HOME JOURNALS CONTACT

Asian Journal of Plant Sciences

Year: 2021 | Volume: 20 | Issue: 3 | Page No.: 406-413
DOI: 10.3923/ajps.2021.406.413
Correlation and Path Coefficient Analysis of Grain Yield and its Components in Toraja Land-Race Aromatic Rice Mutants Induced by Heavy Ion Beam
Andi Muliarni Okasa, Rinaldi Sjahril , Muhammad Riadi, Meta Mahendradatta, Tadashi Sato, Kinya Toriyama, Kotaro Ishii, Yoriko Hayashi and Tomoko Abe

Abstract: Background and Objective: The development of local aromatic rice cultivars has become a distinct segment market for plant breeders and producers. This study aimed to identify the Genotypic Coefficient of Variation (GCV), Phenotypic Coefficient of Variation (PCV), heritability (h2bs), a close relationship between traits and the direct and indirect effect of aromatic rice lines based on traits among the population observed. Materials and Methods: The field experiment was conducted in Enrekang Regency, South Sulawesi, Indonesia (650 m above sea level), from May-October, 2018. Toraja Local Aromatic Rice "Pare Bau" were irradiated with 300 keV μm–1 (10 Gy) Argon ion at RIKEN Nishina Center, Wako-shi, Saitama, Japan. Eighteen aromatic rice lines from M3 generation and one control (non-irradiated) were transplanted in the paddy field. Results: Results showed that the traits observed had a coefficient of variation of moderate to high. All the lines tested showed high heritability associated with the genetic advance as percent of mean (GAM) for all traits, indicating that these traits could be useful to be selected. Based on correlation analysis, the traits that support high yield were the number of panicles, grain weight per panicle and percentage of fertile grain. Moreover, path analysis showed two essential and useful selection traits for grain yield improvement in aromatic rice mutant lines of third generations (M3), i.e., number of panicles and percentage of fertile grain. In the brief of lines, G4 is the highest yield per plant. Conclusion: It can be concluded that variability observed among lines associated with high yield could be exploited in rice breeding, especially mutant aromatic rice.

Fulltext PDF Fulltext HTML

How to cite this article
Andi Muliarni Okasa, Rinaldi Sjahril, Muhammad Riadi, Meta Mahendradatta, Tadashi Sato, Kinya Toriyama, Kotaro Ishii, Yoriko Hayashi and Tomoko Abe, 2021. Correlation and Path Coefficient Analysis of Grain Yield and its Components in Toraja Land-Race Aromatic Rice Mutants Induced by Heavy Ion Beam. Asian Journal of Plant Sciences, 20: 406-413.

Keywords: heritability, heavy ion beam, Aromatic rice, M3 generation and irradiation

INTRODUCTION

Tana Toraja is a region in South Sulawesi, Indonesia, with a large diversity of rice germplasm. Until now, farmers nonetheless used to cultivate local varieties in unfavorable ecosystems. Local varieties, consisting of aromatic rice, have unique characteristics such as aroma, higher cooking quality and better taste which also estimate value in socio-economic aspects1. Aromatic rice 'Pare Bau' is closely associated with the sociocultural in Toraja and consumed during the funeral ceremony and other celebrations2. However, this local variety has low yield potential, tall stems and long harvesting age. The use of indigenous varieties with low yielding capacity also limits rice productivity in Toraja. The potential development of local aromatic rice owned by Toraja is critical to increasing the local aromatic rice production and quality.

Plant breeding is an activity that aimed to improve and enhance the genetic potential of plants so that new varieties that are better than their parents are obtained. One method of plant breeding is by physical mutation. Several types of physical mutagenic sources, such as gamma rays3, ultra-violet light irradiation4, neutrons5 and ion rays6 have been developed and utilized in plant mutation breeding. In ion beam applications, high-energy irradiation of ion beam has been utilized to induce mutations in many plant species7,8. Heavy ion beam irradiation is a useful method for mutation breeding to produce new cultivars9. Ion Beam radiation is known to have several effects on plant growth and development. Abe et al.10 argue that ion beams induce mutations at relatively low doses without severely inhibiting growth at a high rate. There has been little information in the selection, genetic variability and utilization variation observed in selected lines for generating new breeding programs that is required in the development of new cultivars11.

There is considerable interest in the morphological traits that contribute to seed yield because such knowledge is pivotal for breeding. Genotypic Coefficient Variation (GCV), Phenotypic Coefficient Variation (PCV), heritability and Genetic Advance (GA) as genetic parameters may be helpful for the selection of lines with desirable traits. Heritability provides factual information about a specific genetic aspect that is transmitted to the successive generations12,13. High heritability values demonstrate that genetic factors play a part in controlling a trait compared to environmental factors. Furthermore, heritability information is crucial for improvement-based selection since it indicates character to future generations14. Thus, evaluating heritability, alongside genetic advance is reliable and valuable rather than heritability it self15.

Several studies have been published on the heritability of mutation in rice16, wheat17, corn18 and soybean19. However, little is known about heritability and selection criteria for aromatic mutant rice. This could be the first research report related to heritability in local aromatic mutant lines “Pare Bau” using heavy ion beam irradiation to the author's knowledge. It is crucial to distinguish cultivars with better results and desired agronomic traits for increasing the potential of local aromatic rice yields. The study of correlation is necessary to design a suitable selection strategy for genetic improvement in yield and other traits. Several researchers have studied the relationship among yield as well as its fundamental components in mutant rice20-22. The information on the direct and indirect effect of each component's traits toward yield will offer breeders to define the sufficient criteria in selecting desirable lines in population. Given this, a study was undertaken to determine the most important traits for breeding programs by exploiting genotypic and phenotypic coefficient of variation, heritability value, close relationship between traits and direct and indirect effect of yield components among rice lines.

MATERIALS AND METHODS

Study area: Field experiment was carried out at Enrekang Regency, South Sulawesi, at an altitude 650 m above sea level (S: 3°19'47.44", E: 119°50'1.57"), from April-October, 2018. In this study, the materials used were 18 lines of M3 generation from irradiated local aromatic rice "Pare Bau" from Toraja agriculture office induced by heavy-ion beam irradiation. Argon ions with an irradiation dose of 10 Gy (300 keV μm–1) were used as ion irradiation at the RIKEN Nishina Center, Wako-shi, Saitama, Japan.

Research procedure: The experiment used a design without replication. Fifty seeds from each M3 selected line were immersed one night for germination. The seeds of each line were sown into trays containing 1:1 (v/v) mixture soil and manure. The seedlings age 21 days of each line were then transplanted in the paddy field using a single seedling per hill with a plant spacing of 30 and 30 cm, respectively. Lines were transplanted together with unirradiated controls. The experimental field was irrigated with 10 cm water above the ground surface level. Fertilizers used were Urea at a dose 100 kg ha–1, SP-36 (200 kg ha–1) and KCl (100 kg ha–1) at 7 days after planting (DAP) and followed by Urea at a dose 200 kg ha–1 at 45 DAP, respectively. Weeding was treated with herbicides for broad leaves, whereas narrow leaves were conducted manually.

Data collection for agronomic traits: Data were collected on five quantitative traits, i.e., number of panicles, grain weight per panicle (g), percentage of fertile grain per panicle (%), panicle density and grain yield per plant (g). Sampling was recorded from ten plants for each line.

Data analysis: Data were analyzed through several stages. The significance test was performed using t-test with a standard deviation. The GCV and PCV were computed using the formula as suggested by Burton and Devane23. According to Terfa and Gurmu24, GCV and PCV values were categorized as low (0-10%), moderate (10-25%) and high (25% and above). Estimation of heritability in a broad sense was computed following the formula Allard25. The heritability value was classified as low (<0.2), moderate (0.2-0.5) and high (>0.5)26. Genetic advance as a percentage of the mean (GAM) was computed using Assefa et al.27 method and was classified as low (0-10%), moderate (10-20%) and high (>20%)28. Selection intensity (K) was considered to be 20%. Phenotypic correlation coefficients were estimated using the standard procedure suggested by Miller et al.29. The path analysis used was according to Dewey and Lu’s method30.

RESULTS

T-test: T-test showed significant difference in yield and yield component traits among mutant lines (Table 1). The results further informed that most of the traits exhibited a vast extend of variation among 18 lines. The variability observed wide range for the number of panicles (8-11), grain weight per panicle (1-4.6 g), percentage of fertile grain (21.4-74.6%), panicle density ratio (5.6-8.4) and grain yield per plant (8.2-48.1 g). The number of panicles showed that G4 and G15 lines were significant different compared to control. Grain weight per panicle and percentage of fertile grain showed that most of the lines had high significant differences compared to control, except G1, G14, G15, G16, G17 and G18. Panicle density showed that G8, G9, G11, G12 and G17 lines had significant differences, while G1, G2, G7 and G14 lines had high significant differences compared to control. Grain yield per plant showed that most of the lines had high significant differences compared to control, except for G1, G14, G16, G17 and G18.

Phenotypic and genotypic coefficient of variation: The GCV, PCV, heritability and GAM were presented in Table 2. The GCV values computed for five traits ranged from 14.31% for panicle density to 50.74% for grain yield per plant. The PCV ranged from 20.81-53.02%.

Table 1: Mean performance of 18 putative aromatic rice mutant lines for five traits
Lines
Number of panicles
Grain weight per panicle (g)
Percentage of fertile grain (%)
Panicle density
Grain yield per plant (g)
Control
9±1.9ns
1.6±0.7
29.4±16.7
6.2±1.1
14.1±4.7
G1
9±2.2ns
2.6±2.2ns
37.9±15.0ns
8.4±2.2**
23.9±14.4 ns
G2
9±1.2ns
3.6±1.2**
53.6±13.9**
7.5±1.2**
33.5±16.0**
G3
9±0.8ns
3.8±0.8**
60.8±11.4**
6.7±0.8ns
35.1±11.3**
G4
11±1.3*
4.5±1.3**
74.6±9.8**
6.9±1.3ns
48.1±11.8**
G5
9±1.7ns
4.2±1.7**
67.2±4.4**
7.7±1.7ns
39.5±11.8**
G6
9±1.4ns
3.3±1.4**
54.0±12.8*
6.4±1.4ns
29.6±7.3**
G7
9±1.1ns
4.6±1.1**
66.9±13.4**
7.6±1.1**
41.9±16.1**
G8
8±1.8ns
4.5±1.8**
58.3±12.9**
7.7±1.8*
25.5±10.5**
G9
9±1.2ns
4.0±1.2**
60.2±10.6**
7.4±1.2*
36.1±14.3**
G10
10±0.9ns
3.4±0.9**
57.3±12.3**
6.5±0.9ns
33.1±8.4**
G11
8±0.8ns
4.2±0.8**
60.4±15.0**
7.8±0.8*
30.8±10.4**
G12
10±1.9ns
3.4±1.9**
48.3±12.7*
7.5±1.9*
33.4±14.8**
G13
9±1.3ns
3.9±1.3**
57.4±14.3**
7.0±1.3 ns
35.5±20.3*
G14
9±1.7ns
3.1±1.7ns
51.0±26.9ns
7.5±1.7**
28.1±24.7ns
G15
10±1.9*
2.0±1.9ns
28.1±9.6 ns
6.2±1.9 ns
20.6±7.2*
G16
8±0.7ns
1.8±0.7ns
33.9±18.6ns
6.8±0.7 ns
21.5±8.0ns
G17
8±1.1ns
2.2±1.1ns
35.4±20.0ns
7.2±1.1*
17.3±13.9ns
G18
8±1.0ns
1.0±1.0ns
21.4±17.4ns
5.6±1.0 ns
8.2±7.4ns
Data shown are Mean±Standard deviation, ns: Non significant, *Significant at level 5%, **Significant at level 1%


Table 2: Estimation of coefficient of variation, heritability and genetic advance for five traits in putative aromatic rice mutant lines
Coefficient of variation (%)
Traits Mean GCV Criteria PCV Criteria h2b Criteria GAM (%) Criteria
NP 9.09 14.35 Moderate 25.24 High 0.57 High 53.04 High
GWP(g) 3.34 40.92 High 45.51 High 0.90 High 89.55 High
PFG 53.11 19.49 Moderate 36.95 High 0.53 High 61.81 High
PD 7.12 14.31 Moderate 20.81 Moderate 0.69 High 52.96 High
GYP(g) 30.5 50.74 High 53.02 High 0.96 High 99.72 High
NP: Number of panicles, GWP: Grain weight per panicle, PFG: Percentage of fertile grain, PD: Panicle density, GYP: Grain yield per plant, GCV: Genotypic coefficient of variation, PCV: Phenotypic coefficient of variation, h2b: Heritability and GAM: Genetic advance as percent of mean


Table 3: Phenotypic correlation coefficient between five traits in putative aromatic rice mutant lines
Traits NP GWP PFG PD GYP
NP 1.000 0.257ns 0.333ns -0.107ns 0.546*
GWP 1.000 0.964** 0.575** 0.888**
PFG 1.000 0.461* 0.936**
PD 1.000 0.433ns
GYP 1.000
ns: Non-significant, *Significant at level 5%, **Significant at level 1%, NP: Number of panicles, GWP: Grain weight per panicle, PFG: Percentage of fertile grain, PD: Panicle density, GYP: Grain yield per plant


Table 4: Path coefficients analysis of agronomic traits on the direct and indirect effects of the number of panicles, grain weight per panicle, percentage of fertile grain and panicle density on grain yield of aromatic mutant lines
Indirect effect
Trait Direct effect X1 X2 X3 X4 Total effect
X1 0.289** -0.043 0.314 -0.013 0.546*
X2 -0.169ns 0.074 0.911 0.072 0.888**
X3 0.945** 0.096 -0.163 0.058 0.936**
X4 0.125ns -0.031 -0.097 0.436 0.433ns
Residual effect 0.232
ns: Non-significant, *Significant at level 5%, **Significant at level 1%, X1: Number of panicles, X2: Grain weight per panicle, X3: Percentage of fertile grain, X4: Panicle density

Heritability and genetic advance: Heritability is one of the genetic parameters used for the selection criteria in a population. In this study, heritability ranged from 0.53-0.96, respectively. The genetic advance as percent of mean ranged from 52.96% in panicle density to 99.72% in grain yield per plant. High heritability and genetic advance were estimated for all morphological traits, such as the number of panicles, grain weight per panicle, percentage of fertile grain, panicle density and grain yield per plant.

Correlation: The estimates of correlation coefficients were computed between 5 quantitative traits among 18 putative aromatic rice mutant lines in Table 3. The number of panicles were significant and positively correlated with grain yield per plant (0.546), whereas, grain weight per panicle with percentage was highly significant and positively correlated with fertile grain (0.964), panicle density (0.575) and grain yield per plant (0.888), percentage of fertile grain were also highly significant and positively correlated with panicle density (0.461) and grain yield per plant (0.936).

Path coefficient analysis: The result of path coefficient analysis, presented in Table 4, was calculated to get an insight into direct and indirect effects on yield traits. Percentage of fertile grain showed the highest positive direct effect (0.945), followed by the number of panicles (0.289) and panicle density (0.125). Grain weight per panicle had a negative direct effect on grain yield per plant (-0.169) while indirectly increased the yield via grain yield per plant through the percentage of fertile grain (0.911). The residual effect of direct trait was 0.232.

DISCUSSION

The present study determines traits variance among 18 aromatic rice mutant lines which were highly significant indicating considerable genetic variability. The percentage of fertile grain was one of the most critical factors of yield and possibly this character would help break the yield plateau. Enhancement in the number of fertile grains should be linked with effective carbohydrates translocate from the leaves (sources) to the spikelets (sinks), resulting an increase in grain yield31. The more fertile grain, the more grain yield per plant, if other environmental conditions were not limiting. The same result was achieved by Immanuel et al.32. Grain yield per plant showed that G4 line had the highest weight and highly significant differences compared to control. This higher grain weight may be the result of mutation. Several studies have been reported using heavy-ion beam irradiation to increase crop yields, including Sjahril et al.33, who also achieved early maturing lines.

A relationship between GCV and PCV was found in all the traits. Expression of traits as an influence of the environment, PCV values were slightly higher than GCV. Similar studies were earlier recorded by Barik et al.34, Rashmi et al.35 and Akinwale et al.36. The magnitude of genetic variability percentage for a trait was determined by the genotypic coefficient of variation but did not evaluate the number of genetic variations, which is heritable. However, the number of panicles, percentage of fertile grain and panicle density showed moderate levels of GCV. Sabri et al.37 declared moderate levels of GCV for the number of panicles, while Behera et al.38 reported the percentage of fertile grain and Sanghera et al.39 showed a result for panicle density. The high to moderate genotypic coefficients of variation indicated adequate genetic variability for the traits, which might also encourage the selection40.

Traits with high heritability associated with the action of the additive gene have a high selection response. Srujana et al.41 argued that rice breeders could cultivate wide varieties of rice plants with superior genotypes by selecting cultivars with desirable phenotypic traits. In this study, all traits showed high heritability and genetic advance. In addition to high heritability and high genetic advance, higher GCV provided better indicators and can be used as selection criteria. Heritability combined with genetic advance was a more dependable indicator for selections of traits. These suggest the selection could be easily practiced using these traits to improve grain yields in putative aromatic rice mutant lines. Current result support the findings of Sala and Shanthi42, Sandeep et al.43, who also reported such type of heritability in rice. Therefore, selection can also be deferred to the next generations for these traits.

The findings confirmed that positive and significant traits with grain yield could increase grain yield. These traits were given importance while selection as they expressed a positive and significant correlation with grain yield. Therefore, these traits would increase rice yield. Hossain et al.44 argued that the number of panicles is an important characteristic for improving rice lines. Hence, similar associations were already reported for the number of panicles45, grain weight per panicle46 and percentage of fertile grain47. Selection for one trait would directly affect other traits shown by the significant and positive correlation of the traits facilitating effective selection for breeding program.

In the present investigation, grain weight per panicle was highly significant and positively correlated with the percentage of fertile grain and panicle density. Therefore, grain weight per panicle can be increased if more percentage of fertile grain. Current result is supported by the previous finding in the percentage of fertile grain rice presented by Naseer et al.48.

Correlation values could not describe the causal relationship among characters to their direct and indirect effect through other characters. Path analysis, measuring the direct and indirect effects, was applied to partition the correlation coefficient between grain yield per plant and its four component traits. The use of path coefficient analysis is restricted to considering the causal factor's route as the dependent variable and analyzing the direct and indirect effect that leads to the correlation between the traits49. In the present study, the path coefficient analysis was performed at the phenotypic level. The direct effect indicates the direct variance magnitude of a character influencing the main character variance50. The main factor for their relationship with grain yield per plant is the high direct effects on their traits. This means that the number of panicles and percentage of fertile grain were suitable as the best secondary traits, whereas the traits that have negative direct effect will decrease grain yield per plant. This corroborates the works by Prasad et al.51. Surprisingly as it might seem, the direct effect of percentage of fertile grain on plant yield is more significant than the effect of the number of panicles. For these traits, this may be a result of the relative amount of genotypic variation. Moderate positive direct effect but was non-significance, were observed for the traits of panicle density.

Panicle density showed high positive indirect effect towards grain yield via percentage of fertile grain. In the rest of the traits, the correlation was mainly due to indirect selection. Using these associated traits may be useful for breeders in formulating appropriate breeding plans for selection of lines. According to Saleh et al.52, if the direct effect's value is negative, the correlation coefficient will be significant due to high value of indirect effect. With residual effect, the significance of the selected traits used in the study could be calculated. The residual effect of the yield of aromatic rice lines showed that 76.8% of the genotypic level variability was the selected independent traits. This might be due to a significant positive correlation between the number of panicles, grain weight per panicle and the percentage of fertile grain with plant yield. This study demonstrated that the most important determining factor of plant yield was the number of panicles and percentage of fertile grain and it should be possible to breed for increased reproductive system efficiency.

CONCLUSION

The traits had genotypic and phenotypic coefficient of variation from moderate to high in M3 generation of aromatic mutant lines. Lines exhibited high heritability coupled with genetic advance as percent of the mean for all traits, indicating selection may be effective for these traits. Based on correlation analysis, the traits that support high yield were a number of panicles, grain weight per panicle and percentage of fertile grain. By improving these traits in a desirable direction, one can also improve the yield of the plant. Based on path analysis, two essential and useful traits will be rewarding for grain yield improvement in aromatic rice mutant lines of third generations (M3), i.e., number of panicles and the percentage of fertile grain. In the brief of lines, G4 is the highest yield per plant, followed by G7. These lines can be exploited for commercial cultivation.

SIGNIFICANCE STATEMENT

The present study revealed an effective method of improving local Toraja aromatic rice traits from Indonesia, namely Pare Bau, by using heavy ion beam irradiation. As a reference point for local aromatic rice breeders, specifically in Indonesia, the genetic parameters of Pare Bau cultivars have not been reported. The emphasize of the findings of this study is number of panicles and the percentage of fertile grain that resulted as the most significant components of direct yield during simultaneous selection to increase grain yield in rice. Our findings will help breeders develop efficient strategies for breeding aromatic-grade rice cultivars and this line can be used as a donor parent of superior genotypes.

ACKNOWLEDGMENT

The authors are highly thankful to the Indonesian Ministry of Research, Technology and Higher Education for funding this research under the integrated Master-Doctor program for excellent undergraduate students (Contract Number: 149/SP2H/LT/DPRM/2018).

REFERENCES
  • Prom-u-thai, C. and B. Rerkasem, 2020. Rice quality improvement: A review. Agron. Sustain. Dev., Vol. 40.
    CrossRef    

  • Nooy-Palm, H., 2014. The Sa'dan-Toraja: A study of their social life and religion. 1st Edn., Springer, Netherlands, ISBN: 978-90-247-2274-7 Pages: 338
    Direct Link    

  • Ramchander, S., R. Ushakumari and M.A. Pillai, 2015. Lethal dose fixation and sensitivity of rice varieties to gamma radiation. Ind. J. Agri. Rese., 49: 24-31.
    CrossRef    Direct Link    

  • Salama, H.M.H., A.A. Al Watban and A.T. Al-Fughom, 2011. Effect of ultraviolet radiation on chlorophyll, carotenoid, protein and proline contents of some annual desert plants. Saudi Journal of Biological Sciences 18: 79-86.
    CrossRef    Direct Link    

  • Bolon, Y.T., A.O. Stec, J.M. Michno, J. Roessler and P.B. Bhaskar et al., 2014. Genome resilience and prevalence of segmental duplications following fast neutron irradiation of soybean. Genetics, 198: 967-981.
    CrossRef    Direct Link    

  • Kazama, Y., K. Ishii, T. Hirano, T. Wakana, M. Yamada, S. Ohbu and T. Abe, 2017. Different mutational function of low‐ and high‐linear energy transfer heavy‐ion irradiation demonstrated by whole‐genome resequencing of Arabidopsis mutants. Plant J., 92: 1020-1030.
    CrossRef    Direct Link    

  • Abe, T., H. Ryuto and N. Fukunishi, 2012. Ion Beam Radiation Mutagenesis. In: Plant Mutation Breeding and Biotechnology, Shu, Q.Y., B.P. Forster and H. Nakagawa (Eds.), CABI, Oxford, ISBN: 9781780640853, pp: 99-106
    CrossRef    Direct Link    

  • Maekawa, M., Y. Hase, N. Shikazono and A. Tanaka, 2003. Induction of somatic instability in stable yellow leaf mutant of rice by ion beam irradiation. Nuc. Inst. Meth. Phys. Res. B., 206: 579-585.
    CrossRef    Direct Link    

  • Cabanos, C.S., H. Katayama, H. Urabe, C. Kuwata and Y. Murota et al., 2012. Heavy-ion beam irradiation is an effective technique for reducing major allergens in peanut seeds. Mol. Breed., 30: 1037-1044.
    CrossRef    Direct Link    

  • Abe, T., Y. Kazama and T. Hirano, 2015. Ion beam breeding and gene discovery for function analyses using mutants. Nuc. Phys. News, 25: 30-34.
    CrossRef    Direct Link    

  • Babu, B.K., V. Meena, V. Agarwal and P.K. Agrawal, 2014. Population structure and genetic diversity analysis of Indian and exotic rice (Oryza sativa L.) accessions using SSR markers. Mol. Biol. Rep., 41: 4329-4339.
    CrossRef    Direct Link    

  • Bello, O.B., S.A. Ige, M.A. Azeez, M.S. Afolabi, S.Y. Abdulmaliq and J. Mahamood, 2012. Heritability and genetic advance for grain yield and its component characters in maize (Zea mays L.). Int. J. Plant Res., 2: 138-145.
    CrossRef    Direct Link    

  • Raffi, S.A. and U.K. Nath, 2004. Variability, heritability, genetic advance and relationships of yield and yield contributing characters in dry bean (Phaseolus vulgaris L.). J. Biol. Sci., 4: 157-159.
    CrossRef    Direct Link    

  • Sabesan, T., R. Suresh and K. Saravanan, 2009. Genetic variability and correlation for yield and grain quality characters of rice grown in coastal saline low land of Tamilnadu. Electron. J. Plant Breed., 1: 56-59.
    Direct Link    

  • Ogunbayo, S.A., D.K. Ojo, K.A. Sanni, M.G. Akinwale and B. Toulou et al., 2014. Genetic variation and heritability of yield and related traits in promising rice genotypes (Oryza sativa L.) J. Plant Breed. Crop Sci., 6: 153-159.
    CrossRef    Direct Link    

  • da Luz, V.K., S.F. da Silveira Silveira, G.M. da Fonseca, E.L. Groli and R.G. Figueiredo et al., 2016. Identification of variability for agronomically important traits in rice mutant families. Bragantia, 75: 41-50.
    CrossRef    Direct Link    

  • Mosleth, E.F., M. Lillehammer, T.K. Pellny, A.J. Wood and A.B. Riche et al., 2020. Genetic variation and heritability of grain protein deviation in European wheat genotypes. Field Crops Res., Vol. 255.
    CrossRef    

  • Fadhli, N., M. Farid, Rafiuddin, R. Efendi, M. Azrai and M.F. Anshori, 2020. Multivariate analysis to determine secondary characters in selecting adaptive hybrid corn lines under drought stress. Biodiversitas, 21: 3617-3624.
    CrossRef    Direct Link    

  • Espina, M.J., C.M.S. Ahmed, A. Bernardini, E. Adeleke and Z. Yadegari et al., 2018. Development and phenotypic screening of an ethyl methane sulfonate mutant population in soybean. Front. Plant Sci., Vol. 29.
    CrossRef    

  • Devi, K.R., B.S. Chandra, Y. Hari, K.R. Prasad, N. Lingaiah and P.J.M. Rao, 2020. Genetic divergence and variability studies for yield and quality traits in elite rice (Oryza sativa L.) genotypes. Curr. J. Appl. Sci. Technol., 39: 29-43.
    CrossRef    Direct Link    

  • Kumari, N., R. Kumar and A. Kumar, 2019. Genetic variability and association of traits in mutant lines of rice (Oryza sativa L.) for submergence tolerance. Curr. J. Appl. Sci. Technol., 33: 1-7.
    CrossRef    Direct Link    

  • Oladosu, Y., M.Y. Rafii, N. Abdullah, M. Abdul Malek and H.A. Rahim et al., 2014. Genetic variability and selection criteria in rice mutant lines as revealed by quantitative traits. Sci. World J., Vol. 2014.
    CrossRef    

  • Burton, W.G. and E.H. Devane, 1953. Estimating heritability in tall fescue (Festuca arundinacea) from replicated clonal material. Agron. J., 45: 478-481.
    CrossRef    Direct Link    

  • Terfa, G.N. and G.N. Gurmu, 2020. Genetic variability, heritability and genetic advance in linseed (Linum usitatissimum L.) genotypes for seed yield and other agronomic traits. Oil Crop Sci., 5: 156-160.
    CrossRef    Direct Link    

  • Allard, R.W., 1960. Principles of Plant Breeding. 1st Edn., John Wiley and Sons Inc., New York pp: 372-372
    CrossRef    Direct Link    

  • Stevens, L., 1991. Genetics and Evolution of the Domestic Fowl. 1st Ed. Cambridge University Press, Cambridge, UK., ISBN-13: 9780521403177, pp: 125-131
    Direct Link    

  • Assefa, K., S. Ketema, H. Tefera, H.T. Nguyen and A. Blum et al., 1999. Diversity among germplasm lines of the Ethiopian cereal tef Eragrostis tef (Zucc.) Trotter. Euphytica, 106: 87-97.
    CrossRef    

  • Johnson, H.W., H.F. Robinson and R.E. Comstock, 1955. Estimates of genetic and environmental variability in soybeans. Agron. J., 47: 314-318.
    CrossRef    Direct Link    

  • Miller, P.A., J.C. Williams, H.F. Robinson and R.E. Comstock, 1958. Estimates of genotypic and environmental variances and covariances in upland cotton and their implications in selection. Agron. J., 50: 126-131.
    CrossRef    

  • Dewey, D.R. and K.H. Lu, 1959. A correlation and path-coefficient analysis of components of crested wheatgrass seed production. Agron. J., 51: 515-518.
    CrossRef    Direct Link    

  • Xu, Z.Z. and G.S. Zhou, 2007. Photosynthetic recovery of a perennial grass leymuschinensis after different periods of soil drought. Plant Prod. Sci., 10: 277-285.
    CrossRef    Direct Link    

  • Selvaraj, I.C., P. Nagarajan, K. Thiyagarajan, M. Bharathi and R. Rabindran, 2011. Genetic parameters of variability, correlation and path coefficient studies for grain yield and other yield attributes among rice blast disease resistant genotypes of rice (Oryza sativa L.). Afr. J. Biotechnol., 10: 3322-3334.
    Direct Link    

  • Sjahril, R., A.R. Trisnawaty, M. Riadi, R. Rafiuddin and T. Sato et al., 2020. Selection of early maturing and high yielding mutants of Toraja local red rice grown from M2-M3 population after ion beam irradiation. Hayati J. Biosci., 27: 166-173.
    CrossRef    Direct Link    

  • Barik, J., V. Kumar, S.K. Lenka and D. Panda, 2020. Assessment of variation in morpho-physiological traits and genetic diversity in relation to submergence tolerance of five indigenous lowland rice landraces. Rice Sci., 27: 32-43.
    CrossRef    Direct Link    

  • Rashmi, D., S. Saha, B. Loitongbam, S. Singh and P.K. Singh, 2017. Genetic variability study for yield and yield components in rice (Oryza sativa L.). Int. J. Agric. Environ. Biotechnol., 10: 171-176.
    CrossRef    Direct Link    

  • Akinwale, M.G., G. Gregorio, F. Nwilene, B.O. Akinyele, S.A. Ogunbayo and A.C. Odiyi, 2011. Heritability and correlation coefficient analysis for yield and its components in rice (Oryza sativa L.). Afr. J. Plant Sci., 5: 207-212.
    Direct Link    

  • Sabri, R.S., M.Y. Rafii, M.R. Ismail, O. Yusuff, S.C. Chukwu and N.A. Hasan, 2020. Assessment of agro-morphologic performance, genetic parameters and clustering pattern of newly developed blast resistant rice lines tested in four environments. Agronomy, Vol. 10.
    CrossRef    

  • Behera, M., P.N. Jagadev, S. Das, K. Pradhan and B.B. Sahoo, 2020. Assessment of genetic variability, heritability and genetic advance in tomato. Int. J. Chem. Stud., 8: 481-483.
    CrossRef    Direct Link    

  • Sanghera, G.S., S.C. Kashyap and G.A. Parray, 2013. Genetic variation for grain yield and related traits in temperate red rice (Oryza sativa L.) ecotypes. Not. Sci. Biol., 5: 1-7.
    CrossRef    Direct Link    

  • Tiwari, D.N., S.R. Tripathi, M.P. Tripathi, N. Khatri and B.R. Bastola, 2019. Genetic variability and correlation coefficients of major traits in early maturing rice under rainfed lowland environments of Nepal. Adv. Agric., Vol. 2019.
    CrossRef    

  • Srujana, G., B.G. Suresh, G.R. Lavanya, R.B. Jalandhar and V. Sumanth, 2017. Studies on genetic variability, heritability and genetic advance for yield and quality components in rice (Oryza sativa L.). J. Pharmacogn. Phytochem., 6: 564-566.
    Direct Link    

  • Sala, M. and P. Shanthi, 2016. Variability, heritability and genetic advance studies in F2 population of rice (Oryza sativa L.). Int. J. For. Crop Improv., 7: 57-60.
    Direct Link    

  • Sandeep, S., M. Sujatha, L.V. Subbarao and C.N. Neeraja, 2018. Genetic variability, heritability and genetic advance studies in rice (Oryza sativa L.). Int. J. Curr. Microbiol. App. Sci., 7: 3719-3727.
    CrossRef    Direct Link    

  • Hossain, S., M. Salim, M.G. Azam and S. Noman, 2018. Variability, correlation and path analysis in drought tolerant rice (Oryza sativa L.) J. Biosci. Agric. Res., 18: 1521-1530.
    CrossRef    Direct Link    

  • Swapnil, K. Prasad, M. Chakraborty, D.N. Singh, P. Kumari and J.P. Ekka, 2020. Genetic variability, correlation and path coefficient studies in F2 generation of rice (Orzya sativa L.). Int. J. Chem. Stud., 8: 3116-3120.
    CrossRef    Direct Link    

  • Rai, P.K., U.K. Sarker, P.C. Roy and A.K.M.S. Islam, 2013. Character association in F4 generation of rice (Oryza sativa L.). Bangladesh J. Pl. Breed. Genet., 26: 39-44.
    CrossRef    Direct Link    

  • Bhor, T.J., N.V. Kashid and S.M. Kadam, 2020. Genetic variability, character association and path analysis studies for yield components traits in promising rice (Oryza sativa L.) genotypes. J. Pharmacogn. Phytochem., 9: 1953-1956.
    Direct Link    

  • Naseer, S., M. Kashif, H.M. Ahmad, M.S. Iqbal and Q. Ali, 2015. Estimation of genetic association among yield contributing traits in aromatic and non-aromatic rice (Oryza sativa L.) cultivars. Life Sci. J., 12: 68-73.
    CrossRef    Direct Link    

  • Lalitha, R., A. Mothilal, P. Arunachalam, N. Senthil and G. Hemalatha, 2019. Genetic variability, correlation and path analysis of grain yield, grain quality and its associated traits in EMS derived M4 generation mutants ofrice (Oryza sativa L.). Electron. J. Plant Breed., 10: 1140-1147.
    Direct Link    

  • Manjunatha, G.A., M.S. Kumar and M. Jayashree, 2017. Character association and path analysis in rice (Oryza sativa L.) genotypes evaluated under organic management. J. Pharmacogn. Phytochem., 6: 1053-1058.
    Direct Link    

  • Prasad, K.R., K.V.R. Krishna, M.H.V. Bhave and L.V.S. Rao, 2017. Genetic variability, heritability and genetic advance in boro rice (Oryza sativa L.) germplasm. Int. J. Curr. Microbiol. App. Sci., 6: 1261-1266.
    CrossRef    Direct Link    

  • Saleh, M.M., K.F.M. Salem and A.B. Elabd, 2020. Definition of selection criterion using correlation and path coefficient analysis in rice (Oryza sativa L.) genotypes. Bull. Natl. Res. Cent., Vol. 44.
    CrossRef    

    • © Science Alert. All Rights Reserved