In the process to the marketing of cultivars, identification of superior test locations within multi-environment variety trial schemes is of critical relevance. It is relevant to breeding organizations as well as to g...In the process to the marketing of cultivars, identification of superior test locations within multi-environment variety trial schemes is of critical relevance. It is relevant to breeding organizations as well as to governmental organizations in charge of cultivar registration. Where competition among breeding companies exists, effective and fair multi-environment variety trials are of utmost importance to motivate investment in breeding. The objective of this study was to use genotype main effect plus genotype by environment interaction（GGE） biplot analysis to evaluate test locations in terms of discrimination ability, representativeness and desirability, and to investigate the presence of multiple mega-environments in cotton production in the Yangtze River Valley（YaRV）, China. Four traits（cotton lint yield, fiber length, lint breaking tenacity, micronaire） and two composite selection indices were considered. It was found that the assumption of a single mega-environment in the YaRV for cotton production does not hold. The YaRV consists of three cotton mega-environments： a main one represented by 11 locations and two minor ones represented by two test locations each. This demands that the strategy of cotton variety registration or recommendation must be adjusted. GGE biplot analysis has also led to the identification of test location superior for cotton variety evaluation. Although test location desirable for selecting different traits varied greatly, Jinzhou, Hubei Province, China, was found to be desirable for selecting for all traits considered while Jianyang, Sichuan Province, China, was found to be desirable for none.展开更多
To improve multi-environmental trial(MET)analysis,a compound method—which combines factor analytic(FA)model with additive main effect and multiplicative interaction(AMMI)and genotype main effect plus genotype-by-envi...To improve multi-environmental trial(MET)analysis,a compound method—which combines factor analytic(FA)model with additive main effect and multiplicative interaction(AMMI)and genotype main effect plus genotype-by-environment interaction(GGE)biplot—was conducted in this study.The diameter at breast height of 36 open-pollinated(OP)families of Pinus taeda at six sites in South China was used as a raw dataset.The best linear unbiased prediction(BLUP)data of all individual trees in each site was obtained by fitting the spatial effects with the FA method from raw data.The raw data and BLUP data were analyzed and compared by using the AMMI and GGE biplot.BLUP results showed that the six sites were heterogeneous and spatial variation could be effectively fitted by spatial analysis with the FA method.AMMI analysis identified that two datasets had highly significant effects on the site,family,and their interactions,while BLUP data had a smaller residual error,but higher variation explaining ability and more credible stability than raw data.GGE biplot results revealed that raw data and BLUP data had different results in mega-environment delineation,test-environment evaluation,and genotype evaluation.In addition,BLUP data results were more reasonable due to the stronger analytical ability of the first two principal components.Our study suggests that the compound method combing the FA method with the AMMI and GGE biplot could improve the analysis result of MET data in Pinus teada as it was more reliable than direct AMMI and GGE biplot analysis on raw data.展开更多
As one of the secondary metabolites,the isoflavones formed during the development of soybean[Glycine max(L.)Merr.]seeds.The total and individual isoflavone contents,a typical quantitative trait,were affected by signif...As one of the secondary metabolites,the isoflavones formed during the development of soybean[Glycine max(L.)Merr.]seeds.The total and individual isoflavone contents,a typical quantitative trait,were affected by significant genotypes of environments(GE)interaction and controlled by many genes with main or minor effects.In the present study,99 soybean cultivars,collected from northeastern China,were used to analyze the isoflavone performances.Genotype-genotype×environment(GGE)biplot software demonstrated an ability to provide information on genetic main effects than solely on phenotypic perform.Highperformance liquid chromatography(HPLC)system was used to extract and determine the isoflavone contents.The results indicated that most genotypes significantly varied among six tested environments.P40(Xiaolimoshidou)was the best-performed genotype with mean performance and stability for glycitein content across six different environments.P88(L-59Peking)was the super genotype with mean performance and stability on each tested environment for daidzein,genistein and the total isoflavone.E5(Gongzhuling in 2016)was the best environment for optimal environmental factor mining.P70(Charleston),P67(Baichengmoshidou)and P50(Jiunong 20)were the optimal genotypes with the highest field among 99 cultivars on each tested environment for genistein.P70(Charleston),P67(Baichengmoshidou)and P14(Hefeng 25)were the optimal genotypes with the highest field among 99 cultivars on each tested environment for daidzein.P40(Xiaolimoshidou),P45(Jinshanchamodou),P33(Dongnong 48)and P56(L-5)were the optimal genotypes with the highest field among 99 cultivars on each tested environment for glycitein.P70(Charleston)and P67(Baichengmoshidou)were the optimal genotypes with the highest field among 99 cultivars on each tested environment for the total isoflavone.GGE biplot was a rational method for stability and adaptation evaluation of soybean isoflavones,and could assist soybean breeder to select a good culture and a suitable tested site.It provided a scientific basis for the establishment of a breeding site and a selection site of soybean isoflavones.This study was valuable to identify genotypes with stable performances of isoflavones of these 99 cultivars for developing new cultivars.展开更多
The research was carried out with 9 triticale, 3 bread wheat, 3 durum wheat and 3 barley varieties and advanced lines in Tekirdag, Edime and Silivri locations during three years. In the study, the data obtained from c...The research was carried out with 9 triticale, 3 bread wheat, 3 durum wheat and 3 barley varieties and advanced lines in Tekirdag, Edime and Silivri locations during three years. In the study, the data obtained from combined variance analysis were performed and the significance of the differences between the averages was determined by LSD multiple comparison test. GGE biplot analysis and graphics were made by using the statistical package program. The genotypes G2 and G3 for thousand kernel weight, genotype G1 for the heading time and test weight, genotypes G14 and G15 for the maturation time, number of spikelets per spike and grain weight per spike and G13 for the plant height, spike length and grain yield per hectare decare revealed the highest values. The genotypes G6, GS, G4, G14, G9, G8 and G7 gave lower values than the average in terms of grain yield, whereas the other genotypes gave higher values than the general average. According to biplot graphical results, while locations 1 and 8 were closely related, locations 9, 2 and 7 were positively related to these environments. Although the location 7 is slightly different from the other 4 locations, these 5 locations can be seen as a mega environment. Genotypes G12, G2, G3 and G10 for this mega-environment showed the best performances. According to the results of grain yields obtained from 9 different locations, the location 5 was the most discriminating area while the location 1 was the least discriminating. Location 2 was the best representative location, while locations 4 and 7 were with the lowest representation capability. The locations that are both descriptive and representative are good test locations for the selection of adapted genotypes. Test environments, such as location 8, with low ability to represent are useful for selecting genotypes that perform well in specific regions if the target environments can be subdivided into sub-environments.展开更多
基金funded by the Jiangsu Agriculture Science and Technology Innovation Fund,China(CX(12)5035)the National Natural Science Foundation of China(30971735)+1 种基金the China Agriculture Research System(CARS-18-20)the Special Fund for Agro-Scientific Research in the Public Interest of China(Impact of Climate Change on Agriculture Production of China,200903003)
文摘In the process to the marketing of cultivars, identification of superior test locations within multi-environment variety trial schemes is of critical relevance. It is relevant to breeding organizations as well as to governmental organizations in charge of cultivar registration. Where competition among breeding companies exists, effective and fair multi-environment variety trials are of utmost importance to motivate investment in breeding. The objective of this study was to use genotype main effect plus genotype by environment interaction（GGE） biplot analysis to evaluate test locations in terms of discrimination ability, representativeness and desirability, and to investigate the presence of multiple mega-environments in cotton production in the Yangtze River Valley（YaRV）, China. Four traits（cotton lint yield, fiber length, lint breaking tenacity, micronaire） and two composite selection indices were considered. It was found that the assumption of a single mega-environment in the YaRV for cotton production does not hold. The YaRV consists of three cotton mega-environments： a main one represented by 11 locations and two minor ones represented by two test locations each. This demands that the strategy of cotton variety registration or recommendation must be adjusted. GGE biplot analysis has also led to the identification of test location superior for cotton variety evaluation. Although test location desirable for selecting different traits varied greatly, Jinzhou, Hubei Province, China, was found to be desirable for selecting for all traits considered while Jianyang, Sichuan Province, China, was found to be desirable for none.
基金supported by State Key Laboratory of Tree Genetics and Breeding(Northeast Forestry University)(K2013204)co-financed with NSFC project(31470673)Guangdong Science and Technology Planning Project(2016B070701008)
文摘To improve multi-environmental trial(MET)analysis,a compound method—which combines factor analytic(FA)model with additive main effect and multiplicative interaction(AMMI)and genotype main effect plus genotype-by-environment interaction(GGE)biplot—was conducted in this study.The diameter at breast height of 36 open-pollinated(OP)families of Pinus taeda at six sites in South China was used as a raw dataset.The best linear unbiased prediction(BLUP)data of all individual trees in each site was obtained by fitting the spatial effects with the FA method from raw data.The raw data and BLUP data were analyzed and compared by using the AMMI and GGE biplot.BLUP results showed that the six sites were heterogeneous and spatial variation could be effectively fitted by spatial analysis with the FA method.AMMI analysis identified that two datasets had highly significant effects on the site,family,and their interactions,while BLUP data had a smaller residual error,but higher variation explaining ability and more credible stability than raw data.GGE biplot results revealed that raw data and BLUP data had different results in mega-environment delineation,test-environment evaluation,and genotype evaluation.In addition,BLUP data results were more reasonable due to the stronger analytical ability of the first two principal components.Our study suggests that the compound method combing the FA method with the AMMI and GGE biplot could improve the analysis result of MET data in Pinus teada as it was more reliable than direct AMMI and GGE biplot analysis on raw data.
基金Supported by Heilongjiang Provincial Project(Topic JC2018007)
文摘As one of the secondary metabolites,the isoflavones formed during the development of soybean[Glycine max(L.)Merr.]seeds.The total and individual isoflavone contents,a typical quantitative trait,were affected by significant genotypes of environments(GE)interaction and controlled by many genes with main or minor effects.In the present study,99 soybean cultivars,collected from northeastern China,were used to analyze the isoflavone performances.Genotype-genotype×environment(GGE)biplot software demonstrated an ability to provide information on genetic main effects than solely on phenotypic perform.Highperformance liquid chromatography(HPLC)system was used to extract and determine the isoflavone contents.The results indicated that most genotypes significantly varied among six tested environments.P40(Xiaolimoshidou)was the best-performed genotype with mean performance and stability for glycitein content across six different environments.P88(L-59Peking)was the super genotype with mean performance and stability on each tested environment for daidzein,genistein and the total isoflavone.E5(Gongzhuling in 2016)was the best environment for optimal environmental factor mining.P70(Charleston),P67(Baichengmoshidou)and P50(Jiunong 20)were the optimal genotypes with the highest field among 99 cultivars on each tested environment for genistein.P70(Charleston),P67(Baichengmoshidou)and P14(Hefeng 25)were the optimal genotypes with the highest field among 99 cultivars on each tested environment for daidzein.P40(Xiaolimoshidou),P45(Jinshanchamodou),P33(Dongnong 48)and P56(L-5)were the optimal genotypes with the highest field among 99 cultivars on each tested environment for glycitein.P70(Charleston)and P67(Baichengmoshidou)were the optimal genotypes with the highest field among 99 cultivars on each tested environment for the total isoflavone.GGE biplot was a rational method for stability and adaptation evaluation of soybean isoflavones,and could assist soybean breeder to select a good culture and a suitable tested site.It provided a scientific basis for the establishment of a breeding site and a selection site of soybean isoflavones.This study was valuable to identify genotypes with stable performances of isoflavones of these 99 cultivars for developing new cultivars.
文摘The research was carried out with 9 triticale, 3 bread wheat, 3 durum wheat and 3 barley varieties and advanced lines in Tekirdag, Edime and Silivri locations during three years. In the study, the data obtained from combined variance analysis were performed and the significance of the differences between the averages was determined by LSD multiple comparison test. GGE biplot analysis and graphics were made by using the statistical package program. The genotypes G2 and G3 for thousand kernel weight, genotype G1 for the heading time and test weight, genotypes G14 and G15 for the maturation time, number of spikelets per spike and grain weight per spike and G13 for the plant height, spike length and grain yield per hectare decare revealed the highest values. The genotypes G6, GS, G4, G14, G9, G8 and G7 gave lower values than the average in terms of grain yield, whereas the other genotypes gave higher values than the general average. According to biplot graphical results, while locations 1 and 8 were closely related, locations 9, 2 and 7 were positively related to these environments. Although the location 7 is slightly different from the other 4 locations, these 5 locations can be seen as a mega environment. Genotypes G12, G2, G3 and G10 for this mega-environment showed the best performances. According to the results of grain yields obtained from 9 different locations, the location 5 was the most discriminating area while the location 1 was the least discriminating. Location 2 was the best representative location, while locations 4 and 7 were with the lowest representation capability. The locations that are both descriptive and representative are good test locations for the selection of adapted genotypes. Test environments, such as location 8, with low ability to represent are useful for selecting genotypes that perform well in specific regions if the target environments can be subdivided into sub-environments.
