Yielding of winter wheat cultivars across environments — one-year multi-environment post-registration trial

Wiesław Mądry

wieslaw_madry@sggw.ed.pl
Katedra Doświadczalnictwa i Bioinformatyki, SGGW, Warszawa (Poland)

Jakub Paderewski


Katedra Doświadczalnictwa i Bioinformatyki, SGGW, Warszawa (Poland)

Jan Rozbicki


Katedra Agronomii, SGGW, Warszawa (Poland)

Dariusz Gozdowski


Katedra Doświadczalnictwa i Bioinformatyki, SGGW, Warszawa (Poland)

Jan Golba


Katedra Agronomii, SGGW, Warszawa (Poland)

Mariusz Piechociński


Katedra Agronomii, SGGW, Warszawa (Poland)

Marcin Studnicki


Katedra Doświadczalnictwa i Bioinformatyki, SGGW, Warszawa (Poland)

Adriana Derejko


Katedra Doświadczalnictwa i Bioinformatyki, SGGW, Warszawa (Poland)

Abstract

The aim of the paper is adequate modification and presenting a statistical methodology to assess patterns of cultivar adaptive response to agricultural environments (agro-ecosystems) on the basis of complete Genotype × Crop Management × Location (G×M×L) data obtained from yearly multi-location two-factor trials conducted in a net of post-registration trials (PDOiR), empirical illustration of using and usefulness of this methodology applied to winter wheat grain yield. This statistical technique consists of three procedures, i.e. three-way ANOVA based on a fixed model, AMMI procedure for genotype x location interaction modeling and cluster analysis for classification of cultivar adaptive response described by AMMI-modeled (adjusted) genotype × location means calculated across two crop management intensities (A1 and A2). The considered methodology was an efficient tool to reliable classification of 28 winter wheat cultivars into cultivar groups that exhibited homogenous adaptive response to the environments. Then, it permits to identify cultivars showing wide or specific adaptation. It was revealed that in the season 2008/2009 Polish cultivar Bogatka (bred by DANKO Hodowla Roślin sp. z o.o.) and German cultivar Jenga (bred by Nordsaat Saatzucht¬gesellschaft mbH) showed wide adaptation to the testing environments. The remaining cultivars were locally adapted to some testing environments or some of them were not relatively adapted to the environments because they always yielded substantially below environmental means.

Supporting Agencies

The work contains the results of research in the own project of the Ministry of Science and Higher Education No. N N310 091136 entitled

Keywords:

AMMI analysis, cluster analysis, cultivar adaptation, grain yield, post-registration trials, winter wheat

Anderson W.K. 2010. Closing the gap between actual and potential yield of rained wheat. The impacts of environment, management and cultivar. Field Crops Research 116: 14 — 22.
Google Scholar

Anderson W.K., Van Burgel A.J., Sharma D.L., Shackley B.J., Zaicou-Kunesch C.M., Miyan M.S., Amjad M. 2011. Assessing specific agronomic responses of wheat cultivars in a winter rainfall environment. Crop and Pasture Science 62: 115 — 124.
Google Scholar

Annicchiarico P. 2002. Genotype × environment interactions — challenges and opportunities for plant breeding and cultivar recommendations, FAO plant productions and protection paper 174. FAO, 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., Chiapparino E., Perenzin M. 2010. 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., 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 a. Adaptation of landrace and variety germplasm and selection strategies for lucerne in the Mediterranean basin. Field Crops Res. 120: 283 — 291.
Google Scholar

Annicchiarico P., Pecetti L., Bouzerzour H., Kallida R., Khedim A., Porqueddu C., Simoes N.M., Volaire F., Lelièvre F. 2011. Adaptation of contrasting cocksfoot plant types to agricultural environments across the Mediterranean basin. Environ. Experim. Bot. 74: 82 — 89.
Google Scholar

Ayoub M., Guertin S., Fregeau-Reid J., Smith D.L. 1994. Nitrogen fertilizer effect on breadmaking quality of hard red spring wheat in eastern Canada. Crop Sci. 34:.1346 — 1352.
Google Scholar

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

Carr P.M., Horsley R.D., Poland W.W. 2003. Tillage and seeding rate effects on wheat cultivars: I. grain production. Crop Sci. 43: 202 — 209.
Google Scholar

Casanoves F., Baldessari J., Balzarini M. 2005. Evaluation of multi environment trials of peanut cultivars. Crop Sci. 45: 18 — 26.
Google Scholar

Cornelius P.L., Crossa J., Seyedsadr M. 1996 Statistical tests and estimators of multiplicative models for genotype-by-environment interaction. In: Kang M.S., Gauch H.G. (Eds.), Genotype by Environment Interaction. CRC Press, Boca Raton: 199 — 234.
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., Vargas M., Joshi A.K. 2010. Linear, bilinear, and linear-bilinear fixed and mixed models for analyzing genotype environment interaction in plant breeding and agronomy. Can. J. Plant Sci. 90:561 — 574.
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

COBORU. 2002. Zboża. Metodyka badania wartości odmian. COBORU, Słupia Wielka.
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 Research, 69: 47 — 67.
Google Scholar

Denčić S., Mladenov N., Kobiljski B. 2011. Effects of genotype and environment on breadmaking quality in wheat. Int. J. Plant Prod. 5:71 — 82.
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

Dhungana P., Eskridge K.M., Baenziger P.S., Campbell B.T., Gill K.S., Dweikat I. 2007. Analysis of genotype-by-environment interaction in wheat using a structural equation model and chromosome substitution lines. Crop Sci. 47: 477 — 484.
Google Scholar

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

Fan X.M., Kang M.S., Chen H., Zhang Y., Tan J., Xu C. 2007. Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agron. J. 99: 220 — 228.
Google Scholar

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

Gauch H.G. 1992. Statistical analysis of regional yield trials. AMMI analysis of factorial designs. Elsevier Science, New York, NY.
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. 1997. Identifying mega-environments and targeting genotypes. Crop Sci. 37: 311 — 326.
Google Scholar

Geleta B., Atak M., Baenziger P.S., Nelson L.A., Baltenesperger D.D., Eskridge K.M., Shipman M.J., Shelton D.R. 2002. Seeding rate and genotype effect on agronomic performance and end-use quality of winter wheat. Crop Sci. 42: 827 — 832.
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., 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. Int. J. Plant Prod. 5: 299 — 310.
Google Scholar

Mądry W., Iwańska M. 2011. Przydatność metod oraz miar statystycznych do oceny stabilności i adaptacji odmian: przegląd literatury. Biul. IHAR 260/261: 193 — 218.
Google Scholar

McIntosh M.S 1983. Analysis of combined experiments. Crop Sci. 75:153 — 156.
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

Miyan M.S., Impiglia A., Anderson W. K. 2011. Agronomic practices for durum wheat in an area new to the crop. Communications in Biometry and Crop Sci. 6: 64 — 79.
Google Scholar

Oscarsson M., Anderrsson R., Aman P., Jonsson A. 1998. Effects of cultivar, nitrogen fertilization rate and environment on yield and grain quality of barley. J. Sci. Food Agric. 78: 359 — 366.
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

Paderewski J., Mądry W. 2012. Zastosowania modelu AMMI do analizy reakcji odmian na środowiska.
Google Scholar

Biul. IHAR 263: 161 — 188.
Google Scholar

Pena R. J. 2007. Current and future trends of wheat quality needs. In: Buck H. T., Nisi J. E., Salomon N. (Eds.). Wheat production in stressed environments. Springer Verlag: 411 — 424.
Google Scholar

R Development Core Team. 2009. R: A language and environment for statistical computing. Available at http://www.Rproject.org (verified 3 Dec. 2010). R Foundation for Statistical Computing, Vienna, Austria.
Google Scholar

SAS Institute. 2004. SAS system for Windows. v. 8.2. SAS Inst., Cary, NC.
Google Scholar

Schmidt J. P., Lamb J.A., Schmitt M.A., Randall G.W., Orf J. H., Gollany H. T. 2001. Soybean varietal response to liquid swine manure application. Agron. J. 93: 358 — 363.
Google Scholar

Sharma R. C., Morgounov A. I., Braun H.J., Akin B., Keser M., Bedoshvili D., Bagci A., Martius C., van Ginkel M. 2009. Identifying high yielding stable winter wheat genotypes for irrigated environments in Central and West Asia. Euphytica, 171: 53 — 64.
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

Souza E. J., Martin J.M., Guttieri M.J., O'Brien K. M., Habernicht D. K., Lanning S. P., McLean R., Carlson G. R., Talbert L. E. 2004. Influence of genotype, environment, and nitrogen management on spring wheat quality. Crop Sci. 44: 425 — 432.
Google Scholar

Virk D. S., Witcombe J. R. 2008. Evaluating cultivars in unbalanced on-farm participatory trials. Field Crops Res. 106: 105 — 115.
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

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


Published
2012-03-29

Cited by

Mądry, W. (2012) “Yielding of winter wheat cultivars across environments — one-year multi-environment post-registration trial”, Bulletin of Plant Breeding and Acclimatization Institute, (263), pp. 189–204. doi: 10.37317/biul-2012-0083.

Authors

Wiesław Mądry 
wieslaw_madry@sggw.ed.pl
Katedra Doświadczalnictwa i Bioinformatyki, SGGW, Warszawa Poland

Authors

Jakub Paderewski 

Katedra Doświadczalnictwa i Bioinformatyki, SGGW, Warszawa Poland

Authors

Jan Rozbicki 

Katedra Agronomii, SGGW, Warszawa Poland

Authors

Dariusz Gozdowski 

Katedra Doświadczalnictwa i Bioinformatyki, SGGW, Warszawa Poland

Authors

Jan Golba 

Katedra Agronomii, SGGW, Warszawa Poland

Authors

Mariusz Piechociński 

Katedra Agronomii, SGGW, Warszawa Poland

Authors

Marcin Studnicki 

Katedra Doświadczalnictwa i Bioinformatyki, SGGW, Warszawa Poland

Authors

Adriana Derejko 

Katedra Doświadczalnictwa i Bioinformatyki, SGGW, Warszawa Poland

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Copyright (c) 2012 Wiesław Mądry, Jakub Paderewski, Jan Rozbicki, Dariusz Gozdowski, Jan Golba, Mariusz Piechocziński, Marcin Studnicki, Adriana Derejko

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