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1.
The objective of this study was to compare nonparametric stability procedures and apply different nonparametric tests for genotype × environment (G × E) interactions on grain yields of 15 durum wheat genotypes selected from Iran/ICARDA joint project grown in 12 environments during 2004–2006 in Iran. Results of nonparametric tests of G × E interaction and a combined ANOVA across environments indicated the presence of both crossover and noncrossover interactions, and genotypes varied significantly for grain yield. In this study, high values of TOP (proportion of environments in which a genotype ranked in the top third) and low values of sum of ranks of mean grain yield and Shukla’s stability variance (rank-sum) were associated with high mean yield. The other nonparametric stability methods were not positively correlated with mean yield but they characterized a static concept of stability. The results of correlation analysis indicated that only TOP and rank-sum methods would be useful for simultaneous selection for high yield and stability. These two methods identified lines Mrb3/Mna-1, Syrian-4 and Mna-1/Rfm-7 as genotypes with dynamic stability and wide adaptation. According to static stability parameters, the genotypes 12A-Mar8081 and 19A-Mar8081 with lowest grain yield were selected as genotypes with the highest stability.  相似文献   

2.
Multi-environment trials (MET) play an important role in selecting the best cultivars and/or agronomic practices to be used in future years at different locations by assessing a cultivar's stability across environments before its commercial release. Objective of this study is to identify chickpea (Cicer arietinum L.) genotypes that have high yield and stable performance across different locations. The genotypes were developed by various breeders at different research institutes/stations of Iran and the International Center for Agricultural Research in Dray Areas (ICARDA), Syria. Several statistical methods were used to evaluate phenotypic stability of these test chickpea genotypes. The 17 genotypes were tested at six different research stations for two years in Iran. Three non-parametric statistical test of significance for genotype × environment (GE) interaction and ten nonparametric measures of stability analyses were used to identify stable genotypes across the 16 environments. The non-parametric measures (Kubinger, Hildebrand and Kroon/Van der) for G × E interaction were highly significant (P < 0.01), suggesting differential responses of the genotypes to the test environments. Based on high values of nonparametric superiority measure (Fox et al. 1990) and low values of Kang's (1988) rank-sum stability parameters, Flip 94-123C was identified as the most stable genotype. These non parametric parameters were observed to be associated with high mean yield. However, the other nonparametric methods were not positively correlated with mean yield and were therefore not used in classifying the genotypes. Simple correlation coefficients using Spearman’s rank correlation, calculated using ranks was used to measure the relationship between the stability parameters. To understand the nature of relationships among the nonparametric methods, a hierarchical cluster analysis based on non weighted values of genotypes, was performed. The 10 stability parameters fell into three groups.  相似文献   

3.
Reza Mohammadi  Ahmed Amri 《Euphytica》2013,192(2):227-249
The genotype × environment (GE) interaction influences genotype selection and recommendations. Consequently, the objectives of genetic improvement should include obtaining genotypes with high potential yield and stability in unpredictable conditions. The GE interaction and genetic improvement for grain yield and yield stability was studied for 11 durum breeding lines, selected from Iran/ICARDA joint program, and compared to current checks (i.e., one durum modern cultivar and two durum and bread wheat landraces). The genotypes were grown in three rainfed research stations, representative of major rainfed durum wheat-growing areas, during 2005–09 cropping seasons in Iran. The additive main effect and multiplicative interaction (AMMI) analysis, genotype plus GE (GGE) biplot analysis, joint regression analysis (JRA) (b and S2di), six stability parameters derived from AMMI model, two Kang’s parameters [i.e., yield-stability (YSi) statistic and rank-sum], GGE distance (mean performance + stability evaluation), and two adaptability parameters [i.e., TOP (proportion of environments in which a genotype ranked in the top third) and percentage of adaptability (Ad)] were used to analyze GE interaction in rainfed durum multi-environment trials data. The main objectives were to (i) evaluate changes in adaptation and yield stability of the durum breeding lines compared to modern cultivar and landraces (ii) document genetic improvement in grain yield and analyze associated changes in yield stability of breeding lines compared to checks and (iii) to analyze rank correlation among GGE biplot, AMMI analysis and JRA in ranking of genotypes for yield, stability and yield-stability. The results showed that the effects due to environments, genotypes and GE interaction were significant (P < 0.01), suggesting differential responses of the genotypes and the need for stability analysis. The overall yield was 2,270 kg ha?1 for breeding lines and modern cultivar versus 2,041 kg ha?1 for landraces representing 11.2 % increase in yield. Positive genetic gains for grain yield in warm and moderate locations compared to cold location suggests continuing the evaluation of the breeding material in warm and moderate conditions. According to Spearman’s rank correlation analysis, two types of associations were found between the stability parameters: the first type included the AMMI stability parameters and joint regression parameters which were related to static stability and ranked the genotypes in similar fashion, whereas the second type consisted of the rank-sum, YSi, TOP, Ad and GGED which are related to dynamic concept of stability. Rank correlations among statistical methods for: (i) stability ranged between 0.27 and 0.97 (P < 0.01), was the least between AMMI and GGE biplot, and highest for AMMI and JRA and (ii) yield-stability varied from 0.22 (between GGE and JRA) to 0.44 (between JRA and AMMI). Breeding lines G8 (Stj3//Bcr/Lks4), G10 (Ossl-1/Stj-5) and G12 (modern cultivar) were the best genotypes in terms of both nominal yield and stability, indicating that selecting for improved yield potential may increase yield in a wide range of environments. The increase in adaptation, yield potential and stability of breeding lines has been reached due to gradual accumulation of favorable genes through targeted crosses, robust shuttle breeding and multi-locational testing.  相似文献   

4.
Evaluation of genotype × environment interaction (GEI) is an important component of the variety selection process in multi-environment trials. The objectives of this study were first to analyze GEI on seed yield of 18 spine safflower genotypes grown for three consecutive seasons (2008–2011) at three locations, representative of rainfed winter safflower growing areas of Iran, by the additive main effects and multiplicative interaction (AMMI) model, and second to compare AMMI-derived stability statistics with several stability different methods, and two stability analysis approaches the yield-stability (Ysi) and the GGE (genotype + genotype × environment) biplot that are widely used to identify high-yielding and stable genotypes. The results of the AMMI analysis showed that main effects due to genotype, environment, and GEI as well as first six interaction principle component axes (IPCA1 to 6) were significant (P < 0.01). According to most stability statistics of AMMI analyses, genotypes G5 and G14 were the most stable genotypes across environments. According to the adjusted stability variance (s2), the high-yielding genotype, G2, was unstable due to the heterogeneity caused by environmental index. Based on the definition of stable genotypes by regression method (b = 1, S d 2  = 0), genotypes G11, G9, G14, G3, G12 and G13 had average stability for seed yield. Stability parameters of Tai indicated that genotype G5 had specific adaptability to unfavorable environments. The GGE biplot and the Ysi statistic gave similar results in identifying genotype G2 (PI-209295) as the best one to release for rainfed conditions of Iran. The factor analysis was used for grouping all stability parameters. The first factor separated static and dynamic concepts of stability, in which the Ysi and GGED (i.e., the distance from the markers of individual genotypes to the ideal genotype) parameters had a dynamic concept of stability, and the other remaining parameters had static concept of stability.  相似文献   

5.
One approach to improve cotton (Gossypium hirsutum L.) yield is to identify stable genotypes for low or no bollworms damage as it accounts low cost of cultivation to a considerable extent. The objective of the study was to identify stable genotypes for low bollworms damage under protected and unprotected experiments. Fifty cotton (Gossypium hirsutum L.) genotypes were evaluated for open bollworms damage percent (spotted, pink and Heliothis bollworms) for 3 years during 2003–2005 under protected and unprotected conditions. Variance due to genotypes, environments, genotype × environment and genotype × environment (linear) components were highly significant for the trait in protected and unprotected experiments. Under protected experiment, genotypes CSH 3020 and 3045 were found to be desirable and stable while genotype CSH 3058 was suited for poor environments. Fourteen genotypes viz. CSH 3034, 3035, 3043, 3044, 3047, 3051, 3053, 3058, 3059, 3094, 3106, 3114, 3123 and CSH-3157 were fairly stable under unprotected environments. The study lead to the conclude that in general for the two experiments the genotypes differed for stability for low bollworms damage.  相似文献   

6.
Striga gesnerioides (Willd) Vatke, is a major destructive parasitic weed of cowpea (Vigna unguiculata (L.) Walp.) which causes substantial yield reduction in West and Central Africa. The presence of different virulent races within the parasite population contributes to significant genotype × environment interaction, and complicates breeding for durable resistance to Striga. A 3-year study was conducted at three locations in the dry savanna agro-ecology of Nigeria, where Striga gesnerioides is endemic. The primary objective of the study was to identify cowpea genotypes with high yield under Striga infestation and yield stability across test environments and to access suitability of the test environment. Data collected on grain yield and yield components were subjected to analysis of variance (ANOVA). Means from ANOVA were subjected to the genotype main effect plus genotype × environment (GGE) biplot analysis to examine the multi-environment trial data and rank genotypes according to the environments. Genotypes, environment, and genotypes × environment interaction mean squares were significant for grain yield and yield components, and number of emerged Striga plants. The environment accounted for 35.01%, whereas the genotype × environment interaction accounted for 9.10% of the variation in grain yield. The GGE biplot identified UAM09 1046-6-1 (V7), and UAM09 1046-6-2 (V8), as ideal genotypes suggesting that these genotypes performed relatively well in all study environments and could be regarded as adapted to a wide range of locations. Tilla was the most repeatable and ideal location for selecting widely adapted genotypes for resistance to S. gesnerioides.  相似文献   

7.
Durum wheat is grown in the Mediterranean region under stressful and variable environmental conditions. In a 4-year-long experiment, 14 genotypes [including 11 durum breeding lines, two durum (Zardak) and bread (Sardari) wheat landraces, and one durum (Saji) newly released variety] were evaluated under rainfed and irrigated conditions in Iran. Several selection indices [i.e. stress tolerance index (STI), drought tolerance efficiency (DTE), and irrigation efficiency (IE)] were used to characterize genotypic differences in response to drought. The GGE biplot methodology was applied to analyze a three-way genotype-environment-trait data. Combined ANOVA showed that the year effect was a predominant source of variation. The genotypes differed significantly (P < 0.01) in grain yield in the both rainfed and irrigated conditions. Graphic analysis of the relationship among the selection indices indicated that they are not correlated in ranking of genotypes. The two wheat landraces and the durum-improved variety with high DTE had minimum yield reduction under drought-stressed environments. According to STI, which combines yield potential and drought tolerance, the “Saji” cultivar followed by some breeding lines (G11, G8, and G4) performed better than the two landraces and were found to be stable and high-yielding genotypes in drought-prone rainfed environments. The breeding lines G8, G6, G4, and G9 were the efficient genotypes responding to irrigation utilization. In conclusion, the identification of the durum genotypes (G12, G11, and G4) with high yield and stability performance under unpredictable environments and high tolerance to drought stress conditions can help breeding programs and eventually contribute to increasing and sustainability of durum production in the unpredictable conditions of Iran.  相似文献   

8.
Drought tolerance as such is often not considered to be an independent trait by plant breeders. The objective of this study was to evaluate eight drought tolerance indices, namely stress susceptibility index (SSI), yield stability index (YSI), yield reduction ratio (Yr), yield index (YI), tolerance index (TOL), mean productivity (MP), geometric mean productivity (GMP), and stress tolerance index (STI) in upland cotton (G. hirsutum L.) genotypes. For this purpose, 16 genotypes were sampled during the 2013-2014 growing seasons under both normal and drought-stress field conditions at the Main Cotton Research Station of Navsari Agricultural University, Surat, India. The drought tolerance indices were calculated based on seed cotton yield under drought stress and non-stress conditions. Mean comparison of drought tolerance indices and seed cotton yield validated the significant influences of drought stress on yield as well as significant differences among genotypes. Results of calculated correlation coefficients and multivariate analyses showed that GMP, MP and STI indices were able to discriminate drought-sensitive and tolerant genotypes. Cluster analysis using the drought-tolerance indices divided the 16 genotypes into tolerant and susceptible groups. Two genotypes, G.Cot.16 × H-1353/10 and H-1353/10 × G.Cot.16 gave good yield response under drought conditions leading to their stability during water stress conditions. Based on multivariate analyses using the indices individually or in combinations, it was possible to identify the most yield-stable genotypes across the environments. Overall, we concluded that GMP, MP and STI indices can be efficiently exploited not only for screening drought tolerance but also to identify superior genotypessuitable for both stress and non-stress field conditions.  相似文献   

9.
Seed yield of 10 linseed genotypes, tested in a randomized block design with four replications across 18 environments of Ethiopia was analysed using different stability models. The objectives were to assess genotype‐environment (G‐E) interactions, determine stable genotypes, and to compare the stability parameters. Year by location and location variability were the dominant source of interactions. The stability analyses identified ‘R12‐N10D’, ‘Chilalo’ and ‘P13611’ב10314D’ as more stable genotypes, while ‘R11‐N1266’, ‘R10‐N27G’ and ‘R12‐D24C’ were specifically adapted to some environments. The highly significant rank correlations found among the deviations from regression, additive main effects multiplicative interaction, stability values, coefficients of determination, and stability variances indicated their close similarity and effectiveness in detecting stable genotypes over a range of Ethiopian environments.  相似文献   

10.
Twenty parametric and non-parametric measures derived from grain yield of 15 advanced durum genotypes evaluated across 12 variable environments during the 2004–2006 growing seasons were used to assess performance stability and adaptability of the genotypes and to study interrelationship among these measures. The combined ANOVA and the non-parametric tests of Genotype × environment interaction indicated the presence of significant crossover and non-crossover interactions, and of significant differences among genotypes. Principal component analysis based on the rank correlation matrix indicated that most non-parametric measures were significantly inter-correlated with parametric measures and therefore can be used as alternatives. The results also revealed that stability measures can be classified into three groups based on static and dynamic concepts of stability. The group related to the dynamic concept and strongly correlated with mean grain yield of stability included the parameters of TOP (proportion of environments in which a genotype ranked in the top third), superiority index (P i) and geometric adaptability index. The second group reflecting the concept of static stability included, Wricke’s ecovalence, the variance in regression deviation (S 2 di), AMMI stability value, the Huehn’s parameters [S i(1), S i(2)], Tennarasua’s parameter [NPi(1)], Kang’s parameter (RS) and yield reliability index (I i) which were not correlated with mean grain yield. The third group influenced simultaneously by grain yield and stability included the measures S i(3), S i(6), NPi(2), NPi(3), environmental variance (S 2 xi), coefficient of variability and coefficient of regression (b i). Based on the concept of dynamic stability, genotypes G6, G4, and G3 were found to be the most adapted to favorable environments, whereas genotypes G8, G9, and G12 were more stable and are related to the concept of static stability.  相似文献   

11.
Changes in the relative genetic performance of genotypes across environments are referred to as genotype × environment interactions (GEIs). GEIs can affect barley breeding improvement for salt tolerance because it often complicates the evaluation and selection of superior genotypes. The present study evaluated the GEIs over 60 barley genotypes for yield components and grain yield in six salinity environments in North Delta, Egypt. Data were analyzed using the additive main effects and multiplicative interaction (AMMI) and Tai’s stability parameters. GEIs effects on yield explained 20.3, 20.1, 14.6, and 33.0% of the total variation besides, the first two principal components account for 67.3, 56.3, 64.3, and 83.7% of the explained variance in the four sets, respectively. Six genotypes namely G-4, G-7, G-20, G-34, G-36, and G-39 were found to be most stable and high yielding across environments (GY >2.00 t ha-1), and located close to zero projection onto the AEC ordinate. Tai’s stability parameters demonstrated that these genotypes were more responsive to the environmental changes. The genotypes G-50 and G-53 showed perfect/static stability (α = -0.95, -0.91, respectively). In contrast, the genotype; G-36 had α = 0 and λ = 1.10, indicating parallel with the environmental effects followed by G-44. Overall, we found that GEIs for grain yield are highly significant in all sets, suggesting that responded differently across environments. This interaction may be a result of changes in genotypes’ relative performance across environments, due to their differential responses to various abiotic factors.  相似文献   

12.
High and stable yield is a very desirable attribute of soybean (Glycine max (L.) Merr.) cultivars. Stable yield of a cultivar means that its rank relative to other cultivars remains unchanged in a given set of environments. To characterize 12 soybean cultivars chosen from performance trials, data were obtained from 10 environments (five locations in 2 years). Six stability parameters from four statistical models were derived for each cultivar. Regression coefficients were significantly and positively correlated only with coefficients of variation; they are useful in characterizing whether cultivars responded well in favourable or poor environments. Nassar and Huhn's nonparametric measures, Si(1) and Si(2), were significantly and positively correlated with Eberhart and Russell's sdi2 and Wricke's ecovalence (Wi). The stability measures are useful in characterizing cultivars by showing their relative performance in various environments. Results revealed that high-yielding cultivars also can be stable cultivars. Correlations between stability parameters obtained from individual years over the same set of locations and cultivars were very low and nonsignificant, suggesting that single-year data are not reliable as basis for selection. To provide an additional guide for selection, Kang's rank-sum approach was applied, in which both yield (in rank) and measured nonparametric stability (in rank) were considered. In general, selection for yield only would sacrifice stability to some degree, and selection for stability only would sacrifice a certain amount of yield. The rank-sum approach reconciles the two and appeared to provide a useful means to characterize soybean cultivars.  相似文献   

13.
Selecting high yielding genotypes with stable performance is the breeders’ priority but is constrained by genotype × environment (G×E) interaction. We investigated canola yield of 35 genotypes and its stability in multiple environment trials (MET) in south-western Australia and the possibility to breed broadly-adapted high yielding genotypes. The Finlay–Wilkinson (F–W) regression and factor analytic (FA) model were used to investigate the G×E interaction, yield and genotype stability and adaptability. The cross-over response in the F–W regression, substantial genetic variance heterogeneity, and the genetic correlations in the FA model demonstrated substantial G×E interaction for yield. Cluster analysis suggests low, medium and high rainfall mega-environments. F–W regression indicated that genotypes with high stability (e.g. low regression slope values) produced relatively low yield and vice versa, but also identified broadly adapted genotypes with high intercepts and steep regression slopes. The FA model provided a more detailed analysis of performance, dividing genotypes by positive, flat or negative responses to environment. In general, early flowering genotypes responded negatively to favourable environments and vice versa for late flowering genotypes. More importantly, a few early flowering hybrids with long flowering phases were consistently productive in both low and high yielding environments, showing broad adaptability. These productive hybrids were consistent with those identified earlier by high F–W intercept and slope values. Hybrids were higher yielding and more stable than open-pollinated canola, as was Roundup-Ready® canola compared to the three other herbicide tolerance groups (Clearfield®, Triazine tolerant, conventional). We conclude that yield stability and high yield are not mutually exclusive and that breeding for broadly adapted high yielding canola is possible.  相似文献   

14.
The success of plant breeding programs depends on the ability to provide farmers with genotypes with guaranteed superior performance in terms of yield across a range of environmental conditions. We evaluated 49 sugar beet genotypes in four different geographical locations in 2 years aiming to identify stable genotypes with respect to root, sugar and white sugar yields, and to determine discriminating ability of environments for genotype selection and introduce representative environments for yield comparison trials. Combinations of year and location were considered as environment. Statistical analyses including additive main effects and multiplicative interactions (AMMI), genotype main effects and genotype?×?environment interaction effects (GGE) models and AMMI stability value (ASV) were used to dissect genotype by environment interactions (GEI). Based on raw data, root, sugar and white sugar yields varied from 0.95 to 104.86, 0.15 to 20.81, and 0.09 to 18.45 t/ha across environments, respectively. Based on F-Gollob validation test, three interaction principal components (IPC) were significant for each trait in the AMMI model whereas according to F ratio (FR) test two significant IPCs were identified for root yield and sugar yield and three for white sugar yield. For model diagnosis, the actual root mean square predictive differences (RMS PD) were estimated based upon 1000 validations and the AMMI-1 model with the smallest RMS PD was identified as the most accurate model with highest predictive accuracy for the three traits. In the GGE biplot model, the first two IPCs accounted for 60.52, 62.9 and 64.69% of the GEI variation for root yield, sugar yield and white sugar yield, respectively. According to the AMMI-1 model, two mega-environments were delineated for root yield and three for sugar yield and white sugar yield. The mega-environments identified had an evident ecological gradient from long growing season to intermediate or short growing season. Environment-focused scaling GGE biplots indicated that two locations (Ekbatan and Zarghan) were the most representative testing environments with discriminating ability for the three traits tested. Environmentally stable genotypes (i.e. G21, G28 and G29) shared common parental lines in their pedigree having resistance to some sugar beet diseases (i.e. rhizomania and cyst nematodes). The results of the AMMI model were partly in accord with the results of GGE biplot analysis with respect to mega-environment delineation and winner genotypes. The outcome of this study may assist breeders to save time and costs to identify representative and discriminating environments for root and sugar yield test trials and creates a corner stone for an accelerated genotype selection to be used in sweet-based programs.  相似文献   

15.
A combined analysis with three parametricand two nonparametric measures to assess G × E interactions and stability analyses toidentify stable genotypes of linseed across18 environments in Ethiopia wereundertaken. The combined analysis ofvariance for environments (E), genotypes(G) and G × E interaction was highlysignificant (p<0.01), suggestingdifferential responses of the genotypes andthe need for stability analysis. Theparametric stability measures ofcoefficient of variability and thestability variance showed that R12-N10D wasthe most stable genotype, whereascultivars' superiority measure indicatedChilalo to be the most stable cultivar.Like most of the parametric methods, thenon-parametric measures revealed thatR12-N10D had the smallest changes in ranksand thus was the most stable genotype incontrast to R12-D24C, which was unstableand the lowest yielder. A comparison of thefive stability measures showed that thecoefficient of variability, stabilityvariance and variance of ranks were similarin assessing the relative stability of thegenotypes, whereas cultivars' superioritymeasure deviated from the others. Thestability variance and variance of rankswere significantly rank correlated, andwere the best in determining thecomparative stability of linseed genotypes.The coefficient of variability was alsorelatively better than the cultivar'ssuperiority measure. Further studies ofrepeatability tests are, however, needed todetermine the best methods. The stabilitystatistics generally identified R12-N10D,followed by Chilalo, as the most stablevarieties, whereas R12-D24C and R11-M20Gwere the least stable varieties.  相似文献   

16.
Heterosis and mixing effects in barley under drought stress   总被引：1，自引：0，他引：1  
Yield stability is one of the main breeding objectives in breeding for stress environments, such as the semi‐arid areas of Syria. The objectives of this study were to measure the effects of heterogeneity and heterozygosity on yield and yield stability by comparing doubled haploid lines (DHL) in mixed vs. pure stand (influence of heterogeneity) and F2 populations vs. corresponding DHL mixtures (influence of heterozygosity). Six barley lines from two gene pools (LR = landraces, EL = experimental lines) were used to produce nine crosses (two LR × LR, three EL × EL, four LR × EL). The F2 generation and eight DHL per cross were produced from each cross. The six parental DHL, nine F2 populations, nine 8‐line mixtures and 72 DHL in pure stands were tested in five environments under drought stress in north Syria. The mean superiority of F2 populations over DHL mixtures for yield traits across environments and cross combinations ranged between 7.5 and 10%. The effect of heterogeneity was small throughout. For grain yield, harvest index, 1000‐grain weight and plant height significant interactions between heterozygosity levels and environments were observed. The effect of heterozygosity for grain yield increased substantially from ‐1.2% in the highest‐yielding environment to 45.6% in the most stressful environment. Interactions between levels of heterozygosity and cross combinations were significant for most traits. F2 populations were considerably more stable than DHL in pure stands, yet not as stable as DHL mixtures. It is concluded that heterozygosity is more important than heterogeneity in breeding for improved yield and yield stability under drought stress.  相似文献   

17.
Unpredictable rainfall, variations in farm inputs, crop-diseases, and the inherent potential of genotypes are among the major factors for low and variable crop yield. Fourteen elite groundnut genotypes were examined in 14 environments to analyze adaptability and stability of genotypes, and identify mega-environments if they exist. Additive main effect and multiplicative interaction (AMMI) model, cultivar-superiority measure, and genotype plus genotype-by-environment (GGE) biplot analysis were used for data analysis. The environment (69.8%) and genotype-by-environment interaction (GEI) effects (21.4%) were dominating the genotypic effect (8.8%). The GEI was significant (P < 0.01), and two distinct environments (mega-environments) were identified, suggesting separate national groundnut breeding strategies for Babile and Pawe. ICGV-94100 and ICGV-97156 were stable and had the highest-yield at Babile and Pawe, respectively. The higher heritability value was recorded in more homogeneous and favorable environments, indicating the genetic potential of groundnut genotypes were better attained in more homogeneous and favorable environments. AMMI model, cultivar-superiority measure, and GGE biplots were helpful methodologies and complemented each other to evaluate the adaptability and stability of groundnut genotypes in diverse environments.  相似文献   

18.
This study was carried out to identify superior barley genotypes for the rainfed areas of western Iran using a participatory varietal selection (PVS) approach. Three field experiments were conducted in two randomly selected farmers’ fields and in one rainfed research station in the 2006–07 cropping season with 69 genotypes (including one local and one improved check). Several univariate and multivariate methods were used to analyze qualitative (farmers’ scores) and quantitative (grain yield) data. Individual farmers’ scores in each village were positively correlated, indicating that the farmers tended to discriminate genotypes in similar fashion, although the genotypes actually selected by farmers were different in the two villages. In recent years, a greater number of farmers in western Iran preferred the improved variety (Sararood-1) over the local barley (Mahali), while in this project the farmers preferred the new genotypes over the two checks. This was also verified by the quantitative data showing that the checks were outyielded by the new genotypes. Farmers were efficient in identifying the best genotypes for their specific environment, as shown by biplot analysis, indicating their competence in selection. The genotypes selected by the breeder and farmers were almost similar but some differences existed. In conclusion, PVS is a powerful way to involve farmers for selecting and testing new cultivars that are adapted to their needs, systems and environments.  相似文献   

19.
Late blight is an important constraint to potato production and genotype resistance is an effective disease control mesure. Ten late blight resistant potato genotypes (R-gene free) were assessed for yield performance and stability at early (90 days) and late harvest (120 days) at two locations in Kenya during two years. Significant differences (P ≤ 0.05) in area under disease progress curves (AUDPC) were detected among potato genotypes. Resistant genotypes free of R-genes had significantly (P ≤ 0.05) higher yield at late than early harvest, perhaps due to increased tuber bulking period. The rank of genotypes for AUDPC, late blight resistance, and tuber yield varied across seasons and locations (environment). Additive main effects and multiplicative interaction (AMMI) analysis of tuber yield and late blight resistance resulted in significant (P ≤ 0.05) effects of genotypes (G) and environments (E). The proportion of genotypic variance was larger than the environmental variance and the G × E interactions. For tuber yield, the G, E, and G × E interactions accounted for 42.9, 39.6 and 17.5%; and 53.4, 29.7, and 16.9% at early and late harvests, respectively. For AUDPC, G, E, and G × E accounted for 80.2, 5.0, and 14.8%; as well as 82.3, 4.6, and 13% for early and late harvests, respectively. The resistance of potato genotypes without R-genes varied. Selective deployment of resistant genotypes can improve potato tuber yield.  相似文献   

20.
Genotype-by-environment interactions (GEIs) can affect breeding progress because they often complicate the evaluation and selection of superior genotypes. This drawback can be reduced by gaining insights into GEI processes and genotype adaptation. Here, we have studied the GEIs over a set of 24 barley genotypes that were grown across six environments (location-by-year combinations) in Sardinia, Italy. Three groups of genotypes were analysed: barley landraces (LANs), recombinant inbred lines (RILs), and commercial varieties (VARs). The additive main effects and multiplicative interaction (AMMI) model was used for data analysis, and results evidenced no significant differences in grain yield averages for the 24 genotypes. However, there was a relevant GEI for yield mainly between two of the six environments (one characterised by warm pre-anthesis period and high spring rainfalls, and the other characterised by opposite features) and two groups of genotypes (VAR and LAN). Moreover, a negative trade-off between yield levels of genotypes was seen when the barley genotypes were grown in the contrasting environments. Overall, intermediate GEI levels were seen for RILs in comparison to LANs and VARs, and some of the RILs provided valuable yield levels (e.g. RILs 23 and 52). The results thus show the potential usefulness of LANs as a genetic resource for breeding, e.g. to obtain genotypes adapted to Mediterranean environments, such as the RILs analysed in this study. Most of the actual work was carried out when the first author was a PhD student in ‘Agro-meteorology and ecophysiology of agricultural and forest systems’ and she was affiliated to Dipartimento di Scienze Agronomiche e Genetica Vegetale Agraria, Università degli Studi di Sassari, Via E. de Nicola, Sassari 07100, Italy.  相似文献   

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