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Premise of the study: Access to improved crop cultivars is the foundation for successful agriculture. New cultivars must have improved yields that are determined by quantitative and qualitative traits. Genotype-by-environment interactions (GEI) occur for quantitative traits such as reproductive fitness, longevity, height, weight, yield, and disease resistance. The stability of genotypes across a range of environments can be analyzed using GEI analysis. GEI analysis includes univariate and multivariate analyses with both parametric and non-parametric models. Methods and results: The program STABILITYSOFT is online software based on JavaScript and R to calculate several univariate parametric and non-parametric statistics for various crop traits. These statistics include Plaisted and Peterson's mean variance component (θ i ), Plaisted's GE variance component (θ (i) ), Wricke's ecovalence stability index (W i 2 ), regression coefficient (b i ), deviation from regression (S di 2 ), Shukla's stability variance (σ i 2 ), environmental coefficient of variance (CV i ), Nassar and Huhn's statistics (S (1) , S (2) ), Huhn's equation (S (3) and S (6) ), Thennarasu's non-parametric statistics (NP (i) ), and Kang's rank-sum. These statistics are important in the identification of stable genotypes; hence, this program can compare and select genotypes across multiple environment trials for a given data set. This program supports both the repeated data across environments and matrix data types. The accuracy of the results obtained from this software was tested on several crop plants. Conclusions: This new software provides a user-friendly interface to estimate stability statistics accurately for plant scientists, agronomists, and breeders who deal with large volumes of quantitative data. This software can also show ranking patterns of genotypes and describe associations among different statistics with yield performance through a heat map plot. The software is available at
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Genotype- by- environment interactions (GEI) refer to changes
in genotypic performance across dierent environments. e
presence of GEI in multi- environment trials is expressed either
as inconsistent responses of dierent genotypes (relative to oth-
ers) due to changes in genotypic rank, or as the absolute dier-
ence between genotypes without rank change (Crossa, 2012).
is eect can be used to assess quantitative traits of economic
importance—oen investigated in plant and animal breeding,
pharmacogenomics, genetic epidemiology, and conservation
biology research—including longevity, weight, height, biomass,
yield, and even disease resistance. e identication and subse-
quent selection of superior crop varieties in target environments
are important goals of agronomic and plant breeding studies
(Ahmadi etal., 2015; Vaezi etal., 2018). To identify superior va-
rieties across multiple environments, plant breeders undertake
trials across several years and locations, usually during the nal
developmental stages of a crop variety. e GEI eect reduces
the association observed between genotypic and phenotypic val-
ues and complicates the selection of the best variety (Ebdon and
Gauch, 2002). Interpreting the GEI eect in multi- environment
Applications in Plant Sciences 2019 7(1): e1211; © 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-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is
properly cited and is not used for commercial purposes.
STABILITYSOFT: A new online program to calculate
parametric and non- parametric stability statistics for
crop traits
Alireza Pour-Aboughadareh1, Mohsen Yousean2, Hoda Moradkhani3, Peter Poczai4,6 , and Kadambot H. M. Siddique5
Manuscript received 15 August 2018; revision accepted 18
November 2018.
1 Department of Agronomy and Plant Breeding,College of
Agriculture and Natural Resources,University of Tehran, Karaj,
2 Dr. Hashtroudi Pre-University Center, Tehran, Iran
3 Department of Biotechnology and Plant Breeding,Kermanshah
Branch,Islamic Azad University, Kermanshah, Iran
4 Botany Unit,Finnish Museum of Natural History,University of
Helsinki, P.O. Box 7, Helsinki FI-00014, Finland
5 e UWA Institute of Agriculture,e University of Western
Australia, LB 5005, Perth, Western Australia 6001, Australia
6 Author for correspondence: peter.poczai@helsinki.
Citation: Pour-Aboughadareh, A., M. Yousean, H. Moradkhani,
P. Poczai, and K. H. M. Siddique. 2019. STABILITYSOFT: A
new online program to calculate parametric and non- parametric
stability statistics for crop traits. Applications in Plant Sciences
7(1): e1211.
PREMISE OF THE STUDY: Access to improved crop cultivars is the foundation for successful
agriculture. New cultivars must have improved yields that are determined by quantitative
and qualitative traits. Genotype- by- environment interactions (GEI) occur for quantitative
traits such as reproductive tness, longevity, height, weight, yield, and disease resistance.
The stability of genotypes across a range of environments can be analyzed using GEI analysis.
GEI analysis includes univariate and multivariate analyses with both parametric and non-
parametric models.
METHODS AND RESULTS: The program STABILITYSOFT is online software based on JavaScript
and R to calculate several univariate parametric and non- parametric statistics for various
crop traits. These statistics include Plaisted and Peterson’s mean variance component (θi),
Plaisted’s GE variance component (θ(i)), Wricke’s ecovalence stability index (Wi
2), regression
coecient (bi), deviation from regression (Sdi
2), Shukla’s stability variance (σi
2), environmental
coecient of variance (CVi), Nassar and Huhn’s statistics (S(1), S(2)), Huhn’s equation (S(3) and
S(6)), Thennarasu’s non- parametric statistics (NP(i)), and Kang’s rank- sum. These statistics
are important in the identication of stable genotypes; hence, this program can compare
and select genotypes across multiple environment trials for a given data set. This program
supports both the repeated data across environments and matrix data types. The accuracy of
the results obtained from this software was tested on several crop plants.
CONCLUSIONS: This new software provides a user- friendly interface to estimate stability
statistics accurately for plant scientists, agronomists, and breeders who deal with large
volumes of quantitative data. This software can also show ranking patterns of genotypes and
describe associations among dierent statistics with yield performance through a heat map
plot. The software is available at https://mohsenyouse
KEY WORDS adaptability; phenotypic stability; quantitative traits; ranking method;
Applications in Plant Sciences 2019 7(1): e1211 Pour- Aboughadareh etal.—STABILITYSOFT: Online stability statistical software 2 of 6 © 2019 Pour- Aboughadareh etal.
trials assists in the selection of stable varieties for a wide range of
environments (Vaezi etal., 2017).
Many statistical approaches have been proposed for using stabil-
ity analyses to interpret GEI, all of which have been based on uni-
variate and multivariate models (Flores etal., 1998). ere are two
major statistical groups for interpreting GEI by numerical analysis.
e rst group contains parametric methods such as the regression
coecient (bi; Finlay and Wilkinson, 1963), variance of deviations
from the regression (Sdi
2; Eberhart and Russell, 1966), Wrickes eco-
valence stability index (Wi
2; Wricke, 1962), Shuklas stability vari-
ance (σi
2; Shukla, 1972), environmental coecient of variance (CVi;
Francis and Kannenberg, 1978), Plaisted and Petersons mean vari-
ance component (θi; Plaisted and Peterson, 1959), Plaisted’s GE var-
iance component (θ(i); Plaisted, 1960), and the yield- stability index
(YSi; Kang, 1991). ese parametric statistics are primarily used to
assess genotype stability by relating observed genotypic responses
(e.g., yield, plant height, seed oil content) to a sample of environ-
mental conditions (e.g., rainfall, temperature, osmotic stress, soil
type). Parametric stability statistics have suitable properties under
certain statistical assumptions, including a normal distribution and
homogeneity of variance of the errors and their interaction eects.
However, parametric statistics may not be the best method for as-
sessing genotype stability if the assumptions are not met (Huhn,
1990). e second group of analytical methods includes non-
parametric methods such as Nassar and Huhns statistics (S(1), S(2);
Nassar and Huhn, 1987), Huhns equation (S(3) and S(6); Huhn, 1990),
ennarasu’s statistics (NP(i); ennarasu, 1995), Kang’s rank- sum
(KR or Kang; Kang, 1988), and Fox’s top rank (FOX or Top- rank;
Fox etal., 1990). Non- parametric statistics explain environments
and phenotypes relative to both biotic and abiotic factors. Non-
parametric statistics are a feasible alternative to parametric statis-
tics because their performance is based on ranked data (Nassar and
Huhn, 1987) and no assumptions are needed about the distribution
and homogeneity of the variance of the errors. Because each method
has its own merits and weaknesses, most breeding programs now
incorporate both parametric and non- parametric methods for the
selection of stable genotypes (Becker and Leon, 1988).
Parametric and non- parametric statistics are used by researchers
from dierent elds, but the lack of a user- friendly statistical package
makes these methods unavailable for agronomists and plant breed-
ers. A literature review revealed that other studies have attempted
to introduce codes for SAS (Piepho, 1999; Hussein et al., 2000;
Akbarpour etal., 2016; Dia etal., 2016) or R (Branco, 2015; Yaseen
and Eskridge, 2018) to calculate some of the stability indices. Table1
compares the available features and capabilities of these codes and
packages. Currently, researchers interested in applying stability statis-
tics are required to use several programs to obtain the desired results,
and further applications are needed to describe and visualize the
correlations between these parameters, which are crucial for the se-
lection of stable varieties. e general lack of platform- independent
soware capable of calculating all parametric and nonparametric sta-
tistics in one package motivated our work to create an online tool
(STABILITYSOFT) that is able to overcome these diculties by pro-
viding a user- friendly interface for the non- specialist.
STABLITYSOFT is written in JavaScript at the browser- side and
PHP at the server- side and is available as a Web application at
https://mohsenyouse/. Alternately, users can
access the source codes and data sets in GitHub (
pour-aboughadareh/stabilityso). Figure1 shows the information
ow in the STABILITYSOFT program. e soware can be used
online and is available in R programming language for advanced
users, which oers more exibility. e data used for testing the
soware are available online and can be used as example les to run
the program. e input le is in a standard Excel le format, which
is widely supported by other well- known soware. Our program
supports two data types: (1) repeated data across environments
(year, location, and year × location), with genotype i in year n and
location m (or environment j for one year or one location) and rep-
lication k, and (2) matrix data that includes genotypes (rows) and
environments (columns). e program rst calculates the average of
the objective trait for each genotype and then provides a data matrix
by environment. Based on the data matrix, the soware calculates
several univariate parametric and non- parametric statistics, namely
Plaisted and Peterson’s mean variance component (θi), Plaisted’s GE
variance component (θ(i)), Wrickes ecovalence stability index (Wi
regression coecient (bi), deviation from regression (Sdi
2), Shuklas
stability variance (σi
2), environmental coecient of variance (CVi),
Nassar and Huhn’s non- parametric statistics (S(1), Z1, S(2), and Z2),
Huhns nonparametric statistics (S(3) and S(6)), ennarasu’s non-
parametric statistics (NP(i)), and Kangs rank- sum. For further
description and details about these statistics see Appendix1. e
program also calculates ranking patterns of the genotypes, based on
each index. Aer following the instructions outlined on the website,
STABILITYSOFT produces a simple Excel output on two separate
sheets. e rst sheet (named “Statistics”) includes the average of
crop yield along with 16 parametric and non- parametric statistics,
and the second sheet (“Ranks”) consists of the ranking of each gen-
otype for each statistic along with sum of ranks (SR), average of sum
of ranks (ASR), and standard deviation (SD). It is important to note
that the rank of genotypes for the regression coecient (bi) is not
calculated because a signicance test (H0: B ≠ 1) must be conducted
to determine the stability using this parameter. For further details,
see Finlay and Wilkinson (1963). STABILITYSOFT also renders a
heat map plot, based on Pearsons correlation coecients (Pearson,
1895), using Canvas tools (W3C, Cambridge, Massachusetts, USA)
to display the interrelationships between the stability statistics and
yield performance.
To test the program, we are providing two examples and data sets
gathered from ve yield trials in grass pea and barley (advanced and
doubled haploid lines) taken from Ahmadi etal. (2015), Khalili and
Pour- Aboughadareh (2016), and Vaezi etal. (2018). In the rst ex-
ample, we used grain yield (kg ha−1) for 14 advanced grass pea lines
grown in three Iranian semi- arid regions (Kermanshah, Gachsaran,
and Lorestan) during four consecutive years (2005–2008) (see
Ahmadi etal., 2015 for further details on growing conditions and
experimental design). In this example, only the averages of grain
yield across replications were used for calculations. e averages of
grain yield, along with 16 parametric and non- parametric statistics,
are shown in Appendices S1 and S2 together with signicance tests
for S(1) and S(2) statistics, Z1 and Z2, respectively. According to Nassar
and Huhn (1987), if Z1 and Z2 are less than the critical value of χ2, the
results show non- signicant dierences in rank stability among the
studied genotypes grown in the test environments (Huhn, 1990).
In the second sheet of the output le, which is named “Ranks,” the
rank of each genotype for each statistic is calculated (Appendix
S2). e calculated values indicate there are signicant dierences
Applications in Plant Sciences 2019 7(1): e1211 Pour- Aboughadareh etal.—STABILITYSOFT: Online stability statistical software 3 of 6 © 2019 Pour- Aboughadareh etal.
between tested lines in terms of grain yield, and ve lines (G3, G9,
G5, G7, and G12) could be identied with high- yield performance
across nine diverse environments. e calculated S(1), S(2), S(3), S(6),
NP(3), NP(4), and θi statistics showed G3 to be the most stable line,
but according to other parameters (S(3), S(6), NP(1), NP(2), NP(3), NP(4),
θ(i), Wi
2, bi, Sdi
2, σi
2, and CVi), line G6 was also shown to possess
desired stability traits. In these cases, the heat map plot function
of STABILITYSOFT based on Pearsons correlation can be used to
further investigate the interrelationships among dierent stability
statistics. is showed that yield performance only positively cor-
relates with the regression slope (bi) (Appendix S3). Because iden-
tifying stable lines based on grain yield and sole parameters could
be problematic, as shown in this example, our program provides an
estimation of the average of the sum ranks (ASR) for all statistics to
select potentially superior stable lines. Accordingly, a genotype with
low ASR value can be selected as a superior stable genotype. Based
on our results (Appendix S2), lines G6 (ASR = 3.44; SD = 3.01) fol-
lowed by G3 (ASR = 5.69; SD = 5.13) and G11 (ASR = 4.50; SD =
4.50) could be selected as stable lines for cultivation in the semi- arid
regions of Iran.
In the second example, we tested the soware using grain yield
data from a two- year (2016–2017) barley trial using 18 genotypes
grown at four semi- arid regions in Iran (Gonbad, Gachsaran,
Moghan, and Lorestan). At each location, the experimental layout
was a randomized complete block design with four replications.
Each experimental plot consisted of six rows with 17.5- cm row
spacing. Each location received optimal agricultural practices, with
total grain yield for each genotype estimated at harvest. e data le
is available on GitHub as “Example5.xlsx,” and the results are sum-
marized in Appendix S4. Grain yield (Y) ranged from 1820 to 2415
kg ha−1 (average 1985 kg ha−1), which was used as the rst statis-
tic for assessing genotypes. G2 had the highest yield performance,
TABLE1. Statistical capacity and available features of STABILITYSOFT relative to other codes and packages.
Statistical capacity/
SAS codes R packages
Hussein etal.
etal. (2016)
Dia etal.
(2016) Branco (2015)
Yaseen and
Eskridge (2018)
Mean variance
GE variance
Wricke’s ecovalence
stability index
Regression coefficient X X X
Deviation from
Shukla’s stability
coefficient of
Nassar and Huhn’s
non- parametric
statistics and Huhn’s
Thennarasu’s non-
parametric statistics
Kang’s rank- sum X X X X
Ranking pattern of
genotypes through
all statistics
Calculation of
statistics based on
both types of data
(row data and matrix
mean data)
Windows support X X X X X X X
Unix/Linux support X X X X X X X
Mac OS support X X X
Portable (without
GUI (graphical user
Offline usage
Applications in Plant Sciences 2019 7(1): e1211 Pour- Aboughadareh etal.—STABILITYSOFT: Online stability statistical software 4 of 6 © 2019 Pour- Aboughadareh etal.
followed by G13, G5, G1, and G18 (2415, 2246, 2125, 2054, and
2053 kg ha−1, respectively). Two parametric statistics (Wi
2 and σi
showed that genotypes G6 and G18 had the lowest values and are,
therefore, deemed stable lines. According to S(1), S(2), S(3), and S(6),
genotypes G2, G6, G7, and G13 were selected as desirable geno-
types. Two statistics (NP(3) and NP(4)) showed a similar trend, such
that G2 and G13 were selected as stable genotypes. Selection based
on the KR statistic orders the remaining genotypes as G18 > G13 >
G1 > G16. e heat map plot based on Pearsons correlation revealed
that S(1), S(2), S(3), and S(6) were positively and signicantly correlated
with each other and with NP(1), NP(3), and NP(4) (Appendix S5), and
grain yield had a signicant positive correlation with bi and CVi.
Moreover, grain yield also had a positive association with θi, Wi
and σi
2, but a negative association with NP(2), NP(3), NP(4), S(1), S(2),
S(3), S(6), and KR. Two statistics (Wi
2 and σi
2) were negatively associ-
ated with θ(i). Based on these associations and the use of the ranking
method integrated in STABILITYSOFT, we were able to identify the
six most stable genotypes (G18, G13, G12, G6, G2, and G1), which
had the lowest ASR values (4.81, 4.88, 5.13, 5.63, 6.75, and 7.69,
respectively; Appendix S6). With respect to yield performance, two
high- yielding genotypes (G1 [2054 kg ha−1] and G2 [2415 kg ha−1])
with relatively low ASR values are ideally suited for introduction
to desirable growth environments, whereas two genotypes (G13
[2246 kg ha−1] and G18 [2053 kg ha−1]) with high and middle yield
performance along with low ASR values can be introduced to semi-
arid or similar regions of Iran. Furthermore, genotypes G6 (1840
kg ha−1) and G12 (1923 kg ha−1), which have acceptable ASR val-
ues and low grain yields, were identied as low- yielding genotypes,
hence these genotypes can be introduced to marginal cultivation
We developed an online soware to calculate several parametric
and non- parametric stability statistics that are important in the
identication of stable crop genotypes. Some statistical programs
are available for stability analyses, but unlike our program they are
not platform independent and cannot calculate all the required
statistics. In addition to the favorable features listed in Table 1,
STABILITYSOFT has the following advantages over other R and
SAS packages: (1) it directly calculates dierent parametric and
non- parametric statistics along with Pearsons correlation with
high accuracy; (2) it is a cross- platform soware that needs no
additional downloads or installation, calculations are performed
on PHP servers, and users are not limited to the processing power
of their computers when using large data sets; (3) unlike other
codes based on SAS and R packages, which require additional user
knowledge of these packages, STABILITYSOFT has a web- based
user interface; and (4) it is compatible with all major browsers
(e.g., Google Chrome, Mozilla Firefox). In conclusion, we expect
that this soware will be useful for analyzing essential data for
stability studies related to agronomy and plant breeding. Our
program is also able to visualize the interrelationships between
dierent indices, which is crucial for selecting stable varieties.
STABILITYSOFT will be helpful for agronomists and plant breed-
ers who deal with large volumes of quantitative data and require
user- friendly soware to explore GEI and accurately calculate sta-
bility parameters.
e authors thank Dr. Mohsen Shekarbaigi (Department of
Mathematics, Imam Khomeini International University) for his as-
sistance with the dissection of mathematical formulae, Prof. Jafar
Ahmadi and Dr. Valiollah Youse (Department of Genetics and
Plant Breeding, Imam Khomeini International University) for their
suggestions, and Dr. Behroz Vaezi and Dr. Marouf Khalili for pro-
viding the data sets for testing this soware.
e source code used to develop STABILITYSOFT is available
on GitHub ().
STABILITYSOFT is available at https://mohsenyouse
Additional Supporting Information may be found online in the
supporting information tab for this article.
FIGURE 1. Information ow diagram for the STABILITYSOFT software
PHP (server-side)
Extract data and genotype labels from input file
Make Excel file from statistical analysis section
Draw heatmap plot from correlation matrix using Canvas tools
Download output file as an Excel file
Display correlation plot
To start calculating, the data file should be provided in Excel format
Calculation of parametric and non-parametric statistics for each genotype
Calculation of ranks of all statistics except bi statistics for each genotype
Calculation of correlation matrix from input data file
Data Input:
Statistical analysis:
JavaScript (client-side)
JavaScript (client-side)
Applications in Plant Sciences 2019 7(1): e1211 Pour- Aboughadareh etal.—STABILITYSOFT: Online stability statistical software 5 of 6 © 2019 Pour- Aboughadareh etal.
APPENDIX S1. Parametric and non- parametric stability statistics
calculated with STABILITYSOFT for grain yield (kg ha−1) of 14
grass pea advanced lines across nine dierent environments in Iran.
APPENDIX S2. Ranks of parametric and non- parametric stability
statistics calculated with STABILITYSOFT for grain yield (kg ha−1) of
14 grass pea advanced lines across nine dierent environments in Iran.
APPENDIX S3. Heat map plot rendered based on Pearson’s corre-
lation analysis for Example 1. See Appendix 1 for full denitions of
statistics. θi = mean variance component; θ(i) = GE variance com-
ponent; Wi
2 = Wricke’s ecovalence stability index; bi = regression
coecient; Sdi
2 = deviation from regression; σi
2 = Shuklas stability
variance; CVi = environmental coecient of variance; S(1) and S(2) =
Nassar and Huhn’s non- parametric statistics; S(3) and S(6) = Huhn’s
non- parametric statistics; NP(1–4) = ennarasu’s non- parametric
statistics; KR = Kang’s rank- sum; Y = yield.
APPENDIX S4. Parametric and non- parametric stability statistics
calculated with STABILITYSOFT for grain yield (kg ha−1) of 18
barley genotypes across four dierent environments in Iran.
APPENDIX S5. Heat map plot rendered based on Pearson’s corre-
lation analysis for Example 2. See Appendix 1 for full denitions of
statistics. θi = mean variance component; θ(i) = GE variance com-
ponent; Wi
2 = Wricke’s ecovalence stability index; bi = regression
coecient; Sdi
2 = deviation from regression; σi
2 = Shuklas stability
variance; CVi = environmental coecient of variance; S(1) and S(2) =
Nassar and Huhn’s non- parametric statistics; S(3) and S(6) = Huhn’s
non- parametric statistics; NP(1–4) = ennarasu’s non- parametric
statistics; KR = Kang’s rank- sum; Y = yield.
APPENDIX S6. Ranks of parametric and non- parametric stability sta-
tistics calculated with STABILITYSOFT for grain yield grain yield (kg
ha−1) of 18 barley genotypes across four dierent environments in Iran.
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Applications in Plant Sciences 2019 7(1): e1211 Pour- Aboughadareh etal.—STABILITYSOFT: Online stability statistical software 6 of 6 © 2019 Pour- Aboughadareh etal.
APPENDIX 1. Description of stability statistics calculated for the crop traits by STABILITYSOFT.
Statistic Symbol Denition
Mean variance component θiPlaisted and Peterson (1959) proposed the variance component of genotype- by- environment
interactions (GEI) for interactions between each of the possible pairs of genotypes. This
statistic considers the average of the estimate for all combinations with a common genotype
to be a measure of stability. Accordingly, the genotypes that show a lower value for θi are
considered more stable.
GE variance component θ(i) This statistic is a modified measure of stability parameter. In this approach, the ith genotype is
deleted from the entire set of data and the GEI variance from this subset is the stability index
for the ith genotype. According to this statistic, the genotypes that show higher values for
the (i) are considered more stable.
Wricke’s ecovalence stability index Wi
2Wricke (1962) proposed the concept of ecovalence as the contribution of each genotype to
the GEI sum of squares. The ecovalence (Wi) of the ith genotype is its interaction with the
environments, squared and summed across environments. Thus, genotypes with low values
have smaller deviations from the mean across environments and are more stable.
Regression coefficientabiThe regression coefficient (bi) is the response of the genotype to the environmental index
that is derived from the average performance of all genotypes in each environment (Finlay
and Wilkinson, 1963). If bi does not significantly differ from 1, then the genotype is adapted
to all environments. A bi > 1 indicates genotypes with higher sensitivity to environmental
change and greater specificity of adaptability to high- yielding environments, whereas a bi <
1 describes a measure of greater resistance to environmental change, thereby increasing the
specificity of adaptability to low- yielding environments.
Deviation from regression Sdi
2In addition to the regression coefficient, variance of deviations from the regression (Sdi
2) has
been suggested as one of the most- used parameters for the selection of stable genotypes.
Genotypes with an Sdi
2 = 0 would be most stable, while an Sdi
2 > 0 would indicate lower
stability across all environments. Hence, genotypes with lower values are the most desirable
(Eberhart and Russell, 1966).
Shukla’s stability variance σi
2Shukla (1972) suggested the stability variance of genotype i as its variance across environments
after the main effects of environmental means have been removed. According to this statistic,
genotypes with minimum values are intended to be more stable.
Environmental coefficient of variance CViThe coefficient of variation is suggested by Francis and Kannenberg (1978) as a stability statistic
through the combination of the coefficient of variation, mean yield, and environmental
variance. Genotypes with low CVi, low environmental variance (EV), and high mean yield are
considered to be the most desirable.
Nassar and Huhn’s non- parametric
statistics and Huhn’s statisticsb
Huhn (1990) and Nassar and Huhn (1987) suggested four non- parametric statistics: (1) S(1), the
mean of the absolute rank differences of a genotype over all tested environments; (2) S(2),
the variance among the ranks over all tested environments; (3) S(3), the sum of the absolute
deviations for each genotype relative to the mean of ranks; and (4) S(6), the sum of squares of
rank for each genotype relative to the mean of ranks. To compute these statistics, the mean
yield data have to be transformed into ranks for each genotype and environment, and the
genotypes are considered stable if their ranks are similar across environments. The lowest
value for each of these statistics reveals high stability for a certain genotype.
Thennarasu’s non- parametric statistics NP(1)
Four NP(1–4) statistics are a set of alternative non- parametric stability statistics defined by
Thennarasu (1995). These parameters are based on the ranks of adjusted means of the
genotypes in each environment. Low values of these statistics reflect high stability.
Kang’s rank- sum Kang or KR Kang’s rank- sum (Kang, 1988) uses both yield and σi
2 as selection criteria. This parameter gives a
weight of 1 to both yield and stability statistics to identify high- yielding and stable genotypes.
The genotype with the highest yield and lower σi
2 is assigned a rank of 1. Then, the ranks of
yield and stability variance are added for each genotype, and the genotypes with the lowest
rank- sum are the most desirable.
aTo determine stability using this parameter, the significance test (H0: B ≠ 1) must be conducted. For more detail, see Finlay and Wilkinson (1963).
bIn addition to S(i) statistics, two significance tests for S(1) and S(2), namely Z1 and Z2, are calculated.
... Genotype x Environment interactions of cross over type would introduce inconsistency in the behaviour of genotypes evaluated in the various environmental conditions. 1 Adaptability and stability of various crops under multi-environment field trials studied by number of analytic measures as observed in the literature. 2 Moreover non parametric measures to assess GxE interaction and stability analysis had been also reflected. 6 The components of analysis of variance, the regression models, non-parametric methods, AMMI methods, BLUP based mixed models would be most suitable analytic methods. 3 AMMI stability value (ASV), ASV1, Modified AMMI stability value (MASV) & MASV1) have been registered visibility. ...
... 5 Besides that nonparametric measures S i 1 , S i 2 , S i 3 , S i 4 , S i 5 ,S i 6 , S i 7 , NP i (1) , NP i (2) , NP i (3) , NP i (4) have been also utilized for genotypes x environmental conditions. 6 All recent analytic measures have been compared to decipher the Gx E interactions effects for fodder barley genotypes evaluated in northern hills zone of the country. ...
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Field experiments were carried out at six locations in Northern Hill Zone to evaluate twenty three promising fodder barley genotypes in a randomized complete block design (RCBD) during 2020-21 cropping seasons . Using analytic methods Additive Main Effects and Multiplicative Interactions (AMMI), Best Linear Unbiased Predictor (BLUP) along with Non Parametric compared to decipher the GxE interactions under multi environment trials. Highly significant about 67.5% variations accounted by environments, 14.1% of GxE interactions and marginally 3.2% by the genotypes in the total sum square of variations for yield the present study. AMMI1 explained 53.7%, 32.1% by AMMI2, 6.9% for AMMI3, AMMI4 accounted for 4.8% respectivelyof a total variation. ASV and ASV1 measures considered 85.9% of the total variation identified G4, G5, G9 genotypes. MASV1 exploited 97.7% of interactions favoured for G18, G15, G8 genotypes. BLUP-based settled for G6, G11, G5 genotypes. Non parametric measures found G9, G8, G1 as suitable genotypes. Further non parametric composites measures selected G9, G4, G8 as suitable genotypes. Measures Si1, Si2, Si3, Si4, Si5,Si6 ,Si7, HMPRVG, ASV1, ASV, accounted more in first principal component whereas NPi (1), NPi (2), NPi (3), NPi (4), PRVG, Si1, GM, Mean, Average were major contributors in second principal component. Very tight positive relationships observed for IPC3, IPC1with BLUP based measures GM, HM, PRVG, HMPRVG, Average in one quadrant. CV closely related to Stdev, IPC2, IPC4 in opposite quadrant. ASV, ASV1 expressed very tight association with Si6,Si7 whereas NPi(1) , exhibited close affinity with Si1 , Si4, Si2 ,Si5 values. Methods utilized in study showed high to moderate degree of association among themselves, however non parametric measures would be recommended for multi environment trials.
... N = total environment. Rank stability of KR was computed by Kang [31], wherein yield and stability variance of the genotype with a high yield and stability is weighed 1. Software STABILITYSOFT [32] was used to compute yield stability based on parametric and nonparametric methods. ...
... The GGE biplot was composed of the general mean and interaction principal component axes score. STABI-LITYSOFT was also used to estimate combined ANOVA, GGE, and to form their biplots [32]. ...
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The intercropping of maize with other food crops is a current solution to problems in food crop production and crop failures. The objectives of the study were to (i) select adaptive maize hybrids in intercropping as well as sole-cropping systems, and (ii) test the ideal cropping system to evaluate best hybrids for intercropping. This study used 12 maize hybrids with different genetic backgrounds. Planting was carried out for two seasons using four cropping systems. Hybrids were selected according to their adaptability and stability based on parametric, nonparametric, and multivariate analyses. The results showed that G10 had high yield for all cropping systems. G10 was also selected as an adaptive hybrid for sole-cropping, whereas G9 was selected as an adaptive hybrid for intercropping. The L5 and L4 were ideal environments for evaluating hybrids under different cropping systems. The selected hybrids should be evaluated and disseminated for small-holder farmers in Indonesia.
... Additionally, eleven parametric and nine non-parametric common stability statistics were calculated (see Table 3), and furthermore, the investigated genotypes were ranked based on each statistic. All these stability statistics were estimated using a web-based STABILITYSOFT program (Pour-Aboughadareh et al., 2019) and the "metan" package in R. An overview of their equations and how they were used in the analysis of GEI effects is given in a recent review by Pour-Aboughadareh et al. (2022a). ...
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Increasing faba bean production is indispensable to supply the growing demand for plant-based protein on the global scale. A thorough understanding of genotype (G) by environment (E) interaction (GEI) patterns is critical to developing high-yielding varieties with wider adaptation. Thirteen faba bean genotypes were evaluated in 15 environments from 2019 to 2020 in western Canada to estimate their yield stability using different stability statistics. The combined analysis of variance and additive main effects and multiplicative interaction (AMMI) analysis revealed that G, E, and GEI effects were highly significant (P<0.001), indicating differential responses of the genotypes across the environments, enabling the stability analysis. The result of the model comparison found the best linear unbiased prediction (BLUP) to outperform AMMI models. The BLUP-based biplot of the weighted average of absolute scores (WAASB) stability and mean grain yield identified AO1155 (Navi), 1089-1-2, 1310-5, DL Tesoro, and 1239-1 as high-yielding and stable genotypes. The correlation analysis revealed that most of the stability parameters had a strong association with grain yield and with each other, indicating that they should be used in combination with one another to select genotypes with high yield. Overall, the WAASB superiority index (WAASBY) and the average sum of ranks of all stability statistics identified the same genotypes in terms of high yielding and stability, and genotype AO1155 is considered the most stable and highest yielding among the tested genotypes. Genotypes with stable yields across environments would be beneficial for faba bean genetic improvement programs globally. Keywords: Yield stability, WAASB, AMMI model, BLUP, Stability analysis, Western Canada
... In this method, grain yield performance and stability variance that identify high yielding and stable genotypes are given a weighting value of 1. The stable genotypes were identified based on nonparametric and parametric stability measurements using STABILITYSOFT (online software) [33]. To select and to compare high yield maize hybrids based on combined analysis, the results of parametric and nonparametric stability were grouped using cluster analysis (dendrogram) based on the stability rank of each parameter. ...
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Selection of high yielding and stable maize hybrid requires effective method of evaluation. Multienvironment evaluation is a critical step in plant breeding programs that is aimed at selecting the ideal genotype in a wide range of environments. A method of evaluation that combines a variety parameter of stability could provide more accurate information to select the ideal genotype. The aims of the study were (i) to identify the effect of genotype, environment, and genotype × environment interactions (GEIs) on maize hybrid yields and (ii) to select and to compare maize hybrids that have high and stable yields in diverse environments in Sumatra Island based on combined analysis, selection index, and GGE biplot. The study was conducted in five different environments in Sumatra Island, Indonesia, using a randomized complete block design repeated three times. Data were estimated using combined variance analysis, parametric and nonparametric stability, sustainability index, and GGE biplot. The results showed that the genotype had a significant effect on maize hybrid yields with a contribution of 41.797%. The environment contributed to 24.314%, and GEIs contributed 33.889% of the total variation. E1 (Karo, South Sumatra; dry season) and E3 (Tanjung Bintang, Lampung; dry season) were identified as the most ideal environments (representative) for testing the hybrids for wider adaptability. The maize hybrid with high and stable yields can be selected based on combined stability analysis and sustainability index as well as GGE biplot. These three methods are effectively selected high yielding and stable genotypes when they are used together. The three maize hybrids, namely, MH2, MH8, and MH9, are recommended as high yielding and stable genotype candidates.
... For the calculation of linearplateau and quadratic models, SigmaPlot 14.5 was used (Systat Software Inc., Chicago, Illinios, USA). The analysis of stability was performed using the StabilitySoft software [44]. ...
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Based on a long-term experiment in Prague, established in 1954, we analyzed the effect of weather and seven fertilization treatments (mineral and manure treatments) on winter wheat grain yield (GY) and stability. In total, 23 seasons were analyzed, where a wheat crop followed a summer crop of potatoes. A regression analysis showed that, since the experiment started, there has been a significant increase in the annual daily maximum, average, and minimum temperature of 0.5 ◦C, and an increase in annual rainfall of 0.3 mm. Grain yield was positively associated with April precipitation, mean daily temperature in October, and daily maximum temperature in February. Yields were most stable between years with two fertilizer treatments that supplied a mean of 47 kg N ha−1yr−1, 54 kg P ha−1yr−1, and 108 kg K ha−1yr−1. The rate of N at which grain yield was optimized was determined according to the linear-plateau (LP) and quadratic response models as 44 kg N ha−1yr−1 for the long-strawed varieties and 87 kg N ha−1yr−1 for short-strawed varieties. A gradual increase in yields was observed in all treatments, including the unfertilized control, which was attributed to improved varieties rather than to a changing climate.
... In recent years, climate change has increased the intensity of abiotic stressors. Consequently, the effects of environmental stresses on cereal production have become increasingly important [26]. Among cereals, barley is adaptable to a wide range of climates. ...
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Climate change has caused breeders to focus on varieties that are able to grow under unfavorable conditions, such as drought, high and low temperatures, salinity, and other stressors. In recent decades, progress in biotechnology and its related tools has provided opportunities to dissect and decipher the genetic basis of tolerance to various stress conditions. One such approach is the identification of genomic regions that are linked with specific or multiple characteristics. Cereal crops have a key role in supplying the energy required for human and animal populations. However, crop products are dramatically affected by various environmental stresses. Barley (Hordeum vulgare L.) is one of the oldest domesticated crops that is cultivated globally. Research has shown that, compared with other cereals, barley is well adapted to various harsh environmental conditions. There is ample literature regarding these responses to abiotic stressors, as well as the genomic regions associated with the various morpho-physiological and biochemical traits of stress tolerance. This review focuses on (i) identifying the tolerance mechanisms that are important for stable growth and development, and (ii) the applicability of QTL mapping and association analysis in identifying genomic regions linked with stress-tolerance traits, in order to help breeders in marker-assisted selection (MAS) to quickly screen tolerant germplasms in their breeding cycles. Overall, the information presented here will inform and assist future barley breeding programs.
... Two stability parameters were calculated to assess the stability performance of genotypes; b, the linear regression of the phenotypic values on environmental mean [47] and the coefficient of variation (CV) suggested by Francis and Kannenberg [48] using the STABILITYSOFT [49]. ...
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Soybean is generally grown as a rainfed crop worldwide and very often is exposed to drought and high temperatures. The objectives of this study were to determine the performance and stability of 32 elite soybean genotypes for seed protein and oil contents across six dry and eight normal environments and to determine the influence of environment on the relationship between the two traits. In the combined ANOVA, genotypes (G), environments (E) and G × E were significant for both traits with protein content being more sensitive to environmental changes than oil content. Mean seed protein content decreased by 4.5% under drought conditions compared to normal ones and ranged from 2.3% to 7.1% for individual genotypes. At the same time drought caused a slight increase in seed oil content of +1.2%, with a range of −1.3% to +4.5% for individual genotypes. Genotype stability in terms of regression coefficient (b) and coefficient of variation (CV) was in moderate to weak negative correlation with mean genotype performance for protein content, while no correlation was observed between genotype stability and mean genotype performance for oil content. Protein and oil content were significantly negatively correlated in normal environments (r = −0.33), while no correlation between the traits was observed in dry environments (r = −0.02)
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Background: Lentil is a major rabi pulse crop and is known for its protein rich grains. It is important to develop area-specific high yielding varieties of lentil. Methods: The present study was conducted with 24 lentil genotypes grown at three different locations of Uttarakhand for two consecutive years 2018-19 and 2019-20. The average of sum of ranks (ASR) of parametric and non-parametric measures along with yield stability index was used in present study to identify the high yielding and stable lentil genotypes. Result: The pooled ANOVA revealed the presence of significant differences among genotypes, environments and G × E interaction effects. The ASR method in combination with YSI was found to be effective in identifying high yielding as well as stable genotypes. The genotypes PL 8, IPL 315, DPL 15 and PL 7 were found as most stable and high yielding genotypes.
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Magnesium (Mg) is the fourth most abundant element in the human body and plays the role of cofactor formore than 300 enzymatic reactions. In plants,Mg is involved in various key physiological and biochemical processes like growth, development, photophosphorylation, chlorophyll formation, protein synthesis, and resistance to biotic and abiotic stresses. Keeping in view the importance of this element, the present investigation aimed to explore the Mg contents diversity in the seeds of Turkish common bean germplasm and to identify the genomic regions associated with this element. A total of 183 common bean accessions collected from 19 provinces of Turkey were used as plant material. Field experiments were conducted according to an augmented block design during 2018 in two provinces of Turkey, and six commercial cultivars were used as a control group. Analysis of variance depicted thatMg concentration among common bean accessions was statistically significant (p < 0.05) within each environment, however genotype × environment interaction was nonsignificant. A moderate level (0.60) of heritability was found in this study. Overall mean Mg contents for both environments varied from 0.33 for Nigde-Dermasyon to 1.52 mg kg−1 for Nigde-Derinkuyu landraces, while gross mean Mg contents were 0.92 mg kg−1. At the province level, landraces from Bolu were rich while the landraces from Bitlis were poor in seed Mg contents respectively. The cluster constellation plot divided the studied germplasm into two populations on the basis of their Mg contents. Marker-trait association was performed using a mixed linear model (Q + K) with a total of 7,900 DArTseq markers. A total of six markers present on various chromosomes (two at Pv01, and one marker at each chromosome i.e., Pv03, Pv07, Pv08, Pv11) showed statistically significant association for seed Mg contents. Among these identified markers, the DArT-3367607 marker present on chromosome Pv03 contributed tomaximum phenotypic variation (7.5%). Additionally, thismarker was found within a narrow region of previously reportedmarkers.We are confident that the results of this study will contribute significantly to start common bean breeding activities using marker assisted selection regarding improved Mg contents.
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In this study, inter-simple sequence repeats (ISSR) and start codon targeted (SCoT) markers were used for genetic diversity and relationship analysis of nine Salvia species. Twenty-one and twenty selected ISSR and SCoT primers amplified 350 and 329 loci, respectively, of which all were polymorphic. The obtained average polymorphism information content (ISSR, 0.38; SCoT, 0.40), average band informativeness (ISSR, 16.67; SCoT, 16.45) and resolving power (ISSR, 9.75; SCoT, 12.52) revealed high genetic diversity prevailing among Salvia accessions. Considering the both ISSR and SCoT data, the species with the basic chromosomes number x = 8 showed higher values of the percentage polymorphism loci (PPL), the number of observed alleles (Na) and Shannon index value (I) than the other species. The partition of clusters in the neighbor-joining dendrogram based on ISSR, SCoT and combined data were similar and grouped all individuals into four clusters. However, the dendrogram generated based on SCoT separated individuals into sub-clusters in accordance with their species and section. The Mantel test revealed a similar polymorphism distribution pattern between ISSR and SCoT techniques, the correlation coefficient (r) was 0.83, and the results showed that both techniques were effective to assess the genetic diversity. Our results indicated that SCoT marker as a reliable and informative technique can be used for evaluation of genetic diversity and relationships among Salvia species.
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This research was carried out to estimate yield stability of 18 barley breeding lines using different parametric and non-parametric statistics across 15 environments during 2012-2015 growing seasons. The results of combined analysis of variance (ANOVA) and additive main effect and multiplicative interaction (AMMI) analysis showed that environments (E), genotypes (G), GE interaction, as well as the first four interaction principal component axes were highly significant, suggesting that the lines interacted differentially with environments, so further general adaptability and stability analysis across environments should be followed before being introduced for cultivation. Based on correlation coefficient and principal components analysis (PCA), most of the non-parametric statistics were significantly inter-correlated with parametric statistics, hence seem to be suitable alternatives to complement parametric statistics. Furthermore, according to the static and dynamic concepts of stability, the results revealed that stability statistics can be clustered into three groups. The overall stability analysis following different stability methods concluded that four lines (G5, G7, G17 and G20) were highly stable for grain yield in rain-fed conditions of subtropical regions of Iran. Thus, these lines can be recognized as the most stable lines for cultivation in diverse environments of semi-warm regions of Iran.
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Progress in plant molecular tools has been resulted in the development of gene-targeted and functional marker systems. CAAT box region is a different pattern of nucleotides with a consensus sequence, GGCCAATCT, which situated upstream of the start codon of eukaryote genes and plays an important role during transcription. In the present study, several CAAT box-derived polymorphism (CBDP) primers used for fingerprinting in the mini-core collection of durum wheat (including internationally developed breeding lines and Iranian landraces). Twelve selected primers amplified 98 loci, of which all were polymorphic. The average values of the polymorphism information content (PIC) and resolving power (Rp) were 0.31 and 9.16, respectively, indicating a high level of variability among studied genotypes. Analysis of molecular variance (AMOVA) indicated that 92% of the total variation resided among populations. The values of the percentage polymorphic bands (PPL), the observed (Na) and effective (Ne) number of alleles, Nei's gene diversity (He) and Shannon's information index (I) for Iranian landraces were higher than the breeding lines. The Fan-dendrogram obtained by cluster analysis grouped all individuals into three main clusters. Our results showed a remarkable level of genetic diversity among studied durum wheat, especially among Iranian landraces, which can be interesting for future breeding programs. More importantly, the present study also revealed that CBDP technique was the efficient and powerful tool to assess genetic diversity in wheat germplasm. Hence, this technique could be employed individually or in combination with other molecular markers to evaluate genetic diversity and relations among different species.
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We assessed the molecular genetic diversity and relationships among some Aegilops and Triticum species using 15 start codon-targeted (SCoT) polymorphism markers. A total of 166 bands amplified, of which 164 (98.79%) were polymorphic. Analysis of molecular variance and inter-population differentiation (Gst) indicated high genetic variation within the studied populations. Our analyses revealed high genetic diversity in T. boeoticum, Ae. cylindrica, T. durumand Ae. umbellulata, low diversity in Ae. crassa, Ae. caudata and Ae. speltoides, and a close relationship among Ae. tauschii, T. aestivum, T. durum, T. urartu, and T. boeoticum. Cluster analysis indicated 180 individuals divided into 8 genome homogeneous clades and 11 sub-groups. T. aestivum and T. durum accessions were grouped together, and accessions with the C and U genomes were grouped into the same clade. Our results support the hypothesis that T. urartu and Ae. tauschii are two diploid ancestors of T. aestivum, and also that Ae. caudata and Ae. umbellulata are putative donors of C and U genomes for other Aegilops species that possess these genomes. Our results also revealed that the SCoT technique is informative and can be used to assess genetic relationships among wheat germplasm.
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The knowledge about genetic diversity in the wild relatives of wheat provides useful information for breeding programs and gene pool management. In the present study, an assessment of agro-morphological diversity and molecular variability among 70 accessions of Triticum, belonging to T. boeoticum, T. urartu, T. durum and T. aestivum species, collected from different regions of Iran was made. According to phenotypic analysis, all traits except peduncle length, stem diameter and the number of seeds per spike indicated a high level of diversity among studied accessions. Also, principal component analysis identified six components that explained 87.53% of the total variation in agro-morphological traits. In molecular analysis, 15 start codon targeted (SCoT) polymorphism primers produced 166 bands, out of which, 162 (97.59%) were polymorphic. Analysis of molecular variance (AMOVA) indicated the 63% of the variation resided among populations. The maximum value of polymorphism information content (PIC), the observed (Na) and effective (Ne) number of alleles, Nie’s gene diversity (He) and Shannon’s information index (I) was detected for T. boeoticum than the other species. The SCoT-based tree revealed three different groups corresponding to the genomic constitution in Triticum germplasm, which was in part confirmed by STRUCTURE and principal coordinate (PCoA) analyses. Our results indicated a remarkable level of genetic diversity among studied Iranian Triticum species, especially T. boeoticum, which can be of interest for future breeding and other analyses associated with future studies of the wild relatives of wheat. More importantly, our results revealed that SCoT markers could be used to accurate evaluate genetic diversity and phylogenetic relationships among different Triticum species.
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Successful production and development of stable and adaptable cultivars only depend on the positive results achieved from the interaction between genotype and environment that consequently has significant effect on breeding strategies. The objectives of this study were to evaluate genotype by environment interactions for grain yield in barley advanced lines and to determine their stability and general adaptability. For these purposes, 18 advanced lines along with two local cultivars were evaluated at five locations (Gachsaran, Lorestan, Ilam, Moghan and Gonbad) during three consecutive years (2012–2015). The results of the AMMI analysis indicated that main effects due to genotype (G), environment (E) and GE interaction as well as four interaction principal component axes were significant, representing differential responses of the lines to the environments and the need for stability analysis. According to AMMI stability parameters, lines G5 and G7 were the most stable lines across environments. Biplot analysis determined two barley mega-environments in Iran. The first mega-environment contained of Ilam and Gonbad locations, where the recommended G13, G19 and G1 produced the highest yields. The second mega-environment comprised of Lorestan, Gachsarn and Moghan locations, where G2, G9, G5 and G7 were the best adapted lines. Our results revealed that lines G5, G7, G9 and G17 are suggested for further inclusion in the breeding program due to its high grain yield, and among them G5 recommended as the most stable lines for variable semi-warm and warm environments. In addition, our results indicated the efficiency of AMMI and GGE biplot techniques for selecting genotypes that are stable, high yielding, and responsive.
Ramie (Boehmeria nivea (L.) Gaudich) is widely used in the manufacture of garments and industrial fabrics. In the present study, 1151 ramie germplasm resources from 13 regions within China, Brazil, Indonesia, India, and Cuba were analyzed for genetic diversity and population structure using 23 simple sequence repeat (SSR) markers and three phenotypic markers to provide baseline information for ramie’s breeding and conservation plans. Overall, 117 loci were identified and Shannon’s diversity index ranged from 0.3201 to 1.6773 (average, 0.8272). The polymorphism information content varied from 0.1578 to 0.7603 (average, 0.5017), expected heterozygosity from 0.1694 to 0.7815 (average, 0.5282), and genetic diversity from 0.1722 to 0.7884 (average, 0.5228). Genetic diversity was higher in Sichuan, Yunnan, and Guizhou than in the other regions studied. Cluster analysis based on unweighted pair group method with arithmetic mean and genetic distance revealed marked differences between domestic (Chinese) and foreign (other regions) germplasm resources. Population structure analysis also evidenced two groups. Genetic diversity was higher in wild than in domesticated germplasms. Overall, results obtained here will be useful for selecting parents for breeding and performing association analyses in ramie.
We describe a model-based clustering method for using multilocus genotype data to infer population structure and assign individuals to populations. We assume a model in which there are K populations (where K may be unknown), each of which is characterized by a set of allele frequencies at each locus. Individuals in the sample are assigned (probabilistically) to populations, or jointly to two or more populations if their genotypes indicate that they are admixed. Our model does not assume a particular mutation process, and it can be applied to most of the commonly used genetic markers, provided that they are not closely linked. Applications of our method include demonstrating the presence of population structure, assigning individuals to populations, studying hybrid zones, and identifying migrants and admixed individuals. We show that the method can produce highly accurate assignments using modest numbers of loci—e.g., seven microsatellite loci in an example using genotype data from an endangered bird species. The software used for this article is available from
The aim of this study was to find biocatalyst which uses thebaine and extract of different parts of Papaver bracteatum to synthesize morphine alkaloids. The thebaine-resistant strains were obtained from microbial flora of different parts of P.bracteatum. They were purified and treated utilizing different concentrations of thebaine. Those that can grow at a concentration of over 500 µg/ml were chosen for the biotransformation experiments. Biotransformation experiments were carried out utilizing selected cells in the medium containing thebaine and/or the extract of P.bracteatum; the products of such biotransformation were extracted and the profiles of metabolites were evaluated using HPTLC, and LC/ESI-MS methods. Thereafter, the effective isolate for thebaine transformation was characterized by physiological, biochemical and biomolecular methods. The results show that among 67 isolates, 12 strains were selected using the HPTLC screening as candidates that can transform thebaine into codeine and morphine. Among them, 5 strains were identified to transform plant extract, among which, using LC/ESI-MS, a candidate was selected and identified as Bacillus sp. FAR. It can be concluded from this study, that this microbial flora candidate can transform thebaine into important narcotic drugs and it will be a valuable step in biotechnology.