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Euphytica

, 213:221 | Cite as

Replicate allocation to improve selection efficiency in the early stages of a potato breeding scheme

  • M. F. PagetEmail author
  • P. A. Alspach
  • J. A. D. Anderson
  • R. A. Genet
  • W. F. Braam
  • L. A. Apiolaza
Article

Abstract

Field data and simulation were used to investigate replication within trials and the allocation of replicates across trial sites using partial replication as an approach to improve the efficiency of early-stage selection in a potato breeding programme. Analysis of potato trial data using linear mixed models, based on four-plant (clonal) plots planted as augmented partially-replicated (p-rep) designs, obtained genetic and environmental components of variation for a number of yield and tuber components. Heritabilities, trial-to-trial genetic correlations and performance repeatability of clonal selections in p-rep trials and in subsequent fully replicated trial stages were high, and selection was effective for the economically important traits of marketable tuber yield and tuber cooking quality. Simulations using a parameter-based approach, pertaining to the variance components estimated from the p-rep field trials, and the parametric bootstrapping of historic empirical data showed improved rates of genetic gain with p-rep testing over one and two locations compared with testing in fully replicated trials. This potato breeding study suggests that the evaluation and selection of a clonal field crop in fully replicated trials may not be optimal in the early stages of a breeding cycle and that p-rep designs offer a more efficient and practical alternative.

Keywords

Breeding programme Field trials MET Multi-environment trials P-rep Partial replication 

Notes

Acknowledgements

We would like to thank Moe Jeram for the management of the Pukekohe field trials. We thank Satish Kumar and Steve Lewthwaite for helpful suggestions that improved the manuscript. We also gratefully acknowledge the funding assistance for this study provided by Potatoes NZ Charitable Trust.

References

  1. Affleck I, Sullivan JA, Tarn R, Falk DE (2008) Genotype by environment interaction effect on yield and quality of potatoes. Can J Plant Sci 88:1099–1107CrossRefGoogle Scholar
  2. Atlin GN, Baker RJ, McRae KB, Lu X (2000) Selection response in subdivided target regions. Crop Sci 40:7–13CrossRefGoogle Scholar
  3. Bos I (1983) Optimum number of replications when testing lines or families on a fixed number of plots. Euphytica 32:311–318CrossRefGoogle Scholar
  4. Bos I, Caligari PDS (2008) Selection methods in plant breeding, 2nd edn. Springer, DordrechtCrossRefGoogle Scholar
  5. Bradshaw J, Dale M, Mackay G (2009) Improving the yield, processing quality and disease and pest resistance of potatoes by genotypic recurrent selection. Euphytica 170:215–227. doi: 10.1007/s10681-009-9925-4 CrossRefGoogle Scholar
  6. Burdon RD (1977) Genetic correlation as a concept for studying genotype-environment interaction in forest tree breeding. Silvae Genet 26:168–175Google Scholar
  7. Butler D (2009) asreml: asreml() fits the linear mixed model. R package v.3.0-1. www.vsni.co.uk
  8. Butler DG, Cullis BR, Gilmour AR, Gogel BJ (2009) ASReml-R reference manual, VSN International, Hemel Hempstead, UK, www.vsni.co.uk
  9. Caligari PDS, Brown J, Abbott RJ (1986) Selection for yield and yield components in the early generations of a potato breeding programme. Theor Appl Genet 73:218–222CrossRefPubMedGoogle Scholar
  10. Clarke GPY, Stefanova KT (2011) Optimal design for early-generation plant breeding trials with unreplicated or partially replicated test lines. Australian & New Zealand Journal of Statistics 53:461–480CrossRefGoogle Scholar
  11. Coombes N (2011) DiGGer design generator under correlation and blocking. http://www.austatgen.org/software. Accessed: 31 January 2016
  12. Cullis BR, Smith AB, Coombes NB (2006) On the design of early generation trials with correlated data. J Agric Biol Environ Stat 11:381–393CrossRefGoogle Scholar
  13. CycSoftware (2009) CycDesigN 4.0 A package for the computer generation of experimental designs. Version 4.0, CycSoftware Ltd, Hamilton, New Zealand. www.vsni.co.uk
  14. Federer WT (1956) Augmented (or hoonuiaku) designs. Hawaiian Planter’s Record 55:191–208Google Scholar
  15. Gauch HG, Zobel RW (1996) Optimal replication in selection experiments. Crop Sci 36:838–843CrossRefGoogle Scholar
  16. Gilmour AR, Cullis BR, Verbyla AP, Gleeson AC (1997) Accounting for natural and extraneous variation in the analysis of field experiments. J Agric Biol Environ Stat 2:269–293CrossRefGoogle Scholar
  17. Hayes RJ, Thill CA (2003) Genetic gain from early generation selection for cold chipping genotypes in potato. Plant Breed 122:158–163. doi: 10.1046/j.1439-0523.2003.00776.x CrossRefGoogle Scholar
  18. Haynes KG, Gergela DM, Hutchinson CM, Yencho GC, Clough ME, Henninger MR, Halseth DE, Sandsted E, Porter GA, Ocaya PC (2012) Early generation selection at multiple locations may identify potato parents that produce more widely adapted progeny. Euphytica 186:573–583. doi: 10.1007/s10681-012-0685-1 CrossRefGoogle Scholar
  19. Henderson CR (1975) Best linear unbiased estimation and prediction under a selection model. Biometrics 31:423–449CrossRefPubMedGoogle Scholar
  20. Kempton RA (1984) The design and analysis of unreplicated field trials. Vortrage fur Pflanzenzuchtung 7:219–242Google Scholar
  21. Kempton RA, Gleeson AC (1997) Unreplicated trials. In: Kempton RA, Fox PN (eds) Statistical methods for plant variety evaluation. Chapman & Hall, London, pp 86–100CrossRefGoogle Scholar
  22. Lorenz AJ (2013) Resource allocation for maximizing prediction accuracy and genetic gain of genomic selection in plant breeding: a simulation experiment. G3-Genes Genomes. Genetics 3:481–491Google Scholar
  23. Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sinauer Associates Inc., SunderlandGoogle Scholar
  24. Mackay G (2007) Propagation by traditional breeding methods. Potato. In: Razdan MK, Mattoo A (eds) Genetic improvement of Solanaceous crops. Enfield Science Publishers, Enfield, pp 65–81Google Scholar
  25. McCann LC, Bethke PC, Casler MD, Simon PW (2012) Allocation of experimental resources used in potato breeding to minimize the variance of genotype mean chip color and tuber composition. Crop Sci 52:1475–1481. doi: 10.2135/cropsci2011.07.0392 Google Scholar
  26. Meyer K (2009) Factor-analytic models for genotype x environment type problems and structured covariance matrices. Genet Sel Evol. doi: 10.1186/1297-9686-41-21 PubMedPubMedCentralGoogle Scholar
  27. Moehring J, Williams ER, Piepho HP (2014) Efficiency of augmented p-rep designs in multi-environmental trials. Theor Appl Genet 127:1049–1060. doi: 10.1007/s00122-014-2278-y CrossRefPubMedGoogle Scholar
  28. Moreau L, Lemarie S, Charcosset A, Gallais A (2000) Economic efficiency of one cycle of marker-assisted selection. Crop Sci 40:329–337CrossRefGoogle Scholar
  29. Paget MF, Alspach PA, Anderson JAD, Genet RA, Apiolaza LA (2015a) Trial heterogeneity and variance models in the genetic evaluation of potato tuber yield. Plant Breed 134:203–211. doi: 10.1111/pbr.12251 CrossRefGoogle Scholar
  30. Paget MF, Apiolaza LA, Anderson JAD, Genet RA, Alspach PA (2015b) Appraisal of test location and variety performance for the selection of tuber yield in a potato breeding program. Crop Sci 55:1957–1968. doi: 10.2135/cropsci2014.11.0801 CrossRefGoogle Scholar
  31. Piepho HP, Möhring J (2007) Computing heritability and selection response from unbalanced plant breeding trials. Genetics 177:1881–1888. doi: 10.1534/genetics.107.074229 CrossRefPubMedPubMedCentralGoogle Scholar
  32. Piepho HP, Möhring J, Melchinger AE, Büchse A (2008) BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161:209–228. doi: 10.1007/s10681-007-9449-8 CrossRefGoogle Scholar
  33. R Development Core Team (2012) R: a language and environment for statistical computing. In: R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  34. Rattunde HFW, Michel S, Leiser WL, Piepho HP, Diallo C, vom Brocke K, Diallo B, Haussmann B, Weltzien E (2015) Farmer participatory early-generation yield testing of sorghum in West Africa: possibilities to optimise genetic gains for yield in farmers’ fields. Crop Sci 56:2493–2505. doi: 10.2135/cropsci2015.12.0758 CrossRefGoogle Scholar
  35. Robinson GK (1991) That BLUP is a good thing: the estimation of random effects. Stat Sci 6:15–32CrossRefGoogle Scholar
  36. Smith AB, Cullis BR, Thompson R (2001) Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend. Biometrics 57:1138–1147. doi: 10.1111/j.0006-341X.2001.01138.x CrossRefPubMedGoogle Scholar
  37. Smith AB, Cullis BR, Thompson R (2005) The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. J Agric Sci 143:449–462. doi: 10.1017/s0021859605005587 CrossRefGoogle Scholar
  38. Smith AB, Lim P, Cullis BR (2006) The design and analysis of multi-phase plant breeding experiments. J Agric Sci 144:393–409. doi: 10.1017/s0021859606006319 CrossRefGoogle Scholar
  39. Stendal C, Casler MD (2006) Maximizing efficiency of recurrent phenotypic selection for neutral detergent fiber concentration in smooth bromegrass. Crop Sci 46:297–302. doi: 10.2135/cropsci2005.0083 CrossRefGoogle Scholar
  40. Talbot M (1984) Yield variability of crop varieties in the UK. J Agric Sci 102:315–321CrossRefGoogle Scholar
  41. van Berloo R, Hutten RCB, van Eck HJ, Visser RGF (2007) An online potato pedigree database resource. Potato Res 50:45–57CrossRefGoogle Scholar
  42. Williams E, Piepho HP, Whitaker D (2011) Augmented p-rep designs. Biom J 53:19–27. doi: 10.1002/bimj.201000102 CrossRefPubMedGoogle Scholar
  43. Williams ER, John JA, Whitaker D (2014) Construction of more flexible and efficient p-rep designs. Aust NZ J Stat 56:89–96CrossRefGoogle Scholar
  44. Windhausen VS, Wagener S, Magorokosho C, Makumbi D, Vivek B, Piepho HP, Melchinger AE, Atlin GN (2012) Strategies to subdivide a target population of environments: results from the CIMMYT-led maize hybrid testing programs in Africa. Crop Sci 52:2143–2152. doi: 10.2135/cropsci2012.02.0125 CrossRefGoogle Scholar
  45. Yamada Y (1962) Genotype by environment interaction and the genetic correlation of the same trait under different environments. Jpn J Genet 37:498–509. doi: 10.1266/jjg.37.498 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • M. F. Paget
    • 1
    Email author
  • P. A. Alspach
    • 2
  • J. A. D. Anderson
    • 3
  • R. A. Genet
    • 1
  • W. F. Braam
    • 1
  • L. A. Apiolaza
    • 4
  1. 1.The New Zealand Institute for Plant & Food Research LimitedChristchurchNew Zealand
  2. 2.The New Zealand Institute for Plant & Food Research LimitedMotuekaNew Zealand
  3. 3.The New Zealand Institute for Plant & Food Research LimitedPukekoheNew Zealand
  4. 4.School of ForestryUniversity of CanterburyChristchurchNew Zealand

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