Euphytica

, 214:79 | Cite as

Genotype by environment interaction components underlying variations in root, sugar and white sugar yield in sugar beet (Beta vulgaris L.)

  • Mahdi Hassani
  • Bahram Heidari
  • Ali Dadkhodaie
  • Piergiorgio Stevanato
Article
  • 100 Downloads

Abstract

The success of plant breeding programs depends on the ability to provide farmers with genotypes with guaranteed superior performance in terms of yield across a range of environmental conditions. We evaluated 49 sugar beet genotypes in four different geographical locations in 2 years aiming to identify stable genotypes with respect to root, sugar and white sugar yields, and to determine discriminating ability of environments for genotype selection and introduce representative environments for yield comparison trials. Combinations of year and location were considered as environment. Statistical analyses including additive main effects and multiplicative interactions (AMMI), genotype main effects and genotype × environment interaction effects (GGE) models and AMMI stability value (ASV) were used to dissect genotype by environment interactions (GEI). Based on raw data, root, sugar and white sugar yields varied from 0.95 to 104.86, 0.15 to 20.81, and 0.09 to 18.45 t/ha across environments, respectively. Based on F-Gollob validation test, three interaction principal components (IPC) were significant for each trait in the AMMI model whereas according to F ratio (FR) test two significant IPCs were identified for root yield and sugar yield and three for white sugar yield. For model diagnosis, the actual root mean square predictive differences (RMS PD) were estimated based upon 1000 validations and the AMMI-1 model with the smallest RMS PD was identified as the most accurate model with highest predictive accuracy for the three traits. In the GGE biplot model, the first two IPCs accounted for 60.52, 62.9 and 64.69% of the GEI variation for root yield, sugar yield and white sugar yield, respectively. According to the AMMI-1 model, two mega-environments were delineated for root yield and three for sugar yield and white sugar yield. The mega-environments identified had an evident ecological gradient from long growing season to intermediate or short growing season. Environment-focused scaling GGE biplots indicated that two locations (Ekbatan and Zarghan) were the most representative testing environments with discriminating ability for the three traits tested. Environmentally stable genotypes (i.e. G21, G28 and G29) shared common parental lines in their pedigree having resistance to some sugar beet diseases (i.e. rhizomania and cyst nematodes). The results of the AMMI model were partly in accord with the results of GGE biplot analysis with respect to mega-environment delineation and winner genotypes. The outcome of this study may assist breeders to save time and costs to identify representative and discriminating environments for root and sugar yield test trials and creates a corner stone for an accelerated genotype selection to be used in sweet-based programs.

Keywords

AMMI GGE biplot Model diagnosis Hybrid Stability Representativeness Discriminating ability 

Notes

Acknowledgements

The authors thank the Sugar Beet Seed Institute (SBSI) for providing sugar beet germplasm. Authors also appreciate Dr. H. G. Gauch, Cornell University, USA for his help in working with the softwares AMMISOFT and MATMODEL.

Compliance with ethical standards

Conflict of interests

The authors declare no conflict of interest associated with this publication.

Supplementary material

10681_2018_2160_MOESM1_ESM.docx (373 kb)
Supplementary material 1 (DOCX 372 kb)

References

  1. Abate F, Mekbib F, Dessalegn Y (2015) GGE biplot analysis of multi-environment yield trials of durum wheat (Triticum turgidum Desf.) genotypes in north western Ethiopia. Am J Exp Agric 8:120–129.  https://doi.org/10.9734/AJEA/2015/9994 CrossRefGoogle Scholar
  2. Abdollahian-Noghabi M, Sheikholeslami R, Babaei B (2005) Technical terms of sugar beet quantity and quality. J Sugar Beet 21:101–104 in Persian, abstract in English Google Scholar
  3. Acosta-Pech R, Crossa J, de Los Campos G, Teyssèdre S, Claustres B, Pérez-Elizalde S, Pérez-Rodríguez P (2017) Genomic models with genotype × environment interaction for predicting hybrid performance: an application in maize hybrids. Theor Appl Genet 130:1431–1440.  https://doi.org/10.1007/s00122-017-2898-0 CrossRefPubMedGoogle Scholar
  4. Ahmad S, Zubair M, Iqbal N, Cheema NM, Mahmood K (2012) Evaluation of sugar beet hybrid varieties under Thal-Kumbisoil series of Pakistan. Int J Agric Biol 14:605–608Google Scholar
  5. Akbarpour O, Dehghani H, Sorkhi B, Gauch HG (2014) Evaluation of genotype × environment interaction in barley (Hordeum Vulgare L.) based on AMMI model using developed SAS program. J Agric Sci Technol 16:909–920Google Scholar
  6. Akinwale RO, Fakorede MAB, Badu-Apraku B, Oluwaranti A (2014) Assessing the usefulness of GGE biplot as a statistical tool for plant breeders and agronomists. Cereal Res Commun 42:534–546.  https://doi.org/10.1556/CRC.42.2014.3.16 CrossRefGoogle Scholar
  7. Akter A, Jamil Hassan M, UmmaKulsum M, Islam MR, Hossain K (2014) AMMI biplot analysis for stability of grain yield in hybrid rice (Oryza sativa). J Rice Res 2:126–129.  https://doi.org/10.4172/jrr.1000126 CrossRefGoogle Scholar
  8. Anderson TW, Darling DA (1954) A test of goodness of fit. Am Stat Assoc 49:765–769.  https://doi.org/10.2307/2281537 CrossRefGoogle Scholar
  9. Annicchiarico P (1997) Joint regression vs AMMI analysis of genotype-environment interactions for cereals in Italy. Euphytica 94:53–62.  https://doi.org/10.1023/A:100295482 CrossRefGoogle Scholar
  10. Barocka KH (1978) The characterization of performance of sugar beets by variety × environment interaction. Euphytica 27:689–700.  https://doi.org/10.1007/bf00023704 CrossRefGoogle Scholar
  11. Bassi FM, Sanchez-Garcia M (2017) Adaptation and stability analysis of ICARDA durum wheat elite across 18 countries. Crop Sci 57:2419–2430.  https://doi.org/10.2135/cropsci2016.11.0916 CrossRefGoogle Scholar
  12. Beckett JL (1982) Variety × environment interactions in sugar beet variety trials. J Agric Sci 98:425–435.  https://doi.org/10.1017/S0021859600041976 CrossRefGoogle Scholar
  13. Biancardi E (2005) History of sugar beet breeding. In: Biancardi E, Campbell LG, Skaracis GN, DeBiaggi M (eds) Genetics and breeding of sugar beet. Science Publishers, Enfield, pp 38–40.  https://doi.org/10.1002/9780470751114.ch4 Google Scholar
  14. Biancardi E, McGrath JM, Panella LW, Lewellen RT, Stevanato P (2010) Sugar beet. In: Bradshaw JE (ed) Root and tuber crops. Handbook of plant breeding. Springer Science + Business Media, LLC, New York, pp 173–219.  https://doi.org/10.1007/978-0-387-92765-7_6 CrossRefGoogle Scholar
  15. Bloch D, Hoffmann C (2005) Seasonal development of genotypic differences in sugar beet (Beta vulgaris L.) and their interaction with water supply. J Agron Crop Sci 191:263–272.  https://doi.org/10.1111/j.1439-037X.2005.00150.x CrossRefGoogle Scholar
  16. BrewbakerH E (1944) Adaptation of the sugar beet to meet the need of the sugar industry in America. J Am Soc Agron.  https://doi.org/10.2134/agronj1944.00021962003600070004x Google Scholar
  17. Broccanello C, McGrath JM, Panella L, Richardson K, Funk A, Chiodi C, Biscarini F, Barone V, Baglieri A, Squartini A, Concheri G, Stevanato P (2018) A SNP mutation affects rhizomania-virus content of sugar. Euphytica (accepted paper)Google Scholar
  18. Campbell LG, Kern JJ (1982) Cultivar × environment interactions in sugar beet yield trials. Crop Sci 22:932–935.  https://doi.org/10.2135/cropsci1982.0011183X002200050008x CrossRefGoogle Scholar
  19. Cooper M, Delacy IH (1994) Relationships among analytical methods used to study genotypic variation and genotype-by-environment interaction in plant breeding multi-environment experiment. Theor Appl Genet 88:561–572.  https://doi.org/10.1007/BF01240919 CrossRefPubMedGoogle Scholar
  20. Cornelius PL (1993) PL. Statistical test and retention of terms in the ammi main effects and multiplicative interaction model for cultivar traits. Crop Sci 33:1186–1193CrossRefGoogle Scholar
  21. Crossa J (1990) Statistical analyses of multilocation trials. Adv Agron 44:55–85.  https://doi.org/10.1016/S0065-2113(08)60818-4 CrossRefGoogle Scholar
  22. Dehghani H, Ebadi A, Yousefi A (2006) Biplot analysis of genotype environment interaction for barley yield in Iran. Agron J 98:388–393.  https://doi.org/10.2134/agronj2004.0310 CrossRefGoogle Scholar
  23. Delacy IH, Basford KE, Cooper M, Bull JK, McLaren CG (1996) Analysis of multi-environment trials—an historical perspective. In: Hammer G, Cooper M (eds) Plant adaptation and crop improvement. CAB International, Wallingford, pp 39–124Google Scholar
  24. Dias CTD, Krzanowski W (2003) Model selection and cross validation in additive main effect and multiplicative interaction models. Crop Sci 43:865–873CrossRefGoogle Scholar
  25. Dryacott AP (2006) Sugar beet. Blackwell, London.  https://doi.org/10.1002/9780470751114.ch1 CrossRefGoogle Scholar
  26. Ebdon JS, Gauch HG (2002a) Additive main effect and multiplicative interaction analysis of national turfgrass performance trials: I. Interpretation of genotype × environment interaction. Crop Sci 42:489–496.  https://doi.org/10.2135/cropsci2002.4890 CrossRefGoogle Scholar
  27. Ebdon JS, Gauch HG (2002b) Additive main effect and multiplicative interaction analysis of national turfgrass performance trials. II: Cultivar recommendations. Crop Sci 42:497–506.  https://doi.org/10.2135/cropsci2002.4970 CrossRefGoogle Scholar
  28. Eberhart SA, Russell WA (1966) Stability parameters for comparing varieties. Crop Sci 6:36–40.  https://doi.org/10.2135/cropsci1966.0011183X000600010011x CrossRefGoogle Scholar
  29. Falconer DS, Mackay TFC (1996) Introduction to quantitative genetics, 4th edn. Longmans Green, HarlowGoogle Scholar
  30. FAO (2009) Agribusiness handbook: sugar beet white sugar. EastAgri, Food and Agriculture Organization of the United Nations (ed), Rome, ItalyGoogle Scholar
  31. Finaly KW, Wilkinson GN (1963) The analysis of adaptation in a plant-breeding programme. Aust J Agric Res 4:742–754.  https://doi.org/10.1071/AR9630742 CrossRefGoogle Scholar
  32. Gauch HG (1988) Model selection and validation for yield trials with interaction. Biometrics 44:705–715.  https://doi.org/10.2307/2531585 CrossRefGoogle Scholar
  33. Gauch HG (1992) Statistical analysis of regional yield trials: AMMI analysis of factorial designs. Elsevier, Amsterdam.  https://doi.org/10.1016/0308-521x(96)86769-2 Google Scholar
  34. Gauch HG (2007) MATMODEL VERSION 3.0: Open source software for AMMI and related analysis (verfied 27 Feb. 2008). Crop and Soil Science, Cornell Univ., Ithaca, NY. http://www.css.cornell.edu/staff/gauch
  35. Gauch HG (2013) A simple protocol for AMMI analysis of yield trials. Crop Sci 53:1860–1869CrossRefGoogle Scholar
  36. Gauch HG, Zobel RW (1988) Predictive and postdictive success of statistical analyses of yield trials. Theor Appl Genet 76(1):1–10CrossRefPubMedGoogle Scholar
  37. Gauch HG, Piepho HP, Annicchiarico P (2008) Statistical analysis of yield trials by AMMI and GGE: further consideration. Crop Sci 48:866–888CrossRefGoogle Scholar
  38. GENSTAT (2009). ENSTAT, 12th edn. VSN International Ltd. (VSNi), Hertfordshire. http://www.vsni.co.uk
  39. Ggyllenspetz U (1998) Genotype × environment interaction and stability of diploid and triploid sugar beet (Beta vulgaris L.) varieties. Doctoral thesis Swedish University Sveriges Lantbruks, UppsalaGoogle Scholar
  40. Gollob HF (1968) A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33:73–115.  https://doi.org/10.1007/BF02289676 CrossRefPubMedGoogle Scholar
  41. Hoberg F, Ladewig E, Kenter C (2016) Genotype environment interactions in sugar beet in Germany.75.IIRB-Congress, Brussels, 16–17 Feb 2016Google Scholar
  42. Hoffmann CM, Marlander B (2005) Composition of harmful nitrogen in sugar beet (Beta vulgaris L.) amino acids, betaine nitrateas affected by genotype and environment. Eur J Agron 22:255–265.  https://doi.org/10.1016/j.eja.2004.03.003 CrossRefGoogle Scholar
  43. Jalata Z (2011) GGE-biplot analysis of multi-environment yield trials of barley (Hordeom vulgaris L) genotypes in southeastern Ethiopia highlands. Int J Plant Breed Genet 5:59–75.  https://doi.org/10.3923/ijpbg.2011.59.75 CrossRefGoogle Scholar
  44. Jamshidmoghaddam M, Pourdad SS (2013) Genotype × environment interactions for seed yield in rainfed winter safflower (Carthamus tinctorius L.) multi-environment trials in Iran. Euphytica 190:357–369.  https://doi.org/10.1007/s10681-012-0776-z CrossRefGoogle Scholar
  45. Kiliç H (2014) Additive main effects and multiplicative interactions (AMMI) analysis of grain yield in barley genotypes across environments. J Agric Sci 20:337–344.  https://doi.org/10.15832/tbd.44431 Google Scholar
  46. Kumar J, Bratap A, Kumar S (2015) Phenomics in crop plants: trends, options and limitations. Springer, New YorkGoogle Scholar
  47. Liebe S, Varrelmann M (2016) Effect of environment and sugar beet genotype on root rot development and pathogen profile during storage. Phytopathology 106:65–75.  https://doi.org/10.1094/phyto-07-15-0172-r CrossRefPubMedGoogle Scholar
  48. Lin CS, Binns MR (1988) A superiority measure of cultivar performance for cultivar × location data. Canad J Plant Sci 68:193–198.  https://doi.org/10.4141/cjps88-018 CrossRefGoogle Scholar
  49. Lin CS, Binns MR (1994) Concepts and methods for analyzing regional trial data for cultivar and location selection. Plant Breed Rev 12:271–297.  https://doi.org/10.1002/9780470650493.ch10 Google Scholar
  50. Miranda GV, Souza LV, Guimarães LJM, Namorato H, Oliveira LR, Soares MO (2009) Multivariate analyses of genotype × environment interaction of popcorn. Pesq Agropecu Bras 44:45–50.  https://doi.org/10.1590/S0100-204X2009000100007 CrossRefGoogle Scholar
  51. Naroui Rad MR, Abdul Kadir M, Rafii YM, Hawa ZEJ, Naghavi MR, Ahmadi A (2013) Genotype × environment interaction by AMMI and GGE biplot analysis in three consecutive generations of wheat (Triticum aestivum) under normal and drought stress conditions. Aust J Crop Sci 7:956–961Google Scholar
  52. Oliviera EJ, Freitas JPX, Jesus ON (2014) AMMI analysis of the adaptability and yield stability of yellow passion fruit varieties. Sci Agric 71:139–145CrossRefGoogle Scholar
  53. Perkins JM, Jinks JL (1968) Environmental and genotype environmental components of variability. III. Multiple lines and crosses. Heredity 23:339–356.  https://doi.org/10.1038/hdy.1968.48 CrossRefPubMedGoogle Scholar
  54. Phuke RM, Anuradha K, Radhika K, Jabeen F, Anuradha G, Ramesh T et al (2017) Genetic variability, genotype × environment interaction, correlation, and GGE biplot analysis for grain iron and zinc concentration and other agronomic traits in RIL population of sorghum (Sorghum bicolor L. Moench). Front Plant Sci 8:712.  https://doi.org/10.3389/fpls.2017.00712 CrossRefPubMedCentralPubMedGoogle Scholar
  55. Purchase JL, Hatting H, Van Deventer CS (2000) Genotype × environment interaction of winter wheat (T. aestivum) in South Africa: stability analysis of yield performance. S Afr J Plant Soil 17:101–107CrossRefGoogle Scholar
  56. Roostaei M, Mohammadi R, Amri A (2014) Rank correlation among different statistical models in ranking of winter wheat genotypes. Crop J 2:154–163CrossRefGoogle Scholar
  57. Saeed M, Francis CA, Rajweski JF (1984) Maturity effects on genotype × environment interactions in grain sorghum. Agron J 76:55–58.  https://doi.org/10.2134/agronj1984.00021962007600010015x CrossRefGoogle Scholar
  58. Safari H, Moradi F, Jalilian A (2012) Study of genotype × environment interaction for sugar beet monogerm cultivars using AMMI method. J Sugar Beet 28:29–35.  https://doi.org/10.22092/JSB.2012.658 Google Scholar
  59. Shapiro SS, Wilk MB (1965) An analysis of variance for normality (complete samples). Biometrika 52:591–611.  https://doi.org/10.2307/2333709 CrossRefGoogle Scholar
  60. Sousa LB, Hamawaki OT, Nogueira APO, Batista RO, Oliveira VM, Hamawaki RL (2015) Evaluation of soybean lines and environmental stratification using the AMMI, GGE biplot, and factor analysis methods. Genet Mol Res 14:12660–12674.  https://doi.org/10.4238/2015.October.19.10 CrossRefPubMedGoogle Scholar
  61. Stevanato PG, Broccanello C, Pajola L, Biscarini F, Richards C, Panella L, Hassani M, Formentin E, Chiodi C, Concheri G, Heidari B (2017) Targeted next-generation sequencing identification of mutations in disease resistance gene analogs (RGAs) in wild and cultivated beets. J Genes 8:264–276CrossRefGoogle Scholar
  62. Xu Y (2016) Envirotyping for deciphering environmental impacts on crop plants. Theor Appl Genet 129:653–673.  https://doi.org/10.1007/s00122-016-2691-5 CrossRefPubMedCentralPubMedGoogle Scholar
  63. Vargas H, Crossa, J (2000) The AMMI analysis and graphing the biplot. CIMMYT, MexicoGoogle Scholar
  64. Yan W (2002) Singular value partitioning for biplot analysis of multi-environment trial data. Agron J 94:990–996.  https://doi.org/10.2134/agronj2002.9900 CrossRefGoogle Scholar
  65. Yan W, Hunt LA (2001) Interpretation of genotype environment interaction for winter wheat yield in Ontario. Crop Sci 41:19–25.  https://doi.org/10.2135/cropsci2001.41119x CrossRefGoogle Scholar
  66. Yan W, Kang (2003) GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC Press, Boca Raton.  https://doi.org/10.1201/9781420040371 Google Scholar
  67. Yan W, Rajcan I (2002) Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Sci 42:11–20.  https://doi.org/10.2135/cropsci2002.0011 CrossRefPubMedGoogle Scholar
  68. Yan W, Tinker NA (2005) An integrated biplot analysis system for displaying, interpreting, and exploring genotype by environment interactions. Crop Sci 45:1004–1016.  https://doi.org/10.2135/cropsci2004.0076 CrossRefGoogle Scholar
  69. Yan W, Tinker AN (2006) Biplot analysis of multi-environment trial data: Principles and applications. Canad J Plant Sci 86:623–664.  https://doi.org/10.4141/P05-169 CrossRefGoogle Scholar
  70. Yan W, Kang MS, Ma B, Wood S, Cornelius PL (2007) GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci 47:643–655.  https://doi.org/10.2135/cropsci2006.06.0374 CrossRefGoogle Scholar
  71. Zimmermann B, Zeddies J (2002) Productivity progress in sugar beet production—with special emphasis on the contribution of breeding. In Paper presented at the 13th International Farm Management Congress, Wageningen, 7–12 July 2002Google Scholar
  72. Zobel RW, Wright MJ, Gauch HG (1988) Statistical analysis of a yield trials. Agron J 80:388–393.  https://doi.org/10.2134/agronj1988.00021962008000030002x CrossRefGoogle Scholar
  73. Zorić M, Gunjaća J, Šimić D (2017) Genotypic and environmental variability of yield from seven different crops in Croatian official variety trials and comparison with on-farm trends. J Agric Sci 155:804–811.  https://doi.org/10.1017/S0021859616000903 CrossRefGoogle Scholar
  74. Gauch HG (2007) MATMODEL VERSION 3.0: Open source software for AMMI and related analysis (verfied 27 Feb. 2008). Crop and Soil Science, Cornell Univ., Ithaca, NY. http://www.css.cornell.edu/staff/gauch

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Crop Production and Plant Breeding, School of AgricultureShiraz UniversityShirazIran
  2. 2.Sugar Beet Seed Institute (SBSI)KarajIran
  3. 3.Department of Agronomy, Animals, Natural Resources and Environment-DAFNAEUniversity of PadovaLegnaroItaly

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