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Genotype x Environment Interaction Implication: A Case Study of Durum Wheat Breeding in Iran

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Abstract

The genotype x environment (GE) interaction is a major challenge to plant breeders as it complicates testing and selection of superior genotypes and consequently reduces gains from selection. This chapter introduces and compares different statistical models to handle GE interaction by applying them to the durum wheat breeding program in Iran as an example. The results indicate significant crossover GE interaction suggesting the need for applying appropriate analysis for the exploitation and/or the minimization of GE interaction in multi-environment trials (MET) data. The test locations differed in their discriminative ability and representativeness. Highly significant correlations were found between univariate and multivariate statistical models in ranking genotypes for stability and for integrating yield with stability performances, indicating that they can be used interchangeably. Evaluation of genotypes based on multiple traits data identified parental germplasm for earliness, short stature, high grain weight and high grain yield. The proposed statistical analysis can assist in increasing the efficiency of breeding program through (a) selection of the most discriminate locations, (b) identifying superior genotypes based on both strategies dealing with exploitation and minimization of GE interaction and (c) exploring significant genetic gains in yield and yield stability.

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References

  • Akinwale RO, Fakorede MAB, Badu-Apraku B et al (2014) Assessing the usefulness of GGE biplot as a statistical tool for plant breeders and agronomists. Cereal Res Commun 42:534–546

    Google Scholar 

  • Allard RW, Bradshaw AD (1964) Implication of genotype-environmental interaction in applied plant breeding. Crop Sci 5:503–506

    Article  Google Scholar 

  • Alwala S, Kwolek T, McPherson M et al (2010) Comprehensive comparison between Eberhart and Russell joint regression and GGE biplot analyses to identify stable and high yielding maize hybrids. Field Crop Res 119:225–230

    Article  Google Scholar 

  • Annicchiarico P (1997) Joint regression vs AMMI analysis of genotype-environment interactions for cereals in Italy. Euphytica 94:53–62

    Article  Google Scholar 

  • Annicchiarico P (2002) Genotype x environment interactions – challenges and opportunities for plant breeding and cultivar recommendations. FAO, Rome

    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 45:1741–1749

    Article  Google Scholar 

  • Badu-Apraku B, Oyekunle M, Akinwale RO et al (2011) Combining ability of early-maturing white maize inbreds under stress and nonstress environments. Agron J 103:544–557

    Article  Google Scholar 

  • Basford KE, Cooper M (1998) Genotype x environment interactions and some considerations of their implications for wheat breeding in Australia. Aust J Agr Res 49:153–174

    Article  Google Scholar 

  • Becker HC, Leon J (1988) Stability analysis in plant breeding. Plant Breed 101:1–23

    Article  Google Scholar 

  • Bhan MK, Pal S, Rao BL et al (2005) GGE biplot analysis of oil yield in lemongrass. J New Seeds 7:127–139

    Article  Google Scholar 

  • Blanche SB, Myers GO (2006) Identifying discriminating locations for cultivar selection in Louisiana. Crop Sci 46:946–949

    Article  Google Scholar 

  • Blum A (2005) Drought resistance, water-use efficiency, and yield potential—are they compatible, dissonant, or mutually exclusive? Aust J Agr Res 56:1159–1168

    Article  Google Scholar 

  • Breese EL (1969) The measurement and significance of genotype-environment interaction in grasses. Heredity 24:27–44

    Article  Google Scholar 

  • Calderini DF, Slafer GA (1998) Changes in yield and yield stability in wheat during the 20th century. Field Crop Res 57:335–347

    Article  Google Scholar 

  • Casanoves F, Baldessari J, Balzarini M (2005) Evaluation of multienvironment trials of peanut cultivars. Crop Sci 45:18–26

    Article  Google Scholar 

  • Cattivelli LF, Rizza FW, Badeck-Mazzucotelli E et al (2008) Drought tolerance improvement in crop plants: an integrated view from breeding to genomic. Field Crop Res 15:1–14

    Article  Google Scholar 

  • Ceccarelli S (1989) Wide adaptation: how wide? Euphytica 40:197–205

    Google Scholar 

  • Ceccarelli S (1996) Positive interpretation of genotype by environment interactions in relation to sustainability and biodiversity. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CABI Publishing, Wallingford, pp 467–486

    Google Scholar 

  • Crossa J (1990) Statistical analyses of multilocation trials. Adv Agron 44:55–85

    Article  Google Scholar 

  • Crossa J, Fox PN, Pfeiffer WH et al (1991) AMMI adjustment for statistical analysis of an international wheat yield trial. Theor Appl Genet 81:27–37

    Article  CAS  PubMed  Google Scholar 

  • Dardanellia JL, Balzarinic M, Martíneza MJ et al (2006) Soybean maturity groups, environments, and their interaction define mega-environments for seed composition in Argentina. Crop Sci 46:1939–1947

    Article  Google Scholar 

  • DeLacy IH, Fox PN, Corbett JD et al (1994) Long-term association of locations for testing spring bread wheat. Euphytica 72:95–106

    Article  Google Scholar 

  • DeLacy IH, Basford KE, Cooper M et al (1996) Analysis of multi-environment trials–an historical perspective. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CAB International, Wallingford, pp 39–124

    Google Scholar 

  • Dimitrios B, Christos G, Jesus R et al (2008) Separation of cotton cultivar testing sites based on representativeness and discriminating ability using GGE biplots. Agron J 100:1230–1236

    Article  Google Scholar 

  • Donmez E, Sears RG, Shroyer JP et al (2001) Genetic gain in yield attributes of winter wheat in the Great Plains. Crop Sci 41:1412–1419

    Article  Google Scholar 

  • Eberhart SA, Russell WA (1966) Stability parameters for comparing varieties. Crop Sci 6:36–40

    Article  Google Scholar 

  • Egesi CN, Ilona P, Ogbe FO et al (2007) Genetic variation and genotype x environment interaction for yield and other agronomic traits in Cassava in Nigeria. Agron J 99:1137–1142

    Article  Google Scholar 

  • Fan LJ, Hu BM, Shi CH, Wu JG (2001) A method of choosing locations based on genotype x environment interaction for regional trials of rice. Plant Breed 120:139–142

    Article  Google Scholar 

  • Fan XM, Kang MS, Chen H et al (2007) Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agron J 99:220–228

    Article  Google Scholar 

  • Fernandez GCJ (2000) Quick results from statistical analysis. Visited/last modified 16 Aug 2000. http://www.ag.unr.edu/gf

  • Fernandez-Aparicio M, Flores F, Rubiales D (2009) Field response of Lathyrus cicera germplasm to crenate broomrape (Orobanche crenata). Field Crop Res 113:321–327

    Article  Google Scholar 

  • Finlay KW, Wilkinson GN (1963) The analysis of adaptation in a plant-breeding programme. Aust J Agr Res 14:742–754

    Article  Google Scholar 

  • Fischer RA (2007) Understanding the physiological basis of yield potential in wheat. J Agric Sci 145:99–113

    Article  Google Scholar 

  • Flores F, Moreno MT, Cubero JI (1998) A comparison of univariate and multivariate methods to analyze environments. Euphytica 31:645–656

    Google Scholar 

  • Fox PN, Skovmand B, Thompson BK et al (1990) Yield and adaptation of hexaploid spring triticale. Euphytica 47:57–64

    Article  Google Scholar 

  • Gabriel KR (1971) The bi-plot-graphical display of matrices with application to principal component analysis. Biometrika 58:453–467

    Article  Google Scholar 

  • Gauch HG (1992) AMMI analysis of yield trials. In: Kang MS, Gauch HG (eds) Genotype-by-environment interaction. CRC Press, Boca Raton, pp 1–40

    Google Scholar 

  • Gauch HG (2006) Statistical analysis of yield trials by AMMI and GGE. Crop Sci 46:1488–1500

    Article  Google Scholar 

  • Gauch HG, Zobel RW (1988) Predictive and postdictive success of statistical analyses of yield trial. Theor Appl Genet 76:1–10

    Article  PubMed  Google Scholar 

  • Gauch HG, Zobel RW (1996) AMMI analysis of yield trials. In: Kang MS, Gauch HG (eds) Genotype by environment interaction. CRC Press, Boca Raton, pp 85–122

    Chapter  Google Scholar 

  • Gauch HG, Zobel RW (1997) Identifying mega-environment and targeting genotypes. Crop Sci 37:381–385

    Article  Google Scholar 

  • Gauch HG, Piepho HP, Annicchiarico P (2008) Statistical analysis of yield trials by AMMI and GGE: further considerations. Crop Sci 48:866–889

    Article  Google Scholar 

  • Gonzalez AM, Monteagudo AB, Casquero PA et al (2006) Genetic variation and environmental effects on agronomical and commercial quality traits in the main European market classes of dry bean. Field Crop Res 95:336–347

    Article  Google Scholar 

  • Goyal A, Beres BL, Randhawa HS et al (2011) Yield stability analysis of broadly adaptive triticale germplasm in southern and central Alberta, Canada, for industrial end-use suitability. Can J Plant Sci 91:125–135

    Article  Google Scholar 

  • Horner TW, Frey KJ (1957) Methods for determining natural areas for oat varietal recommendations. Agron J 49:313–315

    Article  Google Scholar 

  • Huehn M (1979) Beitrage zur erfassung der phanotypischen stabilitat. EDV Med Biol 10:112–117

    Google Scholar 

  • Huehn M (1990) Non-parametric measures of phenotypic stability: part 1. Theory. Euphytica 47:189–194

    Google Scholar 

  • Huehn M (1996) Non-parametric analysis of genotype x environment interactions by ranks. In: Kang MS, Gauch HG (eds) Genotype by environment interaction. CRC Press, Boca Raton, pp 213–228

    Google Scholar 

  • IRRI (2005) IRRISTAT for Windows, Version 5.0, Metro Manila, Philippines

    Google Scholar 

  • Isik K, Kleinschmit J (2005) Similarities and effectiveness of test environments in selecting and deploying desirable genotypes. Theor Appl Genet 110:311–322

    Article  CAS  PubMed  Google Scholar 

  • Kang MS (1988) A rank-sum method for selecting high-yielding, stable corn genotypes. Cereal Res Commun 16:113–115

    Google Scholar 

  • Kang MS (1993) Simultaneous selection for yield and stability in crop performance trials: consequences for growers. Agron J 85:754–757

    Article  Google Scholar 

  • Kang MS (2002) Genotype-environment interaction: progress and prospects. In: Kang MS (ed) Quantitative genetics, genomics and plant breeding. CABI Publishing, New York, pp 221–243

    Google Scholar 

  • Kang MS, Magari R (1996) New developments in selecting for phenotypic stability in crop breeding. In: Kang MS, Gauch HG Jr (eds) Genotype-by-environment interaction. CRC Press, Boca Raton, pp 1–14

    Google Scholar 

  • Kang MS, Miller JD, Darrah LL (1987) A note on relationship between stability variance and ecovalence. J Hered 78:107

    Google Scholar 

  • Kang MS, Aggarwal VD, Chirwa RM (2006) Adaptability and stability of bean cultivars as determined via yield-stability statistic and GGE biplot analysis. J Crop Improv 15:97–120

    Article  Google Scholar 

  • Kroonenberg PM (1995) Introductions to biplots for G x E tables. Department of Mathematics, Research Report No 51. University of Queensland

    Google Scholar 

  • Lee SJ, Yan W, Ahn JK et al (2002) Effects of year, site, genotype and their interactions on various soybean isoflavones. Field Crop Res 41:1–12

    Google Scholar 

  • Lin CS, Binns MR (1988) A superiority measure of cultivar performance for cultivar x location data. Can J Plant Sci 68:193–198

    Article  Google Scholar 

  • Lin CS, Butler G (1990) Cluster analyses for analyzing two way classification data. Agron J 82:344–348

    Article  Google Scholar 

  • Loomis RS, Connor DJ (1996) Crop ecology. Productivity and management in agricultural systems. Cambridge University Press, Cambridge, pp 91–101

    Google Scholar 

  • Ma BL, Yan W, Dwyer LM et al (2004) Graphic analysis of genotype, environment, nitrogen fertilizer, and their interactions on spring wheat yield. Agron J 96:169–180

    Article  Google Scholar 

  • Malvar RA, Revilla P, Butrón A et al (2005) Performance of crosses among French and Spanish maize populations across environments. Crop Sci 45:1052–1057

    Article  Google Scholar 

  • Mohammadi R, Amri A (2008) Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments. Euphytica 159:419–432

    Article  Google Scholar 

  • Mohammadi R, Amri A (2009) Analysis of genotype x environment interactions for grain yield in durum wheat. Crop Sci 49:1177–1186

    Article  Google Scholar 

  • Mohammadi R, Amri A (2011) Graphic analysis of trait relations and genotype evaluation in durum wheat. J Crop Improv 25:680–696

    Article  Google Scholar 

  • Mohammadi R, Amri A (2012) Analysis of genotype x environment interaction in rain-fed durum wheat of Iran using GGE-biplot and non-parametric methods. Can J Plant Sci 92:757–770

    Article  Google Scholar 

  • Mohammadi R, Amri A (2013) Genotype x environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran. Euphytica 192:227–249

    Article  CAS  Google Scholar 

  • Mohammadi R, Haghparast R (2011) Evaluation of rainfed promising wheat breeding lines on farmers’ fields in west of Iran. Int J Plant Breed 5:30–36

    Google Scholar 

  • Mohammadi R, Haghparast R, Amri et al (2010a) Yield stability of rainfed durum wheat and GGE biplot analysis of multi-environment trials. Crop Pasture Sci 61:92–101

    Article  Google Scholar 

  • Mohammadi R, Roustaii M, Haghparast R et al (2010b) Genotype environment interactions for grain yield in rainfed winter wheat multi-environment trials in Iran. Agron J 102:1500–1510

    Article  Google Scholar 

  • Mohammadi R, Sadeghzadeh D, Armion M et al (2011) Evaluation of durum wheat experimental lines under different climate and water regime conditions of Iran. Crop Pasture Sci 62:137–151

    Article  Google Scholar 

  • Mohammadi R, Vaezi B, Mehraban A et al (2012) Analysis of multi-environment trials of rainfed barley in warm regions of Iran. J Crop Improv 26:503–519

    Article  Google Scholar 

  • Mohammadi R, Haghparast R, Sadeghzadeh B et al (2014) Adaptation patterns and yield stability of durum wheat landraces to highland cold rainfed areas of Iran. Crop Sci 54:944–954

    Article  Google Scholar 

  • Morgounov A, Zykin V, Belan I et al (2010) Genetic gains for grain yield in high latitude spring wheat grown in Western Siberia in 1900–2008. Field Crop Res 117:101–112

    Article  Google Scholar 

  • Morris CF, Campbell KG, King GE (2004) Characterization of the end-use quality of soft wheat cultivars from the eastern and western US germplasm ‘pools’. Plant Genet Res 2:59–69

    Article  Google Scholar 

  • Muir W, Nyquist WE, Xu S (1992) Alternative partitioning of the genotype-by-environment interaction. Theor Appl Genet 84:193–200

    Article  CAS  PubMed  Google Scholar 

  • Munoz P, Voltas J, Igartua E et al (1998) Changes in adaptation of barley releases over time in north eastern Spain. Plant Breed 117:531–535

    Article  Google Scholar 

  • Nassar R, Huehn M (1987) Studies on estimation of phenotypic stability: tests of significance for non- parametric measures of phenotypic stability. Biometrics 43:45–53

    Article  Google Scholar 

  • Ober ES, Bloa ML, Clark CJA et al (2005) Evaluation of physiological traits as indirect selection criteria for drought tolerance in sugar beet. Field Crop Res 91:231–249

    Article  Google Scholar 

  • Ortiz R, Wagoire WW, Hill J et al (2001) Heritability of and correlations among genotype-by-environment stability statistics for grain yield in bread wheat. Theor Appl Genet 103:469–474

    Article  Google Scholar 

  • Paroda RS, Hayes JD (1971) An investigation of genotype-environment interactions for rate of ear emergence in spring barley. Heredity 26:157–175

    Article  Google Scholar 

  • Perkins JM, Jinks JL (1968) Environment and genotype-environmental components of variability. Heredity 23:339–356

    Article  CAS  PubMed  Google Scholar 

  • Peterson DM, Wesenberg DM, Burrup DE et al (2005) Relationships among agronomic traits and grain composition in oat genotypes grown in different environments. Crop Sci 45:1249–1255

    Article  Google Scholar 

  • Pswarayi P, Van Eeuwijk FA, Ceccarelli S et al (2008) Barley adaptation and improvement in the Mediterranean basin. Plant Breed 127:554–560

    Article  Google Scholar 

  • Rao PS, Reddy PS, Ratore A et al (2011) Application GGE biplot and AMMI model to evaluate sweet sorghum (Sorghum bicolor) hybrids for genotype 9 environment interaction and seasonal adaptation. Indian J Agric Sci 81:438–444

    Google Scholar 

  • Reynolds M, Foulkes MJ, Slafer GA et al (2009) Raising yield potential in wheat. J Exp Bot 60:1899–1918

    Article  CAS  PubMed  Google Scholar 

  • Roemer J (1917) Sinde die ertagdreichen sorten ertagissicherer? Mitt DLG 32:87–89

    Google Scholar 

  • Romagosa I, Fox PN (1993) Genotype x environment interaction and adaptation. In: Hayward MD, Bosemark NO, Romagosa I (eds) Plant breeding: principles and prospects. Chapman & Hall, London, pp 373–390

    Chapter  Google Scholar 

  • Rubio J, Cubero JI, Martin LM et al (2004) Biplot analysis of trait relations of white lupin in Spain. Euphytica 135:217–224

    Article  Google Scholar 

  • Saindon G, Schaalje GB (1993) Evaluation of locations for testing dry bean cultivars in western Canada using statistical procedures, biological interpretation and multiple traits. Can J Plant Sci 73:985–994

    Article  Google Scholar 

  • Samonte SOPB, Wilson LT, McClung AM et al (2005) Targeting cultivars onto rice growing environments using AMMI and SREG GGE biplot analyses. Crop Sci 45:2414–2424

    Article  Google Scholar 

  • Samuel CJA, Hill J, Breese EL et al (1970) Assessing and predicting environmental response in Lolium perenne. J Agric Sci 75:l–9

    Article  Google Scholar 

  • Sandhu SK, Brar SS, Singh RS et al (2014) GGE biplot analysis for cane and sugar yield from advanced-stage sugarcane trials in subtropical India. J Crop Improv 28:641–659

    Article  CAS  Google Scholar 

  • Seif E, Evans JC, Balaam LNA (1979) Multivariate procedure for classifying environments according to their interaction with genotypes. Aust J Agric Res 30:1021–1026

    Article  Google Scholar 

  • Shafii B, Price WJ (1998) Analysis of genotype by environment interaction using the additive main effects and multiplicative interaction model and stability estimates. J Agric Biol Environ Stat 3:335–345

    Article  Google Scholar 

  • Shafii B, Mahler KA, Price WJ et al (1992) Genotype- environment interaction effects on winter rape seed yield and oil content. Crop Sci 32:922–927

    Article  CAS  Google Scholar 

  • Shukla GK (1972) Some statistical aspects of partitioning genotype–environmental components of variability. Heredity 28:237–245

    Article  Google Scholar 

  • Simmonds NW (1991) Selection for local adaptation in a plant breeding programme. Theor Appl Genet 82:363–367

    Article  CAS  PubMed  Google Scholar 

  • Slafer GA, Araus JL (2007) Physiological traits for improving wheat yield under a wide range of conditions. In: Spiertz JHJ, Struik PC, van Laar HH (eds) Scale and complexity in plant systems research: gene-plant-crop relations. Springer, Dordrecht, pp 147–156

    Chapter  Google Scholar 

  • Spearman C (1904) The proof and measurement of associations between two different things. Am J Psychiatry 15:72–101

    Google Scholar 

  • Suadric A, Simic D, Vrataric M (2006) Characterization of genotype by environment interactions in soybean breeding programmes of southeast Europe. Plant Breed 125:191–194

    Article  Google Scholar 

  • Tai GCC (1971) Genotypic stability analysis and its application to potato regional trials. Crop Sci 11:184–190

    Article  Google Scholar 

  • Thomason WE, Phillips SB (2006) Methods to evaluate wheat cultivar testing environments and improve cultivar selection protocols. Field Crop Res 99:87–95

    Article  Google Scholar 

  • Tollenaar M, Lee EA (2002) Yield potential, yield stability and stress tolerance in maize. Field Crop Res 75:161–169

    Article  Google Scholar 

  • Voltas J, López-Córcoles H, Borrás G (2005) Use of biplot analysis and factorial regression for the investigation of superior genotypes in multi-environment trials. Eur J Agric 22:309–324

    Article  Google Scholar 

  • Wamatu JN, Thomas E (2002) The influence of genotype-environment interaction on the grain yields of 10 pigeon pea cultivars grown in Kenya. J Agric Crop Sci 188:25–33

    Article  Google Scholar 

  • Wricke G (1962) Über eine Methode zur Erfassung der ökologischen Streubreite in Feldversuchen. Z Pflanzenzüchtg 47:92–96

    Google Scholar 

  • Xiao YG, Qian ZG, Wu K et al (2012) Genetic gains in grain yield and physiological traits of winter wheat in Shandong province, China, from 1969 to 2006. Crop Sci 52:44–56

    Article  Google Scholar 

  • Yan W (2001) GGEbiplot–a Windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agron J 93:1111–1118

    Article  Google Scholar 

  • Yan W (2002) Singular value partitioning for biplot analysis of multi-environment trial data. Agron J 4:990–996

    Article  Google Scholar 

  • Yan W (2015) Mega-environment analysis and test location evaluation based on unbalanced multiyear data. Crop Sci 55:113–122

    Article  Google Scholar 

  • Yan W, Fregeau-Reid J (2008) Breeding line selection based on multiple traits. Crop Sci 48:417–423

    Article  Google Scholar 

  • Yan W, Holland JB (2010) A heritability-adjusted GGE biplot for test environment evaluation. Euphytica 171:355–369

    Article  Google Scholar 

  • Yan W, Kang MS (2003) GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton

    Google Scholar 

  • Yan W, Rajcan IR (2002) Biplot analysis of test sites and trait relations of soybean in Ontario. Can J Plant Sci 42:11–20

    Google Scholar 

  • Yan W, Tinker NA (2005) An integrated biplot system for displaying, interpreting, and exploring genotype 9 environment interaction. Crop Sci 45:1004–1016

    Article  Google Scholar 

  • Yan W, Hunt LA, Sheng Q et al (2000) Cultivar evaluation and mega-environment investigation based on GGE biplot. Crop Sci 40:596–605

    Article  Google Scholar 

  • Yan W, Kang MS, Ma BL et al (2007) GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci 47:643–653

    Article  Google Scholar 

  • Yang RC, Crossa J, Cornelius PL et al (2009) Biplot analysis of genotype x environment interaction: proceed with caution. Crop Sci 49:1564–1576

    Article  Google Scholar 

  • Yates F, Cochran WG (1938) The analysis of groups of experiments. J Agric Sci 28:556–580

    Article  Google Scholar 

  • Zhang Z, Lu C, Xiang ZH (1998) Stability analysis for varieties by AMMI model. Acta Agric Sin 24:304–309

    Google Scholar 

  • Zobel BJ, Talbert BJ (1984) Applied tree improvement. Wiley, New York

    Google Scholar 

  • Zobel RW, Wright MG, Gauch HG (1988) Statistical analysis of yield trial. Agron J 80:388–393

    Article  Google Scholar 

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Acknowledgments

This study was part of the regional durum wheat research project of the Dryland Agricultural Research Institute (DARI) of Iran and sponsored by the Agricultural Research, Education and Extension Organization (AREEO). We thank all members of the durum wheat project for contributions they have made.

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Mohammadi, R., Amri, A. (2016). Genotype x Environment Interaction Implication: A Case Study of Durum Wheat Breeding in Iran. In: Al-Khayri, J., Jain, S., Johnson, D. (eds) Advances in Plant Breeding Strategies: Agronomic, Abiotic and Biotic Stress Traits. Springer, Cham. https://doi.org/10.1007/978-3-319-22518-0_14

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