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Genotype × environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran

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Abstract

The genotype × environment (GE) interaction influences genotype selection and recommendations. Consequently, the objectives of genetic improvement should include obtaining genotypes with high potential yield and stability in unpredictable conditions. The GE interaction and genetic improvement for grain yield and yield stability was studied for 11 durum breeding lines, selected from Iran/ICARDA joint program, and compared to current checks (i.e., one durum modern cultivar and two durum and bread wheat landraces). The genotypes were grown in three rainfed research stations, representative of major rainfed durum wheat-growing areas, during 2005–09 cropping seasons in Iran. The additive main effect and multiplicative interaction (AMMI) analysis, genotype plus GE (GGE) biplot analysis, joint regression analysis (JRA) (b and S2di), six stability parameters derived from AMMI model, two Kang’s parameters [i.e., yield-stability (YSi) statistic and rank-sum], GGE distance (mean performance + stability evaluation), and two adaptability parameters [i.e., TOP (proportion of environments in which a genotype ranked in the top third) and percentage of adaptability (Ad)] were used to analyze GE interaction in rainfed durum multi-environment trials data. The main objectives were to (i) evaluate changes in adaptation and yield stability of the durum breeding lines compared to modern cultivar and landraces (ii) document genetic improvement in grain yield and analyze associated changes in yield stability of breeding lines compared to checks and (iii) to analyze rank correlation among GGE biplot, AMMI analysis and JRA in ranking of genotypes for yield, stability and yield-stability. The results showed that the effects due to environments, genotypes and GE interaction were significant (P < 0.01), suggesting differential responses of the genotypes and the need for stability analysis. The overall yield was 2,270 kg ha−1 for breeding lines and modern cultivar versus 2,041 kg ha−1 for landraces representing 11.2 % increase in yield. Positive genetic gains for grain yield in warm and moderate locations compared to cold location suggests continuing the evaluation of the breeding material in warm and moderate conditions. According to Spearman’s rank correlation analysis, two types of associations were found between the stability parameters: the first type included the AMMI stability parameters and joint regression parameters which were related to static stability and ranked the genotypes in similar fashion, whereas the second type consisted of the rank-sum, YSi, TOP, Ad and GGED which are related to dynamic concept of stability. Rank correlations among statistical methods for: (i) stability ranged between 0.27 and 0.97 (P < 0.01), was the least between AMMI and GGE biplot, and highest for AMMI and JRA and (ii) yield-stability varied from 0.22 (between GGE and JRA) to 0.44 (between JRA and AMMI). Breeding lines G8 (Stj3//Bcr/Lks4), G10 (Ossl-1/Stj-5) and G12 (modern cultivar) were the best genotypes in terms of both nominal yield and stability, indicating that selecting for improved yield potential may increase yield in a wide range of environments. The increase in adaptation, yield potential and stability of breeding lines has been reached due to gradual accumulation of favorable genes through targeted crosses, robust shuttle breeding and multi-locational testing.

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References

  • Alwala S, Kwolek T, McPherson M, Pellow J, Meyer DA (2010) Comprehensive comparison between Eberhart and Russell joint regression and GGE biplot analyses to identify stable and high yielding maize hybrids. Field Crops 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, 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 

  • Argiller O, Hebert Y, Barriere Y (1994) Statistical analysis and interpretation of line × environment interaction for biomass yield in maize. Agronomie 14:661–672

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Brennan JP, Aw-Hassan A, Quade KJ, Nordblom TL (2002) Impact of ICARDA Research on Australian Agriculture. Economic Research Report No. 11. NSW Agriculture

  • Burdon RD (1977) Genetic correlation as a concept for studying genotype–environment interaction in forest tree breeding. Silvae Genet 26:168–175

    Google Scholar 

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

    Article  Google Scholar 

  • Cattivelli L, Rizza F, Badeck FW, Mazzucotelli E, Mastrangelo AM, Francia E, Marè C, Tondelli A, Stanca AM (2008) Drought tolerance improvement in crop plants: an integrated view from breeding to genomic. Field Crops Res 15:1–14

    Article  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 

  • Cooper M, Byth DE (1996) Understanding plant adaptation to achieve systematic applied crop improvement—a fundamental challenge. In: Cooper M, Hammer GL (eds) Plant adaptation and crop improvement. CABI Publishing, Wallingford, pp 5–23

    Google Scholar 

  • Cox TS, Shroyer JP, Ben-Hui L, Sears RG, Martin TJ (1988) Genetic improvement in agronomic traits of hard red winter wheat cultivars from 1919 to 1987. Crop Sci 28:756–760

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Ebdon JS, Gauch HG (2002) Additive main effects and multiplicative interaction analysis of National Turfgrass performance trials: II. Genotype recommendation. Crop Sci 42:497–506

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Falconer DS (1981) Introduction to quantitative genetics, 2nd edn. Longman, London

    Google Scholar 

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

    Article  Google Scholar 

  • Feyerherm A, Paulsen GM, Sebaugh JL (1984) Contribution of genetic improvement to recent wheat yield increases in the USA. Agron J 76:985–990

    Article  Google Scholar 

  • Finlay KW, Wilkinson GN (1963) The analysis of adaptation in a plant-breeding programme. Aust J Agric 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 

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

    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 (1996) AMMI analysis of yield trials. In: Kang MS, Gauch HG Jr (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 

  • Goyal A, Beres BL, Randhawa HS, Navabi A, Salmon DF, Eudes F (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 

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

    Article  PubMed  CAS  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 

  • Khalil IH, Carver BF, Smith EL (1995) Genetic gains in two selection phases of a wheat-breeding program. Plant Breed 114:117–120

    Article  Google Scholar 

  • Lin CS, Morrison MJ (1992) Selection of test locations for regional trials of barley. Theor Appl Genet 83:968–972

    Article  Google Scholar 

  • Mohammadi R, Haghparast R, Amri A, Ceccarelli S (2010) 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, 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

    Article  Google Scholar 

  • Morgounov A, Zykin V, Belan I, Roseeva L, Zelenskiy Y, Gomez-Becerra HF, Budak H, Bekes F (2010) Genetic gains for grain yield in high latitude spring wheat grown in Western Siberia in 1900–2008. Field Crops Res 117:101–112

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Ortiz R, Wagoire WW, Hill J, Chandra S, Madsen S, Stolen O (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 

  • Piepho HP (1998) Methods for comparing the yield stability of cropping systems: a review. J Agron Crop Sci 180:193–213

    Article  Google Scholar 

  • Pswarayi P, Van Eeuwijk FA, Ceccarelli S, Grando S, Comadran J, Russell JR, Francia E, Pecchioni N, Li Destri O, Akar T, Al-Yassin A, Enbelkacem BA, Choumane W, Karrou M, Ouabbou H, Bort J, Araus JL, Molina-Cano JL, Thomas WTB, Romagosa I (2008) Barley adaptation and improvement in the Mediterranean basin. Plant Breed 127:554–560

    Article  Google Scholar 

  • Purchase JL (1997) Parametric analysis to describe G × E interaction and yield stability in winter wheat. PhD thesis. Department of Agronomy, Faculty of Agriculture, University of the Orange Free State, Bloemfontein, South Africa

  • Reynolds M, Foulkes MJ, Slafer GA, Berry P, Parry MAJ, Snape JW, Angus WJ (2009) Raising yield potential in wheat. J Exp Bot 60:1899–1918

    Article  PubMed  CAS  Google Scholar 

  • Rharrabti Y, Garcia del Moral DM, Villegas LF, Royo C (2003) Durum wheat quality in Mediterranean environments. III: stability and comparative methods in analyzing G × E interaction. Field Crops Res 80:141–146

    Article  Google Scholar 

  • Romagosa I, Fox PN (1993) Genotype × 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 

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

    Article  Google Scholar 

  • Schmidt JW (1984) Genetic contributions to yield gains in wheat. In: Fehr WR (ed) Genetic contributions to yield gains of five major crop plants. ASA-CSSA, Madison, WI, pp 89–101

    Google Scholar 

  • Schmidt JW, Worrall WD (1984) Trends in yield improvement through genetic gains. In: Sakamoto S (ed) Proceedings of 6th international wheat genetics symposium. Plant Germplasm Institute, Kyoto, Japan, pp 691–700

    Google Scholar 

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

    Article  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 

  • Sneller CH, Kilgore-Norquest L, Dombek D (1997) Repeatability of yield stability statistics in soybean. Crop Sci 37:383–390

    Article  Google Scholar 

  • St-Pierre CA, Klinck HR, Gauthier FM (1967) Early generation selection under different environments as it influences adaptation of barley. Can J Plant Sci 47:507–517

    Article  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 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Xiao YG, Qian ZG, Wu K, Liu JJ, Xia XC, Ji WQ, He ZH (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, 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, Hunt LA, Sheng Q, Szlavnics Z (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, Woods S, Cornelius PL (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, Burgueno J (2009) Biplot analysis of genotype × environment interaction: proceed with caution. Crop Sci 49:1564–1576

    Article  Google Scholar 

  • Yau SK (1995) Regression and AMMI analysis of genotype × environment interactions: an empirical comparison. Agron J 87:121–126

    Article  Google Scholar 

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

    Google Scholar 

  • Zobel RW (1994) Stress resistance and root systems. In: Proceedings of the workshop on adaptation of plants to soil stress, 1–4 Aug 1993, INTSORMIL Publ. 94-2, Inst. of Agriculture and Natural Resources, Univ. of Nebraska, Lincoln, pp 80–99

  • 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 work was a part of regional durum wheat research project of the Dryland Agricultural Research Institute (DARI) of Iran and sponsored by the Agricultural Research and Education Organization (AREO). We thank all members of the project who contributed to the implementation of the field work. The authors wish to thank two anonymous reviewers for their valuable comments, corrections and suggestions to improve the quality of the manuscript.

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  1. Dryland Agricultural Research Institute (DARI), P. O. Box 67145-1164, Kermanshah, Iran

    Reza Mohammadi

  2. International Center for Agricultural Research in the Dry Areas (ICARDA), Aleppo, Syria

    Ahmed Amri

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  1. Reza Mohammadi
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  2. Ahmed Amri
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Correspondence to Reza Mohammadi.

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Mohammadi, R., Amri, A. Genotype × environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran. Euphytica 192, 227–249 (2013). https://doi.org/10.1007/s10681-012-0839-1

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