Genetic Variability and Morphological Diversity among Open-Pollinated Maize (...Premier Publishers
A study to characterize and determine the magnitude of genetic variation among 60 open-pollinated maize varieties was conducted at two contrasting locations in Sierra Leone during the 2015 wet cropping season. Results revealed that traits such as grain moisture content, anthesis-silking interval, plant and ear heights, number of ears harvested, field weight and grain yield showed moderate to high values of the components of genetic variation while days to 50% anthesis and silking revealed low values of the components of genetic variation. The first two PCA axes explained 54% of the total variation, of which the first principal component (PC1) accounted for 35% and PC2 contributed 19% of the total variation. The cluster diagram grouped the genotypes into seven main clusters and results suggest that crosses involving clusters I and V with any other clusters would produce segregants with low grain yields while the crosses between clusters IV, VI and VII would be expected to manifest higher heterosis and could result in segregants with higher grain yields. There was significant genetic variability observed among the genotypes evaluated thereby suggest the scope to bring about traits improvement of genotypes through direct selection and hybridization.
Genetic Variability and Morphological Diversity among Open-Pollinated Maize (...Premier Publishers
A study to characterize and determine the magnitude of genetic variation among 60 open-pollinated maize varieties was conducted at two contrasting locations in Sierra Leone during the 2015 wet cropping season. Results revealed that traits such as grain moisture content, anthesis-silking interval, plant and ear heights, number of ears harvested, field weight and grain yield showed moderate to high values of the components of genetic variation while days to 50% anthesis and silking revealed low values of the components of genetic variation. The first two PCA axes explained 54% of the total variation, of which the first principal component (PC1) accounted for 35% and PC2 contributed 19% of the total variation. The cluster diagram grouped the genotypes into seven main clusters and results suggest that crosses involving clusters I and V with any other clusters would produce segregants with low grain yields while the crosses between clusters IV, VI and VII would be expected to manifest higher heterosis and could result in segregants with higher grain yields. There was significant genetic variability observed among the genotypes evaluated thereby suggest the scope to bring about traits improvement of genotypes through direct selection and hybridization.
Genetic variability, heritability, genetic advance, genetic advance as percen...Premier Publishers
Field experiment was conducted to estimate genetic variability, heritability, genetic advance, genetic advance as a percent mean and character association for forty nine genotypes of Ethiopian mustards collected from different agro ecologies. The experiment was carried out in a simple lattice design. The analysis of variance showed that there were significant differences among genotypes for all traits compared. The significant difference indicates the existence of genetic variability among the accessions which is important for improvement. High genotypic and phenotypic coefficients of variations were observed in seed yield per plot, oil yield per plot, and plant height. This shows that selection of these traits based on phenotype may be useful for yield improvement. The highest heritability in broad sense was recorded for thousand seed weight (68.80%) followed by days to flowering (65.91%), stand percent (63.14%), linolenic acid(62.58%), days to maturity (60.43%), plant height (59.63%), palmitic acid (58.19%), linoleic acid (57.46%),oil content (50.33%), oil yield (44.84%), seed yield per plot(42.99%),and primary branches(34.20%). This suggests that large proportion of the total variance was due to the high genotypic and less environmental variance. In the correlation coefficient analysis, seed yield per plot showed positive correlation with oil content, oil yield, plant height and seed yield per plant. In the path analysis, number of primary branches and oil yield showed positive direct effect on seed yield per plot. In this study, seed yield per plot, oil content, oil yield and primary branches were found to be the most important components for the improvement of seed and oil. Therefore more emphasis should be given for highest heritable traits of mustard and to those positively correlated traits to improve these characters using the tested genotypes.
Combining Ability Analysis of Maize (Zea Mays L.) Inbred Lines for Grain Yiel...Premier Publishers
A total of 64 test-crosses generated by crossing 32 elite maize inbred lines with two testers and two standard checks were evaluated for grain yield and yield related traits in 6×11 alpha lattice design replicated twice during 2017 cropping season at Bako National Maize Research Center of Ethiopia with the objective of estimating general and specific combining ability effects of the inbred lines for grain yield and yield related traits. Analysis of variance indicated highly significant mean squares due to genotypes for all the studied traits. Mean squares due to line general combining ability (GCA) were significant for all studied traits whereas, mean square due to tester GCA was significant for all traits, except number of kernels per row and grain yield. Mean squares due to specific combining ability (SCA) effects were significant (P<0.01 or P<0.05) for biomass yield, number of ears per plant and thousand kernel weight. Generally, mean squares due to both lines and testers GCA and SCA of line × tester interactions were significant for grain yield and most yield related traits indicating the importance of both additive and non-additive gene actions in controlling these traits.
Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice...Premier Publishers
The experiment was conducted to assess genetic variability, heritability and genetic advance for yield and yield related traits in some upland rice genotypes. A total of 23 rice genotypes were evaluated in a randomized complete block design with three replications in 2017 at Pawe and Assosa. Analysis of variance revealed significant difference among the genotypes for most of the traits at individual and across locations, and error variances of the two locations were homogenous for most of the traits including grain yield. Moreover, the genotypes showed wider variability for grain yield in the range between 3707-6241kg/ha, 4853-7282kg/ha and 4280-6761kg/ha at Pawe, Assosa and over locations, respectively. A relatively high (>20%) phenotypic and genotypic coefficient of variations were estimated merely for number of unfilled grains per panicle. High heritability estimates (> 60%) were obtained for all of the traits, except plant height and Protein content. A relatively high genetic advance was obtained for traits like unfilled grains per panicle and fertile tiller per plant. Thus, this study revealed that there was higher genetic variability among the tested genotypes, which could be potentially exploited in future breeding programs.
Correlation and path analysis for genetic divergence of morphological and fib...Innspub Net
Seventy five genotypes of cultivated cotton (Gossypium hirsutum L.) were studied for morphological characteristics i-e plant height, monopodial branches, sympodial branches, boll weight, seed volume, seed density, seed index and fiber characters. Data were subjected to analysis of variance and estimates were made for genetic advance, broad sense heritability and coefficient of variance for the traits. ANOVA revealed highly significant variability among genotypes for all the characteristics studied. The estimates for heritability were
higher for seed index (0.93) and plant height (0.93). The highest value (6.4) for genetic advance was observed for
sympodial branches whereas lowest value was (0.17) for boll weight. Correlation analysis revealed positive and significant for most of the parameters. In path coefficient, the number of sympodial branches, boll weight, lint index and lint weight had maximum direct and positive effect on fiber fineness of seed cotton. Whereas, the number of monopodial branches, plant height, seed index, seed volume, seed density, staple length, fiber strength and ginning out turn (G.O.T%) had direct and negative effects on fiber of seed cotton. The principle component analysis (PCA) revealed significant differences between genotypes and the first four components with Eigen
values greater than 1 contributed 66.68% of the variability among the genotypes. The grouping of genotypes
possessing excelled traits signifies genetic potential of the germplasm for the improvement of seed and fiber characteristics in cotton crop. Get more articles at: http://www.innspub.net/volume-7-number-4-october-2015-ijaar/
Genomic aided selection for crop improvementtanvic2
In last Several years novel genetic and genomics approaches are expended. Genetics and genomics have greatly enhanced our understanding of the structural and functional aspects of plant genomes.
Variability, heritability and genetic advance analysis for grain yield in riceIJERA Editor
Ten diverse genotypes of rice (Oryza sativa L.) were crossed in a diallel fashion to study variability , heritability and genetic advance for 12 quantitative characters . A considerable amount of variability (gcv) varied from 5.95 for no. of leaves per tiller to 17.40 for grain yield per plant and the estimates of pcv varied from 7.08 for days to 50% flowering to 17.49 for grain yield per plant. The heritability estimates ranged from 0.721 for total biological yield per plant to 1.000 for plant height . Since the heritability in broad sense was estimated , therefore . other parameters should also be considered for selecting the genotypes. The genetic advance varied from 0.71 for no. of leaves per tiller to 46.23 for no. of spikelets per panicle. High estimates of genetic advance was reported for plant height , days to maturity , days to 50% flowering and total biological yield per plant . However, high heritability estimates was associated with high predicted genetic advance for plant height , days to maturity ,days to 50% flowering and no. of spikelets per panicle. The situation is encouraging since selection based on these characters being of additive in nature , is likely to be more effective for their improvement. As such phenotypic selection for those traits is likely to be more effective for their improvement. The estimates of phenotypic coefficient of variation were higher than those of genotypic coefficient of variation for all the traits except plant height. High estimates of heritability and genetic advance were obtained for plant height , number of spikelets per panicle , days to 50 per cent flowering and days to maturity . These traits were mostly governed by additive gene action. And these characters are important for the breeder to construct selection indices.
Correlation and Path analysis studies among yield and yield related traits in...Premier Publishers
The16 Soybean genotypes were evaluated for Association of characters and path coefficient analysis on eleven important yield and grain yield contributing characters at Bako Tibe during the main cropping season of 2015/16. The experiment was designed as RCBD with three- replication. Generally, the magnitudes of genotypic correlation coefficients for most of the characters were higher than their corresponding phenotypic correlation coefficients that indicate the presence of inherent association among various characters. In this study yield was positively correlated with hundred seed weight, number of seed/pod and number of pod per plant so, increasing these traits ultimately increases in grain yield and days to maturity can be exploited through improvement and selection program. Based on findings it can be concluded that pod length, number of pod /plant, biological yield, grain yield and days to maturity can be exploited through selection and improvement program to develop high yielding soybean genotypes.
Genetic variability, heritability, genetic advance, genetic advance as percen...Premier Publishers
Field experiment was conducted to estimate genetic variability, heritability, genetic advance, genetic advance as a percent mean and character association for forty nine genotypes of Ethiopian mustards collected from different agro ecologies. The experiment was carried out in a simple lattice design. The analysis of variance showed that there were significant differences among genotypes for all traits compared. The significant difference indicates the existence of genetic variability among the accessions which is important for improvement. High genotypic and phenotypic coefficients of variations were observed in seed yield per plot, oil yield per plot, and plant height. This shows that selection of these traits based on phenotype may be useful for yield improvement. The highest heritability in broad sense was recorded for thousand seed weight (68.80%) followed by days to flowering (65.91%), stand percent (63.14%), linolenic acid(62.58%), days to maturity (60.43%), plant height (59.63%), palmitic acid (58.19%), linoleic acid (57.46%),oil content (50.33%), oil yield (44.84%), seed yield per plot(42.99%),and primary branches(34.20%). This suggests that large proportion of the total variance was due to the high genotypic and less environmental variance. In the correlation coefficient analysis, seed yield per plot showed positive correlation with oil content, oil yield, plant height and seed yield per plant. In the path analysis, number of primary branches and oil yield showed positive direct effect on seed yield per plot. In this study, seed yield per plot, oil content, oil yield and primary branches were found to be the most important components for the improvement of seed and oil. Therefore more emphasis should be given for highest heritable traits of mustard and to those positively correlated traits to improve these characters using the tested genotypes.
Combining Ability Analysis of Maize (Zea Mays L.) Inbred Lines for Grain Yiel...Premier Publishers
A total of 64 test-crosses generated by crossing 32 elite maize inbred lines with two testers and two standard checks were evaluated for grain yield and yield related traits in 6×11 alpha lattice design replicated twice during 2017 cropping season at Bako National Maize Research Center of Ethiopia with the objective of estimating general and specific combining ability effects of the inbred lines for grain yield and yield related traits. Analysis of variance indicated highly significant mean squares due to genotypes for all the studied traits. Mean squares due to line general combining ability (GCA) were significant for all studied traits whereas, mean square due to tester GCA was significant for all traits, except number of kernels per row and grain yield. Mean squares due to specific combining ability (SCA) effects were significant (P<0.01 or P<0.05) for biomass yield, number of ears per plant and thousand kernel weight. Generally, mean squares due to both lines and testers GCA and SCA of line × tester interactions were significant for grain yield and most yield related traits indicating the importance of both additive and non-additive gene actions in controlling these traits.
Genetic Variability, Heritability and Genetic Advance Analysis in Upland Rice...Premier Publishers
The experiment was conducted to assess genetic variability, heritability and genetic advance for yield and yield related traits in some upland rice genotypes. A total of 23 rice genotypes were evaluated in a randomized complete block design with three replications in 2017 at Pawe and Assosa. Analysis of variance revealed significant difference among the genotypes for most of the traits at individual and across locations, and error variances of the two locations were homogenous for most of the traits including grain yield. Moreover, the genotypes showed wider variability for grain yield in the range between 3707-6241kg/ha, 4853-7282kg/ha and 4280-6761kg/ha at Pawe, Assosa and over locations, respectively. A relatively high (>20%) phenotypic and genotypic coefficient of variations were estimated merely for number of unfilled grains per panicle. High heritability estimates (> 60%) were obtained for all of the traits, except plant height and Protein content. A relatively high genetic advance was obtained for traits like unfilled grains per panicle and fertile tiller per plant. Thus, this study revealed that there was higher genetic variability among the tested genotypes, which could be potentially exploited in future breeding programs.
Correlation and path analysis for genetic divergence of morphological and fib...Innspub Net
Seventy five genotypes of cultivated cotton (Gossypium hirsutum L.) were studied for morphological characteristics i-e plant height, monopodial branches, sympodial branches, boll weight, seed volume, seed density, seed index and fiber characters. Data were subjected to analysis of variance and estimates were made for genetic advance, broad sense heritability and coefficient of variance for the traits. ANOVA revealed highly significant variability among genotypes for all the characteristics studied. The estimates for heritability were
higher for seed index (0.93) and plant height (0.93). The highest value (6.4) for genetic advance was observed for
sympodial branches whereas lowest value was (0.17) for boll weight. Correlation analysis revealed positive and significant for most of the parameters. In path coefficient, the number of sympodial branches, boll weight, lint index and lint weight had maximum direct and positive effect on fiber fineness of seed cotton. Whereas, the number of monopodial branches, plant height, seed index, seed volume, seed density, staple length, fiber strength and ginning out turn (G.O.T%) had direct and negative effects on fiber of seed cotton. The principle component analysis (PCA) revealed significant differences between genotypes and the first four components with Eigen
values greater than 1 contributed 66.68% of the variability among the genotypes. The grouping of genotypes
possessing excelled traits signifies genetic potential of the germplasm for the improvement of seed and fiber characteristics in cotton crop. Get more articles at: http://www.innspub.net/volume-7-number-4-october-2015-ijaar/
Genomic aided selection for crop improvementtanvic2
In last Several years novel genetic and genomics approaches are expended. Genetics and genomics have greatly enhanced our understanding of the structural and functional aspects of plant genomes.
Variability, heritability and genetic advance analysis for grain yield in riceIJERA Editor
Ten diverse genotypes of rice (Oryza sativa L.) were crossed in a diallel fashion to study variability , heritability and genetic advance for 12 quantitative characters . A considerable amount of variability (gcv) varied from 5.95 for no. of leaves per tiller to 17.40 for grain yield per plant and the estimates of pcv varied from 7.08 for days to 50% flowering to 17.49 for grain yield per plant. The heritability estimates ranged from 0.721 for total biological yield per plant to 1.000 for plant height . Since the heritability in broad sense was estimated , therefore . other parameters should also be considered for selecting the genotypes. The genetic advance varied from 0.71 for no. of leaves per tiller to 46.23 for no. of spikelets per panicle. High estimates of genetic advance was reported for plant height , days to maturity , days to 50% flowering and total biological yield per plant . However, high heritability estimates was associated with high predicted genetic advance for plant height , days to maturity ,days to 50% flowering and no. of spikelets per panicle. The situation is encouraging since selection based on these characters being of additive in nature , is likely to be more effective for their improvement. As such phenotypic selection for those traits is likely to be more effective for their improvement. The estimates of phenotypic coefficient of variation were higher than those of genotypic coefficient of variation for all the traits except plant height. High estimates of heritability and genetic advance were obtained for plant height , number of spikelets per panicle , days to 50 per cent flowering and days to maturity . These traits were mostly governed by additive gene action. And these characters are important for the breeder to construct selection indices.
Correlation and Path analysis studies among yield and yield related traits in...Premier Publishers
The16 Soybean genotypes were evaluated for Association of characters and path coefficient analysis on eleven important yield and grain yield contributing characters at Bako Tibe during the main cropping season of 2015/16. The experiment was designed as RCBD with three- replication. Generally, the magnitudes of genotypic correlation coefficients for most of the characters were higher than their corresponding phenotypic correlation coefficients that indicate the presence of inherent association among various characters. In this study yield was positively correlated with hundred seed weight, number of seed/pod and number of pod per plant so, increasing these traits ultimately increases in grain yield and days to maturity can be exploited through improvement and selection program. Based on findings it can be concluded that pod length, number of pod /plant, biological yield, grain yield and days to maturity can be exploited through selection and improvement program to develop high yielding soybean genotypes.
Heterosis, Combining ability and Phenotypic Correlation for Some Economic Tra...Galal Anis, PhD
This investigation was carried out to study heterosis , combining ability and phenotypic correlation in a diallel mating design among 6 Egyptian rice genotypes (excluding reciprocals),including 3 varieties ( Sakha 101, Sakha 104 and Sakha 105),and 3 promising lines (Gz6903, Gz7576 and Gz8479). An experiment was conducted at the research Farm of Rice Research and Training Center (RRTC), Sakha, Kafr EL-sheikh, Egypt during 2013 growing season and designed in a randomize complete block with three replications. Data were recorded on nine traits; days to maturity, chlorophyll content, flag leaf area, plant height, number of panicles / plant, panicle fertility (%), Panicle weight ,1000-grain weight and grain. The results revealed that, the genotypes were highly significant different in all studied characters. The cross (Sakha 101 × GZ6903) showed positive and significant heterosis for mid and better parents for most studied traits. The parent (Sakha 101) was good general combiner for most studied traits. The cross (Sakha 101 × GZ6903) showed positive and highly significant for specific combining ability effects for grain yield and its components.Grain yield was significantly and positively correlated with days to maturity, chlorophyll content, plant height, number of panicles/plant and panicle weight .On the contrary, plant height had significant negative association with days to maturity.
Topic- Genetic Variability and Stability Analysis in Greengram [Vigna radiata (L.) Wilczek]
PRESENTED
BY
CHIRANJEEV
Id. No. – 4213, M. Sc. (Ag.)
In the presence of External examiner and Members of Advisory Committee
Venue: Seminar class room
On date: 27/10/2020
DEPARTMENT OF GENETICS AND PLANT BREEDING
SARDAR VALLABHBHAI PATEL UNIVERSITY OF AGRICULTURE AND TECHNOLOGY MEERUT-250110 (U.P.) India
Genetic Analysis to Improve Grain Yield Potential and Associated Agronomic Tr...Galal Anis, PhD
Grain yield of rice is a complex trait consisting of several yield parameters. It is of
great necessary to reveal the genetic relationships between GY and its yield components. Therefore,
the correlation of agronomic traits contributed of grain yield will be a supplemental advantage in
providing the selection process. The objective of this study was to compare genetic variability and
relationships between nine rice genotypes and their F1 progenies in rice by assessment of heterosis,
yield advantage and correlation coefficient for grain yield improvement. A field experiment were
conducted in a randomized complete block design with three replications in the growing seasons of
2012 and 2013 at Rice Research and Training Center, Sakha, Egypt. Heterosis and correlation
coefficient of various agro-morphological and yield traits were studied by using nine-parent diallel
mating design. The results showed that grain yield was highly significant positive heterosis over
standard heterosis and the highest value was 79.68 for the cross Sakha 101 x Giza 171 and the lowest
value was 32.86 for the cross Sakha 104 x HR5824-B-3-2-3. At the same time, fifteen crosses were
highly significant and positive heterosis over mid-parent, the highest cross was Giza 177 x Sakha
104 with value 32.74 and the lowest cross was Sakha 101 x Sakha 104 with value 19.56 for grain
yield. Significant positive correlation coefficients were observed between grain yield and each of
days to maturing, panicle initiation and number of primary branches panicle-1. Pay special attention
to the cross from Sakha 101 x Giza 171 and as well as Giza 177 x Sakha 104 was achieved the best
grain yield trait. These promising cross would be more valuable materials for breeders engaged in the
development of high yielding cultivars.
STUDY OF MORPHOLOGICAL AND YIELD ATRIBUTING CHARACTERS IN INDIGENOUS RICE (OR...Vipin Pandey
The present study was carried out to study ninety four rice accessions, along with checks, on the basis of sixteen
qualitative and twenty quantitative characters. Analysis of variance for quantitative characters showed differences for
different characters. High coefficient of variation in the entire genotypes was observed for grain yield per plant (27.4 %),
number of effective tillers per plant (22.37 %), test weight (21.14 %) and kernel length breadth ratio (20.59 %).
Correlation analysis revealed positive and highly significant correlation of total number of filled grains per panicle, total
number of grains per panicle, plant height and number of effective tiller per plant; harvest index, test weight, flag leaf
length and days to maturity had positive highly significant correlation with grain yield per plant. Principal Component
Analysis revealed, out of 20, only seven principal components (PCs) exhibited more than 1.00 eigen value, and showed
about 77.42 % variability among the traits studied. So, these 7 PCs were given due importance for further explanation.
Component matrix revealed that the PC1 was mostly related to quality characters while PC2, PC3, PC4, PC5, PC6 and
PC7 mostly associated with yield related traits. Cluster analysis performed by UPGMA method using Euclidean distance
as dissimilarity measure divided the 97 genotypes of rice into ten clusters. The cluster III constituted of 48 genotypes,
forming the largest cluster followed by cluster VI (22 genotypes), cluster V (10 genotypes), cluster II (5 genotypes) and
cluster VIII (4 genotypes), cluster I, IV and VII (two genotypes each), cluster IX and X had (only one genotypes each).
Quality analysis performed for 97 rice genotypes revealed wide range of genetic variability for most of the quality traits.
Genetic control and heterosis depend directly on genetic divergence among the parents in generating promising hybrids
required by plant breeders. The purpose of this study was to determine the relative importance of heterosis, combining
abilities, regression and correlation estimates in order to develop hybrid cultivars in maize. The 28 F1 hybrids obtained by
partial diallel cross of 8 inbreds in a randomized complete block design were evaluated at the Lower Niger River Basin
Authority, Oke-Oyi, Nigeria in three years. General (GCA) and specific combining abilities (SCA) produced significant (P
< 0.01) effects for all the characters, while non-additive genetic effects were predominant. The levels of heterosis for grain
yield varied widely among crosses, ranging from -16.83 to 9.76%. Positive and significant genotypic and phenotypic
correlations among grain yield and some related characters (days to anthesis and silking; plant and ear heights; number of
ears plant-1 and 1000 seed weight) showed that each character could be used indirectly to selection of grain yield. These
results also indicated that SCA was more effective than heterosis for describing hybrid performance. The regression of
actual hybrid characters on the expected hybrid characters, based on parental GCA values, was highly significant.
Regression analysis also showed that all the agronomic characters jointly contributed 19.4% to grain yield of maize. The
inbreds (Pop 66 SR and 2000 EV DT-Y STRC4) and crosses (Pop 66 SR x, 2000 EV DT-Y STRC4, Pop 66 SR x KU1409
and 9006 x KU1409) featured prominently with respect to better GCA × Year and SCA × Year effects with high heterotic
values for maize grain yield and associated characters. These inbreds could serve as donors to obtain early and short
statured hybrids with higher yield, while the crosses may be exploited and adapted to the Nigerian Savannas. This study
also affirmed that genotypic and phenotypic correlation coefficients as well as combining abilities, heterosis and regression
analyses were found to be suitable models for yield improvement in maize breeding.
DOI: 10.21276/ijlssr.2016.2.3.19
ABSTRACT- By using gamma rays (physical) & EMS (chemical) mutagens the various genetic variability parameters
were estimated of two soybean cultivars i.e. PKV-1 & JS-335. Characters studied i.e. Plant height, no. of branches per
plant, no. of clusters per plant, no. of pods per plant, yield per plant, 100 grain wt. shows that genotypic coefficient of
variation (G.C.V.) & phenotypic coefficient of variation (PCV), heritability was significantly high. In both the varieties, all
the mutagenic treatments were effective in inducing genetic variability.
Key-words- Gamma rays, EMS, Mutagens, Genetic Variability
Evaluation of seed storage proteins in common bean by some biplot analysisINNS PUBNET
In order to study of seed storage proteins, proteins samples of common bean genotypes were prepared by 0.2 M
NaCl of extracting soluble. Genotypes were located in two groups by cluster analysis using Wilks’ lambda
statistic. Two groups were different for yield components (number of pods per plant, number of seeds per plant
and seed weight). Factor analysis showed that two factors described 61% of total proteins variation. Correlated
bands with yield components characters had the highest coefficients for the first factor. This factor was named
“yield components proteins”. Protein bands via RM 58 and 64 had relationship with days to flowering.
Therefore, the second factor was named “phenologic proteins”. Genotypes were located in four groups by these
factors. Length, angle and presence of protein bands were important characteristics to explain graphical
information in GGE biplot compared to factor analysis. Get the full articles at: http://www.innspub.net/volume-3-number-5-may-2013/
Correlation and path coefficients analysis studies among yield and yield rela...Premier Publishers
The study was carried out to estimate correlation coefficients among grain yield and yield related traits and work out direct and indirect effects of yield-related traits on grain yield using path- coefficient analysis. Sixty-six F1 crosses and two standard checks were evaluated at Mechara, Ethiopia. The analysis of variance revealed that mean squares due to entries and crosses were highly significant (p<0.01)><0.05) for most traits studied, indicating the existence of variability among the materials evaluated, which could be exploited for the improvement of respective traits. Grain yield showed positive and highly significant correlations with most traits at phenotypic and genotypic levels. Ear diameter and number of kernels per row exerted positive direct effect and also had positive association with grain yield. These traits could be used as a reliable indicator in indirect selection for higher grain yield since their direct effect and association with grain yield were positive at phenotypic and genotypic levels. Traits having strong relationship with grain yield can be used for indirect selection to improve grain yield because grain yield can be simultaneously improved along with the traits for which it showed strong relationship.
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Analysis of combining ability in blackgram (vigna mungo l.hepper)
1. ISSN 2320-5407 International Journal of Advanced Research (2015), Volume 3, Issue 3, 1139-1146
1139
Journal homepage:http://www.journalijar.com INTERNATIONAL JOURNAL
OF ADVANCED RESEARCH
RESEARCH ARTICLE
Analysis of combining ability in Blackgram (Vigna mungo L.Hepper)
Kachave G.A.1
, Parde N.S.2
, Zate D.K.3
and Harer P.N.4
1.Department of Agricultural Botany, Mahatma Phule Krishi Vidyapeeth, Rahuri-413 722, District: Ahmednagar,
Maharashtra State, India.
2.Department of Agricultural Botany, Mahatma Phule Krishi Vidyapeeth, Rahuri-413 722, District: Ahmednagar,
Maharashtra State, India.
3.Assistant Professor, College of Agriculture, Golegaon, VNMKV, Parbhani-431705, District: Hingoli, Maharashtra
State, India.
4.Principal Scientist, Pulses Improvement Project, Mahatma Phule Krishi Vidyapeeth, Rahuri- 413 722, District:
Ahmednagar, Maharashtra, State, India.
Manuscript Info Abstract
Manuscript History:
Received: 22 January 2015
Final Accepted: 25 February 2015
Published Online: March 2015
Key words:
Combining ability, blackgram,
GCA, SCA
*Corresponding Author
Kachave G.A
Seven genetically diverse varieties of blackgram namely TAU-1, AKU-9904,
NUL-7, LBG-402, BDU-1, Pant U-31 and Mesh-1008 were selected from
germplasm maintained at Pulses Improvement Project, MPKV, Rahuri and
Oil seeds Research Station, Jalgaon for making diallel cross to study
combining ability during 2009-10. 21 F1s and their 7 parents were studied in
RBD with three replications. Results indicating that the parents, LBG-402,
BDU 1 and AKU-9904 were proved to be good general combiners with good
per se performance for most of the traits studied, suggesting scope for their
use in breeding programme. The crosses, LBG-402 x BDU-1, BDU-1 x Pant-
U 31, AKU-9904 x Pant-U 31 and AKU-9904 x NUL-7 evinced high SCA
effects for most of the traits, those can be further exploited for isolating
superior genotypes.
Copy Right, IJAR, 2015,. All rights reserved
INTRODUCTION
Blackgram belongs to family Fabaceae with diploid chromosome number 2n=22. Among pulses,
Blackgram is one of the important grain legumes. Its seeds are highly nutritious with protein (24-25%),
carbohydrates (60%), fat (1.5%), minerals, amino acids and vitamins. This crop is highly priced crop and the grain is
richest source of phosphoric acid 5-10 times more than other pulses. Black gram is consumed in the form of ‘Dal’.
The success of breeding procedure is determined by the useful gene combinations organized in the form of high
combining lines and isolation of valuable germplasm. Some lines produce outstanding progenies on crossing with
others, while certain others seem to be equally desirable may not produce good progenies on crossing. The lines
which perform well in combinations are eventually of great importance to the plant breeder. Hence, investigation of
general combining ability (G.C.A.) and specific combining ability (S.C.A.) would yield very useful information.
Accordingly a good knowledge of gene action involved in the inheritance of quantitative characters of economic
importance is required to frame an efficient breeding plan leading to rapid improvement in yield and yield
components.
Material and Methods:
The present investigations aimed at studying combining ability in Blackgram (Vignamungo L. Hepper)
were conducted during 2009-2010 at Pulses Improvement Project, MPKV, Rahuri. The parental material for the
present investigation was selected from the germplasm maintained at Pulses Improvement Project, MPKV, Rahuri
and Oil seeds Research Station, Jalgaon. These parents were crossed in 7 x 7 diallel fashion excluding reciprocals
during summer and kharif, 2009 by hand emasculation and pollination.The experiment with twenty eight treatments
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(7 parents + 21 F1s) and one check (TPU-4) was laid out in a randomized block design (RBD) with three replications
during kharif 2010 at Pulses Improvement Project, MPKV, Rahuri. All the 28 treatments were grown in single
progeny row of 3 meter length with spacing of 45 cm between rows and 10 cm between plants. The experimental
plots were surrounded by non-experimental border rows of variety TAU-4. Observations were recorded in all the
replications on randomly selected competitive five plants in parents and F1s. The combining ability analysis was
carried out according to Model-I (Fixed effect), Method-2 (Parents and one set of F1’s without reciprocals) of
Griffing (1956 a).
Result and discussions
The analysis of variance for combining ability (Table 1) indicated that general and specific combining
ability variances were significant for all the characters studied. This suggested that both additive and non-additive
gene effects were involved in the genetic control of seed yield and its attributes. However, the ratio of estimates of
2 2
SCAsuggested predominant role of non-additive gene effects for seed yield and its attributes
except characters like days to flowering and 100-seeds weight for which additive effects made a major role. These
results are in general in accordance with those reported by Malhotra (1983), Sing et al. (1987), Dasgupta and Das
(1991), Sharma and Pandey (1996), Santha and Veluswamy (1999b), Govindraj and Subramanain (2001).
Vaithiyalingan et al. (2002), Kishnan et al. (2003) and Singh and Singh (2005).
The estimates of general combining ability effects of seven parents for twelve characters are presented in
Table 2. The specific combining ability effects of 21 F1 crosses are given in Table 3. High GCA coupled with high
per se performance is the indication of an outstanding best parent with reservoir of superior genes. Therefore, GCA
effects along with mean performance may be taken into account for parental selection.The summary of relative
magnitude and sign of general combining ability effects of the parents included in the study (Table 2) revealed that,
none of the parents was all round good general combiner for all the twelve characters studied. However, parent
LBG-402 was good combiner for seed yield and yield components while average combiner for earliness. BDU-1
was good combiner for all the characters except days to flowering and days to maturity for earliness and was poor
combiner for earliness. AKU-9904 was good combiner for seed yield and most of the yield components, but this
variety was found poor combiner for earliness, pod length, 100-seed weight and protein content. AKM-8802
exhibited good GCA effects for plant height, number of primary branches per plant, number of clusters per plant and
number of seeds per pod and for other characters it was either average or poor combiner. The variety TAU-1 showed
good general combining ability for earliness but poor combiners for seed yield and yield components. The varieties
from north zone of India Pant U-31 and Mesh-1008 were found good combiners for days to flowering and maturity
but they are poor combiners for seed yield and all other yield components. Similar findings have been reported by
various workers, Malhotra (1983), Dasgupta and Das (1987), Singh et al. (1987) Sood and Gartan (1991), Sawant
(1992), Shanmugasundaram and Rangaswamy (1994), Neog and Talukdar (1999), Dana and Dasgupta (2001),
Krishnan et al. (2003), Thangavel et al. (2004), Srividhya et al. (2005)Chakrabotty et al. (2010).
The specific combining ability effects and heterosis can be regarded as arising primarily from non-additive
genetic effects, which are non-fixable (Sprague and Tatum, 1942). Hence specific combining ability does not
contribute much in the improvement of self-pollinated crops like Blackgram. However, it can be used for identifying
transgressive segregants from segregating populations of the promising cross combinations. Specific combining
ability effects for seed yield per plant (Table 3) indicated that, the top yielding cross AKU-9904 x NUL-7 (good X
good) exhibited significant SCA effects for seed yield and yield components. The cross LBG-402 x BDU-1 showed
highest (0.769) SCA effect for seed yield per plant and also exhibited significant negative SCA effect for days to
50% flowering and days to maturity, while for number of clusters per plant, number of pods per cluster, pod length,
number of seeds per pod, 100-seed weight and protein content it showed positive significant SCA effect. The
desirable combination for days 50% flowering and days to maturity was BDU-1 x Mesh-1008 which showed
negative SCA effect. The cross TAU-1 x BDU-1 was best specific cross for plant height. Significant positive SCA
effect for Number of primary branches per plant, number of clusters per plant, number of pods per cluster, pod
length, number of seeds per pod, 100-seed weight and protein content was exhibited by cross, BDU-1 x Pant U-31.
The cross NUL-7 x Mesh-1008 showed highest significant SCA effect for protein content. Among four crosses with
positive significant SCA effects for seed yield per plant, the best specific combiner was LBG-402 x BDU-1
followed by BDU-1 x Pant U-31, AKU-9904 x Pant U-31 and AKU-9904 x NUL-7. These crosses gave higher seed
yield than the highest yielding parent AKU 9904.
In these superior combinations, the crosses LBG-402 x BDU-1 and AKU-9904 x NUL-7 exhibited
significant positive SCA effects for most of the yield components with high GCA x high GCA type combination,
which indicated additive and additive x additive gene effects were predominant in the expression of these characters.
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Therefore, due to possibility of fixation, single plant selection could be practiced in segregating generations to
isolate superior lines from such combinations. Some of the combinations though had both the parents with high
GCA effects did not show high SCA effect revealing that high x high type combinations not necessarily result into
high SCA effects. This might be due to internal cancellation of gene effects in such parents as suggested by Jinks
and Jones (1953). However, in the crosses, BDU-1 x Pant U-31 and AKU-9904 x Pant U-31 one parent was good
general combiner and other was poor general combiner for most of the traits. This might be due to presence of
genetic diversity in the form of heterozygous loci for these characters. The best specific combinations for different
characters in the present study were combination of good x good, good x average, good x poor combiners. This
suggested that information on GCA effect should be supplemented by SCA effects and hybrid performance of cross
combination to predict the transgressive types possibly be available in segregating generations. Selection is rapid if
the GCA effects of the parents and SCA effects of the crosses are in same direction. Further crosses involving good
x good general combining parent with high SCA effect can be handled by simple varietal improvement programmes
for respective characters. Krishnan et al. (2003), Thangavel et al. (2004), Vaithiyalingan (2004b), Srividhya et al.
(2005) Yadav et al. (2005) advocated practicing inter se mating in F2 generation using biparental approach. It would
help in accumulation of the additive genetic variance.
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Table 1 Analysis of variance for combining ability for twelve characters in a 7 X 7 half diallel cross in Blackgram
*, ** Significant at 5 and 1 per cent levels, respectively
Table 2 Estimates of general combining ability effects of the parents for different characters in a 7 X 7 diallel cross in Blackgram
P a r e n t s
Daysto50%flowering Daystomaturity Plant height Numberofprimarybranchesperplant Numberofclustersperplant Numberofpodspercluster Numberofpodsperplant Pod length Numberofseedsperpod 100-seedsweight Seedyieldperplant Protein content
T A U - 1 - 0 . 1 7 -2.21** - 0 . 0 7 - 0 . 0 7 * - 0.2 3* * -0.20** -1.15** -0.20** - 0 . 0 2 -0.25** -0.76** -0.32**
AKU-9904 0 . 8 7 * * 2 . 3 8 * * 1.62** 0 . 1 1 * * 0 . 1 4 * * 0 . 1 3 * * 1 . 8 9 * * -0.09** 0 . 1 1 * * - 0 . 0 2 0 . 6 4 * * -0.38**
S o u r c e G C A S C A E r r o r 2
g c a 2
s c a 2
g c a / 2
s c a
d . f . 6 2 1 5 4 - - -
D a y s t o 5 0 % f l o w e r i n g 2 1 . 3 8 * * 1 . 0 6 * * 0 . 3 7 2 . 3 3 0 . 7 1 3 . 3 0
D a y s t o m a t u r i t y 4 4 . 1 1 * * 9 . 2 7 * * 1 . 2 9 4 . 7 6 7 . 9 7 0 . 6 0
P l a n t h e i g h t 1 6 . 5 9 * * 8 . 2 9 * * 2 . 0 2 1 . 6 2 6 . 2 8 0 . 2 6
Number of primary branches per plant 0 . 1 7 * * 0 . 0 4 * * 0 . 0 1 0 . 0 2 0 . 0 3 0 . 6 3
N u m b e r o f c l u s t e r s p e r p l a n t 0 . 9 8 * * 0 . 2 0 * * 0 . 0 2 0 . 1 1 0 . 1 8 0 . 6 0
N u m b e r o f p o d s p e r c l u s t e r 0 . 1 8 * * 0 . 0 6 * * 0 . 0 1 0 . 0 2 0 . 0 5 0 . 4 0
N u m b e r o f p o d s p e r p l a n t 2 8 . 2 3 * * 9 . 8 7 * * 1 . 1 8 3 . 0 5 8 . 6 9 0 . 3 5
P o d l e n g t h 0 . 4 2 * * 0 . 0 7 * * 0 . 0 4 0 . 0 5 0 . 0 6 0 . 7 5
N u m b e r o f s e e d s p e r p o d 0 . 1 7 * * 0 . 0 8 * * 0 . 0 1 0 . 0 2 0 . 0 7 0 . 2 8
1 0 0 - s e e d w e i g h t 0 . 5 6 * * 0 . 0 6 * * 0 . 0 4 0 . 0 6 0 . 0 5 1 . 1 6
S e e d y i e l d p e r p l a n t 1 . 8 6 * * 0 . 4 2 * * 0 . 0 9 0 . 2 0 0 . 3 2 0 . 6 1
P r o t e i n c o n t e n t 2 . 5 5 * * 1 . 6 7 * * 0 . 1 3 0 . 2 7 1 . 5 4 0 . 1 8
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N U L - 7 1 . 4 6 * * 3 . 4 2 * * 0 . 8 8 * 0 . 1 5 * * 0 . 1 2 * * 0 . 0 4 0 . 2 1 -0.17** - 0 . 0 1 - 0 . 0 2 0 . 2 6 * * - 0 . 1 9
L B G - 4 0 2 - 0 . 0 6 - 0 . 1 0 - 0 . 0 4 0 . 0 3 0 . 3 9 * * 0 . 1 5 * * 2 . 1 8 * * 0 . 2 9 * * 0 . 1 5 * * 0 . 2 5 * * 0 . 1 9 * 0 . 7 8 * *
B D U - 1 1 . 7 9 * * 0 . 1 9 0 . 9 6 * 0 . 1 2 * * 0 . 3 4 * * 0 . 0 9 * * 0 . 8 9 * 0 . 3 1 * * 0 . 1 3 * * 0 . 4 2 * * 0 . 1 3 0 . 1 5
Pant U-31 - 2 . 4 7 * * -2.25** -2.42** - 0 . 1 4 * * - 0.4 0* * - 0 . 0 6 * -2.29** -0.12** -0.21** -0.18** -0.28** -0.65**
Mesh-1008 - 1 . 4 3 * * -1.43** -0.93* - 0 . 1 9 * * - 0.3 6* * - 0 . 1 6 * * -1.74** - 0 . 0 2 -0.15** -0.20** - 0 . 1 8 0 . 6 0 * *
S E ( g i ) 0 . 1 9 0 . 3 5 0 . 4 4 0 . 0 3 0 . 0 4 0 . 0 3 0 . 3 4 0 . 0 2 0 . 0 3 0 . 0 2 0 . 0 9 0 . 1 1
CD at 5% 0 . 3 7 0 . 7 0 0 . 8 8 0 . 0 6 0 . 0 8 0 . 0 4 0 . 6 7 0 . 0 4 0 . 0 6 0 . 0 4 0 . 1 9 0 . 2 3
CD at 1% 0 . 4 9 0 . 9 4 1 . 1 7 0 . 0 8 0 . 1 2 0 . 0 7 0 . 8 9 0 . 0 5 0 . 0 8 0 . 0 5 0 . 2 6 0 . 3 0
*, ** Significant at 5 and 1 per cent levels, respectively
Table 3Estimates of specific combining ability effects of crosses for different characters in Blackgram
Sr. No. C r o s s ( F 1 ) Days50%toflowering Days to maturity Plant height No.of primary branches/plant No. of clusters/plant No. of pods/cluster
1 T A U 1 X A K U 9 9 0 4 1 . 2 7 8 * - 0 . 9 0 72 . 8 0 7 * 0 . 2 5 7 * *0 . 3 7 6 * * 0 . 2 7 6 * *
2 T A U 1 X N U L 7 - 0 . 6 4 8-5.944**2 . 0 7 4 - 0 . 1 8 7 * 0 . 4 5 0 * * 0 . 2 1 8 * *
3 T A U 1 X L B G 4 0 2 0 . 2 0 4 1 . 5 7 40 . 8 6 7 0 . 1 9 8 * 0 . 2 5 0 0 . 0 5 4
4 T A U 1 X B D U 1 - 0 . 3 1 50 . 2 7 83.726** 0 . 1 8 3 * 0 . 3 7 6 * * - 0 . 2 8 0 * *
5 T A U 1 X P a n t U 3 1 - 0 . 7 2 2- 0 . 9 4 40 . 5 1 1 - 0 . 0 9 80 . 3 8 3 * * - 0 . 1 9 8 *
6 T A U 1 X M e s h 1 0 0 8 - 0 . 4 2 6- 0 . 7 5 90 . 3 5 6 - 0 . 1 7 2- 0 . 2 6 1 *- 0 . 1 6 9 *
7 A K U 9 9 0 4 X N U L 7 -2.352** -3.537**2 . 4 5 2 * 0 . 2 2 8 * 0 . 6 8 0 * * 0 . 2 9 8 * *
8 A K U 9 9 0 4 X L B G 4 0 2 - 0 . 1 6 70 . 3 1 51 . 9 1 1 0 . 0 8 0 - 0 . 2 5 4 * *- 0 . 0 8 0
9 A K U 9 9 0 4 X B D U 1 1 . 3 1 5 * 4 .6 85 **0 . 6 3 7 - 0 . 1 3 5- 0 . 2 6 1 * *0 . 1 2 0
1 0 A K U 9 9 0 4 X P a n t U 3 1 - 0 . 7 5 91 . 7 9 61 . 6 2 2 - 0 . 0 8 3- 0 . 1 2 00 . 0 0 2
1 1 A K U 9 9 0 4 X M e s h 1 0 0 8 0 . 2 0 4 - 1 . 3 5 20 . 4 6 7 0 . 0 4 3 - 0 . 2 9 8 * *0 . 0 3 1
1 2 N U L 7 X L B G 4 0 2 - 0 . 0 9 32 . 6 1 1 *1 . 8 4 4 0 . 2 3 5 * *0 . 2 8 7 * * 0 . 0 0 9
1 3 N U L 7 X B D U 1 2 . 0 5 6 * * 3 .9 81 **0 . 1 0 4 - 0 . 0 4 6- 0 . 1 2 00 . 1 4 3
1 4 N U L 7 X P a n t U 3 1 0 . 9 8 1 4 .0 93 **0 . 8 2 2 0 . 0 7 2 - 0 . 2 4 6- 0 . 0 4 3
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1 5 N U L 7 X M e s h 1 0 0 8 - 0 . 7 2 2- 1 . 3 8 91 . 6 6 7 0 . 1 3 1 0 . 3 7 6 * * - 0 . 0 1 3
1 6 L B G 4 0 2 X B D U 1 - 1 . 0 9 3 * -5.167**1 . 6 7 4 0 . 0 0 6 0 . 3 4 6 * * 0 . 2 4 6 * *
1 7 L B G 4 0 2 X P a n t U 3 1 - 0 . 1 6 7- 2 . 0 5 6 * 1 . 0 8 1 - 0 . 1 4 30 . 2 8 7 * 0 . 2 4 6 * *
1 8 L B G 4 0 2 X M e s h 1 0 0 8 1 . 1 3 0 * 3 .1 30 **0 . 1 2 6 0 . 1 1 7 0 . 3 7 6 * * 0 . 1 4 3
1 9 B D U 1 X P a n t U 3 1 - 0 . 0 1 9- 1 . 0 1 91 . 6 7 4 0 . 3 7 6 * *0 . 3 4 6 * * 0 . 2 4 6 * *
2 0 B D U 1 X M e s h 1 0 0 8 - 0 . 0 5 6-3.833**1 . 5 8 5 0 . 0 3 5 0 . 2 3 5 0 . 1 4 3
2 1 P a n t U 3 1 X M e s h 1 0 0 8 - 1 . 1 3 0 * 0 . 6 1 10 . 9 7 0 0 . 0 8 7 - 0 . 0 2 40 . 1 5 7 *
S E ( S i j ) 0 . 5 4 4 1 . 0 1 21 . 2 1 3 0 . 0 8 8 0 . 1 2 7 0 . 0 7 8
C D a t 5 % 1 . 0 8 8 2 . 0 2 42 . 4 2 7 0 . 1 7 6 0 . 2 5 3 0 . 1 5 6
C D a t 1 % 1 . 4 5 2 2 . 7 0 23 . 2 3 9 0 . 2 3 4 0 . 3 3 9 0 . 2 0 8
*, ** Significant at 5 and 1 per cent levels, respectively
Table 3Contd..
Sr.No. Cross (F1) No. of pods per plant Pod length Number of seeds per pod 100-seeds weight Seed yield per plant Protein content
1 T A U 1 X A K U 9 9 0 4 1 . 9 9 5 * * 0 . 3 5 6 * * 0 . 2 2 4 *0 . 1 8 4 * * - 0 . 3 8 01.640**
2 T A U 1 X N U L 7 1 . 8 1 3 0 . 1 6 7 * * 0 . 0 7 60 . 0 8 8 1 . 0 3 51.584**
3 T A U 1 X L B G 4 0 2 3 . 8 3 9 * * - 0 . 1 3 0 0 . 4 4 6 * *0 . 0 2 1 0 . 1 3 3- 0 . 5 8 8
4 T A U 1 X B D U 1 3 . 0 6 1 * * - 0 . 2 8 5 * * 0 . 1 3 5- 0 . 2 1 6 * * - 0 . 0 3 9- 0 . 6 2 3
5 T A U 1 X P a n t U 3 1 - 3 . 0 7 9 - 0 . 1 5 2 * * 0 . 0 0 9- 0 . 1 5 3 * - 0 . 4 8 1- 0 . 6 7 1 *
6 T A U 1 X M e s h 1 0 0 8 - 0 . 5 0 9 0 . 0 4 40 . 0 0 9- 0 . 0 2 70 . 0 0 9- 0 . 6 0 6
7 A K U 9 9 0 4 X N U L 7 4 . 3 0 2 * * 0 . 3 5 6 * * 0 . 4 8 3 * *0 . 4 5 5 * * 0 . 5 9 4 *0 . 3 7 5
8 A K U 9 9 0 4 X L B G 4 0 2 1 . 4 6 1 - 0 . 3 0 7 * * - 0 . 0 1 30 . 0 8 8 0 . 1 2 61.069**
9 A K U 9 9 0 4 X B D U 1 1 . 3 5 0 - 0 . 0 6 3 - 0 . 1 9 1 * - 0 . 1 4 9 * 0 . 4 5 4- 0 . 3 9 9
1 0 A K U 9 9 0 4 X P a n t U 3 1 2 . 4 1 0 * 0 . 0 7 0- 0 . 1 8 3- 0 . 1 8 6 * * 0 . 6 1 1 *-1.131**
1 1 A K U 9 9 0 4 X M e s h 1 0 0 8 - 0 . 2 2 0 - 0 . 0 3 3 0 . 0 1 70 . 0 4 0 0 . 3 6 91.084**
1 2 N U L 7 X L B G 4 0 2 - 0 . 7 8 7 - 0 . 2 3 0 * * - 0 . 0 2 80 . 0 2 5 - 0 . 0 9 3- 0 . 1 0 3
1 3 N U L 7 X B D U 1 - 0 . 1 6 4 - 0 . 3 5 2 * * 0 . 1 2 8- 0 . 1 1 20 . 3 6 90 . 0 7 9
1 4 N U L 7 X P a n t U 3 1 1 . 8 6 2 - 0 . 1 1 9 *0 . 0 6 9- 0 . 1 1 60 . 1 9 30 . 2 1 4
1 5 N U L 7 X M e s h 1 0 0 8 - 0 . 2 0 1 - 0 . 0 2 2 0 . 0 6 9- 0 . 2 5 6 * * 0 . 5 1 72.512**
1 6 L B G 4 0 2 X B D U 1 - 0 . 5 9 0 0 . 4 9 6 * * 0 . 3 9 4 * *0 . 3 1 4 * * 0 . 7 6 9 * * 1.373**
1 7 L B G 4 0 2 X P a n t U 3 1 3 . 7 2 1 * * 0 . 2 8 5 * * - 0 . 1 6 10 . 3 8 4 * * 0 . 5 2 41.842**
1 8 L B G 4 0 2 X M e s h 1 0 0 8 - 1 . 3 7 6 0 . 3 1 5 * * 0 . 3 0 6 * *- 0 . 3 9 0 * * 0 . 2 3 10 . 0 5 6
1 9 B D U 1 X P a n t U 3 1 - 0 . 5 9 0 0 . 4 0 6 * * 0 . 3 9 4 * *0 . 3 1 4 * * 0 . 6 6 9 * * 1.373**
2 0 B D U 1 X M e s h 1 0 0 8 1 . 9 1 3 0 . 1 5 9 * * 0 . 1 2 80 . 3 7 3 * * 0 . 1 9 3- 0 . 0 7 9
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2 1 P a n t U 3 1 X M e s h 1 0 0 8 1 . 1 7 3 0 . 0 2 6- 0 . 2 6 5 * *0 . 1 3 6 * - 0 . 1 0 00 . 2 2 3
S E ( S i j ) 0 . 9 7 6 0 . 0 5 70 . 0 9 30 . 0 5 9 0 . 2 8 30 . 3 2 8
C D a t 5 % 1 . 9 5 2 0 . 1 1 40 . 1 8 60 . 1 1 8 0 . 5 6 70 . 6 5 6
C D a t 1 % 2 . 6 0 6 0 . 1 5 20 . 2 4 80 . 1 5 8 0 . 7 5 70 . 8 7 6
*, ** Significant at 5 and 1 per cent levels, respectively
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