Evaluation of grain yield, quality characteristics and high molecular weight glutenin subunits of bread wheat cultivars in different agro-ecological regions of Türkiye

doi:10.21203/rs.3.rs-4808752/v1

Download PDF

Research Article

Evaluation of grain yield, quality characteristics and high molecular weight glutenin subunits of bread wheat cultivars in different agro-ecological regions of Türkiye

https://doi.org/10.21203/rs.3.rs-4808752/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

The current study aimed to determine and compare the grain yield, some quality characteristics and high-molecular-weight glutenin subunits (HMW-GS) of bread wheat cultivars cultivated in several agro-ecological regions using a total of 46 registered bread wheat cultivars in the 2016–2017 and 2017–2018 growing seasons. The results determined that the environment had the largest share in the total variation (Genotype + Environment + Genotype×Environment Interaction). The Mediterranean region had the highest average grain yield with 8137 kg ha^− 1, while the Central Anatolia region (under the rainfed conditions) had the lowest average with 4260 kg ha^− 1. The average thousand kernel weight of the cultivars was 35.3–39.9 g, test weight 77.2–79.2 kg hL^− 1, protein content 13.4–14.7%, Zeleny sedimentation 39.2–53.3 mL, and alveograph energy value varied between 191.2-276.4 10^− 4 J. Regarding the HMW-GS, 18 of the 46 cultivars scored 10, and one scored 5. The highest mean protein content and alveograph energy value was determined in cultivars with 9 Glu-1 score. In Zeleny sedimentation, cultivars with 10 Glu-1 score showed the highest mean value. The most common subunits in loci; it is 2* in Glu-A1, 7 + 8 and 7 + 9 in Glu-B1, and 5 + 10 in Glu-D1. The fact that registered cultivars predominantly carry these subunits at Glu-1 loci, which could be the result of yield and quality-oriented selection in the breeding process. It was concluded that high quality new varieties could be developed by HMW-GS oriented crosses and selections in wheat breeding programs.

Triticum aestivum L.

Genotype

Environment

Glu-A1

Glu-B1

Glu-D1

Wheat is one of the most important nutrients in the world and improving its yield and quality characteristics is the target of breeding programs (Kumar et al. 2019; Sirat 2023). Precipitation during the growing season is an important factor in wheat production (He et al. 2016). Changes in temperature and uncertainty in precipitation patterns have an impact on yield in addition to the impact of diverse in an agroecological regions (Gul et al. 2020). Türkiye has 9 agro-ecological regions with different ecological characteristics, plant breeding research and Value for Cultivation and Use (VCU) trials in the registration system are carried out on regional level (Aktas 2019). It is desired that the candidate varieties should be registered as per Distinctness, Uniformity & Stability (DUS) and VCU trials carried out in two growing seasons. Even though VCU trials are conducted regionally, deviations in climatic data have an impact on whether candidate varieties are registered or not. Genotypes that adapt to the ecological conditions of the regions are widely grown by farmers. Annual precipitation and seasonal distribution of precipitation are the most important factors in wheat production in Türkiye. The analysis of climatic data for 2019 and 2020 depicted that even if wheat cultivation was done in equivalent areas, the yield differed by 1.5 million tons in the latter year (FAOSTAT 2022). Türkiye's agro-ecological diversity serves as insurance in years when drought and other stress conditions arise. While there is a decrease in production in one region, increases in production can be seen in other regions and the effect of adverse conditions on total production remains limited.

The yield performance of a genotype is the result of the interaction between the genotypes, environments, and environmental factors play an important role in the yield and quality performance of the genotype (Akcura and Kaya 2008). Therefore, increasing the yield potential of genotypes in different ecologies and variable climatic conditions is an important objective of wheat breeding programs in Türkiye. in addition to ecological conditions, many other factors such as crop rotation, tillage, fertilization, variety, biotic and abiotic factors affect yield potential and grain yield in wheat varieties (Pepo and Kovacevic 2011) and tested in different environments, recommending them for different regions (Yasar et al. 2020) depending on the techniques compatible with ecologies and genotypes. Some farmers sow wheat during freezing cold in late winter to overcome the challenges of ecology to obtain optimum yield and quality.

Nehe et al. (2019) emphasize that genotype and environment are effective on quality traits, but the genotypic effect is more important on summer bread wheat genotypes. The main factor in the grain yield and quality characteristics of bread wheat is environment, followed by the genotype, and the effect of the Genotype×Environment interaction is less (Kaya and Akcura 2014). The effects of monthly precipitation distributions and total precipitation amount on quality parameters as well as grain yield in bread wheat are very important (Olgun et al. 2021). High molecular weight glutenin subunits have an impact on dough quality in bread making (Anjum et al. 2007) and may be beneficial to plant breeders to improve wheat quality (Tohver 2007). There are many studies on the effects of environmental factors and genotype on quality characteristics.

Gluten and gliadin constitute a large part of the storage proteins in wheat. High molecular weight glutenin subunits have an impact on dough quality in bread making (Anjum et al. 2007) and may be beneficial to plant breeders to improve wheat quality (Tohver 2007). Payne et al. (1987) established quality scores according to HMW-GS to predict bread making quality. 5 + 10 encoded by Glu-D1 have the highest quality scores, 1 and 2* encoded by Glu-A1, 17 + 18 and 7 + 8 HMW-GS encoded by Glu-B1 have high quality scores (Payne et al. 1987; Tohver 2007). Sonmez (2022) stated that with a better understanding of the molecular basis of differences in bread making quality, genotypes with desired characteristics can be reached faster in plant breeding and alleles in Glu-A1, Glu-B1 and Glu-D1 loci are important in quality-oriented studies. Dvoracek et al. (2013) reported that electrophoretic evaluation of protein alleles may be suitable for use in the maintenance of varieties. HMW-GS are used as a supporting element in variety identification and in determining the off-type content of candidate varieties. The highest quality scores, 1 and 2* encoded by Glu-A1, 17 + 18 and 7 + 8 HMW-GS and by Glu-B1 are the highest quality scores to measure HMW-GS and predict and understand bread making quality (Payne et al. 1987; Tohver 2007). Sonmez (2022) of genotypes in quality-oriented studies. Similarly, Dvoracek et al. (2013) reported that electrophoretic evaluation of protein alleles may be suitable for use in the maintenance of varieties.

In this study, yield, quality characteristics, and HMW-GS of the widely cultivated varieties were determined. There are studies conducted at different growing seasons and at a limited number of locations. Trials were conducted as many locations as possible in each region during the two growing seasons, and the grain yield and quality characteristics of the genotypes were evaluated. Comparisons were also made between regions. Genotypes were characterized by determining HMW-GS and evaluated together with quality parameters.

Study Regions and Locations

Therefore, the trials were carried out at 4 locations in the Thrace region, 7 locations in Aegean & Southern Marmara region, 5 locations in Mediterranean region, 7 locations in Southeastern Anatolia, 5 locations in the Central Anatolia region under the rainfed conditions, and 6 locations in the Central Anatolia region under the irrigated conditions during 2016-18. The regions where the experiments were conducted are shown in Fig. 1, and the experiment locations within each region are mentioned in Table 1.

Table 1

Experimental locations by region
The Mediterranean Region	The Aegean&Southern Marmara Region	The Southeastern Anatolia Region	The Central Anatolia Region		The Thrace Region
The Mediterranean Region	The Aegean&Southern Marmara Region	The Southeastern Anatolia Region	Rainfed conditions	Irrigated conditions	The Thrace Region
Adana	Adapazari	Adiyaman	Eskisehir	Eskisehir	Edirne
Antalya	Bandirma	Diyarbakir	Gozlu	Gozlu	Kesan
Ceyhan	Dalaman	Kiziltepe	Konya	Karapinar	Luleburgaz
Kahramanmaras	Denizli	Kurtalan	Malya	Konya	Tekirdag
Hatay	Karacabey	Sanliurfa	Ankara	Polatli
	Menemen	Gundas		Ankara
	Pamukova	Serince

Climatic data

In Fig. 2, precipitation amounts on the basis of regions for the years of the study are given (TSMS 2018; TSMS 2019). In 2018, precipitation was above the long-term average in all regions. In 2017, precipitation was below the long-term average in all regions except the Marmara region. Especially in the Southeastern Anatolia region, a precipitation amount that is well below the normal has been measured.

Experimental materials

Experimental materials were obtained from breeder or variety owner organizations in Türkiye. Cultivars in experiments by region are given in Table 2. Winter seasonal type cultivars are cultivated in Central Anatolia and Thrace regions, and spring seasonal type cultivars are cultivated in Aegean & Southern Marmara, Mediterranean and Southeastern Anatolia regions. Wheat cultivation in the Central Anatolian region and the Southeastern Anatolian region; It is done in two ways, under the rainfed conditions and in the irrigated conditions. In other regions, wheat is grown under the rainfed conditions unless there is a necessity. Since Ceyhan 99 and Sagittario cultivars are widely grown in Mediterranean, Aegean & Southern Marmara and Southeastern Anatolia regions, they were used in trials in these three regions. Esperia was used in Central Anatolian region and Thrace region trials. Since the Tosunbey is grown in the Central Anatolian region both under the rainfed conditions and in the irrigated conditions, it was included in both trial sets. Pehlivan, which is a cultivar of the Thrace region, was used in the trials due to the fact that it is also grown in the Southeastern Anatolia region, although it is in the winter seasonal type.

Table 2

Cultivars used in the study by region
The Mediterranean Region¹	The Aegean&Southern Marmara Region¹	The Southeastern Anatolia Region²	The Central Anatolia Region¹	The Central Anatolia Region²	The Thrace Region¹
Osmaniyem	Hanli	Ceyhan 99	Bezostaja 1	Esperia	Gelibolu
Ceyhan 99	Ziyabey 98	Cemre	Bayraktar 2000	Sultan 95	Pehlivan
Sagittario	Basri Bey 95	Sagittario	Tosunbey	Ahmetaga	Krasunia odes’ka
Pandas	Gonen 98	Pehlivan	Sonmez 2001	Tosunbey	Esperia
Karatopak	Sagittario	Adonis	Demirhan	Konya 2002	Flamura 85
Tekfen 1016	Beskopru	Arin	Ayten Abla	Tekfen 2038	Rumeli
Beyazhan	Ceyhan 99	Polathan	Cavus	Yavuz	Aleppo
	Mirsa			Tugra	Pandiya
				Pecenek	Waximum
				Karakalpak	Anafarta
				Baskurt	Abide
				Bilden
				Kipcak
¹ under the rainfed conditions; ² in the irrigated conditions

Experimental design and applications

Experiments were conducted in randomized blocks design with 4 replications. Sowing, fertilization, irrigation, weed control, harvesting and quality analyzes were carried out according to the technical protocol for VCU of bread wheat (TPVCU 2009). SDS-PAGE method was used to determine the band patterns of high molecular weight gluten (UPOV 2017). Quality scores of HMW-GS of cultivars were calculated according to Payne et al. (1987).

Statistical analysis

The data were analyzed in the analysis of variance (ANOVA) according to the randomized blocks experimental design with the SAS program (SAS Institute 1998). While the F test was used to determine the statistical significance levels, the Least Significant Difference (LSD) test was used for the difference grouping of the means. In order to determine the stability of the cultivars and their vectorial positions according to average environment coordinate (AEC), the GGE-biplot method was used and graphics were created with the GenStat package program (GenStat 2009).

The Mediterranean Region

The results from the trials conducted in the Mediterranean Region are shown in Table 3. The grain color of the cultivars in the trials were 3 red and 4 white, and Beyazhan had the highest grain yield performance with 9121 kg ha^− 1. Beyazhan was followed by Tekfen 1016 and the average of all genotypes was 8137 kg ha^− 1. According to the results of variance analysis; 80.4% of the total variation consisting of G, E, and GEI interaction comes from E. G and GEI have equal shares of 9.8% in variation. In the GGE-biplot analysis, 89.23% of the variation was explained and their vectorial appearances according to AEC are shown in Fig. 3. Although the Ceyhan 99 is located above the AEC apsis, it has a negative PC1 value. Beyazhan and Tekfen 1016 cultivars with the highest grain yield performance have similar vectors to AEC. Osmaniyem, Karatopak and Pandas are the cultivars with the longest vectorial position to the AEC apsis.

Table 3

Grain yield, quality parameters, HMW-GS and quality scores of 7 bread wheat cultivars under the rainfed conditions of the Mediterranean region
Cultivar	GC	GY (kg ha^− 1)	TKW (g)	TW (kg hL^− 1)	PC (%)	ZS (mL)	AEV (10^− 4 J)	Glu-A1	Glu-B1	Glu-D1	Glu-1 score
Osmaniyem	R	8167^c	40.6^a	81.0^a	14.3^ab	33.5^d	224.7	1	7 + 9	5 + 10	6
Ceyhan 99	W	7992^c	36.2^cd	78.1^c	13.2^c	47.8^bc	269.0	1	17 + 18	5 + 10	10
Sagittario	R	7982^c	38.1^bc	77.6^cd	14.3^ab	53.7^ab	296.3	1	7 + 9	2 + 12	7
Pandas	R	7128^d	39.2^ab	76.7^d	14.4^ab	50.0^b	291.7	2*	7 + 9	5 + 10	9
Karatopak	W	8050^c	34.3^d	79.5^b	14.5^a	58.7^a	282.8	1	7 + 8	5 + 10	10
Tekfen 1016	W	8518^b	35.4^d	78.8^bc	14.5^a	53.7^ab	297.5	1	17 + 18	2 + 12	8
Beyazhan	W	9121^a	39.6^ab	78.8^bc	14.0^b	42.5^c	273.0	2*	7 + 9	5 + 10	9
Mean F CV (%)		8137 ** 8.4	37.6 ** 5.1	78.6 ** 1.4	14.2 ** 2.6	48.6 ** 10.8	276.4 ns 17.8
**, significant at p < 0.01; ns, not-significant; R, red; W, white; GC, grain color; GY, grain yield; TKW, thousand kernel weight; TW, test weight; PC, protein content; ZS, Zeleny sedimentation; AEV, alveograph energy value

Osmaniyem is the most prominent cultivar with a thousand kernel weight and test weight. In terms of protein content, except for the Ceyhan 99, the other cultivars had values in the range of 14.0-14.5%. Karatopak, Tekfen 1016 and Sagittario showed the highest values in Zeleny sedimentation. There was no statistical difference between cultivars in alveograph energy value. In terms of HMW-GS, subunit 1 in five cultivars and subunit 2* in two cultivars were determined in the Glu-A1 locus. Predominantly genotypes have subunit 7 + 9 in Glu-B1 and subunit 5 + 10 in Glu-D1. Genotypes have values between 6–10 in terms of quality scores given according to HMW-GS. Since Osmaniyem has the 1B/1R rye translocation, 3 points were removed from the total quality score calculation (Nehe et al. 2019). Osmaniyem has a value of 33.5 mL in Zeleny sedimentation, well below the other cultivars.

The Aegean&Southern Marmara Region

Of the 8 cultivars in the Aegean&Southern Marmara Region trials, 5 had white and 3 had red grain color, and Ziyabey 98 cultivar ranks first with a grain yield of 7192 kg ha^− 1 (Table 4). E 90.4%, G 1.5% and GEI 8.1% are effective in total variation (G + E + GEI). 65.50% of the variation was explained by GGE-biplot analysis (Fig. 4). Ziyabey 98 has the highest grain yield performance, as well as a stable cultivar with its location close to the AEC apsis. Especially Hanli and Gonen 98 cultivars have very long vectorial lengths with AEC, which shows their low stability.

Mirsa with a thousand kernel weight and test weight was the genotype with the highest values. In protein content, all cultivars were collected in 2 statistical groups and showed a narrow range of distribution. Although Sagittario and Beskopru had higher values above 50 mL in Zeleny sedimentation, Basri Bey 95 was the genotype showing the lowest sedimentation value in the trials. Mirsa cultivar, which has the highest values in terms of physical quality characteristics (thousand kernel weight and test weight), ranked first in alveograph energy value, and Ziyabey 98, which showed the highest value in grain yield, showed the lowest alveograph energy value. Subunit 1 in four genotypes and subunit 2* in other four genotypes were determined in the Glu-A1 locus, and the majority of genotypes have subunit 5 + 10 in the Glu-D1 locus. At the Glu-B1 locus, cultivars exhibited 7, 7 + 8, 7 + 9 and 17 + 18 band patterns. Genotypes had values between 7 and 10 in Glu-1 quality scores.

Table 4

Grain yield, quality parameters, HMW-GS and quality scores of 8 bread wheat cultivars under the rainfed conditions of the Aegean & Southern Marmara region
Cultivar	GC	GY (kg ha^− 1)	TKW (g)	TW (kg hL^− 1)	PC (%)	ZS (mL)	AEV (10^− 4 J)	Glu-A1	Glu-B1	Glu-D1	Glu-1 score
Hanli	R	6411^de	37.2^bc	78.9^bc	13.4	44.2^bc	206.6^ab	2*	7 + 8	5 + 10	10
Ziyabey 98	W	7192^a	38.6^abc	77.3^d	13.2	40.0^c	120.8^c	2*	7	5 + 10	8
Basri Bey 95	W	6683^bc	34.2^d	78.2^cd	13.2	30.6^d	162.8^bc	2*	7 + 9	5 + 10	9
Gonen 98	W	6335^e	36.7^cd	78.3^bcd	13.5	46.0^bc	185.2^ab	2*	17 + 18	2 + 12	8
Sagittario	R	6652^bcd	40.1^ab	77.2^d	13.9	58.2^a	225.0^a	1	7 + 9	2 + 12	7
Beskopru	R	6453^cde	39.9^ab	79.9^b	13.6	54.8^ab	185.0^ab	1	7 + 9	5 + 10	9
Ceyhan 99	W	6753^b	39.8^ab	79.0^bc	13.0	46.2^bc	205.0^ab	1	17 + 18	5 + 10	10
Mirsa	W	6801^b	41.2^a	81.8^a	13.5	47.0^bc	239.4^a	1	17 + 18	5 + 10	10
Mean F CV (%)		6660 ** 9.7	38.5 ** 5.7	78.8 ** 1.6	13.4 ns 4.6	45.9 ** 18.2	191.2 * 24.9
, Significant at p < 0.05; *, significant at p < 0.01; ns, not-significant

The Southeastern Anatolia Region

In the experiments carried out in irrigated conditions in the Southeastern Anatolia Region, four genotypes had white and three genotypes had red grain color (Table 5). In grain yield, Polathan is in the first place with 7026 kg ha^− 1 and Pehlivan is in the last place with 5693 kg ha^− 1. Pehlivan was well below the other genotypes with this low grain yield value. The share of E in the total variation (G + E + GEI) was 67.70% and the GEI showed the highest value among all regions with 22.20%. In the GGE-biplot analysis, 67.89% of the variation was explained, of which 45.83% was distributed in PC1 and 22.06% in PC2 (Fig. 5). The cultivar with the highest PC1 score is Polathan, and Adonis is located very close to the AEC apsis.

Table 5

Grain yield, quality parameters, HMW-GS and quality scores of 7 bread wheat cultivars in the irrigated conditions of the Southeastern Anatolia region
Cultivar	GC	GY (kg ha^− 1)	TKW (g)	TW (kg hL^− 1)	PC (%)	ZS (mL)	AEV (10^− 4 J)	Glu A1	Glu B1	Glu D1	Glu-1 score
Ceyhan 99	W	6725^b	37.3^b	79.2^b	13.9^c	43.2	320.8^a	1	17 + 18	5 + 10	10
Cemre	W	6633^b	42.8^a	78.6^c	14.9^b	38.0	262.4^bc	2*	17 + 18	2 + 12	8
Sagittario	R	6694^b	39.5^ab	78.7^b	15.0^b	38.2	287.0^ab	1	7 + 9	2 + 12	7
Pehlivan	R	5693^c	42.8^a	79.5^b	13.8^c	35.2	226.0^c	2*	7 + 9	2 + 12	7
Adonis	W	6649^b	39.7^ab	78.7^b	16.0^a	41.0	245.6^bc	2*	7 + 9	5 + 10	9
Arin	R	6446^b	35.1^c	80.8^a	14.9^b	37.0	214.2^c	2*	7 + 9	2 + 12	7
Polathan	W	7026^a	42.3^a	79.0^b	14.6^bc	41.6	270.6^abc	1	7 + 9	5 + 10	9
Mean F CV (%)		6552 ** 10.9	39.9 ** 6.6	79.2 ** 1.0	14.7 ** 4.8	39.2 ns 15.0	260.9 * 16.9
, Significant at p < 0.05; *, significant at p < 0.01; ns, not-significant

Cemre, Pehlivan and Polathan are the cultivars with the highest values at a thousand kernel weight. While Arin cultivar had the lowest value (35.1 g) in thousand grain weight, it was the genotype with the highest value (80.8 kg hL^− 1) in test weight. Protein content of Adonis is well above other genotypes with a value of 16.0%. Ceyhan 99 and Pehlivan are the two genotypes with protein content below 14.0%. In terms of Zeleny sedimentation, the average of the cultivars was 39.2 mL, and no statistical significance was found between the genotypes. Ceyhan 99, which also took part in the trials in the Mediterranean and Aegean & Southern Marmara Regions, showed the highest alveograph energy value in this region with a value of 320.8 10^− 4 J. The cultivars have a Glu-1 quality score between 7 and 10 according to the band pattern they have.

The Central Anatolia Region

The average grain yield of the genotypes was 4260 kg ha^− 1 in the trials in which 7 cultivars were included under the rainfed conditions of the Central Anatolian Region (Table 6). Ayten Abla and Bayraktar 2000 cultivars showed the highest grain yield performance. In the analysis of variance, E was the factor with the highest effect (76.8%) on total variation, then G was the second most influential factor (14.2%). GGE-biplot analysis explained 82.0% of the variation, of which 63.81% was distributed in PC1 and 18.18% in PC2 (Fig. 6a). In general, genotypes have long vector lengths with AEC. Although the vector lengths of Ayten Abla and Bayraktar 2000 cultivars, which show high performance in grain yield, are similar, Ayten Abla has a negative PC2 value.

Sonmez 2001, Bezostaja 1 and Bayraktar 2000 showed high values in thousand kernel weight and were in the same statistical group. Statistical significance was not determined between genotypes in test weight. In terms of protein content, genotypes were distributed in two statistical groups. Cavus, Bezostaja 1 and Demirhan showed protein content over 15%. Large differences were determined between genotypes in Zeleny sedimentation and alveograph energy value. Cultivar Cavus showed very high average values in terms of both parameters. While Cavus showed very high average values in terms of both parameters, Sonmez 2001 and Bayraktar 2000 were the genotypes with the lowest values. The cultivars had values between 5–10 in terms of Glu-1 quality scores. Ayten Abla cultivar had 5 Glu-1 quality score with n, 7 + 9 and 2 + 12 band patterns.

In the experiments carried out with 13 genotypes in irrigated conditions in the Central Anatolia Region, two of the genotypes were white and the others had red grain color (Table 7). Genotypes showed grain yield averages between 6355–7485 kg ha^− 1. The share of E in the variation regarding grain yield was around 71.5%, and the effect of GEI was relatively high (20.0%) in this region, as in the Southeastern Anatolia Region results. GGE-biplot analysis explained 64.9% of the variation (Fig. 6b). Tugra, Tosunbey and Ahmetaga positions close to the apsis of the AEC or their short vector lengths indicate high stability. Although Bilden and Yavuz have relatively longer vectors, they have positive PC1 scores.

While Konya 2002 had the highest values in thousand kernel weight and test weight, Sultan 95 showed the lowest average values. No statistical significance was found between genotypes in protein content. Pecenek, Ahmetaga, Tekfen 2038 and Esperia are cultivars with values above 50 mL in terms of Zeleny sedimentation. Esperia and Tekfen 2038 have the highest values in alveograph energy value, while Bilden is the last cultivar with 135.8 10^− 4 Joule. Genotypes had Glu-1 quality scores between 6 and 10, and 7 cultivars scored 10. The Glu-A1 locus of Pecenek cultivar had no band and was scored with 6. Esperia, Ahmetaga, Tosunbey, Tekfen 2038, Yavuz, Baskurt and Kipcak were the genotypes with a quality score of 10.

Table 6

Grain yield, quality parameters, HMW-GS and quality scores of 7 bread wheat cultivars under the rainfed conditions of the Central Anatolia region
Cultivar	GC	GY (kg ha^− 1)	TKW (g)	TW (kg hL^− 1)	PC (%)	ZS (mL)	AEV (10^− 4 J)	Glu A1	Glu B1	Glu D1	Glu-1 score
Bezostaja 1	R	3654^e	39.2^a	77.3	15.3^a	46.5^b	257.8^bc	2*	7 + 9	5 + 10	9
Bayraktar 2000	W	4768^ab	38.3^a	77.2	13.6^b	34.5^cd	161.0^d	2*	7 + 8	5 + 10	10
Tosunbey	W	4155^c	33.7^b	77.0	14.6^ab	42.3^bc	297.3^b	1	17 + 18	5 + 10	10
Sonmez 2001	R	4610^b	39.9^a	77.5	13.6^b	32.5^d	188.8^cd	1	7	2 + 12	6
Demirhan	R	3761^d	33.5^b	77.6	15.2^a	46.3^bc	253.0^bc	1	7 + 8	5 + 10	10
Ayten Abla	R	4951^a	32.6^b	77.9	14.1^ab	40.8^bcd	219.0^bcd	null	7 + 9	2 + 12	5
Cavus	R	3924^cd	34.6^b	77.8	15.4^a	59.8^a	433.0^a	2*	7 + 9	5 + 10	9
Mean F CV (%)		4260 ** 16.1	36.0 ** 5.4	77.5 ns 1.0	14.5 * 6.6	43.2 ** 18.5	258.6 ** 20.7
, Significant at p < 0.05; *, significant at p < 0.01; ns, not-significant

Table 7

Grain yield, quality parameters, HMW-GS and quality scores of 13 bread wheat cultivars in the irrigated conditions of the Central Anatolia region
Cultivar	GC	GY (kg ha^− 1)	TKW (g)	TW (kg hL^− 1)	PC (%)	ZS (mL)	AEV (10^− 4 J)	Glu A1	Glu B1	Glu D1	Glu-1 score
Esperia	R	6494^fg	34.3^d − g	76.8^bcd	14.5	53.0^ab	279.0^a	2*	7 + 8	5 + 10	10
Sultan 95	W	6355^g	30.1^h	73.7^e	14.3	41.2^cde	154.2^de	2*	7	5 + 10	8
Ahmetaga	R	7243^abc	31.6^fgh	77.1^a − d	14.1	54.8^a	228.2^abc	1	7 + 8	5 + 10	10
Tosunbey	W	7299^ab	34.2^d − g	78.1^abc	14.2	42.6^cde	232.0^abc	1	17 + 18	5 + 10	10
Konya 2002	R	6786^d − g	41.6^a	79.2^a	13.5	39.4^cde	189.4^cde	2*	7 + 9	2 + 12	7
Tekfen 2038	R	6839^c − f	34.9^c − f	77.4^abc	13.7	53.2^ab	275.4^ab	2*	7 + 8	5 + 10	10
Yavuz	R	7188^a − d	36.5^cde	77.6^abc	13.2	36.6^def	208.4^bcd	2*	7 + 8	5 + 10	10
Tugra	R	7485^a	34.9^c − f	78.2^ab	13.7	38.0^c − f	202.8^cde	1	7	5 + 10	8
Pecenek	R	6675^efg	31.2^gh	75.8^cde	14.1	55.4^a	218.2^a − d	null	17 + 18	2 + 12	6
Karakalpak	R	6802^def	37.3^bcd	74.8^de	13.7	30.8^f	168.4^cde	1	7 + 9	2 + 12	7
Baskurt	R	7020^b − e	40.3^ab	77.8^abc	13.7	45.8^bc	191.8^cde	1	7 + 8	5 + 10	10
Bilden	R	7427^ab	38.2^bc	78.7^ab	13.8	36.2^ef	135.8^e	2*	7 + 9	5 + 10	9
Kipcak	R	7049^a − e	33.3^e − h	78.6^ab	13.0	44.4^cd	211.6^a − d	2*	7 + 8	5 + 10	10
Mean F CV (%)		6974 ** 12.8	35.3 ** 7.6	77.2 ** 2.3	13.8 ns 6.7	44.0 ** 14.1	207.3 ** 26.0
**, significant at P < 0.01; ns, not-significant

The Thrace Region

In the trials with 11 cultivars in Thrace Region, Aleppo cultivar has white grain color and other cultivars have red grain color (Table 8). Aleppo (7544 kg ha^− 1) and Pandiya (7308 kg ha^− 1) were the best performing cultivars in grain yield. Waximum, a waxy cultivar, was in the last place with a grain yield of 6167 kg ha^− 1. As in all other regions, the effect of E has the largest share (80.9%) in the total variation. GEI's share was 13.8%, while G's influence remained at 5.3%. By GGE-biplot analysis, 79.69% of the variation was explained, of which 65.44% is represented in PC1 (Fig. 7). While Pehlivan and Waximum have low grain yield performances, they do not have a stable appearance as the cultivars that move the most away from the AEC apsis. Although Aleppo has the highest grain yield performance, its binding to AEC with a long vector indicates a negative situation in terms of stability. Pandiya, which is the cultivar with the highest performance after Aleppo, shows that it is a stable cultivar with its close location to AEC.

While Pehlivan had the highest value in terms of thousand kernel weight, Aleppo and Waximum were the genotypes with the lowest values under 30 g. While Rumeli and Flamura 85 showed average values over 79 kg hL^− 1 in test weight, Waximum was the genotype showing the lowest value with 72.2 kg hL^− 1. Rumeli had the highest performance with 15.7% protein content, followed by Esperia cultivar with 15.0%. Rumeli, which had the highest values in test weight and protein content, differed from other cultivars in terms of Zeleny sedimentation and alveograph energy value and showed very high values. In Glu-1 quality scores, genotypes had values between 6–10, six genotypes had a score of 10 and two genotypes had a score of 6.

Table 8

Grain yield, quality parameters, HMW-GS and quality scores of 11 bread wheat cultivars under the rainfed conditions of the Thrace region
Cultivar	GC	GY (kg ha^− 1)	TKW (g)	TW (kg hL^− 1)	PC (%)	ZS (mL)	AEV (10^− 4 J)	Glu A1	Glu B1	Glu D1	Glu-1 score
Gelibolu	R	7069^bcd	38.2^bc	78.5^a	13.6^ef	49.4^de	189.3^de	2*	7 + 8	2 + 12	8
Pehlivan	R	6390^f	42.9^a	78.4^ab	14.3^b − e	40.8^f	188.0^de	2*	7 + 9	2 + 12	7
Krasunia odes’ka	R	6930^cde	36.9^bcd	77.3^abc	13.2^f	57.0^bc	236.6^bcd	2*	7 + 8	5 + 10	10
Esperia	R	6916^cde	32.7^e	76.2^cd	15.0^ab	62.8^ab	270.2^ab	2*	7 + 8	5 + 10	10
Flamura 85	R	6788^e	39.0^b	79.2^a	14.6^bc	52.2^cd	212.2^cde	2*	7 + 8	5 + 10	10
Rumeli	R	7147^bc	35.5^cde	79.3^a	15.7^a	66.6^a	307.0^a	2*	7 + 8	5 + 10	10
Aleppo	W	7544^a	26.3^f	77.8^abc	14.5^bcd	53.0^cd	258.8^abc	null	7 + 8	2 + 12	6
Pandiya	R	7308^ab	35.3^de	76.4^bcd	14.3^b − e	58.8^bc	206.6^cde	2*	7 + 8	5 + 10	10
Waximum	R	6167^f	29.0^f	72.2^e	13.9^c − f	57.4^bc	240.8^bcd	1	17 + 18	2 + 12	8
Anafarta	R	7135^bc	36.9^bcd	75.3^d	13.8^def	44.4^ef	184.8^de	null	6 + 8	5 + 10	6
Abide	R	6862^de	36.8^bcd	78.5^a	13.7^def	43.6^ef	176.0^e	2*	7 + 8	5 + 10	10
Mean F CV (%)		6932 ** 7.4	35.4 ** 6.3	77.2 ** 2.1	14.2 ** 4.4	53.3 ** 10.1	224.6 ** 20.0
**, significant at p < 0.01; ns, not-significant

Grain yield and quality parameters on the basis of regions

Averages and standard deviations of grain yield and quality parameters are given in Table 9. The Mediterranean Region ranked first in grain yield with 8137 kg ha^− 1. Central Anatolia Region (under the rainfed conditions) showed the lowest grain yield performance with 4260 kg ha^− 1. The highest standard deviation values were determined in Mediterranean and Central Anatolian dry conditions. Other regions showed average grain yield values between 6552–6974 kg ha^− 1. Southeastern Anatolia Region had the highest average values in thousand kernel weight and test weight. Aegean&Southern Marmara and Central Anatolian Region (irrigated conditions) had an average protein content of less than 14%, while other regions showed values above this. The overall average in Zeleny sedimentation was 45.7 mL, and Thrace, Mediterranean and Aegean&Southern Marmara Regions had values above the average. Mediterranean Region showed the highest average in alveograph energy value, followed by Southeastern Anatolia (irrigated conditions) and Central Anatolia Regions (rainfed conditions). The Aegean&Southern Marmara Region was the only region with an alveograph energy value below 200 10^− 4 J.

Table 9

Grain yield and quality characteristics' average values and standard deviations by regions
Region	GY	TKW	TW	PC	ZS	AEV
Mediterranean¹	8137 (604)^*	37.6 (2.4)	78.6 (1.4)	14.2 (0.5)	48.6 (8.4)	276.4 (25.3)
Aegean & Southern Marmara¹	6660 (273)	38.5 (2.3)	78.8 (1.5)	13.4 (0.3)	45.9 (8.5)	191.2 (37.3)
Southeastern Anatolia²	6552 (416)	39.9 (3.0)	79.2 (0.8)	14.7 (0.7)	39.2 (2.8)	260.9 (36.5)
Central Anatolia¹	4260 (516)	36.0 (3.0)	77.5 (0.3)	14.5 (0.8)	43.2 (9.1)	258.6 (89.4)
Central Anatolia²	6974 (352)	35.3 (3.4)	77.2 (1.6)	13.8 (0.4)	44.0 (8.0)	207.3 (41.8)
Thrace¹	6932 (391)	35.4 (4.6)	77.2 (2.1)	14.2 (0.7)	53.3 (8.2)	224.6 (41.8)
Mean	6586	37.1	78.1	14.1	45.7	236.5
¹, under the rainfed conditions; ², in the irrigated conditions; ^*, data in brackets are standard deviation

Grain yield and quality parameters in terms of Glu-1 scores

The average values of the traits examined according to the Glu-1 quality scores calculated on the basis of the HMW-GS are given in Table 10. Of the 46 genotypes used in this study, 18 cultivars had a Glu-1 quality score of 10, while only one cultivar had a Glu-1 quality score of 5. When the test weight and protein content graphs are examined in Fig. 8, it can be said that the slope line is close to horizontal and does not show an increasing trend according to Glu-1 scores. It is observed that there is an increasing trend in other parameters.

Table 10

Average values of grain yield and quality traits of *Glu-1* scores
Glu-1 score	F	GY (kg ha^− 1)	TKW (g)	TW (kg hL^− 1)	PC (%)	ZS (mL)	AEV (10^− 4 J)
5	1	4951	32.6	77.9	14.1	40.8	219.0
6	5	6826	35.0	77.5	14.1	43.8	215.1
7	5	6681	39.7	78.3	14.2	41.7	224.3
8	8	6969	35.7	77.0	14.0	45.5	206.6
9	9	6452	38.5	78.3	14.5	44.8	250.6
10	18	6659	36.0	78.1	14.0	49.2	239.5
F, Frequency

The loci and allele numbers observed in 46 cultivars according to HMW-GS are shown in Table 11. While subunit 2* was observed the most in Glu-A1 locus (26 cultivars), subunit 1 was determined in 16 genotypes. Null allele was observed in Glu-A1 locus in 4 genotypes. The most observed alleles at the Glu-B1 locus are subunit 7 + 8 and 7 + 9. Subunit 17 + 18 was determined in 8 genotypes, subunit 7 in 4 genotypes and subunit 6 + 8 in only one genotype. While 32 genotypes carrying subunit 5 + 10 were determined in the Glu-D1 locus, subunit 2 + 12 was observed in 14 genotypes. The HMW-GS did not show large variation in protein content averages. In Zeleny sedimentation, the highest average is in 7 + 8 subunits. In the alveograph energy value, it has the highest average of 17 + 18 subunits. 7 and 6 + 8 are subunits that show an alveograph energy value below 200 10^− 4 J.

Table 11

Allele numbers and the effect of HMW-GS on grain yield and quality parameters studied on bread wheat cultivars
Glu locus	Subunit	Allele numbers	GY (kg ha^− 1)	TKW (g)	TW (kg hL^− 1)	PC (%)	ZS (mL)	AEV (10^− 4 J)
Glu-A1	null	4	6576.3	31.8	76.7	14.1	48.4	220.2
	1	16	6778.8	37.1	78.1	14.0	46.1	243.2
	2*	26	6578.0	37.3	78.0	14.1	45.7	224.3
Glu-B1	7	4	6410.5	35.9	76.7	13.7	37.9	166.7
	17 + 18	8	6732.1	36.1	77.9	13.9	47.2	251.6
	6 + 8	1	7135.0	36.9	75.3	13.8	44.4	184.8
	7 + 8	17	6743.7	35.3	77.9	14.1	50.8	230.8
	7 + 9	16	6559.4	38.8	78.4	14.3	42.7	236.5
Glu-D1	2 + 12	14	6585.1	37.0	77.7	14.1	44.7	226.7
Glu-D1	5 + 10	32	6691.6	36.7	78.0	14.1	46.7	233.7

Environmental variation in yield and quality traits is more prominent than genotypic variation (Mut et al. 2018, Kaya and Akcura 2014). In this study, which was carried out in regions with different ecological characteristics, the variation in grain yield was mainly caused by the E. Although it varies according to the regions, the share of G and GEI in the variation is around 20%. Precipitation is a more determining factor in yield in wheat cultivation than other factors (He et al. 2016; Irdem 2022). It can be said that the precipitation amounts of the regions in Fig. 2 and the grain yields in Table 9 increased proportionally. A large difference was found between the average grain yield of the dry conditions of the Central Anatolia region, which has the lowest average yield, and the irrigated conditions of the Central Anatolia region. It is seen that the determining factor in grain yield is the amount of precipitation in the Central Anatolia region, which is the region with the most challenging climatic conditions in the study. The limits of genetic performance are being challenged, irrigable areas need to be increased and genotypes resistant to biotic and abiotic stresses should be bred to increase production (Olgun et al. 2018).

In addition to environmental factors, the effects of the genotypes used in the study and the interaction of these genotypes with the environment should not be ignored. For this reason, GGE-biplot graphs for the grain yield of the genotypes were also created. Türkiye's geographical regions have very different ecologies from each other, as well as each region shows differences within itself. In recent years, mostly areal precipitation has been replaced by local precipitation (Aktas 2022). Predictions of climate change are that the increase in temperature will be accompanied by drought (Bento et al. 2021). In recent years, the incidence of extreme temperatures has increased in addition to precipitation. Spring frost damage to winter wheat is exacerbated by the increase in severe weather events and accelerated plant arrival in spring with the effect of a warmer climate (Wang et al. 2020). Long vectors of some genotypes to AEC showed that GEI was an influential factor within the same region as well. Yan and Kang (2003) stated that the increase in vector length to AEC is an indicator of increased sensitivity of genotypes to environmental factors. The fact that the majority of genotypes are located far from the AEC in some regions may also indicate that environmental factors are more challenging or variable. For example, Aegean Region and Southern Marmara Region are accepted as a single region in variety registration trials (TPVCU 2009). While Southern Marmara was considered as a separate VCU region in the past, it was merged with the Aegean Region after 2009 (Aktas and Eren 2014). Breeding programs focus on winter growth type varieties in the Thrace Region, alternative growth type varieties in the Southern Marmara Region, and spring growth type varieties in the Aegean Region. Therefore, it is thought that almost all of the variation in grain yield originates from E and GEI, since the Aegean & Southern Marmara Region also has ecological differences within itself and the seasonal types of the varieties (spring and alternative types) are different. There have been researchers who have reached similar results in grain yield in studies in several locations in Anatolia (Ozturk et al. 2015; Mut et al. 2017; Aydogan and Soylu 2017; Aktas and Eren 2014; Kendal 2013; Koc and Akgun 2019).

A thousand kernel weight is important as it is an indicator of flour yield as well as being one of the yield factors (Posner and Hibbs 1997). Test weight plays a role in determining the market grades and prices of wheat (Yabwalo et al. 2018). Thousand kernel weight and test weight are characteristics that are under the influence of environmental factors as well as genotype (Mut et al. 2005; Zhang et al. 2013). The soil and climate of the cultivation area, crop technology, the prevalence of foliar diseases, pest attacks, agronomic practices, the chemical composition of the kernel, and the condition of the grains at harvest all have an impact on a genotype's test weight (Dumbrava et al. 2019). There are studies to predict flour yield with Artificial Neural Network modeling based on hectoliter weight, thousand-kernel weight, kernel size distribution, and grain hardness (Sabanci et al. 2020). In terms of thousand kernel weight, regional averages varied between 35.3–39.9 g, while a wider range (26.3–42.9 g) was determined on the basis of genotypes. Thrace Region trials were determined as the region with the highest standard deviation in the average of thousand kernel weight. The fact that the genotypes with the lowest and highest thousand kernel weights were in the trials in this region can be seen as the main factor. A wider range of variation was observed in the test weight based on the genotypes, as in the thousand kernel weight. Especially the Southeastern Anatolia Region has the highest average values in terms of thousand kernel weight, test weight and protein content. Protein content is an important parameter in determining wheat quality and is affected by genotype and environment (Dogan and Kendal 2013). The region averages of protein content varied in a range of 1.3%, while genotypes ranged in a range of 3.0%. Protein content is controlled by genetic factors, but when the protein content of cultivars used in trials in more than one region, such as Ceyhan 99, were examined, it was seen that they performed differently in different regions. Genetic factors determine protein content, but environmental factors have a big impact on how those genes are expressed (Groos et al. 2003). Zeleny sedimentation is an indicator of protein quality and although it is under the influence of genotype and environment, the share of genotypic effect is quite high (Grausgruber et al. 2000). The variation in the mean of genotypes in Zeleny sedimentation (30.6–66.6 mL) was quite high. Rheological properties of dough are important in determining protein quality and alveograph analyzes are used for this purpose (Aydogan et al. 2013). Alveograph analyzes are a reliable tool in determining flour quality and energy value has a special place in these analyses. Industrialists prevent this by blending in order to produce standard quality flour from products with different quality characteristics with the effect of genotype and environment (Bayram and Korkut 2018). The genotypes showed a wide range of values for alveograph energy value, between 120.8–433.0. Sanal et al. (2012) reported that the alveograph energy value of red bread wheat in samples taken from different regions of Türkiye varied between 47.3-315.6 10^− 4 joules.

Payne et al. (1987) stated that subunit 5 + 10 had the highest effect in Glu-D1 in quality scores based on high molecular weight glutenin subunits. Later, subunits 1 and 2* in the Glu-A1 locus, and then subunits 7 + 8 and 17 + 18 in the Glu-B1 locus were reported to be effective. Galova et al. (2003) stated that 5 + 10 glutenin subunits are effective in high bread quality, followed by 1, 2* and 7 + 9 subunits. According to Karaduman et al. (2022) reported that subunit 7 had a weakening effect on gluten quality, while subunit 17 + 18 was similar to 7 + 8 but had a more pronounced effect on gluten strength. As seen in Table 11, subunit 7 has the lowest average in terms of protein content, Zeleny sedimentation and alveograph energy value. The 17 + 18 subunit has the highest mean of alveograph energy value and the second highest mean in sedimentation after the 7 + 8 subunit. Bedo et al. (1995) reported that different genetic backgrounds can greatly influence the function of HMW glutenin subunits and consequently the Glu-Dl locus in impacting flour quality, resulting in a complicated system regulating wheat breadmaking quality. High bread making quality can be seen in genotypes with 2 + 12 HMW-GS as well as genotypes with 5 + 10 Glu-D1 subunits. It has been stated by many researchers that the 5 + 10 subunit is the most effective subunit on quality. It is seen in Table 11 that the 2 + 12 and 5 + 10 subunits in the Glu-D1 locus have close average values in terms of the examined characters. This may be the reason why some genotypes have 2 + 12 subunits but show high quality values. For example, Sagittario cultivar showed high quality values despite having 1, 7 + 9, 2 + 12 subunits and 7 quality scores. Akman et al. (2023) reported in their study that some wheat genotypes carried 2 + 12 subunit low quality in the Glu-D1 locus, while the Yellowstone cultivar carried 1 subunit in the Glu-A1 and 5 + 10 subunit in the Glu-D1.

This study, in which cultivars commonly grown in different ecological regions of Türkiye and some newly registered cultivars were used, showed that the effect of the environment on grain yield was the most important factor. The Central Anatolian Region, which has the largest cultivation area in Türkiye and where wheat is grown under rainfed conditions in a large part, showed the lowest value in grain yield. When each region is examined within itself, there are differences in terms of the quality characteristics of the genotypes, while there is no great variation in the average of regional quality parameters. It has been observed that cultivars have subunit 2* in Glu-A1 locus, subunit 7 + 8 and 7 + 9 in Glu-B1 locus, and subunit 5 + 10 in Glu-D1 locus in HMWG loci. The fact that registered cultivars predominantly carry these subunits at Glu-1 loci may be the result of yield and quality-oriented selection in the breeding process. It is thought that this multi-environment and multi-genotype study will provide reference information for future studies.

Author Contribution

The two authors worked together in all stages of the study. The manuscript was written by B. Aktaş and reviewed by H.I. Gökdere.

Akcura M, Kaya Y (2008) Nonparametric stability methods for interpreting genotype by environment interaction of bread wheat genotypes (Triticum aestivum L.). Genetics and Molecular Biology, 31(4): 906-913. https://doi.org/10.1590/S1415-47572008005000004
Akman H, Yesildag ZS, Zengin G (2023) Evaluating Total Phenolic Content, Antioxidant Activity, High Molecular Weight Glutenin Subunits (HMW-GS), and Grain Yield Parameters of Cultivated Wheat and Hybrids. Gesunde Pflanzen 75, 2823-2833. https://doi.org/10.1007/s10343-023-00898-1
Aktas B (2019) Assessment of value for cultivation and use (VCU) trial data by gge-biplot analysis in bread wheat (Triticum aestivum L.). Applied Ecology and Environmental Research, 17(6):12921-12936. http://dx.doi.org/10.15666/aeer/1706_1292112936
Aktas B (2022) Evaluation of yield performance and quality parameters of bread wheat cultivars cultivated in rainfed Central Anatolia. Journal of Animal & Plant Sciences, 32(4): 1035-1045. http://doi.org/10.36899/JAPS.2022.4.0507
Aktas B, Eren H (2014) Determination of quality characteristics and stability analysis of grain yield of some bread wheat (Triticum aestivum L.) varieties. Journal of Central Research Institute for Field Crops, 23(2): 69-76. https://doi.org/10.29136/mediterranean.453777
Anjum FM, Khan MR, Din A, Saeed M, Pasha I, Arshad MU (2007) Wheat gluten: high molecular weight glutenin subunits-structure, genetics, and relation to dough elasticity. Journal of food Science, 72(3): 56-63. https://doi.org/10.1111/j.1750-3841.2007.00292.x
Aydogan S, Akcacik AG, Sahin M, Onmez H, Demir B, Yakisir E (2013) Determination of physicochemical and rheological properties of bread wheat varieties. Journal of Central Research Institute for Field Crops, 22 (2): 74-85.
Aydogan S, Soylu S (2017) Determination of yield, yield components and some quality properties of bread wheat varieties Journal of Central Research Institute for Field Crops, 26 (1):24-30. https://doi.org/10.21566/tarbitderg.323568
Bayram ME, Korkut KZ (2018) Identification and evaluation of alveograph dough parameters of some bread wheat (Triticum aestivum L.) genotypes. Mediterranean Agricultural Sciences, 31(2): 161-168. https://doi.org/10.29136/mediterranean.412208
Bedo Z, Karpati M, Vida G, Kramarik-Kissimon J, Lang L (1995) Good breadmaking quality wheat (Triticum aestivum L.) genotypes with 2+12 subunit composition at the Glu-D1 locus. Cereal Research Communications, 23(3): 283-289.
Bento VA, Ribeiro AS, Russo A (2021) The impact of climate change in wheat and barley yields in the Iberian Peninsula. Scientific Reports, 11: 15484. https://doi.org/10.1038/s41598-021-95014-6
Braun HJ, Atlin G, Payne T (2010) Multi-location testing as a tool to identify plant response to global climate change. In: Reynolds MP, ed. Climate change and crop production. Surrey, UK: CABI Climate Change Series, 115-138.
Dogan Y, Kendal E (2013) Determination of grain yield and some quality traits of some bread wheat (Triticum aestivum L.) genotypes in Diyarbakir ecological conditions. Yuzuncu Yil University Journal of Agricultural Sciences, 23(3):199-208.
Dvoracek V, Bradova J, Capouchova I, Prohaskova A, Papouskova L (2013) Intra-varietal polymorphism of gliadins and glutenins within wheat varieties grown in the Czech Republic and its impact on grain quality. Czech Journal of Genetics and Plant Breeding, 49(4):140-148.
Dumbrava M, Ion V, Basa AG, Dusa EM, Epure LI (2019) Study regarding the yield components and the yield quality at some wheat varieties. Scientific Papers-Series A-Agronomy, 62(2): 77-82.
FAOSTAT (2022) The Food and Agriculture Organization Statistical Database, https://www.fao.org/faostat/en/ (accessed date: 04.09.2022)
Galova Z, Starovicova M, Knoblochova H, Greganova Z (2003) Biochemical and molecular characterization of new wheat genotypes. Biologia, Bratislava, 58(6): 1061-1066.
GenStat (2009) GenStat for Windows (12^th Edition) Introduction. VSN International, Hemel Hempstead.
Grausgruber H, Oberforster M, Werteker M, Ruckenbauer P, Vollmann J (2000) Stability of quality traits in Austrian-grown winter wheats. Field Crops Research 66(3): 257-267. https://doi.org/10.1016/S0378-4290(00)00079-4
Groos C, Robert N, Bervas E, Charmet G (2003) Genetic analysis of grain protein-content, grain yield and thousand-kernel weight in bread wheat. Theoretical and Applied Genetics, 106(6): 1032-1040. https://doi.org/10.1007/s00122-002-1111-1
Gul F, Ahmed I, Ashfaq M, Jan D, Fahad S, Li X, Wang D, Fahad M, Fayyaz M, Shah SA (2020) Use of crop growth model to simulate the impact of climate change on yield of various wheat cultivars under different agro-environmental conditions in Khyber Pakhtunkhwa, Pakistan. Arabian Journal of Geosciences, 13(3):1-14. https://doi.org/10.1007/s12517-020-5118-1
He G, Wang Z, Li F, Dai J, Li Q, Xue C, Cao H, Wang S, Malhi SS (2016) Soil water storage and winter wheat productivity affected by soil surface management and precipitation in dryland of the Loess Plateau, China. Agricultural Water Management 171: 1-9. http://dx.doi.org/10.1016/j.agwat.2016.03.005
Irdem C (2022) Relationship of changes in wheat yield in Türkiye with temperature and precipitation conditions. Turkish Studies - Social, 17(3): 377-392. https://dx.doi.org/10.7827/TurkishStudies.58107
Karaduman Y, Yesildag ZS, Akin A (2022) Evaluating selection efficacy of high molecular weight glutenin subunits (HMW-GS) by relating gluten quality parameters. LWT- Food Science and Technology, 155: 112949. https://doi.org/10.1016/j.lwt.2021.112949
Kaya Y, Akcura M (2014) Effects of genotype and environment on grain yield and quality traits in bread wheat (T. aestivum L.). Food Science and Technology, 34(2): 386-393. https://doi.org/10.1590/fst.2014.0041
Kendal E (2013) Evaluation of some spring bread wheat genotypes in terms of yield and quality in Diyarbakir condition. Kahramanmaras Sutcu Imam University Journal of Agriculture and Nature, 16(3):16-24.
Koc A, Akgun I (2019) Comparing yield and quality of ICARDA- CIMMYT bread wheat lines in the coastal belt. Suleyman Demirel University Journal of Natural and Applied Sciences, 23(1): 157-162. https://doi.org/10.19113/sdufenbed.459660
Kumar A, Mantovani EE, Simsek S, Jain S, Elias EM, Mergoum M (2019) Genome wide genetic dissection of wheat quality and yield related traits and their relationship with grain shape and size traits in an elite × non-adapted bread wheat cross. PLoS ONE 14(9): e0221826. https://doi.org/10.1371/journal.pone.0221826
Mut Z, Akay H, Kose ODE (2018) Grain yield, quality traits and grain yield stability of local oat cultivars. Journal of Soil Science and Plant Nutrition, 18(1): 269-281. http://dx.doi.org/10.4067/S0718-95162018005001001
Mut Z, Aydin N, Ozcan H, Bayramoglu HO (2005) Determination of yield and some quality traits of bread wheat (Triticum aestivum L.) genotypes in the Middle Black Sea Region. Journal of Agricultural Faculty of Gaziosmanpasa University, 22(2): 85-93.
Mut Z, Kose ODE, Akay H (2017) Determination of grain yield and quality traits of some bread wheat (Triticum aestivum L.) varieties. Anadolu Journal of Agricultural Sciences, 32(1): 85-95. https://doi.org/10.7161/omuanajas.288862
Nehe A, Akin B, Sanal T, Evlice AK, Unsal R, Dincer N, Demir L, Geren H, Sevim I, Orhan S, Yaktubay S, Ezici A, Guzman C, Morgounov A (2019) Genotype x environment interaction and genetic gain for grain yield and grain quality traits in Turkish spring wheat released between 1964 and 2010. PLoS ONE, 14(7):e0219432. https://doi.org/10.1371/journal. pone.0219432
Olgun M, Ardic M, Basciftci ZB, Katar D, Arpacioglu NGA, Aydin D, Sezer O, Belen S, Koyuncu O (2018) Analysis of the change of cereal production in Türkiye over long years. Research Journal of Biology Sciences, 11(1): 23-28.
Olgun M, Belen S, Karaduman Y, Turan M, Budak Basciftci Z, Arpacioglu NGA (2021) Determination of precipitation-quality relationship by different statistical methods in bread wheat (Triticum aestivum L.). International Journal of Agriculture Forestry and Life Sciences, 5(2): 171-183.
Ozturk I, Avci R, Tuna B, Kahraman T, Askin OO (2015) Determination of stability parameters and some agronomic traits of the bread wheat (Triticum aestivum L.) cultivars grown in Trakya Region. Harran Journal of Agricultural and Food Sciences, 19(2): 81-93.
Payne PI, Nightingale MA, Krattiger AF, Holt LM (1987) The relationship between HMW glutenin subunit composition and the bread-making quality of British-grown wheat varieties. Journal of the Science of Food and Agriculture, 40(1): 51-56. https://doi.org/10.1002/jsfa.2740400108
Pepo P, Kovacevic V (2011) Regional analysis of winter wheat yields under different ecological conditions in Hungary and Croatia. Acta Agronomica Hungarica, 59(1): 23-33.
Posner ES, Hibbs AN (1997) Experimental and laboratory milling. Wheat Flour Milling ES Posner AN Hibbs, eds. AACC Int. St. Paul, MN. 31-62.
Sabanci K, Aydin N, Sayaslan A, Sonmez ME, Aslan MF, Demir L, Sermet C (2020) Wheat flour milling yield estimation based on wheat kernel physical properties using Artificial Neural Networks. International Journal of Intelligent Systems and Applications in Engineering, 8(2):78-83.
Sanal T, Olgun M, Erdogan S, Pehlivan A, Yazar S, Basciftci ZB, Kutlu I, Ayter NG (2012) Quality analysis of Türkiye in bread wheat by interpolation technique I. Red bread wheat. Biological Diversity and Conservation, 5(3): 69-75.
SAS Institute (1999) SAS user’s guide: Statistics. SAS Institute, Cary, NC.
Sirat A (2023) Determination of Yield, Yield Components, and some Quality Parameters of Bread Wheat (hexaploid) Cultivars with Different Origin in Arid Agricultural Conditions. Gesunde Pflanzen, 75(2), 441-454. https://doi.org/10.1007/s10343-022-00724-0
Sonmez ME, Gulec T, Demir B, Bayrac C, Cakmak M, Aydin N (2022) Molecular screening of the landraces from Türkiye and modern bread wheat (Triticum aestivum L.) cultivars for HMW-GS, wbm, waxy genes and Lr34 gene. Genetic Resources and Crop Evolution, 1-14. https://doi.org/10.1007/s10722-022-01460-0
Tohver M (2007) High molecular weight (HMW) glutenin subunit composition of some Nordic and Middle European wheats. Genetic Resource Crop Evolution, 54(1): 67-81. https://doi.org/10.1007/s10722-005-1885-5
TSMS (2018) 2017 Climate Assessment Report. The Turkish State Meteorological Service, Ankara.
TSMS (2019) 2018 Climate Assessment Report. The Turkish State Meteorological Service, Ankara.
TPVCU (2009) Technical protocol for value for cultivation and use (VCU) of bread wheat. Ministry of Agriculture and Forestry publications, Türkiye.
UPOV (2017) The international union for the protection of new varieties of plants. Guidelines for the conduct of tests for distinctness, uniformity and stability for wheat No. TG/3/12, pp. 39, www.upov.int. (accessed date: 04.09.2022).
Wang S, Chen J, Rao Y, Liu L, Wang W, Dong Q (2020) Response of winter wheat to spring frost from a remote sensing perspective: Damage estimation and influential factors. ISPRS Journal of Photogrammetry and Remote Sensing, 168: 221-235. https://doi.org/10.1016/j.isprsjprs.2020.08.014
Yabwalo DN, Berzonsky WA, Brabec D, Pearson T, Glover KD, Kleinjan JL (2018) Impact of grain morphology and the genotype by environment interactions on test weight of spring and winter wheat (Triticum aestivum L.). Euphytica, 214(7): 1-16. https://doi.org/10.1007/s10681-018-2202-7
Yan W, Kang MS (2003) GGE-Biplot Analysis: A Graphical Tool for Breeders. Geneticists and Agronomists. CRD Press, Boca Raton.
Yasar M, Ekinci R, Sezgin M (2020) Investigation of change of yield and yield components in sesame (Sesamum indicum L.) according to years and locations. Yuzuncu Yil University Journal of Agricultural Science, 30(4):852-857. https://doi.org/10.29133/yyutbd.644514
Zhang K, Wang J, Zhang L, Rong C, Zhao F, Peng T, Li H, Cheng D, Liu X, Qin H, Zhang A, Tong Y, Wang D (2013) Association analysis of genomic loci important for grain weight control in elite common wheat varieties cultivated with variable water and fertiliser supply. Plos One, 8:57853. https://doi.org/10.1371/journal.pone.0057853

No competing interests reported.

supplementary.jpg

Download PDF

Version 1

posted

You are reading this latest preprint version

Evaluation of grain yield, quality characteristics and high molecular weight glutenin subunits of bread wheat cultivars in different agro-ecological regions of Türkiye

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Study Regions and Locations

Climatic data

Experimental materials

Experimental design and applications

Statistical analysis

Results

The Mediterranean Region

The Aegean&Southern Marmara Region

The Southeastern Anatolia Region

The Central Anatolia Region

The Thrace Region

Grain yield and quality parameters on the basis of regions

Discussion

Conclusion

Declarations

Author Contribution

References

Additional Declarations

Supplementary Files

Status:

Version 1