Zastosowania modelu AMMI do analizy reakcji odmian na środowiska

Jakub Paderewski

jakub_paderewski@sggw.edu.pl
Katedra Doświadczalnictwa i Bioinformatyki, SGGW, Warszawa (Poland)

Wiesław Mądry


Katedra Doświadczalnictwa i Bioinformatyki, SGGW, Warszawa (Poland)

Abstrakt

W doświadczalnictwie rolniczym kluczową kwestią są serie doświadczeń odmianowych, stanowiące szczególny przypadek doświadczeń dwuczynnikowych, w których jednym czynnikiem są odmiany, a drugim miejscowości. Do opisu charakteru interakcji dwóch czynników, a więc w powyżej opisanym przypadku, mogą być stosowane wielowymiarowe modele statystyczne takie jak model AMMI, GGE czy JREG. Praca ta ma przybliżyć możliwości zastosowania wybranych modeli statystycznych ze szczególnym uwzględnieniem modelu AMMI. Oprócz analizy AMMI przedstawiono uzupełniającą analizę skupień. Opisane metody statystyczne są celowe w analizie reakcji odmian roślin rolniczych na warunki środowiskowe, czyli agroekosystemy, na podstawie danych z serii doświadczeń.

Instytucje finansujące

Praca zawiera wyniki badań w projekcie własnym Ministerstwa Nauki i Szkolnictwa Wyższego nr N N310 091136 pt. „Badanie uwarunkowania plonu ziarna odmian pszenicy ozimej przez cechy plonotwórcze roślin”.

Słowa kluczowe:

analiza AMMI, analiza GGE, interakcja genotypowo-środowiskowa, modele multiplikatywne, ocena adaptacji

Abamu F.J., Akinsola E.A., Alluri K. 1998. Applying the AMMI models to understand genotype-by-environment (G E) interactions in rice reaction to blast disease in Africa. Internat. J. Pest Manag. 44: 239 — 245.
Google Scholar

Abdelmulla AA., Linke W., Von Kittlitz E., Stelling D. 1999. Heterosis and inheritance of drought tolerance in faba bean Vicia faba L. Plant Breeding 118: 485 — 490.
Google Scholar

Adugna W., Labuschagne M.T. 2002. Genotype-environment interactions and phenotypic stability analyses of linseed in Ethiopia. Plant Breeding 121: 66 — 71.
Google Scholar

Alagarswamy G., Chandra S. 1998. Pattern analysis of international sorghum multi-environment trials for grain-yield adaptation. Theor. Appl. Genet. 96: 397 — 405.
Google Scholar

Annicchiarico P. 1997 a. Additive main effects and multiplicative interaction (AMMI) analysis of genotype location interaction in variety trials repeated over years. Theor. Appl. Genet. 94: 1072 — 1077.
Google Scholar

Annicchiarico P. 1997 b. Joint regression vs AMMI analysis of genotype-environment interactions for cereals in Italy. Euphytica 94: 53 — 62.
Google Scholar

Annicchiarico P. 2002 a. Defining adaptation strategies and yield stability targets in breeding programmers. 165–183. W Kang M.S. (Ed.) Quantitative genetics, genomics and plant breeding. CABI, Wallingford, UK.
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., Perenzin M. 1994. Adaptation patterns and definition of macro-environments for selection and recommendation of common-wheat genotypes in Italy. Plant Breeding 113: 197 — 205.
Google Scholar

Annicchiarico P., Piano E. 2005. Use of artificial environments to reproduce and exploit genotype × location interaction for lucerne in northern Italy. Theor. Appl. Genet. 110: 219 — 227.
Google Scholar

Annicchiarico P., Bellah F., Chiari T. 2005. Defining subregions and estimating benefits for a specific-adaptation strategy by breeding programs: A case study. Crop Sci. 45: 1741 — 749.
Google Scholar

Annicchiarico P., Bellah F., Chiari T. 2006a. Repeatable genotype×location interaction and its exploitation by conventional and GIS-based cultivar recommendation for durum wheat in Algeria. Europ. J. Agronomy 24: 70 — 81.
Google Scholar

Annicchiarico P., Russi L., Piano E., Veronesi F. 2006 b. Cultivar adaptation across Italian locations in four turfgrass species. Crop Sci. 46: 264 — 272.
Google Scholar

Atlin G. N., McRae K.B., Lu X. 2000 a. Genotype region interaction for two row barley yield in Canada. Crop Sci. 40: 1 — 6.
Google Scholar

Basford K. E., Cooper M. 1997. Genotype×environment interactions and some considerations of their implications for wheat breeding in Australia. Austr. J. Agric. Res. 49:153 — 174.
Google Scholar

Becker H. C., Leon J. 1988. Stability analysis in plant breeding. Plant Breeding 101:1 — 23.
Google Scholar

Blanche S. B., Myers G. O. 2006. Identifying Discriminating Locations for Cultivar Selection in Louisiana. Crop Sci. 46:946–949.
Google Scholar

Bradu D. Gabriel K.R. 1978. The biplot as a diagnostic tool for model of two-way tables. Technometrics 1978: 47 — 63.
Google Scholar

Brancourt-Hulmel M., Lecomte C. 2003. Effect of environmental varieties on genotype x environment interaction of winter wheat: a comparison of biadditive factorial regression to AMMI. Crop Sci. 43: 608 — 617.
Google Scholar

Brancourt-Hulmel M., Denis J.B., Biarnes-Dumoulin V. 1997. Comparison of Joint Regression, AMMI model and Factorial regression for efficiency and parsimony in plant breeding. Materiały konferencyjne EUCARPIA sekcja biometrics in Plant Breeding (ed. Krajewski P., Kaczmarek Z.) Poznań 1997: 81 — 86.
Google Scholar

Bujak H., Dopierała A., Dopierała P., Nowosad K. 2006. Analiza interakcji genotypowo-środowiskowej plonu odmian żyta ozimego. Biul. IHAR. 240/241: 151 — 160.
Google Scholar

Burgueño, J., Crossa, J., Vargas, M., 2001. SAS programs for graphing GE and GGE biplots. Biometrics and Statistics Unit, CIMMYT, Int. México.
Google Scholar

Caliński T., Czajka S., Kaczmarek Z. 1979. Analiza interakcji genotypowo-środowiskowej. Zastosowanie analizy regresji oraz analizy składowych głównych. IX Coll. Metodol. z Agrobiom. 5 — 28 .
Google Scholar

Caliński T., Czajka S., Kaczmarek Z. 1980. Analiza jednorocznej serii ortogonalnej doświadczeń odmianowych ze szczególnym uwzględnieniem interakcji odmianowo-środowiskowej. 1. Analiza ogólna. Biul. Oceny Odmian 12: 67 — 81.
Google Scholar

Caliński T., Czajka S., Kaczmarek Z. 1983. Analiza jednorocznej serii ortogonalnej doświadczeń odmianowych ze szczególnym uwzględnieniem interakcji odmianowo-środowiskowej. 1. Analiza szczegółowa. Biul. Oceny Odmian 15: 39 — 60.
Google Scholar

Caliński T., Czajka S., Kaczmarek Z. 1987 a. A model for the analysis of a series of experiments repeated at several places over a period of years. I. Theory. Biul. Oceny Odmian 17 — 18:7 — 33.
Google Scholar

Caliński T., Czajka S., Kaczmarek Z. 1987b. A model for the analysis of a series of experiments repeated at several places over a period of years. II. Example. Biul. Oceny Odmian 17 — 18:35 — 71.
Google Scholar

Caliński T., Czajka S., Kaczmarek Z., Krajewski P., Siatkowski I. 1995. SERGEN-a computer program for the analysis of series of variety trials. Biuletyn Oceny Odmian 26/27: 39 — 41.
Google Scholar

Caliński T., Czajka S., Kaczmarek Z. 1997. A multivariate approach to analysing genotype-environment interactions. W: Krajewski P., Kaczmarek Z (Ed), Advances in Biometrical Genetics, 3 — 14, Poznań.
Google Scholar

Cassida K. A., Muir J. P., Hussey M. A. Read J.C., Venuto B. C., Ocumpaugh W. R. 2005. Biofuel component concentrations and yields of switchgrass in South Central U.S. environments. Crop Sci. 45: 692.
Google Scholar

Ceccarelli S. 1989. Wide adaptation: How wide? Euphytica 40: 197 — 205.
Google Scholar

Ceccarelli S. 1994. Specific adaptation and breeding for marginal conditions. Euphytica 77: 205 — 219.
Google Scholar

Ceccarelli S. 1996. Adaptation to low/high input cultivation. Euphytica 92: 203 — 214.
Google Scholar

Chapman S.C., de la Vega A. J. 2002. Spatial and seasonal effects confounding interpretation of sunflower yields in Argentina. Field Crops Research 73: 107 — 120.
Google Scholar

Chapman S.C., Crossa J., Edmeades G.O. 1997. Genotype by environment effects and selection for drought tolerance in tropical maize. I. Two mode pattern analysis of yield. Euphytica 95: 1 — 9.
Google Scholar

Collaku A., Harrison S.A., Finney P.L., Van Sanford D.A. 2002. Clustering of environments of Southern Soft Red Winter Wheat Region for milling and baking quality attributes. Crop Sci. 42:58 — 63.
Google Scholar

Cooper M., Delacy I. H. 1994. Relationships among analytic methods used to study genotypic variation and genotype-by-environment interaction in plant breeding multi-environment trials. Theor. Appl. Genet. 88: 561 — 572 .
Google Scholar

Cornelius P. L. 1993. Statistical tests and retention of terms in the additive main effects and multiplicative interaction model for cultivar trials. Crop Sci. 33: 1186 — 1193.
Google Scholar

Cornelius P.L., Seyedsadr M., Crossa J. 1992. Using the shifted multiplicative model to search for "separability" in crop cultivar trials. Theor. Appl. Genet. 84: 161 — 172.
Google Scholar

Crossa J. 1990. Statistical analyses of multilocation trials. Adv. Agron. 44: 55 — 85.
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 International Wallingford, UK: 305 — 322.
Google Scholar

Crossa J., Gauch H. G., Zobel R.W. 1990. Additive Main Effects and Multiplicative Interaction Analysis of Two Interaction Maize Cultivar Trials. Crop Sci. 30: 493 — 500 .
Google Scholar

Crossa J., Fox P.N., Pfeiffer W. H., Rajaram S., Gauch H.G. 1991. AMMI adjustment for statistical analysis of an international wheat yield trial. Theor. Appl. Genet. 81: 27 — 37.
Google Scholar

Crossa J., Cornelius P.L., Seyedsadr M., Byrne P. 1993. A shifted multiplicative model cluster analysis for grouping environments without genotypic rank change. Theor. Appl. Genet. 85: 577 — 586.
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

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 la Vega A.J., DeLacy I.H., Chapman S.C. 2007a. Changes in agronomic traits of sunflower hybrids over 20 years of breeding in central Argentina. Field Crops Res. 100: 73 — 81.
Google Scholar

de la Vega A.J., DeLacy I.H., Chapman S.C. 2007b. Progress over 20 years of sunflower breeding in central Argentina. Field Crops Res.100: 61 — 72.
Google Scholar

DeLacy I.H., Cooper M. 1990. Pattern analysis for the analysis of regional variety trials. In: Kang M.S. (ed.) Genotype-by-environments interaction and plant breeding. Louisiana State Univ., Baton Rouge, LA: 301 — 334.
Google Scholar

Dias C., Krzanowski W. 2003. Model selection and cross validation in additive main effect and multiplicative interaction models. Crop Sci. 43: 865 — 873.
Google Scholar

Dixon A.G.O., Ngeve J.M., Nukenine E.N. 2002. Genotype× environment effects on severity of cassava bacterial blight disease caused by Xanthomonas axonopodis pv. Manihotis. European Journal of Plant Pathology 108: 763 — 770.
Google Scholar

Drzazga T., Krajewski P. 2001. Zróżnicowanie środowisk pod względem stopnia interakcji w seriach doświadczeń z pszenicą ozimą. Biul. IHAR. 218/219: 111 — 115.
Google Scholar

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

Ebdon J. S., Gauch H. G. 2002 b. Additive main effect and multiplicative interaction analysis of national turfgrass performance trials: II. Cultivar recommendations. Crop Sci. 42: 497 — 506.
Google Scholar

Fox P.N. Rosielle A.A. 1982. Reducing the influence of environmental main-effects on pattern analysis of plant breeding environments. Euphytica 31: 645 — 656.
Google Scholar

Gabriel K. R. 1971 The biplot graphic display of matrices with application to principal component analysis. Biometrika 58: 453 — 467.
Google Scholar

Gauch H. G. 1988. Model selection and validation for yield trials with interaction. Biometrics 44: 705 — 715.
Google Scholar

Gauch H. G. 1990. Full and reduced models for yield trials. Theor. Appl. Genet. 80: 153 — 160.
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., Furnas R.E. 1991. Statistical analysis of yield trials with MATMODEL. Agron. J. 83: 916 — 920.
Google Scholar

Gauch H. G., Zobel R. W. 1988. Predictive and postdictive success of statistical analyses of yield trials. Theor. Appl. Genet. 76: 1 — 10.
Google Scholar

Gauch H. G., Zobel R. W. 1989. Accuracy and selection success in yield trial analyses. Theor. Appl. Genet. 77: 473 — 481.
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

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

Gollob H. 1968. A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33: 73 — 115.
Google Scholar

Grausgruber H., Oberforster M., Werteker M., Ruckenbauer P., Vollmann J. 2000. Stability of quality traits in Austrian-grown winter wheats. Field Crops Res. 66: 257 —267.
Google Scholar

Haussmann B. I. G., Obilana A. B., Ayiecho P. O., Blum A., Schipprack W., Geiger H. H. 2000. Yield and yield stability of four population types of grain sorghum in a semi-arid area of Kenya. Crop Sci. 40: 319 — 329.
Google Scholar

Hernandez M., Crossa J. 2000. The AMMI analysis and graphing the biplot. CIMMYT, Int. Mexico.
Google Scholar

Hühn M., Truberg B. 2002. Contributions to the analysis of genotype x environment interactions: theoretical results of the application and comparison of clustering techniques for the stratification of field test sites. J. Agron. Crop Sci. 188: 65 — 72.
Google Scholar

Ibanez M. A., Di Renzo M.A., Samami S. S., Bonamico N. C., Poverene M. M. 2001. Genotype-environment interaction of lovegrass forage yield in the semi-arid region of Argentina. Journal of Agricultural Science, Cambridge 137: 329 — 336.
Google Scholar

Joshi A.K., Ortiz-Ferrara G., Crossa J., Singh G., Alvarado G., Bhatta M.R., Duveiller E., Sharma R.C., Pandit D.B., Siddique A.B., Das S.Y., Sharma R.N., Chand R. 2007. Associations of environments in South Asia based on spot blotch disease of wheat caused by Cochliobolus sativus. Crop Sci. 47: 1071— 1081.
Google Scholar

Kang M.S. 1993. Simultaneous selection for yield and stability: Consequences for growers. Agron. J. 85: 754 — 757 .
Google Scholar

Kang M.S. 1998. Using genotype-by-environment interaction for crop cultivar development. Adv. in Agronomy 62: 200 — 252.
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

Kaya Y., Akcura M., Ayranci R., Taner S. 2006. Pattern analysis of multi-environment trials in bread wheat. Commun. Biometry and Crop Sci. 1: 63 — 71.
Google Scholar

Li W., Yan Z.H., Wei Y.M. Lan X.J., Zheng Y.L. 2006. Evaluation of genotype x environment interactions in Chinese spring wheat by the AMMI model, correlation and path analysis. J. Agronomy and Crop Science 192: 221 — 227.
Google Scholar

Lillemo M., van Ginkel M., Trethowan R.M., Hernandez E., Rajaram S. 2004. Associations among international CIMMYT bread wheat yield testing locations in high rainfall areas and their implications for wheat breeding. Crop Sci. 44: 1163 — 1169.
Google Scholar

Lillemo M., van Ginkel M., Trethowan R. M., Hernandez E., Crossa J. 2005. Differential adaptation of CIMMYT bread wheat to global high temperature environments. Crop Sci. 45: 2443 — 2453.
Google Scholar

Lin C.S., Binns M.R. 1994. Concepts and methods for analyzing regional trial data for cultivar and location selection. Plant Breeding Reviews 12: 271 — 297.
Google Scholar

Link W., Schill B., von Kittlitz E. 1996. Breeding for wide adaptation in faba bean. Euphytica 92: 185 — 190. .
Google Scholar

Ma B. L., Yan W., Dwyer L. M., Frégeau-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

Mathews K. L., Chapman S. C., Trethowan R., Singh R. P., Crossa J., Pfeiffer W., van Ginkel M., DeLacy I. 2006. Global adaptation of spring bread and durum wheat lines near-isogenic for major reduced height genes. Crop Sci. 46: 603 — 613.
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., Kang M.S. 2005. Scheffé-Caliński and Shukla models: their interpretation and usefulness in stability and adaptation analyses. Journal of Crop Improvement 14: 325 — 369 .
Google Scholar

Mądry W., Gacek E.S., Paderewski J., Gozdowski D., Drzazga T. 2011. Adaptive yield response of winter wheat cultivars across environments in Poland using combined AMMI and cluster analyses. International Journal of Plant Production 5.
Google Scholar

Mądry W., Paderewski J., Drzazga T. 2006. Ocena reakcji plonu ziarna rodów hodowlanych pszenicy ozimej na zmienne warunki środowiskowe za pomocą analizy AMMI. Fragmenta Agronomica 92: 130 — 143.
Google Scholar

McLaren C.G. 1996. Methods of data standardization used in pattern analysis and AMMI models for the analysis of international multi-environment variety trials, In: Cooper M., Hammer G.L., Eds. Plant adaptation and crop improvement. Wallingford, UK, CAB International.: 225 — 242.
Google Scholar

Mekbib F. 2003. Yield stability in common bean (Phaseolus vulgaris L.) genotypes. Euphytica 130:147-153.
Google Scholar

Motzo R., Giunta F., Deidda M. 2001. Factors affecting the genotype × environment interaction in spring triticale grown in a Mediterranean environment. Euphytica 121: 317 — 324.
Google Scholar

Muurinen S., Peltonen-Sainio P. 2006. Radiation-use efficiency of modern and old spring cereal cultivars and its response to nitrogen in northern growing conditions. Field Crops Research 96: 363 — 373.
Google Scholar

Nabugoomu F., Kempton R.A., Talbot M. 1999. Analysis of series of trials where varieties differ in sensitivity to locations. J. Agric. Biol. Environ. Stat. 4: 310 — 325.
Google Scholar

Nachit M. M., Nachit G., Ketata H., Gauch H. G., Zobel R. W. 1992. Use of AMMI and linear regression models to analyze genotype-environment interaction in durum wheat. Theor. Apel. Genet. 83: 597 — 601.
Google Scholar

Ortiz-Monasterio J.I., Sayre K.D., Rajaram S., McMahon M. 1997. Genetic progress in wheat yield and nitrogen use efficiency under four nitrogen rates. Crop Sci. 37: 898 — 904.
Google Scholar

Pacheco R. M., Duarte J. B., Vencovsky R., Pinheiro J. B., Oliveira A. B. 2005. Use of supplementary genotypes in AMMI analysis. Theor. Appl. Genet. 110: 812 — 818.
Google Scholar

Paderewski J., Mądry W. 2006. Addytywno-multiplikatywny model AMMI do statystycznej analizy danych z serii doświadczeń genotypowych. Coll. Biom. 36: 125 — 148.
Google Scholar

Paderewski J., Mądry W., Rozbicki J. 2010. Yielding of old and modern Polish wheat cultivars under different nitrogen input as assessed by method of joint AMMI and cluster analyses. Plant Breeding and Seed Science 62: 117 — 136.
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

Patterson H. D. 1997. Analysis of series of variety trials. In R.A. Kempton, P.N. Fox (ed.) Statistical methods for plant variety evaluation. Chapman and Hall, London: 139 — 161.
Google Scholar

Piepho H.P. 1996. Analysis of genotype by environment interaction and phenotypic stability. In: M.S. Kang, H.G. Zobel (Eds), Genotype by environment interaction,. CRC Press, Boca Raton: 151 — 174.
Google Scholar

Piepho H.P. 1998. Methods for comparing the yield stability of cropping systems-a review. J. Agron. Crop Sci. 180: 193 — 213.
Google Scholar

Piepho, H.P., van Eeuwijk F.A. 2002. Stability analyses in crop performance evaluation. In: Kang, M. [ed.]: “Crop improvement: Challenges in the twenty-first century”. Food Products Press, Binghamton, New York: 307 — 342.
Google Scholar

Piepho H.P., Möhring J., Melchinger A. E., Büchse A. 2008. BLUP for phenotypic selection in plant breeding and variety testing. Euphytica 161: 209 — 228.
Google Scholar

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

Presterl T., Weltzien E. 2003. Exploiting heterosis in pearl millet for population breeding in arid environments Crop Sci. 43: 767 — 776.
Google Scholar

R Development Core Team 2007. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org.
Google Scholar

Robert N. 1997. Structuring genotype x environment interaction for quality traits in bread wheat, in two multi-location series of trials. Euphytica 97: 53 — 66.
Google Scholar

Robinson J., Jalli M. 1999. Sensitivity of resistance to net blotch in barley. J. Phytopathol. 147: 235 — 241.
Google Scholar

Rodriguez M., Rau D., Papa R., Attene G. 2008. Genotype by environment interactions in barley (Hordeum vulgare L.): different responses of landraces, recombinant inbred lines and varieties to Mediterranean environment. Euphytica Vol. 163, No 2: 231 — 247.
Google Scholar

Roozeboom K. L., Schapaugh W. T., Tuinstra M. R., Vanderlip R.L., Milliken G. A. 2008. Testing wheat in variable environments: genotype, environment, interaction effects, and grouping test locations. Crop Sci. 48: 317 — 330.
Google Scholar

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

SAS Institute. 2001. SAS system for Windows. v. 8.2. SAS Inst., Cary, NC.
Google Scholar

Sharma R. C., Ortiz-Ferrara G.. Crossa J., Bhatta M. R., Sufian M.A., Shoran J., Joshi A. K ., Chand R. Singh G., Ortiz R. 2007. Wheat grain yield and stability assessed through regional trials in the Eastern Gangetic Plains of South Asia. Euphytica 157: 457 — 464.
Google Scholar

Sinebo W. 2005. Trade off between yield increase and yield stability in three decades of barley breeding in a tropical highland environment. Field Crops Research 92: 35 — 52.
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

Tollenaar M., Lee E. A. 2002. Yield potential, yield stability and stress tolerance in maize. Field Crops Research 75: 161 — 170.
Google Scholar

Trethowan R. M., van Ginkel M., Rajaram S. 2002. Progress in breeding wheat for yield and adaptation in global drought affected environments. Crop Sci. 42: 1441 — 1446.
Google Scholar

Trethowan R. M., van Ginkel M., Ammar K., Crossa J., Payne T. S., Cukadar B., Rajaram S., Hernandez E. 2003. Associations among twenty years of international bread wheat yield evaluation environments. Crop Sci. 43: 1698 — 1711.
Google Scholar

Truberg B., Hühn M. 2002. Contributions to the analysis of genotype × environment interactions: Experimental results of the application and comparison of clustering techniques for the stratification of field test sites. J. Agron. Crop Sci. 188: 113 — 122.
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

Vargas M., Crossa J., van Eeuwijk F., Sayre K.D., Reynolds M.P. 2001. Interpreting treatment × environment interaction in agronomy trials. Agron. J. 93:949 — 960.
Google Scholar

Viele K., Srinivasan C. 2000. Parsimonious estimation of multiplicative interaction in analysis of variance using Kullback Leibler Information. J. Stat. Plan. Inf. 84: 201 — 219.
Google Scholar

Voltas J., Romagosa I., Lafarga A., Armesto A.P., Sombrero A., Araus J.L. 1999. Genotype by environment interaction for grain yield and carbon isotope discrimination of barley in Mediterranean Spain. Australian Journal of Agricultural Research 50: 1263 — 1271.
Google Scholar

Wade L.J., McLaren C.G., Quintana L., Harnpichitivitaya D., Rajatasereekul S., Sarawgi A.K., Kumar A., Ahmed H.U., Sarwoto, Singh A.K., Rodriguez R., Siopongco J, Sarkarung S. 1999. Genotype by environment interactions across diverse rained lowland rice environments. Field Crop Reserch 64: 35 — 50.
Google Scholar

Wamatu J.N., Thomas E. 2002. The influence of genotype-environment interaction on the grain yields of 10 pigeonpea cultivars grown in Kenya. J. Agron. Crop Sci. 188: 25 — 33.
Google Scholar

Wamatu J. N., Thomas E.,. Piepho H.P. 2003. Responses of different Arabica coffee (Coffea arabica L.) clones to varied environmental conditions. Euphytica 129: 175 — 182.
Google Scholar

Weber R., Zalewski D., Kotecki A., Kaczmarek J. 2007. Ocena przydatności punktów doświadczalnych do prowadzenia PDO na Dolnym Śląsku. Biul. IHAR 245: 5 — 16.
Google Scholar

Williams W. T. 1976. Pattern analysis in agricultural science. Elsevier, Amsterdam.
Google Scholar

Worku M., Bänziger M., Schulte Erley G., Friesen D., Diallo A.O. Horst W.J. 2007. Nitrogen uptake and utilization in contrasting nitrogen efficient tropical maize hybrids. Crop Sci. 47: 519 — 528 .
Google Scholar

Yan W., Frégeau-Reid J. 2008. Breeding line selection based on multiple traits. Crop Sci. 48: 417 — 423.
Google Scholar

Yan W., Hunt L.A. 2001. Interpretation of genotype x environment interaction for winter wheat yield in Ontario. Crop Sci. 41: 19 — 25.
Google Scholar

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

Yan W., Tinker N.A. 2005. An Integrated Biplot Analysis System for Displaying, Interpreting, and Exploring Genotype × Environment Interaction. Crop Sci. 45: 1004 — 1016.
Google Scholar

Yan W., Hunt L.A. Sheng Q., Szlanics Z. 2000. Cultivar evaluation and mega-environment investigation based on the GGE biplot.. Crop Sci.. 40: 597 — 605.
Google Scholar

Yan W., Kang M.S., Ma B., Woods S., Cornelius P.L.2007. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 47: 643 — 653.
Google Scholar

Yau S.K., Ortiz-Ferrara G. Srivastava J.P. 1991. Classification of diverse bread wheat-growing environments based on differential yield responses. Crop Sci. 31: 571 — 576.
Google Scholar

Zhang Y., He Z., Zhang A., van Ginkel M., Ye G. 2006a. Pattern analysis on grain yield of Chinese and CIMMYT spring wheat cultivars grown in China and CIMMYT. Euphytica 147: 409 — 420.
Google Scholar

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

Zieliński A., Jankowski P., Mądry W. 2005. Biplot typu GGE jako narzędzie do graficznej analizy danych z serii doświadczeń odmianowych. Coll. Biom. 35: 207 — 224.
Google Scholar

Zobel R.W., Wright M.J., Gauch H.G. 1988. Statistical analysis of a yield trial. Agron. J. 80:388 — 393.
Google Scholar

Pobierz


Opublikowane
03/29/2012

Cited By / Share

Paderewski, J. i Mądry, W. (2012) „Zastosowania modelu AMMI do analizy reakcji odmian na środowiska”, Biuletyn Instytutu Hodowli i Aklimatyzacji Roślin, (263), s. 161–188. doi: 10.37317/biul-2012-0082.

Autorzy

Jakub Paderewski 
jakub_paderewski@sggw.edu.pl
Katedra Doświadczalnictwa i Bioinformatyki, SGGW, Warszawa Poland

Autorzy

Wiesław Mądry 

Katedra Doświadczalnictwa i Bioinformatyki, SGGW, Warszawa Poland

Statystyki

Abstract views: 68
PDF downloads: 35


Licencja

Prawa autorskie (c) 2012 Jakub Paderewski, Wiesław Mądry

Creative Commons License

Utwór dostępny jest na licencji Creative Commons Uznanie autorstwa – Na tych samych warunkach 4.0 Miedzynarodowe.

Z chwilą przekazania artykułu, Autorzy udzielają Wydawcy niewyłącznej i nieodpłatnej licencji na korzystanie z artykułu przez czas nieokreślony na terytorium całego świata na następujących polach eksploatacji:

  1. Wytwarzanie i zwielokrotnianie określoną techniką egzemplarzy artykułu, w tym techniką drukarską oraz techniką cyfrową.
  2. Wprowadzanie do obrotu, użyczenie lub najem oryginału albo egzemplarzy artykułu.
  3. Publiczne wykonanie, wystawienie, wyświetlenie, odtworzenie oraz nadawanie i reemitowanie, a także publiczne udostępnianie artykułu w taki sposób, aby każdy mógł mieć do niego dostęp w miejscu i w czasie przez siebie wybranym.
  4. Włączenie artykułu w skład utworu zbiorowego.
  5. Wprowadzanie artykułu w postaci elektronicznej na platformy elektroniczne lub inne wprowadzanie artykułu w postaci elektronicznej do Internetu, lub innej sieci.
  6. Rozpowszechnianie artykułu w postaci elektronicznej w internecie lub innej sieci, w pracy zbiorowej jak również samodzielnie.
  7. Udostępnianie artykułu w wersji elektronicznej w taki sposób, by każdy mógł mieć do niego dostęp w miejscu i czasie przez siebie wybranym, w szczególności za pośrednictwem Internetu.

Autorzy poprzez przesłanie wniosku o publikację:

  1. Wyrażają zgodę na publikację artykułu w czasopiśmie,
  2. Wyrażają zgodę na nadanie publikacji DOI (Digital Object Identifier),
  3. Zobowiązują się do przestrzegania kodeksu etycznego wydawnictwa zgodnego z wytycznymi Komitetu do spraw Etyki Publikacyjnej COPE (ang. Committee on Publication Ethics), (http://ihar.edu.pl/biblioteka_i_wydawnictwa.php),
  4. Wyrażają zgodę na udostępniane artykułu w formie elektronicznej na mocy licencji CC BY-SA 4.0, w otwartym dostępie (open access),
  5. Wyrażają zgodę na wysyłanie metadanych artykułu do komercyjnych i niekomercyjnych baz danych indeksujących czasopisma.

Inne teksty tego samego autora

1 2 3 > >>