Efficiency of using spatial analysis in first-generation coastal Douglas-fir progeny tests in the US Pacific Northwest

Tree Genetics and Genomes

Volumn 4, Issue 4, 2008, Pages 677-692

  Ye, Terrance Z ^a   Jayawickrama, Keith J S ^a  

^a OREGON STATE UNIVERSITY   (United States)

Author keywords

Douglas fir; Multi environmental trial; Spatial analysis

Indexed keywords

PSEUDOTSUGA; PSEUDOTSUGA MENZIESII;

EID: 49749098403     PISSN: 16142942     EISSN: 16142950     Source Type: Journal    
DOI: 10.1007/s11295-008-0142-4     Document Type: Article

References (64)

1. H Akaike 1974 A new look at the statistical model identification IEEE Trans Automat Contr 19 716 723
2. TS Anekonda WJ Libby 1996 Effectiveness of nearest-neighbor data adjustment in a clonal test of redwood Silvae Genet 45 46 51
3. MS Bartlett 1978 Nearest neighbour models in the analysis of field experiments J R Statist Soc B 40 147 174
4. ST Bell DJ Mulla CF Konzak 1993 Spatial heterogeneity affects variety trial interpretation Crop Sci 33 931 935
5. J Besag R Kempton 1986 Statistical analysis of field experiments using neighbouring plots Biometrics 42 231 251
6. Braysher BD, Dear BS, Boschma S, Cullis BR, Sandral GA (2001) Improving the efficiency of pasture breeding programs through the use of spatial analysis. Proceedings of Tenth Australian Agronomy Conference, 29 January-1 February. Australian Society of Agronomy, Hobart, Australia
7. ES Bresler DR Dasberg G Dagan 1981 Spatial variability of crop yield as a stochastic process Soil Sci Soc Am J 45 600 605
8. Cliff AD, Ord JK (1981) Spatial processes, models and applications. Pion, Landon
9. Cochran WG, Cox GM (1957) Experimental designs, 2nd edn. Wiley, New York
10. J Costa e Silva GW Dutkowski AR Gilmour 2001 Analysis of early tree height in forest genetic trials is enhanced by including a spatially correlated residual Can J For Res 31 1887 1893
11. J Costa e Silva GW Dutkowski NMG Borralho 2005 Across-site heterogeneity of genetic and environmental variances in the genetic evaluation of Eucalyptus globulus trials for height growth Ann For Sci 62 183 191
12. BR Cullis AC Gleeson 1989 The efficiency of neighbor analysis for replicated variety trials in Australia J Agric Sci 113 223 239
13. BR Cullis AC Gleeson 1991 Spatial analysis of field experiments-an extension to two dimentions Biometrics 47 1449 1460
14. B Cullis B Gogel A Verbyla R Thompson 1998 Spatial analysis of multi-environment early generation variety trials Biometrics 54 1 18
15. GW Dutkowski J Costa E Silva AR Gilmour GA Lopez 2002 Spatial analysis methods for forest genetic trials Can J For Res 32 2201 2214
16. GW Dutkowski H Wellendorf A Aguiar JC Silva AR Gilmour 2006 Spatial analysis enhances modelling of a wide variety of traits in forest genetic trials Can J For Res 36 1851 1870
17. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Longman, New York
18. JC Fox PK Ades H Bi 2001 Stochastic structure and individual-tree growth models For Ecol Manag 154 261 276
19. Y Fu 2003 On implementation of incomplete block designs in forest genetic field trials For Genet 10 23 33
20. YB Fu GPY Clarke G Namkoong AD Yanchuk 1998 Incomplete block designs for genetic testing: statistical efficiencies of estimating family means Can J For Res 28 977 986
21. YB Fu AD Yanchuk G Namkoong 1999 Spatial patterns of tree height variations in a series of Douglas-fir progeny trials: implications for genetic testing Can J For Res 29 714 723
22. AR Gilmour BR Cullis AP Verbyla 1997 Accounting for natural and extraneous variation in the analysis of field experiments J Agric Biol Environ Stat 2 269 273
23. Gilmour AR, Gogel BJ, Cullis BR, Thompson R (2006) ASReml user guide release 2.0. VSN International, Hemel Hempstead, UK
24. Gleeson AC (1997) Spatial analysis. In: Kempton RA, Fox PN (eds) Statistical methods for plant variety evaluation. Chapman and Hall, London
25. P Green C Jennison A Seheult 1985 Analysis of field experiments by least squares smoothing J R Statist Soc B 47 299 315
26. MO Grondona J Crossa PN Fox WH Pfeiffer 1996 Analysis of variety trials using two-dimensional separable ARIMA processes Biometrics 52 763 770
27. A Hamann G Namkoong MP Koshy 2002 Improving precision of breeding values by removing spatially autocorrelated variation in forestry field experiments Silvae Genet 51 210 215
28. TC Helms RA Scott JJ Hammond 1999 Intrablock variance among duplicate treatments for nearest-neighbor analysis Agron J 91 317 320
29. CR Henderson 1976 Asimple method for computing the inverse of a numerator relationship matrix used in the prediction of breeding values Biometrics 32 69 83
30. WG Hill 1984 Selection among groups with heterogeneous variance Animal production 39 473 477
31. Howe GT, Jayawickrama KJ, Cherry ML, Wheeler NC, Johnson GR (2005) Breeding Douglas-fir. In: Janick J (ed) Plant breed rev. Wiley, New York, pp 245-353
32. Jayawickrama KJS, Ye T (2004) Genetic gain predictions for coastal Douglas-fir in the US Pacific Northwest and their relationships with parent tree location. Forest genetics and tree breeding in the age of genomics: progress and future, IUFRO Joint Conference of Division 2, Conference Proceedings, Charleston, South Carolina, pp 204-206
33. D Joyce R Ford YB Fu 2002 Spatial patterns of tree height variations in a black spruce farm-field progeny test and neighbors-adjusted estimations of genetic parameters Silvae Genet 51 13 18
34. RA Kempton 1982 Adjustment for competition between varieties in plant breeding trials J Agric Sci 98 599 611
35. D Kusnandar N Galwey 2000 A proposed method for estimation of genetic parameters on forest trees without raising progeny: critical evaluation and refinement Silvae Genet 49 15 21
36. Loo-Dinkins JA (1992) Field test design. In: Fins L, Friedman S, Brotschol J (eds) Handbook of quantitative forest genetics. Kluwer, Dordrecht, The Netherlands, pp 96-139
37. JA Loo-Dinkins CG Tauer CC Lambeth 1990 Selection system efficiencies for computer simulated progeny test field designs in loblolly pine Theor Appl Genet 79 89 96
38. MV Lopez JL Arrue 1995 Efficiency of an incomplete block design based on geostatistics for tillage experiments Soil Sci Soc Am J 59 1104 1111
39. Lynch M, Walsh B (1998) Genetics and analysis of quantitative traits. Sinauer, Sunderland, MA
40. S Magnussen 1989 Effects and adjustments for competition bias in progeny trials with single-tree plots For Sci 35 532 547
41. S Magnussen 1990 Application and comparison of spatial models in analyzing tree genetics field trials Can J For Res 20 536 546
42. S Magnussen 1993 Bias in genetic variance estimates due to spatial autocorrelation Theor Appl Genet 86 349 355
43. S Magnussen 1994 A method to adjust simultaneously for spatial microsite and competition effects Can J For Res 24 985 995
44. Meyer K (2006) PX × AI: algorithmics for better convergence in restricted maximum likelihood estimation. 8th World Congress on Genetics Applied to Livestock Production, August 13-18, Belo Horizonte, Brasil
45. Papadakis JS (1937) Méthode statistique pour des expériences sur champ. Institute de Amélioration des Plantes à Salonique Bull. 23
46. SC Pearce 1980 Randomized blocks and some alternatives: a study in tropical conditions Trop Agric 57 1 10
47. DD Reed HE Burkhart 1985 Spatial autocorrelation of individual tree characteristics in loblolly pine stands For Sci 31 575 587
48. AG Salvador TL White DA Huber 2006 Comparison of experimental designs for clonal forestry using simulated data For Sci 52 108 116
49. WM Schutz CC Cockerham 1966 The effect of field blocking on gain from selection Biometrics 22 843 863
50. RG Shaw 1987 Maximum-likelihood approaches applied to quantitative genetics of natural populations Evolution 41 812 826
51. RR Silen JG Wheat 1979 Progressive tree improvement program in coastal Douglas-fir J For 77 78 83
52. M Singh RS Malhotra S Ceccarelli A Sarker S Grando W Erskine 2003 Spatial variability models to improve dryland field trials Exp Agric 39 151 160
53. HF Smith 1938 Anempirical law describing heterogeneity in the yields of agricultural crops J Agric Sci 28 1 23
54. KF Smith MD Casler 2004 Spatial analysis of forage grass trials across locations, years, and harvests Crop Sci 44 56 62
55. A Smith B Cullis R Thompson 2001 Analyzing variety by environment data using multiplicative mixed models and adjustments for spatial field trend Biometrics 57 1138 1147
56. DO Stram JW Lee 1994 Variance components testing in the longitudinal mixed effects setting Biometrics 50 1171 1177
57. WW Stroup PS Baenziger DK Mulitze 1994 Removing spatial variation from wheat yield trials: a comparison of methods Crop Sci 34 62 66
58. Upton GJG, Fingleton B (1985) Spatial analysis by example. John, New York
59. GN Wilkinson SR Eckert TW Hancock O Mayo 1983 Nearest neighbor (NN) analysis of field experiments J R Statist Soc B 45 151 211
60. Williams ER, Matheson AC, Harwood CE (2002) Experimental design and analysis for tree improvement, 2nd edn. CSIRO, Collingwood, Australia
61. Y Yamada 1962 Genotype by environment interaction and genetic correlation of the same trait under different environments Jpn J Genet 37 498 509
62. RC Yang TZ Ye SF Blade M Bandara 2004 Efficiency of spatial analyses of field pea variety trials Crop Sci 44 49 55
63. R Zas 2006 Iterative kriging for removing spatial autocorrelation in analysis of forest genetic trials Tree Genet Genomics 2 177 185
64. DL Zimmerman DA Harville 1991 A random field approach to the analysis of field-plot experiments and other spatial experiments Biometrics 47 223 239