Selection for production-related traits in Pelargonium zonale: improved design and analysis make all the difference

Abstract

Ornamental plant variety improvement is limited by current phenotyping approaches and neglected use of experimental designs. The present study was conducted to show the benefits of using an experimental design and corresponding analysis in ornamental breeding regarding simulated response to selection in Pelargonium zonale for production-related traits. This required establishment of phenotyping protocols for root formation and stem cutting counts, with which 974 genotypes were assessed in a two-phase experimental design. The present paper evaluates this protocol. The possibility of varietal improvement through indirect selection on secondary traits such as branch count and flower count was assessed by genetic correlations. Simulated response to selection varied greatly, depending on the genotypic variances of the breeding population and traits. A varietal improvement of over 20% is possible for stem cutting count, root formation, branch count and flower count. In contrast, indirect selection of stem cutting count by branch count or flower count was found to be ineffective. The established phenotypic protocols and two-phase experimental designs are valuable tools for breeding of P. zonale.

References

[1] Falconer DS, Mackay TFC . Introduction to Quantitative genetics. Prentice Hall: London, UK, 1996.

[2] Walter A, Liebisch F, Hund A . Plant phenotyping: from bean weighing to image analysis. Plant Methods 2015; 11: 14.

[3] Barker J, Zhang N, Sharon J, Steeves R, Wang X, Wei Y et al. Development of a field-based high-throughput mobile phenotyping platform. Comput Electron Agric 2016; 122: 74-85.

[4] Busemeyer L, Ruckelshausen A, Möller K, Melchinger AE, Alheit KV, Maurer HP et al. Precision phenotyping of biomass accumulation in triticale reveals temporal genetic patterns of regulation. Sci Rep 2016; 3: 1-6.

[5] Panguluri SK, Kumar AA (ed.). Phenotyping for Plant Breeding. Springer: New York, NY. 2013.

[6] Mutui TM, Mibus H, Serek M . Effect of meta-topolin on leaf senescence and rooting in Pelargonium× hortorum cuttings. Postharvest Biol Technol 2012; 63: 107-110.

[7] Edmondson RN . Past developments and future opportunities in the design and analysis of crop experiments. J Agric Sci 2005; 143: 27-33.

[8] Fisher RA . The Design of Experiments. Edinburgh Oliver & Boyd: Edinburgh, Scottland, 1935.

[9] McIntyre GA . Design and analysis of two phase experiments. Biometrics 1995; 11: 822-828.

[10] Brien CJ, Harch BD, Correll RL, Bailey RA . Multiphase experiments with at least one later laboratory phase. I. Orthogonal designs. J Agric Biol Environ Stat 2011; 16: 422-450.

[11] Johnl JA, Williams ER . Cyclic and Computer Generated Designs. London Chapman & Hall: London, UK, 1995.

[12] Brien CJ, Demétrio CGB . Formulating mixed models for experiments, including longitudinal experiments. J Agric Biol Environ Stat 2009; 14: 253-280.

[13] Piepho HP, Büchse A, Emrich K . A hitchhiker’s guide to mixed models for randomized experiments. J Agron Crop Sci 2003; 189: 310-322.

[14] Cook DR, Weisberg S . Residuals and influence in regression. In: Cos DR, Hinkley DV (ed.). Monographs on Statistics and Applied Probability. Chapman & Hall: New York, NY. 1984.

[15] Littell RC, Milliken GA, Stroup WW, Wolfinger RD, Shabenberger O . SAS for mixed models. SAS Institute Inc: Cary, NC, 2006.

[16] Dufner J, Jensen U, Schuhmacher E . Statistic with SAS. Vieweg & Teubner Verlag: Tübingen. 2004, 171-180 German.

[17] Searle SR, Casella G, McCulloch CE . Variance Components. John Wiley & Sons: Hoboken, NJ, 1992.

[18] Piepho HP, Büchse A . A mixed modelling approach for randomized experiments with repeated measures. J Agron Crop Sci 2004; 247: 230-247.

[19] Piepho HP, Eckl T . Analysis of series of variety trials with perennial crops. Grass Forage Sci 2014; 69: 431-440.

[20] Akaike H . A new look at the statistical model identification. IEEE Trans Automat Contr 1974; 19: 716-723.

[21] Wolfinger R. Covariance structure selection in general mixed models. Commun Stat Simul Comput 1993; 22: 1079-1106.

[22] Piepho HP, Möhring J . Computing heritability and selection response from unbalanced plant breeding trials. Genetics 2007; 177: 1881-1888.

[23] Piepho HP, Möhring J . On estimation of genotypic correlations and their standard errors by multivariate REML using the MIXED procedure of the SAS System. Crop Sci 2011; 51: 2449-2454.

[24] Holland JB . Estimating genotypic correlations and their standard errors using multivariate restricted maximum likelihood estimation with SAS Proc MIXED. Crop Sci 2006; 46: 642-654.

[25] Müller BU, Kleinknecht K, Möhring J, Piepho HP . Comparison of spatial models for sugar beet and barley trials. Crop Sci 2010; 50: 794-802.

[26] Schützenmeister A, Piepho HP . Residual analysis of linear mixed models using a simulation approach. Comput Stat Data Anal 2012; 56: 1405-1416.

[27] Bernal-Vasquez AM, Utz HF, Piepho HP . Outlier detection methods for generalized lattices: a case study on the transition from ANOVA to REML. Theor Appl Genet 2016; 129: 787-804.

[28] Piepho HP, Möhring J, Williams ER . Why randomize agricultural experiments? J Agron Crop Sci 2013; 199: 374-383.

[29] Stroup WW . Power analysis based on spatial effects mixed models: a tool for comparing design and analysis strategies in the presence of spatial variability. J Agric Biol Environ Stat 2002; 7: 491-511.

[30] Mead R, Curnow RN, Hasted AM . Statistical Methods in Agriculture and Experimental Biology. Chapman & Hall: Boca Raton, FL, 2002.

[31] Kelen M, Ozkan G . Relationships between rooting ability and changes of endogenous IAA and ABA during the rooting of hardwood cuttings of some grapevine rootstocks. Eur J Hortic Sci 2003; 68: 8-13.

[32] Kadner R, Druege U . Role of ethylene action in ethylene production and poststorage leaf senescence and survival of pelargonium cuttings. Plant Growth Regul 2004; 43: 187-196.

[33] Loach K. Water relations and adventitious rooting. In: Sankhla N (ed.). Adventitious Root Formation in Cuttings. Dioscorides Press: Portland, OR. 1989.

[34] Zimmermann PW, Hitchcock AE . Root formation and flowering of Dahlia cuttings when subjected to different day lengths. Bot Gaz 1929; 87: 1-13.

[35] Serek M, Prabucki A, Sisler EC, Andersen AS . Inhibitors of ethylene action affect final quality and rooting of cuttings before and after storahe. Hort Sci 1998; 33: 153-155.

[36] Mutui TM, Mibus H, Serek M . Effects of thidiazuron, ethylene, abscisic acid and dark storage on leaf yellowing and rooting of Pelargonium cuttings. J Hort Sci Biotechnol 2005; 80: 543-550.

[37] Kumar J, Kumar S, Pratap A (ed.). Phenomics in Crop Plants: Trends, Options and Limitations. Springer: New Delhi. 2015.