African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6714

Full Length Research Paper

Stability analysis for yield and yield contributing characters in hybrid maize (Zea mays L.)

M. Q. I. Matin
  • M. Q. I. Matin
  • Plant Breeding Division, Bangladesh Agricultural Research Institute (BARI), Gazipur City, Bangladesh.
  • Google Scholar
Md. Golam Rasul
  • Md. Golam Rasul
  • Department of Genetics and Plant Breeding, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur, Bangladesh.
  • Google Scholar
A. K. M. Aminul Islam
  • A. K. M. Aminul Islam
  • Department of Genetics and Plant Breeding, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur, Bangladesh.
  • Google Scholar
M. A. Khaleque Mian
  • M. A. Khaleque Mian
  • Department of Genetics and Plant Breeding, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur, Bangladesh.
  • Google Scholar
J. U. Ahmed
  • J. U. Ahmed
  • Department of Crop Botany, Bangabandhu Sheikh Mujibur Rahman Agricultural University (BSMRAU), Gazipur, Bangladesh.
  • Google Scholar
M. Amiruzzaman
  • M. Amiruzzaman
  • Plant Breeding Division, Bangladesh Agricultural Research Institute (BARI), Gazipur City, Bangladesh.
  • Google Scholar


  •  Received: 15 March 2017
  •  Accepted: 04 July 2017
  •  Published: 14 September 2017

 ABSTRACT

Twenty one hybrids along with two check varieties of maize were assessed for genotype environment interaction (GEI) and stability for selection of the best hybrid maize in three different Agro-Ecological Zones (AEZ) of Bangladesh during rabi season 2014/2015. The experiment was laid out in Randomized Complete Block Design with 3 replications. The additive main effects and multiplicative interaction (AMMI) model was used to analyze the genotype-environment interaction over three locations to select the hybrids having higher yield (yld) and other potential attributes. The mean sum of square for genotypes was highly significant for the studied characters. Similarly, environmental variances were also highly significant for all characters. Variances due to genotype × environment interaction were highly significant for cob length and thousand seed weight (TSW). AMMI Component 1 showed variation for TSW. But AMMI Component 2 and G×E (Linear) revealed insignificant variation for all the characters. The highly significant effects of environment indicated high differential genotypic response across the different environments. The environments of Gazipur (Ij=-1.42**) and Barisal (Ij=-0.068) were poor but Rangpur (Ij=1.49)) was a favourable environments due to positive environmental index for tested maize hybrids. Considering the mean, bi and S2di for all the parameters, it was evident that all the genotypes showed different response of adaptability under different environmental conditions. Among the hybrids , WL4×WL5, WL1×WL3, WL2×WL3, WL1×WL4 and the check NK 40 exhibited the higher grain yield; bi~1 and S2di~0 indicated that these hybrids were stable across the environment. All the hybrids showed insignificant values for regression co-efficient and also deviation from regression except WL1×WL5. The AMMI biplot showed four grouping of genotypes having none of them, low yielding and unstable; one hybrid was low yielding but moderately stable; eight were high yielding and stable hybrids, and fourteen were high yielder but highly unstable.

 

Key words: Additive main effects and multiplicative interaction (AMMI) model, hybrid maize, stability analysis.


 INTRODUCTION

Maize (Zea mays L.; 2n=20) is a monoecious, C4 plant which belongs to the tribe Maydeae of the family Poaceae. It is a tall, robust, annual, usually with  a  single dominant stem, although there may be few tillers in some genotypes and environments. Its leaves are districhous (two ranks of single  leaves  borne  in  alternate  position) with overlapping sheaths and relatively long broad leaves.
 
Maize is a crop with versatile nature. It grows in a wide range of landscapes and agro-ecological settings across the world. This crop has brought an agricultural revolution in the United States (U.S.) and is the first crop in the world that had high-yielding varieties within a decade of the Mendelian discovery in 1900 (Dasgupta, 2014).
 
Presently maize is cultivated in 165 countries on 184 million hectares, and has a production of 1,016 million tons (t) and productivity of 5.52 t/ha globally. In terms of production, India, with 9.4 million hectare, ranks fourth globally, after the USA States (35.5 million hectare), China (35.3 million hectare) and Brazil (15.4 million hectare) (Yadav et al., 2014)
 
DAE (2015) showed that production was 11.37, 13.70, 15.52, 19.54, 21.78 and 25.16 lac metric ton in the year of 2008-09, 2009-10, 2010-11, 2011-12, 2012-13 and 2013-14 with an area occupying 1.74, 2.02, 2.27, 2.83, 3.12 and 3.64 lac ha in Bangladesh. The yield (yld) was 6.53, 6.78, 6.84, 6.90, 6.98 and 6.91 t/ha respectively.
 
In Bangladesh, although maize can be grown in both kharif (monsoon) and rabi (winter) seasons but the potentiality of realizing very high yields is possible only during the rabi season. In kharif cultivation, farmers face various problems such as waterlogging, high infestation of diseases and pests, etc. Due to this reason it is now extensively being grown in rabi season under irrigated conditions. However, kharif cultivation is also possible in some suitable areas; the area of growth is now gradually increasing mainly due to T. Aman-potato-maize cropping pattern (Mondal et al., 2014)
 
Maize in Bangladesh is becoming an important crop in the rice based cropping system. It is the third important cereal crop after rice and wheat. In recent year’s maize is gaining popularity among the farmers mainly due to high yield, more economic return and versatile uses; it is the highest yielding grain crop having multiple uses. The need for demand of maize is increasing gradually. The area and production of maize is increasing day by day in Bangladesh and it continues to expand rapidly at an average rate of 20% year-1 (CIMMYT, 2008).
 
Stability refers to the consistency of phenotype in varying environment. It is one of the most important properties of a genotype to be released as a variety for wide cultivation. For quantitative traits like yield the relative performance of different genotypes often varies from one environment to another. Thus, genotype×environment interaction does exist when phenotypic response made by a change in environment is not the same for all genotypes (Comstock and Moll, 1963). Stability analysis helps to select genotypes adaptable for wide and specific environments and divides large geographical areas into subareas. It provides effective allocation of resources for testing genotypes across location years.
 
A commercial variety must have stable performance and broad adaptation over a range of environments in addition to high yield potential. But its evaluation becomes complicated due to genetic heterogeneity, a complex biological basis, and genotype × environment interactions (Austin and Lee, 1988). Thus, genotype × environment interaction limits the effectiveness of selection when selection is done based only on mean yields (Dehganpour and Moghadam, 1999). The potential of genotypes and stability of their performance can be judged by multi environment testing (Mahajan and Khehra, 1992). It is more practical to develop and release varieties which are adapted to more than a single environment and can be successfully grown over a range of environments.
 
The analysis of G×E, therefore, becomes an important tool employed by breeders for evaluating varietal adaptation and also for selecting parents for base populations. The additive main effects and multiplicative interaction (AMMI) model was found suitable to handle both the main effects and G×E interactions in multilocational yield trials more effectively and efficiently than other statistical packages (Gauch, 1993). Considering these the experiment was laid out with the following objectives:
 
(i) To estimate G×E interaction of single cross hybrids and
(ii) To identify stable single cross hybrids for yield and yield related characters.


 MATERIALS AND METHODS

The experiment was carried out at three different Agro -Ecological Zones (AEZ) of Bangladesh such as Gazipur, Barisal and Rangpur during rabi season 2014/2015. Twenty one hybrids and two check varieties were evaluated in this study. The experiment was laid out in randomized complete block design with 3 replications. The twenty one hybrids were developed from seven parental lines using diallel mating without the reciprocals during the rabi season in 2012 to 2013. The source materials were from both BARI and CIMMYT such as WL1=BIL20, WL2=BML36, WL3=BIL77, WL4=BIL106, WL5=CLQRCY44, WL6=BIL79 and WL7=BIL 31. The hybrids developed through diallel mating design were as follows: WL1×WL2, WL1×WL3, WL1×WL4, WL1×WL5, WL1×WL6, WL1×WL7, WL2×WL3, WL2×WL4, WL2×WL5, WL2×WL6, WL2×WL7, WL3×WL4, WL3×WL5, WL3×WL6, WL3×WL7, WL4×WL5, WL4×WL6, WL4×WL7,WL5×WL6, WL5×WL7 and WL6×WL7.
 
Seeds of each entry were sown in 2 rows, 4 m long plots with 60 and 20 cm  spacing  between  rows  and  hills,  respectively.  Seeds were sown at Gazipur on 24 November, Barisal on 27 November, Rangpur 1 December. One healthy seedling per hill was kept after thinning. Fertilizers were applied at the rate of 250, 55, 110, 40, 5 and 1.5 kg/ha of N, P2O5, K2O, S, Zn and B, respectively. Standard agronomic practices were followed (Quayyum, 1993) and plant protection measures were taken as required. Two border rows at both end of each replication were used to minimize the border effect. Data on cob length (CL), cob diameter (CD), thousand seed weight (TSW) and yield (t/ha) were recorded. All the plants in two rows were considered for plot yield and converted to t/ha.
 
The analysis of variance (ANOVA) was used and the GE interaction was estimated by the AMMI model (Zobel et al., 1988). In this procedure, the contribution of each genotype and each environment to the GE interaction was assessed by use of the biplot graph display in which yield means were plotted against the scores of the first principal component of the interaction (IPCA1). The computational program for AMMI analyses was supplied by Duarte and Vencovsky (1999). The stability parameters, regression coefficient (bi) and deviation from regression (S2di) were estimated according to Eberhart and Russel (1966). Significance of differences among bi value and unity was tested by t-test while between S2di and zero by F-test. All the data were processed and analyzed using Cropstat 7.2 program and PB Tools.


 RESULTS AND DISCUSSION

Analysis of variance
 
Twenty one single cross maize hybrids and two check hybrids [BARI Hybrid Maize7 (BHM 7) and NK 40] were evaluated in three AEZ for four characters. The combined analysis of variance for four characters was presented in Table 1. The mean square for genotypes was highly significant for the characters studied. Similarly, environmental variances were also highly significant for all characters. Variances due to genotype × environment interaction were highly significant for cob length and TSW. AMMI Component 1 showed variation for TSW. But AMMI Component 2 and G×E (Linear) revealed insignificant variation for all the characters. The pooled deviation (nonlinear portion of variance) which is unpredictable portion of G×E interaction was significant for only TSW. The highly significant effects of environment indicated high differential genotypic response across the different environments. Environment relative magnitude was much higher than the genotypic effect, suggesting that performance of each genotype was influenced more by environmental factors. Significant differences in Genotype (G), Environments (E), Interaction G×E and Pooled deviation of maize were also recorded in the studies of Shiri (2013), Patel and Kathiria (2016), Banik et al. (2010), Lata et al. (2010), Miah et al. (2011), Islam et al. (2011), Rahman et al. (2010), Fan et al. (2007), Akbar et al. (2009) and Kaundal and Sharma (2006).
 
 
Cob length (cm)
 
Cob length (cm) along with the value of phenotypic indices (Pi), regression coefficient (bi) and deviation from regression (S2di) were presented in Table 2. The environmental mean and genotypic mean ranged from 18.91 to 28.9 and 19.29 to 28.15 cm, respectively.
 
 
The highest overall mean for CL was recorded in WL6×WL7 (28.15 cm) followed by WL3×WL4 (26.16 cm), WL1×WL7 (26.12 cm) and WL3×WL6 (26.03 cm) and WL4×WL6 (25.59 cm) indicating dwarf hybrids. The lowest CL was found in WL1×WL5 (19.29 cm).
 
Thirteen hybrids including one check (BHM 7) showed positive phenotypic index while the other ten hybrids including the check (NK 40) had negative phenotypic index for CL. Thus, positive phenotypic index represented higher yielder and negative index represented lower yielder hybrids.
 
Among the twelve, nine hybrids such as WL6×WL7, WL3×WL4, WL1×WL7, WL3×WL6, WL2×WL3, WL2×WL6, WL2×WL5, WL1×WL2 and WL1×WL3 having positive phenotypic index, coupled with near unit regression co-efficient and non-significant deviation from regression recorded stable. Though the hybrid WL4×WL6 had positive phenotypic index, deviation from regression (S2di)=0, but the regression coefficient (bi) was significant, so it was the unstable one.
 
Again, positive  and  negative  environmental  index  (Ij) reflects the favourable environment and unfavourable environment for this character, respectively. The environment Gazipur (Ij =3.80) and Rangpur (Ij =2.38) were favourable for hybrids and Barisal (Ij = -6.18) was an unfavourable environments for cob length.
 
The regression coefficient (bi) and deviation from regression (S2di) values of these hybrids ranged from 0.67 to 1.36 and 0.00 to 0.15, respectively.
 
Cob diameter (cm)
 
Cob diameter (cm) along with the value of phenotypic indices (Pi), regression coefficient (bi) and deviation from regression (S2di) were presented in Table 3. The environmental mean and genotypic mean ranged from 4.10 to 4.95 and 3.83 to 4.75 cm. The highest overall mean for CD was recorded in WL3×WL4 and WL4×WL7 (4.75 cm) followed by WL2×WL3 (4.69 cm), WL4×WL6 (4.66 cm) and WL4×WL5 (4.56 cm) and WL1×WL3 (4.56 cm) higher yields of the hybrids. The lowest CD was found in WL1×WL5 (3.83 cm). Twelve hybrids including two checks showed positive phenotypic index while the other eleven hybrids had negative phenotypic index for CD. Thus, positive phenotypic index represented higher yield and negative index represented the lower yield hybrids. 
 
 
Among the twelve, six hybrids such as WL4×WL7, WL3×WL4, WL2×WL3, WL3×WL6, WL1×WL3 and WL1×WL4 have positive phenotypic index, coupled with near unit regression co-efficient and non-significant deviation from regression recorded stable. Though the hybrid WL4×WL6 had positive phenotypic index, deviation from regression (S2di)=0, but the regression coefficient (bi) was significant, so it was the unstable one according to Eberhart and Russel (1966).
 
Again, positive and negative environmental index (Ij) reflected the favourable environment and unfavourable environment for this character, respectively. The environment Barisal (Ij = 0.47) was favourable and Gazipur (Ij =-3.80) and Rangpur (Ij =-0.86) were unfavourable environments for this character of hybrids.
 
The regression coefficient (bi), deviation from regression (S2di) values of these hybrids ranged from 0.53 to 1.41 and 0 to 0.03, respectively.
 
Thousand seed weight (TSW)
 
The thousand seed weight (TSW) along with the value of phenotypic indices (Pi), regression coefficient (bi) and deviation from regression (S2di) were presented in Table 4. The environmental mean and genotypic mean ranged from 338.2 to 397.9 and 330.11 to 405.89 g.
 
 
 
In Gazipur, the highest TSW was found from WL1×WL7 (432 g) followed by WL1×WL6 (426.7 g) and WL1×WL3 (424 g) and WL3×WL5 (420 g). In Barisal, WL3×WL4 produced the highest TSW (457.7 g) followed by WL2×WL7 (447.7 g) and WL1×WL2 (442.7 g) in Rangpur the maximum production recorded in WL3×WL6 (377.7 g) which was identical with WL2×WL6 (368 g) and WL1×WL2 (364.7 g). But the cross WL1×WL5 produced the  lowest  TSW  in  Gazipur  and  Rangpur.  The  hybrid WL1×WL6 was the lowest yielder in Barisal.
 
Eleven hybrids including one check NK 40 showed positive phenotypic index while the other eleven genotypes had negative phenotypic index for yield. Thus, positive phenotypic index revealed the higher TSW and negative index represented the lower TSW among the genotypes. Again, positive and negative environmental index (Ij) reflected the rich or favourable and poor or unfavourable environments for this character, respectively. Thus the environment of Gazipur and Barisal were rich whereas Rangpur was negative environments for this character. Barisal was highly suitable for hybrid maize cultivation followed by Gazipur.
 
The regression coefficient (bi) deviation from regression (S2di) values of these genotypes ranged from -0.02 to 2.04 and 33.11 to 10457.82, respectively. These differences in bi values indicated that all the genotypes responded differently to different environments. Considering the mean, bi and S2di three parameters, it was evident that all the genotypes showed different response of adaptability under different environmental conditions. Among the hybrids , WL3×WL4, WL3×WL7, WL5×WL7, WL1×WL2, WL1×WL7 and check NK 40 exhibited the higher TSW, bi~1 and S2di~0 indicating that the hybrids were stable across the environment according to Eberhart and Russel (1966).
 
Grain yield (t/ha)
 
The grain yield along with the value of phenotypic indices (Pi), regression coefficient (bi) and deviation from regression (S2di) were presented in Table 5. The environmental mean and genotypic mean ranged from 7.58 to 10.48 t/ha and 3.94 to 10.49 t/ha, respectively.
 
 
In Gazipur, the highest yield was found from WL1×WL6 (9.61 t/ha) followed by WL4×WL5 (9.02 t/ha) and WL3×WL5 (8.71 t/ha). In Barisal, WL4×WL5 produced the highest yield (10.61 t/ha) followed by WL1×WL4 (10.28 t/ha) and WL2×WL7 (10.22 t/ha). The maximum production recorded in WL4×WL5 (11.83 t/ha) in Rangpur which  was  identical  with   WL1×WL3   (10.82 t/ha)   and WL1×WL4 (10.28 t/ha). But the cross WL1×WL5 (3.61-4.31 t/ha) produced the lowest yield in all three locations.
 
Thirteen hybrids showed positive phenotypic index while the other eight genotypes had negative phenotypic index for yield. Thus, positive phenotypic index represented the higher yield and negative index represents the lower yield among the genotypes. Again, positive and negative environmental index (Ij) reflected the rich or favourable and poor or unfavourable environments for this character, respectively. Thus the environment of Gazipur and Barisal were poor whereas Rangpur was positive environments for hybrid maize production. Rangpur was highly suitable for hybrid maize cultivation followed by Barisal.
 
The regression coefficient (bi), deviation from regression (S2di) values of these genotypes ranged from 0.242 to 1.924 and 0.00 to 4.17, respectively. These differences in bi values indicated that all the genotypes responded differently to different environments. Considering the mean, bi and S2di three parameters, it was evident that all the genotypes showed different response  of  adaptability  under  different   environmental conditions. Among the hybrids , WL4×WL5, WL1×WL3 , WL2×WL3, WL1×WL4 and check NK 40 exhibited the higher grain yield, bi~1 and S2di~0 indicated that the hybrids are stable across the environment. All the hybrids showed insignificant values for regression co-efficient except WL1×WL5 and also deviation from regression.
 
In the present study, it was found that the level of stability for a particular genotype was not similar for all characters. Grada and Ciulca (2012), Patel and Sanghi (1989) and Sharma and Hore (1991) reported that the levels of stability were not similar to any genotype for all characters, no uniform pattern of association between stability of yield and yield components and stress components could be established for individual genotype.
 
Figure 1 represents the Q-Q plot, histogram and scatter plot which revealed distribution pattern of the data. Figure 2 indicated yield range of hybrid maize.
 
 
 
AMMI biplot
 
The  AMMI  biplot  provides  a  visual  expression  of   the relationships between the first interaction principal component axis (IPCAI) and means of genotypes and environments (Figure 3) with the biplot accounting up to 95.3% of the treatment sum of squares. The IPCA1 was highly significant and explained the interaction pattern better than other interaction axes. The mean genotypes or environments in AMMI biplot located on the same parallel line, relative to the ordinate, had similar yield, while those located on the right side of the center of the axis had higher yields than those on the left hand side (Figure 1). 
 
 
The biplot showed four grouping of genotypes having none of them, low yielding and unstable; 4(WL1×WL5) was low yielding but moderately stable, 17(WL4×WL6), 21(WL6×WL7), 13(WL3×WL5), 10(WL2×WL6), 6(WL1×WL7), 9(WL2×WL5), 5(WL1×WL6) and 23(NK 40-check) were high yielding and stable hybrids. 15(WL3×WL7), 14(WL3×WL6), 20(WL5×WL7), 12(WL3×WL4), 7(WL2×WL3), 8(WL2×WL4), 2(WL1×WL3), 16(WL4×WL5), 22(BHM7-check), 4(WL1×WL5), 1(WL2×WL3), 18(WL4×WL7), 11(WL2×WL7) and 19(WL5×WL6) were high yielder but highly unstable.
 
Since IPCA2 scores also played a significant role in explaining the GEI; the IPCA1 scores were plotted against the IPCA2 scores for further exploring adaptation (Figure 4). According to Figure 4, the hybrids 19 (WL5×WL6), 22(BHM7- check), 4 (WL1×WL5), 17(WL4×WL6), 10(WL2×WL6) and 11 (WL2×WL7) were unstable due to their dispersed position. 23 (NK 40-check), 10(WL2×WL6), 16(WL4×WL5), 12(WL3×WL4), 7(WL2×WL3), 6(WL1×WL7), 8(WL2×WL4), 21(WL6×WL7), 13 (WL3×WL5) and 5(WL1×WL6) showed more stability when plotting the IPCA1 and IPCA2 scores. The present findings were in accordance with Banik et al. (2010) and Oliveira et al. (2010).
 
 
Genotypes with IPCA1 scores near zero had little interaction  across  environments  while  genotypes   with very high IPCA1 values had considerable interactions across environments. Of the 23 hybrids, six had negligible interactions characterized by 16(WL4×WL5), 20(WL5×WL7), 14(WL3×WL6), 12(WL3×WL4), 21(WL6×WL7) and 13(WL3×WL5) and were relatively stable showing broad adaptation across environments. Six hybrids with higher IPCA scores were highly interactive and were unstable across environments; such as 19(WL5×WL6), 4(WL1×WL5), 22((BHM7-check)), 5(WL1×WL6), 10(WL2×WL6) and 11(WL2×WL7). The underlying causes of the interaction observed can therefore be based on both the genetic differences between these genotypes and the different environments (Wallace et al., 1995).
 
As shown in Figure  5,  criss  cross  line  reflected  high interaction with environment but parallel line less interaction. The hybrid 23(NK40), 18(WL4×WL7), 5(WL1×WL6) and 19(WL5×WL6) were less influenced by the environment comprised of higher yield. But the entry 4(WL1×WL5) had the least yield.
 
 
Figure 6 showed which won where. The hybrids 23(NK40), 5(WL1×WL6), 18(WL4×WL7) and 19(WL5×WL6) were stable in all locations; 23(NK40) was suitable for Gazipur and Rangpur location and 18(WL4×WL7) best adapted to Rangpur and Barisal location. The hybrid 4(WL1×WL5) was unstable in all locations.
 


 CONCLUSION

Among the hybrids , WL4×WL5, WL1×WL3, WL2×WL3, WL1×WL4 and check NK 40 exhibited higher grain yield; bi~1 and S2di~0 indicated that the hybrids found stable across the environment. All the hybrids showed insignificant values for regression co-efficient and also deviation from regression except WL1×WL5. The AMMI biplot showed four grouping of genotypes having none of them, low yielding and unstable; one hybrid was low yielding but moderately stable; eight hybrids including check NK 40 are high yielding and stable hybrids; 14 hybrids including BHM7-check were high yielder but highly unstable. Rangpur was highly suitable for hybrid maize cultivation followed by Barisal and Gazipur. 


 CONFLICT OF INTERESTS

The authors have not declared any conflict of interests.



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