جستجوی مقالات مرتبط با کلیدواژه « برهمکنش ژنوتیپ × محیط » در نشریات گروه « زراعت »

The Production of high-yielding and stable cultivars is the most important objective of crop breeding programs, including wheat. Wheat is one of the key crops cultivated in Iran. The final yield of each plant is determined by the genotype potential, the environmental effect, and the interaction effect of genotype × environment. Studies on genotype × environment interactions can help determine whether a genotype is stable in performance across a wide range of environments. Various methods (univariate and multivariate methods) have been introduced to evaluate the interaction effect, each of which examines the nature of the interaction effect from a specific point of view. The results of different methods may not be the same, but the best result is obtained when a genotype with different evaluation methods shows similar results in terms of stability. Univariate methods do not provide a complete view of the complex and multidimensional nature of genotype × environment interaction, therefore, the use of multivariate methods is suggested to solve this problem. Among the multivariate methods, genotype × genotype-environment (GGE) biplot methods are more important. Therefore, this study aimed to identify promising and stable top-performing lines of bread wheat for warm and dry climates using the GGE biplot method.

The adaptability and stability of 37 promising bread wheat lines were evaluated in 10 environments, along with three checks (Chamran2, Sarang, and Mehregan). The experiment was conducted using a randomized complete block design with three replications in two cropping seasons (2020-2021 and 2021-2022) at five research stations (Darab, Ahvaz, Dezful, Khorramabad, and Zabol). In the field, each plot was planted with a density of 450 seeds/m2. Each line was planted in plots with six four-meter lines with 20 cm line spacing. At the end of the growing season, six rows of five-meter spikes from each plot were harvested and threshed by a Wintersteiger combine. The weight of the obtained grains was measured by a digital scale and reported in hectares.Grain yield was determined using combined analyses of variance. The GGE biplot statistical method (genotype effect + genotype × environment interaction) was used to study the stability of genotypes in the studied environments. SPSSv22 software was used to analyze the experimental data using the analysis of the combined experiment. The data were analyzed with GGE-Biplot software using the GGE biplot graphic method.

The Smirnov-Kolmogorov test was conducted to examine residual errors in each environment. The results for each environment separately showed that the residual data were normal in all environments. Bartlett's test results for the environments indicated the homogeneity of error variances, allowing for a combined analysis of variance, which showed the significant main effects of the environment, genotype, and genotype × environment interaction for grain yield. The significance of the interaction effects of genotypes in this study showed that the genotypes responded differently in different environments; in other words, the difference between genotypes is not the same from one environment to another, and the stability of grain yield can be evaluated in these conditions. The environment, genotype, and genotype × environment interaction effects accounted for 70.12%, 1.24% and 9.57% of the total variation, respectively. The results showed that the three PCAs explained 54% of the total agronomical variability residing in the tested wheat genotypes. The first two PCAs accounted for 29% and 25% of the total variation, respectively. The GGE biplot analysis revealed four mega-environments and five superior genotypes. The polygonal diagram obtained from the analysis showed that the genotypes GT biplot arising G31،G21 ،G29, G27, and G32, which were located at the vertices of the polygon, were the superior genotypes. The average environmental coordinate of the GGE biplot analysis showed that genotypes G29, G28, and G16 had high grain yield and stability. The biplot of the correlation among environments revealed that the environmental vectors of Ahwaz and Zabol were near 90◦, thus these locations were different environments.  Based on the results, the environment of Zabol can be introduced as a favorable environment for selecting the best bread wheat genotypes.

Given the climate change in Iran, particularly in the hot and dry regions of the south, there is always a pressing need for using sustainable varieties with high performance. This study has clearly and easily aided in the identification of stable and superior genotypes graphically. Wheat breeders worldwide consider breeding varieties specifically adapted to different geographical and climatic agricultural regions. The general adaptability of varieties to several regions was identified in this study, indicating that the Zabol environment could be introduced as a suitable environment for selecting superior genotypes of bread wheat. Finally, it is recommended to select genotypes G29, G28, and G16 for further testing and promotion after seed multiplication and selection under farm conditions, eventually introducing them as new wheat varieties. The results obtained in this study demonstrate the efficiency of the GGE biplot technique for selecting high-yielding and stable varieties.

Durum wheat (Triticum turgidum L. var. durum) is an industrial and agricultural product that is mainly used in pasta production industries. One of the important goals of durum wheat breeding programs is to produce high-yielding cultivars that have suitable characteristics for cultivation in different regions of the country. Therefore, the purpose of this research was to investigate the genotype × environment interaction using GGE biplot graphic method in durum wheat promising lines and to identify and introduce lines with economic and stable yield for introduction and cultivation in different regions of the country.

In this research, 18 promising lines of durum wheat with two check Hana and Aran in five research stations of Karaj, Kermanshah, Khorramabad, Dezful and Darab (under two conditions of normal irrigation and interruption of irrigation during the flowering stage) in the form of randomized complete blocks design in 3 replications and in two cropping seasons (2019-2021) were cultivated and compared.

The results of two-year combined variance analysis for grain yield under normal irrigation conditions in five research stations of Darab, Dezful, Khorramabad, Karaj and Kermanshah and dry conditions at the end of the season in Darab showed that the effect of year in both conditions and all stations was significant. It has been in terms of grain yield under normal irrigation conditions, the difference between genotypes was significant in Darab, Dezful and Kermanshah stations, but not significant in Karaj and Khorramabad stations. Genotype × year interaction was significant under normal conditions in Darab and Kermanshah, but not significant in Karaj, Dezful and Khorramabad. Based on the average grain yield, lines G8, G9, G10, G14 and G18 (respectively with the average grain yield of 7725, 7597, 7742, 7661 and 7558 kg ha-1) were superior to the checks. According to the GGE biplot, three large environments were identified. The first large environment included Dezful and Khorramabad regions, and G8 and G10 genotypes were among the top genotypes in these two regions, respectively. The second largest environment was Kermanshah and Karaj, and G9 and G14 genotypes were among the top genotypes in these two regions. The third large environment included two test conditions in Darab and the superior genotype in Darab was G18 in both drought stress and non-stress conditions. GGE Biplot results showed that G8 and G10 genotypes were among the most stable lines and also had the highest grain yield. The comparison of the studied lines with the ideal genotype showed that G10 and G8 are the closest genotypes to the ideal genotype, which have the highest grain yield and stability. The results of qualitative characteristics showed that the hardness index mean of different genotypes was in the range of 57-58% and the promising lines were not significantly different from the check cultivars. G17 genotype had the lowest (14%) and G14 genotype had the highest average of grain yellow berry (45%). Also, a relatively large variation was observed in terms of the amount of wet gluten, so that genotypes G9 and G13 had the lowest and highest amount of wet gluten with 23.8 and 27.3%, respectively.

In general, according to the average grain yield and GGE biplot results in both normal and drought conditions and according to some quality characteristics of the grain, lines G8 (D-98-8) and G10 (D-98-10) were selected as the most suitable lines for both normal and dry conditions. These lines will be tested in on-farm yield trials of next crop season and finally one of them will be introduced as a new cultivar.

IntroductionWater deficit compared to other abiotic stresses is the most important factor limiting the growth and production in all crops, especially wheat. Investigating the response of crop plants under stress conditions is the best way to produce drought-tolerant cultivars and improve yield under stress conditions. The challenge of breeding for drought stress tolerance in all crop plants, is to achieve a rapid screening method of genotypes and genetic improvement of yield under these difficult environmental conditions. The objective of this experiment was to identify high-yielding and water deficit tolerant genotypes in an F4 generation population of bread wheat.Materials and methodsThe plant materials of this experiment were 90 genotypes of the F4 generation resulting from a cross between two bread wheat cultivars (Arta, a spring cultivar sensitive to salinity and drought stresses, and Arg, a tolerant cultivar to salinity and drought stresses). These genotypes along with the parents, were evaluated in split plots based on randomized complete block design with three replications under two conditions (normal irrigation and non-irrigation from the pollination stage) in the research farm of Faculty of Agriculture, Tabriz University, Tabriz, Iran, in 2014. Evaluating the genotypes were done by simultaneous application of stress tolerance index (STI) based on yield-related traits including grain yield (STI-Y), spike weight (STI-S), 1000-grain weight (STI-1000) and harvest index (STI-HI) and identifying the relationship between these indices using genotype × trait biplot analysis. The studied genotypes were ranked based on the relationships in the biplot, and the high yield and water deficit tolerant genotypes were identified.Research findingsThe results of biplot showed that the first two principal components explained 72% (46% and 26%, respectively) of total variance in the studied population. Acute and closed angle between the STI-Y, STI-S and STI-HI vectors indicated a positive correlation between these traits, while the open and obtuse angle between the STI-Y and STI-HI traits with STI-1000 showed a negative correlation between them. In contrast, an quadrant angle was observed between STI-S and STI-1000 vectors, indicating that these two traits were independent and uncorrelated. In total, according to the relationships in the biplot and based on the combination of STI-Y and STI-1000 vectors, genotypes No. 84, 45, 89 and 15 were identified and introduced as the highest grain yield and most water deficit tolerant genotypes.ConclusionThe results of this experiment led to identification of superior and promising genotypes with higher yield potential and more tolerance to water deficit stress. Genotypes No. 84, 45, 89 and 15, which were superior to the stress-tolerant parent, after the purification steps, can be introduced as suitable genotypes for cultivation under water stress conditions as well as in normal environments. These genotypes can be used as drought tolerant parental lines in future breeding programs.

The aim of the present study was to evaluate and interpret the genotype × environment interaction for advanced rainfed lentil lines. Fifteen advanced rainfed lentil lines (genotypes) along with two commercial cultivars (cv. Gachsaran and cv. Sepehr) as check were evaluated using randomized complete block design with three replications in Gachsaran, Moghan, Khorramabad and Ilam field stations in Iran in 2019-20 and 2020-21 cropping seasons. The mean seed yield of trials was 891 kg ha-1. Genotype 10 with 1031 kg ha-1 had the highest mean seed yield, and genotype 16 (cv. Gachsaran) with 728 kg ha-1 had the lowest. Since genotype × environment interaction was significant, yield stability analysis was performed. Evaluation of the seed yield stability of genotypes by rank method showed that genotypes 12 and 10 had high seed yield and yield stability. In addition, yield stability analysis using WAASB index, as a multivariate method, was also performed. The seed yield × WAASB index biplot showed that most of the lentil genotypes had higher seed yield and yield stability than the check cultivars (cv. Gachsaran and cv. Sepehr). Genotypes 12, 13, 11, 10 and 1 had higher seed yield and yield stability than the superior check cultivar (cv. Sepehr). Considering the equal contribution for mean seed yield and yield stability in the calculation of WAASBY index, genotypes 10, 13, 11, 12, 1 were ranked as the most desirable genotypes for target environments.

To assess root and white sugar yield stability of 11 spring sugar beet genotypes together with three check cultivars, a field experiment was carried out using randomized complete block design with four replications in six agricultural research stations; Toroq (Mashhad), Zarghan, Khoy, Kermanshah Miandoab, and Karaj, Iran, in 2020 and 2021. Combined analysis of variance showed that genotype × environment interaction was significant (P < 0.01), and genotypes had different performance in different environmental conditions. Based on regression coefficient, deviation from regression, Shukla’s stability variance, Wrick's ecovalence, and coefficient of determination, GB-6 and GB-10 genotypes were identified with high root and white sugar yield and yield stability. Although the number of genotypes with yield stability increased by using superiority measure and coefficient of variation, three genotypes including; GB-11, GB-10, and GB-2 showed the highest yield stability for root and white sugar yield, respectively. Using GGE biplot method, GB-11, GB-10, GB-2 and GB-6 were identified with higher root and white sugar yield, than average of all genotypes, and higher yield stability, respectively. Considering the results of this research, GB-6, GB-11, GB-2, and GB-10 were identified as high-yielding genotypes with high yield stability.

Chickpea is one of the most important legumes in Iran and accounts for almost 84% of dietary legumes. Chickpea seed yield is strongly influenced by environments, and breeders often determine the stability of high-yielding genotypes across different environments before being introduced as a cultivar. Accurate study of the nature of genotype × environment allows breeders to identify stable and compatible genotypes and has always been one of the important issues in the production and release of new stable and high-yielding cultivars in breeding programs. . Adaptation of chickpea genotypes to environmental conditions is important for crop production stability in different years and places. Existence of the interaction of genotype and environment affects the value of genotypes in different places. The present study was conducted to investigate the effect of genotype by environment interaction on grain yield of chickpea genotypes and cultivars in four environments and to identify stable and high-yielding genotypes under rainfed conditions as autumn planting.

In this study, twelve cultivars and advanced genotype of chickpea were planted during two cropping years (2016-2018) in a randomized complete block design with three replications in semi-warm (Kuhdasht) and temperate (Khorramabad) areas of Lorestan province. Various nonparametric methods such as non-parametric statistics of Si (1), Si (2), Si (3) and Si (6), Thennarasus statistics of NPi (1), NPi (2), NPi (3) and NPi (4), mean rank stability statistics (R), stability statistics of Ketata et al (σr, σmy), Kang stability statistics (Ysi), Fox stability statistics (TOP, MID and LOW) and Genotype Stability Index (GSI) were used for estimating the stability of genotypes. the principal component analysis method was used to better understand the relationships between different statistics..

The results of combined analysis of variance showed that the main effects of environment (including location, year and location ×year) and genotype ×environment were significant at the 1% probability level and the main effects of genotype, location × genotype and year × genotype were significant at the 5% probability level. The effects of genotype, environment and the genotype by environment interaction accounted for 6.48, 77.4 and 13.03% of the total squares, respectively.The biplot of the first principal component (PC1) versus the second principal component (PC2) classified the studied nonparametric stability statistics into three groups.Based on the statistics of NPi (1), NPi (2), NPi (3) and NPi (4), the genotypes with the lowest values are considered as stable genotypes. According to NPi (1) statistics, G1, G6 and G9 genotypes were identified as the most stable and G3 and G5 genotypes as the most unstable genotypes. Based on NPi (2) and NPi (4) parameters, G1, G10 and G9 genotypes were the most stable and G5, G12 and G4 genotypes were the most unstable. Based on the results, the first two principal components explained 68% (42% and 26%, respectively), of the variance of the main variables). Cluster analysis using mean seed yield and non-parametric statistics, placed chickpea genotypes in two main groups. The first cluster included mean seed yield, TOP, MID, σr and σmy. The second cluster consisted of four sub-clusters .

Based on Si (1), Si (2), Si (3) and Si (6) statistics, G1, G10 and G9 genotypes with the lowest values were identified as the most stable genotypes. G1 and G9 genotypes with the lowest GSI were recognized as the best genotypes in terms of grain yield and stability. Based on the parameters with the dynamics concept of stability, genotypes G1, G10 and G9 were identified as stable genotypes with high yield.

Sixteen chickpea genotypes and two commercial cultivars; Adel and Azad as check were evaluated under rainfed conditions using randomized complete block design with three replications at four filed stations; Gachsaran, Gonbad, Khorramabad and Ilam for three cropping seasons (2016-2019). Genotypes 5, 12, 11, 17 and 4 had the highest seed yield. Genotypes 18, 16, 6, and 3 had high seed yield stability based on Shukla variance (2i ), Wrick's equvalence (Wi), root mean square error (RMSE), Si1 and Si2 statistics. Genotypes 16 and 18 with regression coefficient close to one, seed yield higher than average yield and the lowest deviation from regression line were the superior genotypes. Analysis of variance using Eberhart and Russell method showed a significant genotype × environment interaction effect (linear) which indicated different response of genotypes to environmental conditions. Genotypes 4, 5, 9, 11, 12, 15, 16 and 18 were the best genotypes based on yield-stability index (YSi). Based on TOP nonparametric statistics, genotypes 5, 12, 15, 9 and 4 were superior genotypes. Based on Lin and Binns priority index, genotypes 12, 5, 11, 17 and 18 were desirable genotypes in all environments. In conclusion, genotypes 4, 5 and 12 can be considered as high yielding with yield stability and suitable for further verification in research and development projects.

To determine the yield stability of barley promising lines, 17 lines along with three control genotypes were evaluated in five warm zone stations during two years (2017-2019) in randomized complete block design with three replications and their stability was determined using AMMI (additive main effects and multiplicative interaction) and SHMM (Shifted multiplicative model). Analysis of variance of grain yield using AMMI model showed that the effect of genotype, environment and genotype × environment interaction was significant at 1% probability level. Genotype × environment interaction analysis based on AMMI model showed that the four main components of interaction were significant at the level of 1% probability. These four components explained 84.7% of the changes in genotype × environment interaction. The lowest value of RMS PD was related to AMMI1 model. Therefore, the interpretation of the results using the AMMI1 model is more valid than the AMMI2 model. According to the AMMI2 model, lines WB-96-8 and WB-96-9 had specific adaptability with the Zabol region and line WB-96-12 had specific adaptability with Moghan. Lines WB-96-10, WB-96-17, WB-96-18 and WB-96-19 were the high-performance lines in this study. The grouping of locations based on the SHMM model created two groups. The first group includes Darab, Ahvaz and Zabol, which are part of the warm zone stations in the south of the country. The second group included Moghan and Gonbad stations (warm northern zone).

Evaluating of the rapeseed genotypes under different environmental conditions would be useful to identify genotypes with stable and high yield potential. In order to study yield stability of winter rapeseed genotypes, 9 new advanced lines along with four cultivars Okapi, Ahmadi, Nima and Nafis were evaluated in a randomized complete block design in six experimental field stations including Esfahan, Hamedan, Karaj, Kermanshah, Khoy and Zarghan during 2015–2017 growing seasons. Results of combined analysis of variance indicated that the effects of environments, genotypes and genotype × environment interaction were significant, suggesting that the genotypes responded differently in the studied environment conditions. So, there was the possibility of stability analysis. Cluster analysis based on the mean yield and nonparametric stability statistics showed that there were three main clusters. According to mean rank of nonparametric stability parameters, the genotypes G1, G2, G5 and G13 with the lowest value for mean rank were stable, whereas genotypes G4, G11, G7 and G9 with highest values were unstable. Also, the results indicated that the nonparametric statistics Si(3), Si(6), NPi(2), NPi(3), NPi(4) and KR were associated with mean seed yield and the dynamic concept of stability. Therefore, these methods were suitable for selecting stable and high yielding genotypes in winter rapeseed. Based on these parameters, the genotypes G13 and G2 with mean seed yield 4086 and 3829 kg h-1 respectively, were identified as high yield stable genotypes. So, these genotypes could be used for cultivation in cold and mild cold regions of the country.

To investigate the response of new maize hybrids to different environmental conditions and determine their grain yield stability, an experiment was conducted using eight maize hybrids in a randomized complete block design with three replications in six locations in 2016. Due to the significant hybrid × environment (G × E) interaction, stability analysis was performed using two multivariate methods, AMMI and GGE-biplot. The results of the AMMI model showed that only the first principal component (AMMI1) was significant and accounted for 72.52% of the G×E variation. Based on AMMI model statistics (SPCA1 and ASV), the hybrids No. 1 ( KSC715B) and 5 (KSC706) were selected as the highest stable hybrids. The results of stability anlaysis by GGE-biplot procedure showed that 83.32% of the total grain yield variation was explained by the first and second component of GGE-biplot and the hybrids No. 1 (SC715B) and 7 (SC703) were identified as the hybrids with higher stability. In total, based on grain yield and the results of stability analysis using these two methods, the hybrids No. 7 (SC703) and 1 (SC715B) with grain yield of 13.16 and 12.82 t/ha, respectively, were identified as the most stable hybrids with most high yielding and due to high general adaptability, cab be cultivated in different regions of Iran. Also, biplot of correlation among environments revealed that Moghan, Shiraz and Kerman as well as Ghaemshahr, Kermanshah and Karaj, were very close to each other and were similar in ranking the studied hybrids. Considering high dicreament power of hybrids in Shiraz, Kerman, Karaj and Ghaemshahr regions and in order to save the costs of future experiments, it is recomanded that the experiments be carried out in these four regions instead of six.

The genotype by environment interaction is a major challenge in the study of quantitative characters because it complicates the interpretation of genetic experiments and do predictions difficult, also it reduces grain seed yield stability in different environments. In order to determine the yield stability, adaptability and analysis of the genotype × environment interaction of oily sunflower inbred lines under normal and salt stress conditions, 100 genotypes coming from different geographical regions were evaluated using a randomized complete block design with three replications for two successive years (2006 to 2007). In stability analysis using statistics such as environmental variance and coefficient of variation, lines 71 and 33 showed minimum variations compared to other lines. Based on Eberhart and Russell regression method, lines 71, 45, 40 and 25 was the most stable genotypes. AMMI statistics revealed lines with code numbers of 71, 77, 93 and 51 as the most stable genotypes. Biplot technique was used to identify the appropriate genotypes for special environments. Based on this method, lines 71, 61 and 17 showed the lowest interaction and considered as the most stable genotypes. However, line 71 showed highest seed yield compared two other lines. The line with code number of 50 showed special stability and high yield under normal conditions whereas lines 90, 48 showed special stability and high yield under salt stress conditions. In conclusion, based on different stability analysis statistics and AMMI analysis the line 71 are introduced as most stable line under normal and salt stress conditions.

Breeding for yield and yield stability has always been the main goal for breeders. In the current research, yield and yield stability of 51 wheat cultivars introduced over the last half century were evaluated in a randomized complete block design with two replications during five years. Analysis of variance revealed that genotype × environment interaction was significant. Therefore, the pattern of genotype × environment variation was analyzed using graphical method. According to the results of GGE biplot analysis, the first and second components as genotype and genotype × environment interaction components, explained 41.98% and 20.28% of the total variation, respectively. Simultaneous pattern of yield and yield stability divided the studied cultivars into three groups including desirable, moderate and undesirable. Among the studied cultivars, Mahdavi, Maroon and Sistan showed high yield and yield stability, while Shahpassand and Zagros had a low yield and yield stability. The results of this study showed that breeders have successfully introduced the genotypes with high yield and good stability over time, however due to the complexity of yield and yield stability traits, 10% of genotypes with low yield and 30% with poor stability have been introduced during 1986-2006, which confirms the need for more accurate tests in the future. Winter genotypes showed lower yield and yield stability compared to spring and facultative cultivars. Since, spring cultivars showed high yield in autumn cultivation, it is therefore recommended to cultivate spring cultivars in autumn as well.

The stability of yield performance is one of the most desirable characters of a genotype to be released as a variety, which allows the developed varieties to be adopted in large areas. To obtain high yielding and stable durum wheat genotypes, 18 durum wheat lines and promising lines along with two commercial durum (Behrang) and bread wheat (Chamran) check cultivars were evaluated in four warm and dry locations of Iran including Darab, Ahvaz, Khoramabad and Dezful  during two cropping seasons (2013-2015). The experiments were conducted in form of RCBD with three replications. Seed yield and some agronomic characteristics were recorded in each location. Results of SIIG criterion, calculated based on all parametric methods, indicated that lines DW-93-10, DW-93-7, DW-93-15, DW-93-16, DW-93-4 and DW-93-18, in comparison with the other lines, had the least deviation from the ideal genotype (d+) and had the greatest deviation from the non-ideal (d-) one; therefore these lines with maximum SIIG were the stable lines. Results of SIIG index, calculated based on all non-parametric methods, indicated that the lines DW-93-18, DW-93-15, DW-93-10, DW-93-16, DW-93-7, DW-93-14, DW-93-2, DW-93-4 and DW-93-5 were characterized with maximum SIIG and were introduced as stable genotypes. Based on parametric and non-parametric procedures, the lines DW-93-10, DW-93-7, DW-93-15, DW-93-4 and DW-93-18 were introduced as stable genotypes. Finally, by using average of yield, grain quality indices and resistance to diseases, lines DW-93-4 and DW-93-5 along with DW-93-19 as the earliest maturity lines were selected to be tested in on-farm yield trials of next cropping seasons.

To study the stability of 20 bread wheat genotypes forgrain yield and yield components, an experiment was carried outinrandomized complete block design with two replications in five environmentsin East Azarbaijan provience, Tabriz and Meyane agricultural stations, Iran,during 2011-2014. The results of combined analysis of variance showed that the genotype × environment interaction was significant (at least p