ASSESSMENT OF GENOTYPE ENVIRONMENT INTERACTIONS FOR GRAIN YIELD IN MAIZE HYBRIDS IN RAINFED ENVIRONMENTS

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1 RESEARCH ARTICLE SABRAO Journal of Breeding and Genetics 46 (2) , 2014 ASSESSMENT OF GENOTYPE ENVIRONMENT INTERACTIONS FOR GRAIN YIELD IN MAIZE HYBRIDS IN RAINFED ENVIRONMENTS R. KUMAR 1 *, A. SINGODE 1, G.K. CHIKKAPPA 1, G. MUKRI 1, R.B. DUBEY 3, M.C. KOMBOJ 4, H.C. SINGH 5, D.S. OLAKH1, B. AHMAD 1, M. KRISHNA 6, P.H. ZAIDI 2, M.K. DEBNATH 2, K. SEETHARAMA 2 and O.P. YADAV 1 1 Directorate of Maize Research, New Delhi, India 2 CIMMYT-Asia, Hyderabad, India 3 MPU&AT, Udaipur, India 4 CCS Haryana Agricultural University, Uchani Karnal, India 5 CSAUA&T, Kanpur, India 6 ANGRAU, Rajender Nagar, Hyderabad, India *Corresponding author s rk.phagna@gmail.com SUMMARY A study was conducted to identify maize hybrids with stable grain yield among 24 commercial hybrids in 5 test environments. The variation in genotypes, environments and genotype environment interaction (G x E) was significant. Maximum variation was explained by difference in environmental conditions (55.92%) and least by genotypes (9.81%). The 2 AMMI principal coordinates axes (PCA) estimates explained 80% of the phenotypic variation. For the quantitative measure of stability, AMMI Stability Value (ASV) was calculated using PCA scores. Among these hybrids HQPM-1 was found to be most stable with ASV of Based on this experiment using various biplot methods, it is concluded that Hyderabad environment is best for testing the hybrids for wider adaptability and Karnal and Kanpur locations can be used to identify location specific hybrids. The hybrids G5 (TNAU Co-6), G14 (LVN 99) and G 18(VS 71) performed best in Udaipur, G23 (Bio 9544), G12 (VN 8960) and G21 (HTMH 5101 Sona) and G24 (Bio 9522 S) were found best in Hyderabad and Delhi. At Karnal G20 (900M Gold), G6 (PMH-1), G13 (LCH 9) and G22 (HTMH 5401) are considered to be the best genotypes whereas in Kanpur G7 (PMH-2), G11 (HQPM-1) and G 2 (WLS-F B-2-BBB/CL02450-BBB) were found to be best. Keywords: Maize, genotype x environment interaction (G E), AMMI, PCA Manuscript received: July 8, 2014; Decision on manuscript: September 9, 2014; Manuscript accepted: September 28, Society for the Advancement of Breeding Research in Asia and Oceania (SABRAO) 2014 Communicating Editor: Bertrand Collard INTRODUCTION Maize is cultivated in diverse agro-climatic conditions across the world. It is cultivated in the tropics, sub-tropics and temperate regions; from sea level to > 4000 m above sea level, under irrigated to semi-arid conditions. Maize is staple food crop in African countries and in other part of the world it is majorly utilized in feed industry. Maize ranks first among cereal food crops in world production (868 million tons from 168 million hectares) followed by wheat and rice (FICCI, 2014). It represents 38% of the total grain production as compared to 30% for wheat and 20% for rice. Globally more than 160 m ha of maize is cultivated under rainfed condition (Edmeades, 2013). Among the cereal crops in India, Maize with annual production of around 21 million tones covering 8.5 million hectares ranks third in

2 SABRAO J. Breed. Genet. 46 (2) production. India, ranks 6th in global maize production, contributing to 2.4% of world production with almost 5% share in world harvested area. However, the country lags far behind in productivity with 2.47 t/ha against world average of 5.14 t/ha. Maize is cultivated widely in India in diverse geographies from Karnataka to Jammu and Kashmir, which results in the produce of varying quality and productivity. With a large area under cultivation and low productivity, it has a strong potential for increasing food production in coming years but, one of the most challenging task in maize breeding is to develop hybrids with stable performance in diverse environments. There is involvement of genotype environment interaction when we test same genotype at different locations, due to which differential expression of genotype in different environment is usually observed. Generally Finlay and Wilkinson, (1963), Eberhart and Russel, (1966) stability methods which are based on simple and multiple regression are used to assess the Genotype Environment. The regression based methods, though have been used widely, have limitations that are frequently reported by many workers. Crossa (1990) argues that linear regression analysis is not informative if linearity fails, it is highly dependent on the group of genotypes and environments included and tends to simplify regression model explaining the variation caused by interaction in one dimension whereas in fact it is quite complex. The additive nature of the common analysis of variance (ANOVA) allows for an adequate description of the main effects (genotype and environmental effects). However, G x E may not be additive and other techniques are required to identify the existing relationship. The Principal Component Analysis can be used but it is also faulty in identification of main significant effects (Shaffi and Price, 1998). However, Additive Main effect and Multiplicative Interaction (AMMI) model is being used for environmental stratification based on winning genotypes more efficiently than other methods. But, a modification of AMMI analysis proposed by Yan et al. (2000) denoted by GGE (Genotype and Genotype Environment interaction) biplot is being used widely to study the G E interaction. Hence, the aim of this study was to generate information regarding yield stability and adaptability of the maize hybrids across the environments. MATERIALS AND METHODS Twenty-four maize hybrids belonging to different public and private institutions were planted at 5 locations i.e. CCSHAU, Karnal (E1), Directorate of Maize Research, New Delhi (E2), CSAU&T, Kanpur (E3), MPUAT, Udaipur (E4) and ANGRAU, Hyderabad (E5) during kharif, 2012 in randomized complete block design (RCBD) with spacing of 0.70 m inter and 0.25 m intra rows in a row length of 4.0 meter. All necessary agronomic and cultural practices were timely followed to ensure good plant stand. The trials were conducted purely under rainfed conditions, and all locations were considered as different environments as there were differences in average rainfall, mean temperature, humidity and soil type. Observations were recorded on several traits including days to 50% tasseling, days to 50% silking, plant height, ear height, cob weight/plot, shelling percentage and grain moisture percent at harvest however only grain yield (q/ha, at 15% moisture) was used for AMMI analysis using software R v package agricolae. The combined ANOVA for all 5 locations was done to estimate the variations in the genotypes under study and partitioning of G x E interaction. AMMI combines ANOVA into a single model with additive and multiplicative parameters. The model equation is: Y ij = µ + g i + e j + N n= 1 λ γ n in δ jn + ρ Where, Y ij is the yield of the i th genotype in the j th environment; μ is the grand mean; g i and e j are the genotype and environment deviations from the grand mean, respectively; λ n is the eigenvalue of the PC analysis axis n; ij 285

3 Kumar et al. (2014) Table 1. Code and pedigree of maize hybrids along with their source of seed. Code Name of hybrid Source of seed 1 CA B-1-BBB/CL02450-BBB CMMYT-Asia, Hyderabad 2 WLS-F B-2-BBB/CL02450-BBB CMMYT-Asia, Hyderabad 3 CML451-B*4/CL02450-BBB CMMYT-Asia, Hyderabad 4 CMH TNAU, Coimbatore 5 TNAU CO-6 TNAU,Coimbatore 6 PMH-1 PAU, Ludhiana 7 PMH-2 PAU, Ludhiana 8 MHM-2 BHU, Varanasi 9 DHM-117 ANGRAU, Hyderabad 10 HM 4 CCSHAU, Uchani (Karnal) 11 HQPM1 CCSHAU, Uchani (Karnal) 12 VN LCH9 14 LVN61 15 LVN99 16 LVN66 17 VS26 18 VS V92 Pioneer MG Monsanto 21 HTMH-5101Sona Hytech Seeds Pvt. Limited 22 HTMH5401 Hytech Seeds Pvt. Limited 23 BIO 9544 Bioseeds Pvt. Limited 24 BIO 9220 S Bioseeds Pvt. Limited γ in and δjn are the genotype and environment principal component scores (eigenvectors) for axis n; n is the number of principal components retained in the model and ρ ij is the error term. The AMMI model does not make provision for a quantitative stability measure; however, such measures are essential in order to quantify and rank genotypes according to their yield stability. AMMI stability values were calculated to study the stability of genotypes across environments. The R graphic interface GGEBiplotGUI package was used to generate biplots (Yan and Kang, 2003). RESULTS AND DISCUSSION The combined analysis of variance showed highly significant differences for environment, genotype and interactions (Table 2). The combined analysis of variance showed that grain yield was 286

4 SABRAO J. Breed. Genet. 46 (2) Table 2. AMMI analysis of variance over 5 environments. Source of variation Degrees of freedom TSS MSS % TSS Treatments ** Genotypes ** 9.81 % Environments ** % Blocks ** Interactions ** % IPCA ** IPCA ** Residuals Error Total * Significant at 5% probability level ** Significant at 1% probability level significantly affected by environments. Sum of squares due to environments was high which was due to the large differences in environmental mean for yield, this indicates the selected environments are diverse. In terms of percent variation it explained % of the total variation (G+E+GEI) whereas, G x E interaction captured % of the total sum of squares. A smaller G x E interaction and large environmental effect implies that the genotypes are stable across environments and the maximum variation in hybrid performance is contributed by the environmental differences. Genotypic differences are low in the analysis which is quite possible because the hybrids selected are the commercial hybrids nationally released only after extensive testing over many locations. However, hybrid response in the tested environment was variable as indicated by G x E interaction. The G x E interaction vis-àvis main effect is well explained by AMMI analysis. The AMMI model demonstrated the presence of significant G x E interaction. The G x E variance is partitioned into 2 Principal Components (PC1 and PC2). Cumulatively these 2 principal components are able to explain 80.85% variation, PC1 accounted for 57.66% and PC %. The remaining ~19 is left as unexplained residual variation. This indicates sufficient approximation of data by the 2 PC scores for grain yield genotypes in different environments. However, AMMI model suffers from a limitation that it does not provide quantitative measure of stability. AMMI Stability Value can be estimated, it is the distance from zero in a 2 dimensional scatterplot of IPCA1 scores against IPCA2 (Purchase et al., 2000). It is definite value of stability measure, using this genotypes can be ranked. The lower stability value indicates high stability and viceversa. In the present study we found that G11 (HQPM 1) had the lowest ASV value and this is the most stable genotype, while G19 (30V92), G8 (MHM 2) and G4 (CMH08-381) were unstable as these genotypes exhibited highest ASV (Table 3). After stability using AMMI, Genotype-Environment Interaction (GGE) biplot analysis was performed in order to study the relationship among and between environments and genotypes. The GGE biplot is modification of AMMI analysis which provides graphical display and is considered as an innovative methodology for applied plant breeding (Yan et al., 2000). 287

5 Kumar et al. (2014) Table 3. Mean grain yield (t/ha), IPCA1 and IPCA2 scores and AMMI stability values of 24 maize hybrids. Genotype Karnal Delhi Kanpur Udaipur Hyderabad Mean IPCA1 IPCA2 ASV G G G G G G G G G G G G G G G G G G G G G G G G Relationship among and between environments and genotypes The five test environments showed significant variation. Each environment provided different conditions for manifestation of yield in the hybrids tested. The environmental differences are depicted as vector lines originating from the biplot origin (Figure 1). The cosine of the angle between the vectors of two environments approximates the correlation between them. In the biplot, Hyderabad and Udaipur vectors form acute angle showing close relation between the environments. In contrast, Delhi and Karnal were negatively correlated with Kanpur. However, both Hyderabad and Udaipur had weak correlation with Delhi, Karnal and Kanpur. The presence of wide obtuse angle among test environments is an indication of strong cross over effect. The distance between two environments measures their dissimilarity in discriminating the genotypes and presence of close associations among test environments suggest that the same information can be obtained from the fewer environments and this will reduce the testing cost. In the present study Hyderabad and Udaipur were associated closely. Hence, we can drop one testing sites in this case. The discriminativeness versus representativeness biplot (Figure 2) strongly suggests, Hyderabad test environment is both discriminating and representative. It is good test environment for selecting generally adapted genotypes. It is also the best environment among the test locations for selecting genotypes with wide adaptability (Figures 3). Although, Kanpur and Karnal test environments are discriminating 288

6 SABRAO J. Breed. Genet. 46 (2) Figure 1. Similarities between test environments in discriminating the genotypes. Figure 2. The discriminating ability and representativeness of the test environment. 289

7 Kumar et al. (2014) Figure 3. Ranking of test environments vis-à-vis identification of ideal test environments. Figure 4. Potential mega-environments and genotype performance in environments. 290

8 SABRAO J. Breed. Genet. 46 (2) Figure 5. Average- environment coordinate (AEC) for assessing stability of genotypes across environment. but are non-representative and are also the poorest site for selecting genotypes with wide adaptability. These environments are useful in selecting genotypes specifically adapted to that environment only. Yan et al. (2000) and Yan and Hunt (2001) suggested which won where biplot to identify potential mega-environments. The genotypes which are farthest from the origin are connected with a straight line forming a polygon. The lines starting from the origin are normal to the polygon sides and divide the polygon into different sectors. The locations within one sector are the ones where certain genotypes had the best yield and can be considered as mega-environments for that genotype. Figure 4 indicates G5, G14 and G 18 performed best in Udaipur, however G23 recorded the highest yield. As mentioned earlier there is strong correlation between Hyderabad and Udaipur, the inferences for 1 location applies to other also. Specifically, the performance of G23, G12, G21 and G24 was best in Hyderabad. At Karnal G20, G6, G13 and G22 are considered to be the best genotypes 291 whereas in Kanpur G7, G11 and G 2 were best. In this study, stable hybrids were identified based on the ranking of genotypes and their relationship with the location (Figure 5). The double arrowed line is the Average Environment Coordinate (AEC) and it points to higher mean yield across environments. It runs from the origin in both positive and negative direction. Hence, G23 has the mean yield as per this biplot on arrow followed by G5, G14, G21 and G12. Among these genotypes G12 is highly stable as this genotype having shorter distance from the Average-Environment Coordinate line (AEC). G4 is the lowest yielding genotype in this study. The double arrow line is the AEC coordinate and it points to greater variability (poor stability) in either direction. Thus, G20 is the unstable genotype. Their yield was more than expected in E3, E4 and E5 whereas, it has yielded lower than expected in E1 and E2. The AMMI model and GGE interaction biplots are useful tools with plant breeders. The finding has an important implication in strategizing the test locations for development of

9 Kumar et al. (2014) cultivars with broader adaptability. This study was undertaken in rainfed condition. In India the rainfed condition is different in different ecology. Hence, cross-over effects are commonly observed due to G x E interactions. The most stable or locally adapted hybrids identified can be used as local checks while developing stress resilient hybrids. ACKNOWLEDGEMENTS Authors duly acknowledge help provided in the form of financial support by CIMMYT and Directorate of Maize Research, ICAR for providing all the facilities for carrying out the present study. REFERENCES Admassu S, Nigussie M, Zelleke H (2008). Genotype x environment interaction and stability analysis for grain yield (Zea mays L.) in Ethiopia. Asian J. Plant Sci. 7 (2): Choukan, R (2011). Genotype, environment and genotype x environment interactions effects on performace of maize (Zea mays) inbred lines. Crop Breeding Journal 1(2): Crossa J (1990). Statistical analysis of Multiloation trials. Advances in Agronomy. 44: Eberhart SA & Russell WA (1966). Stability parameters for comparing varieties. Crop Science 6: Edmeades GO (2013). Progress in Achieving and Delivering Drought Tolerance in Maize - An Update, ISAAA: Ithaca, NY FICCI (2014). Website: Accessed on 1 st Jan., Finlay KW, Wilkinson GN (1963).The analysis of adaptation in a plant breeding programme. Australian Journal of Agricultural Research. 14: Mitrovic B, Stanisavijevi D, Treski S, Stojakovi M, Ivanovic M, Bekavac G and Rajkvic M (2012). Evaluation of experimental maize hybrids tested in multilocation trials using AMMI and GGE biplot analysis. Turkish J. Field Crops. 17(1): Motzo R, Guinta F, Deidda (1962). Factors affecting the genotype x environment interaction in spring triticale grown in Mediterranean environment. Euphytica. 121: Shafii B, Price WJ (1998). Analysis of genotype-byenvironment interaction using the additive main effects and multiplicative interaction model and stability estimates. J. Agric. Biol. Environ. Stat. 3: Sivapalan S, Brien LO, Ferrara GO, Hollamby GI, Barclay I, Martin PJ (2000). An adaptation analysis of Australian and CIMMYT/ICARDA wheat germplasm in Australian production environments. Aust. J. Agric. Res. 51: Yan W (2001). GGE biplot - a windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agron. J. 93: Yan W, Kang M (2003). GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists. CRC Press, Boca Raton, FL, USA. Yan W, Tinker NA (2005). An integrated biplot analysis system for displaying, interpreting and exploring genotypes by environment interactions. Crop Sci. 45: Yan W, Tinker NA (2006). Biplot analysis of multienvironment trial data: Principles and applications. Can. J. Plant Sci. 86: Yan W, Hunt LA, Sheng Q, Szlavnics Z (2000).Cultivar evaluation and megaenvironment investigation based on GGE biplot. Crop Sci. 40: Zali H, Sabaghpom SH, Farshadfar H, Pezeshkpour P, Safikhani M, Sarparast R, Beyagi AH (2009). Stability analysis of Chick pea genotypes using ASV parameters compare to other stability methods. Iranian Field Crop Sci. 40: Zobel RW, Wright MJ, Gauch JHG (1988). Statistical analysis of a yield trial. Agron. J. 80: