Study the relationships between seed cotton yield and yield component traits by different statistical techniques

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Research Paper 01/05/2016
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Study the relationships between seed cotton yield and yield component traits by different statistical techniques

Ashraf Abd El-Aala Abd El-Mohsen, Mohamed Mostafa Amein
Int. J. Agron. Agri. Res.8( 5), 88-104, May 2016.
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Two field experiments were conducted in 2013 and 2014 growing seasons at the experimental farm of the Faculty of Agriculture, Cairo University, Giza, Egypt. Twenty Egyptian cotton genotypes were evaluated in a randomized complete blocks design with three replications for six traits. The aim of this study was to determine the relationships between seed cotton yield and yield components and to show efficiency of components on seed cotton yield by using different statistical procedures. Data of seed cotton yield and yield components over the two years in the study were evaluated by statistical procedures; correlation and regression analysis, path coefficient analysis, stepwise multiple linear regression and factor analysis. Differences among all the traits were statistically highly significant. Seed cotton yield plant-1 was significantly and positively correlated with number of bolls plant-1 (r = 0.85**), boll weight (r = 0.68**), seed index (r = 0.91**) and lint percentage (r = 0.70**). Regression analysis by using step-wise method revealed that 96.51 percent of total variation exist in seed cotton yield accounted for by traits entered to regression model namely; number of bolls plant-1, boll weight and lint percentage. The path analysis indicated high positive direct effect of number of bolls plant-1 (0.57), boll weight (0.39) and lint percentage had moderate positive direct effect (0.24) on seed cotton yield plant-1. Factor analysis indicated that three factors could explain approximately 73.96% of the total variation. The first factor which accounted for about 53.21% of the variation was strongly associated with number of bolls plant-1, boll weight, seed index and lint percentage, whereas the second factor was strongly associated and positive effects on earliness index only, which accounts for about 20.75% of the variation. Stepwise multiple regression and path analysis techniques were more efficient than other used statistical techniques. Based on the five of statistical analysis techniques, agreed upon that high seed cotton yield of Egyptian cotton could be obtained by selecting breeding materials with high number of bolls plant-1 , boll weight and lint percentage.


Afiah SAN, Ghoneim EM. 2000. Correlation, stepwise and path coefficient analysis in Egyptian cotton under saline conditions. Arab University Journal of Agricultural Science 8, 607-618.

Ahmad W, Khan NU, Khalil MR, Parveen A, Aimen U, Saeed, Samiullah M, Shah SA. 2008. Genetic variability and correlation analysis in upland cotton. Sarhad Journal of Agriculture 24, 573-580.

Ahuja L, Dhayal LS, Prakash R. 2006. A correlation and path coefficient analysis of components in G. hirsutum L. hybrids by usual and fiber quality grouping. Turkish Journal of Agriculture and Forestry 30, 317-324.

Alishah O, Bagherieh-Najjar MB, Fahmideh L. 2008. Correlation, path coefficient and factor analysis of some quantitative and agronomic traits in cotton (Gossypium hirsutum L.). Asian Journal of Biological Sciences 1(2), 61-68.

Bartlett MS. 1937. Some examples of statistical methods of research in agriculture and applied biology. Journal of the Royal Statistical Society Supplement 4, 137- 185.

Bramel PJ, Hinnze PN, Green DE, Shibles RM. 1984. Use of principal actor analysis in the study of three stem termination type’s of soybean. Euphytica 33, 387-400.

Brejda JJ. 1998. Factor analysis of nutrient distribution patterns under shrub live-oak two contrasting soils. Soil Science Society of America Journal 62, 805-809.

Cooper JCB. 1983. Factor analysis. An overview Am. Statist 37, 141-147.

Copur O. 2006. Determination of yield and yield components of some cotton cultivars in semi arid conditions. Pakistan Journal Biological Sciences 9(14), 2572-2578.

DeGui Z, FanLing K, QunYuan Z, WenXin L, FuXin Y, NaiYin X, Qin L, Kui Z. 2003. Genetic improvement of cotton varieties in the Yangtse valley in China since 1950s. I. Improvement on yield and yield components. Acta Agronomica Sinica Journal 29(2), 208-215.

Dewey DR, Lu KH. 1959. A correlation and path coefficient analysis of components of crusted wheat grass seed production, Agronomy Journal 51, 515-518 05100090002x

Di Rienzo JA, Casanoves F, Balzarini MG, Gonzalez L, Tablada M, Robledo CW. 2010. InfoStat Versi´on, Grupo InfoStat, FCA, Universidad Nacional de C´ordoba, Brujas, Argentina.

Draper NR, Smith H. 1981. Applied regression analysis .2nd ed. John Willy and Sons, New York.

El-Badawy MElM. 2006. The relative contribution of yield components by using specific statistical techniques in corn. Annals of Agricultural Science, Moshtohor 44(3), 899-909.

El-Kady DA, Abd El-Mohsen AA, Abdel Latif HM. 2015. Evaluating bivariate and multivariate statistical analysis of yield and agronomic characters in Egyptian cotton. Scientia Agriculturae 9(3), 150-164.

Farshadfar E. 2004. Multivariate principles and procedures of statistics. Tagh Bostan Publications Kermanshah, Iran. 734 P.

Freed R, Einensmith SP, Gutez S, Reicosky D, Smail VW, Wolberg P. 1989. User’s Guide to MSTAT-C Analysis of agronomic research experiments. Michigan State University, East Lansing, U.S.A.

Gomaa MAM, Shaheen AMA and Khattab SAM. 1999. Gene action and selection indices in two cotton (Gossypium barbadense L.) crosses. Annals of Agricultural Science, Cairo 44(1), 293-308.

Gomez KA, Gomez AA. 1984. Statistical Procedures for Agricultural Research. 2nd ed., New York: John Wiley and Sons, Inc. p: 108-116.

Hair JF, Anderson RE, Tatham RL, Black WC. 1995. Multivariate Data Analysis (3rd  ed). New York: Macmillan.

Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL. 2006. Multivariate Data Analysis. New Jersey: Pearson University Press.

Ikiz F, Puskulcu H, Eren S. 2006. Introduction to Statistics, Barıs Press, Bornova, Izmir, 548 P.

Johnson RA, Wicheren DW. 1996. Applied multivariate statistical analysis. Prentice Hall of India, New Delhi.

Khadijah M, Khan NU, Batool S, Z. Bibi, Farhatullah, S. Khan, Mehmood F, Hussain D, Raziuddin M, Sajad Khan N. 2010. Genetic aptitude and correlation studies in Gossypium hirsutum L. Pakistan Journal Botany 42(3), 2011-2017.

Khan NU, Hassan G, Kumbhar MB, Parveen A, Aiman U, Ahmad W, Shah SA, Ahmad S. 2007. Gene action of seed traits and oil content in upland cotton (G. hirsutum). Sabrao Journal of Breeding and Genetics 39, 17-30.

Kim J, Kohout FJ. 1975. Multiple regression analysis, in: Nie N.H., Hull C.H., Jenkins J.G., Steinbrenner K., Bent D.H. (Eds.), Statistical package for the social sciences, McGraw Hill, New York, p. 320-367.

Kim J. 1975. Factor analysis, in: Nie NH, Hull CH, Jenkins JG, Steinbrenner K., Bent DH (Eds.), Statistical package for the social sciences, McGraw Hill, New York, p: 468-514.

Lenka D, Mishra B. 1973. Path coefficient analysis of yield in rice varieties. Indian Journal of Agricultural Sciences 43, 376-379.

Mahdi AHA. 2014. Correlation and path coefficient analysis of lint yield and its components in Egyptian cotton. Bulletin of Faculty of Agriculture, Cairo University 65, 398-404.

Massart DL, Vandeginste BGM, Buydens LMC, de Jong S, Lewi PG, Smeyers-Verbeke J. 1997. Straight line regression and calibration. In Handbook of chemometrics and qualimetrics, Part A, p: 171-231, Amsterdam, The Netherlands: Elsevier.

Meena RA, Monga D, Kumar R. 2007. Undescriptive cotton cultivars of north zone: an evaluation. Journal of Cotton Research and Development 21(1), 21-23.

Minitab. 2015. Minitab 16 statistical software, Minitab Inc. httb://

Neter  J,  Wasserman  W,  Kutner  MH.  1989. Applied Linear Regression Models. Homewood, IL: Irwin.

Rauf S, Khan TM, Sadaqat HA, Khan AI. 2004. Correlation and path coefficient analysis of yield components in cotton (G. hirsutum L.). International Journal of Agriculture and Biology 6(4), 686-688.

Salahuddin S, Abro S, Rehman A, Iqbal K. 2010. Correlation analysis of seed cotton yield with some quantitative traits in upland cotton ( Gossypium hirsutum L.). Pakistan Journal Botany 42(6), 3799-3805.

Santoshkumar M, Urbi B, Rajasekaran R, Krishnasamy T, Boopathi M, Sankaran R. 2012. Association analysis of yield and fiber quality characters in inter specific population of cotton (Gossypium spp.). Journal of Crop Science and Biotechnology 15 (3), 239-243.

Seiller GJ, Stafford RE. 1985. Factor analysis of components in guar. Crop Science 25, 905-908.

Snedecor GW, Cochran GW. 1980. Statistical methods. Iowa, U.S.A. The Iowa University Press.

Soomro AR, Kakar RG, Ali H, Abid SA. 2005. Comparison of yield and its components in some commercial cotton varieties. Industrial Journal of Plant Science 4(4), 545-552.

Soomro ZA, Larik AS, Kumbhar MB, Khan NU, Panhwar NA. 2008. Correlation and path analysis in hybrid cotton. Sabrao Journal of Breeding and Genetics 40, 49-56.

SPSS. 2009. SPSS for Windows, version 17.0.0. SPSS Inc., Chicago, USA.

Steel R, Torrie J, Dicky D. 1997. Principles and Procedures of Statistics; A Biometrical Approach. 3rd Ed. W.C.B/McGraw-Hill, New York.

Suinaga, FA, Bastos CS, Rangel LEP. 2006. Phenotypic adaptability and stability of cotton cultivars in Mato Grosso State, Brazil. Pesquisa Agropecuaria Tropical 36(3), 145-150.

Tousi M, Ghanadha MR, Khodarahimi M, Shahabi S. 2005. Factor analysis for grain yield and other attributes in bread wheat. J. Pazhohesh, Sazandegi 66, 9-16.

Walton PD. 1971. The use of factor analysis in determining characters for yield selection in wheat. Euphytica 20, 416-421.

Wright S. 1921. Correlation and causation. Journal of Agricultural Research 20, 557-587.