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GGE BIPLOT and AMMI ANALYSIS OF YIELD STABILITY IN MULTI-ENVIRONMENT TRIAL OF SOYBEAN [Glycine max (L.) Merrill] GENOTYPES UNDER RAINFED CONDITION OF NORTH WESTERN HIMALAYAN HILLS.

Byline: A. Bhartiya, J. P. Aditya, V. Kumari, N. Kishore, J. P. Purwar, A. Agrawal and L. Kant

ABSTRACT

Soybean [Glycine max (L.) Merrill] is major oilseed crop globally. It is grown in diverse agro-ecological conditions and the performance of quantitative traits often varies due to significant genotype x environment interaction (GEI) therefore, the integration of yield and stability is one of the common objective of soybean breeding. The present investigation was carried out to study genotype x environment interaction (GEI) through GGE biplot and AMMI analysis over four environments (Majhera, Palampur, Bajaura and Almora) with 32 genetically diverse genotypes for four traits viz., grain yield, days to 50% flowering, days to maturity and 100 seed weight under rainfed condition of North Western Himalayan hills using randomised complete block design. The analysis of variance revealed that environments (E), genotypes (G) and genotype x environment interactions (GEI) accounted about 19.61%, 26.18% and 40.71% of the total variation, respectively.

GGE biplot graphically displayed interrelationships between test locations as well as genotypes and facilitated visual comparisons through two-dimensional biplot between the first two principal components (PCI and PCII) which explained 74.40% variation for grain yield, 91.98% for days to 50% flowering, 83.27% for days to maturity and 84.68% for 100 seed weight. The GGE biplot suggested suitability of all the four test locations to be used for multi-location trials on the basis of discrimination ability and representativeness. Genotypes, 'C 17' ('PS 1556') was found the best performing genotypes in terms of grain yield followed by 'C 11' ('VLS 89'), 'C 4' ('PS 1550') and 'C 10' ('DS 3102') whereas, in terms of high grain yield and stability both 'C 11' ('VLS 89') was found as the ideal genotype. In the test locations Majhera, Palampur and Almora, winning genotypes for grain yield were 'C 17' ('PS 1556') and 'C 11' ('VLS 89') while, 'C 34' ('VLS 59') was the winning genotype at Bajaura.

Keywords: AMMI, GGE biplot, GEI, MET and soybean [Glycine max (L.) Merrill].

INTRODUCTION

Soybean [Glycine max (L.) Merrill] is one of the most important crop in the world because of its versatile end uses and presently, largest source of edible oil among the oilseed crops (Bhartiya et al., 2014). It is grown worldwide in a diverse range of environments in tropical, subtropical and temperate climates. It was introduced in India during 1960's for exploring its potential as a commercial crop (Bisaliah, 1986) and presently, it occupies 10.02 million ha area with the production and productivity of 11.64 million tonnes and 1162 kg/ha, respectively (Anonymous, 2014-15). The phenomenal gain in area and production of soybean in the country was owing to the concerted efforts for developing high yielding cultivars and their wide cultivation.

In the development and identification of suitable genotypes for target environment, multi-environment testing is an important activity but generally, grain yield superiority is considered and the interaction of the cultivar with target environment i.e. genotype x environment interaction (GEI) is given little or no attention in this process (Rakshit et al., 2012). Besides, multi-environment trial (MET) data are not sufficiently exploited to their full potential and the genotypic evaluation is limited only to genotype main effects (G) whereas, genotype and environment interaction are ignored as noise (Yan and Tinker, 2006). Grain yield of each cultivar is a measure of genotype main effect (G), environment main effect (E) and the genotype x environment interaction (GEI) in each test environment (Yan and Tinker, 2005) but the genotype x environment interaction (GEI) reduces the correlation between phenotype and genotype as well as influence selection progress (Rao et al., 2011).

Genotype evaluation trials leading to variety identification generally faces two major difficulties, first existence of a strong genotype x environment interaction (GEI) for key trait like yield and secondly an undesirable association between the key trait and some trait of significant importance like days to flowering, days to maturity and 100 grain weight etc. Data analysis of key trait address the genotype x environment interaction (GEI) for key trait whereas, joint analysis along with the traits of significant importance accommodates the associations among them (Yan, 2014). Ideally, the variety assessment should be done through multi-year data, however, such data of coordinated trials are usually unbalanced due to change (dropping) of genotypes in different years making interpretations of data difficult. An alternative could be inclusion of all check genotypes to get sufficient number of test genotypes however, this also runs a risk of major influence of regional check.

Under such condition, analysis of single year multi-location data can be an option. Although, this may not be the best choice as the estimated heritability would be low, data from single year multi-location evaluation are sufficient enough for identifying location specific superior genotypes deserving promotion. In addition, the extent to which heritability is lowered depends on the magnitude of the genotype-year variance/ genotypic variance and genotype-year-location variance/genotypic variance (Yan, 2014).

In India, soybean is grown in diverse agro ecological zones ranging from Northern hill and plain, North Eastern, Central and Southern Zone. Among all soybean growing zones, in hills agro-climatic conditions are most challenging and are highly variable in terms of soil characteristics, rainfall, temperature etc. and differences in altitude and sunshine hours renders significant impact on genotype x environment interaction (GEI) in determining crop yield (Shukla et al., 2006). In North Western Himalayan region, soybean is grown as major Kharif crop mainly at altitudinal range 700-1700 masl (Dangwal et al., 2007). Genotype x environment interactions (GEI) remained a challenge in selecting genotypes across diverse agro-climatic conditions and make the identification of the best genotypes with high yield and stability difficult (Kumar et al., 2014).

Genotype x environment interactions (GEI) has great influence on the performance of cultivar especially, in marginal fragile environments of hills and also one of the main reasons for the failure of cultivars to attain predicted yield levels. In this situation, multi-environment trials (MET) can be used effectively to accurately evaluate the performance of cultivars across locations, predict the yield level as well as examine the stability of genotypes for target location for the selection of the best genotypes for target environments (Mustapha et al., 2014).

In general, genotype x environment interactions (GEI) are usually small in genotypic variation whereas, genotype (G) and environment (E) explains most of the variation (80% or higher) (Yan and Kang, 2003). It is, therefore, essential that genotypes are identified based on detailed understanding of their genotype x environment interactions (GEI), so that environment specific recommendations could be made. A wide array of statistical techniques have been developed to study and reveal the nature of this complex genotype x environment interactions (GEI), among them additive main effects and multiplicative interaction (AMMI) can effectively assess the stability and adaptability of genotypes (Pacheco and Vencovsky, 2005) whereas, GGE biplot enables the simplistic graphical visualization of complex genotype x environment interactions (GEI) (Yan et al., 2000).

The present investigation was done with the objective to study genotype x environment interaction (GEI) through GGE biplot and AMMI analysis over four environments in 32 genetically diverse soybean genotypes for traits viz., grain yield, days to flowering, days to maturity and 100 seed weight in rainfed condition of North Western Himalayan hills.

MATERIALS AND METHODS

The genetic materials of the present investigation comprised 32 new soybean genotypes along with 3 commercial checks viz., 'PS1092', 'VL Soya 59' and 'VL Soya 63'. The genotypes in this study were from the Initial Varietal Trial conducted under AICRP on soybean during rainy season of 2014. Multi-environment testing (MET) conducted at 4 locations namely, Almora, Palampur, Bajaura and Majhera which represented rain-fed agro-ecology at different altitudes of North Western Himalayan hills. General information on soybean genotypes used in the study are presented in Table 1 and the detailed features of the testing locations and sowing time at particular location are given in Table 2.

The experiment was conducted in randomized complete block design at each location, with plots accommodating standard plant population (90-95 plants) in 3 rows each of 3 m length with 45 x 10 cm2 crop geometry in three replications and standard crop management practices were adopted across all the locations. Crop was harvested plot wise at physiological maturity to record the grain yield data (kg/ha), 100 seed weight (g) of each treatment and observations on phenological stages were collected using standard methods (IBPGR, 1984). Plot yield data were converted to kg/ha using the plot size as factor. The grain yield (kg/ha), 100 seed weight(g), days to 50% flowering and days to maturity data were subjected to AMMI analysis of variance using JMP genomics (6.0 version) for the interpretation GxE interaction and for the graphical representation of MET data, GGE biplot analysis was executed using R software (version 3.1.3) and GGE Biplot GUI package.

The MET data was analysed without scaling (Scaling=0) to generate a tester centred (Centering=2) GGE biplot, genotype focused singular value partitioning (SVP=1) for visualising mean versus stability of genotypes, environment-focused single value partitioning (SVP=2) was employed for location evaluation and which won where option was used to identify which genotype was winner in given set of environment (Yan and Tinker, 2006).

RESULTS AND DISCUSSION

Analysis of variance: The analysis of variance over environments revealed the relative magnitude of genotypes (G), environments (E), Genotype x environment interactions (GEI) which clearly exhibited that G, E and GEI effects are significant for all of the traits under study (Table 3). ANOVA is an additive model that effectively describes the main effects and determines if genotype x environment interaction (GEI) is a significant source of variation (Samonte et al., 2005).The ANOVA for grain yield using the AMMI method explained that the soybean grain yield was significantly (p<0.01) affected by environments (E), genotype (G) and genotype x environment interactions (GEI). As per AMMI analysis, environments (E), genotypes (G) and genotype x environment interactions (GEI) accounted about 19.61%, 26.18% and 40.71% of the total variation, respectively and genotype x environment interactions (GEI) is more than the genotypic and environment effects.

However, the first principal component (PCI), second principal component (PCII) and third principal component (PCIII) explained 24.88%, 11.37% and 6.61% respectively, of the total GxE variation (40.71%). The genotype x environment interactions (GEI) refers to differential ranking of genotypes across environments and only genotype (G) and genotype x environment interactions (GEI) are relevant to cultivar evaluation particularly, when genotype x environment interaction (GEI) is determined as repeatable (Hammer and Cooper, 1996). The genotype x environment interactions (GEI) may complicate the process of selecting superior genotypes, recommendation of a genotype for a target environment and reduces the selection efficiency in different breeding programs (Ebdon and Gauch, 2002; Gauch, 2006).

The large GxE effects depicted genotypic differences in the performance, different wining genotypes at different locations as well as possibility of different mega environments in testing locations (Mohahamadi et al., 2009; Rakshit et al., 2012). However, multiyear data is required for the confirmation of the observed pattern of mega environments (Yan and Tinker, 2006).

Mean performance and stability of genotypes: In the GGE biplot technique developed by Gabriel (1971), the complex genotype x environment interactions (GEI) are simplified in different PCs and the data are presented graphically against various PCs where, PCI approximates the G (mean performance), PC II approximates the GxE (a measure of instability) for each genotype (Yan and Tinker 2006). Genotypic performance and stability were graphically visualized through GGE biplot (Fig.1a-d) using environment centered (centering=2) and genotype metric preserving (SVP=1) model for traits viz., grain yield, days to 50% flowering, days to maturity and 100 seed weight. The first two PCs explained 74.40% variation for grain yield, 91.98% for days to 50% flowering, 83.27% for days to maturity and 84.68% for 100 seed weight. Thus, the biplots may safely be interpreted as effective graphical representation of the variability in MET data.

If the first two PCs explain more than 60% of the variability in the data and the combined effect account for more than 10% of the total variability that implies the biplot adequately approximates the variability in genotype x environment data (Yang et al., 2009; Rakshit et al., 2012).

In biplot, mean performance of genotype is measured by the average environment coordination (AEC) abscissa which represents average environment and points towards higher mean values (Farshadfar et al., 2012).Whereas, stability is represented by AEC ordinate which points towards greater genotype x environment interactions (GEI) effect i.e. poor stability in either direction of genotypes i.e. greater the absolute length of the projection in either direction shows greater variability or less stability (Yan et al., 2000). Accordingly, C17 (PS 1556) was the best performing genotypes in terms of grain yield of 2556kg/ha followed by C11 (VLS 89) and C4 (PS 1550) with 2403kg/ha and 2305kg/ha, respectively whereas, C15 (MACS 1460) and C12 (RVS 2008-24) were poor yielders. It may be observed that the genotype C1 (AMS 1002), C31 (NRC 100), C23 (DSb 24) and C34 (VLS 59) are least stable for grain yield contrary to genotype C30 (KDS 780) with high stability though not high grain yielder.

Among all genotypes C11 (VLS 89) and C13 (MAUS 706) exhibited considerable stability with high mean performance for grain yield. For flowering duration genotype C22 (RSC 10-15 took maximum days for maturity (65 days) among all the entries whereas, C30 (KDS 780) was the earliest (52 days). Interestingly, though C11 (VLS 89) was among the best yielders and as in C17 (PS 1556) and C10 (DS 3102) but it was relatively early (111 days) in maturity than 'C10' ('DS 3102') and 'C17' ('PS 1556') which was having high mean values 116 days and 118 days, respectively i.e. long reproductive phase. Among all the genotypes under study 'C11' ('VLS 89') was found most stable for days to maturity as well as early than the best performing soybean genotypes. For 100 seed weight, 'C9' ('NRC 116') was highest (18.42 g), whereas, 'C18' ('JS 20-87') was lowest (9.03g) among other high yielding genotypes.

Although, C11 (VLS 89) ranked second for mean grain yield after C17 (PS 1556) but found promising for 100 seed weight.

Since the main attention was to get high yielding genotypes across the environment therefore, for further analysis more weight was given on grain yield. Graphical representation of GEI through GGE biplot offers benefit of identification of genotypes which are closest to ideal genotype and ideal environment as well. Ideal genotype situated at the centre of the concentric circles in GGE biplot, having greatest vector length of highest yielding genotype with zero GEI and selection of superior genotypes provided that the genotypic PCI scores have a near-perfect correlation with the genotype main effects, ideal genotypes should have a large PC1 score (high yielding ability) and a small (absolute) PC2 score (high stability). Genotypes located closer to the 'ideal genotype' are more desirable than others (Rakshit et al., 2012; Yan and Tinker 2006).

The ranking of the genotypes for the grain yield in terms of ideal genotype which is high performer with high stability across the environments is depicted in Fig.2. Thus, it may be stated that 'C11' ('VLS 89') could be considered as ideal genotype which had high grain yield among all genotypes and most stable across the test environments, therefore, 'C 4' ('PS 1550') and 'C 10' ('DS 3102') were closest to ideal genotype followed by 'C 17' ('PS 1556') and more desirable than the rest of soybean genotypes for North Western Himalayan hills. The most ideal genotype, 'C 11' ('VLS 89), performed best at Bajaura (2543kg/ha), while almost similar yield levels at rest three locations viz., Almora (2337kg/ha), Palampur (2387 kg/ha) and Majhera (2346kg/ha) (Table 4).

The above result suggests slight crossover GE interaction (order of genotypes based on their performance varied depending on the testing environment) for the most ideal genotype 'C 11' ('VLS 89). Whereas, 'C22' ('RSC 10-15') and 'C15' ('MACS 1460') were unfavourable as situated very far from the ideal genotype.

Environment evaluation: GGE biplots are useful to understand the relationship between the testing environments and identify the test environment that effectively identifies superior genotypes for mega environments and representative of mega environments (Yan et al., 2007). The relationship among test environments were studied based on environment-centered (centering=2) and environment-metric preserving (SVP=2) without scaling. In the environmental vector view of GGE biplot (Fig.3e-h), the lines that connects test environment to the biplot origin are called environment vectors and the cosine of the angle between the vectors of two environments approximates the correlation between them i.e. acute angle represents positive, obtuse angle depicts negative correlation, large GxE and strong crossovers and right angle represents no correlation between environments (Yan and Tinker, 2006).

However, the test environments with longer vectors are more discriminating of the genotypes whereas, a test environment marker with a very short vector provided little or no information about the genotype differences (Yan et al., 2007). Representativeness of test environments can be measured by angle between test environment with Average Environment Axis (AEA) and environment with smaller angles with AEA are most representative of the average test environments (Yan and Tinker, 2006). Accordingly, for days to 50% flowering, Palampur was found most discriminating and representative therefore, suitable environment for selecting generally adapted genotypes. Palampur was found most discriminating and Almora was found representative for days to maturity whereas, for grain yield, Bajaura was most discriminating whereas, Almora was most representative location.

All the test locations are discriminating and representative for one or more traits and show its suitability to be used as test environment for multi environment trials (MET). Almora was the most closest to average environment followed by Palampur. Whereas, ranking the genotypes in most near to average environment i.e. most representative (Almora) showed that 'C17' ('PS 1556') yielded maximum followed by 'C11' ('VLS 89'), 'C34' ('VLS 59'), 'C4' ('PS 1550') and 'C10' ('DS 3102'). For selecting generally adapted genotypes for grain yield, Palampur was found suitable based on both discrimination and representativeness.

Which-won-where and mega environment identification: Most attractive feature of GGE biplot is 'Which-won-where' analysis, in which crossover genotype x environment interactions (GEI), mega-environment differentiation, specific adaptation of genotypes etc. are graphically addressed (Rakshit et al., 2014). Visualization of the which-won-where pattern of multi environment trial data is important for studying the possible existence of different mega-environments (ME) in a region (Kaya et al., 2006). Which-won-where graph is constructed first by joining the farthest genotypes from the biplot origin so that, all other genotypes are contained within polygon and subsequently, perpendicular lines/equality lines drawn from biplot origin on each side of the polygon, separating the biplot into several sectors with one genotype at the vertex of the polygon.

The polygon view of a GGE biplot explicitly displays the which-won-where pattern, and hence is a laconic summary of the GxE interaction pattern (GEI) of a MET data set. All the genotypes in biplot were arranged in such a way that some of them were on the vertices whereas, the rest were inside the polygon. These vertex genotypes were the most responsive since, they have the longest distance from the biplot origin. Responsive genotypes were those having either the best or poorest performance in one or all environments (Yan and Rajcan, 2002) falling within the sectors. In the 'Which-won- where biplot', the environments distributed by equality lines into different sectors for grain yield (7), days to 50% flowering (6), days to maturity (7) and 100 seed weight (7) (Fig.4i-l). For grain yield, the polygon had seven genotypes, viz., C17 (PS 1556), C34 (VLS 59), C1 (AMS 1002), C15 (MACS 1460), C22 (RSC 10-15), C31 (NRC 100) and C23 (DSb 24) at the vertices.

All the environments were retained into two sectors and may be due to latitudinal and longitudinal differences the testing locations may be partitioned into two mega environments one with Majhera (29o30' N and 79o28' E), Palampur (32o6'N and 76o23'E) and Almora (29o36' N and 79o 40' E ) and second mega environment encompassed Bajaura (31o8'N and 77degE). In first mega environment (Majhera, Palampur and Almora) C17 (PS 1556) and C11 (VLS 89) and in Bajaura with C34 (VLS 59) were the winning genotypes.

Table 1. Soybean genotypes used for multi-environment trial (MET) at different locations in North Western Himalayan hills.

Code###Genotype###Pedigree###Source

C1###AMS 1002###Mutant of JS 93-05###PDKV, Amravati

C2###RVS 2007-6###JS 20-10/MAUS 162###RVSKV, Sehore

C3###MACS 1442###MACS 1037/JS 335###ARI, Pune

C4###PS 1550###PS 1029 /PS 1241###GBPUA and T, Pantnagar

C5###JS 20-98###JS 98-52 /SL 710###JNKVV, Jabalpur

C6###KDS 869###JS 335/EC 538800###K. Digraj, Maharashtra

C7###RSC 10-46###Bragg/JS 335###IGAU, Raipur

C8###DSb 28-3###JS 93-05/EC 241780###UAS, Dharwad

C9###NRC 116###-###DSR, Indore

C10###DS 3102###-###IARI, Delhi

C11###VLS 89###VLS 47/EC 361364###VPKAS, Almora

C12###RVS 2008-24###JS 335/PK 1042###RVSKV, Sehore

C13###MAUS 706###-###MAU, Parbhani

C14###AMS1004###Mutant of JS 93-05###PDKV, Amravati

C15###MACS 1460###RKS 24/ JS 95-60###ARI, Pune

C16###KDS 753###JS 93-05/EC 241780###K. Digraj, Maharashtra

C17###PS 1556###(PS1042/ MACS 450)/(PS1024/PS 1241)###GBPUA and T, Pantnagar

C18###JS 20-87###JS 97-52/JS(15)90-5-12-1###JNKVV, Jabalpur

C19###NRC99###EC546882/PS 1024###DSR, Indore

C20###SL 1028###PK 1223/SL(E)4###PAU, Ludhiana

C21###VLS 88###VLS 47/EC 361363###VPKAS, Almora

C22###RSC 10-15###RSC 4/Indian soya 9###IGAU, Raipur

C23###DSb 24###MACS 450/Local black soybean###UAS, Dharwad

C24###DS 3101###-###IARI, Delhi

C25###RVS 2008-8###JS93-05/RKS24###RVSKV, Sehore

C26###MACS 1454###PS 1347/TS 99-76###ARI, Pune

C27###PS 1552###PS 1214/Hardee###GBPUA and T, Pantnagar

C28###JS 20-96###JS 97-52/JSM 286###JNKVV, Jabalpur

C29###AMS 1003###Mutant of JS 93-05###PDKV, Amravati

C30###KDS 780###JS 93-05/ AMS 51###K. Digraj, Maharashtra

C31###NRC 100###G 841/NRC 7###DSR, Indore

C32###KBS 23-2014###JS 335/KHSb-2###UAS, Bengaluru

C33###PS 1092###(Check)###GBPUA and T, Pantnagar

C34###VL Soya 59###(Check)###VPKAS, Almora

C35###VL Soya 63###(Check)###VPKAS, Almora

Table 2. Locations used for evaluation of soybean genotypes in North-Western Himalayan hills.

Parameters###Almora###Bajaura###Palampur###Majhera

Altitude (m)###1250###1090###1290###905

Latitude###29deg36' N###31deg8'N###32deg6'N###29deg30'N

Longitude###79deg40'E###77degE###76deg23'E###79deg28'E

Total Rainfall (mm)###630.8###338.3###1010.4###634.0

Average Temp 0C (max)###29.44###30.43###27.3###30.35

Average Temp 0C (min)###17.14###17.37###17.5###21.72

Date of sowing###30.06.2014###08.07.2014###24.06.2014###07.07.2014

Table 3. Additive Main Effects and Multiplicative Interaction (AMMI) Analysis of variance of soybean genotypes

Source###DF###Mean Square Values of Different Traits###% SS###GE (%)

###Days to 50% flowering###Grain yield###(GY)###(GY)

Env###3###1561.56**###8746.67**###118.04**###10477608**###19.61###-

Rep(Env)###8###7.02**###3.51###3.15**###79358.32###0.40###-

###**###**

Genotypes###34###151.01###143.48###49.95**###1234084**###26.18###-

EnvxGenotypes###102###15.96**###69.70**###6.14**###639711**###40.71###-

###**###**###**

IPCA I###36###26.43###140.39###6.82###1107874.4**###-###24.88

###**###**###**

IPCA II###34###13.07###35.09###6.72###536096.6**###-###11.37

IPCA III###32###8.46**###25.55**###4.70**###330999.9**###-###6.61

Error###272###1.41###2.23###0.58###73232.5###-###12.43

Table 4. Mean values for days to 50% flowering, days to maturity, 100 seed weight (g) and grain yield (kg/ha) of soybean genotypes (C1 to C35) tested at four locations (Majhera, Palampur, Bajaura and Almora) of NW Himalayan hills.

###Days to 50% flowering###Days to maturity###100 seed weight (g)###Grain yield (kg/ha)

Code

###E1###E2###E3###E4###Mean E1###E2###E3###E4###Mean###E1###E2###E3###E4###Mean###E1###E2###E3###E4###Mean

C1###59###63###67###56###62###112###111###128###98###112###8.97###10.93###9.70###9.80###9.85###1144###2988###1391###1556###1770

C2###54###63###60###52###57###114###114###125###98###113###10.65###12.38###12.45###13.83###12.33###1654###2074###1679###2716###2031

C3###55###56###60###49###55###111###111###133###100###114###11.89###14.67###16.19###13.74###14.12###1802###2650###1580###1975###2002

C4###56###62###63###56###59###120###114###131###103###117###13.52###11.09###13.42###15.19###13.30###1778###2148###2502###2790###2305

C5###54###56###60###51###55###105###106###123###95###107###9.93###12.52###14.07###10.88###11.85###1407###2173###1992###1901###1868

C6###57###67###62###54###60###112###107###124###95###110###11.28###12.42###11.59###10.07###11.34###1770###2938###1449###2049###2052

C7###57###58###66###57###59###113###114###124###107###115###9.03###11.61###11.48###11.70###10.95###1465###2675###1506###2642###2072

C8###63###58###63###51###59###112###105###123###96###109###11.92###14.26###13.90###12.12###13.05###2091###2008###1827###1975###1975

C9###51###52###55###51###52###114###109###123###108###113###15.39###19.57###19.28###19.43###18.42###971###1202###2058###2049###1570

C10###55###57###62###50###56###117###112###129###106###116###8.90###12.66###10.72###10.21###10.62###1621###2198###2461###2840###2280

C11###55###56###62###50###56###114###105###123###102###111###12.85###15.76###14.91###14.41###14.48###2337###2543###2387###2346###2403

C12###56###57###63###52###57###114###107###127###106###114###11.96###13.03###14.43###13.51###13.23###1202###1984###1128###2025###1585

C13###54###52###60###50###54###107###101###123###96###107###10.39###14.87###13.70###13.41###13.09###1778###2609###2016###2148###2138

C14###56###57###61###52###57###112###99###127###97###109###9.16###9.84###10.64###10.65###10.07###897###1638###1498###1901###1484

C15###55###56###62###49###56###106###102###123###97###107###8.39###12.44###13.65###11.87###11.59###444###2115###1185###790###1134

C16###63###57###64###53###59###115###114###126###111###117###11.52###15.56###11.06###13.84###12.99###1152###2420###1679###2790###2010

C17###56###56###62###51###56###119###113###129###111###118###9.57###14.13###12.31###13.52###12.38###1465###2527###3342###2889###2556

C18###63###63###68###58###63###115###113###126###106###115###8.36###10.07###9.90###7.80###9.03###1556###1712###1761###1926###1739

C19###52###57###59###51###55###117###108###127###100###113###12.27###13.94###14.10###12.52###13.21###2362###1844###2074###1284###1891

C20###57###63###66###57###61###117###111###126###108###115###9.22###15.58###11.90###11.38###12.02###1111###1737###1877###2148###1718

C21###54###56###61###52###56###114###109###124###105###113###10.83###16.58###14.92###16.69###14.75###1572###2691###2477###1432###2043

C22###64###65###70###60###65###120###117###129###112###120###10.28###10.57###11.28###14.23###11.59###872###872###1556###1556###1214

C23###63###63###68###56###63###115###114###127###107###116###11.25###12.36###11.97###13.20###12.20###1317###1416###2634###2716###2021

C24###58###64###67###56###61###114###119###124###106###116###11.13###15.83###10.05###15.73###13.19###1613###2156###1926###1975###1918

C25###55###52###56###49###53###110###102###126###96###108###10.18###11.83###12.76###12.35###11.78###872###1556###1029###2790###1562

C26###60###63###68###56###62###116###117###104###110###112###10.33###12.21###12.07###16.14###12.69###1078###1045###1827###1901###1463

C27###62###63###70###58###63###122###119###105###112###114###13.44###12.36###15.03###17.15###14.49###1045###1457###2189###2049###1685

C28###62###56###67###56###60###111###109###127###98###111###9.26###11.33###12.14###10.16###10.72###1267###1333###1704###2642###1737

C29###57###58###64###52###58###111###107###123###96###109###11.03###12.17###11.33###10.10###11.16###1103###1909###1309###1975###1574

C30###54###51###51###50###52###111###109###123###97###110###8.81###14.08###15.22###12.26###12.59###881###1885###1276###2049###1523

C31###51###56###59###52###55###95###108###123###94###105###12.43###9.74###11.57###9.57###10.83###1243###889###1399###2840###1593

C32###54###56###60###52###56###112###111###124###96###111###15.97###14.62###12.81###12.55###13.99###1597###1695###1399###2346###1759

C33###48###59###50###51###52###107###109###133###98###111###13.66###18.18###16.67###16.80###16.33###1366###2765###1144###1930###1801

C34###51###52###58###50###53###106###112###125###97###110###14.49###19.08###17.67###16.34###16.90###2148###3169###1613###2148###2270

C35###51###49###60###48###52###111###104###126###107###112###13.90###17.67###17.17###15.09###15.96###2000###2864###1572###1901###2084

Mean###56###58###62###53###-###113###110###125###102###-###11.2###13.59###13.2###13.10###-###1428###2054###1784###2142###-

CD###1.85###0.85###2.2###1.36###-###2.95###0.69###2.29 1.83###-###0.84###1.20###0.85###1.75###-###389.69###142.43###423.95###580.61 -

CV###2.01###0.9###2.18 1.59###-###1.61###0.38###1.12 1.10###-###4.65###5.42###3.96###8.24###-###16.74###4.25###14.58###16.63###-

(5%)

Conclusion: Genotype x environment interaction (GEI) plays a crucial role in the performance of genotypes in an environment but its importance generally ignored and stability of the genotypes is not given any consideration. Development of cultivars is a time consuming, resource and labour intensive task and in the existing procedure of varietal release, mean performance of a genotype over years and locations, and its superiority over the checks is only considered and multi-environment trial (MET) data is not utilized to its full potential. In the present investigation, effort was made to identify high yield and stable genotypes by analysing multi environment data to take the stability of the genotypes into consideration and graphical visualization has expediently aided in identification of stable and superior soybean genotypes across testing environments.

Although, all the testing environments exhibited their suitability to be used for multi-location trials on the basis of their discriminative ability and representativeness but their ability to discriminate differ trait wise. It was also found that genotype showing stability for one trait not necessarily stable for other trait as well as differing with test locations. Thus, during soybean breeding programme the trait of interest need to be prioritized as per the need of particular geographical and agro-climatic regions.

Acknowledgements: Authors are highly thankful to the Director, ICAR-VPKAS, Almora and Head, Crop Improvement Division for providing necessary facilities for carrying out the work as well as improving this research manuscript by providing highly valuable intellectual inputs. Assistance of Shri M.S. Khati and Shri Chandan Singh Kanwal in pulses and oilseeds improvement project is also duly acknowledged.

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