GENETIC DIVERSITY ANALYSIS FOR SEED COTTON YIELD AND FIBER QUALITY IN COTTON (Gossypium hirsutum L.)

Transcription

1 GENETIC DIVERSITY ANALYSIS FOR SEED COTTON YIELD AND FIBER QUALITY IN COTTON (Gossypium hirsutum L.) A THESIS SUBMITTED TO NAVSARI AGRICULTURAL UNIVERSITY NAVSARI IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF THE DEGREE OF MASTER OF SCIENCE (AGRICULTURE) IN GENETICS AND PLANT BREEDING BY CHAUDHARI MAHENDRAKUMAR NANJIBHAI B.Sc. (Hons.) Agriculture DEPARTMENT OF GENETICS AND PLANT BREEDING N. M. COLLEGE OF AGRICULTURE NAVSARI AGRICULTURAL UNIVERSITY NAVSARI GUJARAT STATE May-2017 Registration No

2 Abstract

3 GENETIC DIVERSITY ANALYSIS FOR SEED COTTON YIELD AND FIBER QUALITY IN COTTON (Gossypium hirsutum L.) Name of Student Chaudhari Mahendrakumar N. Major Advisor Dr. G. O. Faldu DEPARTMENT OF GENETICS AND PLANT BREEDING N. M. COLLEGE OF AGRICULTURE NAVSARI AGRICULTURAL UNIVERSITY NAVSARI ABSTRACT A field experiment comprised of 40 genotypes of cotton (Gossypium hirsutum L.) was laid-out in Randomized Block Design replicated thrice, at the experimental farm of Main Cotton Research Station, Navsari Agricultural University, Surat during to investigate the magnitude of genetic variability, correlation, path analysis and D 2 statistics in cotton (Gossypium hirsutum L.). Analysis of variance revealed significant differences among the genotypes indicating a high degree of variability among the genotypes tested. The higher estimates of genotypic and phenotypic coefficient of variation were obtained for number of sympodia per plant, number of bolls per plant, seed cotton yield per plant, gossypol content, phenol content and reducing sugar content and also had high heritability coupled with high

4 genetic advance indicating better scope for improvement of these traits by a productive selection programme. Correlation coefficient analysis revealed that seed cotton yield per plant exhibited positive and highly significant correlation with number of sympodia per plant, number of bolls per plant and oil percentage at genotypic and phenotypic levels. On the contrary, it expressed negative and highly significant genotypic and phenotypic association with days to 50% flowering. These findings of correlation revealed that emphasis should be given on selection for plant having more sympodial branches with more number of bolls per plant for improvement in seed cotton yield. Besides this, fiber quality traits, gossypol content, phenol content, leaves protein content, seed protein content and reducing sugar content were not significantly affect seed cotton yield. The path analysis described that number of bolls per plant and boll weight had high direct effect on seed cotton yield per plant. These traits can be considered as principal components and suggested to use these traits as selection criteria for seed cotton yield improvement. On other hand seed cotton yield per plant was not much affected by other component traits, fiber quality and bio chemical traits. Based on D 2 values, forty genotypes of cotton (Gossypium hirsutum L.) clustered by Tocher s method into three clusters which revealed less genetic diversity present among genotypes. Among different 17 traits studied, oil percentage and reducing sugar was the main contributors to the total divergence. The genotype of cluster III (K-3259-EC- 9, PD 9363, DS 28, Ibadem allon, SDN-24 and Co American 8-

5 29) showed maximum genetic divergence with cluster II (C 1579, EL-174C, BP52 and G 3637 IP) and genotypes of these clusters may be selected for hybridization for generating genetic variability. Cluster III seems to be most promising for agronomic traits followed by cluster II and I. The genetically diverse genotypes were helpful in selecting parental combinations for creating diverse segregating populations for genetic dissection of quantitative trait loci (QTL) followed by marker assisted selection (MAS). On the basis of present investigations, number of sympodia per plant, number of bolls per plant, boll weight and oil percentage are the most important component traits for improving seed cotton yield in cotton (Gossypium hirsutum L.). D 2 analysis suggested that selection of parents for hybridization programme from most diverse cluster II and III will produce superior segregants.

6 Dr. G. O. Faldu Assistant Research Scientist, Main Cotton Research Station, Navsari Agricultural University, Surat C E R T I F I C A T E This is to certify that the thesis entitled Genetic diversity analysis for seed cotton yield and fiber quality in cotton (Gossypium hirsutum L.) submitted by Mr. CHAUDHARI MAHENDRAKUMAR NANJIBHAI in partial fulfillment of the requirements for the award of the degree of MASTER OF SCIENCE (Agriculture) in the subject of GENETICS AND PLANT BREEDING to the Navsari Agricultural University, Navsari is a record of bonafide research work carried out by him under my guidance and supervision and the thesis has not previously formed the basis for the award of any degree, diploma or other similar title. Place: Navsari Date: 09/05/2017 (G. O. Faldu) Major Advisor

7 DECLARATION This is to declare that the whole of the research work reported in this thesis for the partial fulfillment of the requirements for the degree of MASTER OF SCIENCE (AGRICULTURE) in GENETICS AND PLANT BREEDING by the undersigned is the result of investigation done by the undersigned under the direct guidance and supervision of Dr. G. O. Faldu, Assistant Research Scientist, Main Cotton Research Station, Navsari Agricultural University, Surat and no part of the work has been submitted for any other degree so far. Place: Navsari Date: 09/05/2017 (Chaudhari Mahendrakumar N.) Countersigned by (G. O. Faldu) Assistant Research Scientist, Main Cotton Research Station, Navsari Agricultural University, Surat

8 A C K N O W L E D G E M E N T After successful completing the long educational journey, I look back and find that though mine has been a fairly sail, it has been mesmerizing extravaganza of memorable experiences. I also realize how difficult it is to wrap my feelings with plumage of worldly words more so as I am inundated with emotions. At this gratifying moment completion of my research problem, I feel obliged to record my gratitude to those who have helped me. Every author owes a debt to his teachers. Therefore, I seize this opportunity to express my deep sense of indebtedness and profound gratitude to my major advisor Dr. G. O. Faldu, Assistant Research Scientist, Main Cotton Research Station, Navsari Agricultural University, Surat, for his keen interest, scientific guidance, constructive criticism and inspiration during the course of investigation and preparation of this dissertation. I could not have imagined having a better advisor and mentor for my M.Sc. study, most valuable and inspiring guidance with him. I fail in words to express my heartfelt thanks to the members of my advisory committee, Dr. Chintan Kapdia, Assistant Professor, Dept. of Plant Molecular Biology and Biotechnology, ASPEE college of H & F., N.A.U., Navsari, Dr. R. K. Patel, Associate Professor and I/C Head, Dept. of GPB, N. M. C. A., N. A. U., Navsari and Prof. H. N. Chhatrola, Asst. Professor, Dept. of Stat. & computer centre, ASPEE college of H & F., N.A.U., Navsari for their valuable guidance and ever willing co -operation. Thanks to Dr. R. K. Patel, Associate Professor and I/C Head, Dept. of GPB, N. M. C. A., N. A. U., Navsari, Dr. K. G. Modha, Asst. Professor, Dept. of GPB, N. M. C. A., N. A. U., Navsari, Prof. G. D. Vadodariya, Asst. Professor, Dept. of GPB, N. M. C. A. and Dr. Madhubala, Asst. Professor, Dept. of GPB, N. M. C. A. I am highly thankful to Honourable Vice Chancellor, Director of Research and Dean PG studies, Principal, Registrar, Library staff and all the department of N. M. College of Agriculture, NAU, Navsari for providing necessary facilities

9 during the course of studies and investigation. I am also thankful to Dr. B. G. Solanki, Research Scientists, Main Cotton Research Station, N. A. U., Surat, Dr. H. R. Ramani, Assistant Research Scientist (Bio Chemistry), MCRS, N. A. U., Surat and all the staff members of MCRC farm, N. A. U., Surat for rendering helps during the course of field experimentation. I want to extend a multitude of thanks to my best friends Romie Anajna, Rameshbhai, Nitesh, Dinesh, Shubham, Mendal, Prakash, Shailesh, Mukesh, Vishwas, Mahesh, Kamlesh, Lalji, Lav, Abhishek, Shankar, Guru, Vivek, Pravin, Bhurabhai, Kandarp, Arvind, Shamal, Bharat, Deep, Henil, Hirabhai, Dilipbhai, Kinjal Bhemani and classmates Hardy, Manat, Ashish, Mitesh Pratik Rakesh, Dipesh, Mavani, Nisarg, Aditi, Nidhi, Kanak, Kunjan, Kajal, Jyoti, Shreya, Gayatri for their help and cooperation during the course of investigation. The moments I shared with them are unforgettable and will cherish in my mind for the rest of my life. I am also thankful to my brothers Prakashbhai, Vinod, Vijay, Dinesh, Nilam, Naresh, Kiran, Mukesh, Vishnubhai, Maheshbhai, Rameshbhai, Pirabhai, Madhubhai for his self-less love throughout journey and encouragement during my study. On my personal note, I would like to express my gratitude and respect to my beloved family members mother Smt Shantaben, father Shri Nanjibhai, my Grand Maa Smt Jethiben and my cousins Mahesh, Harsh, Prakash, Dasrath, Bharat, Vashram also my loving sisters Shejal, Vaibhavi, Vinal, Gomati, Pravina, Sukhi, Geetaben, Chetanaben and all family members and other who always wanted my success, inspired me with their love and affections and for the sacrifice made by them to shape my career. I express my infinite sense of gratitude to the God and Godness for continuously providing me spiritual energy, which has inspired me to reach at the highest excellence during my academic career and life. Place: Navsari Date: 09/05/2017 (M. N. Chaudhari)

10 CHAPTER CONTENTS TITLE PAGE NO. I INTRODUCTION 1-5 II III IV V REVIEW OF LITERATURE 2.1 Variance components 2.2 Correlation coefficient 2.3 Path analysis 2.4 Genetic divergence MATERIALS AND METHODS 3.1 Location and Climate condition 3.2 Experimental material 3.3 Experimental details 3.4 Character studied 3.5 Statistical analysis RESULTS 4.1 Variance components analysis 4.2 Correlation coefficient analysis 4.3 Path analysis 4.4 Genetic divergence DISCUSSION 5.1 Variance components analysis 5.2 Correlation coefficient analysis 5.3 Path analysis 5.4 Genetic divergence VI SUMMARY AND CONCLUSION REFERENCES i-xvi APPENDIX xvii-xxii

11 TABLE NO. LIST OF TABLES TITLE PAGE NO Details of cotton (G. hirsutum L.) genotypes used in study. Analysis of variance including expected mean squares. Analysis of variance (Mean Square) for different characters in cotton (G. hirsutum L.). Mean, variability, heritability and genetic advance as per cent of mean for seed cotton yield and its components traits in cotton (Gossypium hirsutum L.). Genotypic (r g ) and phenotypic (r p ) correlation coefficients of seventeen characters in cotton (G. hirsutum L.). Path coefficient analysis showing direct and indirect effects of sixteen characters on seed cotton yield per plant of cotton (G. hirsutum L.). 4.5 Composition of cluster based on D 2 values Intra and inter-cluster distances (D) between 40 genotypes of cotton (G. hirsutum L.). (D = 2 ) Cluster mean for seventeen characters in cotton (G. hirsutum L.). Contribution of different characters towards total divergence in cotton (G. hirsutum L.)

12 Appendix No. LIST OF APPENDICES TITLE Page No. I Meteorological data recorded at Main Cotton Research Station, Surat during the period of experiment. II Mean of 40 genotypes of cotton (G. hirsutum L.) for seventeen characters. xx xxi III ABBREVIATIONS xxii LIST OF FIGURES Fig. No. I II III IV TITLE Genotypic path analysis of cotton (G. hirsutum L). Clustering of genotype by tocher method Intra and Inter-cluster distances (D) between 40 genotypes of cotton (G. hirsutum L). Relative contribution of different characters towards diversity Page No

13 Introduction

14 I. INTRODUCTION Cotton has a proud place among the cash crops from the earliest times. It finds mention in the Rigveda the oldest scripture of the Hindus. Manu, the law giver also referred to it in his Dharma Shastra. It was the excellence of Indian cotton fibres famed as webs of woven wind which compelled European countries to seek new trade routes with India. Since the discovery of the Mohen-jodaro relics the history of cotton and cotton manufacture came to be treated as beginning from times of the ancient Indus valley civilization, which flourished in India about 5000 years ago. Despite the advent of a multitude of other fibres, cotton, white gold rules the world of textile. Even today, it is unchallenged as a natural textile fibre. It is an important fibre and oilseed crop of nearly 100 countries with India, China, United States, Pakistan and Brazil being five of the largest producers of cotton. World cotton production is based on four cultivated species namely Gossypium hirsutum, Gossypium arboreum, Gossypium barbadense and Gossypium herbaceum. It is a premier industrial crop of major cotton growing countries like China, United States, India, Pakistan, Brazil, Turkey, Greece, Australia, Syria and Mali which accounts nearly 90 percent of the total global production (Nation master.com). Among the cotton growing countries of the world, India ranks first in acreage of 105 lakh ha as well as in production of 351 lakh bales of with a productivity of 568 kg/ha (AICRP on Cotton Annual Report ). 1 P a g e

15 Introduction India is the only country where all the four cultivated species being grown commercially. Gujarat, Maharashtra and Telangana are the major cotton growing states contributing around 70% of the area and 67% of cotton production in India. During the year in Gujarat cotton was grown in lakh ha area with production of lakh bales and productivity of 673 kg/ha (AICRP on Cotton Annual Report ). Cotton plays a key role in the national economy in terms of generation of direct and indirect employment of about 60 million people in the agricultural and industrial sectors of cotton production and processing, textile and related exports which accounts for nearly 33 per cent (76,000 crores) of total foreign exchange earnings of our country. Cotton thoug h mainly grown for fibre is also ranked as major oilseed crop in the international market. India holds the distinction of being pioneer in the world for development of cotton hybrids for commercial cultivation. The release of world first commercial intra - hirsutum hybrid H-4 (Patel, 1971) and inter specific hybrid, Varalaxmi (Katarki, 1971) during the seventies was an important milestone in the history of cotton improvement not only in India but also in the world. Since then several hundreds of hybrids have been developed and released for cultivation. Breeding for high productivity is still the primary goal of breeding programms, but improving fibre quality has also become increasingly important. All the changes in spinning technology require unique and often superior fibre 2 P a g e

16 Introduction quality, especially fibre strength for processing. The present day cultivars lack in fibre strength and fail to meet the standards specified by Central Institute for Research on Cotton Technology (CIRCOT). So there is an urgency to develop cultivars with high fibre strength in order to meet the requirements of spinning industry. Beside this, bio chemical paramaters are also important traits as oilseed crop. Knowledge of the nature and magnitude of genetic variance present in the breeding material is the most important pre requisite for successful breeding programme. Mahalanobis s D 2 statistics is an effective tool in quantifying the degree of genetic divergence at genotypic level and provide a seed cotton yield and important yield components measure of association between geographic distribution and genetic diversity based on generalized distance (Mahalanobis, 1928). Multivariate analysis, utilizing Mahalanobis s D 2 statistics has been found to be a potent biometrical tool in quantifying the degree of divergence in germplasm collection of various crop plants (Rao, 1952). D 2 statistic can be used for the choice of parent s combination without making crosses before the initiation of hybridization programme (Bhatt, 1970). An assessment of magnitude of genetic variability in breeding population and its precise characterization enables the breeder to decide intensity and direction of selection pressure to evolve better varieties. There is a general apprehension in cotton breeding as suggested by many cotton researchers that an increase in productivity will always lead to a decline in the fibre strength and vice versa. So, what should be the feature of parents used in hybrid programme so that we 3 P a g e

17 Introduction get hybrids with high productivity and acceptable fibre quality? The focus of attention of this study was to find an answer to this question. Seed cotton yield itself being a complex characters, is dependent on component traits. These traits show different type of association among themselves, knowledge of interrelationship between yields, its components trait is necessary for simultaneously improvement in these characters. Further the relative contribution i.e., both direct and indirect effects of these traits on yield and interrelations and linkage between them can be examined by path coefficient analysis (Wright, 1921). Such analysis will be more useful because sometimes correlation studies alone are misleading. The genetic diversity of different fibre quality parameters has been studied only up to a limited extent. To attain sustainability in quality aspects, access is made for diversity present in the cotton lines both for seed cotton yield and important yield components and fibre quality traits. Existence of genetic diversity is an essential requirement for successful hybridization programme. In order to take the programme of development of hybrid cotton successfully, choice of suitable parent through careful and critical evaluation is of paramount importance. This is because per se performance of parents is not always true indicator of its potential in hybrid combination. An estimate of genetic diversity among parents is an important tool by which a breeder can choose suitable parents for successful hybridization programme. 4 P a g e

18 Introduction Keeping in view the above points, the present investigation will be carried out with the following objectives: 1. To study the variability parameters for various characters among genotypes. 2. To estimate genotypic and phenotypic correlations for yield and yield contributing characters. 3. To analyze path coefficient for assessing the direct and indirect effect of individual characters on yield. 4. To study the genetic divergence among the genotypes and to provide a basis for selection of parents for hybridization. 5 P a g e

19 Review of Literature

20 II. REVIEW OF LITERATURE Cotton belongs to the family Malvaceae and genus Gossypium. Out of the 50 recognized cotton species in the world four are cultivated. Two of them (Gossypium arboreum and G. herbaceum) are diploid with 2n=26 (old world cotton or Asiatic cotton) and remaining two (G. hirsutum and G. barbadense) are tetraploids with 2n=52 (new world cotton or American and Egyptian cotton) and are used for commercial cultivation. The present investigation is concerned with genetic parameters of G. hirsutum. Literature pertaining to genetic diversity and stability are available on different characters. The available literature, especially on this aspect, irrespective of the crop has been reviewed under following headings. 2.1 Genetic variability 2.2 Correlation studies 2.3 Path co-efficient analysis 2.4 Genetic diversity 2.1 Genetic Variability The efficiency of selection in plant breeding largely depends on the extent of genetic variability present in the population. Knowledge of nature and magnitude of genotypic and phenotypic variability present in any crop species plays an important role in evolving superior cultivars. Importance of estimates of genotypic and phenotypic variability in formulating efficient breeding procedures in cotton have been emphasized by Hutchinson (1940) and Miller 6 P a g e

21 Review of literature et al. (1958). They have established that genetic variance has a direct bearing on the prospect of advance in cotton breeding programmes. Balakrishnan (2006) analyzed total phenols and gossypol and he observed that total phenols and gossypol considerably varied among plant parts as well as varieties. The phenolic content showed a range of 7.4 (RCH 2) to 14.1 (K 11), 12.0 (LRA 5166) to 20.4 (K 11) and 8.4 (RCH 2) to 9.6 (K 11) mg/g of fresh sample in leaves, squares and bolls, respectively among the different varieties/ hybrid. The gossypol content was varied from 2.3 to 4.2, 3.4 to 5.0 and 2.4 to 4.4 mg/g of fresh sample in leaves, squares and bolls, respectively. Mishra and Satpute (2007) studied the twelve phenotypically diverse genotypes of diploid cotton (Gossypium arboreum L.). They observed genetic variability for seed oil contributing traits. High phenotypic coefficients of variation for oil content and morphological traits along with high genotypic coefficient of variation and low environmental coefficient of variation for oil content were analyzed. Ashokkumar and Ravikesavan (2010) evaluated eleven genotype for morphological diversity, they concluded that significant variation found for Plant height (cm), number of sympodia per plant, number of bolls per plant, boll weight per plant (g), number of seeds per boll, seed cotton yield per plant (g), ginning outturn (%), lint index (g), seed index (g), 2.5% span length (mm), fibre strength (g/tex), micronaire 7 P a g e

22 Review of literature value (μg/inch), uniformity ratio and fibre elongation (%) in all the genotypes. Aqsa et al. (2012) estimated variability for seed cotton yield with yield related traits among thirty upland cotton (Gossypium hirsutum L.) genotypes. Fuzzy seed weight and non fuzzy seed weight showed lesser genetic variability while cotyledonary leaf area, gossypol glands, emergence %, and seed cotton yield exhibited intermediate range of variability. Sufficient genetic variability was observed only for fuzz weight. Dinakaran et al. (2013) evaluated 32 upland cotton genotypes for genetic variability under both salinity and normal conditions revealed high GCV and genetic gain for number of bolls per plant, boll weight, lint yield per plant, 2.5 per cent span length, leaf area index, Na-K ratio and seed cotton yield. Patel et al. (2013) studied that genetic variability of 122 Bt cotton hybrids of various seed companies for lint yield and its component characters. They reported that all the genotypes showed a wide range of variability for lint yield, days to 50% flowering, number of bolls per plant, number of monopodia, number of sympodia and bolls weight per plant. Vinodhana et al. (2013) studied variability of eight lines and seven testers and their 56 F1s made with the parents of G.hirsutum and G. barbadense genotypes of diverse origin. Estimates of genetic variability revealed that the GCV and PCV were comparatively high for seed cotton yield per plant, number of bolls per plant, plant height and boll weight. 8 P a g e

23 Review of literature Moderate values for GCV and PCV were observed for seed index, lint index, fibre strength, micronaire value and fibre length. The trait ginning percentage was observed for low GCV and PCV values. High heritability value was recorded in seed cotton yield, fibre length, fibre strength, plant height and number of bolls per plant. Low GCV/PCV ratio in number of sympodia per plant, boll weight, ginning percentage, seed index and lint index indicated these characters were highly influenced by environmental factors while high GCV/PCV ratio was recorded for rest of the characters. Dhivya et al. (2014) studied the analysis of variance indicated the presence of significant difference among all the traits in Gossypium hirsutum accessions. The highest phenotypic coefficient of variation (GCV) and genotypic coefficient of variation (GCV) were recorded by seed index, plant height, lint index and boll weight. Genotypic co-efficient of variation had a similar trend as PCV. The lowest PCV and GCV values were observed in days to 50% flowering. High heritability along with high genetic advance was observed in traits viz., number of sympodia per plant, single plant yields, seed index and micronaire value. Pujer et al. (2014) evaluated sixty eight diverse genotypes of American cotton Gossypium hirsutum L. for 13 quantitative and fibre quality traits. The variability studies indicated that high PCV and GCV was observed in case of seed cotton yield per plant and number of bolls per plant while moderate PCV and GCV was observed in case of days to first flower, plant height and boll weight. Low GCV and PCV ratio 9 P a g e

24 Review of literature in number of monopods per plant, seed index, lint index, ginning percentage, 2.5% span length, fibre fineness, bundle strength and uniformity ratio. Seed cotton yield per plant, days to first flower, plant height, number of bolls per plant and boll weight shows high heritability with high genetic advance over mean. Muhammad et al. (2015) observed significant genetic variation for traits like plant height (cm), number of bolls per plant, boll weight (g), seed index (g), lint index (%), ginning out turn (%), seed cotton yield per plant (kg) among the accessions of the upland cotton. The highest genotypic (GCV) and phenotypic coefficient of variation (PCV) were exhibited by the number of bolls per plant, lint index and seed cotton yield per plant. High heritability and high genetic advance was observed in the lint index, number of bolls per plant and seed cotton yield per plant. Latif et al. (2015) studied genetic variability in sixty cotton (Gossypium hirsutum L.) genotype and they observed wide spread variability among all investigated traits like plant height, bolls per plant, monopodia per plant, sympodia per plant, boll weight, seed index, ginning out turn and yield per plant. Baloch et al. (2015) studied the analysis of variance exhibited that twenty six tested genotypes performed significantly different for bolls per plant, seed cotton yield per plant, micronaire value, GOT %, lint index and staple length, suggesting that studied materials possess useful genetic 10 P a g e

25 Review of literature resources for variety of traits thus can extensively be used for upcoming breeding programme. Dahiphale et al. (2015) studied genotypic and phenotypic variability for seed cotton yield and its component traits in fifteen diverse genotypes of cotton (Gossypium hirsutum L.). The analysis of variance revealed significant differences among the genotypes for all the characters studied. The high genotypic and phenotypic coefficient of variation observed for lint (kg/ha), seed cotton yield and monopodia per plant. The moderate to least amount of variations were observed for number of bolls per plant, sympodia per plant, GOT %, seed index, fibre length, plant height, days to first flowering and plant stand. 2.2 Correlation studies Ashokkumar and Ravikesavan (2010) studied correlation using four lines, seven testers and their 28 made with parents F 1 s of upland cotton (Gossypium hirsutum L.) revealed that seed cotton yield has positive significant correlation with days to fifty percent flowering, number o f sympodia per plant, number of bolls per plant, boll weight, number of seed per boll, ginning outturn, lint index, seed index, and micronaire. In parents and hybrids, seed oil had negative correlation with seed cotton yield and days to first flowering. Seed oil had positive correlation with number of sympodia per plant, boll weight, number of seeds per boll, lint index, seed index and 2.5 per cent span length. Thiyagu et al. (2010) studies correlation with parents, F 1 s of fifteen (lines) four (testers) and one check 11 P a g e

26 Review of literature hybrid (TCHB 213) in interspecific crosses of cotton (Gossypium spp). Seed cotton yield was significant and positively correlated with seven traits namely number of bolls per plant followed by number of sympodial branches per plant, plant height, 2.5 per cent span length, bundle strength, seed index and elongation percentage. Following these characters, lint index and boll weight recorded positive correlation with yield. Fibre quality traits like 2.5 per cent span length, fibre fineness and elongation percentage had positively significant inter correlation with plant height, number of sympodial branches per plant and number of bolls per plant. Dinakaran et al. (2013) studied correlation with 32 upland cotton genotypes revealed that the significant positive correlations exists between Bartlett's rate index with uniformity ratio, 2.5 per cent span length with bundle strength, uniformity ratio with micronaire and elongation percent, specific leaf area with leaf area index. Patel et al. (2013) studied genotypic and phenotypic correlations of 122 Bt cotton hybrids of various seed companies for lint yield and its component characters. Lint yield had significant and positive correlation with number of bolls per plant and number of monopodia as well as sympodia per plant at both genotypic and phenotypic levels. They observed positive correlation of number of sympodia per plant, number of bolls per plant and ginning percentage with seed cotton yield at both genotypic and phenotypic levels. Though days to 50% flowering had non significant correlation 12 P a g e

27 Review of literature with lint yield, but it showed significant and positive genotypic and phenotypic correlations with number of bolls per plant and number of monopodia per plant. Number of bolls per plant had significant and positive association with both number of monopodia and sympodia per plant. Vinodhana et al. (2013) studied genotypic and phenotypic correlations of G.hirsutum and G. barbadense genotypes of diverse origin. Correlation studies revealed that seed cotton yield had positive significant correlation with number of bolls per plant and fibre length. Number of bolls per plant had significant positive association with plant height and fibre length. The positive significant correlation was observed for seed index, lint index and micronaire value with boll weight at genotypic and phenotypic level. Farooq et al. (2014) reported genotypic, phenotypic correlation coefficients between seed cotton yield, earliness, fibre and yield contributing traits in fifty three cotton cultivars. He observed seed cotton yield have positive genotypic correlation with bolls per plant, plant height, boll weight, staple length and strength, earliness index and GOT%. Pradeep et al. (2014) studied correlation for yield and component traits with 60 cotton (Gossypium hirsutum L.) genotype. The character association studies revealed that seed cotton yield per plant had positive significant association with plant height, number of monopodia per plant, number of sympodia per plant, number of bolls per plant, boll weight, ginning out turn, seed index, lint index, and lint yield per 13 P a g e

28 Review of literature plant suggesting that these were the major yield contributing traits. Pujer et al. (2014) evaluated sixty eight diverse genotypes of American cotton Gossypium hirsutum L. for genotypic and phenotypic correlations. The correlation study revealed that seed cotton yield was found to be positively and significantly correlated with traits like days to first flower, plant height, number of monopodial branches, number of bolls per plant, seed index, lint index, ginning out turn, and uniformity ratio, whereas it had negative association with boll weight, 2.5% span length, fibre fineness, and bundle strength. Asha et al. (2015) studied correlation for 15 characters in 40 genotypes of upland cotton. Correlation studies indicated that plant height, sympodia and bolls per plant, boll weight, bundle strength and fibre elongation recorded significant positive association with seed cotton yield per plant. Dahiphale et al. (2015) studied genotypic and phenotypic correlations for seed cotton yield and its component traits in fifteen diverse genotypes of cotton (Gossypium hirsutum L.). Seed cotton yield was found significant positively associated with monopodia per plant, sympodia per plant, GOT %, lint kg per ha and fibre length at genotypic level. However, plant height and seed index showed negative correlation. Among yield components, plant stand registered positive and significant association with fibre length at genotypic level. Similarly, days to 50% flowering revealed negative and significant association with plant height 14 P a g e

29 Review of literature at genotypic level. Plant height revealed positive and significant correlations with GOT% and number of bolls per plant at genotypic level. Monopodia per plant had significant and positive association with sympodia per plant at genotypic level and lint kg /ha at both levels. Conversely they GOT % had positive and significant association with lint kg per ha and fibre length at genotypic and phenotypic levels, while the character lint kg per ha had positive and significant association with fibre length and negative correlation with seed index at both levels. Number of bolls per plant and fibre length showed negative and significant correlations with seed index at genotypic level. The phenotypic correlation coefficients for monopodia per plant, lint kg per ha and fibre length had revealed positive and significant association with seed cotton yield per plant. Mandhania et al. (2015) studied nutritional quality constituents relationship in seventeen desi (Gossypium arboreum) cotton genotype. The results showed that oil content exhibited a significant positive correlation with sugar and gossypol, an anti nutrient factor. Protein showed non significant positive correlation with sugar and oil content. An inverse non significant correlation was found between protein and gossypol. Padmavathi et al. (2015) studied correlation for 16 characters in 60 genotypes of upland cotton. Plant height, monopodia per plant, sympodia per plant, bolls per plant, boll weight, seed index, lint index and lint yield per plant recorded 15 P a g e

30 Review of literature significant positive association with seed cotton yield per plant. An experiment was conducted by Reddy et al. (2015) on correlation for yield and yield contributing characters in upland cotton with sixty three genotypes of cotton for seventeen characters. Seed cotton yield per plant was significantly and positively correlated with number of monopodia per plant, number of bolls per plant, boll weight, 2.5% span length and lint yield per plant at phenotypic level, where as with number of monopodia per plant, number of per bolls plant, boll weight, 2.5% span length and lint yield per plant at genotypic level. 2.3 Path co-efficient analysis Rauf et al. (2004) studied path coefficient analysis of yield components on five cotton germplasm lines and their twenty F 1 crosses of cotton (Gossypium hirsutum L.). They observed that number of bolls per plant had maximum positive direct effect on seed cotton yield per plant followed by boll weight, whereas, internodal length had maximum negative direct effect on seed cotton yield. Ashokkumar and Ravikesavan (2010) studied path coefficient analysis using four lines, seven testers and their twenty eight made with parents F 1 s of upland cotton (Gossypium hirsutum L.) revealed that boll weight, number of sympodia per plant, lint index, and number of seeds per boll, uniformity ratio and micronaire exerted high and positive direct effect on seed cotton yield. Indirect effects of days to first flowering influenced the seed cotton yield positively 16 P a g e

31 Review of literature through fifty per cent flowering, seed index, micronaire and seed protein. Days to fifty per cent flowering influenced seed cotton yields indirectly through days to first flowering, plant height, number of sympodia, number of seeds per boll, ginning outturn, bundle strength, and seed oil. Number of bolls per plant exerted positive effects on seed cotton yield through fifty per cent flowering, number of sympodia per plant, number of monopodia per plant, boll weight, number of seeds per boll, ginning outturn, lint index, seed index and bundle strength. The direct effects of seed cotton yield was influenced in negative direction by days to first flower, number of monopodia, uniformity ratio, micronaire, and elongation percentage and seed oil. Thiyagu et al. (2010) studies on path coefficient analysis with parents, F 1 s of fifteen (lines) four (testers) and one check hybrid (TCHB 213) in interspecific crosses of cotton (Gossypium spp). This study revealed very high positive direct effect for number of bolls per plant (1.030) and high positive effect for boll weight (0.411) on seed cotton yield. The characters 2.5 per cent span length, number of sympodial branches per plant, ginning percentage, seed index, fibre fineness, uniformity ratio, days to 50 per cent flowering and elongation percentage recorded positive effect on seed cotton yield per plant. The high indirect positive effect on seed cotton yield per plant was noticed by plant height, number of sympodial branches per plant, 2.5 per cent span length, bundle strength, elongation percentage through number 17 P a g e

32 Review of literature of bolls per plant followed by lint index and seed index via boll weight. Dinakaran et al. (2013) studied path analysis with 32 upland cotton (G. hirsutum L.) genotypes revealed that the seed cotton yield was highly influenced by lint yield per plant in both normal and saline alkaline condition. The characters boll weight, ginning out turn, 2.5% span length and uniformity ratio registered high order negative direct effect on seed cotton yield. Vinodhana et al. (2013) studied path coefficient analysis in eight lines and seven testers and their fifty six F 1 s made with the parents of G. hirsutum and G. barbadense genotypes of diverse origin. Path coefficient analysis revealed that number of bolls per plant, number of sympodia per plant, boll weight, seed index, lint index exerted high and positive direct effect on seed cotton yield. Indirect effects of plant height influenced the seed cotton yield through number of bolls per plant, boll weight, ginning percentage, seed index, micronaire value and lint index. Number of sympodia per plant influenced the seed cotton yield indirectly through number of bolls, ginning percentage, seed index, lint index and micronaire value. The indirect effect of number of bolls per plant was positive through number of sympodia, number of bolls, ginning percentage and micronaire value. Boll weight exerted positive effects on seed cotton yield through seed index, lint index, fibre length, ginning percentage and fibre strength. Seed index influenced the seed cotton yield through 18 P a g e

33 Review of literature number of sympodia, boll weight, seed index, fibre length and fibre strength. Pradeep et al. (2014) studied path coefficient for yield and component traits with 60 cotton (Gossypium hirsutum L.) genotype. Lint yield per plant exerted strong direct positive effect on seed cotton yield per plant signifying the importance of this trait while selecting for improvement of seed cotton yield of cotton Pujer et al. (2014) evaluated sixty eight diverse genotypes of American cotton Gossypium hirsutum L. for path coefficient analysis. This study revealed that days to first flower, number of monopods per plants, number of bolls per plant, boll weight, seed index, lint index, ginning out t urn and uniformity ratio exerted high and positive direct effect on seed cotton yield. Indirect effects of plant height influenced the seed cotton yield through lint index, boll weight, ginning out turn, bundle strength, number of bolls per plant, number of monopods per plant and seed index. The indirect effect of uniformity ratio was positive through fibre fineness, number of monopods per plant and days to first flower. Fibre fineness influenced the seed cotton yield indirectly through number of bolls per plant, boll weight and lint index. 2.5% span length and bundle strength influenced the seed cotton yield positively through days to first flower. Asha et al. (2015) studied path coefficient analysis for 15 characters in 40 genotypes of upland cotton. Days to 50 per cent flowering, bolls per plant and boll weight had positive direct effect on seed cotton yield per plant. 19 P a g e

34 Review of literature Dahiphale et al. (2015) studied path coefficient for assessing the direct and indirect effect of individual characters on yield in fifteen diverse genotypes of cotton (Gossypium hirsutum L.). Lint kg/ha exhibited the highest magnitude of direct effects on seed cotton yield, followed by fibre length, plant height, number of bolls per plant and sympodia per plant. Latif et al. (2015) studied direct and indirect effects of yield with other attributes in cotton (Gossypium hirsutum L.) using path coefficient analysis on sixty cotton genotype. Path analysis depicted that bolls per plant exhibited highest positive and undeviating effect on cotton yi eld. While other attributes depicted low direct effects on yield. Negative direct effect was observed for plant height and seed index on yield. Sympodia per plant and boll weight depicted maximum indirect positive effect on seed yield via number of bolls p er plant. In this study monopodial branches put undeviating and highly positive effect on yield via number of bolls per plant. In our investigation ginning out turn illustrated good positive indirect effect on yield via bolls per plant. Padmavathi et al. (2015) studied path coefficient analysis for 16 characters in 60 genotypes of upland cotton. Sympodia per plant, bolls per plant, boll weight, seed index and lint yield per plant had positive direct effect on seed cotton yield per plant. 2.4 Genetic diversity Kumar et al. (2000) used D 2 statistic to assess genetic divergence among 43 genotypes. Nine fibre quality 20 P a g e

35 Review of literature characters were studied to group them into five clusters. Cluster I has the largest with 39 genotypes. The characters, yellowness followed by fibre length, fineness & elongation contributed maximum towards divergence Pushpam et al. (2004) reported that yield and number of bolls followed by micronaire value contributed more towards the expression of genetic divergence in G. hirsutum and also revealed the absence of any parallelism between genetic divergence and geographic diversity of the genotypes. Sambamurthy et al. (2004) reported the nature and magnitude of genetic diversity in 135 G. herbaceum lines for seed cotton yield and its component characters. Sambamurthy et al. (2005) grouped 37 arboreum genotypes into nine clusters based on D 2 statistics and reported that number of bolls per plant contributed maximum towards divergence followed by micronaire and boll weight. Chovatia et al. (2006) used D 2 statistic to assess 38 hybrids of upland cotton and grouped into seven clusters. Thus, distribution of different hybrids in different clusters indicated that large amount of variability was generated due to development of different hybrids and materials possessed considerable diversity within and between groups which could be exploited in further breeding programmes of development of complex clusters. Guang and Xiong Ming (2006) assessed genetic diversity for agronomic and quality characteristics in 43 ba sal germplasms of upland cotton in China and reported very 21 P a g e

36 Review of literature significant differences among germplasm with genetic diversity index of Gumber et al. (2006) evaluated twenty five hybrids of Gossypium hirsutum for genetic divergence using Tocher s method. The genotypes were grouped in to eleven clusters. Cluster I was the largest with ten genotypes. Pathak et al. (2007) assessed genetic divergence among 30 elite lines of cotton using D 2 statistic. Grouping of genotypes was independent of geographical origin of the genotypes. American cotton genotype formed separate clusters from that of arboreum cotton genotypes and suggested the need for broadening the genetic base of arboreum cotton. Gopinath et al. (2009) sixty cotton genotypes were evaluated for genetic divergence using Mahalanobis D 2 statistics. Nine characters boll number, boll weight, seed index, lint index, ginning per cent, 2.5 per cent span length, micronaire, fibre strength and seed cotton yield were evaluated for their contribution to total divergence. These 60 genotypes were grouped into eight clusters. The characters, boll weight, boll number and 2.5 per cent length contributed maximum towards total divergence. Genotypes in cluster V and VI could be utilized in the breeding programme for improvement. Kulkarni et al. (2011) studied genetic divergence in twenty nine upland cotton genotypes for 19 yield attributes and quality characters. The pattern of grouping of genotypes revealed that the genetic diversity is not fully related to the geographical diversity. The inter-cluster distances were found 22 P a g e

37 Review of literature to be greater than intra-cluster distances, revealing considerable amount of genetic diversity among genotype studied. The hybridization programme with the selected genotypes by considering inter-cluster distances may produce high magnitude of heterosis or desirable segregants. XianTao et al. (2011) studied 38 self-bred upland cotton varieties for genetic diversity index of fruit branches was highest, followed by micronaire value, seed weight and boll number of each plant and the span length had lowest value. The whole growth and development period was shortened, fruit branch gradually increased, and cotyledon node height and plant height increased. The yields of varieties in different years changed markedly. The boll weight increased significantly. Seed weight increased trend with boll weight variation. Fibre strength gradually increased. Micronaire value appeared to decrease. Clustering analysis showed that these varieties were classified into two groups. The first group included 30 varieties, and the second one included 8 varieties. The first group was further divided into two sub-groups, with the first sub-group including 16 varieties, and the second 14 varieties. Varieties with similar phenotypic characteristics, similar genetic backgrounds, the same breeding units and the same species were clustered together better. Clustering results were more consistent with the true characteristics of their own species and trend of evolutionary genetic background. Kavithamani et al. (2013) studied genetic divergence in forty eight genotypes of G. barbadense these 23 P a g e

38 Review of literature genotypes groups in to thirteen clusters. The grouping of genotypes into different clusters was independent of their geographical origin. The distribution indicated that the geographical diversity and genetic diversity were not related and there were other forces responsible for diversity. The intra and inter cluster distances revealed that inter cluster distance values were greater than intra cluster distance values. Haritha et al. (2014) studied genetic diversity of 40 genotypes of upland cotton using Mahalanobis D 2 statistic for 21 characters which indicated considerable diversity in the material. The 40 genotypes were grouped into 8 clusters. Nine characters viz., crop growth rate at DAS followed by bolls per plant, boll weight, leaf area index at 120 DAS, seed cotton yield per plant, specific leaf weight, days to 50 per cent flowering, sympodia per plant, plant height and uniformity ratio contributed maximum towards genetic divergence. The mutual relationships between the clusters revealed that inter cluster distance values were greater than intra cluster values. The maximum inter cluster distance observed between clusters IV and VI. Rajeev et al. (2014) studied genetic diversity analysis in cotton (Gossypium hirsutum L.) based on morphological traits and microsatellite markers. All the 156 genotypes were grouped into ten clusters based on D 2 values by following Tocher s method. The D 2 values ranged from 8.00 to among the large collection of hirsutum lines, the highest D 2 values was noticed between KH 134 (robust plant type) and SC (compact plant type) and lowest D 2 24 P a g e

39 Review of literature values was noticed between R-14 (102) and DH-348 (intra plant types). Ranjan et al. (2014) studied on genetic divergence in Gossypium arboretum L. On the basis of D 2 values, sixty genotypes were grouped into eight clusters containing one to fourteen genotypes. These clusters consisted of genotypes with different geographical origins and indicated no correlation between genetic divergence and geographical divergence. The genotypes of Cluster VIII showed maximum genetic divergence with Cluster I and cluster V. The genotypes belonging to cluster VIII and cluster I may be selected for hybridization for generating genetic variability. Cluster VI having six genotypes was found to be best performing for agronomic characters followed by cluster VIII with one genotype and cluster V with eight genotypes. Thus to generate desirable genetic variability the crossing between cluster VI, VIII and V genotypes would be useful. It is suggested that hybridization among the genotypes of above said clusters would produce segregants for more than one economic character which can serve as parents of hybrids. Days to first flower followed by seed cotton yield per plant, number of monopods and plant height contribute maximum toward divergence. Shakeel et al. (2015) studied the genetic divergence among fifty upland cotton genotypes. In this study cluster -I consisted of two sub clusters i.e., Ia and Ib, respectively. Sub cluster Ia further portioned into Ic and Id and sub cluster Ic consisted of fifteen genotypes whereas sub-cluster Id 25 P a g e

40 Review of literature exhibited twelve genotypes and sub cluster Ib consisted of five genotypes, whereas Cluster-II is partitioned into two subclusters IIa, IIb. Sub-cluster IIa composed of eleven cotton genotypes whereas sub-cluster IIb consists of five genotypes. Cluster III was composed of two genotypes. Accessions DPL-6 and QUALANDARI showed 92.02% similarity in sub-cluster IIa whereas, genotypes COKER-307 and FH-113 showed 87.15% similarity. The BH had 34% level of similarity with both COKER-307 and FH-113 in sub cluster Ib. Similarly in sub cluster Ia, FH-87 and LRA-5166 showed 86.68% similarity with other varieties i.e. STONEVILLE-731, VH-61, VH P a g e

41 Materials and Methods

42 III. MATERIALS AND METHODS 3.1 Location and climatic conditions The present investigation was carried out at the Main Cotton Research Station, Surat (Gujarat) of Navsari Agricultural University during This station falls in the tropical zone characterized by fairly hot summer, moderately cold winter and humid monsoon. Geographically it is situated at 20 o - 12 N latitude 72 o - 52 E longitude and at altitude of 12 meters above mean sea level. The meteorological data for the cropping season are presented in Appendix-I. 3.2 Experimental materials The experimental material for present investigation consisted of forty genotypes of Cotton (G. hirsutum L.) obtained from the Main Cotton Research Station, Navsari Agricultural University, Surat, Gujarat. The details of genotypes used are listed in Table Experimental details The experiment consisting of forty entries was laid out in randomized block design with three replications. Each genotype was planted in row to row spacing of 120 cm. The seeds were dibbled in rows at a distance of 45 cm. The experiment was conducted at Main Cotton Research Station, Navsari Agricultural University, Surat. Appropriate and uniform agronomical operations and timely plant protection measures were followed to raise healthy crop. 27 P a g e

43 Materials and methods Table-3.1 Details of the cotton genotypes (All the genotypes were obtain from Main Cotton Research Station, Surat) 1) 134-CO 2 -M ) Girja 2) K 22) GS 34 3) 23F 23) GS-M-3 4) 68-4-B 24) H 144 5) Acala ) IAN ) BC ) IAN ) BC-68 27) ISC 77 8) BP 52 28) JLH 59 9) BTE-22 29) JR-5 10) C ) K-3259-EC ) Co American ) K ) DHY ) KOP ) DS 28 33) KOP 8 14) EC ) Ibadem allon 15) EC ) LL54 16) EC ) MUCU-2 17) EC ) NH ) EL-174-C 38) PD ) G 3637 IP 39) RG 1 20) G.Cot-12 40) SDN P a g e

44 Materials and methods 3.4 Characters studied Five random competitive plants were chosen from each plot in each replication to record data on following characters Days to 50% flowering The numbers of days from date of sowing to appearance of flower on 50 per cent plants in a plot were recorded Number of sympodia per plant Total numbers of sympodial branches (fruiting branch) on the main stem were counted when the plants were fully matured Number of bolls per plant The fully matured and open bolls on each of the observational plant which had yielded seed cotton were counted and averaged as the number of bolls per plant Plant height (cm) The observation was taken at the time of maturity of the crop. The measurement was taken from the cotyledonary node to the apex of the main stem Boll weight (g) Twenty fully matured healthy and open bolls from each of the plots were collected randomly, picked, weighed, averaged and recorded as average boll weight in gram. 29 P a g e

45 Materials and methods Seed index (g) The weight of the 100 matured healthy seeds in gram obtained from each of the individual selected plants were recorded as Seed Index and averaged over all the plants Seed cotton yield per plant (g) The seed cotton yield obtained from each of the randomly selected plant was recorded in gram separately and averaged Ginning percentage (%) The random samples of approximately 300 g of seed cotton from five selected plants were used to calculate the ginning percentage. The sample was ginned on the electrically operated laboratory model gin designed by Central Institute for Research on Cotton Technology (CIRCOT), Mumbai. Ginning percentage or ginning outturn is the ratio of weight of lint to that of seed cotton expressed as percentage. It is calculated as follows: Ginning Percentage (G. P. ) = Testing of fibre characteristics Weight of lint Weight of seed cotton 100 All the major fibre characteristics were tested with HFT 9000 system. In the HFT 9000 system, various conventional instruments are integrated into a single compact operating system by using the state-of-the art technology in optics, mechanics and electronics. The HFT 9000 system provides for accurate and precise measurement of fibre length, 30 P a g e

46 Materials and methods length uniformity, strength at 3.2 mm gauge, micronaire, maturity and short fibre index per cent span length (mm) Span length is a new concept of length parameter. This term can be defined as the distance spanned by a specified percentage of fibres in the specimen being tested when the fibres are parallelized and randomly distributed. The most commonly used measure is 2.5 per cent span length, which corresponds well with the American Classers staple length Fibre strength (g/tex) Fibre strength was measured at 3.2 mm guage on the stelometer. It was calculated by following formula: Tenacity (g/tex) = Breaking load of bundle in kg x 11.8 Weight of bundle in mg Fibre fineness (mv) The micronaire value indicates the extent of resistance of flow by fibre plugs. It is expressed as micrograms per inch (i.e g/inch). The higher value of micronaire (above 4.5 mv) indicates coarseness of fibre, while (below 3.5 mv) lower indicates finer fibres. Biochemical parameters Oil percentage (%) The oil content was determined by non-destructive 31 P a g e

47 Materials and methods method using Newport Magnetic Resonance (NMR) technique at Main Cotton Research Station, NAU, Surat Gossypol content (%) The gossypol extraction and estimation elucidated by the method of Bell. Gossypol in the sample was estimated at 550 nm using Gossypol acetate as standard and expressed amount of gossypol in mg per g and then converted to percent (%) Phenol content (%) The phenol extraction and estimation elucidated by the method of Malick and Singh (1980). Phenol in the sample was estimated at 650 nm using Catechol as standard and expressed amount of phenol mg per g and then converted to percent (%) Leaves Protein content (%) The protein extraction and estimation elucidated by the method of Lowry et al. (1951). Protein in the sample was estimated at 660 nm using bovine serum albumin as standard and expressed amount of protein mg per g and then converted percent (%) Seed Protein content (%) The protein extraction and estimation elucidated by the method of Lowry et al. (1951). Protein in the sample was estimated at 660 nm using bovine serum albumin as standard and expressed amount of protein mg per g and then converted percent (%). 32 P a g e

48 Materials and methods Reducing Sugar (%) The Reducing sugar extraction and estimation elucidated by the method of Somogyi (1952) and Miller (1959). Reducing sugar in the sample was estimated at 510 nm using D-Glucose as standard and expressed amount of reducing sugar mg per g and then converted to percent (%). 3.5 Statistical Analysis The mean values based on five randomly selected plants formed the basis for statistical analysis. The experimental data pertaining to various characters were subjected to suitable statistical analysis. The analysis was carried out at Department of Agricultural Statistics, N. M. College of Agriculture, Navsari Agricultural University, Navsari. Under five broad categories Analysis of variance for the experimental design Estimation of variance components Correlation between different characters Path coefficient analysis Genetic Divergence Analysis of variance for experimental design The analysis of variance proposed by Snedecor and Cochran (1989) was followed to test the significance of differences between the genotypes for all the characters. The form of analysis of variance as given in Table 3.2 provides comparison by partitioning of variance due to various sources and the statistical model was, yij= μ+ r i + t j + e ij 33 P a g e

49 Materials and methods Where, y ij = an observation of j th genotype in i th replication. μ = General mean r i = The effect of i th replication t j = The effect of j th genotype e ij = Uncontrolled random error associated with j th genotype in i th replication. Table 3.2 Analysis of variance including expected mean squares Source of Degree of Mean Expected mean Variation Freedom squares squares (EMS) Replications (r-1) Mr σe 2 + g σr 2 Genotypes (g-1) Mg σe 2 + r σg 2 Error (r-1) (g-1) Me σe 2 Total (rg-1) Where, r and g are number of replications and number of genotypes, respectively; σe 2, σr 2 and σg 2 are variance due to error, replication and genotype, respectively; and Mr, Mg and Me are the mean sum of squares of replication, genotype and error, respectively. Significance of replication mean squares and genotype mean squares were tested against error mean squares. The standard error of mean, critical differences and coefficient of variation were estimated as stated below 34 P a g e

50 Materials and methods S.Em. = Me r CD at 5 % = S.Em. x 2 x t at error d.f. (P= 0.05) Me CV % = ( ) 100 General mean Variance components The mean squares due to genotypes and error were used for calculation of variance components by manipulation of expected mean squares as shown below Estimate of genotypic, phenotypic and environmental variances Genotypic, phenotypic and environmental variances were calculated as suggested by Johnson et al. (1955). (a) Genotypic variance ( ˆ 2 g Mg - Me ) = r (b) Environmental variance ( ˆ 2 e ) = Me (c) Phenotypic variance ( ˆ 2 p ) = ˆ 2 g Genotypic coefficient of variation (GCV %) + ˆ 2 e Genotypic coefficient of variation was computed using the following formula given by Burton (1952). GCV (%) 2 ˆ g X x 100 Where, X = General mean of the character under study 35 P a g e

51 Materials and methods Phenotypic coefficient of variation (PCV %) Phenotypic coefficient of variation was computed using the following formula given by Burton (1952). PCV (%) 2 ˆ p X x 100 X = General mean of the character under study GCV and PCV values were categorized as low, moderate and high value as suggested by Shivasubramanian and Menon (1973), which is as follows. 0 10% - Low 10 20% - Moderate 20% and above - High Heritability in broad sense (H 2 ) It is the proportion of phenotypic variability that is due to genetic reasons. It was computed in per cent using the formula given by Allard (1960). 2 2 ˆ g H (%) 2 ˆ p x 100 The heritability percentage was categorized as low, medium, high and very high as given by Searle (1965). Below 40% - Low 40 60% - Moderate % - High Above 80 % - Very high 36 P a g e

52 Materials and methods Genetic advance as per cent of mean The expected genetic advance (GA) was calculated for each character by adopting the procedure as suggested by Allard (1960). GA = K 2 ˆ g 2 ˆ p σ p Where, K = Standardized selection differential (K = 2.06 at 5% selection intensity) 2 ˆ g 2 ˆ p = Heritability in broad sense σ p = Phenotypic standard deviation The genetic advance expressed as per cent of mean was estimated as follows GA (%of mean) GA X x Correlation coefficient The form of analysis used for estimating covariance components between different pairs of observations was the same as that for analysis of variance, except that the sum of square and mean square were replaced by sum of products and mean products, respectively. The following 37 P a g e

53 Materials and methods estimates of co-variance were worked out as per Galton (1889). ( ˆ p i p j = ˆ g i g j + ˆ e i e j ) ˆ g i g j = Genotypic covariance between i th and j th character. ˆ p i p j = Phenotypic covariance between i th and j th character ˆ e i e j = Environmental covariance between i th and j th character The estimates of co-variance and variance were utilized in computing genotypic and phenotypic correlation coefficient Genotypic correlation coefficient (r gigj) The genotypic correlation is chiefly caused by pleiotropy and linkage action of gene and r gigj was estimated as suggested by Hazel et al. (1943). r gigj ˆ ˆ gigj 2 gi x ˆ 2 gj Where, r gigj = Genotypic correlation coefficient between i th and j th character. 2 ˆ gi = Genotypic variances of i th character ˆ 2 gj = Genotypic variances of j th character 38 P a g e

54 Materials and methods Phenotypic correlation coefficient (r pipj) The genetic and environmental causes of correlation combine together to give phenotypic correlation coefficient and r pipj were estimated with help of following formula. r pipj ˆ ˆ pipj 2 pi x ˆ 2 pj Where, r pipj = Phenotypic correlation coefficient between i th and j th character, ˆ 2 pi and 2 ˆ pj respectively. = Phenotypic variances of i th and j th character, The genotypic and phenotypic correlation coefficients were tested against standardized tabulated significant values of r with (g-2) degrees of freedom as per the procedure suggested by Fisher and Yates (1963) Path coefficient analysis The cause and effect, interrelationship between two variables cannot be estimated from simple correlation coefficient analysis. Therefore, the path analysis suggested by Wright (1921) and applied in plant selection by Dewey and Lu (1959) was followed in order to partition genotypic correlation 39 P a g e

55 Materials and methods of different variables with grain yield into direct and indirect effects of these variables on yield. The path coefficients were obtained by solving simultaneous equations which represent the basic relationship between correlation and path coefficient. r ny = p ny + r n2 p 2y + r n3 p 3y + + r nx p xy Where, r ny = correlation coefficient between one causal factor and dependent character y i.e. yield. p ny = path coefficient between the characters and yield. r n2, r n3..r nx = represents the correlation coefficient between those characters and each other yield component in turn The above equation was written in a matrix form as: Matrix-A Matrix-B Matrix-C r 1 y r 11 r 12 r p 1 y r 1 n r 2 y r 21 r 22 r p 2 y r 2 n r 3 y = r 31 r 32 r X p 3 y r 3 n : : : :... : : : : : :... : : r n y r n1 r n2 r n1... r nn p n y 40 P a g e

56 Materials and methods or A =B. C Then, B = C -1 x A Where, C 11 C 12 C C 1n C 21 C 22 C C 2n C -1 = C 31 C 32 C C 3n : : :... : : : :... : r n1 r n2 r n1... r nn r 12 = r 21 and so on and r 1y = Correlation between first component character and yield. The technique given by Goulden (1962) was followed for inversion of the C matrix using partitioning method of matrix inversing the following correlation matrix as per Doolittle method given by Steel and Torrie (1960). Path coefficient (P ij ) was obtained as follows: P ij = (C -1 ) x A 41 P a g e

57 Materials and methods Where, (C -1 ) is the inverse of mutual correlation matrix of character. The indirect effect for particular characters through other characters was obtained by multiplication of direct path and particular correlation coefficient between those two characters, respectively. Indirect effect = r ij x p ij Where, I = 1,2,3, n J = 1,2,3.n P ij = P 1y x P 2y x xp ny The residual effect, which represents the variation in yield unaccounted for these associations, was calculated from the following formula: Residual effect (x) = (1-R 2 ) 0.5 R 2 = p 1y r 1y + p 2y r 2y +. +P n y r ny Genetic Divergence D 2 statistics Rao (1952) described the multivariate analysis of 42 P a g e

58 Materials and methods genetic divergence using Mahalanobis s D 2 statistics. Transformation of original means of various characters (X 1 s) to uncorrelated varieties (Y 1 s) was carried out by pivotal condensation as the common dispersion matrix calculated on computer. This made D 2 value as simple sum of squares of difference in transformed values of various characters. The generalized distance between any two populations is defined as. D 2 P = b 1 d 1 + b 2 d b p d p Where, X 1, X 2, X 3.., Xp as a multiple measurements available on -1 each individual d 1 d 2.., dp as x, X 1 X , X 2 X 2.. Xp -1 Xp -2, respectively, is the difference in the mean of two populations. In terms of variance and co-variance, the D 2 value is obtained as follows. D P = W ij (X i X i ) (X j X j ) Where, W ij = is the inverse estimated variance, covariance matrix Determination of clusters No formal rules can be laid down for formation of clusters because a cluster is not a well-defined term. The only criterion appears to be that any two groups belonging to the 43 P a g e

59 Materials and methods same cluster should at least on an average show smaller D 2 than those belonging to two different clusters. There are several methods available of the formation of clusters viz., Tocher s method (Rao, 1952), Ward s minimum variance method (SAS, 1988), Average Linkage method (Anderberg, 1973) and Complete Linkage method (SAS, 1988). After the formation of clusters average intra cluster distance was calculated by using the formula Di n 2 Where, ΣDi 2 = Sum of distance between all possible combinations of the population included in the cluster N = Number of population in a cluster The inter cluster distance was calculated by measuring the distance between cluster I and II, between cluster I and III, between cluster II and III and so on. Likewise, one by one cluster was taken and their distances from other were calculated. For graphical presentation, the square roots of the average intra and inter cluster D 2 values were used to obtain mutual relationship between clusters. 44 P a g e

60 Experimental Results

61 IV. RESULTS The present study on Genetic diversity analysis for seed cotton yield and fiber quality in cotton (G. hirsutum L.) was undertaken to estimate various genetically important parameters which helps in framing an effective breeding programme for making desirable improvement of seed cotton yield in cotton genotypes under study. 4.1 ANALYSIS OF VARIANCE 4.2 ANALYSIS OF VARIABILITY COMPONENTS Mean performance Genotypic, phenotypic and environmental components of variance Genotypic and phenotypic coefficients of variation Estimation of heritability and expected genetic advance 4.3 CORRELATION COEFFICIENTS 4.4 PATH ANALYSIS 4.5 GENETIC DIVERGENCE The results obtained for each of the above aspects are presented in the subsequent paragraph: 4.1 ANALYSIS OF VARAIANCE: Analysis of variance depicting mean squares for different characters studied are presented in Table 4.1. The results revealed that mean squares due to genotypes were highly significant for all the characters studied indicating the presence of considerable genetic variation for different traits among the genotypes under study. 45 P a g e

62 Results 4.2 ANALYSIS OF VARIABILITY COMPONENTS Presence of genetic variability is unambiguously the most important prerequisite for crop improvement programme. The assessment of extent of variation present in the genetic material becomes an essential step to know the magnitude of improvement that can be attained for various characters and to decide the ways to achieve it. The results of variability analysis are presented as under Mean performance and range The mean data for different traits across the genotypes are presented in Table 4.2. The mean performance and range of forty genotypes with respect of seventeen characters is summarized in Appendix-II Days to 50 % flowering The genotype, KOP 8 was found the earlier in days to 50% flowering (44.00 days). IAN 1327 had found as late genotype (66.67 days) for days to 50% flowering Number of sympodia per plant The genotype GS-M-3 had the highest number of sympodia per plant (24.00) which was at par with two genotypes EC (23.00) and GS 34 (22.33) viz., While lowest value showed genotypes NH 239, BP- 52 and 68-4-B (7.00) Number of boll per plant The genotype MUCU-2 had the highest number of boll per plant (45.33) which was at par with genotypes 134- CO2-M (42.67). While lowest value showed genotypes ISC77(9.33). 46 P a g e

63 Results Table 4.1: Analysis of variance (Mean Square) for different characters in cotton Source of Variation d. f. Days to 50% flowering Sympodia per plant Boll per Plant Plant height (cm) Boll weight (g) Seed index Seed cotton yield (g) Ginning percentage (%) Replication Treatment ** ** ** ** ** ** ** ** Error Source of Variation d. f. 2.5% span length (mm) Fibre strength (g/tex) Fibre fineness (mv) Oil percentage (%) Gossypol content (%) Phenol content (%) Leaf protein content (%) Seed protein content (%) Reducing sugar content (%) Replication Treatment ** ** ** ** ** ** ** ** ** Error ** Significant at P =0.01 level 47 P a g e

64 Results Plant height (cm) Out of forty genotypes, genotype LL54 possessed the maximum plant height ( cm) and it was at par with genotypes K-3259-EC-9280 ( cm), JLH 59 ( cm), 23F ( cm), SDN-24 ( cm), EL-174-C ( cm), Girja ( cm), K-3299 ( cm), and BC 761 ( cm). On the other hand, genotype Acala possessed minimum plant height ( cm) Boll weight (g) The genotype ISC 77 had the highest boll weight (5.35 g) which was at par with genotypes K-3299 (5.20 g). While genotypes 23F (3.00 g) showed lowest value for boll weight Seed index (g) The genotype KOP 236 possessed the highest seed index (10.63 g) and it was at par with genotypes like EC (10.43 g), SDN-24 (10.30 g), KOP 8 (10.27 g), K EC-9280 (10.23 g), G.Cot-12 (10.17 g). On the other hand, genotype 68-4-B possessed minimum seed index (6.03 g) Seed cotton yield per plant (g) Out of 40 genotypes of cotton, the genotype MUCU-2 yielded the highest seed cotton yield per plant ( g), which was statistically at par with genotypes 134- CO2-M ( g). Whereas the lowest seed cotton yield per plant (42.67 g) was observed in G 3637 IP Ginning percentage (%) The genotype C 1579 possessed the maximum ginning percentage (39.68 %) and it was at par with genotypes 23F (38.66 %), G 3637 IP (37.21 %), H 144 (36.86 %), 48 P a g e

65 Results MUCU-2 (36.80 %) and 134-CO2-M (36.58 %). On the other hand, genotype SDN-24 possessed minimum ginning percentage (28.80 %) % span length (mm) The genotype EC and ISC 77 had showed highest 2.5% span length (29.40 mm) and it was statistically at par with genotype BTE-22 (28.80 mm) while lowest value found (23.47 mm) in genotype IAN Fibre strength (g/tex) The highest fibre strength was found (29.23 g/tex) in genotype EC and it was statistically at par with genotype DS 28 (29.00 g/tex) while lowest fibre strength found (22.30 g/tex) in genotype IAN Fibre fineness (mv) The highest micronaire value (5.03 mv) was observed for genotype Co American 8-29 and minimum micronaire value (3.07 mv) was observed in genotype BTE Oil percentage (%) The genotype PD 9363 recorded the maximum oil content (18.53 %) followed by genotypes K-3259-EC-9280 (18.33 %), DS 28 (18.28 %), JLH 59 (18.08 %), SDN-24 (18.04 %). On the other hand, genotype G 3637 IP recorded minimum oil content (15.91 %) Gossypol content (%) The genotype SDN-24 recorded the maximum gossypol content (0.68 %) while the genotype BP 52 recorded minimum gossypol content (0.11 %). 49 P a g e

66 Results Phenol content (%) The genotype BC-68 contain the maximum gossypol content (4.05 %) followed by genotype BC 761 (3.55 %), Acala (3.36 %) and Girja (3.33 %). The genotype MUCU-2 recorded minimum gossypol content (0.57 %) Leaves protein content (%) The genotype GS 34 had recorded the maximum leaves protein content (19.46 %) followed by genotype EL- 174-C (18.73 %) and LL54 (18.56). The genotype 68-4-B had recorded minimum leaves protein content (10.39 %) Seed protein content (%) The genotype Co American 8-29 recorded the maximum seed protein content (25.73 %) followed by genotype NH 239 (23.17 %), 134-CO2-M (22.88 %) and PD 9363 (22.60 %). The genotype H 144 recorded minimum seed protein content (12.88 %) Reducing sugar (%) The genotype BC 761 contains the maximum leaves protein content (10.24 %). It was at par with genotype 68-4-B (10.11 %). The genotype Co American 8-29 contains minimum leaves protein content (1.11 %) Genotypic, phenotypic and environmental components of variance The genotypic (σ 2 g) and phenotypic (σ 2 p) components of variance computed for all the traits are presented in Table 4.2 The high values of genotypic and phenotypic variance were observed for plant height ( and ) and seed cotton yield per plant ( and ). 50 P a g e

67 Results The medium value values of genotypic and phenotypic variance were observed for days to 50% flowering (16.50 and 20.30), number of sympodia per plant (18.50 and 21.85) and number of bolls per plant (59.68 and 69.50). The lower estimates of σ 2 g and σ 2 p were exhibited by boll weight (0.31 and 0.37), seed index (1.98 and 2.11), ginning percentage (5.18 and 9.43), 2.5% span length (2.31and 2.49), ), fibre strength (2.74 and 2.97), fibre fineness (0.15 and 0.17), oil percentage (0.26 and 0.26), gossypol content (0.01 and 0.01), phenol content (0.65 and 0.66), leaf protein content (7.10 and 7.23), seed protein content (7.86 and 7.96) and sugar content (8.01 and 8.03). A perusal of the estimation of environmental component of variance in relation to their genotypic counterpart revealed that the estimation of σ 2 g were higher than σ 2 e in all character. The highest magnitude of genotypic variance suggested little influence of environments in the expression of genetic variability. Moreover, there was narrow difference between genotypic and phenotypic component of variance for majority of the traits Genotypic and phenotypic coefficient of variation The estimation of genotypic and phenotypic components of variance described in the previous sect ion reflected the amount of variability present in the population for different traits. However, such estimation cannot be utilized for comparing relative degree of variability for various characters as these estimates are associated with squared unit of measurement for certain characters. Thus, 51 P a g e

68 Results reliable variation can be achieved by estimating genotypic coefficient of variation (GCV%) and phenotypic coefficient of variation (PCV%). The genotypic and phenotypic coefficients of variation for all traits are summarized in Table 4.2. High genotypic coefficient of variation (GCV%) and phenotypic coefficient of variation (PCV%) was observed for number of sympodia per plant (27.98 and 30.41), number of boll per plant (32.46 and 35.03), seed cotton yield per plant (31.86 and 33.78), gossypol content (38.98 and 39.15), phenol content (49.56 and 50.08) and reducing sugar content (67.10 and 67.18). Moderate genotypic coefficient of variation (GCV%) and phenotypic coefficient of variation (PCV%) was observed for boll weight (15.35 and 16.75), seed index (17.19 and 17.76), leaf protein content (19.52 and 19.70) and seed protein content (15.53 and 15.62). Low genotypic coefficient of variation (GCV%) moderate phenotypic coefficient of variation (PCV%) was observed for plant height (8.65 and 10.72). Low genotypic coefficient of variation (GCV%) and phenotypic coefficient of variation (PCV%) was observed for days to 50% flowering (7.17 and 7.95), ginning percentage (6.73 and 9.08), 2.5% span length (5.69 and 5.91), fibre strength (6.44 and 6.71), fibre fineness (9.00 and 9.75) and oil percentage (2.96 and 2.96). 52 P a g e

69 Results Table 4.2 Mean, variability, heritability and genetic advance as per cent of mean for seed cotton yield and its components traits in cotton (Gossypium hirsutum L.). Coefficient of Heritability Genetic S. Range Character Mean σ 2 g σ 2 p σ 2 e Variation (%) Advance (% No. Min Max GCV (%) PCV (%) Broad Sense mean) 1 Days to 50% flowering No. of sympodia per plant No. of bolls per plant Plant height (cm) Boll weight (g) Seed index (g) Seed cotton yield per plant (g) Ginning percentage (%) % span length (mm) Fibre strength (g/tex) Fibre fineness (mv) Oil percentage (%) Gossypol content (%) Phenol content (%) Leaves protein content (%) Seed protein content (%) Reducing sugar content (%) Where, σ 2 g, σ 2 p and σ 2 e are the genotypic, phenotypic and environmental variance, respectively. GCV% and PCV% are genot ypic and phenot ypic coefficient of variation, respectivel y. 53 P a g e

70 Results Estimation of heritability and expected genetic advance (% of mean) The heritability estimation in broad sense and expected genetic advance as percent mean for different characters are presented in Table 4.2. Higher estimates of heritability were observed for oil percentage (99.90%) followed by reducing sugar content (99.80%), gossypol content (99.10%), seed protein content (98.80%), leaves protein content (98.10%), phenol content (98.00%), seed index (93.70%), 2.5% span length (92.80%), fibre strength (92.20%), seed cotton yield per plant (89.00%), number of bolls per plant (85.90 %), fibre fineness (85.30%), number of sympodia per plant (84.70%), boll weight (84.00%) and days to 50% flowering (81.30%). Moderate estimate of heritability were observed for ginning percentage (55.00%). The high estimate of genetic advance (% of mean) was observed for the reducing sugar content (138.06%) followed by phenol content (101.06%), gossypol content (79.95%), number of boll per plant (61.97%), seed cotton yield per plant (61.90%), number of sympodia per plant (53.03%), leaves protein content (39.84%), seed index (34.28%), seed protein content (31.80%), boll weight (28.99%). The moderate estimate of genetic advance (% of mean) was observed for the fibre fineness (17.14%), plant height (14.46%), days to 50% flowering (13.32%), fibre strength (12.74%), 2.5% span length (11.30%), ginning percentage (10.29%), whereas lower genetic advance (% of mean) was observed for the oil percentage (6.08%) 54 P a g e

71 Results The high heritability associated with high genetic advance (% of mean) was observed for reducing sugar content (99.80% and %), gossypol content (99.10% and 79.95%), seed protein content (98.80% and 31.80%), leaves protein content (98.10 % and %), phenol content (98.00% and %), seed index (93.70% and 34.28%), seed cotton yield per plant (89.00% and 61.9%), number of boll per plant (85.90% and 61.97%), fibre fineness (85.3% and 17.14%), number of sympodia per plant (84.7% and 53.03%) and boll weight (84.00% and 28.99%). The high heritability associated with moderate genetic advance (% of mean) was observed for 2.5% span length (92.80% and 11.30%), fibre strength (92.20% and 12.74%), days to 50% flowering (81.30% and 13.32%), plant height (65.50% and 14.46%). The medium heritability associated with moderate genetic advance (% of mean) was found for ginning percentage (55.00% and 10.29%) while high heritability along with low genetic advance (% of mean) was expressed by oil percentage (99.90% and 6.08%). 55 P a g e

72 Results 4.3 CORRELATION COEFFICIENTS: The correlation coefficients between seed cotton yield per plant and its component characters and among themselves were estimated at genotypic and phenotypic levels and presented in Table 4.3. Genotypic (r g ) and phenotypic (r p ) correlations are described as under Days to 50 % flowering The days to 50 % flowering was showed positive and highly significant correlated with seed protein content (r g = ) at genotypic level and positively significant (r p = ) at phenotypic level, whereas negative and highly significant correlated with boll per plant (r g = and r p = ), phenol content (r g = and r p = ) and seed cotton yield per plant (r g = and r p = ) at both genotypic and phenotypic level Number of sympodia per plant The number of sympodia per plant to was observed positive and highly significant correlated with seed index (r g = and r p = ), fibre fineness (r g = and r p = ), oil percentage (r g = and r p = ) at both genotypic and phenotypic level, whereas positive and highly significant correlated with boll weight (r g = ) and seed cotton yield per plant (r g = ) at genotypic level. Negative and highly significant correlated with reducing sugar (r g = and r p = ) at both genotypic and phenotypic level. 56 P a g e

73 Table 4.3 Genotypic (rg) and phenotypic (rp ) correlation coefficients of seventeen character in cotton Character C 50% F SP/P BP/P PH BW SI GP SL FS FF Oil Gos Phenol LP SP RS SP/P BP/P PH BW SI GP SL FS FF Oil Gos Phenol r g r p r g ** r p ** r g r p r g ** ** r p * ** r g ** * ** ** r p ** * ** r g ** ** ** r p * * ** r g ** ** r p ** ** r g * ** ** r p * ** ** r g ** * ** r p ** * r g ** ** * ** ** ** r p ** ** ** ** ** r g ** ** ** ** r p ** ** ** ** r g ** ** ** * * * ** r p * ** ** * * ** ** LP r g * ** r p ** SP r g ** * ** ** * ** r p * ** ** ** ** RS r g ** ** ** ** * ** r p ** ** ** * * ** SCY r g ** ** ** ** r p ** ** ** *, * * S i g n i f i c a n t a t P = l e v e l a n d P = l e v e l W h e r e, 5 0 % D F = D a y s t o 5 0 % f l o w e r i n g, S P / P = N u m b e r o f s y m p o d i a p e r p l a n t, B P / P = N u m b e r of b o l l s p e r p l a n t, P H = P l a n t h e i g h t, B W = B o l l w e i g h t, S I = S e e d i n d e x, G P = G i n n i n g p e r c e n t a g e, S L = 2. 5 % s p a n l e n g t h, F S = F i b r e s t r e n g t h, F F = F i b r e f i n e n e s s, G os = G o s s y p o l c o n t e n t, S P = S e e d p r o t e i n c o n t e n t, L P = L e a v e s p r o t e i n c o n t e n t, R S = R e d u c i n g s u g a r, S C Y = S e e d c ot t o n y i e l d p e r p l a n t 57 P a g e

74 Results Number of boll per plant The number of boll per plant was noticed positive and highly significant correlated with oil percentage (r g = and r p = ) and seed cotton yield per plant (r g = and r p = ) at both genotypic and phenotypic level. Positive and highly significant correlated with ginning percentage ( r g = ) at genotypic level. It was negative and highly significantly correlated with boll weight (r g = and r p = ) at both genotypic and phenotypic level, whereas negative and significant correlation with seed index (r g = ) at genotypic level Plant height The plant height possessed positive and highly significant correlation with seed index (r g = ) and significantly positive correlation with oil percentage ( r g = ) at genotypic level, whereas positive and significant correlation with seed index (r p = ) at phenotypic level Boll weight Highly positive and significant association was seen with seed index (r g = and r p = ) and 2.5% span length (r g = and r p = ) at both genotypic and phenotypic level. Negative and highly significant association was seen with phenol content (r g = and r p = ) and reducing sugar (r g = and r p = ) at both genotypic and phenotypic level. Negative and highly significant association was seen with ginning percentage (r g = ) at genotypic 58 P a g e

75 Results level while negative and significant association was seen with ginning percentage (r p = ) Seed index Seed index exhibited positive and highly significant association with oil percentage (r g = and r p = ) and gossypol content (r g = and r p = ) at both genotypic and phenotypic level. Positive and significant association with fibre strength (r g = and r p = ) at both genotypic and phenotypic level. Negative and highly significant association with ginning percentage (r g = and r p = ), phenol content (r g = and r p = ) and reducing sugar (r g = and r p = ) at both genotypic and phenotypic level Ginning percentage Positive and highly significant association with reducing sugar (r g = ) at genotypic level while positive and significant (r p =0.2028) at phenotypic level. Positive and significant association with leaves protein content (r g = ) at genotypic level, whereas negative and highly significant association with 2.5% span length (r g = and r p = ), fibre strength (r g = and r p = ), oil percentage (r g = and r p = ) and gossypol content (r g = and r p = ) at both genotypic and phenotypic level. Negative and significant association with leaf protein content (r g = ) at genotypic level % span length The trait had positive and highly significant association with fibre strength (r g = and r p = ), 59 P a g e

76 Results whereas Positive and significant association with phenol content (r g = and r p = ) at genotypic level. Negative and highly significant association with gossypol (r g = and r p = ) while negative and significant with fibre fineness (r g = ) at genotypic level Fibre strength Trait had negative and highly significant association with fibre fineness (r g = ) at genotypic level and negative and significant association (r p = ) at phenotypic level Fibre fineness Positive and highly significant association with oil percentage (r g = and r p = ) and seed protein content (r g = and r p = ), whereas negative and significant association with phenol content (r g = and r p = ) at both genotypic and phenotypic level Oil percentage The oil percentage possessed and positive correlation highly significant was observed with gossypol content (r g = and r p = ), seed protein (r g = and r p = ) and seed cotton yield per plant (r g = and r p = ), whereas highly significant and negative correlation with leaves protein content (r g = and r p = ) at both genotypic and phenotypic level. Significant and negative correlation was found with phenol (r g = and r p = ) at both genotypic and phenotypic level. 60 P a g e

77 Results Gossypol content The gossypol content exhibited positive and highly significant association with seed protein content (r g = and r p = ), whereas negative and highly significant correlation observed with phenol content (r g = and r p = ). Negative and significant correlation observed with reducing sugar (r g = and r p = ) at both level Phenol content The phenol content exhibited positive and highly significant association with reducing sugar (r g = and r p = ), whereas highly significant and negative correlation with seed protein content (r g = and r p = ) was seen at both level. There is no significant correlation was observed between leaves protein content, seed protein content and reducing sugar. 4.4 PATH COEFFICENT ANALYSIS: Path coefficient analysis was carried out with the objective of partitioning the genotypic correlation of yield component with seed cotton yield into direct and indirect effects for sixteen variables. The seed cotton yield was regarded as resultant variable while the other traits as causal variable. The estimates of direct and indirect effects of various traits along with their genotypic correlations with yield are presented in Table 4.4. The path coefficient is depicted diagrammatically as shown in figure I. 61 P a g e

78 Results Days to 50% flowering vs seed cotton yield per plant The genotypic correlation between days to 50% flowering and seed cotton yield per plant was found to be negative and highly significant (r g = ). The direct effect of days to 50 % flowering was positive but negligible (0.0096). This character manifested negative indirect effects on seed cotton yield per plant through sympodia per plant ( ), boll per plant ( ), seed index ( ), ginning percentage (0.0017), 2.5% span length ( ), oil percentage ( ), phenol content ( ) and leaves protein content ( ). It also depicted pronounced positive indirect effects on seed cotton yield per plant through plant height (0.0015), boll weight (0.0006), fibre strength (0.0007), fibre fineness (0.0016), gossypol (0.0004) and seed protein content (0.0023) Number of sympodia per plant vs seed cotton yield per plant The genotypic correlation between days to 50% flowering and seed cotton yield per plant was found to be positive and highly significant (r g = ). The direct effect of number of sympodia per plant was positive but negligible (0.0073). This character manifested negligible negative indirect effects on seed cotton yield per plant through days to 50% flowering ( ), ginning percentage ( ), phenol content ( ) and leaves protein content ( ) and reducing sugar content ( ). 62 P a g e

79 Results It also depicted pronounced negligible positive indirect effects on seed cotton yield per plant through number of boll per plant (0.0004), height (0.0011), boll weight (0.0020), seed index (0.0030), 2.5% span length (0.0008), fibre strength (0.0004), fibre fineness (0.0022), oil percentage (0.0019), gossypol (0.0011) and seed protein content (0.0010) Number of boll per plant vs seed cotton yield per plant The genotypic correlation between number of boll per plant and seed cotton yield per plant was found to be positive and highly significant (r g = **). The high positive direct effect of number of sympodia per plant on seed cotton yield (1.0401). This character manifested high negative indirect effects on seed cotton yield per plant through days to 50% flowering ( ) and moderate negative indirect effects boll weight ( ). Whereas low negative indirect effects through seed index ( ), fibre fineness ( ), gossypol ( ). Negligible negative indirect effects through 2.5% span length ( ), fibre strength ( ), seed protein content ( ). It also depicted pronounced high positive indirect effects on seed cotton yield per plant through ginning percentage (0.3266) and moderate positive indirect effects through oil percentage (0.2690). Low positive indirect effects through leaves protein content (0.1503). Negligible positive indirect effects through sympodia per plant (0.0622), plant height (0.0894), phenol content ( ) and reducing sugar content (0.0923).. 63 P a g e

80 Results Plant height vs seed cotton yield per plant The genotypic correlation between plant height and seed cotton yield per plant was found to be positive but non significant (r g = ). The negligible positive direct effect of plant height on seed cotton yield (0.0349). This character manifested negligible negative indirect effects on seed cotton yield per plant through ginning percentage ( ), gossypol ( ), seed protein content ( ). It also depicted pronounced negliglible positive indirect effects on seed cotton yield per plant through days to 50% flowering (0.0055), number of sympodia per plant (0.052), number of boll per plant (0.0030), boll weight (0.0006), seed index (0.0089), 2.5% span length (0.0010), fibre strength (0.0030), fibre fineness (0.0014), oil percentage (0.0063), phenol content (0.0002), leaves protein content (0.0030) and reducing sugar (0.0052) Boll weight vs seed cotton yield per plant The genotypic correlation between boll weight and seed cotton yield per plant was found to be positive and non significant (r g = ). The direct effect of boll weight was positive and high (0.4347). This character manifested low negative indirect effects on seed cotton yield per plant through days to boll per plant ( ), ginning percentage ( ), phenol content ( ), reducing sugar content ( ). Negligible negative indirect effect through fibre strength ( ), gossypol ( ) and leaves protein content ( ). 64 P a g e

81 Results Table 4.4: Path coefficient analysis showing direct and indirect effects of sixteen characters on seed cotton yield per plant of cotton. Residual effect= Characters 50% F SP/P BP/P PH BW SI GP SL FS FF Oil Gos Phenol LP SP RS 50% F SP/P BP/P PH BW SI GP SL FS FF Oil Gos Phenol SP LP RS Correlation with ** ** ** ** SCY *,** Significant at P=0.05 level and P=0.01 level 50% DF= Days to 50% flowering, SP/P= Number of sympodia per plant, BP/P= Number of boll per plant, PH= Plant height, BW=Boll weight, SI= Seed index, GP= Ginning percentage, SL= 2.5% span length, FS= Fibre strength, FF= Fibre fineness, Gos= Gossypol content, SP= Seed protein content, LP= Leaves protein content, RS= Reducing sugar, SCY= Seed cotton yield per plant 65 P a g e

82 Results Figur: I Genotypic path analysis of cotton (G. hirsutum L). 66 P a g e

83 Results It also depicted pronounced low positive indirect effects on seed cotton yield per plant through number of sympodia per plant (0.1219), seed index (0.1210), 2.5% span length (0.1457). Negligible positive and indirect effect through 50% flowering (0.0285), plant height (0.0069), fibre fin eness (0.0220), oil percentage (0.0322) and seed protein content (0.0692) Seed index vs seed cotton yield per plant The genotypic correlation between seed index and seed cotton yield per plant was found to be negative but non significant (r g = ). The direct effect of seed index was negligible and negative on seed cotton yield ( ). This character manifested negligible negative indirect effects on seed cotton yield per plant through number of sympodia per plant ( ), plant height ( ), boll weight ( ), fibre strength ( ), fibre fineness ( ), oil percentage ( ), gossypol ( ) and seed protein content ( ). It also described pronounced negligible positive indirect effects on seed cotton yield per plant through days to 50% flowering (0.0015), number of boll per plant (0.0024), ginning percentage (0.0070), 2.5% span length (0.0012), phenol content (0.0049), leaves protein content (0.0021) and reducing sugar (0.0047) Ginning percentage vs seed cotton yield per plant The genotypic correlation between ginning percentage and seed cotton yield per plant was found positive and non significant (r g = ). The trait showed negative negligible 67 P a g e

84 Results direct effect of ginning percentage on seed cotton yield ( ). This character manifested negligible negative indirect effects on seed cotton yield per plant through number of boll per plant ( ), fibre fineness ( ), phenol content ( ), leaves protein content ( ) and reducing sugar ( ). It also showed pronounced negligible positive indirect effects on seed cotton yield per plant through days to 50% flowering (0.0067), number of sympodia per plant (0.0069), plant height (0.0026), boll weight (0.0126), seed index (0.0204), 2.5% span length (0.0149), fibre strength (0.0211), oil percentage (0.0139), gossypol (0.0154), seed protein content (0.0090) % span length vs seed cotton yield per plant The genotypic correlation between 2.5% span length and seed cotton yield per plant was found to be positive and non significant (r g = ). The direct effect of 2.5% span length was negative but negligible ( ). This character manifested negligible negative indirect effects on seed cotton yield per plant through number of sympodia per plant ( ), plant height ( ), boll weight ( ), fibre strength ( ), oil percentage ( ) and phenol content ( ), It also recorded pronounced negligible positive indirect effects on seed cotton yield per plant through number of days to 50% flowering (0.0021), number of boll per plant (0.0010), seed index (0.0072), ginning percentage (0.0302), fibre fineness (0.0149), gossypol (0.0284), leaves protein content 68 P a g e

85 Results (0.0006), seed protein content (0.0032) and reducing sugar content (0.0025) Fibre strength vs seed cotton yield per plant The genotypic correlation between fibre strength and seed cotton yield per plant was found positive and non significant (r g = ). The direct effect of fibre strength was positive but negligible (0.0444). This character manifested negligible negative indirect effects on seed cotton yield per plant through number of boll per plant ( ), boll weight ( ), ginning percentage ( ), fibre fineness ( ), seed protein content ( ) and reducing sugar content ( ). It also recorded pronounced negligible positive indirect effects on seed cotton yield per plant through number of days to 50% flowering (0.0033), number of sympodia per plant (0.0024), plant height (0.0038), seed index (0.0101), 2.5% span length (0.0210), oil percentage (0.0063), gossypol (0.0034) and phenol content (0.0041) Fibre fineness vs seed cotton yield per plant The genotypic correlation between fibre fineness and seed cotton yield per plant was found negative and non significant (r g = ). The direct effect of fibre fineness was positive but negligible (0.0371). This character manifested negligible negative indirect effects on seed cotton yield per plant through number of boll per plant ( ), 2.5% span length ( ), fibre strength (0.0090), phenol content ( ), leaves protein content ( ) and reducing sugar content ( ). 69 P a g e

86 Results It also observed pronounced negligible positive indirect effects on seed cotton yield per plant through days to 50% flowering (0.0062), number of sympodia per plant (0.0112), plant height (0.0014), boll weight (0.0019), seed index (0.0062), ginning percentage (0.0056), oil percentage (0.0100), gossypol (0.0001), phenol content (0.0041) and seed protein content (0.0121) Oil percentage vs seed cotton yield per plant The genotypic correlation between oil percentage and seed cotton yield per plant was found positive and highly significant (r g = ). The direct effect of oil percentage was positive but negligible (0.0140). This character manifested negligible negative indirect effects on seed cotton yield per plant through days to 50% flowering ( ), ginning percentage ( ), phenol content ( ) and leaves protein content ( ). It also expressed pronounced negligible positive indirect effects on seed cotton yield per plant through number of sympodia per plant (0.0037), number of boll per plant (0.0036), plant height (0.0025), boll weight (0.0010), seed index (0.0060), 2.5% span length (0.0015), fibre strength (0.0020), fibre fineness (0.0038), gossypol (0.0054) and seed protein content (0.0045) Gossypol content vs seed cotton yield per plant Negative and non significant (r g = ) genotypic correlation was recorded between gossypol content and seed cotton yield per plant. The direct effect of gossypol content was negative but negligible ( ). This character manifested 70 P a g e

87 Results negligible negative indirect effects on seed cotton yield per plant through days to 50% flowering ( ), number of sympodia per plant ( ), seed index ( ), fibre strength ( ), oil percentage ( ) and seed protein ( ). It also observed pronounced negligible positive indirect effects on seed cotton yield per plant through number of boll per plant (0.0009), plant height (0.0013), boll weight (0.0003), ginning percentage (0.0033), 2.5% span length (0.0030), phenol content (0.0033), leaves protein content (0.0010) and reducing sugar content (0.0019) Phenol content vs seed cotton yield per plant The genotypic correlation between phenol content and seed cotton yield per plant was found to be negative and non significant (r g = ). The direct effect of phenol content was positive but negligible (0.0327). This character manifested negligible negative indirect effects on seed cotton yield per plant through days to 50% flowering ( ), number of sympodia per plant ( ), boll weight ( ), seed index ( ), fibre fineness ( ), oil percentage ( ), gossypol content ( ) and seed protein content ( ). It also exhibited pronounced negligible positive indirect effects on seed cotton yield per plant through number of boll per plant (0.0004), plant height (0.0002), ginning percentage (0.0041), 2.5% span length (0.0069), fibre strength (0.0030), leaves protein content (0.0039) and reducing sugar content (0.0200). 71 P a g e

88 Results Leaves protein content vs seed cotton yield per plant The genotypic correlation between leaves protein content and seed cotton yield per plant was found positive and non significant (r g = ). The direct effect of leaves protein content was negative but negligible ( ). This character manifested negligible negative indirect effects on seed cotton yield per plant through number of boll per plant ( ), plant height ( ), ginning percentage ( ) and phenol content ( ). It also depicted pronounced negligible positive indirect effects on seed cotton yield per plant through days to 50% flowering (0.0027), number of sympodia per plant (0.0035), boll weight (0.0036), seed index (0.0042), 2.5% span length (0.0002), fibre fineness (0.0050), oil percentage (0.0064), gossypol content (0.0032), seed protein content (0.0047) and reducing sugar content (0.0028) Seed protein content vs seed cotton yield per plant The genotypic correlation between seed protein content and seed cotton yield per plant was showed negative and non significant (r g = ). The direct effect of seed protein content was negative but negligible ( ). This character manifested negligible negative indirect effects on seed cotton yield per plant through days to 50% flowering ( ), number of sympodia per plant ( ), boll weight ( ), seed index ( ), fibre fineness ( ), oil percentage ( ) and gossypol content ( ). 72 P a g e

89 Results It also presented pronounced negligible positive indirect effects on seed cotton yield per plant through number of boll per plant (0.0026), plant height (0.0031), ginning percentage (0.0077), 2.5% span length (0.0013), fibre fineness (0.0007), phenol content (0.0111), leaves protein content (0.0057) and reducing sugar content (0.0034) Reducing sugar content vs seed cotton yield per plant The genotypic correlation between reducing sugar content and seed cotton yield per plant was found to be negative and non significant (r g = ). The direct effect of reducing sugar content was negative but negligible ( ). This character manifested negligible negative indirect effects on seed cotton yield per plant through number of boll per plant ( ), plant height ( ), ginning percentage ( ), oil percentage ( ) and phenol content ( ). It also expressed pronounced negligible positive indirect effects on seed cotton yield per plant through days to 50% flowering (0.0001), number of sympodia per plant (0.0069), boll weight (0.0100), seed index (0.0098), 2.5% span length (0.0009), fibre strength (0.0027), fibre fineness (0.0003), gossypol content (0.0061), leaves protein content (0.0028) and seed protein content (0.0028). 73 P a g e

90 Results 4.5 GENETIC DIVERGENCE: The genetic divergence among forty genotypes of cotton was estimated based upon observation of seventeen characters. The Mahalanobis s D 2 statistics was computed for all possible pair of populations under study Distribution of genotypes into clusters. Forty genotypes of cotton were grouped into three clusters on the basis of relative magnitude of D 2 values following Tocher s method (Rao, 1952). (Table 4.5 and Fig II) Thirty genotypes were grouped in cluster I while six genotypes grouped in cluster III and cluster II having four genotypes Intra and inter cluster distance Intra and inter cluster distance (D) between all possible pairs of three clusters were computed with the help of method given by Singh and Chaudhary (1976). Intra and inter cluster average (D) values are summarized in Table 4.6. The mutual relationship among the D 2 clusters is diagrammatically depicted in figure 3. The intra cluster distance ranged from to The cluster I exhibited maximums intra cluster distance (29.92) followed by the cluster II (29.00) and cluster III (26.75). The maximum inter cluster distance observed between cluster II and cluster III (72.92) followed by cluster I and cluster II (47.37).Whereas as minimum inter cluster distance observed between I and III (44.55). 74 P a g e

91 Results Cluster mean for different characters Cluster mean for the seventeen characters are presented in Table 4.7. The results clearly revealed appreciable differences among cluster means for most of the traits. Cluster III exhibited high mean value for almost all the characters except plant height, ginning percentage, phenol content, seed protein and reducing sugar. Cluster II exhibited high mean value for plant height ( cm), ginning percentage (35.93 %), phenol content (2.07 %), seed protein content (15.59 %) and reducing sugar (2.42 %). Cluster I exhibited moderate mean value for number of sympodia per plant (15.18), boll weight (3.65 g), seed index (8.23 g), ginning percentage (33.88 %), fibre fineness (4.31 mv), oil percentag e (17.40 %), gossypol content (0.31 %), phenol content (1.68 %), leaves protein content (13.56 %) and seed protein content (17.91 %) Character contribution towards divergence The component of D 2 due to each character variable was ranked in descending order of magnitude, rank-i being assigned to the highest value. This percent contribution of different characters to diversity is presented in Table 4.8 and figure 4. The present study revealed oil percentage (47.18 %) and reducing sugar (37.69 %) was the main contributors to the total divergence. These two characters accounted for % of total divergence. The contribution gossypol content (5.90 %), seed protein content (4.36 %), leaves protein content (3.08 %), phenol content (1.54 %), seed index (0.13 %) and fibre fineness (0.13 %) was negligible. 75 P a g e

92 Results Table 4.5: Composition of cluster based on D 2 values Cluster No. of genotype Genotypes 134-CO2-M K 23F 68-4-B Acala BC 761 BC-68 BTE-22 DHY-286 EC EC EC I. 30 EC-1777 G.Cot-12 Girja GS 34 GS-M-3 H 144 IAN 1327 IAN ISC 77 JLH 59 JR-5 K-3299 KOP 236 KOP 8 LL54 MUCU-2 NH 239 RG 1 II. 4 C 1579 EL-174C BP52 G 3637 IP III. 6 K-3259-EC-9 PD 9363 DS 28 lbadem allon SDN-24 Co American P a g e

93 Results Fig. II Clustering of genotype by tocher method 77 P a g e

94 Results Table 4.6: Intra and inter-cluster distances (D) between 40 genotypes of cotton (D = 2 ) Cluster I II III I II III Fig. III Intra and Inter- cluster distances (D) between 40 genotypes of cotton 78 P a g e

95 Results Table 4.7 Cluster mean for seventeen characters in cotton (G. hirsutum L.) Clusters Days to 50% flowering Number sympodia per plant Number Boll per Plant Plant height (cm) Boll weight (g) Seed index (g) Seed cotton yield per plant (g) Ginning percentage (%) I II III Clusters 2.5% span length (mm) Fibre strength (g/tex) Fibre fineness (mv) Oil percentage (%) Gossypol content (%) Phenol content (%) Leaf protein content (%) Seed protein content (%) Reducing sugar content (%) I II III P a g e

96 Results Table 4.8 Contribution of different characters towards total divergence in cotton (G. hirsutum L.) Sr. No. Characters Times ranked first Contribution % 1. Days to 50% flowering 0 0 % 2. Number sympodia per plant 0 0 % 3. Number of boll per plant 0 0 % 4. Plant height (cm) 0 0 % 5. Boll weight (g) 0 0 % 6. Seed index (g) % 7. Seed cotton yield per plant (g) 0 0 % 8. Ginning percentage (%) 0 0 % % span length (mm) 0 0 % 10. Fibre strength (g/tex) 0 0 % 11. Fibre fineness (mv) % 12. Oil percentage (%) % 13. Gossypol content (%) % 14. Phenol content (%) % 15. Leaves protein content (%) % 16. Seed protein content (%) % 17. Reducing sugar content (%) % 80 P a g e

97 Results Fig. IV Relative contribution of different characters towards diversity Seed index (g) 0.13% Fibre fineness (mv) 0.13% Reducing sugar content (%) 37.69% Oil percentage (%) 47.18% Protein content (Seed) (%) 4.36% Protein content (Leaves) (%) 3.08% Phenol content (%) 1.54% Gossypol content (%) 5.90% 81 P a g e