STRUCTURAL EQUATION MODEL AS A TOOL TO ASSESS THE RELATIONSHIP BETWEEN GRAIN YIELD PER PLANT AND YIELD COMPONENTS IN DOUBLED HAPLOID SPRING BARLEY LINES (HORDEUM VULGARE L.)



Abstract

The aim of this study was to describe and characterize the relationships between yielding factors and grain yield per doubled haploid (DH) plant of spring barley as well as relation between yield components and duration of each stage of plant development. To describe these relations structure equation modeling was used. The study included plants of doubled haploid spring barley lines (Hordeum vulgare L.) derived from tworowed form of Scarlett cultivar. The SAS® system was used to analyze the model of relationships between grain yield per plant and yield components. Our results indicate that the number of spikes per plant and grain yield per spike had a direct and decisive influence on the grain yield of the investigated DH plants of spring barley. Based on the path model analysis it was found that the most important factor determining grain yield per DH plants of spring barley was the number of spikes per plant and the duration of tillering and shooting stages.


Keywords

doubled haploids; Hordeum vulgare L.; Structural Equation Modeling (SEM); yield related traits

Armitage, P., Colton, T. (eds.), 2005. Encyclopedia of Biostatistics, 2nd edition. John Wiley & Sons Inc., Hoboken, USA.

Bentler, P.M., Yuan, K.-H., 1999. Structural equation modeling with small samples: Test statistics. Multivariate Behavioral Research, 34(2), 181–197.

Bollen, K.A., 1989. Structural Equations with Latent Variables. Wiley & Sons Inc., NY, USA.

Eaton, G.W., 1986. Two-dimensional partitioning of yield variation. Hort. Sci., 21(4): 1052–1053.

Eaton, G.W., Kyte, T.R., 1978. Yield component analysis in strawberry. Journal of the American Society for Horticultural Science 103: 578–583.

Eaton, G.W., McPherson, E.A., 1978. Morphological component of yield in cranberry. Horticultural Research 17: 73–82.

Freaser, J., Eaton, G.W., 1983. Applications of yield component analysis to crop research. Field Crop Abstracts 36, 787–796. del Moral, G.M.B., del Moral, G.L.F., 1995. Tiller production and survival in relation to grain yield in winter and spring barley. Fields Crops Research, 44: 85–93.

Gołaszewski, J., 1996. A method of yield component analysis. Biometrical Letters, 33(2): 79–88.

Gołaszewski, J., Idźkowska, M., Milewska, J., 1998. The TDP method of seed yield component analysis in grain legume breeding. J. Appl. Genet., 39(4): 299–308.

Gozdowski, D., Kozak, M., Kang, M.S., Wyszyński, Z., 2007. Dependence of grain weight of spring barley genotypes on trials of individual stems. Journal of Crop Improvement 20(1/2), 223–233.

Guillen-Portal, F.R., Stougaard, R.N., Xue, Q., Eskridge, K.M., 2006. Compensatory mechanisms associated with the effect of spring wheat seed size on wild oat competition. Crop Science, 46, 935–945.

Hatcher, H., 1994. A Step-by-Step Approach to Using SAS® for Factor Analysis and Structural Equation Modeling. Carry, NC: SAS Institute Inc.

Hay, R., Porter, J., 2006. The physiology of crop yield. Balckwell Publishing, Oxford, UK.

Hu L.-T., Bentler P.M. 1998. Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3(4), 424—453.

Hu L.-T., Bentler P.M. 1999. Cutoff criteria for fit indices in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1—55.

Kenny, D.A., 1979. Correlation and causality. John Wiley & Sons Inc., Hoboken, USA.

Khazaei, J., Naghavi, M.R., Jahansouz, M.R., Salimi-Khorshidi, G., 2008. Yield estimation and clustering of chickpea genotypes using soft computing techniques. Agronomy Journal 100, 1077–1087.

Kirby, E.J.M., Appleyard, M., 1984. Cereal plant development and its relation to crop management. In: Cereal production. Proceedings of the Second International Summer School in Agriculture held by the Rogal Dublin Society in cooperation with WK Kellog Foundation. Gallagher E. J. (Ed.) Dublin. 161–173.

Klepper, B., Rickman, R.W., Waldman, S., Chevalier, P., 1998. The physiological life cycle of wheat: Its use in breeding and crop management. Euphytica 100, 341–347.

Kozak, M., 2004. New concept of yield component analysis. Biometrical Letters 41(2), 59–69.

Kozak, M., 2006. Two-dimensional partitioning of yield variation: A critical note. Plant Breeding and Seed Science, 53: 37–42.

Kozak, M., 2007. Ontogenetic chain and Markov condition in crop science. Nature and Science, 3(5): 5–8.

Kozak, M., Azevedo, R.A., 2010. Does using stepwise variable selection to build sequential path analysis models make sense? Physiologia Plantarum, DOI: 10.1111/j.1399-3054.2010.014341.x.

Kozak, M., Bocianowski, J., Rybiński, W., 2008. Selection of promising genotypes based on path and cluster analyses. Journal of Agricultural Science, 146: 85–92.

Kozak, M., Kang, M.S., 2006. Note on modern path analysis in application to crop science. Communications in Biometry and Crop Science Vol. 1, No. 1: 32–34.

Kozak, M., Singh, P.K., Verma, R.M., Hore, D.K., 2007. Causal mechanism for determination of grain yield and milling quality of lowland rice. Field Crops Research 102, 178–184.

Kozdój, J., Mańkowski, D.R., Oleszczuk, S., 2010. Analysis of the potential for yield formation in doubled haploids of spring barley (Hordeum vulgare L.) obtained via androgenesis (in Polish). Biull. PBAI, 256: 97–116.

Levenberg, K., 1944. A method for the solution of certain non-linear problems in least squares. The Quarterly of Applied Mathematics, 2, 164–168.

MacCallum, R.C., Browne, M.W., Sugawara, H.M., 1996. Power analysis and determination of sample size for covariance structure modeling. Multivariate Behavioral Research, 1(2), 130–149.

Madsen, K., Nielsen, H.B., Tingleff, O., 2004. Methods for non-linear least squares problems. 2nd edition.

Informatics and Mathematical Modeling, Technical University of Denmark.

Maman, N., Mason, S.C., Lyon, D.J., Dhungana, P., 2004. Yield components of pearl millet and grain sorgum across environments in the central great plains. Crop Science, 44, 2138–2145.

Mańkowski D.R., 2013. Structural equation models SEM in agricultural research. PBAI-NRI Monographs and Dissertations No. 42, Radzików, Poland. (in polish)

Marquardt, D., 1963. An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal of Applied Mathematics, 11, 431–441.

McDonald, R.P., Hartmann, W., 1992. A procedure for obtaining initial values of parameters in the RAM model. Multivariate Behavioral Research, 27, 57–176.

Mulaik S.A., van Alstine J., James L.R., Bennett N., Lind S., Stilwell C.D. 1989. An evaluation of goodnessof-fit indices for structural equation models. Psychological Bulletin, 105, 430—445.

Nátrová, Z., Jokeš, M., 1993. A proposal for a decimal scale of the inflorescence development of wheat. Rostl. Vyr. 4: 315–328.

Naylor, R.E.L., Munro, L.M. 1993. Apical development in barley and wheat varieties. Aspects of Applied Biology. 34: 105–110.

Peltonen-Sainio,P., Jauhiainen L., Rajala, A., Muurinen, S., 2009. Tiller traits of spring cereals under tillerdepressing long day conditions. Field Crops Research, 113: 82–89.

SAS Institute Inc., 2009. SAS/STAT 9.2 User’s Guide, Second Edition. SAS Publishing, SAS Institute Inc., Cary, NC, USA.

Shipley B. 2002. Cause and correlation in biology. A user’s guide to path analysis, structural equations and causal inference. Cambridge University Press, Cambridge.

Sreenivasulu, N., Schnurbusch, T., 2012. A genetic playground for enhancing grain number in cereals. Trends Nátrová, Z., Jokeš, M., 1993. A proposal for a decimal scale of the inflorescence development of wheat. Rostl. Vyr. 4: 315–328.

Naylor, R.E.L., Munro, L.M. 1993. Apical development in barley and wheat varieties. Aspects of Applied Biology. 34: 105–110.

Peltonen-Sainio,P., Jauhiainen L., Rajala, A., Muurinen, S., 2009. Tiller traits of spring cereals under tillerdepressing long day conditions. Field Crops Research, 113: 82–89.

SAS Institute Inc., 2009. SAS/STAT 9.2 User’s Guide, Second Edition. SAS Publishing, SAS Institute Inc., Cary, NC, USA.

Shipley B. 2002. Cause and correlation in biology. A user’s guide to path analysis, structural equations and causal inference. Cambridge University Press, Cambridge.

Sreenivasulu, N., Schnurbusch, T., 2012. A genetic playground for enhancing grain number in cereals. Trends in Plant Science. 17, 2: 91–101.

Timm, N.H., 2002. Applied multivariate analysis. Springer-Verlag Inc., New York, USA.

Ugarte, C., Calderini, D.F., Slafer, G.A., 2007. Grain weight and grain number responsiveness to pre-anthesis temperature in wheat, barley and triticale. Field Crops Res. 100: 240–248

Vargas, M., Crossa, J., Reynolds, M.P., Ghungana, P., Eskridge, K.M., 2007. Structural equation modeling for studying genotype × environment interactions for physiological traits affecting yield in wheat. Journal of Agricultural Science 145, 151–161.

Williams, R.F., 1975. The growth o fan inflorescence. In: The shoot apex and Lear growth. A study in quantitative biology. Cambridge Univ. Press: 183–198.

Wright, S., 1921. Correlation and causation. Journal of Agricultural Research 20, 557–585.

Yung, Y.-F., 2008. Structural Equation Modeling and Path Analysis Using PROC TCALIS in SAS®

2. SAS

Global Forum 2008 Proceedings; paper 384-2008.

Zadoks, J.C., Chang, T.T., Konzak, G.F., 1974. A decimal code for growth stages of cereals. Weed Research

, 415–421. in Plant Science. 17, 2: 91–101.

Timm, N.H., 2002. Applied multivariate analysis. Springer-Verlag Inc., New York, USA.

Ugarte, C., Calderini, D.F., Slafer, G.A., 2007. Grain weight and grain number responsiveness to pre-anthesis temperature in wheat, barley and triticale. Field Crops Res. 100: 240–248

Vargas, M., Crossa, J., Reynolds, M.P., Ghungana, P., Eskridge, K.M., 2007. Structural equation modeling for studying genotype × environment interactions for physiological traits affecting yield in wheat. Journal of Agricultural Science 145, 151–161.

Williams, R.F., 1975. The growth o fan inflorescence. In: The shoot apex and Lear growth. A study in quantitative biology. Cambridge Univ. Press: 183–198.

Wright, S., 1921. Correlation and causation. Journal of Agricultural Research 20, 557–585.

Yung, Y.-F., 2008. Structural Equation Modeling and Path Analysis Using PROC TCALIS in SAS® 9.2. SAS Global Forum 2008 Proceedings; paper 384-2008.

Zadoks, J.C., Chang, T.T., Konzak, G.F., 1974. A decimal code for growth stages of cereals. Weed Research 14, 415–421.

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Published : 2016-06-20


Mańkowski, D. R., Kozdój, J., & Janaszek-Mańkowska, M. (2016). STRUCTURAL EQUATION MODEL AS A TOOL TO ASSESS THE RELATIONSHIP BETWEEN GRAIN YIELD PER PLANT AND YIELD COMPONENTS IN DOUBLED HAPLOID SPRING BARLEY LINES (HORDEUM VULGARE L.) . Plant Breeding and Seed Science, 73, 63-77. Retrieved from http://ojs.ihar.edu.pl/index.php/pbss/article/view/233

Dariusz R. Mańkowski  d.mankowski@ihar.edu.pl
Department of Seed Science and Technology, Plant Breeding and Acclimatization Institute – NRI, Radzików, 05-870 Błonie, Poland  Poland
Janusz Kozdój 
Department of Plant Biotechnology and Cytogenetics, Plant Breeding and Acclimatization Institute – NRI, Radzików, 05-870 Błonie, Poland  Poland
Monika Janaszek-Mańkowska 
Department of Engineering, Warsaw University of Life Science; Nowoursynowska 166, 02-787 Warsaw, Poland  Poland