Research Article

Association of Leaf Related Traits and Boll Weight in Cotton

Muhammad Qadir Ahmad¹*, Farheen Ashraf Raza¹, Abdul Qayyum¹, Waqas Malik¹, Rao Wali Muhammad2, Muhammad Asif Saleem1, Amir Hamza2, Ahmad Baksh Mahar3

1Department of Plant Breeding and Genetics, Faculty of Agricultural Sciences and Technology, Bahauddin Zakariya University, Multan; 2PARC, Research and training station, Faculty of Agricultural Sciences and Technology, Bahauddin Zakariya University, Multan; 3Pakistan Agricultural Research Council, Islamabad, Pakistan.

Received | 12 December, 2016; Accepted | 07 April, 2017; Published | June 18, 2017

*Correspondence | Muhammad Qadir Ahmad, Department of Plant Breeding and Genetics, Faculty of Agricultural Sciences and Technology, Bahauddin Zakariya University, Multan; Email: mqadirahmad@bzu.edu.pk

Citation | Ahmad, M.Q. , F.A. Raza, A.Qayyum, W. Malik, R.W. Muhammad, M.A. Saleem, A. Hamza and A.B. Mahar. 2017. Association of Leaf Related Traits and Boll Weight in Cotton. Pakistan Journal of Agricultural Research, 30(2): 129-135.

DOI | http://dx.doi.org/10.17582/journal.pjar/2017/30.2.129.135

Keywords | Association, Yield, Leaf, Genotype, Variation

Introduction

Cotton, (Gossypium hirsutum L.) popularly referred as “White Gold”, is an important fiber crop grown in tropical and sub-tropical regions around the globe (Dinakarana, 2012). Cotton is extensively cultivated in more than eighty countries across the globe and it provides financial stability to many people (Latif et al., 2015). Pakistan’s economy largely depends upon cotton production.

Cotton yield can be determined on the basis of following traits such as boll weight, number of bolls per plant, number of plants, seed index and number of seeds per boll. Leaf plays important role in plants phenotype. Leaf is a “power house” of plant as it synthesizes food with the help of chlorophyll (Gitelson et al., 2003). Researchers have theshape and size of leaf is a balance between leaf energy exchange, leaf temperature and photosynthesis. Physiological process such as photorespiration protects cotton plant from damaging? of oxygen level and high intensities of light (Givnish et al., 1976). Different leaf traits have shown vital significance in final plant yield. Some leaf traits such as leaf color, leaf hairiness have been extensively used by plant breeders to produce

resistant genotypes (Nawab, 2014). In cotton, life cycle of a leaf starts with initiation of a bud and become

Table 1: Mean square values of the studies traits.

net exporter of carbohydrates within few days. Emerging bolls extract maximum of their nutrition from the subtending leaves. During the period of boll filling, leaves age quickly. Therefore, breeding efforts and management practices should be designed to incorporate additional carbohydrates into bolls and fewer into leaves. Competition for photosynthates prevails during various developmental stages (Stewart, 1989). Raper et al. (2013) reported close positive correlation between leaf nitrogen status and yield. Leaf area was also found positively associated with yield (Samba Murthy and Chamundeswari, 2006). Similarly, Nawab (2014) reported a strong association between leaf hairiness and insect resistance. Yield is defined as a composite measureable trait, which is influenced by environment. Therefore, yield is not only single effective measurable trait for selection. Selection had to be made for the components of yield also. Boll weight is considered as a major component of yield. Previously some leaf traits were found to be associated with boll weight. Therefore, current research was carried out to (i) observe association of leaf and yield related traits (ii) to observe direct and indirect effect of different traits on boll weight.

Materials and Methods

The experiment was undertaken at the research area of the department of Plant Breeding and Genetics, Faculty of Agricultural Sciences and Technology Bahauddin Zakariya University, Multan, situated on latitude: 30°15’29.09” and longitude: 71°30’50.54” during 2014. The physio-chemical exploration of soil indicated loamy in texture (silt 46.36%, sand 38.36% and clay 15.28%). During crop cultivation temperature was ranged from30° 76.C to 37° C. The study comprises of 20 genotypes of tetraploid cotton. The experiment was replicated three times using randomized complete block design. Plant to plant (P*P) and Row to row (R*R) distance was maintained 9 cm and 12 cm, respectively. The data was collected from five tagged plants from each replication for the leaf related traits such as leaf area (LA), leaf angle position (LP), number of lobs per leaf (NL), leaf chlorophyll contents/spad value (SV), relative water content (RWC), leaf hairiness (HL), leaf color grading (LC) and yield related traits such as boll weight (BW), number of seeds per boll (NS), number of locules per boll (NLB), seed index (SI) and seed volume (SEV). For leaf color grading, leaves showed dark green color were assigned with letter (A), medium green color (B) and light green color with (C). Genetic divergence among genotypes was accounted using analysis of variance (ANOVA) with the help of Statistix 8.1 (Ahmad et al., 2012). Correlation analysis was performed with the help of SAS code v 9.4 software and path coefficient analysis was done using Ms XL stats, diagrammatic illustration was developed with the help of Smart Draw Ci v 22.0.1

Results and Discussion

Please describe your results

Analysis of variance showed significant variation among studied genotypes for leaf related traits LA, LP, NL, SV, RWC and yield traits such as BW, NSl, NLB, SI, SEV as illustrated in Table 1. This high degree of variation suggested that genetic material is diverse in nature and should be given due importance for further studies. Some other researchers also found variation for leaf and yield related traits (Ahmad et al., 2012; Haidar et al., 2012; Rahman et al., 2013; kitajima, 1996). This variation provides sufficient scope to plant breeders because high degree of variability is essential for effective breeding programs to enhance various traits (Ahsan et al., 2015).

First discuss your results and conclude your work with other subtests

Several researchers have executed mean performance of cotton genotypes for physiological, morphological and yield traits in upland cotton (Arshad et al., 1993). In current study, for LA, MPS-11 showed maximum value (14.16cm²) and genotype Trend-1 showed minimum

Table 2: Average performance of genotypes.

Table 3: Pearson correlation analysis among studied traits.

value (9.44cm²). Similarly for BW, the genotype V-89 accounted maximum boll weight (3.72gm) whereas; the genotype CRIS-510 depicted lowest value (1.44gm) of boll weight. Widespread ranges of values for rest of the traits are depicted in Table 2.

Some traits of leaves were qualitative in nature. Therefore, there biometrical measurements could not be made. Qualitative leaf traits such as leaf color and leaf hairiness also contributes to yield. Leaf color is commonly used to measure plant leaf nitrogen level

Table 4: Path coefficient analysis results showing direct and indirect effect of various traits in boll weight.

(Murakami et al., 2005). Cotton genotypes V-85, Sayban-201, Trend-1and V-68 showed (grad A) dark green color of leaf. Similarly VH-300, CIM-598, VS-303, MNH-846, CIM-506, MNH-456, V-89, AGC-707 and V-93 showed (grade B) green color leaves. Moreover, CAM-5, SLH-4, MPS-11, CRIS-510, Gomal-93, V-17, and CRIS-510 were ranked grade C (light green) leaves. Qualitative grading was done to find out distribution of trichomes on leaves demonstrated by Kloth (1995). VH-300, V-85, CIM-598, VS-303, MNH-846, Gomal-93 and AGC-707 have pilose type hairiness on leaves. While CAM-5, SLH-4, MPS-11, MNH-456, V-89, CRIS-510, Sayban-201, Trend-1, V-68, V-17, CRIS-510 and V-93 showed intermediate type of hairiness.

To determine the association among different traits, Pearson correlation analysis was performed (Farooq et al., 2014) which revealed various levels of association among these traits Table 3. LA expressed significant and positive association with NL. NL showed positive and significant association with BW. Positive and significant association was found between SV and SI. The association among BW was highly significant with LB, SB and SI. NLB showed highly significant association with SB and significant correlation with SI. Similarly high significant correlation was found among SB and SEV. Significant and positive association was found among SI and SEV.

High significant and positive correlation of BW with NS and SI had been reported by many researchers (Boggs et al., 2003; Killi et al., 2005; Karademir, 2009). Ahmad et al. (2008) revealed positive association between BW and cotton seed yield. In this study chlorophyll value showed positive significant association with seed index, whereas previously some researchers reported conversely. Researchers suggested that chlorophyll reduction in leaf may provide better yield because chloroplasts are rich in nutrients and reduction in their quantity may give rise to existing nutrients for progress and development of plant fruit bearing (Hamblin et al., 2014).

A straightforward association does not provide ideal picture of the contribution of numerous traits in total yield. Therefore, Path coefficient analysis was performed to partition the correlation into direct and indirect effects on boll weight (Dinakaran et al., 2012; Shahriari et al., 2014). Path coefficient revealed positive direct effect of SB on BW Table 4. Similar results were also reported by (Hazem and Baytay, 2005; Ahmed et al., 2003). SI illustrated positive indirect effect on BW via LA (Wendt et al., 1967). V-89 showed moderate trichomes and medium green color of leaf. Similar results had been reported by (Knight, 1952; Niles, 1980). Direct and indirect effects of various plant traits on boll weight are well represented in Figure 1.

Cotton genotype V-89 performed best in yield traits but the leaf traits found in V-89 were surprisingly moderate. Varieties having larger LA like MPS-11 (14.17cm²), VH-300 (13.81cm²) and MNH-456 (13.28cm²) or LP like CIM-506 (100.00°) and V-68 (83.33°) or SV like V-85 (66.97μmol/m²) and Trend-1 (59.87μmol/m²) or RWC like CAM-5 (36.10%) and CIM-506 (34.55%) were found having moderate or low yield traits. Previously some researchers also reported that genotypes having moderate leaf traits perform better in terms of yield (Monks et al., 1999; Kerby et al., 1980; Ekinci et al., 2008; Hamblin, 2014). Therefore on the basis of results of this research we are of the view that breeders should develop plants having moderate leaf traits instead of bigger in size to get higher yields. When plant uses all its energy to strengthen the vegetation, less potential remains in it to perform best in terms of yield (Hamblin, 2014). It does not mean that plant with poor leaf traits will perform best in yield. A moderate and balance performance is required to achieve goals.

Authors Contribution

MQA planned the research work; FHA recorded the data and wrote the manuscript and AQ and WM reviewed the manuscript. RWA and AH performed statistical analysis. MAS presented the data graphically and ABM did the final revision of the manuscript.

References

Ahmad, M.Q., S.H. Khan and F.M. Azhar. 2012. Decreasing level of genetic diversity in germplasm and cultivars of upland cotton (Gossypium hirsutum L.) in Pakistan. J. Agric. Soc. Sci. 8: 92–96.

Ahmad, W., N.U. Khan, M.R. Khalil, A. Parveen, U. Aiman, M. Saeed, Samiullah and S.A. Shah. 2008. Genetic variability and correlation analysis in upland cotton. Sarhad J. Agric. 24: 573-580.

Ahmed, H.M., B.M. Khan, S. Khan, N. Kissana and S. Laghari. 2003. Path coefficient analysis in bread wheat, Asian J. Plant Sci. 2: 491-494. https://doi.org/10.3923/ajps.2003.491.494

Ahsan, M.Z., M.S. Majidano, H. Bhutto, A.W. Soomro, F.H. Pahnwar, A.R. Channa and K.B. Sial. 2015. Genetic Variability, Coefficient of Variance, Heritability and Genetic Advance of Some Gossypium hirsutum L. Accessions. J. Agric. Sci. 7(2): 147-151. https://doi.org/10.5539/jas.v7n2p147

Arshad, M., M. Hanif, N. Ilahi and S.M. Shah. 1993. Correlation studies on some commercial cotton varieties of G. hirsutum. Sarhad J. Agric. 9: 49-53.

Boggs, J.L., T.D. Tsegaye, T.L. Coleman, K.C. Reddy and F. Ahmed. 2003. Relationship between hyperspectral reflectance, soil nitrate-nitrogen, cotton leaf chlorophyll, and cotton yield. J. Sustain. Agric. 22: 5-16. https://doi.org/10.1300/J064v22n03_03

Dinakaran, E., S. Thirumeni and K. Paramasivam. 2012. Yield and fibre quality components analysis in upland cotton (Gossypium hirsutum L.) under salinity. Annals Biol. Res. 3(8): 3910–3915.

Ekinci, R., O. Gencer and S. Basbag. 2008. Correlation between some physio-morphological formations and yield on okra and normal leaf cottons. J. Agric. Sci. (In Press).

Farooq, J., M. Anwar, M. Riaz, A. Farooq, A. Mahmood, M.T.H. Shahid, M.S. Rafiq and F. Ilahi. 2014. Correlation and path coefficient analysis of earliness, fiber quality and yield contributing traits in cotton (Gossypium hirsutum L.). JAPS. 24: 781-790.

Gitelson, A.A., Y.Gritz and M.N. Merzlyak. 2003. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol. 160: 271-282. https://doi.org/10.1078/0176-1617-00887

Givnish, T.J. and G.J. Vermeij. 1976. Sizes and shapes of liane leaves. Am.erican naturalist. 743-778. https://doi.org/10.1086/283101

Haidar, S., M. Aslam, M.U. Hassan, H.M. Hassan and A. Ditta. 2012. Genetic diversity among upland cotton genotypes for different economic traits and response to cotton leaf curl virus (CLCV) disease. Pak. J. Bot. 44(5): 1779–1784.

Hamblin, J., K. Stefanova and T.T. Angessa. 2014. Variation in chlorophyll content per unit leaf larea in spring wheat and implications for selection in segregating material. PLoS ONE 9(3): e92529. https://doi.org/10.1371/journal.pone.0092529

Hazem, M. and A. Bayaty. 2005. Path coefficient analysis in upland cotton. (Gossypium hirsutum L.) Mesopatamia J. Agric. 33(3): 1-8.

Karademir, C., E. Karademir, R. Ekinci and O. Gencer. 2009. Correlations and path coefficient analysis between leaf chlorophyll content, yield and yield components in cotton (Gossypium hirsutum L.) under drought stress conditions, Not. Bot. Hort. Agrobot. Cluj. 37: 241-244.

Kerby, T.A., D.R. Buxton and K. Matsuda. 1980. Carbon sourcesink relationships within narrow-row cotton canopies. Crop Sci. 20: 208–213. https://doi.org/10.2135/cropsci1980.0011183X002000020015x

Killi. F., L. Efe and S. Mustafayev. 2005. Genetic and environmental variability in yield, yield components and lint quality traits of cotton. Int. J. Agric. Biol. 6: 1007- 1010.

Kitajima, K. 1996. Ecophysiology of tropical tree seedlings, tropical forest plant ecophysiology New York, USA. 559–597. https://doi.org/10.1007/978-1-4613-1163-8_19

Kloth, R.H. 1995. Interaction of two loci that affect trichome density in upland cotton. Heredity. 86: 78-80. https://doi.org/10.1093/oxfordjournals.jhered.a111536

Knight RL, 1952. The genetics of jassid resistance in cotton. I. The genes H1 and H2. Genetics. 51: 46-66.

Latif, A., M. Bilal, S.B. Hussain and F. Ahmad. 2015. Estimation of genetic divergence, association, direct and indirect effects of yield with other attributes in cotton (Gossypium hirsutum L.) using biplot correlation and path coefficient analysis. Trop. Plant Res. 2(2): 120–126.

Monks, C.D., M.G. Patterson, J.W. Wilcut and D.P. Delaney. 1999. Effect of pyrithiobac, MSMA, and DSMA on cotton (Gossypium hirsutum L.) growth and weed control. Weed Technol. 13: 6-11.

Murakami, P.F., M.R. Turner, A.K. Berg and P.G. Schaberg. 2005. An instructional guide for leaf color analysis using digital imaging software, General Technical Report. Department of Agriculture, Forest Service, Northeastern Research Station. U.S.A.

Nawab, N.N., A. Mehmood, G. Jeelani, M. Farooq and T.N. Khan. 2014. Inheritence of okra leaf type, gossypol glands and trichomes in cotton. JAPS. 24: 526-533.

Niles, G.A. 1980. Breeding cotton for resistance to insects. In: Cotton Breeding, II nd edition, 2004 (Eds): Phundun Singh., Kalyani Publishers, New Delhi. pp. 136-146.

Raper, T.B., J.J. Varco and K.J. Hubbard. 2013. Canopy-based normalized difference vegetation index sensors for monitoring cotton nitrogen status. Agron. J. 105(5): 1345. https://doi.org/10.2134/agronj2013.0080

Rahman, S.A., M.S. Iqbal, M. Riaz, A. Mahmood, M.R. Shahid, G. Abbas and J. Farooq. 2013. Cause and effect estimates for yield contributing and morphological traits inupland cotton (Gossypium hirsutum L.) J. Agric. Res. 51(4): 393-398.

Samba Murthy, J.S.V. and N. Chamundeswari. 2006. Yield component analysis in introgressed lines of upland cotton (Gossypium hirsutum L.) J. Cotton Res. Dev. 20 (1): 1-4.

Shahriari, A.A.B.P., G.B. Saleh and A.B.A. Rahim, 2014. Germination at low osmotic potential as selection criteria for drought stress tolerance in sweet corn. Afr. J. Biotechnol. 13(2): 294–300. https://doi.org/10.5897/AJB11.3435

Stewart, J.M.D. 1989. Integrated events in the flower and fruit. Cotton Physiol. 1: 261-300.

Wendt, C.W. 1967. Use of a relationship between leaf length and leaf area to estimate the leaf area of cotton (Gossypium hirsutum L.), Agron. J. 59: 484-48. https://doi.org/10.2134/agronj1967.00021962005900050034x