Overview of applications of statistical methods in the analysis of data from a series of experiments
Adriana Derejko
adriana_derejko@sggw.edu.plKatedra Doświadczalnictwa i Bioinformatyki, Szkoła Główna Gospodarstwa Wiejskiego, Warszawa (Poland)
Wiesław Mądry
Katedra Doświadczalnictwa i Bioinformatyki, Szkoła Główna Gospodarstwa Wiejskiego, Warszawa (Poland)
Abstract
Introduction of new varieties to the cultivation is associated with the risk of failure in the production which increases with decreasing knowledge of their response to different environmental conditions and crop management factors. Thus, the implementation of each variety does not stop at the stage of preliminary tests and its registration, but next to the reproduction of the seed, it also includes an assessment of the economic value of variety in multiple series of post-registration trials. A steady supply to agriculture of newly registered varieties requires from the post-registration experimental variety testing, the efficient and reliable verification of the economic value of tested varieties. These field experiments allow to significantly reduce the risk of introducing the cultivation of varieties unsuitable for agriculture, i.e. those which do not provide high production and economic effects in different or only selected (specific) environmental conditions and systems of crops and agricultural techniques. Data from these series of experiments require the use of specialized and complementary statistical methodology. In this work, an overview of a series of varietal and agrotechnical experiments is presented and the statistical methodology described.
Keywords:
statistical methods, multi-environment experimental series, combined analysis of varianceReferences
Anderson W. K. 2010. Closing the gap between actual and potential yield of rainfed wheat. The impacts of environment, management and cultivar. Field Crops Res. 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 Sci. 62: 115 — 124.
Google Scholar
Annicchiarico P. 2002 a. Defining adaptation strategies and yield stability targets in breeding programmers W: 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., 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
Becker H. C., Leon J. 1988. Stability analysis in plant breeding. Plant Breeding 101, 1: 1 — 23.
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
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 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., Krajewski P., Pilarczyk W. 2005. Analyzing Multi-environment Variety Trials Using Randomization-Derived Mixed Models. Biometrics 61 (2): 448 — 55.
Google Scholar
Caliński T., Czajka S., Kaczmarek Z., Krajewski P., Pilarczyk W. 2009. Analyzing the genotype-by-environment interactions under a randomization-derived mixed model. JABES 14 (2).
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. Biul. Oceny Odmian 26–27: 39 — 41.
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
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., Yan W. 2002. Biplots of linear-bilinear models for studying crossover genotype × 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 × 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 la Vega A. J., Chapman S. 2010. Mega-environment differences affecting genetic progress for yield and relative value of component traits. Crop Sci. 50: 574 — 583.
Google Scholar
de la Vega A. J., DeLacy I. H., Chapman S.C. 2007. Changes in agronomic traits of sunflower hybrids over 20 years of breeding in central Argentina. Field Crops Res. 100: 73 — 81.
Google Scholar
DeLacy I. H., Kaul S., Rana B. S., Cooper M. 2010 a. Genotypic variation for grain and stover yield of dry land (rabi) sorghum in India, 1. Magnitude of genotype × environment interactions. Field Crops Res. 118: 228 — 235.
Google Scholar
DeLacy I. H., Kaul S., Rana R. A., Cooper M. 2010 b. Genotypic variation for grain and stover yield of dryland (rabi) sorghum in India, 2. A characterization of genotype × environment interactions. Field Crops Res. 118: 236 — 242.
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
Finlay K. W., Wilkilson G. N. 1963. The analysis of adaptation in a plant breeding programme. Aust. J. Agric. Sci. 14.
Google Scholar
Gan Y., Johnston A., Knight D., McDonald C., Stevenson C. 2010. Nitrogen dynamics of chickpea: Effects of cultivar choice, N fertilization, Rhizobium inoculation, and cropping systems. Can. J. Plant Sci. 90: 655 — 666.
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
GenStat 2002. The guide to Genstat. Release 6.1. VSN International. Oxford, UK.
Google Scholar
Gilmour A., Cullis B., Welham S., Gogel B., Thompson R. 2004. An efficient computing strategy for prediction in mixed linear models. Comput. Stat. Data An. 44: 571 — 586.
Google Scholar
Gilmour A. Thompson R., Cullis B. R. 1995. Average information REML: An efficient algorithm for variance parameter estimation in linear mixed models. Biometrics 51: 1440 — 1450.
Google Scholar
Gogel B. J., Cullis, B. R., Thompson R. 2009. ASReml UserGuide Release 3.0 VSN International Ltd, Hemel Hempstead, HP1 1ES, UK.
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
Iwańska M., Mądry W., Drzazga T., Rajfura A. 2008. Zastosowanie miar statystycznych do oceny stopnia szerokiej adaptacji odmian pszenicy ozimej na podstawie serii doświadczeń przedrejestrowych. Biul. IHAR 250: 67 — 86.
Google Scholar
Kang M. S. 1993. Simultaneous selection for yield and stability in crop performance trials: Consequences for growers. Agron. J. 85: 754 — 757.
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
Loyce C., Meynard J. M., Bouchard C., Rolland B., Lonnet P., Bataillon P., Bernicot M. H., Bonnefoy M., Charrier X., Debote B., Demarquet T., Duperrier B., Felix I., Hedddadj D., Leblanc O., Leleu M., Mangin P., Meausoone M., Doussinault G. 2008. Interaction between cultivar and crop management effects on winter wheat diseases, lodging, and yield. Crop Protect. 27: 1131 — 1142.
Google Scholar
Loyce C., Meynard J. M., Bouchard C., Rolland B., Lonnet P., Bataillon P., Bernicot M. H., Bonnefoy M., Charrier X., Debote B., Demarquet T., Duperrier B., Felix I., Hedddadj D., Leblanc O., Leleu M., Mangin P., Meausoone M., Doussinault G. 2011. Growing winter wheat cultivars under different management intesities in France: A multicriteria assessment based on economic, energetic and environmental indicators. Field Crops Res. 125: 167 — 178.
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., Kang M. S. 2005. Scheffé-Caliński and Shukla models: their interpretation and usefulness in stability and adaptation analyses. J. Crop Improv. 14: 325 — 369.
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. Fragm. Agron. 92: 130 — 143.
Google Scholar
Mądry W., Paderewski J., Gazdowski 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
Mohammadi R., Amri A. 2009. Analysis of genotype × environment interactions for grain yield in durum wheat. Crop Sci. 49: 1177 — 1186.
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
Murphy K. M., Campbell K. G., Lyon S. R., Jones S. S. 2007. Evidence of varietal adaptation to organic farming systems. Field Crops Res. 102: 172 — 177.
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
Paderewski J., Mądry W. 2012. Zastosowania modelu AMMI do analizy reakcji odmian na warunki środowisk rolniczych. Biul. IHAR 263: 161 — 188.
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
Peltonen-Sainio P., Jauhiainen L., Laurila I. P. 2009. Cereal yield in northern European conditions. Changes in yield potential and its realisation. Field Crops Res. 110:85 — 90.
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
Piepho, H. P., van Eeuwijk F.A. 2002. Stability analyses in crop performance evaluation. In: Kang M.S. [ed.]: Crop improvement: Challenges in the twenty-first century”. Food Products Press, Binghamton. New York: 307 — 342.
Google Scholar
Pilarczyk W. 1983. Wykorzystanie analizy skupień do podziału stacji doświadczalnych na grupy o małej interakcji odmianowo-środowiskowej. Colloqium Biometryczne 13: 133 — 147.
Google Scholar
Pilarczyk W., Kamiński J. 1995. Application of some traditional models and AMMI model for analysis of a series of cereal trials. Biuletyn Oceny Odmian. 26–27: 179 — 188.
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
Raman A., Ladha J. K., Kumar V., Sharma S., Piepho H. P. 2011. Stability analysis of farmer participatory trials for conservation agriculture using mixed models. Field Crops Res. 121: 450 — 459.
Google Scholar
SAS Institute Inc. 2004 SAS OnlineDoc® 9.1.3. Cary, NC.
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
So Y.S, Edwards J. 2009. A Comparison of mixed-model analyses of the Iowa Crop Performance Test for corn. Crop Sci. 49: 1593 — 1601.
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
Surprenant J., Drapeau R., Fernet C. 1993. Cultivar by management interactions effects on timothy yield and quality evaluation. Can. J. Plant Sci. 73: 445 — 460.
Google Scholar
Thangavel P., Anandan A., Eswaran R. 2011. AMMI analysis to comprehend genotype by environment (G × E) interactions in rainfed grown mungbean (Vigna radiata L.). Aust. J. Crop Sci. 5: 1767 — 1775.
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
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
Ward J. H. 1963. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58: 236 — 244.
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
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., 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
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
Authors
Adriana Derejkoadriana_derejko@sggw.edu.pl
Katedra Doświadczalnictwa i Bioinformatyki, Szkoła Główna Gospodarstwa Wiejskiego, Warszawa Poland
Authors
Wiesław MądryKatedra Doświadczalnictwa i Bioinformatyki, Szkoła Główna Gospodarstwa Wiejskiego, Warszawa Poland
Statistics
Abstract views: 55PDF downloads: 64
License
Copyright (c) 2014 Adriana Derejko, Wiesław Mądry
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Upon submitting the article, the Authors grant the Publisher a non-exclusive and free license to use the article for an indefinite period of time throughout the world in the following fields of use:
- Production and reproduction of copies of the article using a specific technique, including printing and digital technology.
- Placing on the market, lending or renting the original or copies of the article.
- Public performance, exhibition, display, reproduction, broadcasting and re-broadcasting, as well as making the article publicly available in such a way that everyone can access it at a place and time of their choice.
- Including the article in a collective work.
- Uploading an article in electronic form to electronic platforms or otherwise introducing an article in electronic form to the Internet or other network.
- Dissemination of the article in electronic form on the Internet or other network, in collective work as well as independently.
- Making the article available in an electronic version in such a way that everyone can access it at a place and time of their choice, in particular via the Internet.
Authors by sending a request for publication:
- They consent to the publication of the article in the journal,
- They agree to give the publication a DOI (Digital Object Identifier),
- They undertake to comply with the publishing house's code of ethics in accordance with the guidelines of the Committee on Publication Ethics (COPE), (http://ihar.edu.pl/biblioteka_i_wydawnictwa.php),
- They consent to the articles being made available in electronic form under the CC BY-SA 4.0 license, in open access,
- They agree to send article metadata to commercial and non-commercial journal indexing databases.
Most read articles by the same author(s)
- Wiesław Mądry, Dariusz Gozdowski, Jan Rozbicki, Mirosław Pojmaj, Stanisław Samborski, Grain yield formation strategies in advanced lines of winter triticale grown in different environments , Bulletin of Plant Breeding and Acclimatization Institute: No. 244 (2007): Regular issue
- Marcin Studnicki, Wiesław Mądry, Tadeusz Śmiałowski, Comparison of statistical methods to development of a core collection for a spring wheat collection , Bulletin of Plant Breeding and Acclimatization Institute: No. 252 (2009): Regular issue
- Wiesław Mądry, Barbara Roszkowska-Mądra , The beauty and importance of old traditional orchards , Bulletin of Plant Breeding and Acclimatization Institute: No. 299 (2023): Regular issue
- Wiesław Mądry, Adriana Derejko, Statistical methods for data analysis in the complete classification Cultivar × Crop Management × Location × Year (G×M×L×Y) from PVTS , Bulletin of Plant Breeding and Acclimatization Institute: No. 273 (2014): Regular issue
- Dariusz Gozdowski, Wiesław Mądry, Zdzisław Wyszyński, Maria Kalinowska-Zdun, Characteristics and empirical comparison of simple and complex path analysis in grain yield determination by yield - related traits. Part II. Example on spring barley , Bulletin of Plant Breeding and Acclimatization Institute: No. 249 (2008): Regular issue
- Marcin Studnicki, Wiesław Mądry, Jan Schmidt, Multivariate analysis of genotypic diversity of agronomic traits in orchardgrass (Dactylis glomerata L.) germplasm collection , Bulletin of Plant Breeding and Acclimatization Institute: No. 263 (2012): Regular issue
- Wiesław Mądry, Marzena Iwańska, Usefulness of statistical methods and measures for evaluating cultivar stability and adaptation: an overview of research , Bulletin of Plant Breeding and Acclimatization Institute: No. 260/261 (2011): Regular issue
- Jakub Paderewski, Wiesław Mądry, Use of AMMI model in the analysis of cultivar responses to environments , Bulletin of Plant Breeding and Acclimatization Institute: No. 263 (2012): Regular issue
- Wiesław Mądry, Marzena Iwańska, Quantitative measures of the cultivar wide adaptation degree and their using in preliminary yield trials for winter wheat , Bulletin of Plant Breeding and Acclimatization Institute: No. 260/261 (2011): Regular issue
- Anna Rajfura, Wiesław Mądry, The clustering of locations based on multi-environment trials with different cultivars across years using the SEQRET package. Part I. Theoretical development of retrospective analysis , Bulletin of Plant Breeding and Acclimatization Institute: No. 250 (2008): Regular issue
