|
|
|
|
|
| The Application of GGE Biplot Analysis for Evaluat ng Test Locations and Mega-Environment Investigation of Cotton Regional Trials |
| XU Nai-yin, Fok Michel, ZHANG Guo-wei, LI Jian , ZHOU Zhi-guo |
1、Key Laboratory of Corp Growth Regulation, Ministry of Agriculture/Nanjing Agricultural University, Nanjing 210095, P.R.China
2、Key Laboratory of Cotton and Rapeseed, Ministry of Agriculture/Institute of Industrial Crops, Jiangsu Academy of Agriculture Sciences,
Nanjing 210014, P.R.China
3、Center of International Cooperation on Agronomic Research for Development (CIRAD), TA B102/02, 34398 Montpellier Cedex 5, France |
|
|
|
摘要 In the process to the marketing of cultivars, identification of superior test locations within multi-environment variety trial schemes is of critical relevance. It is relevant to breeding organizations as well as to governmental organizations in charge of cultivar registration. Where competition among breeding companies exists, effective and fair multi-environment variety trials are of utmost importance to motivate investment in breeding. The objective of this study was to use genotype main effect plus genotype by environment interaction (GGE) biplot analysis to evaluate test locations in terms of discrimination ability, representativeness and desirability, and to investigate the presence of multiple mega-environments in cotton production in the Yangtze River Valley (YaRV), China. Four traits (cotton lint yield, fiber length, lint breaking tenacity, micronaire) and two composite selection indices were considered. It was found that the assumption of a single mega-environment in the YaRV for cotton production does not hold. The YaRV consists of three cotton mega-environments: a main one represented by 11 locations and two minor ones represented by two test locations each. This demands that the strategy of cotton variety registration or recommendation must be adjusted. GGE biplot analysis has also led to the identification of test location superior for cotton variety evaluation. Although test location desirable for selecting different traits varied greatly, Jinzhou, Hubei Province, China, was found to be desirable for selecting for all traits considered while Jianyang, Sichuan Province, China, was found to be desirable for none.
Abstract In the process to the marketing of cultivars, identification of superior test locations within multi-environment variety trial schemes is of critical relevance. It is relevant to breeding organizations as well as to governmental organizations in charge of cultivar registration. Where competition among breeding companies exists, effective and fair multi-environment variety trials are of utmost importance to motivate investment in breeding. The objective of this study was to use genotype main effect plus genotype by environment interaction (GGE) biplot analysis to evaluate test locations in terms of discrimination ability, representativeness and desirability, and to investigate the presence of multiple mega-environments in cotton production in the Yangtze River Valley (YaRV), China. Four traits (cotton lint yield, fiber length, lint breaking tenacity, micronaire) and two composite selection indices were considered. It was found that the assumption of a single mega-environment in the YaRV for cotton production does not hold. The YaRV consists of three cotton mega-environments: a main one represented by 11 locations and two minor ones represented by two test locations each. This demands that the strategy of cotton variety registration or recommendation must be adjusted. GGE biplot analysis has also led to the identification of test location superior for cotton variety evaluation. Although test location desirable for selecting different traits varied greatly, Jinzhou, Hubei Province, China, was found to be desirable for selecting for all traits considered while Jianyang, Sichuan Province, China, was found to be desirable for none.
|
|
Received: 02 August 2013
Accepted: 24 September 2014
|
| Fund: This work was funded by the Jiangsu Agriculture Science and Technology Innovation Fund, China (CX(12)5035), the National Natural Science Foundation of China (30971735), the China Agriculture Research System (CARS-18-20), and the Special Fund for Agro-Scientific Research in the Public Interest of China (Impact of Climate Change on Agriculture Production of China, 200903003). |
|
Corresponding Authors:
ZHOU Zhi-guo, Tel/Fax: +86-25-84396813, E-mail: giscott@njau.edu.cn
E-mail: giscott@njau.edu.cn
|
| About author: XU Nai-yin, E-mail: naiyin@126.com |
Cite this article:
XU Nai-yin, Fok Michel, ZHANG Guo-wei, LI Jian , ZHOU Zhi-guo.
2014.
The Application of GGE Biplot Analysis for Evaluat ng Test Locations and Mega-Environment Investigation of Cotton Regional Trials. Journal of Integrative Agriculture, 13(9): 1921-1933.
|
| Annicchiarico P. 1997. Additive main effects and multiplicativeinteraction (AMMI) analysis of genotype-locationinteraction in variety trials repeated over years. Theoreticaland Applied Genetics, 94, 1072-1077Anothai J, Patanothai A, Pannangpetch K, Jogloy S, Boote KJ, Hoogenboom G. 2009. Multi-environment evaluationof peanut lines by model simulation with the cultivarcoefficients derived from a reduced set of observed fielddata. Field Crops Research, 110, 111-122Badu-Apraku B, Akinwale R O. 2011. Cultivar evaluationand trait analysis of tropical early maturing maize underStriga-infested and Striga-free environments. Field CropsResearch, 121, 186-194Baker R J. 1988. Tests for crossover genotype-environmentalinteractions. Canadian Journal of Plant Science, 68,405-410Baxevanos D, Goulas C, Rossi J, Braojos E. 2008. Separationof cotton cultivar testing sites based on representativenessand discriminating ability using GGE biplots. AgronomyJournal, 100, 1230-1236Blanche S B, Myers G O. 2006. Identifying discriminatinglocations for cultivar selection in Louisiana. Crop Science,46, 946-949Cody R P, Smith J K. 1997. Applied Statistics and the SASProgramming Language. Prentice-Hall, New Jersey, USA.Cooper M, Woodruff D R, Eisemann R L, Brennan P S, DelacyI H. 1995. A selection strategy to accommodate genotypeby-environment interaction for grain yield of wheat:managed-environments for selection among genotypes.Theoretical and Applied Genetics, 90, 492-502Cooper M, Woodruff D R, Phillips I G, Basford K E, GilmourA R. 2001. Genotype-by-management interactions forgrain yield and grain protein concentration of wheat. FieldCrops Research, 69, 47-67Cravero V, Espósito M A, Anido F L, García S M, CointryE. 2010. Identification of an ideal test environment forasparagus evaluation by GGE-biplot analysis. AustralianJournal of Crop Science, 4, 273-277Crossa J, Fox P N, Pfeiffer W H, Rajaram S, Gauch H G.1991. AMMI adjustment for statistical analysis of aninternational wheat yield trial. Theoretical and AppliedGenetics, 81, 27-31Ebdon J S, Gauch H G. 2002. Additive main effectand multiplicative interaction analysis of nationalTurfgrass performance trials: I. Interpretation of genotype environment×interaction. Crop Science, 42, 489-496Fan X, Kang M S, Chen H, Zhang Y, Tan J, Xu C. 2007. Yieldstability of maize hybrids evaluated in multi-environmenttrials in Yunnan, China. Agronomy Journal, 99, 220-228Fok M, Xu N Y. 2010. Technological integration and seedsector development two factors of the Bt-cotton diffusionin Yangtze River Valley. Economie Rurale, 317, 40-56Fok M, Xu N Y. 2011. Variety market development: A Btcotton cropping factor and constraint in China. Agbioforum,14, 47-60Gauch H G. 1992. Statistical Analysis of Regional YieldTrials: AMMI Analysis of Factorial Designs. Elsevier,Amsterdam, the Netherlands.Gauch H G. 2006. Statistical analysis of yield trials by AMMIand GGE. Crop Science, 46, 1488-1500Gauch H G, Zobel R W. 1997. Identifying mega-environmentsand targeting genotypes. Crop Science, 37, 311-326Guillen-Portal F R, Russell W K, Eskridge K M, BaltenspergerD D, Nelson L A, D’Croz-Mason N E, Johnson B E. 2004.Selection environments for maize in the U.S.Western HighPlains. Crop Science, 44, 1519-1526Kang M S. 1993. Simultaneous selection for yield and stabilityin crop performance trials: Consequences for growers.Agronomy Journal, 85, 754-757Lin C S, Binns M R. 1988. A superiority measure of cultivarperformance for cultivar×location data. Canadian Journalof Plant Science, 68, 193-198Moreno-Gonzalez J, Crossa J, Cornelius P L. 2003. Additivemain effects and multiplicative interaction model. I. Theoryon variance components for predicting cell means. CropScience, 43, 1967-1975Nurminiemi M, Madsen S, Rognli O A, Bjornstad A, Ortiz R.2002. Analysis of the genotype-by-environment interactionof spring barley tested in the Nordic Region of Europe:Relationships among stability statistics for grain yield.Euphytica, 127, 123-132SAS-Institute. 2002. SAS User’s Guide: Statistics.v.9.0. SASInstitut, Cary, NC.Trethowan R M, van Ginkel M, Ammar K, Crossa J, Payne TS, Cukadar B, Rajaram S, Hernandez E. 2003. Associationsamong twenty years of international bread wheat yieldevaluation environments. Crop Science, 43, 1698-1711Xu N Y, Fok M. 2010. The Bt-cotton market: Analysis ofthe Chinese situation within an international perspective.Cahiers Agricultures, 19, 34-42Yan W K, Glover K D, Kang M S. 2010. Comment on “biplotanalysis of genotype×environment interaction: Proceedwith caution”. Crop Science, 50, 1121-1123Yan W K, Holland J B. 2010. A heritability-adjusted GGEbiplot for test environment evaluation. Euphytica, 171,355-369Yan W K, Hunt L A, Sheng Q, Szlavnics Z. 2000. Cultivarevaluation and mega-environment investigation based onthe GGE biplot. Crop Science, 40, 597-605Yan W K, Hunt L A. 2001. Genetic and environment causesof genotype by environment interaction for winter wheatyield in Ontario. Crop Science, 41, 19-25Yan W K, Kang M S, Ma B, Woods S, Cornelius P L.2007. GGE biplot vs. AMMI analysis of genotype-byenvironmentdata. Crop Science, 47, 643-655Yan W K, Kang M S. 2003. GGE biplot analysis: A graphicaltool for breeders, geneticists, and agronomists. CRC Press,Boca Raton, FL.Yan W K, Rajcan I. 2002. Biplot analysis of test Sites and traitrelations of soybean in Ontario. Crop Science, 42, 11-20Yan W K. 2001. GGE biplot - A windows application forgraphical analysis of multi-environment trial data and othertypes of two-way data. Agronomy Journal, 93, 1111-1118Yang R C, Blade S F, Crossa J, Stanton D, Bandara M S. 2005.Identifying isoyield environments for field pea production.Crop Science, 45, 106-113Yang R C, Crossa J, Cornelius P L, Burgueño J. 2009. Biplotanalysis of genotype×environment interaction: Proceedwith caution. Crop Science, 49, 1564-1576. |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
