GGE叠图法─分析品种×环境互作模式的理想方法

本文介绍一种分析作物区域试验结果的方法－ＧＧＥ叠图法。首先，将原始产量数据减去各地 点的平均产量，由此形成的数据集只含品种主效应Ｇ和品种－环境互作效应ＧＥ，合称为ＧＧＥ。对ＧＧＥ 作单值分解，并以第一和第二主成分近似之。按照第一和第二主成分值将各品种和各地点放到一个平 面图上即形成ＧＧＥ叠图。借助于辅助线，可以直观回答以下问题：（１）什么是某一特定环境下最好的 品种；（２）什么是某一特定品种最适合的环境；（３）任意两品种在各环境下的表现如何；（４）试验中品 种×环境互作的总体模式是怎样的；（５）什么是高产、稳产品种；（６）什么是有利于筛选高产、稳产品 种的环境。     

Adaptability and stability analysis of groundnut genotypes using AMMI model and GGE-biplot

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.     

Genotype by environment interaction components underlying variations in root,sugar and white sugar yield in sugar beet (<Emphasis Type="Italic">Beta vulgaris</Emphasis> L.)

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 (F_R) 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.     

Characterization of genotype by environment interactions in soybean breeding programmes of southeast Europe

Southeast Europe is Europe's major production area of soybean of maturity groups (MG) 0, I and II, but genotype by environment interaction (GEI) for soybean traits in this region is still not characterized. The objective of this study was to characterize GEI in soybean grown in Croatia for seed yield, protein and oil content. Fourteen soybean cultivars of MG 0 and I were evaluated at 15 environments. In the combined anova , genotypes, environments and GEI were significant. All three seed traits were more affected by year than by location and seed yield and protein content were more sensitive to environmental changes than oil content. Genotype by environment interaction is generally of less importance than effects of genotypes and years, especially for oil content. High‐yielding genotypes had values of regression coefficient (b) close to 1, indicating that they were not responsive to extreme environments, either positive or negative. Means and b values were not correlated for yield and negatively correlated for protein and oil content, probably because of low‐input practice in the region. The absence of recognizable differences in means, b values and principal component scores of ammi analysis between two MGs for all traits indicated that soybean cultivars of MG 0 may be recommended for growing in the region because they responded similarly to environmental changes as do full‐season genotypes of MG I.     

Trial heterogeneity and variance models in the genetic evaluation of potato tuber yield

Genetic evaluation aims to identify genotypes with high empirical breeding values (EBVs) for selection as parents. In this study, 2157 potato genotypes were evaluated for tuber yield using 8 years of early‐stage trial data collected from a potato breeding programme. Using linear mixed models, spatial parameters to target greater control of localised spatial heterogeneity within trials were estimated and variance models to account for across‐trial genetic heterogeneity were tested. When spatial components improved model fit, correlations of errors were mostly small and negative for marketable tuber yield (MTY) and total tuber yield (TTY), suggesting the presence of interplot competition in some years. For the analysis of multi‐environment trials, a variance model with a simple correlation structure (with heterogeneous variances) was the most favourable variance structure fitted for TTY and PTY (per cent marketable yield). There was very little difference in model fit when comparing a factor analytic structure of order 2 (FA2) with either FA1 or simple correlation structures for MTY, indicating that simple variance models may be preferable for early‐stage genetic evaluation of potato yield.     

Using GGE Biplot Analysis to Evaluate Interrelationships between Yield and Yield Components of Oat Genotypes in Different Growing Seasons

The aim of this study was to evaluate oat genotypes for grain yield and yield components in both 2014-2015 and 2015-2016 growing seasons using GGE biplot analysis. Experiments were laid out according to lattice design. Biomass at harvest, grain yield, number of grain per panicle, weight of grain per panicle, flag leaf width, flag leaf height, plant height, stem diameter, thousand kernel weight, time of panicle emergence, harvest index, panicle length, and spikelet per panicle were evaluated for 56 oat genotypes. GGE biplot graphics of the first and second years explained 54.4 and 55% of total variation, respectively. Grain yield, number of grains per panicle, and weight of grain per panicle were closely and positively associated in both growing seasons. Recently registered cultivar Sari and lines 26, 29, and 30 were found as promising genotypes for Çanakkale conditions. Traits of genotype at different growing seasons can be visually studied using different genotype-by-trait (GT) biplots.     

Identifying high yielding stable winter wheat genotypes for irrigated environments in Central and West Asia

Improved winter wheat (Triticum aestivum L.) cultivars are needed for the diverse environments in Central and West Asia to improve rural livelihoods. This study was conducted to determine the performance of elite winter wheat breeding lines developed by the International Winter Wheat Improvement Program (IWWIP), to analyze their stability across diverse environments, and to identify superior genotypes that could be valuable for winter wheat improvement or varietal release. One hundred and one advanced winter wheat breeding lines and four check cultivars were tested over a 5-year period (2004–2008). Grain yield and agronomic traits were analyzed. Stability and genotypic superiority for grain yield were determined using genotype and genotype × environment (GGE) biplot analysis. The experimental genotypes showed high levels of grain yield in each year, with mean values ranging from 3.9 to 6.7 t ha⁻¹. A set of 25 experimental genotypes was identified. These were either equal or superior to the best check based on their high mean yield and stability across environments as assessed by the GGE biplot analysis. The more stable high yielding genotypes were ID800994.W/Falke, Agri/Nac//Attila, ID800994W/Vee//F900K/3/Pony/Opata, AU//YT542/N10B/3/II8260/4/JI/Hys/5/Yunnat Esskiy/6/KS82W409/Spn and F130-L-1-12/MV12. The superior genotypes also had acceptable maturity, plant height and 1,000-kernel weight. Among the superior lines, Agri/Nac//Attila and Shark/F4105W2.1 have already been proposed for release in Kyrgyzstan and Georgia, respectively. The findings provide information on wide adaptation of the internationally important winter wheat genotypes, and demonstrate that the IWWIP program is enriching the germplasm base in the region with superior winter wheat genotypes to the benefit of national and international winter wheat improvement programs.     

基于HA-GGE双标图的甘蔗试验环境评价及品种生态区划分

采用遗传力校正的GGE双标图(heritability adjusted GGE,HA-GGE),分析基因型(G)、环境(E)、基因型与环境互作效应(GE)对产量变异的影响,对14个试验点的分辨力、代表性和理想指数进行分析,并对这些试验点的生态区进行划分。结果表明,甘蔗试验环境对产量变异的影响大于基因型和基因型与环境互作;互作因素中以环境×基因型的互作效应最大,基因型×年份的互作效应最小。广东遂溪(E3)和广西崇左(E6)为最理想试验环境,对筛选广适性新品种和鉴别理想品种的效率最高;福建福州(E1)、福建漳州(E2)、广东湛江(E4)、云南保山(E11)、云南临沧(E13)、云南瑞丽(E14)为理想试验环境;广西百色(E5)、广西河池(E7)、海南临高(E10)、云南开远(E12)为较理想试验环境;广西来宾(E8)、广西柳州(E9)为不太理想的试验环境。根据HA-GGE双标图分析结果,可将我国甘蔗生态区划分为3个,即以广西百色、河池、来宾和柳州为代表的华南内陆甘蔗品种生态区,以云南保山、开远、临沧、瑞丽为代表的西南高原甘蔗品种生态区,涵盖福建福州、漳州、广东湛江、遂溪、广西崇左等试点的华南沿海甘蔗品种生态区。     

Performance of cowpea varieties under <Emphasis Type="Italic">Striga gesnerioides</Emphasis> (Willd.) Vatke infestation using biplot analysis

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.     

Evaluation of Indian mustard genotypes to heat stress in irrigated environment - seed yield stability and physiological model

Indian mustard is mostly targeted for commercial cultivation as an early-sown or late-sown crop with the expectation of higher seed yield and oil content. With this lacuna, 45 genotypes were analyzed for yield traits by growing them as early, timely, and late-sown crops over 2 years in Pantnagar, India. The results of the ‘Eberhart and Russell Model’ and ‘GGE Biplot’ analyses were in accordance to each other but Eberhart and Russell’s model was more appropriate for judging the genotype(s) to environment specificity/adaptation while GGE Biplot was the best approach to evaluate the concerned environments for their discriminating power to genotypes. Inverse and counteracting relationships were observed among model parameters, i.e., crop growth rate (C), partitioning coefficient (P), and duration of reproductive phase (Dr) with seed yield and oil content. Seed yield was positively (P ≤ 0.01) related to all related traits except Dr, while oil content was positively (P ≤ 0.01) related to only Dr. Both C and P contributed to final yields, but P had a greater contribution particularly under terminal heat stress.     

Comparison of shifted multiplicative model,rank correlation,and biplot analysis for clustering winter wheat production environments

Categorization of locations with similar environments helps breeders to efficiently utilize resources and effectively target germplasm. This study was conducted to determine the relationship among winter wheat (Triticum aestivum L.) yield testing locations in South Dakota. Yield trial data containing 14 locations and 38 genotypes from 8 year were analyzed for crossover genotype (G) × environment (E) interactions according to the Azzalini-Cox test. G × E was significant (P < 0.05) and contributed a small proportion of variation over the total phenotypic variation. This suggested that for efficient resource utilization, locations should be clustered. The data were further analyzed using the Shifted Multiplicative Model (SHMM), Spearman’s rank correlation and GGE biplot to group testing locations based on yield. SHMM analysis revealed four major cluster groups in which the first and third had three locations, with four locations in each of the second and fourth groups. Spearman rank correlations between locations within groups were significant and positive. GGE biplot analysis revealed two major mega-environments of winter wheat testing locations in South Dakota. Oelrichs was the best testing location and XH1888 was the highest yielding genotype. SHMM, rank correlation and GGE biplot analyses showed that the locations of Martin and Winner in the second group and Highmore, Oelrichs and Wall in the third group were similar. This indicated that the number of testing locations could be reduced without much loss of grain yield information. GGE biplot provided additional information on the performance of entries and locations. SHMM clustered locations with reduced cross-over interaction of genotype × location. The combined methods used in this study provided valuable information on categorization of locations with similar environments for efficient resource allocation. This information should facilitate efficient targeting of breeding and testing efforts, especially in large breeding programs.     

Performance stability of maize genotypes across diverse hill environments in Nepal

The national maize improvement program in Nepal regularly receives elite maize (Zea mays L.) genotypes from CIMMYT and other countries and tests them for their performance stability in highly diverse environments. Studies were conducted on research stations and farmers’ fields at five sites in three years to determine performance stability of exotic maize genotypes. Replicated on-station and on-farm studies were conducted using 25 and 10 genotypes, respectively, including a local check and an improved check (Manakamana-3), in 2004–2006. We analyzed grain yield, days to flowering, plant and ear height, plant population, husk cover, and plant and ear aspect. Stability and genotype superiority for grain yield was determined using genotype and genotype × environment (GGE) biplot analysis that compares among a set of genotypes with a reference ‘ideal’ genotype, which has the highest average value of all genotypes and is absolutely stable. Several genotypes produced significantly higher grain yield than the local check. Four genotypes (‘Across9942 × Across9944’, ‘Open Ended White Hill Population’, ‘Population 44C10’ and ‘ZM621’), that produced significantly higher grain yield than the improved check, also had other agronomic traits (days to flowering, plant and ear height, number of ears, resistance to leaf blight, plant and ear aspect and husk cover tightness) equal to or better than the improved check. GGE-biplot analysis showed that Across9942 × Across9944 and ZM621 were the most superior genotypes in the on-station and on-farm trials, respectively. The findings from this study provide new information on the stability of the maize genotypes that are also adapted to other regions of the world. Such information could be useful for maize improvement program for the highlands in Nepal and other similar environments.     

Assessment of Groundnut under Combined Heat and Drought Stress

In semi‐arid regions, particularly in the Sahel, water and high‐temperature stress are serious constraints for groundnut production. Understanding of combined effects of heat and drought on physiological traits, yield and its attributes is of special significance for improving groundnut productivity. Two hundred and sixty‐eight groundnut genotypes were evaluated in four trials under both intermittent drought and fully irrigated conditions, two of the trial being exposed to moderate temperature, while the two other trials were exposed to high temperature. The objectives were to analyse the component of the genetic variance and their interactions with water treatment, year and environment (temperature) for agronomic characteristics, to select genotypes with high pod yield under hot‐ and moderate‐temperature conditions, or both, and to identify traits conferring heat and/or drought tolerance. Strong effects of water treatment (Trt), genotype (G) and genotype‐by‐treatment (GxTrt) interaction were observed for pod yield (Py), haulm yield (Hy) and harvest index (HI). The pod yield decrease caused by drought stress was 72 % at high temperature and 55 % at moderate temperature. Pod yield under well‐watered (WW) conditions did not decrease under high‐temperature conditions. Haulm yield decrease caused by water stress (WS) was 34 % at high temperature and 42 % under moderate temperature. Haulm yield tended to increase under high temperature, especially in one season. A significant year effect and genotype‐by‐environment interaction (GxE) effect were also observed for the three traits under WW and WS treatments. The GGE biplots confirmed these large interactions and indicated that high yielding genotypes under moderate temperature were different to those at high temperature. However, several genotypes with relatively high yield across years and temperature environments could be identified under both WW and WS conditions. Correlation analysis between pod weight and traits measured during plant growth showed that the partition rate, that is, the proportion of dry matter partitioned into pods, was contributing in heat and drought tolerance and could be a reliable selection criterion for groundnut breeding programme. Groundnut sensitivity to high‐temperature stress was in part related to the sensitivity of reproduction.     

Specific adaptation of barley varieties in different locations in Ethiopia

Barley is one of the most important cereal crops grown for the livelihoods of the poor farmers of Tigray region in northern Ethiopia. As many low input and marginal environments it has benefited less from the yield increases achieved by modern breeding. This has been largely attributed due to genotype × environment intraction (GEI). To investigate the causes of GEI, ten barley varieties including local checks (two farmers developed varieties, four modern varieties and three rare local varieties) were tested over 21 environments. Participatory methods were applied to sample an adequate number of environments spanning the regional diversity. The yielding ability and stability of the varieties was graphically depicted by GGE and PLSR biplot. There were two major groups of environments, the central and northern highlands, the latter with less rainfall and poorer soils. Rainfall per month and total nitrogen level were the environmental variables that differentiated these two groups. In Tigray, rainfall in June and July were negatively correlated with yield, reflecting waterlogging problems. The different varieties were either specifically or widely adapted across the two environments. The variety ‘Himblil’, originating in Tigray, was the highest yielding and also most stable in the region of origin. However, it was inferior to improved varieties (Shege and Dimtu) at high yield levels. The association of earliness with grain yield indicates that the trait can be effectively manipulated within the existing materials. We recommend breeding for drought/water logging resistance based on selection in the target environment as the best strategy to provide stable and high yielding varieties for Tigray.     

Genotype × environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran

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.     

Genotype x environment interaction for durum wheat grain yield and selection for drought tolerance in irrigated and droughted environments in Iran

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.     

Evaluation of a durum wheat selection scheme under Mediterranean conditions: adjusting trial locations and replications

The effectiveness of a cultivar evaluation scheme is impeded by the cost of experimentation. The aim of this study was to explore whether the locations employed in durum wheat evaluation program in Greece constituted a mega-environment (ME) and to adjust the number of trial replications and locations for realizing an optimum heritability (H) of 0.75. The analysis was conducted for grain yield (GY), agronomic and quality parameters in a 10-year (2002–2011) dataset and included a variable across years, number of genotypes and locations. The GGE biplot analyses revealed that trial locations can be considered as a single, complex ME. The existence of the ME was also confirmed by the high H across locations. The number of replications and locations for realizing an optimum H for GY was five replications compared to the four currently used, and five locations in lieu of 3–4 now tested. Plant height in March, final plant height and days to heading required three replications and four locations, winter frost three and five, powdery mildew three and seven, stem rust five and nine, whereas lodging 10 replications and 10 locations, respectively. Regarding quality, thousand-kernel weight required four replications and three locations, whereas vitreous kernel percentage six and eight, grain protein concentration four and seven, black point percentage 17 replications and was of zero H across locations. Finally, for the traits assessed only across locations, ash content required seven, wet gluten content five while gluten index and β-carotene three locations.     

Assessment of mungbean genotypes for durable resistance to Yellow Mosaic Disease: Genotype × Environment interactions

Yellow mosaic disease (YMD) is the major constraint of mungbean for realizing high productivity worldwide. Moreover, management of disease using YMD‐resistant genotypes is the simplest approach. Therefore, based on a preliminary screening of 220 genotypes during the year 2010 and 2011 at 17 locations, a set of 25 genotypes was further selected to evaluate at six locations over 2 years for identification of more stable resistant genotypes. The genotype and genotype × environment (GGE) analysis indicated that the genotypes and environment effects were significant (P < 0.001) for YMD incidence. Interestingly, the GGE biplot analysis successfully accounted for 74.71 per cent of the total variation with three genotypes (ML 818, ML 1349 and IPM 02‐14) showing high degree of resistance and stability over the locations. Notably, a strong positive association was observed between disease reaction and temperature, relative humidity and rainfall. As crop is grown in diverse growing environments, aforementioned genotypes can be used as stable/durable sources for future breeding programme to develop YMD‐resistant cultivars.     

GGE biplot analysis to evaluate genotype, environment and their interactions in sorghum multi-location data

Sorghum [Sorghum bicolor (L.) Moench] is a very important crop in the arid and semi-arid tropics of India and African subcontinent. In the process of release of new cultivars using multi-location data major emphasis is being given on the superiority of the new cultivars over the ruling cultivars, while very less importance is being given on the genotype?×?environment interaction (GEI). In the present study, performance of ten Indian hybrids over 12 locations across the rainy seasons of 2008 and 2009 was investigated using GGE biplot analysis. Location attributed higher proportion of the variation in the data (59.3–89.9%), while genotype contributed only 3.9–16.8% of total variation. Genotype?×?location interaction contributed 5.8–25.7% of total variation. We could identify superior hybrids for grain yield, fodder yield and for harvest index using biplot graphical approach effectively. Majority of the testing locations were highly correlated. ‘Which-won-where’ study partitioned the testing locations into three mega-environments: first with eight locations with SPH 1606/1609 as the winning genotypes; second mega-environment encompassed three locations with SPH 1596 as the winning genotype, and last mega-environment represented by only one location with SPH 1603 as the winning genotype. This clearly indicates that though the testing is being conducted in many locations, similar conclusions can be drawn from one or two representatives of each mega-environment. We did not observe any correlation of these mega-environments to their geographical locations. Existence of extensive crossover GEI clearly suggests that efforts are necessary to identify location-specific genotypes over multi-year and -location data for release of hybrids and varieties rather focusing on overall performance of the entries.