Genetic analysis for grain yield and yield related traits in maize ( Zea mays L . ) inbred lines in line × tester mating design

Maize is one of the most important cereal crop widely grown in the world. Maize crosses along with similar maturing checks were evaluated at Hawassa in 2015 to 2016 cropping season to understand the nature of gene action governing yield and its attributes through line × tester analysis and to study genetic contribution of line, tester and the interaction of line × tester to total variation. The experiment was done using α-lattice design 6 × 11 arrangement. Cross L31×T2 showed the highest mean grain yield than both checks. The result showed that, lines played an important role towards days to anthesis, days to silking, ear length, number of rows per cob and number of kernels per row, indicating predominance of maternal lines. Based on analysis of genetic variance, traits variance due to specific combining ability (σ2SCA) was higher than variance due to general combining ability (σ2GCA) indicated, non-additive gene action was important than additive gene action in the inheritance of these traits. These best cross combinations could be effectively utilized in maize breeding for the improvement of yield components and thus their incorporation in further breeding program is suggested.


INTRODUCTION
Maize is one of the most important cereal crops in the world after wheat and rice.Maize is nutritionally, an important crop used as food and feed.It is a source of industrial materials for the production of fuel, oil, starch, syrup; gluten, alcohol, glucose, ethanol and many more products.Its cultivation extends over a wide range of geographical and environmental conditions ranging from 58°N to 40°S.Portuguese traders introduced maize to Ethiopia in 16 th or 17 th century (Haffnagel, 1961).
Currently, in Ethiopia, maize is one of the most important cereal crops grown in almost all parts of the country.The popularity of maize in Ethiopia is partly because of its high value as a food, fodder and source of fuel for rural area.Approximately, 88% of maize produced in Ethiopia is consumed as food, both as green and dry grain (Abate et al., 2015).The total annual production and productivity of maize in Ethiopia exceeds all other cereal crops except Tef in area coverage (Mosisa et al., 2011).
The objective of maize breeding programs is the evaluation of best yielding and adaptive.Improvement of varieties (genotypes) needs deep genetic information.Breeders conducted a genetic analysis for yield and yield related traits of genotypes.In fact, maize has been subjected to extensive genetic studies than any other crop (Hallauer and Miranda, 1988).Several biometrical techniques were used to study genetic analysis of quantitative traits.Among them, line × tester is suggested by Kempthorne (1957) and is used to breed both self and cross pollination plants.This method of efficient study on large number of lines provides reliable estimates of genetic components, estimates of specific combining ability (SCA), general combining ability (GCA) and gene action governing quantitative traits.Therefore, the current study aimed to understand the nature of gene action governing yield and its attributes through line × tester analysis and to study genetic contribution of line, tester and the interaction of line × tester and total variation.
Data like days to maturity (DM), field weight, seed moisture content and thousand kernel weight (TKW) were collected plot bases while data like plant height (PH), ear height (EH), ear length (EL), number of rows per ear (NRPE), ear diameter (ED) and number of kernels per row (NCPR) were collected on plant bases.Biomass (BM) and grain yield (GY) was calculated by using the following formula: Where: MC = moisture content of grain at harvest, 0.8 = shelling percentage, 85 = standard moisture content of grain, n = number of plants harvested, 17 = total number of plants in a plot, 10000 = area of hectare in square meters.

Data analysis
All data obtained were subjected to SAS computer software to test the significance genotypes (Gomez and Gomez, 1984).Genetic parameter analysis and proportional contribution of line tester and line × tester were done only for trait.

Genetic parameter analysis σ²GCA= σ²SCA=
The ratio of σ²GCA to σ²SCA was expressed as

Proportional contribution of line, tester and line × tester to total variation in hybrid combinations
The percentage contribution of lines (females), testers (males) and line × tester to the hybrids were calculated according to Abuali et al.

Genetic parameters analysis
The analysis of variance showed that, there was a Fresher weigh × (100-MC) × 0.8*17 × 10,000 Grain yield (t ha -1 ) = n × 85 × 3.85 × 100 kg significance difference between genotypes for all traits.The analysis of variance indicated that, sufficient genetic variability is present among genotypes for all characters (Table 1b).Variance due to SCA (σ² SCA ) was higher than variance due to general combining ability (σ² GCA ) and the ratio of σ² GCA to σ²SCA was less than one for traits like days to anthesis, days to silking, days to maturity, ear diameter, thousand kernel weight, grain yield and cob per plant which indicate, non-additive gene action was more important than additive gene action in the inheritance of these traits (Table 1).Non additive gene action is not easily fixable, implies that best hybrids were not easily identified for the following traits.Similarly, Atanaw et al. (2010) reported that, non-additive gene effects were important than additive gene effect for grain yield.Also, Kamara et al. (2014) found similar result for ear diameter and thousand kernel weight.σ² GCA was larger than σ² SCA in plant height, ear length, number of rows per cob, number of kernels per row and biomass which indicates the additive gene action played the great role in governing the inheritance of these traits than non-additive gene action (Table 1).Additive gene action is easily fixable, implying that, best hybrids were easily identified for the following traits.Alamnie et al. (2007) and Panhwar et al. (2008) reported that, additive gene effects were more important than non-additive gene effects for plant height and number of kernels per row.The result was pact with that of Sharma et al. (2004) who found preponderance of additive genetic effects in the control of traits like plant height, ear length, number of rows per cob, number of kernels per row and biomass.Similar result has been reported by different researchers (Irshad-El-Haq et al., 2010;El-Badawy, 2012;Aminu et al., 2014) for grain yield.
The value of additive gene effects was more than the value of dominance gene effect for plant height, ear length, number of kernels per row, number of rows per cob, while the value of dominance gene effects was higher than the value of additive gene effects for days to anthesis, days to silking, days to maturity, ear height, ear diameter, thousand seed weight and grain yield (Table 1).The average degree of dominance was more than one for days to anthesis, days to silking, days to maturity, ear height, ear diameter, thousand seed weight and grain yield, indicating these traits were under control of the over dominance gene effect, whereas the average dominance was zero for traits like plant height, ear length, number of kernels per row, number of row per cob indicate there was no dominance for the traits (Table 1).

Proportional contribution of line, tester and line × tester
The proportional contribution of lines, testers and the interaction of line ×tester to the total variances are presented in Table 3.The result showed that, lines played an important role in days to anthesis, days to silking, ear length, number of rows per cob and number of kernels per row, indicating that predominant of maternal (lines) influence these traits and higher estimates of variance due to GCA (Table 3).The contribution of testers was low for all traits, which indicates higher estimates of variances due to SCA.The contribution of line × tester interactions played an important role in days to maturity, plant height, ear height, ear diameter, thousand kernel weight and grain yield, which indicate higher estimates of variances due to nonadditive genetic effects and the importance of SCA.Shams et al. (2010) observed higher estimates of SCA variance due to line × tester.Aminu et al. (2014) also found the proportional contribution of line × tester was greater than tester for grain yield, plant height, ear height, thousand kernels weight and ear length of their study on combining ability and heterosis for phenologic and agronomic traits in maize (Zea mays L.) under drought conditions.In contrast, Shams et al. (2010) found proportional contribution of line × tester interaction was greater than line and tester for number of kernels per row and proportional contribution of tester was greater than line and the interaction line × tester in number of rows per   cob in maize using line × tester method.

Conclusion
The analysis of variance showed sufficient genetic variability among genotypes for all characters.σ²SCA was greater than σ²GCA for traits like days to anthesis, days to silking, days to maturity, ear diameter, thousand kernel weight, grain yield.σ²GCA was larger than σ²SCA in plant height, ear length, number of rows per cob, number of kernels per row and biomass.The proportional contribution of line is greater than tester and the interaction of line × tester for traits like days to anthesis, days to silking, ear length, number of rows per cob and number of kernels per row.The proportional contribution of line × tester is greater than line and tester for days to maturity, plant height, ear height, ear diameter, thousand kernel weight and grain yield.

CONFLICT OF INTERESTS
The author declares that there is no conflict of interest.Means with the same letter are not significantly different from each other.DA = days to anthesis, DS=days to silking, DM=days to maturity, PH=plant height, EH=Ear height, EL=ear length, NRPC=number of rows per cob, NCPR=number of kernel per row, ED=ear diameter, TSW=thousand seed weight, GY=grain yield, CV=coefficient of variance.
Table 3. Proportional contribution of line, tester and line × tester interaction to total variance for 11 traits of maize hybrids tested at Hawassa.
= variance of general combining ability, σ²SCA = variance of specific combining ability, MSl = mean square of line, MSt = mean square of tester, MSl×t = mean square of line × tester, l = line, t = tester, r = replications.

Table 2 .
Top ten high yielding crosses relative to both checks were L31×T2 (8.68 t ha

Table 1a .
List of crosses and checks used in the experiment.

Table 1b .
Analysis of variance for phenological, growth parameters and yield of maize in maize crosses.

Table 2 .
Mean performance of maize 64 crosses and 2 checks for phenological, growth parameters, grain yield and yield related traits of maize (Zea mays L.) in southern Ethiopia, Hawassa.