A comparison between joint regression analysis and the AMMI model: a case study with barley

References

• Kang , M. S. and Gauch , H. G. 1996 . Genotype-by-Environment Interaction , Boca Raton , FL : CRC Press .
   Google Scholar
• Finlay , K. W. and Wilkinson , G. N. 1963 . Analysis of adaptation in a plant-breeding programme . Aust. J. Agric. Res. , 14 : 742 – 754 .
   Google Scholar
• Gauch , H. G. 1992 . Statistical Analysis of Regional Yield Trials: AMMI Analysis of Factorial Designs , Amsterdam : Elsevier .
   Google Scholar
• Gusmão , L. 1985 . An Adequate Design for Regression Analysis of Yield Trials . Theor. Appl. Genet. , 71 : 314 – 319 .
   PubMed Web of Science ®Google Scholar
• Mexia , J. T. , Pereira , D. G. and Baeta , J. 1999 . L2 environmental indexes . Biom. Lett. , 36 : 137 – 143 .
   Google Scholar
• Mexia , J. T. , Amaro , A. P. , Gusmao , L. and Baeta , J. 1997 . Upper contour of a joint regression analysis . J. Genet. Breed. , 51 : 253 – 255 .
   Google Scholar
• Gauch , H. G. 1988 . Model Selection and Validation for Yield Trials with Interaction . Biometrics. , 44 : 705 – 715 .
   Web of Science ®Google Scholar
• Gauch , H. G. and Zobel , R. W. 1997 . Identifying mega-environments and targeting genotypes . Crop Sci. , 37 : 311 – 326 .
   Web of Science ®Google Scholar
• Pereira , D. G. and Mexia , J. T. 2009 . Comparing double minimization and zigzag algorithms in joint regression analysis: The complete case . J. Stat. Comput. Simul. , 79 : 1563 – 5163 .
   Google Scholar
• Pereira , D. G. and Mexia , J. T. 2010 . Comparing double minimization and zigzag algorithms in joint regression analysis: The complete case . J. Stat. Comput. Simul. , 80 : 133 – 141 .
   Web of Science ®Google Scholar
• Rodrigues , P. C. , Pereira , D. G. and Mexia , J. T. A comparison between JRA and AMMI: The robustness with increasing amounts of missing data Scientia Agricola (2011), accepted for publication
   Google Scholar
• Pereira , D. G. and Mexia , J. T. 2008 . Selection proposal of cultivars of spring barley in the years from 2001 to 2004, using joint regression analysis . Plant Breed. , 127 : 452 – 458 .
   Web of Science ®Google Scholar
• Scheffé , H. 1959 . The Analysis of Variance , New York : Wiley .
   Google Scholar
• Paderewski , J. , Gauch , H. G. , Madry , W. , Drzazga , T. and Rodrigues , P. C. 2011 . Yield response of winter wheat to spatial conditions using AMMI and cluster analysis . Crop Sci. , 51 : 969 – 980 .
   Web of Science ®Google Scholar
• Bradu , D. and Gabriel , K. R. 1978 . Biplot as a diagnostic tool for models of 2-way tables . Technometrics. , 20 : 47 – 68 .
   Web of Science ®Google Scholar
• Annicchiarico , P. , Bellah , F. and Chiari , T. 2005 . Defining subregions and estimating benefits for a specific-adaptation strategy by breeding programs: A case study . Crop Sci. , 45 : 1741 – 1749 .
   Web of Science ®Google Scholar
• Gauch , H. G. 2007 . MATMODEL Version 3.0: Open Source Software for AMMI and Related Analyses software available at http://www.css.cornell.edu/staff/gauch
   Google Scholar
• Piepho , H. P. 1995 . Robustness of statistical tests for multiplicative terms in the additive main effects and multiplicative interaction model for cultivar trials . Theor. Appl. Genet. , 90 : 438 – 443 .
   PubMed Web of Science ®Google Scholar
• Annicchiarico , P. 1997 . Joint regression vs AMMI analysis of genotype–environment interactions for cereals in Italy . Euphytica. , 94 : 53 – 62 .
   Web of Science ®Google Scholar
• Brancourt-Hulmel , M. , Biarnès-Dumoulin , V. and Denis , J. B. 1997 . Points de repère dans l'analyse de la stabilité et de l'interaction génotype-milieu en amélioration des plants . Agronomie. , 17 : 219 – 246 .
   Google Scholar
• Annicchiarico , P. 2002 . Genotype × Environment Interactions – Challenges and Opportunities for Plant Breeding and Cultivar Recommendations , Rome : FAO Plant Production and Protection Papers, FAO . Available at http://www.fao.org/docrep/005/y4391e/y4391e00.htm
   Google Scholar
• Annicchiarico , P. , Bellah , F. and Chiari , T. 2006 . Repeatable genotype × location interaction and its exploitation by conventional and GIS-based cultivar recommendation for durum wheat in Algeria . Eur. J. Agron. , 24 : 70 – 81 .
   Web of Science ®Google Scholar
• Ebdon , J. S. and Gauch , H. G. 2011 . Direct validation of AMMI predictions in turfgrass trials . Crop Sci. , 51 : 862 – 869 .
   Web of Science ®Google Scholar
• Annicchiarico , P. Coping with and exploiting genotype-by-environment interactions . in Plant Breeding and Farmer Participation, S. Ceccarelli, E.P. Guimarães. and E. Weltzien, eds., FAO, Rome, 2009, pp. 519–564
   Google Scholar