Genetic Diversity Analysis among Capsicum annuum Mutants Based on Morpho-Physiological and Yield Traits
Abstract
:1. Introduction
2. Materials and Methods
2.1. Experiment Location
2.2. Plant Materials
2.3. Experimental Design and Layout
2.4. Data Collection
2.4.1. Measuring the Morphological, Physiological, and Yield Components
2.4.2. Genetic Variance, Heritability, and Advance
- (a)
- Calculation of genotypic variance using following formulae:
- (b)
- Calculation of phenotypic variance using following formula:σ2p = σ2g + MSE
- (c)
- Phenotypic and Genotypic Coefficient of Variation (PCV and GCV). Estimates of phenotypic and genotypic coefficient of variation were calculated according to Singh and Choudhary [25] as follows:
- (d)
- Broad sense heritability h2B ratio of genetic variance (σ2g) to phenotypic variance (σ2g).
- (e)
- Estimated and Expected Genetic Advance. Expected genetic advance (GA) (as percentage of the mean) was calculated using the method of Assefa et al. [28] and selection intensity (K) was assumed to be 5%. Genetic advance was marked as low (0–10%), moderate (10–20%), and high (>20%) by following Johnson et al. [29].
2.5. Data Analysis
3. Results and Discussion
3.1. Morpho-Physiological and Yield Component
3.1.1. Growth and Physiological Components
3.1.2. Yield and Yield Contributing Traits
3.2. Correlation between Different Traits
3.3. Genetic Analysis, Broad-Sense Heritability, and Genetic Advance
3.4. Clustering and Principal Components Analysis
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Nsebiyera, V.; Logose, M.; Ochwo-Ssemakula, M.; Sseruwagi, P.; Gibson, P.; Ojiewo, C.O. Morphological characterization of local and exotic hot pepper (Capsicum annuum L.) collections in Uganda. Bioremediation Biodivers. Bioavailab. 2013, 7, 22–32. [Google Scholar]
- Dias, G.B.; Gomes, V.M.; Moraes, T.M.S.; Zottich, U.P.; Rabelo, G.R.; Carvalho, A.O.; Moulin, M.; Goncalves, L.S.A.; Rodrigues, R.; da Cunha, M. Characterization of Capsicum species using anatomical and molecular data. Gene. Mol. Res. 2013, 12, 6488–6501. [Google Scholar] [CrossRef]
- Wahyuni, Y.; Ballester, A.R.; Tikunov, Y.; De Vos, R.C.; Pelgrom, K.T.; Maharijaya, A.; Sudarmonowati, E.; Bino, R.J.; Bovy, A.G. Metabolomics and molecular marker analysis to explore pepper (Capsicum sp.) biodiversity. Metabolomics 2013, 9, 130–144. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Virga, G.; Licata, M.; Consentino, B.B.; Tuttolomondo, T.; Sabatino, L.; Leto, C.; La Bella, S. Agro-morphological characterization of sicilian chili pepper accessions for ornamental purposes. Plants 2020, 9, 1400. [Google Scholar] [CrossRef] [PubMed]
- Sreelathakumary, I.; Rajamony, L. Variability, heritability and genetic advance in chilli (Capsicum annuum L.). J. Trop. Agri. 2006, 42, 35–37. [Google Scholar]
- Singh, Y.; Sharma, M.; Sharma, A. Genetic variation, association of characters and their direct and indirect contributions for improvement in chilli peppers. Int. J. Veg. Sci. 2009, 15, 340–368. [Google Scholar] [CrossRef]
- Chattopadhyay, A.; Sharangi, A.B.; Dai, N.; Dutta, S. Diversity of genetic resources and genetic association analysis of green and dry chillies of Eastern India. Chil. J. Agric. Res. 2011, 71, 350–356. [Google Scholar] [CrossRef]
- Pandit, M.K.; Adhikary, S. Variability and heritability estimates in some reproductive characters and yield in chilli (Capsicum annuum L.). Int. J. Plant Soil Sci. 2014, 3, 845–853. [Google Scholar] [CrossRef]
- Maurya, A.K.; Kushwaha, M.L.; Jain, V.K.; Singh, N. Evaluation of chilli (Capsicum annuum L.) genotypes for yield and performance against diseases. Prog. Res. Intl. J. 2016, 11, 4615–4617. [Google Scholar]
- Rani, P.U. Fruit seed weight and seed attributes on the quality characteristics in chilli. Madras Agric. J. 1996, 83, 259–264. [Google Scholar]
- Syukur, M.; Sujiprihati, S.; Yunianti, R. Estimation of genetic parameter for quantitative characters of pepper (Capsicum annuum L.). J. Trop. Crop Sci. 2012, 1, 4–8. [Google Scholar] [CrossRef]
- Bello, O.B.; Ige, S.A.; Azeez, M.A.; Afolabi, M.S.; Abdulmaliq, S.Y.; Mahamood, J. Heritability and genetic advance for yield and its component character in chilli. Intl. J. Plant Res. 2014, 2, 138–145. [Google Scholar]
- Parkash, C. Estimation of genetic variability and implications of direct effects of different traits on leaf yield in bathua (Chenopodium album). Indian J. Agri. Sci. 2012, 82, 71–74. [Google Scholar]
- Kadwey, S.; Ashwini, D.; Sunil, P. Genotypes performance and genetic variability studies in Hot Chilli. Indian J. Agric. Res. 2016, 50, 56–60. [Google Scholar]
- Gupta, A.M.; Singh, D.; Kumar, A. Genetic variability, genetic advance and correlation in chilli. Indian J. Agric. Sci. 2009, 79, 221–223. [Google Scholar]
- Bendale, V.W.; Palsuledesai, M.R.; Bhave, S.G.; Sawant, S.S.; Desai, S.S. Genetic evaluation of some economic traits in chilli. Crop Res. 2006, 31, 401–403. [Google Scholar]
- Bharadwaj, D.N.; Singh, S.K.; Singh, H.L. Genetic variability an association of component characters for yield in chilli. Intl. J. Plant Sci. 2007, 2, 93–96. [Google Scholar]
- Lahbib, K.; Bnejdi, F.; Gazzah, E.I. Genetic diversity evaluation of pepper in Tunisia based on morphologic characters. Afr. J. Agric. Res. 2012, 7, 3413–3417. [Google Scholar]
- Khodadadi, M.; Fotokian, M.H.; Miransari, M. Genetic diversity of wheat genotypes based on cluster and principal component analyses for breeding strategies. Aust. J. Crop Sci. 2011, 5, 17–24. [Google Scholar]
- Farhad, M.; Hasanuzzaman, M.; Biswas, B.K.; Arifuzzaman, M.; Islam, M.M. Genetic divergence in chilli. Bangladesh Res. Pub. J. 2010, 3, 1045–1051. [Google Scholar]
- Krishna, U.C.; Madalageri, M.B.; Patil, M.P.; Ravindra, M.; Kotlkal, Y.K. Variability studies in green chilli (Capsicum annuum L.). Karnataka J. Agric. Sci. 2007, 20, 102–104. [Google Scholar]
- Yatung, T.; Dubey, R.K.; Singh, V.; Upadhyay, G. Genetic diversity of chilli (Capsicum annuum L.) genotypes of India based on morphochemical traits. Aust. J. Crop Sci 2014, 8, 97–102. [Google Scholar]
- Hoque, M.N.; Rahman, L. Estimation of Euclidean distance for different morpho-physiological characters in some wild and cultivated rice genotypes (Oryza sativa L.). J. Biol. Sci. 2007, 7, 86–88. [Google Scholar] [CrossRef] [Green Version]
- Sen, N.; Biswas, K.; Sinha, S.N. Assessment of genetic divergence through cluster analysis of chilli varieties. J. Biosci. 2021, 46, 52. [Google Scholar] [CrossRef] [PubMed]
- Singh, R.K.; Choudhary, B.D. Biometrical Methods in Quantitative Genetic Analysis; Kalyani Publishers: New Delhi, India, 1977. [Google Scholar]
- Sivasubramanian, S.; Madhavamenon, P. Genotypic and phenotypic variability in rice. Madras Agric. J. 1973, 60, 1093–1096. [Google Scholar]
- Johnson, H.W.; Comstock, R.E.; Harvey, P.H. Genotypic and phenotypic correlations in corn and their implications in selection. Agron. J. 1951, 43, 282–287. [Google Scholar]
- Assefa, K.; Ketema, S.; Tefera, H.; Nguyen, H.T.; Blum, A.; Ayele, M.; Bai, G.; Simane, B.; Kefyalew, T. Diversity among germplasm lines of the Ethiopian cereal tef [Eragrostistef (Zucc.) Trotter]. Euphytica 1999, 106, 87–97. [Google Scholar] [CrossRef]
- Johnson, H.W.; Robinson, H.F.; Comstock, R.E. Estimation of genetic and environmental variability in soybeans. Agron. J. 1955, 47, 314–318. [Google Scholar] [CrossRef]
- Ridzuan, R.B. Development of Anthracnose Resistant Chili Varieties through Marker-assisted Pedigree Selection. Ph.D. Thesis, Universiti Putra Malaysia, Serdang, Malaysia, 2018. [Google Scholar]
- Usman, M.G.; Rafii, M.Y.; Ismail, M.R.; Malek, M.; Latif, M.A. Capsaicin and dihydrocapsaicin determination in chili pepper genotypes using ultra-fast liquid chromatography. Molecules 2014, 19, 6474–6488. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- George, A. Principles of Plant Genetics and Breeding; Backwell Publishing: Victoria, Australia, 2007; pp. 246–248. [Google Scholar]
- Ridzuan, R.; Rafii, M.Y.; Ismail, S.I.; Mohammad Yusoff, M.; Miah, G.; Usman, M. Breeding for anthracnose disease resistance in chili: Progress and prospects. Int. J. Mol. Sci. 2018, 19, 3122. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Falconer, D.S. Introduction to Quantitative Genetics. Pearson Education: New Delhi, India, 1996; ISBN 8131727408. [Google Scholar]
- Kumar, D.; Bahadur, V.S.; Rangare, D.; Singh, D. Genetic variability, heritability and correlation studies in chilli (Capsicum annuum L.). HortFlora Res. Spectrum 2012, 1, 248–252. [Google Scholar]
- Agasimani, S.; Kumar, H.D. Genetic variability, heritability and genetic advance for yield and its components in byadgikaddichilli (Capsicumannuum L.) accessions. BIOINFOLET-A Q. J. Life Sci. 2013, 10, 50–53. [Google Scholar]
- Sreelathakumary, I.; Rajamony, L. Variability, heritability and correlation studies in chilli (Capsicum spp.) under shade. Indian J. Hortic. 2002, 59, 77–83. [Google Scholar]
- Tembhurne, B.V.; Kuchanur, P.H. Varietal performance, genetic variability and correlation studies in chilli (Capsicum annuum L.). Karnataka J. Agric. Sci. 2010, 21, 541–543. [Google Scholar]
- Surya Kumari, S.; Uma Jyothi, K.; Srihari, D.; Siva Sankar, A.; Ravi Sankar, C. Variability and genetic divergence in paprika (Capsicum annuum L.). J. Spices Aromat. Crops 2011, 19, 71–75. [Google Scholar]
- Guerra, E.P.; Destro, D.; Miranda, L.A.; Montalván, R. Parent selection for intercrossing in food type soybean through multivariate genetic divergence. Acta Sci. Agron. 1999, 21, 429–437. [Google Scholar]
- Geleta, L.F.; Labuschagne, M.T.; Viljoen, C.D. Genetic variability in pepper (Capsicum annuum L.) estimated by morphological data and amplified fragment length polymorphism markers. Biodivers. Conserv. 2005, 14, 2361–2375. [Google Scholar] [CrossRef]
Code | Variety | Gamma Source Type |
---|---|---|
G1 | Chilli Bangi 3 | acute |
G2 | Chilli Bangi 3 | acute |
G3 | Chilli Bangi 3 | acute |
G4 | Chilli Bangi 3 | acute |
G5 | Chilli Bangi 3 | chronic |
G6 | Chilli Bangi 3 | chronic |
G7 | Chilli Bangi 3 | chronic |
G8 | Chilli Bangi 3 | chronic |
G9 | Chilli Bangi 3 | chronic |
G10 | Chilli Bangi 3 | chronic |
G11 | Chilli Bangi 3 | chronic |
G12 | Chilli Bangi 3 | chronic |
G13 | Chilli Bangi 3 | chronic |
G14 | Chilli Bangi 5 | acute |
G15 | Chilli Bangi 5 | acute |
G16 | Chilli Bangi 5 | acute |
G17 | Chilli Bangi 5 | acute |
G18 | Chilli Bangi 5 | chronic |
G19 | Chilli Bangi 5 | chronic |
G20 | Chilli Bangi 5 | chronic |
G21 | Chilli Bangi 5 | chronic |
G22 | Chilli Bangi 5 | chronic |
G23 | Chilli Bangi 5 | chronic |
G24 | Chilli Bangi 5 | chronic |
G25 | Chilli Bangi 5 | chronic |
G26 | Chilli Bangi 5 | chronic |
G27 | Chilli Bangi 5 | chronic |
Sl. No. | Parameter | Denotation | Description |
---|---|---|---|
1 | Germination% | GP | Germination was counted at tenth day after sowing. |
2 | First bifurcation length (cm) | FBL | The length between soil base and first bifurcation is measured. |
3 | Number of primary branches (nos.) | PB | Number of branches produced from the main stem wascounted. |
4 | Number of secondary branches (nos.) | SB | Number of branches produced from the primary branch wascounted |
5 | Plant height (cm) | PH | Each plant’s height was measured from the soil surface up to the tip of the plant with a measuring tape. |
6 | Stem diameter (mm) | SD | The stem diameter was taken using an Absolute Digimatic calliper 5 cm from the base of the plant (Mitutoyo, Japan). |
7 | Number of leaf/plant (nos.) | NLP | Total number of leaves were counted for each plant. |
8 | Days to first flowering (nos.) | DF | The days from transplanting to the first fully open flower was observed. |
9 | Days to first fruit maturity (nos.) | DM | The days tofirst fruit ripening on the plant were counted. |
10 | Number of fruits/plant (nos.) | NFP | Total numberof fruits collected from the first harvest to 90 days after transplanting. |
11 | Fruit length (mm) | FL | The matured fruit length from calyx to the tip of fruit. |
12 | Fruit breadth (mm) | FB | The girth of one mature fruit (0.3 cm below the calyx). |
13 | Pedicle length (mm) | PL | From the base of calyx to the attachment point of branch. |
14 | Single fruit weight (gm) | FW | Weight of one mature fruit per plant. |
15 | Single fruit dry weight (gm) | FDW | Per plant, the weight of one dried ripe fruit. |
16 | Seed number/fruit (nos.) | NSF | Total number of seeds for each fruit were counted. |
17 | 100 seeds weight (gm) | HSW | Counted hundred seeds’ weight was taken by using electronic weighing balance. |
18 | Fruit wall thickness (mm) | FWT | The wall thickness of fully matured fruit was recorded at harvest using slide calliper. |
19 | TYP (kg) | YLD | All fruits’weight from the first harvest to 90 days after transplanting. |
20 | Relative chlorophyll content (SPAD value) | RCC | The relative amount of chlorophyll content present in the leaf, measured on the third or fourth leaf from the tips using SPAD-502 Plus (Konica Minolta, Japan). |
21 | Photosynthesis rate (µmol CO2 m−2 s−1) | PR | Photosynthetic rate, stomata conductance, and transpiration rate measured on third or fourth leaf from the tips using a photosynthesis portable system (LI-6400xt, LI-COR, Lincoln, NE, USA). |
22 | Stomata conductance (molH2Om−2s−1) | SC | |
23 | Transpiration rate (mmolH2Om−2s−1) | TR |
SOV | GP | FBL | PB | SB | PH | SD | NLP | DF |
---|---|---|---|---|---|---|---|---|
Blocks(season) | 11.72 ns | 1.05 ns | 0.67 ns | 0.56 ns | 5.22 ns | 3.67 ns | 1259.45 ns | 37.00** |
Seasons (S) | 29.38 ns | 10.88 * | 0.09 ns | 0.15 ns | 42.91 ns | 10.31 * | 74.69 ns | 9.97 * |
Genotypes (G) | 190.11 ** | 7.44 ** | 0.93 ** | 1.65 ** | 177.68 ** | 30.52 ** | 23,229.31 ** | 31.96 ** |
G × S | 8.65 ns | 1.20 ns | 0.23 ns | 0.33 ns | 52.77* | 1.41 ns | 4032.98 * | 2.41 * |
Error | 15.55 | 1.74 | 0.31 | 0.54 ns | 29.70 ns | 1.53 ns | 2097.35 ns | 1.46 |
SOV | DM | NFP | FL | FB | CL | FW | FDW | NSF |
Blocks(season) | 57.85 * | 91.98 ns | 3.08 ns | 0.10 ns | 10.24 ns | 0.02 ns | 0.02 ns | 3.82 ns |
Seasons (S) | 90.37 * | 71.02 ns | 1.77 ns | 0.37 ns | 26.53 ns | 0.38 ns | 0.002 ns | 1.04 ns |
Genotypes (G) | 131.49 ** | 2689.08 ** | 399.71 ** | 33.25 ** | 118.58 ** | 9.64 ** | 0.11 ** | 935.50 ** |
G × S | 10.24 ns | 15.14 ns | 6.48 ns | 0.54 ns | 2.57 ns | 0.12 ns | 0.02 ns | 1.97 ns |
Error | 8.01 | 81.20 | 31.54 | 1.42 | 6.86 | 0.19 | 0.006 | 32.46 |
SOV | HSW | FWT | RCC | PR | SC | TR | YLD | |
Blocks(season) | 0.001 ns | 0.004 ns | 3.72 ns | 0.12 ns | 0.0005 ns | 0.001 ns | 0.0006 ns | |
Seasons (S) | 0.0009 ns | 0.0006 ns | 12.21 ns | 0.22 ns | 0.002 ns | 0.02 ns | 0.003 ns | |
Genotypes (G) | 0.007 ** | 0.64 ** | 109.15 ** | 31.26 ** | 0.11 ** | 2.99 ** | 0.29 ** | |
G × S | 0.001 ns | 0.001 ns | 6.23 ns | 0.25 ns | 0.0004 ns | 0.01 ns | 0.006 ns | |
Error | 0.001 | 0.03 | 10.41 | 0.57 | 0.002 | 0.05 | 0.03 |
Genotypes | GP | FBL | PB | SB | PH | SD | NLP | DF | DM | NFP | FL | FB | CL | FW | FDW | NSF | HSW | FWT | RCC | PR | SC | TR | YLD |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Gen 1 | 93.5 | 9.83 | 3.3 | 5.5 | 79.53 | 16.48 | 574.7 | 24.4 | 75.7 | 133.2 | 83.68 | 20.13 | 28.27 | 13.49 | 1.13 | 67.7 | 0.496 | 2.33 | 50.20 | 17.13 | 0.43 | 4.72 | 1.43 |
Gen 2 | 94.0 | 7.83 | 3.5 | 7.5 | 82.20 | 14.57 | 710.5 | 20.1 | 66.7 | 144.2 | 89.68 | 18.40 | 39.63 | 11.00 | 1.03 | 79.3 | 0.518 | 2.70 | 55.01 | 21.52 | 0.68 | 6.47 | 1.74 |
Gen 3 | 93.7 | 7.33 | 3.5 | 6.8 | 83.70 | 14.41 | 731.5 | 20.3 | 68.2 | 110.7 | 88.31 | 21.22 | 35.45 | 11.97 | 1.13 | 77.8 | 0.584 | 2.46 | 55.91 | 20.86 | 0.61 | 6.72 | 1.57 |
Gen 4 | 83.7 | 9.50 | 2.7 | 6.5 | 84.20 | 11.57 | 631.7 | 25.4 | 76.8 | 131.8 | 93.06 | 22.71 | 35.03 | 9.67 | 0.91 | 66.3 | 0.487 | 1.71 | 47.15 | 15.65 | 0.41 | 5.09 | 1.54 |
Gen 5 | 94.2 | 7.33 | 3.2 | 7.5 | 82.70 | 13.10 | 685.2 | 20.9 | 66.8 | 139.0 | 72.25 | 22.70 | 22.10 | 14.05 | 1.21 | 84.5 | 0.527 | 2.66 | 56.07 | 20.41 | 0.55 | 6.68 | 1.62 |
Gen 6 | 86.3 | 8.00 | 3.2 | 6.8 | 71.53 | 14.80 | 739.2 | 20.4 | 69.5 | 140.0 | 95.50 | 18.79 | 28.82 | 13.22 | 1.26 | 69.0 | 0.509 | 2.73 | 55.61 | 21.06 | 0.73 | 6.76 | 1.60 |
Gen 7 | 87.5 | 9.83 | 3.0 | 6.8 | 77.03 | 14.56 | 662.5 | 19.6 | 69.8 | 124.0 | 78.37 | 21.54 | 33.14 | 13.59 | 1.26 | 68.8 | 0.487 | 2.71 | 55.71 | 22.02 | 0.62 | 6.53 | 1.29 |
Gen 8 | 80.3 | 9.67 | 2.7 | 5.8 | 77.87 | 17.48 | 633.3 | 24.1 | 77.5 | 97.5 | 83.67 | 17.54 | 29.89 | 11.09 | 0.89 | 68.7 | 0.497 | 2.03 | 46.78 | 18.09 | 0.42 | 4.83 | 1.29 |
Gen 9 | 87.0 | 8.50 | 3.2 | 7.5 | 84.70 | 14.22 | 706.0 | 19.6 | 68.5 | 154.3 | 82.09 | 17.17 | 23.75 | 13.23 | 1.16 | 71.5 | 0.516 | 2.56 | 55.99 | 21.85 | 0.61 | 6.60 | 1.63 |
Gen 10 | 83.0 | 10.33 | 2.8 | 6.8 | 81.20 | 10.58 | 555.0 | 24.5 | 77.3 | 107.5 | 79.23 | 18.62 | 29.30 | 12.07 | 0.96 | 65.5 | 0.480 | 2.45 | 48.49 | 17.62 | 0.43 | 5.13 | 1.39 |
Gen 11 | 75.3 | 10.33 | 2.5 | 7.2 | 79.03 | 17.35 | 671.3 | 24.5 | 77.3 | 107.7 | 86.68 | 17.02 | 30.48 | 14.09 | 1.31 | 63.5 | 0.497 | 2.17 | 47.92 | 16.95 | 0.49 | 5.18 | 1.33 |
Gen 12 | 81.2 | 9.67 | 2.8 | 6.2 | 83.72 | 12.72 | 614.3 | 24.4 | 75.7 | 109.8 | 95.01 | 18.85 | 36.32 | 12.26 | 1.03 | 63.8 | 0.493 | 2.58 | 48.12 | 17.81 | 0.51 | 5.36 | 1.26 |
Gen 13 | 84.2 | 9.67 | 2.5 | 6.7 | 83.05 | 11.19 | 593.3 | 23.8 | 76.0 | 128.2 | 95.55 | 19.11 | 32.38 | 14.96 | 1.19 | 60.5 | 0.500 | 2.90 | 46.32 | 18.00 | 0.39 | 5.45 | 1.25 |
Gen 14 | 91.8 | 10.50 | 2.3 | 7.2 | 80.05 | 11.17 | 618.3 | 24.8 | 76.3 | 95.7 | 87.68 | 16.93 | 35.23 | 12.52 | 1.25 | 45.2 | 0.447 | 2.65 | 47.83 | 16.45 | 0.43 | 5.37 | 1.15 |
Gen 15 | 93.3 | 8.17 | 2.2 | 7.2 | 69.59 | 14.56 | 707.0 | 18.6 | 66.7 | 125.0 | 102.78 | 16.77 | 39.44 | 11.69 | 1.23 | 53.2 | 0.461 | 1.60 | 57.20 | 22.02 | 0.76 | 6.81 | 1.28 |
Gen 16 | 90.3 | 8.67 | 2.5 | 7.5 | 74.93 | 14.13 | 706.5 | 21.1 | 65.0 | 120.2 | 80.24 | 17.36 | 39.09 | 11.67 | 1.08 | 51.5 | 0.461 | 1.82 | 56.23 | 22.10 | 0.59 | 6.67 | 1.25 |
Gen 17 | 77.0 | 9.67 | 2.2 | 6.8 | 83.59 | 16.87 | 669.8 | 24.3 | 75.2 | 157.2 | 81.71 | 13.53 | 29.37 | 9.97 | 0.93 | 54.7 | 0.443 | 2.44 | 46.63 | 17.52 | 0.39 | 5.42 | 1.18 |
Gen 18 | 81.0 | 10.50 | 2.5 | 6.2 | 74.96 | 11.27 | 609.0 | 23.9 | 73.3 | 104.8 | 70.36 | 12.40 | 31.36 | 10.91 | 1.01 | 39.5 | 0.435 | 2.33 | 46.61 | 17.22 | 0.43 | 5.32 | 0.98 |
Gen 19 | 91.5 | 8.83 | 2.5 | 6.8 | 75.63 | 17.43 | 552.2 | 23.5 | 76.0 | 122.3 | 75.14 | 20.59 | 35.44 | 12.34 | 1.03 | 55.2 | 0.452 | 2.19 | 46.37 | 16.94 | 0.42 | 5.41 | 1.35 |
Gen 20 | 85.0 | 8.83 | 2.2 | 7.3 | 78.46 | 16.95 | 734.5 | 23.4 | 73.8 | 107.7 | 77.89 | 19.61 | 37.96 | 12.77 | 1.20 | 52.0 | 0.462 | 2.18 | 47.47 | 17.20 | 0.38 | 5.62 | 1.25 |
Gen 21 | 80.0 | 8.50 | 2.8 | 7.2 | 80.29 | 11.60 | 655.0 | 24.2 | 76.3 | 104.3 | 73.58 | 20.62 | 30.94 | 12.25 | 1.22 | 44.5 | 0.463 | 2.35 | 47.84 | 17.29 | 0.42 | 5.92 | 1.09 |
Gen 22 | 86.2 | 10.00 | 3.2 | 6.5 | 67.46 | 15.13 | 739.5 | 19.0 | 67.2 | 121.7 | 89.03 | 19.65 | 36.58 | 13.24 | 1.33 | 48.0 | 0.449 | 1.99 | 54.24 | 22.04 | 0.68 | 6.62 | 1.42 |
Gen 23 | 89.2 | 10.17 | 2.8 | 7.5 | 72.96 | 14.72 | 652.3 | 20.4 | 65.5 | 114.3 | 79.11 | 17.13 | 32.34 | 11.35 | 1.32 | 49.3 | 0.450 | 2.13 | 55.06 | 21.73 | 0.74 | 6.44 | 1.24 |
Gen 24 | 88.5 | 10.33 | 3.2 | 6.8 | 66.96 | 16.82 | 725.7 | 19.7 | 66.2 | 146.7 | 77.53 | 19.35 | 34.21 | 12.48 | 1.22 | 51.8 | 0.480 | 2.28 | 55.66 | 21.79 | 0.72 | 6.46 | 1.47 |
Gen 25 | 77.5 | 11.33 | 2.8 | 6.5 | 76.63 | 16.46 | 551.3 | 24.4 | 74.3 | 78.2 | 73.24 | 18.87 | 31.33 | 10.98 | 0.94 | 40.7 | 0.440 | 2.13 | 46.96 | 17.13 | 0.41 | 5.40 | 0.89 |
Gen 26 | 84.3 | 11.17 | 2.5 | 6.5 | 69.96 | 17.84 | 615.0 | 25.7 | 76.0 | 104.0 | 86.60 | 17.47 | 33.67 | 12.88 | 1.02 | 39.7 | 0.438 | 2.10 | 49.12 | 17.17 | 0.42 | 5.58 | 0.91 |
Gen 27 | 90.3 | 8.17 | 2.5 | 6.5 | 71.29 | 14.52 | 743.7 | 20.0 | 64.5 | 71.0 | 82.84 | 19.37 | 37.74 | 13.37 | 1.27 | 52.3 | 0.459 | 2.52 | 56.86 | 21.51 | 0.71 | 6.86 | 1.04 |
LSD (p < 0.05) | 4.52 | 1.51 | 0.63 | 0.85 | 6.24 | 1.42 | 52.43 | 1.38 | 3.24 | 10.32 | 6.43 | 1.37 | 2.99 | 0.5 | 0.09 | 6.52 | 0.04 | 0.20 | 3.69 | 0.86 | 0.05 | 0.24 | 0.18 |
Season 1 | 8586 | 9.09 | 2.75 | 6.79 | 77.01 | 14.28 | 658.1 | 22.2 | 71.2 | 117.9 | 83.63 | 18.60 | 32.53 | 12.29 | 1.12 | 58.60 | 0.47 | 2.31 | 50.96 | 19.11 | 0.53 | 5.89 | 1.30 |
Season 2 | 86.72 | 9.62 | 2.80 | 6.85 | 78.05 | 14.79 | 659.5 | 22.7 | 72.7 | 119.2 | 83.83 | 18.69 | 33.34 | 12.39 | 1.13 | 58.77 | 0.48 | 2.32 | 51.51 | 19.18 | 0.52 | 5.91 | 1.32 |
LSD (p < 0.05) | 1.22 | 0.41 | 0.17 | 0.23 | 1.69 | 0.39 | 14.27 | 0.38 | 0.88 | 2.81 | 1.75 | 0.37 | 0.82 | 0.14 | 0.02 | 1.78 | 0.01 | 0.05 | 1.01 | 0.23 | 0.01 | 0.07 | 0.05 |
FBL | PB | SB | PH | SD | NLP | DF | DM | NFP | FL | FB | CL | FW | FDW | NSF | HSW | FWT | RCC | PR | SC | TR | YLD | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
GP | −0.363 ** | 0.199 * | 0.206 ** | −0.073 | −0.068 | 0.195 * | −0.386 ** | −0.429 ** | 0.152 | 0.130 | 0.251 * | 0.150 | 0.134 | 0.249 * | 0.268 ** | 0.197 * | 0.085 | 0.484 ** | 0.429 ** | 0.406 ** | 0.469 ** | 0.296 ** |
FBL | −0.086 | −0.165 * | −0.129 | 0.123 | −0.261 ** | 0.297 ** | 0.293 ** | −0.189 * | −0.110 | −0.204 * | −0.031 | −0.042 | −0.146 | −0.351 ** | −0.291 ** | −0.121 | −0.282 ** | −0.265 ** | −0.282 ** | −0.381 ** | −0.347 ** | |
PB | 0.078 | −0.099 | −0.072 | 0.057 | −0.229 * | −0.127 | 0.206 * | −0.058 | 0.297 ** | −0.164 * | 0.101 | 0.039 | 0.297 ** | 0.343 ** | 0.169 * | 0.225 * | 0.252 * | 0.252 * | 0.212 * | 0.350 ** | ||
SB | 0.005 | −0.068 | 0.182 * | −0.220 * | −0.301 ** | 0.121 | −0.073 | 0.039 | −0.032 | 0.057 | 0.286 ** | 0.048 | 0.124 | 0.027 | 0.164 * | 0.245 * | 0.244 * | 0.334 ** | 0.089 | |||
PH | −0.216 * | −0.155 * | 0.240 * | 0.242 * | 0.080 | 0.041 | 0.103 | −0.242 * | −0.110 | −0.269 * | 0.359 ** | 0.274 * | 0.278 * | −0.218 * | −0.258 * | −0.335 ** | −0.269* | 0.131 | ||||
SD | 0.114 | −0.055 | −0.077 | 0.046 | −0.046 | −0.029 | 0.047 | 0.013 | −0.003 | −0.046 | −0.097 | −0.244 * | 0.051 | 0.079 | 0.048 | −0.020 | −0.009 | |||||
NLP | −0.453 ** | −0.503 ** | 0.193 * | 0.152 | 0.031 | 0.156 * | 0.096 | 0.366 ** | 0.154 * | 0.120 | 0.001 | 0.502 ** | 0.546 ** | 0.531 ** | 0.604 ** | 0.259 * | ||||||
DF | 0.646 ** | −0.248 * | −0.073 | −0.096 | −0.181 * | −0.170 * | −0.405 ** | −0.217 | −0.238 * | −0.077 | −0.668 ** | −0.788 ** | −0.739 ** | −0.740 ** | −0.307 ** | |||||||
DM | −0.188 * | −0.045 | −0.059 | −0.147 | −0.079 | −0.375 ** | −0.129 | −0.104 | −0.008 | −0.624 ** | −0.735 ** | −0.699 ** | −0.757 ** | −0.202* | ||||||||
NFP | 0.173 * | 0.046 | −0.233 * | 0.028 | 0.012 | 0.421 ** | 0.231 * | 0.134 | 0.207 * | 0.263 * | 0.214 ** | 0.214 ** | 0.537 ** | |||||||||
FL | 0.074 | 0.277 ** | 0.069 | 0.102 * | 0.181 * | 0.096 | −0.053 | 0.120 | 0.125 | 0.228 * | 0.072 | 0.183 * | ||||||||||
FB | 0.018 | 0.308 ** | 0.142 | 0.379 ** | 0.296 ** | 0.069 | 0.140 | 0.085 | 0.079 | 0.146 | 0.332 ** | |||||||||||
CL | −0.207 ** | 0.034 | −0.256 ** | −0.175 | −0.293 ** | 0.099 | 0.151 | 0.198 * | 0.140 | −0.145 | ||||||||||||
FW | 0.565 ** | 0.146 | 0.183 * | 0.405 ** | 0.173 * | 0.169 * | 0.120 | 0.176 * | 0.098 | |||||||||||||
FDW | −0.044 | 0.031 | 0.193 * | 0.380 ** | 0.428 ** | 0.490 ** | 0.487 ** | 0.128 | ||||||||||||||
NSF | 0.592 ** | 0.310 ** | 0.263 * | 0.254 ** | 0.145 | 0.168 * | 0.608 ** | |||||||||||||||
HSW | 0.312 ** | 0.185 * | 0.152 | 0.149 | 0.173 * | 0.422 ** | ||||||||||||||||
FWT | 0.008 | 0.033 | −0.013 | 0.095 | 0.178 * | |||||||||||||||||
RCC | 0.746 ** | 0.702 ** | 0.735 ** | 0.262 ** | ||||||||||||||||||
PR | 0.847 ** | 0.837 ** | 0.318 ** | |||||||||||||||||||
SC | 0.784 ** | 0.322 ** | ||||||||||||||||||||
TR | 0.257 ** |
Traits | Mean | (σ2e) | (σ2g) | (σ2p) | PCV (%) | GCV (%) | RD (%) | (h2B) % | GA (%) |
---|---|---|---|---|---|---|---|---|---|
GP | 86.3 | 14.3 | 29.3 | 43.6 | 7.7 | 6.3 | 18.0 | 67.3 | 10.6 |
FBL | 9.4 | 1.7 | 1.0 | 2.7 | 17.4 | 10.5 | 39.9 | 36.1 | 13.0 |
PB | 2.8 | 0.3 | 0.1 | 0.4 | 22.8 | 11.8 | 48.3 | 26.8 | 12.6 |
SB | 6.8 | 0.5 | 0.2 | 0.7 | 12.2 | 6.4 | 47.0 | 28.1 | 7.0 |
PH | 77.5 | 33.6 | 24.0 | 57.6 | 9.8 | 6.3 | 35.4 | 41.7 | 8.4 |
SD | 14.5 | 1.6 | 4.8 | 6.4 | 17.4 | 15.1 | 13.4 | 75.0 | 26.9 |
NLP | 658.8 | 2415.5 | 3469.0 | 5884.5 | 11.6 | 8.9 | 23.2 | 59.0 | 14.1 |
DF | 22.4 | 2.3 | 5.0 | 7.2 | 12.0 | 9.9 | 17.1 | 68.8 | 17.0 |
DM | 71.9 | 9.8 | 20.3 | 30.1 | 7.6 | 6.3 | 17.9 | 67.4 | 10.6 |
NFP | 118.5 | 67.9 | 436.9 | 504.7 | 19.0 | 17.6 | 7.0 | 86.6 | 33.8 |
FL | 83.7 | 26.0 | 62.3 | 88.3 | 11.2 | 9.4 | 16.0 | 70.6 | 16.3 |
FB | 18.7 | 1.2 | 5.3 | 6.6 | 13.7 | 12.4 | 9.8 | 81.4 | 23.0 |
CL | 32.9 | 6.1 | 18.7 | 24.9 | 15.2 | 13.1 | 13.2 | 75.3 | 23.5 |
FW | 12.3 | 0.2 | 1.6 | 1.8 | 10.7 | 10.2 | 5.2 | 90.0 | 19.9 |
FDW | 1.1 | 0.0 | 0.0 | 0.0 | 13.4 | 11.8 | 11.6 | 78.2 | 21.5 |
NSF | 58.7 | 25.8 | 151.6 | 177.5 | 22.7 | 21.0 | 7.6 | 85.4 | 40.0 |
HSW | 0.5 | 0.0 | 0.0 | 0.0 | 9.5 | 6.6 | 31.2 | 47.4 | 9.3 |
FWT | 2.3 | 0.0 | 0.1 | 0.1 | 15.3 | 13.8 | 10.2 | 80.6 | 25.4 |
RCC | 51.2 | 9.5 | 16.6 | 26.1 | 10.0 | 8.0 | 20.3 | 63.6 | 13.1 |
PR | 19.2 | 0.5 | 5.1 | 5.6 | 12.4 | 11.8 | 4.5 | 91.2 | 23.3 |
SC | 0.5 | 0.0 | 0.0 | 0.0 | 25.9 | 24.8 | 4.2 | 91.8 | 49.0 |
TR | 5.9 | 0.0 | 0.5 | 0.5 | 12.3 | 11.9 | 3.6 | 92.9 | 23.6 |
YLD | 1.3 | 0.0 | 0.1 | 0.1 | 19.7 | 16.3 | 17.3 | 68.5 | 27.8 |
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Karim, K.M.R.; Rafii, M.Y.; Misran, A.; Ismail, M.F.; Harun, A.R.; Ridzuan, R.; Chowdhury, M.F.N.; Hosen, M.; Yusuff, O.; Haque, M.A. Genetic Diversity Analysis among Capsicum annuum Mutants Based on Morpho-Physiological and Yield Traits. Agronomy 2022, 12, 2436. https://doi.org/10.3390/agronomy12102436
Karim KMR, Rafii MY, Misran A, Ismail MF, Harun AR, Ridzuan R, Chowdhury MFN, Hosen M, Yusuff O, Haque MA. Genetic Diversity Analysis among Capsicum annuum Mutants Based on Morpho-Physiological and Yield Traits. Agronomy. 2022; 12(10):2436. https://doi.org/10.3390/agronomy12102436
Chicago/Turabian StyleKarim, K. M. Rezaul, Mohd Y. Rafii, Azizah Misran, Mohd Firdaus Ismail, Abdul Rahim Harun, Raihana Ridzuan, Mst. Farhana Nazneen Chowdhury, Monir Hosen, Oladosu Yusuff, and Md Azadul Haque. 2022. "Genetic Diversity Analysis among Capsicum annuum Mutants Based on Morpho-Physiological and Yield Traits" Agronomy 12, no. 10: 2436. https://doi.org/10.3390/agronomy12102436