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Premise of the Study: In crop breeding programs, breeders use yield performance in optimal and stressful environments as key indicator for screening the most tolerant genotypes. Over four decades, several yield-based indices have been suggested for evaluating stress tolerance in crops. Despite the well-established use of these indices in agronomy and plant breeding, a user-friendly software that would provide access to these methods is still lacking. Methods and Results: The Plant Abiotic Stress Index Calculator (iPASTIC) is an online program based on Javascript and R, which calculates common stress tolerance and susceptibility indices for various crop traits including the tolerance index (TOL), relative stress index (RSI), mean productivity (MP), harmonic mean (HM), yield stability index (YSI), geometric mean productivity (GMP), stress susceptibility index (SSI), stress tolerance index (STI), and yield index (YI). Along with these indices, this easily accessible tool can also calculate their ranking patterns, estimate the relative frequency for each index, and create heat maps based on Pearson’s and Spearman’s rank-order correlation analyses. In addition, it can also render 3D plots based on both yield performances and each index to separate entry genotypes into Fernandez’s groups (A, B, C and D), and performs principal component analysis. The accuracy of the results calculated from our software was tested using two different datasets obtained from previous experiments testing the salinity and drought stress in wheat genotypes, respectively. Conclusion: iPASTIC can be widely used in agronomy and plant breeding programs as a user-friendly interface for agronomists and breeders dealing with large volume data. The software is available at https://mohsenyousefian.com/ipastic/.
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e climate crisis has increased the frequency and intensity of both
abiotic and biotic stressors. In recent decades, the eects of these
stressors on crop production have become increasingly important
(Vaughan etal., 2018). e eciency of breeding programs in di-
verse environments can be signicantly improved by gaining an
understanding of the associations between yield performance and
dierent selection criteria, as well as by accurately estimating stress
tolerance in genetic materials (Collard and Mackill, 2008; Xu, 2016).
In most crops, yield performance is the main criterion for evaluat-
ing tolerance to dierent environmental stressors. For example, in
crop improvement programs, breeders use yield performance and
its stability under dierent growth conditions (e.g., drought, salin-
ity, temperature extremes, and biotic stressors) as a major indicator
of stress tolerance. erefore, screening for tolerance to a specic
stress is based on high performance in non‐stressed and stressed
environments (Clarke etal., 1992), such that genotypes with high
yields in both environments are considered tolerant.
Based on Fernandez’s theory (Fernandez, 1992), genotypes can
be categorized into four groups, based on their yield response to
stressful conditions: (1) relatively uniform performance in both
non‐stressed and stressed environments (Group A), (2) high
performance in non‐stressed environments (Group B), (3) high
performance in stressed environments (Group C), and (4) low
performance in both non‐stressed and stressed environments
(Group D). In relation to these classications, several yield‐based
stress tolerance and susceptibility indices have been formulated to
Applications in Plant Sciences 2019 7(7): e11278; http://www.wileyonlinelibrary.com/journal/AppsPlantSci © 2019 Pour‐Aboughadareh etal.
Applications in Plant Sciences is published by Wiley Periodicals, Inc. on behalf of the Botanical Society of America. This is an open access article under the
terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly
cited.
iPASTIC: An online toolkit to estimate plant abiotic stress
indices
Alireza Pour‐Aboughadareh1,8 , Mohsen Yousean2, Hoda Moradkhani3, Mohammad Moghaddam Vahed4, Peter Poczai5,6,8 , and
Kadambot H. M. Siddique7
SOFTWARE NOTE
Manuscript received 27 February 2019; revision accepted 16 June
2019.
1 Seed and Plant Improvement Institute,Agricultural Research,
Education and Extension Organization (AREEO), Karaj, Iran
2 Department of Computer Science,University of Manitoba,
Winnipeg, Manitoba, Canada
3 Department of Plant Breeding,Kermanshah Branch,Islamic
Azad University, Kermanshah, Iran
4 Department of Plant Breeding and Biotechnology,University of
Tabriz, Tabriz, Iran
5 Botany Unit,Finnish Museum of Natural History,University of
Helsinki, P.O. Box 7, Helsinki FI‐00014, Finland
6 Department of Molecular Plant Physiology,Institute for Water
and Wetland Research,Radboud University, 6500 GL Nijmegen,
e Netherlands
7 e UWA Institute of Agriculture,e University of Western
Australia, LB 5005, Perth, Western Australia 6001, Australia
8 Authors for correspondence: peter.poczai@helsinki.,
a.poraboghadareh@gmail.com
Citation: Pour‐Aboughadareh, A., M. Yousean, H. Moradkhani,
M. Moghaddam Vahed, P. Poczai, and K. H. M. Siddique. 2019.
iPASTIC: An online toolkit to estimate plant abiotic stress indices.
Applications in Plant Sciences 7(7): e11278.
doi:10.1002/aps3.11278
PREMISE: In crop breeding programs, breeders use yield performance in both optimal and
stressful environments as a key indicator for screening the most tolerant genotypes. During
the past four decades, several yield‐based indices have been suggested for evaluating stress
tolerance in crops. Despite the well‐established use of these indices in agronomy and plant
breeding, a user‐friendly software that would provide access to these methods is still lacking.
METHODS AND RESULTS: The Plant Abiotic Stress Index Calculator (iPASTIC) is an online
program based on JavaScript and R that calculates common stress tolerance and suscepti-
bility indices for various crop traits including the tolerance index (TOL), relative stress index
(RSI), mean productivity (MP), harmonic mean (HM), yield stability index (YSI), geometric
mean productivity (GMP), stress susceptibility index (SSI), stress tolerance index (STI), and
yield index (YI). Along with these indices, this easily accessible tool can also calculate their
ranking patterns, estimate the relative frequency for each index, and create heat maps based
on Pearson’s and Spearman’s rank‐order correlation analyses. In addition, it can also render
three‐dimensional plots based on both yield performances and each index to separate entry
genotypes into Fernandez’s groups (A, B, C, and D), and perform principal component anal-
ysis. The accuracy of the results calculated from our software was tested using two dierent
data sets obtained from previous experiments testing the salinity and drought stress in wheat
genotypes, respectively.
CONCLUSIONS: iPASTIC can be widely used in agronomy and plant breeding programs as a
user‐friendly interface for agronomists and breeders dealing with large volumes of data. The
software is available at https ://mohse nyous ean.com/ipast ic/.
KEY WORDS abiotic stresses; online software; principal component analysis; selection index;
three‐dimensional plot; tolerance and susceptibility indices.
Applications in Plant Sciences 2019 7(7): e11278 Pour‐Aboughadareh etal.—iPASTIC to estimate plant abiotic stress indices 2 of 6
http://www.wileyonlinelibrary.com/journal/AppsPlantSci © 2019 Pour‐Aboughadareh etal.
characterize the response of genotypes in dierent environments,
and to select for tolerant genotypes. ese are: tolerance index
(TOL; Rosielle and Hamblin, 1981), relative drought index (RDI;
Fischer and Wood, 1979; herein referred to as relative stress index
[RSI]), mean productivity (MP; Rosielle and Hamblin, 1981), har-
monic mean (HM; Bidinger etal., 1987), yield stability index (YSI;
Bouslama and Schapaugh, 1984), geometric mean productivity
(GMP; Fernandez, 1992), stress susceptibility index (SSI; Fischer
and Maurer, 1978), stress tolerance index (STI; Fernandez, 1992),
and yield index (YI; Gavuzzi etal., 1997).
e nine proposed indices were rst used to screen for drought‐
tolerant genotypes and are more commonly known as drought‐
stress indices. Nonetheless, these indices can be used in other
studies—including those of abiotic and biotic stressors—for screen-
ing tolerant and susceptible genotypes. During the past four de-
cades, these indices have been developed and used independently
in numerous breeding programs. However, a soware package that
amalgamates all of these indices into a single source has thus far
not been developed. erefore, we oer the rst user‐friendly on-
line soware that meets this need, the Plant Abiotic Stress Index
Calculator (iPASTIC).
METHODS AND RESULTS
Description of iPASTIC software and its functionalities
Table 1 shows the mathematical formulas and selection pattern
for each index. iPASTIC is written in the JavaScript programming
language on the browser‐side and PHP on the server‐side, and is
available as a web application (https ://mohse nyous ean.com/ipast
ic/). Alternatively, users can access the source codes in R language
(R Development Core Team, 2014) and supporting data sets on
GitHub (https ://github.com/pour-aboug hadar eh/iPAST IC/). In ad-
dition to the web application, iPASTIC is available in R language
for more advanced users. Figure1 shows the information ow of
this soware. e soware reads standard Microso Excel for-
mats, hence it is easy and approachable even for users with lim-
ited knowledge of computer programming languages. As its core
functionality, iPASTIC calculates the nine indices and the per-
centage of relative change due to stress relative to the non‐stress
environment for a set of genotypes. It also calculates the ranking
patterns of the genotypes, based on each index. Using the WebGL
and ree.js frameworks (Cabello, 2014), this soware renders an
interactive three‐dimensional (3D) plot based on yield (Yp: yield
performance under non‐stressed conditions, and Ys: yield perfor-
mance under stressed conditions) for each index. As a result, users
can assign the genotypes to groups A, B, C, and D, as described by
Fernandez (1992). Based on Pearson’s and Spearman’s rank‐order
correlation coecients (Pearson, 1895; Spearman, 1904), iPASTIC
can identify interrelationships among the indices and their ranks
using heat map(s), which are displayed with the Canvas tool. e
relative frequency of each index can also be estimated. Principal
component analysis (PCA) is another tool available in this soware,
which enables users to visualize the associations between the tested
genotypes and index vectors in a PCA‐based biplot.
Aer following the instructions on the website, the results are
displayed in ve separate tabs. e rst tab, Indices, includes two
separate sheets. e rst sheet displays average yield (for each trait)
under non‐stressful and stressful conditions, relative change due to
stress, and actual values of the nine measured indices. e second
sheet displays genotype rankings for each index, along with sum
ranks, average sum of ranks (ASR), and standard deviation (SD), all
of which are downloadable in Excel format.
e second tab, Frequencies, provides the relative frequency of
genotypes based on yield and each index. is tab enables users to
obtain more information regarding the distribution of the geno-
types into dierent classes. When one index is selected, the gen-
otypes belonging to each class are displayed at the bottom of the
frequency plot.
In the third tab, Correlation Plots, associations among dierent
indices and yield are shown in two distinct heat maps. Pearson’s
correlation analysis estimates the correlation coecients of the cor-
relation among the actual values of the indices; Spearman’s rank‐
order correlation analysis shows the relationships among the ranks
of the indices. e user has the option of displaying the results as
one of three dierent heat maps (i.e., square, circle, or mixed values
and circle).
e fourth tab, Three-dimensional, renders a 3D plot for each
index along with yield. e third dimension is adjustable, and us-
ers can select any of the indices from the menu bar at the bottom
of the page to create a 3D plot. iPASTIC also has a tool to check
the position of each genotype individually. Selecting one or more
genotypes in the “Genotypes control panel” on the right side of the
TABLE 1. Mathematical formulas of tolerance and susceptibility indices calculated by iPASTIC software.
Index Formula Pattern of selection Reference
Tolerance TOL = YP − YSMinimum value Rosielle and Hamblin (1981)
Mean productivity
MP
=
Y
P+
YS
2
Maximum value Rosielle and Hamblin (1981)
Geometric mean productivity
GMP
=
Y
S
×Y
P
Maximum value Fernandez (1992)
Harmonic mean
HM
=2
(Y
S×
Y
P
)
(
Y
S
+Y
P)
Maximum value Bidinger et al. (1987)
Stress susceptibility index
SSI
=1
(Y
S
Y
P
)
1
(
̄
Y
S
̄
Y
P)
Minimum value Fischer and Maurer (1978)
Stress tolerance index
STI
=
Y
S×
YP
(
̄
Y
P)
2
Maximum value Fernandez (1992)
Yield index
YI
=
Y
S
̄
Y
s
Maximum value Gavuzzi et al. (1997)
Yield stability index
YSI
=
Y
S
Y
P
Maximum value Bouslama and Schapaugh (1984)
Relative stress index
=
S
P
̄
Y
̄
Y
Maximum value Fischer and Wood (1979)
Applications in Plant Sciences 2019 7(7): e11278 Pour‐Aboughadareh etal.—iPASTIC to estimate plant abiotic stress indices 3 of 6
http://www.wileyonlinelibrary.com/journal/AppsPlantSci © 2019 Pour‐Aboughadareh etal.
index’s menu bar will display the position of the selected genotypes.
Clicking on the bars in the 3D plot will display the label for each
genotype. e viewing angle of the plot can be changed by dragging
the 3D plot. Aer selecting the best position and viewing angle, the
nal graph can be downloaded as an image le.
e h tab, PCA, shows the results of the PCA analysis, which is
mostly used as a multivariate approach in investigative data analysis
and for predictive models. It can also be used to visualize distance
and relatedness between entries. PCA can be done by eigenvalue
decomposition of a data correlation (or covariance) matrix, or sin-
gular value decomposition of a data matrix, usually aer a normal-
ization step of the initial data. e results of PCA are downloaded in
an Excel le. In the output le, the summary of descriptive statistics
(including minimum, maximum, mean, and SD), correlation (or
covariance) matrix, eigenvalues, eigenvectors, factor loading, con-
tribution of variables in each component, and factor scores for each
genotype are displayed in separate sheets. In this section, the bi-
plot is initially rendered on the rst two principal components, but
iPASTIC provides a useful tool that enables users to render the bi-
plot on any two arbitrary principal components.
Testing iPASTIC software
To test of accuracy the soware, two data sets gathered from two ex-
periments were used to screen for the most tolerant genotypes in re-
sponse to severe salinity and water decit stresses. In Data Set 1, we
tested 90 genotypes and accessions of cultivated and wild relatives
of wheat under control and saline conditions. A greenhouse pot ex-
periment was conducted in 2015–2016 at the Crop Production and
Breeding Department, Imam Khomeini International University,
Qazvin, Iran. Information on experimental setup, growth condi-
tions, salinity treatments, and collection of aboveground biomass
yield under control (Yp) and saline (Ys) conditions for each seed-
ling plant is in Ahmadi etal. (2018b). Detailed information on the
tested genotypes is in AppendixS1. Results of the nine yield‐based
indices, along with relative change due to stress for each geno-
type, are shown in Appendix S2. In the control conditions, shoot
dry weight (Yp) ranged from 37.65 to 99.08 mg·plant−1, and gen-
otypes G1, G3, G25, G20, and G30 had the highest mean perfor-
mance. Under salinity stress, shoot dry weight (Ys) ranged from
25.43 to 84.38 mg·plant−1, and genotypes G3, G47, G2, G20, and
G46 showed the highest values. e relative change due to salin-
ity stress for each tested genotype revealed that the genotypes G55,
G47, G69, G71, and G46 had the smallest changes, being 2.19%,
2.53%, 2.86%, 4.59%, and 5.46% lower than the controls. Using
the TOL index, genotypes with lower values are more tolerant to
stress. Accordingly, genotypes G55, G69, G47, G71, and G23 were
the most tolerant to salinity, and genotypes G88, G25, G34, G59,
and G4 were the most sensitive. Genotypes that perform well un-
der non‐stress and stressful conditions will have high values for the
STI, MP, GMP, and HM indices and will be identied as tolerant.
In this case, genotypes G2, G3, G20, G46, G47, and G50 had the
highest values for these indices. e SSI identies only those gen-
otypes with minimal reductions under stressful compared to non‐
stressful conditions (Fischer and Maurer, 1978); an SSI > 1 indicates
above‐average susceptibility to drought stress (Guttieri etal., 2001).
As shown in Appendix S2, the majority of genotypes had an SSI ≤ 1;
with G55, G47, G69, G71, and G46 having the lowest values. ree
indices (YI, YSI, and RSI) can be used to evaluate genotypic sta-
bility in both stressful and non‐stressful conditions. ese indices
are based on tolerance or susceptibility of genotypes, and have been
used in many crops, including bread wheat (Sardouei‐Nasab etal.,
2019), durum wheat (Etminan etal., 2019), barley (Khalili et al.,
2016), saower (Khalili etal., 2014), chickpea (Pour‐Siabidi and
Pour‐Aboughadareh, 2013), and potato (Cabello et al., 2013). YSI
and RSI produced similar ranking patterns in the characterization
of tolerant genotypes, with G55, G47, G69, G71, and G46 having
the highest values.
Identifying tolerant genotypes based on a single index could be
problematic, as seen here. Our program can estimate an ASR for all
FIGURE 1. Information ow diagram for iPASTIC software.
Data Input:
To begin calculating, the data file
should be provided in Excel format
• Note: When calculating indices, the
data should be entered as genotype
codes
JavaScript Code Extract data and genotype
labels from input file
• Calculation of indices for
each genotype
• Calculation of ranks of all
indices for each genotype
Data Input:
Estimation of the relative
frequency based on actual
values of estimated indices
Estimation of Pearson
and Spearman correlation
coefficients based on actual
values and ranking patterns
of estimated indices
Calculation of principal
components
Canvas tools
Make heat map plots
Download plots as an image file
Canvas tools
Make frequency plots
Download plots as an image file
Canvas tools
Make biplot
Download plots as an image file
WebGL + Three.js 3D Engine
tools
Make 3D plot based on Yp,
Ys, and desired index
Download 3D plot as
an image file
Data Table tools
Make online table including
genotype codes, measured
indices, and ranking pattern
Download output table
as an excel file
Applications in Plant Sciences 2019 7(7): e11278 Pour‐Aboughadareh etal.—iPASTIC to estimate plant abiotic stress indices 4 of 6
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indices to select potentially superior genotypes; the lower the value,
the more superior the genotype. In this case, G47 (ASR = 3.73; SD =
2.90), G46 (ASR = 6.82; SD = 4.09), and G2 (ASR = 9.64; SD = 9.08)
were the most salinity‐tolerant genotypes in severe salinity condi-
tions (Appendix S3). e relative frequency results provided more
information on the distribution of genotypes into dierent classes.
For example, under control conditions, half of the genotypes
had a yield potential from 51 to 65 mg·plant−1, but under salinity
stress the yield potential of most genotypes ranged from 34 to 62
mg·plant−1 (Appendix S4). e relative frequencies of the genotypes
based on other indices are presented in Appendix S4–S6. Two heat
maps based on the actual values of indices and their ranking pat-
terns across all genotypes revealed that STI, MP, GMP, and HM are
strongly correlated with crop performance (Yp and Ys) (Appendix
S7). e highly signicant correlations between these indices and
yield under control and saline conditions indicate their capacity to
identify genotypes with high potential yield and tolerance to saline
conditions. Furthermore, the highly signicant correlation between
these indices suggests that they can be used interchangeably to
select tolerant genotypes. In contrast, SSI, TOL, YSI, and YI were
strongly correlated to Ys but not Yp, and therefore cannot be used
to identify Group A genotypes. e ability to separate Group A gen-
otypes from others using STI, GMP, and MP is consistent with the
ndings reported for common bean (Fernandez, 1992), chickpea
(Ganjeali etal., 2011), and canola (Khalili etal., 2012). Appendix
S8 shows rendered 3D plots based on the STI index and yield (Yp
and Ys). To demonstrate the opt‐in functionality of the soware,
we have shown plots from dierent angles. Genotypes G2, G3, G8,
G9, G10, G20, G43, G45, G46, G47, G48, G49, G50, G54, G61, and
G83 were placed in Group A. e PCA results based on the cor-
relation matrix indicated that the rst two principal components
with eigenvalues >1 accounted for 99.26% of the total variation in
yield performance and nine yield‐based indices (outputs including
descriptive statistics, correlation and/or covariance matrix, eigen-
values, eigenvectors, factor loading, contribution of variables in
each component, and factor scores for each genotype not shown).
PC1 was positively inuenced by yield (Yp and Ys) and all indices
except SSI and TOL, whereas PC2 was positively inuenced by Yp,
TOL, MP, GMP, HM, and SSI. Hence, selection based on high val-
ues of PC1 and intermediate values of PC2 could help to identify
salt‐tolerant genotypes. Several genotypes, including G2, G3, G20,
G46, G47, and G50, were identied as superior genotypes, which is
supported by the ndings in the 3D plot (Appendix S9).
In Data Set 2, we tested the soware using shoot dry weight data
from a water‐stress experiment that included nine genotypes from
several species of cultivated and wild wheat—Triticum aestivum
L., T. durum Desf., T. urartu umanjan ex Gandilyan, T. boeoti-
cum Boiss., Aegilops tauschii Coss., Ae. neglecta Req. ex Bertol., Ae.
triuncialis L., Ae. crassa Boiss., and Ae. caudata L. All genotypes
were grown in a greenhouse maintained at an optimal photope-
riod and growing temperature at the Department of Genetics and
Plant Breeding, Imam Khomeini International University, Qazvin,
Iran, during 2016–2017. e experiment was arranged as factorial
using randomized complete block design with three replications.
e plants were well watered every 1–2 days to maintain 90% ± 5%
eld capacity (FC). e water stress treatment (FC = 25 ± 5%) was
initiated at the three‐leaf stage of growth. Details on the growing
conditions, stress treatments, and data collection are available in
Ahmadi etal. (2018a). e results of this data set are summarized
in Appendix S10. Under both control and water‐stress conditions,
T. urartu had the highest dry matter, followed by T. aestivum and T.
durum, whereas Ae. crassa and Ae. tauschii had the lowest. However,
Ae. neglecta, Ae. tauschii, and T. durum had the smallest changes in
dry weight in response to water stress. Based on the lowest values
for TOL and SSI and highest values for RSI and YSI, Ae. neglecta
and Ae. tauschii were selected as the most tolerant genotypes. In
contrast, T. urartu, T. durum, and T. aestivum had the highest val-
ues for STI, MP, GMP, HM, and YI. Based on ASR values, the most
tolerant genotypes were T. durum (2.27), T. aestivum (3.64), Ae.
neglecta (4.18), and Ae. caudata (4.36) (Appendix S11). e cor-
relation coecients for yield performance and the nine indices re-
vealed that STI, MP, GMP, HM, and YI were strongly correlated
with both Yp and Ys (Appendix S12); hence, these indices were
used to generate a 3D plot to identify genotypes into Fernandez’s
groups. As shown in Appendix S13, Group A comprised T. durum,
T. aestivum, T. urartu, and Ae. caudata; Group B contained Ae. tri-
uncialis; Group C contained Ae. neglecta; and Group D comprised
Ae. tauschii, Ae. crassa, and T. boeoticum.
Because PCA is a multivariate analysis commonly used for re-
ducing data through decomposing the total variance into a few new
independent components, achieving acceptable results will depend
on the size of the data set. For this data set, we only used PCA anal-
ysis to demonstrate the applicability of the soware. Here, the rst
two principal components accounted for 98.86% (PC1 = 73.60%
and PC2 = 25.26%) of the total variation in yield performance and
the measured indices. Eigenvector coecients revealed that Yp and
Ys along with the indices, except YSI and RSI, had a positive asso-
ciation with PC1, whereas SSI and TOL had a negative association
with PC2. Hence, using the PC1 results, tolerant genotypes will be
selected based on high‐ranking yield performance and tolerance
indices such as MP, GMP, and STI. In this case, T. aestivum and
T. durum were identied as the tolerant genotypes with accept-
able performance under both non‐stress and water‐stress condi-
tions (Appendix S14). Regarding the PCA tool, it is worth noting
that while this method provides a good way of summarizing data
when interesting patterns increase the variance of projections onto
orthogonal components, it also has limitations worth considering
when interpreting the output. First, the underlying structure of the
data must be linear. Second, patterns that are highly correlated may
be unresolved because all principal components are uncorrelated.
Finally, the goal is to maximize variance and not necessarily to nd
clusters (Lever etal., 2017).
CONCLUSIONS
We developed a novel online soware (iPASTIC) to calculate sev-
eral yield‐based stress tolerance and susceptibility indices that are
important in the identication of tolerant crop genotypes. In addi-
tion to the useful and practical tools described above in the Methods
and Results, iPASTIC also has the following advantages: (1) It can
analyze large data sets in minimal time; (2) It is a cross‐platform so-
ware that does not require additional downloads or installation; (3)
Unlike other codes based on SAS and R packages, which require ad-
ditional user knowledge, iPASTIC has a web‐based user‐friendly in-
terface; and (4) It is compatible with the major browsers (e.g., Google
Chrome, Mozilla Firefox, Safari). ese advantages, combined with
its user‐friendly interface and tools for better selection of entry geno-
types, make iPASTIC valuable for use in agronomy and plant breed-
ing programs by students, teachers, and researchers alike.
Applications in Plant Sciences 2019 7(7): e11278 Pour‐Aboughadareh etal.—iPASTIC to estimate plant abiotic stress indices 5 of 6
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ACKNOWLEDGMENTS
e authors thank Dr. James ompson from the U.S. Department
of Agriculture (USDA) for revising rst dra of the manuscript.
Financial support was received from the LUOMUS Trigger Fund
and the Organisation for Economic Co‐operation and Development
(OECD) Co‐operative Research Programme (CRP) grant (to P.P.).
DATA ACCESSIBILITY
e R script source codes used to develop iPASTIC, as well as the
supporting data sets, are available on GitHub (https ://github.com/
pour-aboug hadar eh/iPAST IC/) and the iPASTIC web application is
available at https ://mohse nyous ean.com/ipast ic/.
SUPPORTING INFORMATION
Additional Supporting Information may be found online in the
supporting information tab for this article.
APPENDIX S1. Label, GenBank accession number, and species of
the 90 wheat genotypes and accessions tested in Data Set 1.
APPENDIX S2. Yield performance of 90 wheat genotypes and ac-
cessions under control (Yp) and saline (Ys) conditions along with
the relative change (RC) due to stress and tolerance and susceptibil-
ity indices calculated using iPASTIC soware for Data Set 1.
APPENDIX S3. Yield performance rankings of 90 wheat genotypes
and accessions under control (Yp) and saline (Ys) conditions along
with the calculated tolerance and susceptibility indices using iPAS-
TIC soware for Data Set 1.
APPENDIX S4. (A–B) Relative frequency of yield performances
under (A) control conditions and (B) stress conditions in 90 wheat
genotypes and accessions. (C–D) Relative frequency of (C) mean
productivity (MP) indices and (D) geometric mean productivity
(GMP) indices calculated by iPASTIC soware for Data Set 1.
APPENDIX S5. Relative frequency of (A) relative stress index
(RSI), (B) stress tolerance index (STI), (C) stress susceptability in-
dex (SSI), and (D) yield index (YI) indices calculated by iPASTIC
soware for Data Set 1.
APPENDIX S6. Relative frequency of (A) yield stability index
(YSI), (B) harmonic mean (HM), and (C) tolerance index (TOL)
indices calculated by iPASTIC soware for Data Set 1.
APPENDIX S7. Rendered heat‐map plot based on Pearson’s correla-
tion analysis for Data Set 1. See Table1 for full denitions of indices.
APPENDIX S8. Rendered three‐dimensional plot based on the STI
index and yield performance (Yp and Ys) of the 90 wheat genotypes
and accessions in Data Set 1. Each plot shows a view angle of distri-
bution of entry genotypes into Fernandez’s groups (A–D).
APPENDIX S9. Rendered principal components analysis–based
biplot based on the correlation matrix of Yp, Ys, and nine tolerance
and susceptibility indices calculated using iPASTIC soware for
Data Set 1.
APPENDIX S10. Yield performance of nine wheat genotypes un-
der control (Yp) and saline (Ys) conditions along with the relative
change (RC) due to stress and tolerance and susceptibility indices
calculated using iPASTIC soware for Data Set 2.
APPENDIX S11. Yield performance rankings of nine wheat geno-
types under control (Yp) and saline (Ys) conditions along with the
calculated tolerance and susceptibility indices using iPASTIC so-
ware for Data Set 2.
APPENDIX S12. Rendered heat‐map plot based on Pearson’s cor-
relation analysis for Data Set 2. See Table1 for full denitions of
indices.
APPENDIX S13. Rendered three‐dimensional plot based on the
STI index and yield performance (Yp and Ys) of the 90 wheat geno-
types and accessions in Data Set 2.
APPENDIX S14. Rendered principal components analysis–based
biplot based on the correlation matrix of Yp, Ys, and nine toler-
ance and susceptibility indices calculated using iPASTIC soware
for Data Set 2.
LITERATURE CITED
Ahmadi, J., A. Pour‐Aboughadareh, S. Fabriki Ourang, A. A. Mehrabi, and K.
H. M. Siddique. 2018a. Wild relatives of wheat: Aegilops–Triticum acces-
sions disclose dierential antioxidative and physiological responses to water
stress. Acta Physiologiae Plantarum 40: 90.
Ahmadi, J., A. Pour‐Aboughadareh, S. Fabriki Ourang, A. A. Mehrabi, and K. H.
M. Siddique. 2018b. Screening wild progenitors of wheat for salinity stress at
early stages of plant growth: Insight into potential sources of variability for
salinity adaptation in wheat. Crop and Pasture Science 69: 649–658.
Bidinger, F. R., V. Mahalakshmi, and G. D. Rao. 1987. Assessment of drought re-
sistance in pearl millet (Pennisetum americanum (L.) Leeke). II. Estimation
of genotype response to stress. Australian Journal of Agricultural Research
38: 49–59.
Bouslama, M., and W. T. Schapaugh. 1984. Stress tolerance in soybean. Part 1:
Evaluation of three screening techniques for heat and drought tolerance.
Crop Science 24: 933–937.
Cabello, R. 2014. ree.js License. Website https ://github.com/mrdoo b/three.js/
blob/maste r/LICENSE [accessed 1 May 2014].
Cabello, R., P. Monneveux, F. D. Mendiburu, and M. Bonierbale. 2013.
Comparison of yield based drought tolerance indices in improved varieties,
genetic stocks and landraces of potato (Solanum tuberosum L.). Euphytica
193: 147–156.
Clarke, J. M., R. M. De Pauw, and T. M. Townley‐Smith. 1992. Evaluation of
methods for quantication of drought tolerance in wheat. Crop Science 32:
728–732.
Collard, B. C. Y., and D. J. Mackill. 2008. Marker‐assisted selection: An ap-
proach for precision plant breeding in the twenty‐rst century. Philosophical
Transactions of the Royal Society of London Series B 363: 557–572.
Etminan, A., A. Pour‐Aboughadareh, R. Mohammadi, L. Shoshtari, M.
Youseazarkhanian, and H. Moradkhani. 2019. Determining the best
drought tolerance indices using articial neural network (ANN): Insight
into application of intelligent agriculture in agronomy and plant breeding.
Cereal Research Communication 47: 170–181.
Fernandez, G. C. J. 1992. Eective selection criteria for assessing plant stress
tolerance. In C. G. Kuo [ed.], Adaptation of food crops to temperature and
Applications in Plant Sciences 2019 7(7): e11278 Pour‐Aboughadareh etal.—iPASTIC to estimate plant abiotic stress indices 6 of 6
http://www.wileyonlinelibrary.com/journal/AppsPlantSci © 2019 Pour‐Aboughadareh etal.
water stress, 257–270. Asian Vegetable Research and Development Center,
Shanhua, Taiwan.
Fischer, R. A., and R. Maurer. 1978. Drought resistance in spring wheat culti-
vars. I. Grain yield responses. Australian Journal of Agricultural Research
29: 897–912.
Fischer, R. A., and T. Wood. 1979. Drought resistance in spring wheat culti-
vars III. Yield association with morphological traits. Australian Journal of
Agricultural Research 30: 1001–1020.
Ganjeali, A., H. Porsa, and A. Bagheri. 2011. Assessment of Iranian chickpea
(Cicer arietinum L.) germplasms for drought tolerance. Agricultural Water
Management 98: 1477–1484.
Gavuzzi, P., F. Rizza, M. Palumbo, R. G. Campaline, G. L. Ricciardi, and B.
Borghi. 1997. Evaluation of eld and laboratory predictors of drought and
heat tolerance in winter cereals. Canadian Journal of Plant Science 77:
523–531.
Guttieri, M. J., J. C. Stark, K. Brien, and E. Souza. 2001. Relative sensitivity of
spring wheat grain yield and quality parameters to moisture decit. Crop
Science 41: 327–335.
Khalili, M., M. R. Naghavi, A. Pour‐Aboughadareh, and J. Talebzadeh. 2012.
Evaluating of drought stress tolerance based on selection indices in spring
canola cultivars (Brassica napus L.). Journal of Agricultural Science 4:
78–85.
Khalili, M., A. R. Pour‐Aboughadareh, M. R. Naghavi, and E. Mohammad
Amini. 2014. Evaluation of drought tolerance in saower genotypes based
on drought tolerance indices. Notulae Botanicae Horti Agrobotanici Cluj-
Napoca 42: 214–218.
Khalili, M., A. Pour‐Aboughadareh, and M. R. Naghavi. 2016. Assessment of
drought tolerance in barley: Integrated selection criterion and drought toler-
ance indices. Environmental and Experimental Biology 14: 33–41.
Lever, J., M. Krzywinski, and N. Altman. 2017. Principal component analysis.
Nature Methods 14: 641–642.
Pearson, K. 1895. Notes on regression and inheritance in the case of two parents.
Proceedings of the Royal Society of London 58: 240–242.
Pour‐Siahbidi, M. M., and A. Pour‐Aboughadareh. 2013. Evaluation of grain
yield and repeatability of drought tolerance indices for screening chickpea
(Cicer aritinum L.) genotypes under rainfed conditions. Iranian Journal of
Genetics and Plant Breeding 2: 28–37.
R Development Core Team. 2014. R: A language and environment for statis-
tical computing. R Foundation for Statistical Computing, Vienna, Austria.
Website https ://www.r-proje ct.org/ [accessed September 2018].
Rosielle, A. A., and J. Hamblin. 1981. eoretical aspects of selection for yield in
stress and non‐stress environments. Crop Science 21: 943–946.
Sardouei‐Nasab, S., G. Mohammadi‐Nejad, and B. Nakhoda. 2019. Yield stabil-
ity in bread wheat germplasm across drought stress and non‐stress condi-
tions. Agronomy Journal 111: 175–181.
Spearman, C. 1904. e proof and measurement of association between two
things. American Journal of Psychology 15: 72–101.
Vaughan, M. M., A. Block, S. A. Christensen, L. H. Allen, and E. A. Schmelz.
2018. e eects of climate change associated abiotic stresses on maize phy-
tochemical defenses. Phytochemistry Reviews 17: 37–49.
Xu, Y. 2016. Envirotyping for deciphering environmental impacts on crop plants.
Theoretical and Applied Genetics 129: 653–673.
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Suitable screening techniques are required to select wheat (Triti-cum aestivum L.) lines exhibiting both high and stable yields in environments where drought stress is commonplace. Two recom-binant inbred line (RIL) populations bred from crosses between a drought-tolerant landrace Roshan and the cultivars Sabalan and Falat were evaluated in the field under both well-watered and water-stressed conditions. The drought stress was imposed by stopping irrigation at the flowering stage. Discriminant function analysis, based on a set of stress tolerance/sensitivity indices, was applied to identify the most desirable drought tolerance criterion. To quantify the size of the variation due to genotype × environment interaction (GEI), the grain yield over field trials was analyzed using the residual maximum likelihood (REML). The additive main effects and multiplicative interaction (AMMI) model was employed to determine the yield stability of the RILs. Finally, we screened a set of 10 drought-tolerant lines with consistent performance across the test environments. The results of the AMMI and REMEL analysis showed that environment was the major source of variability (69.98%) followed by GEI (12%). The two AMMI biplots revealed that a set of three RILs yielded stably in all environments with the high mean yield response. These promising lines should be valuable as donors of favorable alleles to wheat improvement programs targeting stress-prone environments. © 2019 by the American Society of Agronomy 5585 Guilford Road, Madison, WI 53711 USA All rights reserved.
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Wild relatives of wheat have served as a pool of genetic variation for understanding salinity tolerance mechanisms. Two separate experiments were performed to evaluate the natural diversity in root and shoot Na+ exclusion and K+ accumulation, and the activity of four antioxidant enzymes within the extensive collection of ancestral wheat accessions. In the initial screening experiment, salinity stress (300 mM NaCl) significantly increased Na+ concentration in roots and leaves but led to a significant decline in root and shoot fresh weights, dry weights, and K+ contents. Principal component analysis (PCA) of the 181 accessions and 12 species identified two first accounted for 48.06 and 60.60% of the variation under salinity stress. We identified 12 accessions of each species with superior tolerance to salinity for further assessment of their antioxidant defense systems in response to salinity. Both mild (250 mM NaCl) and severe (350 mM NaCl) levels of salinity significantly increased four enzymatic activities, indicating an enhanced antioxidant scavenging system for minimizing the damaging effects of H2O2. Some of the wild relatives—Ae. speltoides (putative B genome), Ae. caudata (C genome), Ae. cylindrica (DC genome) and T. boeoticum (Ab genome)—responded to salinity stress by increasing antioxidants as the dominant mechanism to retain oxidative balance in cells. Consequently, further evaluation of salt-tolerance mechanisms in these superior wild relatives will help us to understand the potential of wheat progenitors in the development of more salt-tolerant varieties.