Current Opinion in Agriculture Curr. Opin. Agric. 2013 2(1), 20–24.

Analysis of yield stability in multi-environment trials of barley (Hordeum vulgar L.) genotypes using AMMI model M Mohammadi1, R Karimizadeh1, AA Noorinia2, H Ghojogh2, T Hosseinpour3, GR Khalilzadeh4, A Mehraban5, M Roustaii6, M Hasanpor Hosni7 1Cereal breeder, Dryland Agricultural Research Institute, Gachsaran, Iran. 2Cereal breeder, Agricultural and national Resources Research center of Golestan Province, Gorgan, Iran. 3Cereal breeder, Agricultural and national Resources Research center of Lorestann Province, Khorramabad, Iran. 4Barley breeder, Agricultural and national Resources Research center of West Azarbayjan Province, Oromiieh, Iran. 5Barley breeder, Agricultural and national Resources Research center of Ardabil Province, Moghan, Iran. 6Wheat breeder, Dryland Agricultural Research Institute, Maragheh, Iran. 7Cereal pathologist, Dryland Agricultural Research Institute, Maragheh, Iran. *Corresponding author's E-mail: mohtashammohammadi@yahoo.com

Abstract Analysis of multi-environment trials for investigation of genotype × environment (GE) interaction for yield performance across test environments, and selection of the most favorable genotypes is an important issue in barley breeding programs. The multi-environmental trials were conducted at test locations; Gachsaran, Gonbad, Khoramabad and Moghan, for three years. Sixteen barley genotypes including 14 new improved lines, and the two cultivars; Izeh and local check, were cultivated in randomized complete block design with four replicates. An AMMI model with AMMI1 and AMMI2 biplots were used to determine the mega-environments and wining genotypes. The results of combined analysis of variance indicated main effects of genotypes and environments as well as GE interaction effects were significant. The results of the polygon view of AMMI2 biplot verified relatively the results of AMMI1 and AMMI2 biplots and showed that there of test locations (Gonbad, Khoramabad and Moghan) are three main mega-environments in barley producing areas. Regarding this pattern, genotypes G1, G6, G13 and G16 were as the most favorable genotypes, had specific adaptability for these mega-environments. The best recommended genotypes according to the this investigation were G6 (3591 kg ha-1) for location Khoramabad, G16 (3192 kg ha-1) for location Gonbad, and G13 (3119 kg ha-1) for location Moghan which had high mean yield and was the most stable for related mega- environments. Keywords: Barley, Genotype × environment interaction, Stability analysis Abbreviations: AMMI, Additive main effects and multiplicative interaction; GE, genotype by environmental interaction

Introduction Genotype by environment (GE) interaction is of major importance, because it provides information about the effects of test environments on genotype performance and plays an important key role for assessment of performance yield stability of the new breeding genotype. Increasing genetic gain in yield performance is possible in part from narrowing the adaptation of genotypes and so maximizing yield in particular environments are described by GE interaction (Sabaghnia et al. 2012b). The GE interaction is a complex phenomenon which is involves environmental conditions such as agroecological, climate and agronomic factors as well as genetic factors which are determine the plant growth and development (Annicchiarico, 2002). Some types of GE interaction (additive or non-crossover) will lead to different variances at each environment but can be managed during data analysis (Annicchiarico, 2002), but some others (crossover or non- additive) can be a practical problem for improvement programs as it shows that genotypes may have specific environment preferences (Truberg and Huehn, 2000). Also, from other aspect, some GE interaction may be predictable such as for genotypes growing in their native

environment (Wu and Ying, 2004) while other GE interaction is not predictable and may reduce across environment heritability and genetic gain (Raymond, 2011). For exploration of GE interaction, some statistical methods have been suggested in yield performance analysis of crop genotypes. These include partitioning of GE interaction variance (Wricke, 1962), the coefficient of linear regression model (Finlay and Wilkinson, 1963), deviation from linear regression model (Eberhart and Russell, 1966), nonparametric statistics (Huehn, 1990; Sabaghnia et al. 2006), and multivariate methods such as the additive main effects and multiplicative interaction (AMMI) model (Zobel et al. 1988). In many aspects of barley research and especially in its genetic improvement, the analysis of GE interaction is of primary importance (Dehghani et al. 2008). This issue is particularly critical in arid and semi-arid areas, where barley planted under the harmful influence of drought and high temperatures, and inter-annual changes in climate factors can occur (Voltas et al. 1999). Therefore, experimental investigation requires to be performed via multi-environment trials in order to detection of the major factors that are responsible for genotype stability (De

Analysis of yield stability of barley using AMMI model

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Lacy et al. 1996). Barley landraces have an important source of plant material in poor environments, as they are often grown in stressful environmental conditions with zero to limited agronomic input (Ceccarelli and Grando, 2000), and so these materials must be used in breeding programs. Dehghani et al. (2008) studied the yield stability of several genotypes with different origins and found that genotypes with broad genetic background are more useful form adaptation and stability aspects. Table 1. Geographical properties of test locations.

Longitude

Latitude

Altitude

(m)

Soil

Type

Rainfall

(mm) Location

50 ْ◌ 50 َ◌ E

30 ْ◌ 20 َ◌ N 710 Regosols 460.8 Gachsaran

55 ْ◌ 12 َ◌ E

37 ْ◌ 16 َ◌ N 45 Regosols 367.5 Gonbad

23 ْ◌ 26 َ◌ E

48 ْ◌ 17 َ◌ N 1148 Regosols 433.1 Khoramabad

48° 03´E

39° 01´N 32 Cambisols 271.2 Moghan

The major objective of this study is to understand the adaptation of barley genotypes using AMMI analysis to evaluate the significance of the GE interaction on seed yield, identify mega-environments, determine the best performing genotype for each mega-environment, and discuss the implication of the GE interaction to barley breeding. Material and methods The barley multi-environmental trials were conducted at four test locations; Gachsaran, Gonbad, Khoramabad and Moghan, for three years. These test locations were chosen because they show main barley growing regions characterized by some differences in soil fertility and climatic conditions (Table 1). The trial of Moghan location at third year was destroyed and so it is deleted from total analysis and the number of environments decreased to eleven. Sixteen barley genotypes (14 new improved lines, and the two cultivars (Izeh and local check) were used (Table 2). Trials performed in a randomized complete block design with four replicates. Each experimental unit consisted of a 7.35 m2 plot (six rows 7 m long with 0.175 m between rows). A combined ANOVA was done to test significant differences among genotypes and environments due to non-significance of Bartlett’s homogeneity of variance test. The AMMI model (Gauch, 1988) was performed using blow formula:

N

nijjninnjiij egY

1

where ijY is the yield of the ith genotype in the jth

environment; is the general mean; ig and je are the

genotype and environment deviations from the general mean,

Table 2. The properties of 16 barley genotypes.

Pedigree or Name Yield Code Wi2291/Wi2269//ER/Apm ICB86-0629-0AP-2APH-0AP

3805 G1

Pld10342//Cr.115/Por/3/Bahtim/4/Ds/Apro/5/wi2291/ Wi2291/Wi2269/7/Wi2291/Wi2291/Wi2291/Wi2269// Wi2291/Bgs ICB94-0402-0AP

3690 G2

7028/2759/3/6982//Ds/Apro/4/H272//Wi2198/ ID601810/5/Mazurka ICB95 –0437-0AP

3474 G3

Zanbaca/3/H.Spont.21-3/Arar 84//Wi2291/Bgs ICB94-0314-0AP

3393 G4

Hml/WI2291/4/Zanbaca/3/Er/Apm/Lignee131 ICB94 –0587-0AP

3165 G5

Er/Apm//Cerise/3/lignee131/3/Er/Apm ICB83-1985-2AP-0AP

3591 G6

Lignee 124/Hml 024 ICB 82-0757-10AP-0AP-23AP-0AP

3367 G7

Alanda/Harma01/7/Gustoe/6/M6476/Bon//Jo/ York/3/Ms/Colt/As46/4/Hy3480/Astrix/5/NK1272 ICB95–0791-0AP-0AP

3483 G8

IPA7//As46/Rhn-05 ICB95 –0162-0AP-0AP

3347 G9

Weahll/Wi2291/Bgs/3/Er/Apm//Ac253 ICB 94 – 0707-0AP-0AP

3561 G10

Roho Alger/Ceres 362 1-1/3/Kantara/4/Bowman ICB93 –0791-21AP-0AP

3549 G11

Mari/Aths×2//Avt/Attiki/3/Aths/Lignee 686 ICB 91 –0368-3AP-0TR-3AP-0AP

3440 G12

IPA 265/PA 7 ICB95 –0127-0AP

3119 G13

Lignee 131/ArabiAbiad/3/Chiem/An57//Albert 3488 G14

Izeh 3488 G15

Local check 3192 G16

respectively; n is the eigenvalue of the IPC (interaction

principal component) analysis axis n; in and jn are the

genotype and environment eigenvectors for axis n; n is the

number of IPCs retained in the model and ij is the residual

component. A special F-test as FRatio (Cornelius et al. 1992) was used to determine of significant numbers of IPCs in AMMI model. The AMMI1 and AMMI2 biplots were drawn and used to determine the mega-environments and wining genotypes. All statistical analyses were carried out using GENSTAT 12.0 (VSN International, 2010) and SAS 6.12 (SAS, 1996), which calculate different stability statistics. Results and discussion The results of combined analysis of variance indicated main effects of genotypes and environments were significant at P < 0.01 (Table 2). Also, the GE interaction effect was significant at P < 0.01 (Table 2) and explained only 1.8% of total variance. The high significant GE interaction would result in different ranking of barley genotypes (crossover interaction), and complicate selection because it measures the degree to which performance in one environment fails to predict performance in the other (Baker, 1988). Of the total variance, a larger portion was described by the environment main effect (94.2%) while the genotypes accounted about 4% of total variation (GE+E+G). When no distinct source of the GE interaction can be found, selection of genotypes with broad adaptation would be expected to yield dependably across a wide range of environments

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(Annicchiarico, 2002; Sabaghnia et al., 2012b), and grouping of genotypes with minimal GE was relatively successful. Table 3. Combined analysis of variance and AMMI analysis for grain yield of barley genotypes.

% of G×E FRatio Mean Squares DF‡ SOV†

35222228.3** 10 Environment (E)

702331.2 33 Replication/E

1489296.7** 15 Genotype (G)

676638.1** 150 G×E

37.2 3.31** 1572839.2 24 IPC1

60.0 2.57** 1040859.6 22 IPC2

2182573 104 Residual

153069.3 495 Error † Sources of variation ‡ Degrees of freedom ** Significant at the 0.01 probability level The GE interaction of barley multi-environment trials was further analyzed with the AMMI model which AMMI2 model including IPC1 and IPC2 accounted for 60% of the GE variation in studied barley genotypes. Table 2 shows the two IPC axes were significant by the FRatio (Cornelius et al., 1992). Therefore, it could be concluding that the AMMI model revealed that there was not a more complex interaction of GE and which it could facilitate graphical visualization of the genotypes in low dimensions (Gauch et al., 2008). Figure 1 indicates the AMMI1 biplot (mean yield versus IPC1 scores) for the barley multi-environment experiments and provides a good explanation of the data pattern, even though it misses some additional information in IPC2 (Gauch, 2006). Genotypes G4 and G12 had the low IPC1 scores and moderate mean yield which is reflected static concept of yield stability. In other word, these genotypes and relatively similar ones (G2, G3, G9, G10, G11, G12 and G15) could be recommended for most of the test environments with regarding broad adaptability. In most multi-environment trials, a genotype is regarded to be most stable if its performance is relatively constant across different test environments. According to Becker and Leon (1988) static concept of stability (biological type), a stable genotype is the one with minimal variance across different environments.

Fig. 1. The AMMI1 model biplot for yield data from 16 barley genotypes grown at 11 environments which showing genotypes’ positions.

Fig. 2. The AMMI2 model biplot for yield data from 16 barley genotypes grown at 11 environments which showing genotypes’ positions.

However, static concept has received little attention from plant breeders and especially agronomists as they prefer genotypes with high mean yields in favorable environments (Becker, 1981). Genotype with a constant high yield referred to as dynamic stability concept is the preferred option in commercial plant breeding (Flores et al., 1998). Despite the relatively low potential of barley grain yield at semi-arid areas, G1 had the relatively highest mean yield, implying that it had good dynamic stability concept. There were two mega-environments including ME-A as KH1, KH2, GA2, GA3 and GO3 with wining genotypes G1 and G6; and ME-B as the other remained test environments with wining genotype G13 (Fig. 1). Similar findings were reported by Sabaghnia et al. (2008) in lentil, and Karimizadeh et al. (2012) in durum wheat and Sabaghnia et al. (2012a) in durum wheat which mentioned that static stability may be more useful than dynamic in a wide range of conditions. The IPC1 often correlates highly with PC2 from GGE biplot model and so the above conclusion could be expected from GGE biplot methodology (Gauch et al. 2008). Figure 2 indicates the AMMI2 biplot (IPC1 versus IPC2 scores) and provides a good explanation of the data pattern, regarding first two IPCs. Genotype G10 had the low IPC scores with relatively moderate mean yield and is reflected static concept of yield stability (Fig. 2). In other word, this genotype could be recommended for most of the test environments with regarding broad adaptability. There were four sectors as mega-environments including; ME-A as GA2, GA3, KH1 and KH2 with wining genotype G1; ME-B as GO1 and GO3 with wining genotypes G3; ME-C as KH3 with wining genotype G14; and ME-D as MO1, MO2, GA2 and GO2 with wining genotype G13 (Fig. 2). Polygon view of AMMI2 biplot has been used to identify “which-wins-where” patterns in lentil multi-environmental trials data analysis. In Figure 3, lines are drawn to connect the furthest genotypes in the biplot and then a line is drawn perpendicular to that side of the polygon so as to pass through the origin. The furthest genotype is the best performer in the environment included in that sector. There are six rays in Fig. 3 which divide the biplot into six sectors, and the test environments fall into four of them. The test environment GH2 fell into sector 1 and the vertex genotype for this sector was G1, suggesting that the most favorable genotype for this test environment was G9. The test environments KH1, KH2 and

Analysis of yield stability of barley using AMMI model

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Fig. 3. The polygon of AMMI2 model biplot for yield data from 16 barley genotypes grown at 11 environments which showing genotypes’ positions.

GA3 fell into sector 2 and the vertex genotype for this sector was G6. This genotype was better than the other genotypes which fell into sector 2 (such as genotypes G7 and G8). The test environments GO1 and GO3 fell into sector 3 and the vertex genotype for this sector was G16. This genotype was better than genotypes G2 and G3 which fells into sector 3. The test environments MO1, MO2, GO2, GA1 and KH3 fell into sector 4 and the vertex genotype for this sector was G13. This genotype was better than genotypes G10, G11, G12, G14 and G15 which fells into sector 4. The results of the polygon view of AMMI2 biplot verified relatively the results of Figures 1 and 2. Therefore, it could be concluding that, there of test locations (GO: Gonbad; KH: Khoramabad; MO: Moghan) are three main mega-environments in barley producing areas. The response of location Gachsaran in three years was not constant and every year of this trial located in different main mega-environment. Therefore, Fig. 3 suggests that there exist at least, three possible barley mega-environments in semi-arid areas of Iran. However, this mega-environment pattern needs verification through other multi-environment trials for this target region. Regarding this pattern, genotypes G1, G6, G13 and G16 were as the most favorable genotypes, had specific adaptability for these mega-environments. As discussed by Sabaghnia et al. (2010), the above inferences about polygon view patterns are mostly, but not totally, validated from the original data. However, the model outcome is worthwhile for recommendation purposes since, as first demonstrated by Gauch (1988), and later applied to GE modeling by Sabaghnia et al. (2012c). Successful genotypes of barley need to be adapted to a broad range of environmental conditions in order to ensure their yield stability. It is clear that the AMMI biplots are excellent tools for visual multi-environment trials analysis. Compared with conventional methods, the AMMI model approach has some advantages (Gauch, 2006; Gauch et al., 2008; Sabaghnia, 2012). The first gain of the AMMI biplot is its graphical presentation, which greatly enhances our ability to understand the data pattern. The second gain is its interpretative option (Gauch et al., 2008). Selection of genotypes for stability is needed in most dry-land environments, where the environment is variable and

unpredictable. Therefore, simultaneous selection for yield and stability is the most valuable selection index that can be used in any breeding program. Such a similar outcome could be employed in the future to delineate predictive, more rigorous recommendation strategies as well as to help define stability concepts for recommendations for barley and other crops in other areas of the world. Conclusions Despite the fact that the various stability methods are indicative, the AMMI method provides more useful information for achieving definitive results and the identification of mega-environments and wining genotypes are indispensable. The best recommended genotypes according to the this investigation were G6 (3591 kg ha-1) for location Khoramabad, G16 (3192 kg ha-1) for location Gonbad, and G13 (3119 kg ha-1) for location Moghan which had high mean yield and was the most stable for related mega- environments. Acknowledgments The authors thank the Iranian Dry Agricultural Research Institute for making available the plant materials, experimental locations and financial supports. References Annicchiarico P, 2002. Genotype × environment interactions: challenges

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