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
The experiments were conducted for three consecutive years across 14 locations using 9 non-waxy proso millet genotypes and 16 locations using 7 waxy proso millet genotypes in China. The objectives of this study were t...The experiments were conducted for three consecutive years across 14 locations using 9 non-waxy proso millet genotypes and 16 locations using 7 waxy proso millet genotypes in China. The objectives of this study were to analyze yield stability and adaptability of proso millets and to evaluate the discrimination and representativeness of locations by analysis of vari- ance （ANOVA） and genotype and genotype by environment interaction （GGE） biplot methods. Grain yields of proso millet genotypes were significantly influenced by environment （E）, genotype （G） and their interaction （GxE） （P〈0.1%）. GxE inter- action effect was six times higher than G effect in non-waxy group and seven times in waxy group. N04-339 in non-waxy and Neimi 6 （NM6） in waxy showed higher grain yields and stability compared with other genotypes. Also, Neimi 9 （NM9, a non-waxy cultivar） and 90322-2-33 （a waxy cultivar） showed higher adaptability in 7 and in 11 locations, respectively. For non-waxy, Dalat, Inner Mongolia （E2） and Wuzhai, Shanxi （E5） were the best sites among all the locations for maximizing the variance among candidate cultivars, and Yanchi, Ningxia （El0） had the best representativeness. Wuzhai, Shanxi （e9） and Yanchi, Ningxia （e14） were the best representative locations, and Baicheng, Jilin （e2） was better discriminating location than others for waxy genotypes. Based on our results, El0 and e14 have enhanced efficiency and accuracy for non-waxy genotypes and waxy genotypes selection, respectively in national regional test of proso millet varieties.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
This study was conducted to determine the performance of wheat landraces cultivated under organic conditions and to analyze their stability across diverse environments. Six wheat landraces with specific characteristic...This study was conducted to determine the performance of wheat landraces cultivated under organic conditions and to analyze their stability across diverse environments. Six wheat landraces with specific characteristics (high protein content, drought tolerance, stay green) were tested under organic growing environment. The experiments were applied in three locations (Larisa (LAR), Thessaloniki (THES), Kilkis (KIL)) for three growing seasons. The role of specific agronomic traits (stay green, lodging) and their correlation with yield components were analyzed. Stability and genotypic superiority for grain yield were determined using ANOVA and genotype × environment (GGE) biplot analysis. Furthermore, the interrelationships among wheat traits and genotype-by-trait using regression analysis, coefficient of variation and (GT)-biplot technique were studied. Significant differences were found in yield among wheat landraces tested, and also in yield components, as related to specific traits expressed into organic environment. Best varieties in terms of yield were the medium statured landraces Skliropetra and M. Argolidas, characterized by lowest weight of 1000 grains, large number of spikes per m2 meter and the highest number of grains per spike as compared to the other landraces. The statistical model GGE biplot provides useful information for experimentation of wheat landraces when grown under organic environment. It identifies clearly the ideal and representative environment for experimentation and underlines the effect of specific traits for each wheat cultivar on yield performance and stability across environments.展开更多
This study was to evaluate the high yielding and stability of candidate cultivars, depict the adaptive planting region, analyze trial location discrim-ination ability and representativeness, as wel as identify the ide...This study was to evaluate the high yielding and stability of candidate cultivars, depict the adaptive planting region, analyze trial location discrim-ination ability and representativeness, as wel as identify the ideal cultivar and trial location, with the aim to provide theory background for cultivar selection and rea-sonable scheme of test location in Jiangsu Province. [Method] The GGE biplot method was used to analyze the lint cotton yield of 12 experimental genotypes in the 6 test locations （three replicates in each） of the cotton regional trial in Jiangsu Province in 2013. [Result] The effects of genotype （G）, environment （E）, and geno-type by environment interaction （G＆#215;E） on lint cotton yield were al highly significant （P〈0.01）, which made it necessary to further explore the specific pattern of geno-type by environment interaction. Jinmian118 （G4） and SF3303 （G5） were the best ideal genotypes screened by the ＂ideal cultivar＂ and ＂ideal location＂ view of GGE biplot, and the ordination of test sites based on the ideal index were in the order of Dafeng （DF）, Yanliang （YL）, Liuhe （LH）, Dongtai （DT）, Yancheng （YC）, and Nantong （NT）, among which NT was relatively weak in representing of the whole target cot-ton planting region in Jiangsu Province. The ＂similarity among locations＂ view of GGE biplot clustered al trial locations into one group, showing that the test sites in the cotton planting region in Jiangsu Province were in the same mega-environment. The ＂which-won-where＂ view of GGE biplot indicated that cotton cultivar Jinmian118 （G4） was the most appropriate cultivar in the homogeneous cotton planting region in Jiangsu Province. [Conclusion] Among the candidate cultivars, Jinmian118 and SF3303 were identified as the most ideal cultivars in this set of conventional cotton regional trial in Jiangsu Province; the test site of Dafeng ranked the first out of al locations in terms of discrimination and representativeness, and al test locations were clustered into the same mega-environmet, which indicated the high efficiency of cultivar selection in the cotton regional trial in Jiangsu Province.展开更多
A study of combining ability for improving rice cooking quality was carried out via genotype plus genotype x environment （GGE） biplot. Four restorer lines and three male sterile lines were used to obtain F1 in a lin...A study of combining ability for improving rice cooking quality was carried out via genotype plus genotype x environment （GGE） biplot. Four restorer lines and three male sterile lines were used to obtain F1 in a line x tester trial at the Rice Research Institute, Amol, Iran in 2009. GGE biplot analysis showed that Neda and IR56 were the best general combiners for amylose content （AC）, whereas Nemat and IR28 had the highest general combining ability （GCA） effects for gelatinization temperature （GT）, and IR58 and IR59 showed the highest GCA effects in terms of gel consistency （GC）. Meanwhile IR58 and IR59 showed large specific combining ability （SCA） effects for AC, while Neda and SA13 had high SCA effects for GT. Nemat and IR28 had large SCA effects for GC. Because intermediate levels ofAC, GT and GC are ideal, Nemat × IR59 was considered as the best possible cross. Based on these results, the GGE biplot showed good potential for identifying suitable parents, heterotic crosses and the best hybrids in line x tester 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funded by the National Key Technologies R&D Program of China during the 12th Five-Year Plan period (2014BAD07B03)the National Natural Science Foundation of China (31371529)+2 种基金the Postdoctoral Science Foundation of Heilongjiang Province, China (LBH-Z14177)the project of Education Department in Heilongjiang Province, China (12541599)the China Agricultural Research System (CARS07-13.5-A9)
文摘The experiments were conducted for three consecutive years across 14 locations using 9 non-waxy proso millet genotypes and 16 locations using 7 waxy proso millet genotypes in China. The objectives of this study were to analyze yield stability and adaptability of proso millets and to evaluate the discrimination and representativeness of locations by analysis of vari- ance （ANOVA） and genotype and genotype by environment interaction （GGE） biplot methods. Grain yields of proso millet genotypes were significantly influenced by environment （E）, genotype （G） and their interaction （GxE） （P〈0.1%）. GxE inter- action effect was six times higher than G effect in non-waxy group and seven times in waxy group. N04-339 in non-waxy and Neimi 6 （NM6） in waxy showed higher grain yields and stability compared with other genotypes. Also, Neimi 9 （NM9, a non-waxy cultivar） and 90322-2-33 （a waxy cultivar） showed higher adaptability in 7 and in 11 locations, respectively. For non-waxy, Dalat, Inner Mongolia （E2） and Wuzhai, Shanxi （E5） were the best sites among all the locations for maximizing the variance among candidate cultivars, and Yanchi, Ningxia （El0） had the best representativeness. Wuzhai, Shanxi （e9） and Yanchi, Ningxia （e14） were the best representative locations, and Baicheng, Jilin （e2） was better discriminating location than others for waxy genotypes. Based on our results, El0 and e14 have enhanced efficiency and accuracy for non-waxy genotypes and waxy genotypes selection, respectively in national regional test of proso millet varieties.
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.
文摘This study was conducted to determine the performance of wheat landraces cultivated under organic conditions and to analyze their stability across diverse environments. Six wheat landraces with specific characteristics (high protein content, drought tolerance, stay green) were tested under organic growing environment. The experiments were applied in three locations (Larisa (LAR), Thessaloniki (THES), Kilkis (KIL)) for three growing seasons. The role of specific agronomic traits (stay green, lodging) and their correlation with yield components were analyzed. Stability and genotypic superiority for grain yield were determined using ANOVA and genotype × environment (GGE) biplot analysis. Furthermore, the interrelationships among wheat traits and genotype-by-trait using regression analysis, coefficient of variation and (GT)-biplot technique were studied. Significant differences were found in yield among wheat landraces tested, and also in yield components, as related to specific traits expressed into organic environment. Best varieties in terms of yield were the medium statured landraces Skliropetra and M. Argolidas, characterized by lowest weight of 1000 grains, large number of spikes per m2 meter and the highest number of grains per spike as compared to the other landraces. The statistical model GGE biplot provides useful information for experimentation of wheat landraces when grown under organic environment. It identifies clearly the ideal and representative environment for experimentation and underlines the effect of specific traits for each wheat cultivar on yield performance and stability across environments.
基金Supported by the Fund for Independent Innovation of Agricultural Sciences in Jiangsu Province(CX-12-5035)
文摘This study was to evaluate the high yielding and stability of candidate cultivars, depict the adaptive planting region, analyze trial location discrim-ination ability and representativeness, as wel as identify the ideal cultivar and trial location, with the aim to provide theory background for cultivar selection and rea-sonable scheme of test location in Jiangsu Province. [Method] The GGE biplot method was used to analyze the lint cotton yield of 12 experimental genotypes in the 6 test locations （three replicates in each） of the cotton regional trial in Jiangsu Province in 2013. [Result] The effects of genotype （G）, environment （E）, and geno-type by environment interaction （G＆#215;E） on lint cotton yield were al highly significant （P〈0.01）, which made it necessary to further explore the specific pattern of geno-type by environment interaction. Jinmian118 （G4） and SF3303 （G5） were the best ideal genotypes screened by the ＂ideal cultivar＂ and ＂ideal location＂ view of GGE biplot, and the ordination of test sites based on the ideal index were in the order of Dafeng （DF）, Yanliang （YL）, Liuhe （LH）, Dongtai （DT）, Yancheng （YC）, and Nantong （NT）, among which NT was relatively weak in representing of the whole target cot-ton planting region in Jiangsu Province. The ＂similarity among locations＂ view of GGE biplot clustered al trial locations into one group, showing that the test sites in the cotton planting region in Jiangsu Province were in the same mega-environment. The ＂which-won-where＂ view of GGE biplot indicated that cotton cultivar Jinmian118 （G4） was the most appropriate cultivar in the homogeneous cotton planting region in Jiangsu Province. [Conclusion] Among the candidate cultivars, Jinmian118 and SF3303 were identified as the most ideal cultivars in this set of conventional cotton regional trial in Jiangsu Province; the test site of Dafeng ranked the first out of al locations in terms of discrimination and representativeness, and al test locations were clustered into the same mega-environmet, which indicated the high efficiency of cultivar selection in the cotton regional trial in Jiangsu Province.
文摘A study of combining ability for improving rice cooking quality was carried out via genotype plus genotype x environment （GGE） biplot. Four restorer lines and three male sterile lines were used to obtain F1 in a line x tester trial at the Rice Research Institute, Amol, Iran in 2009. GGE biplot analysis showed that Neda and IR56 were the best general combiners for amylose content （AC）, whereas Nemat and IR28 had the highest general combining ability （GCA） effects for gelatinization temperature （GT）, and IR58 and IR59 showed the highest GCA effects in terms of gel consistency （GC）. Meanwhile IR58 and IR59 showed large specific combining ability （SCA） effects for AC, while Neda and SA13 had high SCA effects for GT. Nemat and IR28 had large SCA effects for GC. Because intermediate levels ofAC, GT and GC are ideal, Nemat × IR59 was considered as the best possible cross. Based on these results, the GGE biplot showed good potential for identifying suitable parents, heterotic crosses and the best hybrids in line x tester 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.
