Retrospective study of yield response to environmental conditions in winter wheat cultivars using combined AMMI and cluster analysis to incomplete data: genetic progress for adaptability

Jakub Paderewski

jakub_paderewski@sggw.edu.pl
Katedra Doświadczalnictwa i Bioinformatryki SGGW w Warszawie (Poland)

Wiesław Mądry


Katedra Doświadczalnictwa i Bioinformatryki SGGW w Warszawie (Poland)

Wiesław Pilarczyk


Katedra Metod Matematycznych i Statystycznych Uniwersytetu Przyrodniczego w Poznaniu (Poland)

Tadeusz Drzazga


Hodowla Roślin Rolniczych "Nasiona Kobierzyc" Sp. z o.o. w Kobierzycach (Poland)

Abstract

Data for winter wheat grain yield which were analyzed in the paper come from 14 multiple-environment trials (METs), called pre-registration trials, done across the years 1991–2004. Each year, new tested cultivars entered the trials network but some entries and check-cultivars retained in the network for a few year period. Among them 21 tested and check cultivars, evaluated across at least 3 years period, were taken to these considerations. The entries were released during the years the 1982 to 2004. Adjusted means of grain yield for cultivars in locations across years using BLUE estimator in REML method were calculated. These means constituted an incomplete GL classification. Some missing means in the classification were approximated by the EM-AMMI procedure. The AMMI analysis based on additive main effects and multiplicative interaction fixed model. In the second step of the statistical procedure cluster analysis was performed taking into account estimates of GL interaction effects. This method enables grouping cultivars with similar profiles of GL interaction effects and also similar genotypic means of grain yield. In this way different types of yield response of homogenous cultivar group of winter wheat to variable environmental conditions in locations (types of adaptations) were distinguished. The combined AMMI and cluster analysis including specific modifications for incomplete data was efficient to study patterns of yield response to environmental conditions in winter wheat cultivars using data from incomplete GLY classification obtained in pre-registration trials. As a result of the study, genetic progress was estimated with regard to both yield and its stability and then wide adaptability.


Keywords:

winter wheat, AMMI analysis, cluster analysis, cultivars, adaptation, grain yield

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

Allard R. W., Bradshaw A. D. 1964. Implications of genotype-environmental interactions in applied plant breeding. Crop Sci. 4: 503 — 508.
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

Austin R. B. 1999. Yield of wheat in the United Kingdom: recent advances and prospects. Crop Sci. 39: 1604 — 610.
Google Scholar

Bänziger M., Cooper M. 2001. Breeding for low input conditions and consequences for participatory plant breeding: Examples from tropical maize and wheat. Euphytica 122: 503 — 519.
Google Scholar

Benesi I. R. M., Labuschagne M. T., Herselman L., Mahungu N. M., Saka J. K. 2008. The effect of genotype, location and season on cassava starch extraction. Euphytica 160: 59 — 74.
Google Scholar

Brancourt-Hulmel M., Doussinault G., Lecomte C., Bérard P., Le Buanec 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

Caliński T, Corsten L. C. A. 1985. Clustering Means in ANOVA by Simultaneous Testing. Biometrics 41: 39 — 48.
Google Scholar

Caliński T., Czajka S., Denis J. B., Kaczmarek Z. 1992. EM and ALS algorithms applied to estimation of missing data in series of variety trials. Biul. Oceny Odmian 24–25: 7 — 31.
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. 1987 b. 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

Carr P. M., Kandel H. J., Porter P. M., Horsley R. D., Zwinger S. F. 2006. Wheat cultivar performance on certified organic fields in Minnesota and North Dakota. Crop Sci. 46: 1963 — 1971.
Google Scholar

Casler M.D., van Santen E. 2000. Patterns of variation in a collection of meadow fescue accessions. Crop Sci. 40: 248 — 255.
Google Scholar

Charakterystyka i technologia uprawy odmian pszenicy ozimej. 2003. IHAR Radzików.
Google Scholar

Chauhan Y. S., Wallace D. H., Johansen C., Singh L. 1998. Genotype-by-environment interaction effect on yield and its physiological bases in short-duration pigeonpea. Field Crops Research 59: 141 — 150.
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., Crossa J., Seyedsadr M. 1996 Statistical tests and estimators of multiplicative models for genotype-by-environment interaction. W Kang M. S., Gauch H. G. (Eds.), Genotype by Environment Interaction. CRC Press, Boca Raton: 199 — 234.
Google Scholar

DeLacy, I. H., K. E. Basford, M. Cooper J. K., Bull C.G., McLaren. 1996 a. Analysis of multi-environment trials — an historical perspective. In M. Cooper and G. L. Hammer (ed.) Plant adaptation and crop improvement. CAB International. Wallingford, UK: 39 — 124.
Google Scholar

DeLacy, I. H., K. E. Basford, M. Cooper, Fox P.N. 1996 b. Retrospective analysis of historical data sets from multi-environment trials–theoretical development. In: M. Cooper and G.L. Hammer (ed.) Plant adaptation and crop improvement. CAB International. Wallingford, UK: 243 — 267.
Google Scholar

de la Vega A. J., Chapman S.C., Hall A. J. 2001. Genotype by environment interaction and indirect selection for yield in sunflower I. Two-mode pattern analysis of oil and biomass yield across environments in Argentina. Field Crops Res. 72: 17 — 38.
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

De Vita P., Li Destri O., Nigro F., Platani C., Riefolo C., Di Fonzo N., Cattivelli L. 2007. Breeding progress in morpho-physiological, agronomical and qualitative traits of durum wheat cultivars released in Italy during the 20th century. Europ. J. Agronomy 26: 39 — 53.
Google Scholar

Donmez E., Sears R. G., Shroyer J. P., Paulsen G. M. 2001. Genetic gain in yield attributes of winter wheat in the Great Plains. Crop Sci. 41: 1412 — 1419.
Google Scholar

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

Fufa H., Baenziger P.S., Beecher B. S., Graybosch R. A., Eskridge K. M., Nelson L. A. 2005. Genetic improvement trends in agronomic performances and end-use quality characteristics among hard red winter wheat cultivars in Nebraska. Euphytica 144:187 — 198.
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

GenStat. 2002. The guide to Genstat. Release 6.1. VSN International. Oxford, UK.
Google Scholar

Giunta F., Motzo R. Pruneddu G. 2007. Trends since 1900 in the yield potential of Italian-bred durum wheat cultivars. Europ. J. Agron. 27: 12 — 4.
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

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

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

Lopez-Pereira M.L., Sadras V.O., Trapani, N. 1999. Genetic improvement of sunflower in Argentina between 1930 and 1995. I. Yield and its components. Field Crops Res. 62: 57 — 166.
Google Scholar

Mądry W., Talbot M., Ukalski K., Drzazga T., Iwańska M. 2006. Podstawy teoretyczne znaczenia efektów genotypowych i interakcyjnych w hodowli roślin na przykładzie pszenicy ozimej. Biul. IHAR 240/241: 13 — 31.
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. Wilingford, UK, CAB International: 225 — 242.
Google Scholar

Munoz P., Voltas J., Araus J. L., Igartua E., Romagosa I. 1998. Changes over time in the adaptation of barley releases in north-eastern Spain. Plant Breeding 117: 531 — 535.
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

Patterson H. D., Thompson R. 1971. Recovery of inter-block information when block sizes are unequal. Biometrika 58: 545 — 554.
Google Scholar

Piepho, H. P., Möhring J. 2005. Best linear unbiased prediction of cultivar effects for subdivided target regions. Crop Sci. 45: 1151 — 1159.
Google Scholar

Piepho, H. P., Möhring J. 2006. Selection in cultivar trials — is it ignorable? Crop Sci. 46:192 — 201.
Google Scholar

Putto C., Patanothai A., Jogloy S., Pannangpetch K., Boote K. J., Hoogenboom G. 2008. Determination of efficient test sites for evaluation of peanut breeding lines using the CSM-CROPGRO-peanut model. Field Crops Research.
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

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 (w druku, Online 1573–5060).
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

Searle S. R. 1987. Linear models for unbalanced data. J. Wiley & Sons, New York.
Google Scholar

Searle S. R., Casella G., McCulloch C.E. 1992. Variance components. Wiley, New York.
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

Sipaseuth Basnayake J., Fukai S., Inthapanya P., Changphengxay M. 2009. Consistency of genotypic performance of lowland rice in wet and dry season in Lao PDR. Field Crops Research 111: 47 — 54.
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

Slafer G. A., Satorre E. H., Andrade F. H. 1993. Increases in grain yield in wheat from breeding and associated physiological changes. In: Slafer, G. A. (Ed.). Genetic Improvement of Field Crops. Marcel Dekker, New York: 1 — 68.
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. Agr. Sci. Cam. 143: 449 — 462.
Google Scholar

Tinker N. A., Yan W. 2006. Information systems for crop performance data. Can. J. Plant Sci. 86: 647 — 662.
Google Scholar

Tollenaar M., Lee E. A. 2002. Yield potential yield, 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

van Eeuwijk F. A. 1995. Linear and bilinear models for the analysis of multi-environment trials: I. An inventory of models. Euphytica 84: 1 — 7.
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

Zhang Y., He Z., Zhang A., van Ginkel M., Ye G. 2006 a. 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. 2006 b. 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

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


Published
2008-12-31

Cited by

Paderewski, J. (2008) “Retrospective study of yield response to environmental conditions in winter wheat cultivars using combined AMMI and cluster analysis to incomplete data: genetic progress for adaptability”, Bulletin of Plant Breeding and Acclimatization Institute, (250), pp. 87–106. doi: 10.37317/biul-2008-0007.

Authors

Jakub Paderewski 
jakub_paderewski@sggw.edu.pl
Katedra Doświadczalnictwa i Bioinformatryki SGGW w Warszawie Poland

Authors

Wiesław Mądry 

Katedra Doświadczalnictwa i Bioinformatryki SGGW w Warszawie Poland

Authors

Wiesław Pilarczyk 

Katedra Metod Matematycznych i Statystycznych Uniwersytetu Przyrodniczego w Poznaniu Poland

Authors

Tadeusz Drzazga 

Hodowla Roślin Rolniczych "Nasiona Kobierzyc" Sp. z o.o. w Kobierzycach Poland

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