Statistical methods for data analysis in the complete classification Cultivar × Crop Management × Location × Year (G×M×L×Y) from PVTS

Annicchiarico P. 2002 a. Defining adaptation strategies and yield stability targets in breeding programmes In: Kang M. S. (Ed.) Quantitative genetics, genomics and plant breeding. CAB, Wallingford, UK: 165 — 183.
Google Scholar

Annicchiarico P. 2002 b. Genotype-environment interactions: challenges and opportunities for plant breeding and cultivar recommendations. FAO Plant Production and Protection Paper No. 174. Food and Agriculture Organization, Rome.
Google Scholar

Annicchiarico P. 2009. Coping with and exploiting genotype × environment interactions. In: Ceccarelli S., Guimarães E. P., Weltzien E. (eds). Plant Breeding and Farmer Participation. Food and Agricultural Organization, Rome: 519 — 564.
Google Scholar

Annicchiarico P., Bellah F., 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.
Google Scholar

Annicchiarico P., Chiapparino E., Perenzin M. 2010 a. Response of common wheat varieties to organic and conventional production systems across Italian locations and implications for selection. Field Crops Res. 116: 230 — 238.
Google Scholar

Annicchiarico P., Scotti C., Carelli M., Pecetti L. 2010 b. Questions and avenues for lucerne improvement. Czech J. Genet. Plant Breed. 46: 1 — 13.
Google Scholar

Annicchiarico P., Iannucci A. 2008. Adaptation strategy, germplasm type and adaptive traits for field pea improvement in Italy based on variety responses across climatically contrasting environments. Field Crops Res. 108: 133 — 142.
Google Scholar

Annicchiarico P., Pecetti L., Abdelguerfi A., Bouizgaren A., Carroni A. M., Hayek T., M’Hammadi Bouzina M., Mezni M. 2011. Adaptation of landrace and variety germplasm and selection strategies for lucerne in the Mediterranean basin. Field Crops Res. 120: 283 — 291.
Google Scholar

Basford K. E., Cooper M. 1998. Genotype × environment interactions and some considerations of their implications for wheat breeding in Australia. Aust. J. Agric. Res. 49: 153 — 174.
Google Scholar

Brancourt-Hulmel M., Doussinault G., Lecomte C., Berard P., Le Buaec B., Trottet M. 2003. Genetic improvement of agronomic traits of winter wheat cultivars released in France from 1946 to 1992. Crop Sci. 43: 37 — 45.
Google Scholar

Bujak H., Tratwa G. 2011. Ocena stabilności plonowania odmian pszenicy ozimej na podstawie doświadczeń porejestrowych w Polsce. Biul. IHAR 260/261: 69 — 79.
Google Scholar

Caliński T., Czajka S., Kaczmarek Z, Krajewski P. 1998. SERGEN 4 — Analysis of series of variety trials and plant genetic or breeding experiments. Institute of Plant Genetics, Polish Academy of Sciences, and Department of Mathematical and Statistical Methods, Agricultural University, Poznań, Poland.
Google Scholar

Cooper, M., DeLacy I. H., Basford K. E. 1996. Relationships among analytical methods used to study genotypic adaptation in multienvironment trials. in M. Cooper and G. L. Hammer (ed.) Plant adaptation and crop improvement. CABI, Wallingford, UK: 193 — 224.
Google Scholar

Cooper M., Woodruff D. R., Phillips I. G., Basford K. E., Gilmour A. R. 2001. Genotype-by-management interactions for grain yield and grain protein concentration of wheat. Field Crops Res. 69: 47 — 67.
Google Scholar

Crossa J., Cornelius P. L. 2002. Linear-bilinear models for the analysis of genotype-environment interaction In: Kang M.S. (Ed.) Quantitative genetics, genomics and plant breeding. CAB, Wallingford, UK: 305 — 322.
Google Scholar

Crossa J., Cornelius P. L., Yan W. 2002. Biplots of linear-bilinear models for studying crossover genotype x environment interaction Crop Sci. 42: 619 — 633.
Google Scholar

Crossa J., Franco J. 2004. Statistical methods for classifying genotypes. Euphytica 137: 19 — 37.
Google Scholar

Crossa J., Vargas M., Joshi A.K. 2010. Linear, bilinear, and linear-bilinear fixed and mixed models for analyzing genotype x environment interaction in plant breeding and agronomy. Can. J. Plant Sci. 90:561 — 574.
Google Scholar

de la Vega A. J., Chapman S. C. 2006. Defining sunflower selection strategies for a highly heterogeneous target population of environments. Crop Sci. 46: 136 — 144.
Google Scholar

De Vita P., Mastrangelo A. M., Matteu L., Mazzucotelli E., Virzi N., Palumbo M., Lo Storto M., Rizza F., Cattivelli L. 2010. Genetic improvement effects on yield stability in durum wheat genotypes grown in Italy. Field Crops Res. 119: 68 — 77.
Google Scholar

Derejko A., Mądry W., Gozdowski D., Rozbicki J., Golba J., Piechociński M., Studnicki M. 2011. Wpływ odmian, miejscowości i intensywności uprawy oraz ich interakcji na plon pszenicy ozimej w doświadczeniach PDO. Biul. IHAR 259: 131 — 146.
Google Scholar

Ebdon J. S., Gauch H. G. 2002. Additive main effect and multiplicative interactions analysis of national turfgrass performance trials. Interpretation of genotype x environment interactions. Crop Sci. 42: 489 — 496.
Google Scholar

Elandt R. 1964. Statystyka matematyczna w zastosowaniu do doświadczalnictwa rolniczego. PWN, Warszawa.
Google Scholar

Gauch H. G. 1992. Statistical analysis of regional yield trials. AMMI analysis of factorial designs. Elsevier Science, New York.
Google Scholar

Gauch H. G. 2006. Statistical analysis of yield trials by AMMI and GGE. Crop Sci. 46: 1488 — 1500.
Google Scholar

Gauch H. G., Piepho H.P., Annicchiarico P. 2008. Statistical analysis of yield trials by AMMI and GGE: Further considerations. Crop Sci. 48: 866 — 889.
Google Scholar

Gauch H. G., Zobel R. W. 1996. AMMI analysis of yield trials. In: M. S. Kang, H. G. Gauch (Ed.) Genotype by environment interaction. CRC Press, Boca Raton: 85 — 122.
Google Scholar

Gauch H. G., Zobel R. W. 1997. Identifying mega-environments and targeting genotypes. Crop Sci. 37: 311 — 326.
Google Scholar

Johnson R. A., Wichern D. W. 2002. Applied multivariate statistical analysis. Prentice-Hall, Inc. Upper Saddle River, NJ, USA.
Google Scholar

Kang M. S. 1993. Simultaneous selection for yield and stability in crop performance trials: Consequences for growers. Agron. J. 85: 754 — 57.
Google Scholar

Kang M. S. 1998. Using genotype-by environment interaction for crop cultivar development. Adv. Agron. 62: 199 — 253.
Google Scholar

Kang M. S. 2002. Genotype-environment interaction: Progress and prospects In: Kang M.S. (Ed.), Quantitative genetics, genomics and plant breeding, CAB International Wallingford, UK: 221 — 243.
Google Scholar

Krzanowski W. J. 1988. Principles of multivariate analysis: a users’ perspective. Oxford University Press, Oxford.
Google Scholar

Ma B. L., Yan W., Dwyer L. M., Fregeau-Reid J., Voldeng H. D., Dion Y., Nass H. 2004. Graphic analysis of genotype, environment, nitrogen fertilizer, and their interactions on spring wheat yield. Agron. J. 96: 169 — 180.
Google Scholar

Mądry W. 2003. Analiza statystyczna miar stabilności na podstawie danych w klasyfikacji genotypy × środowiska. Część II Model mieszany Shukli i model regresji łącznej. Coll. Biom. 33: 207 — 220.
Google Scholar

Mądry W., Paderewski J., Gozdowski D., Drzazga T. 2011. Adaptive yield response of winter wheat cultivars across environments in Poland using joint AMMI and cluster analyses. Intern. J. Plant Prod. 5: 299 — 310.
Google Scholar

Mądry W., Paderewski J., Rozbicki J., Gozdowski D., Golba J., Piechociński M., Studnicki M., Derejko A. 2012. Plonowanie odmian pszenicy ozimej w różnych środowiskach- jednoroczna seria PDOiR. Biul. IHAR 263:189 — 204.
Google Scholar

McIntosh M. S. 1983. Analysis of combined experiments. Agron. J. 75: 153 — 155.
Google Scholar

Mejza I. 1999. Planowanie serii doświadczeń dwuczynnikowych z rozszczepionymi jednostkami i analiza wyników. Roczniki Akademii Rolniczej w Poznaniu, vol. 301.
Google Scholar

Mintenko A. S., Smith S. R., Cattani D. J. 2002. Turfgrass evaluation of native grasses for the Northern Great Plains Region. Crop Sci. 42: 2018 — 2024.
Google Scholar

Mohammadi R., Amri A. 2013. Genotype × environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran. Euphytica 192: 227 — 249.
Google Scholar

Mohammadi R., Sadeghzadeh D., Armion M., Amri A. 2011. Evaluation of durum wheat experimental lines under different climate and water regime conditions of Iran. Crop Pasture Sci. 62: 137 — 151.
Google Scholar

Quinn G. P., Keough M. J. 2003. Experimental design and data analysis for biologists. Cambridge University Press, Cambridge.
Google Scholar

Paderewski J. 2008. Przydatność modelu AMMI do badania reakcji roślin rolniczych na warunki środowiskowe. Praca doktorska, Wydział Rolnictwa i Biologii, SGGW.
Google Scholar

Paderewski J., Gauch H. G., Mądry W., Drzazga T., Rodrigues P. C. 2011. Yield response of winter wheat to agro-ecological conditions using additive main effects and multiplicative interaction and cluster analysis. Crop Sci. 51: 969 — 980.
Google Scholar

Pecetti L., Annicchiarico P., Abdelguerfi A., Kallida R., Mefti M., Porqueddu C., Simoes N., Volaire F., Lelievre F. 2011. Response of Mediterranean tall fescue cultivars to contrasting agricultural environments and implications for selection. J. Agron. Crop Sci. 197: 12 — 20.
Google Scholar

Pinnschmidt H. O., Hovmøller M. S. 2002. Genotype × environment interactions in the expression of net blotch resistance in spring and winter barley varieties. Euphytica 125: 227 — 243.
Google Scholar

Samonte S. O. Pb., Wilson L. T., McClung A. M., Medley C. 2005. Targeting cultivars onto rice growing environments using AMMI and SREG GGE biplot analyses. Crop Sci. 45: 2414 — 2424.
Google Scholar

Sivapalan S., O’Brien L., Ortiz-Ferrera G., Hollamby G.J., Barclay I., Martin P.J. 2000. An adaptation analysis of Australian and CIMMYT/ICARDA wheat germplasm in Australian production environments. Aust. J. Agric. Res. 51: 903 — 915.
Google Scholar

Smith A. B., Cullis B.R., 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.
Google Scholar

Steinberg W. J. 2011. Statistics Alive. SAGE Publications Inc.: 527 — 529.
Google Scholar

Stiller W. N., Reid P. E., Constable G. A. 2004. Maturity and leaf shape as traits influencing cotton cultivar adaptation to dryland conditions. Agron. J. 96: 656 — 664.
Google Scholar

van Eeuwijk F. A., Keizer L. C. E., Bakker J. J. 1995. Linear and bilinear models for the analysis of multi-environment trials: II. An application to data from the Dutch Maize Variety Trials. Euphytica 84: 9 — 22.
Google Scholar

Welham S. J., Gogel B. J., Smith A. B., Thompson R., Cullis B. R. 2010. A comparison of analysis methods for late stage variety evaluation trials. Aust. N. Z. J. Stat. 52: 125 — 149.
Google Scholar

Wójcik A. R., Laudański Z. 1989. Planowanie i wnioskowanie statystyczne w doświadczalnictwie. PWN.
Google Scholar

Yan W., M.S. Kang. 2003. GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton, FL.
Google Scholar

Zhang Y., He Z., Zhang A., van Ginkel M., Pena R. J., Ye G. 2006. Pattern analysis on protein properties of Chinese and CIMMYT spring wheat cultivars sown in China and CIMMYT. Austr. J. Agric. Res. 57: 811 — 822.
Google Scholar