Genotype-Environment Interaction Studies Over Seasons for Kernel Yield in Maize (Zea mays L.)

International Journal of Environmental & Agriculture Research (IJOEAR) ISSN:[2454-1850] [Vol-8, Issue-11, November- 2022] Genotype-Environment Interaction Studies Over Seasons for Kernel Yield in Maize (Zea mays L.) Dr. N. Sabitha1*, Dr. D. Mohan Reddy2 Assistant Professor, Dept. of Genetics and Plant Breeding, Acharya N.G. Ranga Agricultural University, Agricultural College, Mahanandi - 518502 *Corresponding Author Received:- 14 November 2022/ Revised:- 20 November 2022/ Accepted:- 24 November 2022/ Published: 30-11-2022 Copyright @ 2022 International Journal of Environmental and Agriculture Research This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted Non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract— Forty five single cross hybrids derived from 10 inbred lines of maize were tested for kernel yield across three seasons viz., rabi, summer and kharif adopting AMMI model to assess the G × E interaction and to identify the stable hybrids for kernel yield. Seasons were found to contribute to the variations in performance of hybrids indicating that unpredictable seasonal conditions are one of the constraints in selecting superior and adaptable hybrids. The hybrids viz., BML 6 × PDM 1474, BML 7 × DFTY, BML 15 × PDM 1474, DFTY × Heypool, DFTY × PDM 1452 and Heypool × PDM 1474 across seasons recoded significantly higher kernel yield over general mean. The first two interaction principal components viz., PC 1 (74.00 %) and PC 2 (16.00 %) of GGE-biplot analysis explained 90.00 % of total variation caused by genotype × environment interaction. Hybrids viz., DFTY × Heypool, BML 15 × PDM 1452 and Heypool × PDM 1474 were the vertex hybrids or winners indicating that they are the best performing or responsive hybrids. Summer season was found to be the most discriminating season in culling the unproductive ones and also to save time and expenditure. Kharif and rabi seasons were the most representative testing seasons for kernel yield. Hybrids viz., BML 2 × DFTY, BML 2 × Heypool, BML 6 × PDM 1474, BML 7 × DFTY, BML 15 × PDM 1474, DFTY × PDM 1452, Heypool × PDM 1474 and PDM 1452 × PDM 1474 were more stable as well as high yielding, whereas DFTY × Heypool, BML 15 × PDM 1452, BML 15 × Heypool and DFTY × PDM 1474 were more variable but high yielding. The hybrids BML 6 × PDM 1474, BML 7 × DFTY, BML 15 × PDM 1474, DFTY × Heypool and Heypool × PDM 1474 were located near to ideal genotype with high mean and stability and could be ranked as desirable hybrids for kernel yield. Keywords— Maize, AMMI GGE biplot analysis, Kernel yield, Genotype × environment interaction. I. INTRODUCTION Maize is an important cereal crop worldwide and is ranked third after wheat and rice for its nutritional quality and uses Cassamon,; Ali et al, 2014. It is mostly used as a food, feed, forage, green fuel, vegetable oil and starch and is the backbone of the poultry feed industry. Kernel yield is a quantitative character, which depends on several yield contributing factors. Genotype × environment interaction reduces the association between the phenotype and genotype which in-turn reduces the selection response (Yan and Kang, 2003). Genotype–environment interactions may cause inconsistencies in genotype ranking across environments. Therefore, testing of identification and interpretation of G × E interaction is essential to make genetic progress (Kang, 2002 and Crossa, 2012). In the process of breeding, newly developed hybrids should be tested in multiple environments to determine the performance and stability before their commercial release. Multi environment trials aids in identification and recommendation of superior stable genotypes in mega environments. Seasons were found to contribute to the variations in performance of hybrids indicating that unpredictable seasonal conditions are one of the constraints in selecting superior and adaptable hybrids. AMMI model combines analysis of variance for the genotype and environment main effects with principal components analysis of the G × E interactions (Gauch and Zobel, 1996). It is useful in statistical analysis of comparative experimental yield clarify the effect of genotype in the environment, patterns and relationship of genotypes and the environment and also for improving the precision of yield estimation (Zobel et al, 1988; Crossa et al., 1990 and Annicchiarico, 2002). The Page | 29 International Journal of Environmental & Agriculture Research (IJOEAR) ISSN:[2454-1850] [Vol-8, Issue-11, November- 2022] present study was carried out to identify superior experimental hybrids as well as to select the best environment (Season) for testing hybrids developed in the maize breeding through AMMI biplot method. II. MATERIAL AND METHODS Forty five single cross hybrids developed from 10 inbred lines (BML 2, BML 6, BML 7, BML 15, DFTY, Heypool, PDM 1416, PDM 1428, PDM 1452 and PDM 1474) of maize through diallel mating design were evaluated for their performance over three seasons viz., rabi, summer and kharif from 2016-17 to 2017-18 at Agricultural Research Station, Perumallapalli, A.P. The experiment was laid out in a randomized block design with three replications with five meters row length. A spacing of 75 × 20 cm in kharif and 60 × 20 cm in summer and rabi between rows and plant to plant, respectively was followed. The two seeds per hill were dibbled and thinning operation was carried out one week after germination to maintain single plant per hill. All the recommended package of practices were adopted in raising a healthy crop. Data were recorded for 15 morphophysiological and yield contributing characters on five randomly selected plants and whole plot basis in each replication. The mean values for different characters were analysed according to Panse and Sukhatme (1978). The AMMI model (The Additive Main Effects and Multiplicative Interaction) was used to assess the G × E interaction (Hybrids × Seasons) according to Gauch and Zobel (1996). Statistical data analysis was performed using Genstat 12 th computer statistical program (Genstat, 2009). AMMI analysis was performed in Excel biplot Macros (Johnson and Bhattacharya, 2020). III. RESULTS AND DISCUSSION Pooled mean data analysis of variance over seasons was carried out after testing for homogeneity of error variances using Bartlett,s test. Pooled analysis of the variance for kernel yield was presented in Table 1. Partitioning of total sum of squares to the additive (genetic) and non-additive (ecological) component through analysis of variance indicated the significant differences among hybrids, seasons and hybrids × seasons interactions. The expression of the character not only depends on genetic factors but also on the external environment (Borojevic, 1965). The results of analysis of variance reveal that the proportion of the total variance of kernel yield attributable to seasons (41.66 %) was higher than the hybrids (34.28 %) and hybrids × seasons interaction (12.29 %) (Table 1). Significant hybrids × seasons interaction indicated that rank of genotypes varry at all the three seasons. TABLE 1 POOLED DATA ANALYSIS OF VARIANCE FOR KERNEL YIELD (g plant-1) OF MAIZE OVER SEASONS S.No Source of variation DF Mean sum of squares Per cent contribution (%) 1 Hybrids 44 909.32** 34.28 2 Seasons 2 24310.68** 41.66 3 Hybrids × Seasons 88 163.01** 12.29 4 Pooled Error 264 51.11 1.19 5 Total 404 116713.89 Note: per cent contribution were worked out based on sum of squares; *Significant at 5% level, **Significant at 1% level Kernel yield among hybrids ranged from 103.93 (BML 15 × PDM 14298) to 146.70 (BML 7 × DFTY) with a mean of 129.90 g in rabi; from 96.27 (BML 7 × BML 15) to 142 57 (Heypool × PDM 1474) with a mean of 126 26 g in kharif and from 86.94 (PDM 1428 × PDM 1452) to 129.03 (DFTY × Heypool) with a mean of 105.18 g per plant in summer. Pooled mean across seasons varied from 98.77 (BML 15 × PDM 1428) to139.19 (Heypool × PDM 1474) with a general mean of 120.56 g per plant. The hybrids viz., BML 6 × PDM 1474, BML 7 × DFTY, BML 15 × PDM 1474, DFTY × Heypool, DFTY × PDM 1452 and Heypool × PDM 1474 across seasons recoded significantly higher kernel yield over general mean (Table 2). Page | 30 International Journal of Environmental & Agriculture Research (IJOEAR) ISSN:[2454-1850] [Vol-8, Issue-11, November- 2022] TABLE 2 MEAN PERFORMANCE OF MAIZE HYBRIDS ACROSS SEASONS FOR KERNEL YIELD (g plant-1) IN MAIZE S.No 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 Hybrid(s) No. H1 H2 H3 H4 H5 H6 H7 H8 H9 H10 H11 H12 H13 H14 H15 H16 H17 H18 H19 H20 H21 H22 H23 H24 H25 H26 H27 H28 H29 H30 H31 H32 H33 H34 H35 H36 H37 H38 H39 H40 H41 H42 H43 H44 H45 Parentage BML2×BML6 BML2×BML7 BML2×BML15 BML2×DFTY BML2×Heypool BML2×PDM1416 BML2×PDM1428 BML2×PDM1452 BML2×PDM1474 BML6×BML7 BML6×BML15 BML6×DFTY BML6×Heypool BML6×PDM1416 BML6×PDM1428 BML6×PDM1452 BML6×PDM1474 BML7×BML15 BML7×DFTY BML7×Heypool BML7×PDM1416 BML7×PDM1428 BML7×PDM1452 BML7×PDM1474 BML15×DFTY BML15×Heypool BML15×PDM1416 BML15×PDM1428 BML15×PDM1452 BML15×PDM1474 DFTY×Heypool DFTY×PDM1416 DFTY×PDM1428 DFTY×PDM1452 DFTY×PDM1474 Heypool×PDM1416 Heypool×PDM1428 Heypool×PDM1452 Heypool×PDM1474 PDM 1416 × PDM 1428 PDM 1416 × PDM 1452 PDM 1416 × PDM 1474 PDM 1428 × PDM 1452 PDM 1428 × PDM 1474 PDM 1452 × PDM 1474 Grand Mean Rabi 135.50 142.73 124.77 129.17 143.88 127.50 116.13 128.57 131.33 120.43 114.87 138.90 126.03 137.57 122.30 137.53 145.83 135.10 146.70 134.43 107.80 120.62 136.07 122.07 129.40 143.90 110.47 103.93 144.73 143.77 143.20 111.03 128.83 143.40 138.17 120.73 124.13 138.80 145.77 105.97 114.53 138.73 114.07 133.67 142.40 129.90 Summer 102.27 108.23 95.73 107.40 116.33 96.93 110.90 97.27 118.00 94.87 94.67 117.10 107.83 105.47 98.57 106.33 122.93 91.13 126.47 90.49 90.00 108.40 91.80 113.33 92.47 100.33 99.03 90.07 108.93 125.73 129.03 111.93 114.43 120.27 97.53 96.75 107.83 104.97 129.23 97.67 93.63 99.53 86.94 102.67 111.73 105.18 Kharif 138.63 131.60 119.73 133.17 128.23 136.67 117.27 131.60 132.37 118.47 118.52 120.10 130.83 130.50 120.53 124.03 132.13 96.27 138.40 135.53 117.48 118.13 120.57 131.67 135.93 131.27 108.73 102.30 139.80 141.53 138.93 120.80 136.37 135.37 139.73 126.90 121.40 138.63 142.57 104.00 113.43 131.10 105.87 129.33 130.70 126.60 Mean over season 125.47 127.52 113.41 123.24 129.48 120.37 114.77 119.14 127.23 111.25 109.35 125.37 121.57 124.51 113.80 122.63 133.63 107.50 137.19 120.15 105.09 115.72 116.14 122.36 119.27 125.17 106.08 98.77 131.16 137.01 137.06 114.59 126.54 133.01 125.14 114.79 117.79 127.47 139.19 102.54 107.20 123.12 102.29 121.89 128.28 120.56 Page | 31 International Journal of Environmental & Agriculture Research (IJOEAR) ISSN:[2454-1850] [Vol-8, Issue-11, November- 2022] The hybrids × seasons interaction was further partitioned in to two principal components (PCA 1 and PCA 2) through AMMI analysis. The first two interaction principal components viz., PC 1 (74.00 %) and PC 2 (16.00 %) of GGE-biplot analysis explained 90.00 % of total variation caused by genotype + genotype × environment interaction and hence is considered satisfactory. The use of GGE biplot analysis helps in determining stable performing hybrids for kernel yield. Hybrids in different ecological conditions possessing the higher value of the first component close to zero were noted as stable (Sabaghniaa et al. 2006) .The high value of PCA 2 indicates that the best expression of the character in a specific environmental conditions (Bozovic et al., 2018). In this regard, AMMI is more suitable in the initial statistical analysis of yield trials which provides estimate of G × E interactions and summarizes the various pattern and relationships among genotypes and environments (Crossa et al., 1990). PCA scores of hybrids showed both positive and negative values in the present study. The GGE biplot analysis which provides graphical display is considered as an innovative methodology or applied plant breeding (Yan et al. 2000). The which-won-where pattern, relationships among test seasons and hybrids were visualized using their respective GGE biplots. GGE analysis was performed to study the relationship between and among seasons. The principal components of GGE biplots for kernel yield of hybrids evaluated in three seasons viz., first principal component (PCA 1) and the second principal component (PCA 2) sores were plotted against X axis Y axis, respectively. The polygon view of tested hybrids during three seasons was presented in Fig 1. All three seasons fell into one sector, whereas hybrids were grouped in all the sectors indicating that a single cultivar had the highest yield in all the environments. Hybrids viz., 31 (DFTY × Heypool), 29 (BML 15 × PDM 1452) and 39 (Heypool × PDM 1474) were the vertex hybrids or winners indicating that they are the best performing or responsive hybrids (Fig. 1). Lengths of season vectors are proportional to standard deviation of genotype yield in a corresponding treatment. Seasons having long vectors classify hybrids more when compared to seasons with short vector. Summer season was the most discriminative season for kernel yield. The test seasons presenting shorter angles were the most representative ones. Accordingly, in the present study rabi and kharif seasons were found most representative seasons for kernel yield (Fig. 2). FIGURE 1: Which won where pattern of GGE biplot for kernel yield in maize FIGURE 2: Discriminativeness vs representativeness of seasons for kernel yield in maize Yield performance and stability of hybrids was evaluated by an average environment coordination (AEC) method. Hybrids viz., 4 (BML 2 × DFTY), 5 (BML 2 × Heypool), 17 (BML 6 × PDM 1474), 9 (BML 7 × DFTY), 30 (BML 15 × PDM 1474), 34 (DFTY × PDM 1452) and 45 (PDM 1452 × PDM 1474) were more stable as well as high yielding, whereas 31 (DFTY × Heypool), 29 (BML 15 × PDM 1452), 26 (BML 15 × Heypool) and 35 (DFTY × PDM 1474) were more variable but high yielding (Fig. 3). Kaplan et al. (2017), Mebratu et al., (2019), Garoma et al., (2020) and Ramesh Kumar et al., (2020) have also reported that GGE Biplot method can be used to reliably in the evaluation of different maize genotypes grown in different environments. Page | 32 International Journal of Environmental & Agriculture Research (IJOEAR) ISSN:[2454-1850] [Vol-8, Issue-11, November- 2022] Genotypes with high average yield with relatively stable in performance across environments is referred as ideal genotypes and such genotypes are present at the center of concentric circle in GGE-biplot. Hybrids ranking on the basis of mean yield and stability in comparison to ideal genotype were depicted in Fig 4. Hybrids viz., 17 (BML 6 × PDM 1474), 19 (BML 7 × DFTY), 30 (BML 15 × PDM 1474), 31 (DFTY × Heypool), 34 (DFTY × PDM 1452) and 39 (Heypool × PDM 1474) were located near to ideal genotype and could be ranked as desirable hybrids stable with high mean yield and stable in performance for kernel yield. FIGURE 3: Mean vs Stability for kernel yield in maize IV. FIGURE 4: Ranking pattern of hybrids in relation to ideal genotype for kernel yield in maize CONCLUSIONS Seasons were found to contribute to the variations in performance of hybrids indicating that unpredictable seasonal conditions are one of the constraints in selecting superior and adaptable hybrids. The hybrids viz., BML 6 × PDM 1474, BML 7 × DFTY, BML 15 × PDM 1474, DFTY × Heypool, DFTY × PDM 1452 and Heypool × PDM 1474 across seasons recoded significantly higher kernel yield over general mean. Hybrids viz., DFTY × Heypool, BML 15 × PDM 1452) and Heypool × PDM 1474 were the vertex hybrids or winners indicating that they are the best performing or responsive hybrids. Summer season was found to be the most discriminating season in culling the unproductive ones and to save time and expenditure. Kharif and rabi seasons were the most representative testing seasons for kernel yield. Hybrids viz., BML 2 × DFTY, BML 2 × Heypool, BML6 × PDM 1474, BML 7 × DFTY, BML 15 × PDM 1474, DFTY × PDM 1452, Heypool × PDM1474 and PDM 1452 × PDM 1474 were more stable as well as high yielding. Hybrids close to the ideal genotype were ranked as the ones with high mean and phenotypic stability. The hybrids viz., BML 6 × PDM 1474, BML 7 × DFTY, BML 15 × PDM 1474, DFTY × Heypool and Heypool × PDM 1474 were located near to ideal genotype with high mean and stability and could be ranked as desirable hybrids for kernel yield. REFERENCES [1] Anncchiarico, P. Defining adaptation strategies and yield stability targets in breeding programs. In: Kang, M.S.(ed) Quantitative genetics, genomics and plant breeding. CABI, Wallingford, pp. 365-383,2002. [2] Borojevic, S. Model of inheritance and heritability of qualitative properties in crossings various varieties of wheat. Contemporary Agriculture, 7(8): 587-607, 1965. [3] Bozovic, D., Zivanovic, T., Popovic, V., Tatic, M., Gospavic, Z and Dokic, M. Assessement stability of maize lines yield by GGE biplot analysis. Genetika. 50(3): 1-10, 2018. [4] Crossa, J. Statistical analysis of multilocation trials. Advances in Agronomy. 44:55-85, 1990. [5] Crossa, J. Genotype × environment interaction. Current Genomics. 13: 225-244, 2012. [6] Garoma, B., Alamirew, S and Tilahun, B. Genotype × environment interaction and grain yield stability of maize (Zea mays L.) hybrids tested in multi-environment trials. International Journal of Plant Breeding and Crop Science. 72(2): 763-770, 2020. Page | 33 International Journal of Environmental & Agriculture Research (IJOEAR) ISSN:[2454-1850] [Vol-8, Issue-11, November- 2022] [7] Gauch, H.G and Zobel, R.W. Predictive and postdictive success of statistical analysis of yield trials. Theoretical and Applied Genetics. 76: 1-10, 1988. [8] Gauch, H.G. and R.W. Zobel.1996.AMMI analysis of yield trials. In:Genotype x Environment Interaction . CRC Press, Boca rato, F.L.:8 5-122, 1996. [9] Johnson, R and Bhattacharya, G. Statistics, Principles and Methods. 6th Edition. John wiley and Sons.2010. [10] Kaplan, M., Kokten, K and Akcura, M. Assessment of genotype × trait and environment interactions of silage maize genotypes through GGE Biplot. Chil.J Agric. Res. 77(3): 212-217, 2017. [11] Mebratu, A., Wegary, D., Mohammed, W., Teklewold, A and Tarekegne, A. Genotype × environment interaction of quality protein maize hybrids under contrasting management conditions in Eastern and Southern Africa. Crop Science. 59: 1576-1589,2019. [12] Ramesh Kumar., Jyoti Kaul., Kaur, Y., Das, A.K., Singode, A., Choudhary, M., Dubey, R.B., Sravani, D., Mukri, G and Rakshit, S. Response of quality protein maize (QPM) hybrids for grain yield in diverse environments. Indian Journal of Agricultural sciences. 90(4): 756-61,2020. [13] Sabaghniaa, N., Dehghania, H and Sabaghpour, B. Nonparametric methods for interpreting genotype × environment interaction of lentil genotypes. Crop Science. 46: 1100-1106, 2006. [14] Yan, W and Kang, M.S. GGE biplot analysis: Graphical tool for breeders, geneticists and agronomists. CRC press, New York, 2003. Page | 34