IJERD – International Journal of Environmental and Rural Development (2020) 11-2

Research article
erd

Selection Index for Yield and Yield Contributing Traits in
Improved Rice Genotypes
NYO MAR HTWE*
Yezin Agricultural University, Nay Pyi Taw, Myanmar
Email: dr.nyomarhtwe@yau.edu.mm

MYINT AYE
Yezin Agricultural University, Nay Pyi Taw, Myanmar

CHAN NYEIN THU
Yezin Agricultural University, Nay Pyi Taw, Myanmar
Received 15 January 2020

Accepted 29 November 2020(*Corresponding Author)

Abstract To select improved rice lines according to a selection index, a field experiment was
conducted using fifty improved rice genotypes, during the dry season of 2017 at the
Department of Plant Breeding, Physiology and Ecology, Yezin Agricultural University,
Myanmar. The examination used a Randomized Complete Block Design with three
replications. Days to 50% flowering, plant height, number of effective tillers per hill, filled
grain percentage, 1000 seed weight, number of spikelets per panicle, harvest index and yield
per plant exhibited highly significant differences indicating the existence of genetic
variability among the improved rice lines. There was not much variability in panicle length.
Seed yield per plant was positively and significantly correlated with days to reach 50%
flowering, plant height, the number of effective tillers per hill, filled grain percentage, numberof spikelets per panicle and the harvest index at phenotypic and genotypic levels. This showsthat indirect selection on these traits would be effective in improving grain yield. At the
genotypic level, path coefficient analysis shows that number of spikelets per panicle, the 1000
grain weight, filled grain percentage, number of effective tillers per hill, plant height andharvest index has a positive influence on seed yield. The selection index based on the
combination of number of effective tiller per hill, filled grain percentage, number of spikelets
per panicle and seed yield per plant (ETPP+FGP+SPP+SYPP) has the highest genetic
advance and relative efficiency. This indicated that indirect selection via these traits would
be more efficient than direct selection using yield alone. Therefore, the genotypes; YAU1215-S-S-S-41-1-1, YAU-1211-9-3-1, YAU-1211-71-1-1, YAU-1211-118-2-1 and YAU1201-151-1-1 could be selected based on multiple traits selection for further evaluation.
Keywords correlation, genotypic, path analysis, phenotypic, selection index

INTRODUCTION
Improving grain yield is one of the main objectives of many breeding programs. Seed yield is linked
to a complex inheritance and it is an ultimate expression of different factors. It is highly affected byenvironmental variations. The knowledge of the interrelationship among various yield and yieldcomponent traits is necessary for an effective breeding program (Sarwar et al., 2004). Path coefficient
analysis can estimate the causes and measurement of the relative importance of each of the yieldcomponents can also separate the direct effects from the indirect ones that are due to other characters(Ibrahim et al., 2012). Moreover, the effectiveness of a plant breeding programs depends on theability of a breeder to select superior individuals or families for many traits of interest (Strefeler andWehner, 1986a).
The economic value of a plant depends on the value of its different traits, therefore, plant
breeders should consider simultaneous selections for several traits to maximize the economic valueof a plant (Rabiei et al., 2004). Moreover, breeding and selection programs often incorporate severalⒸ ISERD

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characters simultaneously (Hill et al., 1998). It is necessary to choose individuals with the best
combination of desirable traits when considering several traits. The basis for this selection is aselection index, which is a combination of traits to select, according to their relative weighting. Gainsfrom selection for any given trait is expected to decrease as additional traits are included in the index,
therefore the choice for traits to be included must be done accurately (Hallauer and Miranda, 1981).
The Smith Hazel index is considered the optimum index when accurate estimates of variancesand covariance are available (Hazel, 1943; Strefeler and Wehner, 1986b). However, this index
requires a quantitative genetic study to estimate genetic variances and covariance and the assignmentof relative economic weights to each trait (Strefeler and Wehner, 1986a). The Smith (Smith, 1936)
and Hazel (Hazel, 1943) index and its various modifications (Kempthorne and Nordskog, 1959;
Tallis, 1962; Elston, 1963; Pešek and Baker, 1969) have been shown to be the most efficient methods
to achieve aggregate genetic progress compared with any other direct single trait selection methods.
OBJECTIVE
The objectives of this study were to determine the interrelationship between yield and yieldcontributing characters and to select the rice line(s) based on the selection indices and their relative
efficiencies in providing improvement in yield.
MATERIALS AND METHODS
Fifty rice genotypes developed at Yezin Agricultural University (YAU), Myanmar were used in thisstudy. These genotypes were developed by crossing parental lines such as Sin Thwe Latt, YadanarToe, Sin Thu Kha, Long 8, Shwe Thwe Yin and Long 6. Pedigree, bulk and single seed descentmethods were used to develop these lines. The genotypes were evaluated at the Department of Plant
Breeding, Physiology and Ecology at YAU during the 2017 dry season. The experiment site was
located in Yezin Agricultural University, Zeyar Thiri Township, Nay Pyi Taw, Myanmar, latitude
19° 10' N and longitude 96° 07' E with an elevation of 102 m above sea level.
These genotypes were grown in a randomized complete block design, with three replications.
The spacing was 20 cm × 20 cm with 100 hills per block. Standard agronomic practices and plant
protection measures were applied to ensure good crop growth and complete grain development. The
data collected were; days to 50% flowering (days) (DTF), plant height (cm) (PH), number of effective
tillers per hill (no.) (ETPP), panicle length (cm) (PL), filled grain percentage (FGP), 1000 grain
weight (g) (TGW), number of spikelets per panicle (no.) (SPP), harvest index (HI) and seed yield per
plant (g) (SYPP). All the traits were measured following the guidelines provided by the Standard
Evaluation System for Rice (SES) prepared by the International Rice Research Institute, Philippines(IRRI, 2002).
Analysis of variance (ANOVA) and correlations were generated using STAR v.2.0.1 (STAR,
2014). Path coefficient analysis was worked out using SPAR v 2.0 (Statistical Package for
Agricultural Research, Version 2.0) software and R programs. Selection indices were constructedusing the method developed by (Smith, 1936) based on the discriminate function of (Fisher, 1936).
The economic weight for Smith Hazel Index was used to evaluate the direct effects from path analysis.
The 10 % selection intensity was used to estimate genetic advances. The estimates of genetic advance
were expressed as a percentage of the genetic progress obtained from seed yield per plant alone,which was assumed to initially be 100%, and it was used to compare the relative efficiencies of thedifferent selection indices.
RESULTS AND DISCUSSION
Mean Performances and Analysis of Variances for Yield and Yield Component TraitsⒸ ISERD

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The analysis of variance revealed statistically significant differences at 0.1% probability level among
the genotypes, for all traits except panicle length (Table 1). This indicates that there is much genetic
variation among genotypes in all characters.
Table 1 Analysis of variance for yield and yield components in 50 rice genotypes
Trait
Days to 50% flowering
Plant height
Panicle length
No. of effective tillers per hill
Filled grain percentage
No. of spikelets per panicle
1000 grain weight
Harvest index
Seed yield per plant

Replication
2.007
179.554
6.125
4.903
14.992534.0683.079
0.000919.227

Genotype
79.728**657.449**
5.679
6.509**
94.504**2422.678**
28.801**0.0067**145.634**

Error
5.367
294.131
4.012
0.747
4.966
102.3841.013
0.0004
7.989** Significant at 1% probability level

Correlation Coefficient Analysis
There were 12 positive and 3 negatively significant correlations at phenotypic level and 14 positiveand 4 negatively significant correlations at genotypic level for the nine traits measured (Fig. 1 a andb). At genotypic and phenotypic levels, seed yield per plant was positively and significantly
correlated with days to 50% flowering, plant height, number of effective tillers per hill, filled grain
percentage, number of spikelet per panicle and harvest index. This showed that indirect selection
based on these traits will improve grain yield.

(b)

(a)

Fig. 1 Phenotypic correlation (a) and genotypic correlation (b) among agronomic traits
in 50 rice genotypes
The tests of significance were indicated as **, * at 1% and 5 % probability level, respectively. The blue colour
shows the positive association and red colour indicates the negative association and the intensity of the colour
indicates the degree of association.

Path Coefficient Analysis
At phenotypic level and genotypic level, all of the direct effects, with the exception of one, were
below one, suggesting that inflation due to multicollinearity was minimal (Fig. 2). At both levels, thedirect effects for number of spikelets per panicle were positive and of greater magnitude than direct
effects for the other traits. At phenotypic level, number of spikelets per panicle (1.07), 1000 grain
weight (0.60), number of effective tillers per hill (0.42), filled grain percentage (0.30), plant heightⒸ ISERD

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(0.04) and harvest index (0.01) showed a significant positive direct effect with seed yield per plant.
At genotypic level, path coefficient analysis showed that number of spikelets per panicle (1.05), 1000
grain weight (0.67), filled grain percentage (0.34), number of effective tillers per hill (0.32), plantheight (0.14) and harvest index (0.02) influenced seed yield directly in a positive direction. These
are the primary yield components in tested genotypes. In other words, when there is an increase in
the performance of these characters, seed yield per plant can also increase. However, days to 50%
flowering (-0.15) and panicle length (-0.17) had direct negative effect on seed yield per plant.
At genotypic level, days to 50% flowering (0.61) followed by harvest index (0.55), plant height(0.37) and filled grain percentage (0.30) exhibited a high order of positive indirect effect on seedyield per plant via number of spikelets per panicle. The remaining estimates of indirect effects in thisanalysis were too low to be considered of any consequence. The estimates of residual factors (0.0581)
were negligible in the present study. The residual effect determines how best the causal factors
account for the variability of the dependent factor such as the standard evaluation score. Residual
effect indicated that the characters which are selected in this study contributed 94% of variability ofthe standard evaluation score.
Therefore, the number of effective tillers per hill, filled grain percentage and number of spikelets
per panicle had significant positive correlation with seed yield as well as exercising a positive direct
effect on seed yield, suggesting the selection for these traits would be helpful for the improvementof seed yield. Path analysis studies revealed that the traits, such as the number of effective tillers perhill, filled grain percentage and number of spikelets per panicle can be considered as selection criteria
in improving the seed yield of rice as these have a prominent direct effect. Hence, selection for these
traits will improve the yield.

(a)

(b)
Fig. 2 Direct and indirect effects of seed yield and other related traits
in path analysis at (a) phenotypic level and (b) genotypic level

Selection IndicesFifteen selection indices containing two or more traits simultaneously were constructed. Out offifteen, four selection indices were more efficient than direct selection for seed yield per plant. Using
different combinations of yield and yield contributing traits, different selection indices were
formulated and their expected genetic gain and relative efficiencies were estimated (Table 2). It wasobserved that among all selection indices, the index based on number of effective tiller per hill +filled grain percentage + number of spikelets per panicle + seed yield per plant (ETPP+ FGP+ SPP+
SYPP) had the maximum genetic advance (57.79) with relative efficiency (498.65). Among theothers, the indices based on filled grain percentage + number of spikelets per panicle + seed yield
per plant (FGP+SPP+SYPP), number of effective tillers per hill + number of spikelet per panicle +seed yield per plant (ETPP+SPP+SYPP), number of spikelets per panicle + seed yield per plantⒸ ISERD

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(SPP+SYPP) showed high genetic gain with relative efficiency over straight selection for grain yieldalone. Therefore, an improvement of grain yield is suggested through these selection indices.
Table 2 Selection indices for seed yield and their relative efficiency
in YAU promising rice genotypes
Index
ETPP + FGP + SPP + TGW + SYPP
FGP + SPP + TGW + SYPP
ETPP + SPP + TGW + SYPPETPP+ FGP + TGW + SYPP
ETPP + FGP + SPP + SYPPSPP + TGW + SYPP
FGP +TGW + SYPP
FGP + SPP + SYPP
ETPP + TGW + SYPPETPP+ SPP + SYPPETPP+ FGP + SYPP
ETPP + SYPP
FGP +SYPPSPP + SYPPTGW + SYPP
SYPP

Genetic advance (g)/plant
55.94
55.93
54.51
13.82
57.79
54.60
13.51
57.67
11.68
56.36
13.54
11.25
13.28
56.31
11.40
11.59

Relative efficiency (%)
482.63482.54470.36119.20498.65471.10116.57497.61100.80486.32116.8497.03
114.55
485.8398.33
100.00

Table 3 Comparison between relative genetic score of 50 rice genotypes
based on the best selection index (ETPP + FGP + SPP+ SYPP)
Rank

Genotypes

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

YAU-1215-S-S-S-41-1-1
YAU-1211-9-3-1
YAU-1211-71-1-1
YAU-1211-118-2-1
YAU-1201-151-1-1
YAU-1201-151-1-3
YAU-1215-S-S-S-78-3-1
YAU-1211-116-3-4
YAU-1211-26-1-2
YAU-1201-1-2-1
YAU-1215-S-S-S-40-2-1
YAU-1211-22-2-1
YAU-1201-39-2-1
YAU-1215-S-S-S-55-2-1
YAU-1211-223-3-1
YAU-1215-B-B-B-153-3-1
YAU-1215-S-S-S-77-2-1
YAU-1215-73-2-3-1-1-1
YAU-1201-74-1-2
YAU-1201-187-1-2
YAU-1214-S-S-S-77-1-1
YAU-1215-188-3-1-1-1-1
YAU-1201-202-1-2
YAU-1201-9-1-1
YAU-1214-183-35-1-1-1-1

Selection Index(Selection Score)
187.776
185.773
173.982
172.725
171.689169.348168.001
165.081164.421162.572160.616158.096155.626138.238
137.691137.037134.943134.882
134.623132.461131.854129.958
129.379129.155
127.852

Rank

Genotypes

26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50

YAU-1201-187-1-1
YAU-1215-B-B-B-168-1-1
YAU-1215-B-B-B-10-1-1
YAU-1201-206-2-1
YAU-1211-121-2-1
YAU-1201-202-2-1
YAU-1214-B-B-B-33-1-1
YAU-1201-90-2-2
YAU-1201-90-1-1
YAU-1214-183-3-3-1-1-1
YAU-1201-202-2-2
YAU-1201-61-3-3
YAU-1215-188-3-1-2-1-1
YAU-1215-B-B-B-139-3-1
YAU-1215-B-B-B-134-1-1
YAU-1214-183-3-4-1-1-1
YAU-1201-61-3-1
YAU-1215-S-S-S-115-1-1
YAU-1215-B-B-B-141-3-1
YAU-1201-16-2-1
YAU-1201-121-3-1
YAU-1215-80-1-2-1-1-1
YAU-1215-B-B-B-52-3-1
YAU-1215-S-S-S-113-1-1
YAU-1214-187-1-1-1-1-1

Selection Index(Selection Score)
127.507126.567
125.120122.602122.587
122.458
122.174
120.390117.818
117.552
116.408
115.993115.741112.413112.031109.247
107.198
103.131102.619101.81299.83095.56495.30479.83977.424

Selection based on yield alone may be often misleading due to high environmental influenceand multiple traits selection can improve selection efficiency and can attain largest economic gain.
Therefore, utilizing the best selection index (number of effective tiller per hill + filled grain
percentage + number of spikelets per panicle + seed yield per plant) (ETPP+FGP+SPP+SYPP), therelative genetic score of each genotype was determined (Table 3). It was found that the genotypes
YAU-1215-S-S-S-41-1-1, YAU-1211-9-3-1, YAU-1211-71-1-1, YAU-1211-118-2-1 and YAUⒸ ISERD90

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1201-151-1-1 were superior among all genotypes tested, having the optimal combination of attributes.
These genotypes should be used for further evaluation.
CONCLUSION
Except panicle length, all the tested genotypes have a high variation in all studied traits. Number ofeffective tillers per hill, filled grain percentage and number of spikelets per panicle had significant
positive genetic correlation with seed yield as well as employed positive direct effect on seed yieldat genotypic and phenotypic levels. The selection index based on the combination of number ofeffective tiller per hill, filled grain percentage, number of spikelets per panicle and seed yield per
plant (ETPP+FGP+SPP+SYPP) resulted the highest genetic advancement and relative efficiency.
This indicates that indirect selection via these traits would be more efficient than direct selection
considering yield alone. Therefore, the genotypes; YAU-1215-S-S-S-41-1-1, YAU-1211-9-3-1,
YAU-1211-71-1-1, YAU-1211-118-2-1 and YAU-1201-151-1-1 are superior among all genotypes
and could be selected for further evaluation.
ACKNOWLEDGEMENTS
The authors would like to express their sincere thanks to Japan International Cooperation Agency(JICA), Project for Capacity Development of Yezin Agricultural University for financial andtechnical support to conduct this study.
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