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Interrelationship of Yield and Quality Attributing Traits in JNPT Lines of Rice

Pratibha Choudhary, D. K. Mishra, G. K. Koutu, Amita Pachori and S. K. Singh

  • Page No:  330 - 340
  • Published online: 07 Jun 2018
  • DOI : HTTPS://DOI.ORG/10.23910/IJBSM/2018.9.3.3C0934

  • Abstract
  •  prratibha08@gmail.com

In present study, one hundred eighty five JNPT (Jawahar New Plant Type) lines including five checks were evaluated for twenty eight morphological and quality traits planted in Randomized Complete Block Design with three replications. The experiment was conducted during kharif seasons of 2014 and 2015 at Seed Breeding Farm, Department of Plant Breeding and Genetics, College of Agriculture, JNKVV, Jabalpur (M.P.), India. Observations were recorded on the basis of middle five random competitive plants selected from each line in every replication for yield and quality traits. Considerable genetic variability was exhibited by all the yield and quality traits under study. It was observed that for selecting the high yielding lines in the rice the characters viz., spikelet density, fertile spikelets panicle-1, number of spikelets per panicle, panicle weight plant-1 and biological yield plant-1 might be considered. On the basis of high PC score in principal component analysis 10 most prominent JNPT lines JNPT 813, JNPT 811, JNPT 845, JNPT 770, JNPT 779, JNPT 777, JNPT 778, JNPT 749, JNPT 781 and JNPT(S) 10H were identified. Thus, these JNPT lines will be utilized as inbred for production of hybrid rice, with higher yield and better quality. However, after evaluation under different agro-ecological rice growing situations, these lines might be released as high yielding variety with better quality.

Keywords :   Rice, JNPT lines, PC score, PCA, variability

  • Introduction

    Rice (Oryza sativa L.) is the basic food crop of Asia, providing over 30% of the calories consumed in the region (Narciso and Hossain, 2002). Rice production in Asia has increased by 2.6 times since 1961, primarily as a result of the “Green Revolution”, which dramatically increased the rice productivity in the high-input irrigated systems (Khush, 1999). Enhancing crop yield is one of the top most priorities in crop breeding programmes. Results have indicated that an effective way to develop super rice lies first in developing the new plant type and strong vigour by crossing Indica with Japonica subspecies, and then consolidating the two advantages by optimizing the combination of desirable traits via multiple crossing and backcrossing (Cheng et al., 2001). In the late 1980s, to increase yield potential in Indica inbred varieties under a tropical environment, a breeding programme to develop new plant type (NPT) rice was launched at IRRI. The NPTlines had several traits from tropical Japonica: low tillering habit, few unproductive tillers, large panicles, thick culm, lodgingresistance, and large and dark green flag leaves (Khush, 1995). Especially, the NPT lines had elite characteristics such as larger flag leaves, higher spikelet number, and heavier grain weight than IR64. Thus, these lines were thought to be useful materials to improve the yield potential of IRRI-bred varieties, including IR64. Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur (India) has made JNPT (Jawahar New Plant Type) lines through tropical Japonica×Indica hybridization using wide compatibility gene source. The Indica lines used to develop the JNPT lines were very popular for high yield and quality aspects. Derived lines combine strong culm, short stature, dark green erect leaves, long panicles, high grain numbers with improved quality.

    Magnitude of genetic variability present in the plant population decides the efficiency of selection as well as development of an effective plant breeding strategy. Correlations help the breeder to understand the mutual component characters on which selection can be based for genetic improvement (Chakravorty et al.,2013). Information on association of the characters, direct and indirect effects contributed by each trait towards yield will be an added advantage in aiding the selection process and helps the breeder to design his selection strategies for improving the grain yield (Ravindrababu et al., 2012). Principal Component Analysis (PCA) is one of the tools available for summarizing and describing the inherent genetic variation in crop genotypes. This technique helps in identification of traits that help in distinguishing selected genotypes based on similarities in one or more traits and classify the genotypes into separate groups (Ariyo, 1987 and Nair et al., 1998). The PCA has been used by Nassir, 2002 and Chakravorty et al., 2013 in rice for partitioning observed variation and studying inter relationships among different traits. PCA helps to identify the traits with high variability, correlations reveal the strength of relationship between different traits with yield. Thus, to identify the yield and quality traits of utmost importance the present investigation was conducted in rice by studying genetic parameters and interrelationship of yield and quality traits in JNPT lines.


  • Materials and Methods

    The experimental material consists of 180 JNPT lines derived from Indica×Japonica subspecies crosses (F14- F15 generation’s) developed by JNKVV, Jabalpur with 5 checks were grown during Kharif seasons of 2014 and 2015 at Seed Breeding Farm, JNKVV, Jabalpur (M.P.), India. These lines were planted in Randomized Complete Block Design with three replications. Twenty one days old seedlings were transplanted in the experimental site with spacing of 20 cm between plant to plant and 30 cm between the rows, keeping single seedling per hill. Gap filling was done within a week in order to maintain uniform plant population.  Fertilizer dose of 120 kg N, 60 kg P2O5 and 60 kg K2O was applied. Observations were recorded on the basis of middle five random competitive plants selected from each line in every replication for yield and quality traits. The data for each trait was statistically analyzed using analysis of variance recommended for randomized complete block design. The mean values were used to obtain analysis of variance as per methodology advocated by Panse and Sukhatme (1967). PCV and GCV were calculated by the formula given by Burton (1952), heritability in broad sense by Burton (1952) and Burton and De Vane (1953) and genetic advance i.e. the expected genetic gain was calculated by using the procedure given by Johnson et al., 1955. Correlation coefficient and path coefficient analysis was worked out as method suggested by Al-Jibouri et al., 1958 and Dewey and Lu (1959) respectively. Genetic and phenotypic correlations among the traits were determined by Singh and Chaudhary (2005) method. The simplified procedure of (Juliano, 1971) is used for the amylose contentanalysis. PCA analysis was done using the methodology given by (Massy, 1965; Jolliffe, 1986).


  • Results and Discussion

    3.  Results and Discussion  3.1.  Genetic parameter The mean value of different characters were tested for homogeneity by Bartlett’s test and found non-significant. Thus, the pooled result of two years was obtained by computing the mean value of different traits from two years. All yield attributing traits showed the considerable amount of variability. The total number of spikelets panicle-1 (34420.0178) showed maximum variability, whereas, stem thickness (0.0674) exhibited minimum variability, which was in agreement with the findings of Kumar et al. (2015). Highest genotypic and phenotypic coefficient of variation observed by spikelet density (34.77 and 34.91) while, low GCV and PCV by hulling percentage (4.53 and 4.59). High heritability coupled with high genetic advance expressed by spikelet density followed by fertile spikelets panicle-1, number of spikelets panicle-1, number of productive tillers plant-1, panicle weight per plant, amylose content, number of tillers plant-1, grain yield plant-1, biological yield plant-1, 1000-grain weight, flag leaf width, harvest index, flag leaf length, panicle index, stem length, grain length, plant height, grain breadth, decorticated grain l/b ratio, panicle length and decorticated grain length (Table 1). This was in consonance with the findings of Bekele et al. (2013), Rajput et al. (2014); Shrivastava et al. (2014) and Dongre et al. (2014). 3.2.  Character association Grain yield plant-1 revealed significant and positive association with panicle weight plant-1, biological yield per plant, number of productive tillers plant-1, harvest index, number of tillers plant-1, fertile spikelets per panicle, spikelet density, number of spikelets panicle-1, flag leaf length, panicle index, days to 50% flowering, grain breadth, days to maturity, spikelet fertility, hulling percentage and amylose content in continues two years and pooled result of two years. Similar findings were reported by Sohgaura et al. (2014), Singh et al. (2014), Dongre et al. (2014); Shrivastava et al. (2014). Considering the results from correlation and path coefficient analysis, it is concluded that for selecting the high yielding lines in rice the characters viz., panicle weight plant-1, panicle index, biological yield per plant, harvest index, number of productive tiller plant-1, spikelet density, panicle length, spikelet fertility and 1000-grain weight might be considered (Table 2(a), 2(b), 3(a) & 3(b)). 3.3.  Principal component analysis To find out independent impact of all the characters under study principal component analysis was conducted. In JNPT lines, the first principal component accounted for maximum proportion of total variability in the set of all variables and remaining components accounted for progressively lesser and lesser amount of variation. Out of 28 principal components (PCs) only eight PCs exhibited more than 1.00 Eigen value and about 75.61% variability among the traits studied (Table 4). So, these eight PC’s were given due importance for further explanation. Highest variability (20.69%) was exhibited by PC1 however, PC2, PC3, PC4, PC5, PC6, PC7 and PC8 revealed 14.20%, 9.89%, 8.90%, 6.45%, 5.62%, 5.30% and 4.54% variability, respectively among the lines for the traits under


    study. Rotated component matrix revealed that each principal component separately loaded with various yield and quality attributing traits under study (Table 5). The PC1 was more related to the yield attributing traits viz., spikelet density, fertile spikelets per panicle and number of spikelets per panicle. PC2 exhibited positive effect for grain yield per plant, panicle weight per plant and biological yield plant-1, which were more loaded with yield contributing traits. Thus, PC1 and PC2 allowed for simultaneous selection of yield related traits and it can be regarded as yield factor. The 5th principal component was more linked to quality attributing traits i.e., decorticated grain length and decorticated grain l/b ratio. Similarly, eighth PC also more dominated with quality traits such as hulling percentage and milling percentage. Remaining principal components (PC3, PC4, PC6 and PC7) more loaded with physiological traits (Table 6). This result was in agreement with Yang et al. (2009), Ashfaq et al. (2012) and Kumar et al. (2014). On the basis of PCA study, it was cleared that the JNPT 810, JNPT 754, JNPT 800, JNPT 752, JNPT 811, JNPT 751, JNPT 748, JNPT 820, JNPT 822 and JNPT 830 were the selected 10 promising lines for both yield and quality attributes. Because, these genotypes performing their presence with high PC score in both yield (PC1or PC2) as well as quality (PC5 or PC8) related PC’s. Therefore, selection of genotype in these PCs will be more accurate in comparison to other PCs. (Table 7).


  • Conclusion

    Selecting the high yielding lines in rice the characters viz., panicle weight per plant, panicle index, biological yield per plant, harvest index, number of productive tiller per plant, spikelet density, panicle length, spikelet fertility and 1000-grain weight might be considered. On the basis of PC score, JNPT 810, JNPT 754, JNPT 800, JNPT 752, JNPT 811, JNPT 751, JNPT 748, JNPT 820, JNPT 822 and JNPT 830 were the selected 10 promising lines for both yield and quality attributes.


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Cite

1.
Choudhary P, Mishra DK, Koutu GK, Pachori A, Singh SK. Interrelationship of Yield and Quality Attributing Traits in JNPT Lines of Rice IJBSM [Internet]. 07Jun.2018[cited 8Feb.2022];9(1):330-340. Available from: http://www.pphouse.org/ijbsm-article-details.php?article=1142

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