegratedLikelihoodTreatment_1_2.txt”,
“IntegratedLikelihoodTreatment_2_1.txt”, and “IntegratedLikelihoodTreatment_2_2.txt”,

respectively, store the combined likelihood values for each treatment in each round
of GLUE. The “IntegratedLikelihoodTreatment_1_1.txt” file, for example, stores
the combined likelihood value for GLUE 1 and treatment 1 for all generated
parameter sets, so do other files. One can see these files in the DSSAT45/GLWork
directory after any GLUE estimation procedure is run.

4. How to Add a New Crop
When a new crop is added to DSSAT, cultivar coefficients for this crop can also be
estimated after adding appropriate information in the ParameterProperty.csv file if the
naming conventions for measurements and simulated outputs are standardized and the
same as for other crops. However, if headers are different for a new crop, then additional
information must be added to MeasurementVariance_All.csv file. The
MeasurementVariance_All.csv file shows how additional sheets in the spreadsheet must
be set up for the crop.

Appendices
Appendix A
Example Genotype Parameters and Ranges from ParameterProperty.csv File for
Maize and Soybean
Minimum

Maximum

Flag1

MZ_P1

450

MZ_P2

MZ_P5

580

999

MZ_G2

248

990

MZ_G3

16.5

MZ_PHINT

SB_CSDL

11.78

14.6

SB_PPSEN

0.100

0.385

SB_EM.FL

23.5

SB_FL.SH

SB_FL.SD

SB_SD.PM

37.7

SB_FL.LF

SB_LFMAX

0.95

1.15

SB_SLAVR

300

400

SB_SIZLF

140

230

SB_XFRT

SB_WTPSD

0.158

0.195

SB_SFDUR

25.5

SB_SDPDV

1.7

2.44

SB_PODUR

SB_THRSH

SB_SDPRO

0.4

SB_SDLIP

0.2

The FLAG column indicates which coefficients are to be estimated using phenology measurements
(FLAG=1), which are to be estimated using growth measurements (FLAG=2) and which
coefficients are not to be estimated (FLAG=0).

Appendix B
Variances of Observations for Most Crops
STD

Variance

ADAP

Anthesis day (dap).

MDAP

Physiological maturity day (dap).

PD1T

PWAM

0.3

Flag

Description

First pod date (YrDoy).

Pod/Ear/Panicle weight at maturity (kg [dm]/ha).

HWAM

0.3

Yield at harvest maturity (kg [dm]/ha).

CWAM

0.3

Tops weight at maturity (kg [dm]/ha).

LAIX

0.4

Leaf area index, maximum.

HWUM

0.1

Grain unit weight at maturity (g/seed)
Leaf number per stem at maturity. The symbol "#" was changed to ".", since it
is the symbol of comments in R.

L.SM

Appendix C
Batch File “COBB.SBC” Created with GLUESelect
$BATCH(CULTIVAR):SBIB0002 COBB
@FILEX
TRTNO
CO
C:\DSSAT45\Soybean\UFGA8101.SBX
0
0
C:\DSSAT45\Soybean\UFGA8501.SBX
0
0

Appendix D
Batch File “GLUE.BAT”
C:\Progra~1\R\R-2.10.1\bin\Rterm
C:\DSSAT45\Tools\GLUE\GLUE.r

--slave

Appendix E
Output Files of GLUE Procedure
1. Optimal parameter set saved as a CUL file (SBIB0002 COBB.CUL)
IB0002 COBB (8)

. SB0801 12.54 0.373 23.83 9.200 20.11 32.63 18.00 1.090 346.0 162.7 1.000 0.184 17.16 1.846 10.00 78.00 0.400 0.200

2. Posterior distribution in first round GLUE (PosteriorDistribution_1.csv)
Param
Mean
STDEV
MaxProbability

SB_CSDL

SB_PPSEN

SB_EM.FL

SB_FL.SH

SB_FL.SD

SB_SD.PM

SB_FL.LF

SB_LFMAX

SB_SLAVR

SB_SIZLF

SB_XFRT

SB_WTPSD

SB_SFDUR

SB_SDPDV

SB_PODUR

SB_THRSH

SB_SDPRO

SB_SDLIP

12.174

0.303

23.286

9.2

18.558

31.511

1.03

375

190

0.158

1.9

0.4

0.2

0.327

0.073

3.602

2.587

2.99

12.538

0.373

23.831

9.2

20.113

32.635

1.03

375

190

0.158

1.9

0.4

0.2

3. Posterior distribution in second round GLUE (PosteriorDistribution_2.csv)
Param
Mean

SB_CSDL

SB_PPSEN

SB_EM.FL

SB_FL.SH

SB_FL.SD

SB_SD.PM

SB_FL.LF

SB_LFMAX

SB_SLAVR

SB_SIZLF

SB_XFRT

SB_WTPSD

SB_SFDUR

SB_SDPDV

SB_PODUR

SB_THRSH

SB_SDPRO

SB_SDLIP

12.538

0.373

23.831

9.2

20.113

32.635

1.187

345.327

178.795

0.184

18.651

2.042

0.4

0.2

0.105

25.85

27.183

0.008

1.324

0.22

12.538

0.373

23.831

9.2

20.113

32.635

1.09

346.041

162.69

0.184

17.165

1.846

0.4

0.2

STDEV
MaxProbability

4. Example random parameter sets and their Likelihood values and in first round GLUE (RandomParameterSetsAndProbability_1.txt)
CSDL

PPSEN

EM.FL

FL.SH

FL.SD

SD.PM

FL.LF

LFMAX

SLAVR

SIZLF

XFRT

WTPSD

SFDUR

SDPDV

PODUR

THRSH

SDPRO

SDLIP

Probability

12.538

0.373

23.831

9.200

20.113

32.635

18.000

1.030

375.000

190.000

1.000

0.158

23.000

1.900

10.000

78.000

0.400

0.200

0.227

12.549

0.364

26.064

9.200

21.883

29.549

18.000

1.030

375.000

190.000

1.000

0.158

23.000

1.900

10.000

78.000

0.400

0.200

0.117

11.791

0.200

26.731

9.200

15.802

34.909

18.000

1.030

375.000

190.000

1.000

0.158

23.000

1.900

10.000

78.000

0.400

0.200

0.115

11.895

0.187

27.931

9.200

16.092

34.792

18.000

1.030

375.000

190.000

1.000

0.158

23.000

1.900

10.000

78.000

0.400

0.200

0.103

11.784

0.262

21.206

9.200

13.915

29.413

18.000

1.030

375.000

190.000

1.000

0.158

23.000

1.900

10.000

78.000

0.400

0.200

0.095

12.339

0.376

19.642

9.200

19.395

28.400

18.000

1.030

375.000

190.000

1.000

0.158

23.000

1.900

10.000

78.000

0.400

0.200

0.085

5. Example random parameter sets and their Likelihood values and in second round GLUE (RandomParameterSetsAndProbability_2.txt)
CSDL

PPSEN

EM.FL

FL.SH

FL.SD

SD.PM

FL.LF

LFMAX

SLAVR

SIZLF

XFRT

WTPSD

SFDUR

SDPDV

PODUR

THRSH

SDPRO

SDLIP

Probability

12.538

0.373

23.831

9.200

20.113

32.635

18.000

1.090

346.041

162.690

1.000

0.184

17.165

1.846

10.000

78.000

0.400

0.200

0.016

12.538

0.373

23.831

9.200

20.113

32.635

18.000

1.029

342.632

189.759

1.000

0.186

17.602

1.775

10.000

78.000

0.400

0.200

0.015

12.538

0.373

23.831

9.200

20.113

32.635

18.000

1.110

330.573

174.342

1.000

0.186

17.317

2.364

10.000

78.000

0.400

0.200

0.015

12.538

0.373

23.831

9.200

20.113

32.635

18.000

1.011

307.951

203.991

1.000

0.190

17.585

2.005

10.000

78.000

0.400

0.200

0.015

12.538

0.373

23.831

9.200

20.113

32.635

18.000

1.082

339.885

225.893

1.000

0.194

18.123

1.792

10.000

78.000

0.400

0.200

0.015

12.538

0.373

23.831

9.200

20.113

32.635

18.000

1.150

375.298

163.908

1.000

0.191

17.539

1.719

10.000

78.000

0.400

0.200

0.014

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