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Journal ArticleDOI

Pattern analysis of turkish bread wheat landraces and cultivars for grain and flour quality

TL;DR: The results showed that, based on the quality traits, the pure lines in different groups were belonged to different provinces of Turkey, and were also proved to be highly diverse for 8 quality trait values (both mixograph and grain quality) to breeders and end-users.
Abstract: This research was carried out to characterize both 200 pure lines selected from Turkish bread wheat landraces and 25 Turkish bread wheat cultivars based on 3 grain quality traits [thousand kernel weight (TKW), protein content (PC), Zeleny sedimentation test (ZSDS)] and 5 mixograph parameters. Univariate and Multivariate (clustering and ordination) techniques were used to investigate the diversity present among the pure lines and cultivars. Both cluster and ordination analyses suggested that there were ten groups of studied genotypes. Applying genotype-by-trait (GT) biplot analysis to the multiple quality trait data revealed that GT-biplot graphically displayed the interrelationships among traits and facilitated visual comparison of pure lines and selection. The results also showed that, based on the quality traits, the pure lines in different groups were belonged to different provinces of Turkey. They were also proved to be highly diverse for 8 quality trait values (both mixograph and grain quality) to breeders and end-users. Especially most of the pure lines had higher PC, midline peak value of mixogram (MPV), midline time x = 8 min integral of mixogram (MTxI) and ZSDS values than some of the cultivars. Also, our results were very contributive in selection of precious pure lines for further breeding programs.

Summary (2 min read)

INTRODUCTION

  • A landrace may display variation for many traits, because of natural selection and by traditional farmers to a limited extent in the environment, where it is inhabited, due to its admixtured genotypes (Belay et al.
  • Bread wheat landraces, in Turkey, also may be classified according to expected usage; different landraces are used for flour, bulgur, lavas, tandir, asure etc. 1998; Karcicio and Izbirak 2003; Ozbek et al. 2011; 2012).

Plant Material

  • Totally 225 bread wheat genotypes (200 landraces pure lines and 25 registered Turkish bread wheat cultivars) were used as the experimental plant material.
  • Sowing was done on first week of October in both growing seasons.
  • Grain samples were dried and cleaned before quality analysis commenced.
  • Quality analyses were performed on the complete set (200 pure lines, 25 cultivars and two replicates) of samples both seasons.

Grain quality characteristics

  • Thousand grain weights (TKW) of each wheat genotype were determined as described by Akcura (2011).
  • The Zeleny sedimentation test volumes (ZSDS) were determined according to the method of AACC method 56- 81 and International Association of Cereal Chemistry standard method 115-116 (AACC 2000).
  • Grain protein content (PC) was determined with a LECO FP-2000 (Leco Corporation Michigan USA).

Flour Rheology

  • To test dough quality, flour was obtained with the Chopin Dubois CD1 mill, which gave a 70% extraction rate.
  • The software used the top envelope curves and the midline to analyze mixograms.
  • Two parameters were the height and the width of curves: at peak time (midline peak value (MPV)) and at 8 min (midline time x = 8 min value (MTxV)).
  • Dough weakening was expressed as the difference in the curve heights at peak time and after 8 min of mixing; this parameter was called the weakening slope, WS = MPV – MRV (Bordes et al. 2008).

Statistical analysis

  • Pattern analysis, defined by Williams (1976) as the joint use of classification and ordination methods, was applied to characterize 225 genotypes (200 pure lines selected from bread wheat landraces and 25 cultivars) mean of data across growing seasons (DeLacy et al. 1996).
  • Both genotype-by-trait (GT) and genotype group-bytrait (GGT) biplots were used to assess the patterns of relations among quality characters, genotypes and their interactions.
  • Biplots were conducted in the dimension of first two principal components (PC1 and PC2), using a singular-value decomposition procedure (Gabriel 1971; Kempton 1984; Yan 2014).
  • The CROPSTAT statistical software and biplot Macro for Excel were used to generate all statistical analyses (Lipkovich and Smith 2002; IRRI 2013).

Mean performance of genotypes

  • Data recorded on the 225 bread wheat genotypes (200 landraces pure lines and 25 cultivars) across growing seasons for the quality and rheological traits were given in Table 1.
  • Data in Table 1 indicated that all studied quality traits were remarkably influenced by genotypes (Data not given).
  • The lowest CV values were observed for the traits PC followed by MTxI, MPV, and TKW indicating the least variation among the all cultivars for these characters, while the highest CV values were found for the MTxV, WS, MPTi and ZSDS (Table 1).

Relationship between traits

  • Biplot procedure was used to evaluation relationships between studied traits.
  • Therefore, according to these results fundamental patterns among the traits should be captured by both genotype trait biplot and genotype group trait biplot .
  • MTxV shows negative correlation with MPTi and WS (Table 2).
  • Since the cosine of the angles does not precisely translate into correlation coefficients, some associations with traits couldn’t be seen in biplots.

Classification of Genotypes

  • Cluster analysis was done to classification of genotypes for studied traits (Table 3).
  • The results of classification for bread wheat genotypes by hierarchical cluster analysis were given in Figure 4.
  • For each attribute the individual groups exhibited different ranges of distribution.

DISCUSSION

  • Results of two growing seasons indicated a significant variation among the bread wheat genotypes, traits and their interactions.
  • The authors correlation coefficients between TKW and PC or dough properties were similar to those reported by Bordes et al. (2008) for 372 bread wheat’s core collection.
  • The parameter MPTi exhibited significantly positive correlations with most of the investigated traits (MPV, MTxI and WS) except MTxV.
  • The biplot technique was used as a useful statistical tool for visualizing genotype-by-trait data and helped correctly for showing interrelationships among the traits.

CONCLUSION

  • In conclusion, pure lines of bread wheat landraces from seven geographical regions of Turkey were proved to be highly diverse for 8 traits of quality values (mixograph and grain quality) to breeders and as well as end-users.
  • Also, their results may be very useful in choosing the precious pure lines or pure line group in further breeding programs .
  • In addition, results of pure line classification revealed that pure lines within each cluster belonged to different regions of Turkey which suggested that there was no clear relationship between pure lines and regional diversity.
  • Cultivars and pure lines showed some similarities, most of pure lines demonstrated higher PC, MPV, MTxI and ZSDS values than some of cultivars.
  • The information, thus, obtained will be informative for wheat breeders, millers and bakers for their intended use.

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120
Turk J
Field Crops
2016, 21(1), 120-130
DOI: 10.17557/tjfc.72407
PATTERN ANALYSIS OF TURKISH BREAD WHEAT LANDRACES AND
CULTIVARS FOR GRAIN AND FLOUR QUALITY
Mevlut AKCURA
1*
, Kagan KOKTEN
2
, Aysun GOCMEN AKCACIK
3
,
Seydi AYDOGAN
3
1
University of Çanakkale Onsekiz Mart, Faculty of Agriculture, Department of Field Crops, Çanakkale,
TURKEY
2
University of Bingol, Faculty of Agriculture, Department of Field Crops, Bingol, TURKEY
3
Bahri Dagdaş International Agricultural Research Institute, Konya, TURKEY
*
Corresponding author: makcura@comu.edu.tr
Received: 05.08.2015
ABSTRACT
This research was carried out to characterize both 200 pure lines selected from Turkish bread wheat landraces
and 25 Turkish bread wheat cultivars based on 3 grain quality traits [thousand kernel weight (TKW), protein
content (PC), Zeleny sedimentation test (ZSDS)] and 5 mixograph parameters. Univariate and Multivariate
(clustering and ordination) techniques were used to investigate the diversity present among the pure lines and
cultivars. Both cluster and ordination analyses suggested that there were ten groups of studied genotypes.
Applying genotype-by-trait (GT) biplot analysis to the multiple quality trait data revealed that GT-biplot
graphically displayed the interrelationships among traits and facilitated visual comparison of pure lines and
selection. The results also showed that, based on the quality traits, the pure lines in different groups were
belonged to different provinces of Turkey. They were also proved to be highly diverse for 8 quality trait values
(both mixograph and grain quality) to breeders and end-users. Especially most of the pure lines had higher PC,
midline peak value of mixogram (MPV), midline time x = 8 min integral of mixogram (MTxI) and ZSDS values
than some of the cultivars. Also, our results were very contributive in selection of precious pure lines for further
breeding programs.
Key Words: Landraces, Mixograph, Pattern Analysis, Pure Lines, Quality Traits, Turkey
INTRODUCTION
A landrace may display variation for many traits,
because of natural selection and by traditional farmers to a
limited extent in the environment, where it is inhabited, due
to its admixtured genotypes (Belay et al. 1995; Jaradat
2006; Ahmadizadeh et al. 2011). Wheat landraces comprise
the major genetic resource of cultivated wheat in Turkey
(Akcura 2011).
Germplasm collections continue to play a vital role in
providing the genetic resources needed for improving bread
wheat. During the last 70 years of the 20
th
century, an
individual study resulted in collecting and conserving these
landraces in gene banks; their vernacular names and some
of their characteristics have been documented (Gokgol
1939). As distinct plant populations, landraces are named
and maintained by traditional farmers to meet their social,
economic, cultural, and environmental needs. Bread wheat
landraces, in Turkey, also may be classified according to
expected usage; different landraces are used for flour,
bulgur, lavas, tandir, asure etc. Consumption attributes of a
variety are reported to be important for a farm household,
on farm cultivation is the best solution to guarantee its
availability (Brush and Meng 1998). Wheat landraces, such
as Kirik, is still grown in some areas of Eastern Anatolian
Region, especially, in the least favorable areas. Advantages
of Kirik landrace can be listed, in East Anatolia, as high
quality and white grain for white unleavened lavash bread,
a high value marketable product locally, short growing
season, facultative wheat, low risk of production, good
straw, no awns (Bardsley and Thomas 2005; Karagoz
2013). Similarly, Asure is a landraces is grown in Elazig
and Malatya provinces, its grains are sought for asure
dessert. Genetic variation of Turkish bread wheat landraces
different traits such as biochemical characters, endosperm
proteins and isoenzymes studied by some scientists (Ozkan
et al. 1998; Peskircioglu et al. 1998; Karcicio and Izbirak
2003; Ozbek et al. 2011; 2012). Genetic variability and
interrelationship among grain yield and some quality traits
in Turkish wheat landraces evaluated by Akcura (2009) and
Sayaslan et al. (2012). In addition, Turkish durum wheat
(Akcura 2009; Sayaslan et al. 2012) and bread wheat
(Akcura and Topal 2006; Kara and Akman 2007) landraces
were assessed to determine the genetic diversity by several
authors according to some quality related morphological
traits.Turkish wheat landraces have a great potential to have
different traits such as grain yield, yield component and
some quality traits for rainfed breeding programs. Although

121
Turkish bread wheat landraces have all these great breeding
potential, nevertheless, they were not used broadly in
breeding programs nationally (Akcura 2006; Akcura 2011;
Ozbek 2013).
Germplasm evaluation and variety selection must be
based on multiple traits or breeding objectives. For most
crops, although yield is the first primary breeding objective,
quality is also a very important point. Furthermore, quality
is not a single trait; rather, it is measured by many
characteristics, which may be negatively associated. Also,
quality means different things for different end-uses (Yan
and Fregeau-Reid 2008). Different methods have been used
to evaluate the data structures, although strategies may
differ in overall fitness, these methods usually lead to the
same or similar conclusions for a given dataset (Flores et
al. 1998; Rubio et al. 2004; Akcura 2011). Alternative
statistical methods, ranging from simple univariate to the
more complex multivariate techniques, have been used in
the analysis of description of data in the durum wheat
landraces of Iran (Aghaee et al. 2010). Pattern analysis
techniques have been used broadly to evaluate the diversity
among genetic material of different plant (Harch et al.
1995; Jahufer et al. 1997; DeLacy et al. 2000; Rosso and
Pagano 2001; Aghaee et al. 2010). In addition to clustering
technique, the genotype-by-trait (GT) biplot has been
applied to understand the relations among traits and the trait
profiles of the genotypes, particularly among those that are
key breeding objectives (Yan and Kang 2003; Rubio et al.
2004; Peterson et al. 2005; Yan and Fregeau-Reid 2008;
Yan 2014). Furthermore, GGT is an application of the GGE
biplot to evaluate genotype comparison, and selection for
different traits (Yan and Kang 2003; Ilker et al. 2009; 2011;
Yan 2014).
However, Turkey is one of the main centers of very
little work on quality and rheology of landraces has been
carried out so far, so the present study was planned to
investigate the quality and rheology in both 200 pure lines
selected from Turkish bread wheat landraces and 25 widely
growing Turkish bread wheat cultivars by using mixograph
tests and to find out the relationships with different quality
attributes.
MATERIALS AND METHODS
Plant Material
Totally 225 bread wheat genotypes (200 landraces pure
lines and 25 registered Turkish bread wheat cultivars) were
used as the experimental plant material. These pure lines
were selected from 340 bread wheat landraces by pure line
selection method between 2002-2005 growing seasons at
Konya Location in Turkey (Akcura 2006). Origins of
landrace pure lines were given at Figure 1. Other
experiment materials were consisted of 25 cultivars (Altay-
2000, Bagci-2002, Bayraktar-2000, Bezostaja-1, Dagdas-
94, Demir-2000, Dogu-88, Flamura-85, Gelibolu, Gerek-
79, Gun-91, Harmankaya-99, Karahan-99, Kenanbey,
Kirac-66, Kirgiz-95, Kirik, Konya-2002, Mufitbey,
Pehlivan, Seval, Sonmez-01, Tekirdag, Tosunbey,
Zencirci-2000). The field experiments were carried out
under rain fed conditions at Canakkale Onsekiz Mart
University, Dardanos Campus field experiment area in
2012 and 2013 growing seasons. The plant materials (225
genotypes) were sown in 4 rows of 2 m long incomplete
block design with two replications. Sowing was done on
first week of October in both growing seasons. Weeds were
controlled manually. Fertilizer application was 27 kg N ha
-
1
and 69 kg P
2
O
5
ha
-1
at sowing, 43 kg ha
-1
N was applied
at the end of tillering stage in both growing seasons.
Experiments were harvested near the same date between
June 16 and June 28 for each year.
Figure 1. Origin of pure lines selected from Turkish bread wheat landraces

122
Grain samples were dried and cleaned before quality
analysis commenced. Quality analyses were performed on
the complete set (200 pure lines, 25 cultivars and two
replicates) of samples both seasons.
Grain quality characteristics
Thousand grain weights (TKW) of each wheat genotype
were determined as described by Akcura (2011). The
Zeleny sedimentation test volumes (ZSDS) were
determined according to the method of AACC method 56-
81 and International Association of Cereal Chemistry
standard method 115-116 (AACC 2000). Grain protein
content (PC) was determined with a LECO FP-2000 (Leco
Corporation Michigan USA).
Flour Rheology
To test dough quality, flour was obtained with the
Chopin Dubois CD1 mill, which gave a 70% extraction
rate. To assess dough properties during mixing, the
mixograph test was done on 10 g of flour with added water,
according to the approved AACC method 54-40A (AACC
2000). The mixograph curves (two envelopes and one
midline) were computed with Mixsmart software. The
software used the top envelope curves and the midline to
analyze mixograms. The height of the curves and the width
of the mixogram were recorded at three time points: at peak
time, 8 min after peak time and at the end of the mixing
procedure (10 min). Five parameters, previously described
by Martinant et al. (1998), Bordes et al. (2008) and Neacsu
et al. (2009), were used. Two parameters were the height
and the width of curves: at peak time (midline peak value
(MPV)) and at 8 min (midline time x = 8 min value
(MTxV)). The other parameters were the peak time
(midline peak time (MPTi)) and the area under the midline
curve after 8 min of mixing (midline time x = 8 min integral
(MTxI)). Dough weakening was expressed as the
difference in the curve heights at peak time and after 8 min
of mixing; this parameter was called the weakening slope,
WS = MPV MRV (Bordes et al. 2008).
Statistical analysis
Pattern analysis, defined by Williams (1976) as the joint
use of classification and ordination methods, was applied
to characterize 225 genotypes (200 pure lines selected from
bread wheat landraces and 25 cultivars) mean of data across
growing seasons (DeLacy et al. 1996).
Both genotype-by-trait (GT) and genotype group-by-
trait (GGT) biplots were used to assess the patterns of
relations among quality characters, genotypes and their
interactions. Biplots (Figure 2 and 3) were conducted in the
dimension of first two principal components (PC1 and
PC2), using a singular-value decomposition procedure
(Gabriel 1971; Kempton 1984; Yan 2014). The
CROPSTAT statistical software and biplot Macro for Excel
were used to generate all statistical analyses (Lipkovich and
Smith 2002; IRRI 2013).
[*: The best lines; TKW: Thousand grain weight (g); ZSDS: Zeleny sedimentation test volume (ml); PC: protein content (%); MPV: midline peak value
(%); MTxV: midline time x = 8 min value (%); MTxI: midline time x = 8 min integral (Tq%*min); MPTi: midline peak time (min); WS: weakening
slope (min); Cultivars abbreviation: Alt: Altay-2000, Bag: Bagci-2002, Bay: Bayraktar-2000, Bez: Bezostaja-1, Dag: Dagdas-94, Dem: Demir-2000,
Dog: Dogu-88, Fla: Flamura-85, Gel: Gelibolu, Ger: Gerek-79, Gun: Gun-91, Har: Harmankaya-99, Kar: Karahan-99, Ken: Kenanbey, Kir: Kirac-66,
Krz: Kirgiz-95, Krk: Kirik, Kon: Konya-2002, Muf: Mufitbey, Peh: Pehlivan, Sev: Seval, Son: Sonmez-01, Tek: Tekirdag, Tos: Tosunbey, Zen:
Zencirci-2000]
Figure 2. Genotype by quality trait (GT) biplot of 225 genotypes across growing seasons

123
[TKW: Thousand grain weight (g); ZSDS: Zeleny sedimentation test volume (ml); PC: protein content (%); MPV: midline peak value (%); MTxV:
midline time x = 8 min value (%); MTxI: midline time x = 8 min integral (Tq%*min); MPTi: midline peak time (min); WS: weakening slope (min)]
Figure 3. Group by trait biplot of ten genotype clusters
RESULTS
Mean performance of genotypes
Data recorded on the 225 bread wheat genotypes (200
landraces pure lines and 25 cultivars) across growing
seasons for the quality and rheological traits were given in
Table 1. For each trait the descriptive statistics were also
presented in Table 1. Data in Table 1 indicated that all
studied quality traits were remarkably influenced by
genotypes (Data not given). In pure lines from landraces,
the CV% of the traits varied from 6.98% (for PC) to 40.09%
(for MTxV). The lowest CV- values were observed for the
traits PC followed by MPV, MTxI and TKW indicating the
least variation among the all pure lines for these traits,
while the highest values were found for the MTxV, WS,
MPTi and ZSDS.
Table 1. Descriptive statistics of traits across growing seasons (n= 200 for pure lines, n= 25 for cultivars)
Pure Lines Selected from Turkish Bread Wheat Landraces
§
Traits
Max
Min
Mean
CV
The Best Lines
TKW
53.35
23.55
41.56
12.24
15, 41 and 44
PC
14.48
10.43
12.37
6.98
11,57 and 28
ZSDS
35.50
12.00
21.81
22.49
166, 150 and 165
MPTi
3.87
0.89
1.82
23.05
106, 103 and 22
MPV
68.29
34.43
54.13
9.12
50, 167 and 53
MTxV
42.27
6.34
23.64
40.09
180, 163 and 106
MTxI
336.44
154.36
268.66
11.20
29, 50 and 22
WS
5.45
1.13
2.20
32.73
103, 106 and 22
Cultivars
Traits
Max
Min
Mean
CV
The Best Cultivars
TKW
51.93
38.75
46.13
9.45
Konya-2002, Tekirdag, Mufitbey
PC
12.29
10.64
11.26
3.49
Kirik, Dagdas-94 and Zencirci-2000
ZSDS
30.75
19.50
22.99
10.95
Bezostaja-1, Kirik and Harmankaya-99
MPTi
3.84
1.84
2.54
17.95
Flamura-85, Harmankaya-99 and Tekirdag
MPV
62.83
48.90
56.46
5.86
Demir-2000, Seval and Mufitbey
MTxV
36.32
6.72
18.09
58.07
Bayraktar-2000, Harmankaya-99 and Flamura-85
MTxI
313.39
263.67
290.58
4.68
Demir-2000, Mufitbey and Seval
WS
5.69
1.55
3.46
34.90
Harmankaya-99, Bezostaja-1 and Seval
[
§
: The best genotypes had the highest values each trait except MTxV; TKW: Thousand grain weight (g); ZSDS: Zeleny sedimentation test volume
(ml); PC: protein content (%); MPV: midline peak value (%); MTxV: midline time x = 8 min value (%); MTxI: midline time x = 8 min integral
(Tq%*min); MPTi: midline peak time (min); WS: weakening slope (min); CV: Coeffient of variation (%)]
Regarding cultivars; the CV % of the traits varied from
3.49% (for PC) to 58.07% (for MTxV). The lowest CV
values were observed for the traits PC followed by MTxI,
MPV, and TKW indicating the least variation among the all
cultivars for these characters, while the highest CV values
were found for the MTxV, WS, MPTi and ZSDS (Table 1).

124
Relationship between traits
Biplot procedure was used to evaluation relationships
between studied traits. The GT biplots revealed the
interrelationships between traits and it was also used as
independent selection criteria based on several traits (Yan
and Kang 2003; Akcura 2011). The cosine of the angle
between two traits approximates the correlation between
them; therefore, associations between all traits can be easily
visualized from the biplot. Two traits were positively
correlated if the angle between their vectors was <90°,
negatively correlated if the angle was >90°, independent if
the angle was 90° (Yan 2014).
The genotype by trait (GT) biplot explained 67%
(Figure 2), genotype-group (GGT) biplot explained 92%
(Figure 3) of the total variation of the standardized data.
Therefore, according to these results fundamental patterns
among the traits should be captured by both genotype trait
biplot (Figure 2) and genotype group trait biplot (Figure 3).
The most prominent relations shown by genotype (group)
trait biplot were: (i) a close correlation between WS and
MPTi as indicated by the near perpendicular vectors, (ii) a
positive relation between ZSDS and MTxI, (Figure 2 and
3) and between MPV and PC in addition to between MTxV
and PC (Figure 3), (iii) a negative association between
TKW and all studied traits and between MTxV and other
traits (Figure 2) except PC (Figure 3). Although these
results usually reflected the correlation among the
measured traits (Table 2), the biplot does not explain all of
the variation in a dataset. WS exhibited significantly
positive correlation with MPTi (r=0.74**). These traits also
positively associated with ZSDS (r=0.49**), MTxI
(r=0.39**) and MPV (r=0.17**). MTxI had positive
correlations with PC (r=0.29**), ZSDS (r=0.67**), MPV
(r=0.74**) as well as WS (r=0.39**) and MPTi (r=0.47**).
MTxV shows negative correlation with MPTi and WS
(Table 2). Since the cosine of the angles does not precisely
translate into correlation coefficients, some associations
with traits couldn’t be seen in biplots. So that between
MTxV and other traits were not evaluated in biplots.
Table 2. Pearson correlation coefficient between quality traits (n=225)
Traits
PC
MPTi
MPV
MTxV
MTxI
WS
TKW
-0.50**
0.02
-0.13*
-0.11
-0.10
0.09
PC
-0.04
0.27**
0.22**
0.29**
-0.10
ZSDS
0.63**
0.47**
0.03
0.67**
0.49**
MPTi
0.16*
-0.20**
0.47**
0.74**
MPV
0.18**
0.74**
0.17**
MTxV
-0.01
-0.19**
MTxI
0.39**
[*: P<0.05; **: P<0.01; TKW: Thousand grain weight (g); ZSDS: Zeleny sedimentation test volume (ml); PC: protein content (%); MPV: midline peak
value (%); MTxV: midline time x = 8 min value (%); MTxI: midline time x = 8 min integral (Tq%*min); MPTi: midline peak time (min); WS:
weakening slope (min)]
Classification of Genotypes
Cluster analysis was done to classification of genotypes
for studied traits (Table 3). The results of classification for
bread wheat genotypes by hierarchical cluster analysis were
given in Figure 4. Clustering of the 200 bread wheat pure
lines, along with the 25 cultivars, was truncated at the ten-
group level which retained 92.0% of the genotype-by-trait
SS. Group V, the first largest group contained 45 pure lines
and a cultivar (Dogu-88), the second largest group III
contained 24 pure lines and two cultivars (Konya-2002 and
Kirgiz-95), the third largest group VI contained 25 pure
lines and a cultivar (Gelibolu) while groups II, VIII, I, IX,
X and IV the smallest groups, each consisted of 23, 21, 19,
15, 15 and 9 genotypes. As indicated in Figure 4, the
cultivars were separate into six groups (group III, V, VI,
VII, IX and X). Interestingly group IX contained only 3
pure lines and 12 check cultivars (Altay-2000, Bagci-2002,
Bezostaja-1, Demir-2000, Flamura-85, Gun-91, Pehlivan,
Kenanbey, Harmankaya-99, Seval, Sonmez-2000 and
Tosunbey) (Figure 4). For each attribute the individual
groups exhibited different ranges of distribution.
Genotypes in groups VII and VIII (including pure lines
11, 57 and 28) had the highest PC, ZSDS, MTxI and MTxV
(Table 3 and Figure 3).
The highest MPTi and WS were found for genotypes in
group IX [(including only 3 pure lines and 12 check
cultivars (Altay-2000, Bagci-2002, Bezostaja-1, Demir-
2000, Flamura-85, Gun-91, Pehlivan, Kenanbey,
Harmankaya-99, Seval, Sonmez-2000 and Tosunbey)]
(Table 3, Figure 4 and 5).

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Journal ArticleDOI
TL;DR: The new genotype × yield × trait (GYT) biplot approach was used to identify the best cultivar among 10 durum wheat cultivars based on multiple environments and multiple traits, and ‘Sariçanak’ was ranked as the best combination of physio-morphological traits with grain yield.
Abstract: The specification of the most convenient cultivars based on multiple trait indices is a new approach in durum wheat (Triticum durum Desf.) adaptation and stability studies. This approach helps to define the best cultivar based on multiple traits and multiple locations because cultivars are affected by unpredictable climatic conditions. Some traits (ears per square meter, spike length, number of grains per spike, spike yield, and leaf chlorophyll content among others) can be produced for primary breeding purposes because they are influenced by environmental factors and indirectly affect grain yield and quality. Therefore, in the present study, the new genotype × yield × trait (GYT) biplot approach was used to identify the best cultivar among 10 durum wheat cultivars based on multiple environments (8) and multiple traits (18). Cultivar ranking was examined by a superiority index that combined yield and other target traits with the GYT biplot. The general adaptability of each cultivar in terms of all the traits indicated differences based on environment means, and significant differences were found between varieties for the GYT biplot. In the GYT biplot, yield-trait combinations clearly indicated the most stable cultivars, whereas in the genotype × trait (GT) biplot, the best cultivars were not defined for all traits. ‘Sariçanak’ was ranked as the best combination of physio-morphological traits with grain yield, ‘Zühre’ was the best for more quality traits, and ‘Güneyyildizi’ was the best for both physio-morphological and quality traits in the GYT biplot. The GYT biplot combines traits with yield and can help the visual identification of the best cultivars; it is better than the GT biplot method.

34 citations


Cites methods from "Pattern analysis of turkish bread w..."

  • ...In addition, the GT biplot technique has been used for a long time by many researchers to understand the effect of genotype and environment on the relationships between agronomic, physiological and quality characters, and yield (Yan and Tinker, 2006; Kendal and Dogan, 2015; Akcura et al., 2016; Oral et al., 2018)....

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  • ...…GT biplot technique has been used for a long time by many researchers to understand the effect of genotype and environment on the relationships between agronomic, physiological and quality characters, and yield (Yan and Tinker, 2006; Kendal and Dogan, 2015; Akcura et al., 2016; Oral et al., 2018)....

    [...]

Journal ArticleDOI
14 Nov 2019-Agronomy
TL;DR: Three neglected or underutilized subspecies of hexaploid wheat could be a source of genes for quality improvement in common wheat, which would permit both their recovery as new crops and development of modern cultivars with similar quality characteristics but better agronomic traits.
Abstract: Club wheat (Triticum aestivum L. ssp. compactum (Host) Mackey), macha wheat (T. aestivum L. ssp. macha (Dekapr. & A.M. Menabde) Mackey) and Indian dwarf wheat (T. aestivum L. ssp. sphaerococcum (Percival) Mackey) are three neglected or underutilized subspecies of hexaploid wheat. These materials were and are used to elaborate modern and traditional products, and they could be useful in the revival of traditional foods. Gluten proteins are the main grain components defining end-use quality. The high molecular weight glutenin subunit compositions of 55 accessions of club wheat, 29 accessions of macha wheat, and 26 accessions of Indian dwarf wheat were analyzed using SDS-PAGE. Three alleles for the Glu-A1 locus, 15 for Glu-B1 (four not previously described), and four for Glu-D1 were detected. Their polymorphisms could be a source of genes for quality improvement in common wheat, which would permit both their recovery as new crops and development of modern cultivars with similar quality characteristics but better agronomic traits.

8 citations


Cites background from "Pattern analysis of turkish bread w..."

  • ...Further studies are necessary to determine the grain quality components and properties that led to this preference, something that has already started in that country [40]....

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Journal ArticleDOI
12 Nov 2021-Agronomy
TL;DR: In this paper, a total of 32 stable mutants (M7) were developed, followed by characterization by conducting multilocation trials over two seasons, and the mutants were grouped into three clusters based on high yield index values.
Abstract: Exploiting new genetic resources is an effective way to achieve sustainable wheat production. To this end, we exposed wheat seeds of the “Punjab-11” cultivar to gamma rays. A total of 32 stable mutants (M7) were developed, followed by characterization by conducting multilocation trials over two seasons. Principal component analysis (PCA) showed that the first six components accounted for 90.28% of the total variation among the plant height, tillers per plant, 1000-kernel weight, grain yield, and quality traits. All mutants were grouped into three clusters based on high yield index values. The genotype by trait (GT) bi-plot revealed significant associations between yield and its components among the mutants. Positive correlations were estimated for tillers per plant, plant height, 1000-kernel weight, and grain yield; however, yield components showed negative associations with protein, moisture, and gluten contents. The mutant lines Pb-M-59 waxy, Pb-M-1272 waxy, Pb-M-2260, Pb-M-1027 waxy, Pb-M-1323 waxy, and Pb-M-1854 exhibited maximum grain yield, 1000-grain weight, and tillers per plant values. Likewise, Pb-M-2725, Pb-M-2550, and Pb-M-2728 were found to be the best mutant lines in terms of grain quality; thus, the use of gamma radiation is effective in improving the desirable traits, including yield and grain quality. It is suggested that these traits can be improved beyond the performance of corresponding traits in their parent genotypes. The newly produced mutants can also be used to explore the genetic mechanisms of complex traits in the future.

8 citations

References
More filters
Book
09 Dec 2019
TL;DR: GGE Biplot Continues to Evolve Cultivar Evaluation Based on Multiple Traits Why multiple Traits?
Abstract: GENOTYPE-BY-ENVIRONMENT INTERACTION AND STABILITY ANALYSIS Genotype-by-Environment Interaction Heredity and Environment Genotype-by-Environment Interaction Implications of GEI in Crop Breeding Causes of Genotype-by-Environment Interaction Stability Analyses in Plant Breeding and Performance Trials Stability Analysis in Plant Breeding and Performance Trials Stability Concepts and Statistics Dealing with Genotype-by-Environment Interaction GGE Biplot: Genotype + GE Interaction GGE BIPLOT AND MULTI-ENVIRONMENTAL TRIAL ANALYSIS Theory of Biplot The Concept of Biplot The Inner-Product Property of a Biplot Visualizing the Biplot Relationships among Columns and among Rows Biplot Analysis of Two-Way Data Introduction to GGE Biplot The Concept of GGE and GGE Biplot The Basic Model for a GGE Biplot Methods of Singular Value Partitioning An Alternative Model for GGE Biplot Three Types of Data Transformation Generating a GGE Biplot Using Conventional Methods Biplot Analysis of Multi-Environment Trial Data Objectives of Multi-Environment Trial Data Analysis Simple Comparisons Using GGE Biplot Mega-Environment Investigation Cultivar Evaluation for a Given Mega-Environment Evaluation of Test Environments Comparison with the AMMI Biplot Interpreting Genotype-by-Environment Interaction GGE BIPLOT SOFTWARE AND APPLICATIONS TO OTHER TYPES OF TWO-WAY DATA GGE Biplot Software-The Solution for GGE Biplot Analyses The Need for GGE Biplot Software The Terminology of Entries and Testers Preparing Data File for GGE Biplot Organization of GGE Biplot Software Functions for a Genotype-by-Environment Dataset Function for a Genotype-by-Strain Dataset Application of GGE Biplot to Other Types of Two-way Data GGE Biplot Continues to Evolve Cultivar Evaluation Based on Multiple Traits Why Multiple Traits? Cultivar Evaluation Based on Multiple Traits Identifying Traits for Indirect Selection for Loaf Volume Identification of Redundant Traits Comparing Cultivars as Packages of Traits Investigation of Different Selection Strategies Systems Understanding of Crop Improvement Three-Mode Principal Component Analysis and Visualization QTL Identification Using GGE Biplot Why Biplot? Data Source and Model Grouping of Linked Markers Gene Mapping Using Biplot QTL Identification via GGE Biplot Interconnectedness among Traits and Pleiotropic Effects of a Given Locus Understanding DH Lines through the Biplot Pattern QTL and GE Interaction Biplot Analysis of Diallel Data Model for Biplot Analysis of Diallel Data General Combining Ability of Parents Specific Combining Ability of Parents Heterotic Groups The Best Testers for Assessing General Combining Ability of Parents The Best Crosses Hypothesis on the Genetic Constitution of Parents Targeting a Large Dataset Advantages and Disadvantages of the Biplot Approach Biplot Analysis of Host Genotype-by-Pathogen Strain Interactions Vertical vs. Horizontal Resistance Genotype-By-Strain Interaction for a Barley Net Blotch Genotype-by-Strain Interaction for Wheat Fusarium Head Blight Biplot Analysis to Detect Synergism between Genotypes of Different Species Genotype-by-Strain Interaction for Nitrogen-Fixation Wheat-Maize Interaction for Wheat Haploid Embryo Formation References Index

1,213 citations

Journal ArticleDOI
TL;DR: To determine relative contributions of genotype, environment, and G×E interaction to variation in quality characteristics of hard red winter wheat, 18 winter wheat genotypes were grown in replicated trials at six locations in Nebraska and one site in Arizona in 1988 and 1989.
Abstract: Improvement of end-use quality in wheat (Triticum aestivum L.) depends on thorough understanding of the influences of environment, genotype, and their interaction. Our objectives were to determine relative contributions of genotype, environment, and G×E interaction to variation in quality characteristics of hard red winter wheat. Eighteen winter wheat genotypes were grown in replicated trials at six locations in Nebraska and one site in Arizona in 1988 and 1989. Harvested grain was micromilled to produce flour samples for evaluation of protein concentration, mixing characteristics, and sodium dodecylsulfate (SDS) sedimentation(.)

265 citations


"Pattern analysis of turkish bread w..." refers background in this paper

  • ...In addition, previous studies carried out for both bread wheat cultivars and landraces generally showed a negative relationship between thousand kernel weight with protein content and sedimentation (Peterson et al. 1992; Akcura 2011)....

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Journal ArticleDOI
TL;DR: In this article, 22 different methods (parametric, nonparametric and multivariate) used for analysing genotype-environment (G×environment) interaction were compared by applying them to two sets of experimental data (15 faba bean cultivars and 11 pea cultivars) and a principal components analysis was performed on the rank correlation matrix arising from the application of each method.

254 citations

Book
01 Jan 1976

187 citations

Journal ArticleDOI
TL;DR: Relations have also measured and selected for certain grain physiships among agronomic traits and grain composition as influenced by cal traits such as test weight, kernel weight, and groat genotype and environment.
Abstract: age improves both milling yield and energy density for livestock. Genotypeandenvironmentaremajordeterminantsofplantpheno- Improvement of agronomic traits has been the pritype. Economically important quantitative traits include agronomic mary objective of oat breeders for many years. Breeders characteristics and grain composition. This study examined relationhave also measured and selected for certain grain physiships among agronomic traits and grain composition as influenced by cal traits such as test weight, kernel weight, and groat genotype and environment. Thirty-three oat (Avena sativa L.) geno

172 citations

Frequently Asked Questions (1)
Q1. What have the authors contributed in "Pattern analysis of turkish bread wheat landraces and cultivars for grain and flour quality" ?

This research was carried out to characterize both 200 pure lines selected from Turkish bread wheat landraces and 25 Turkish bread wheat cultivars based on 3 grain quality traits [ thousand kernel weight ( TKW ), protein content ( PC ), Zeleny sedimentation test ( ZSDS ) ] and 5 mixograph parameters. Both cluster and ordination analyses suggested that there were ten groups of studied genotypes. Also, their results were very contributive in selection of precious pure lines for further