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Targeting resilient lentil genotypes with an adding value of nutritional quality by using AMMI and GGE biplots analysis

DOI
https://doi.org/10.14719/pst.2310
Submitted
18 December 2022
Published
30-08-2023
Versions

Abstract

The current study aims to assess the impact of different genotypes, environmental conditions, and their interactions (G×E) on lentil yield and nutritive traits in various agro-ecological locations across Morocco. To achieve this, two analysis methods, Analysis of Main Additive Effects and Multiplicative Interaction (AMMI), and Genotype and Genotype by Environment (GGE) were used. The study involved evaluating sixty-four lentil genotypes in six diverse environments during the 2017–2018 and 2019–2020 seasons. Results from the analysis of variance revealed that environmental variation significantly influenced grain yield (75.7%), zinc (48.4%), and magnesium (73.3%). In contrast, genotype by environment interaction (G×E) played a more substantial role in determining protein (45.7%), iron (53.2%), and manganese (49.6%) content. The first two components explained 69.2%, 78.3%, 90.5%, 79.3%, 71.4%, and 74.3% of the variation in grain yield, protein content, iron, zinc, manganese, and magnesium, respectively. The GGE biplot analysis identified specific environments (E3 and E5) as representative and discriminative for yield, zinc, and manganese. Similarly, E3 and E4 were discriminative for iron and protein and magnesium, respectively. Seventeen lentil genotypes exhibited high performance, combining yield and nutritional quality. Notably, genotypes LN34 and VR28 performed well in the Marchouch 2019-2020 environment, while genotype LN54 excelled in the Douyet and Sidi el Aydi environments during 2019-2020. Furthermore, three advanced lines (LN34, LN58 and LN64) expressed stability in yield and most nutrient traits, outperforming released lentil varieties. These promising lines hold potential for developing novel, resilient lentil varieties with both high yield and nutritive quality.

References

  1. Yadav SS, McNeil DL, Stevenson PC. Lentil an ancient crop for modern times edited. Springer. 2007;3:54-67 p. http://repositorio.unan.edu.ni/2986/1/5624.pdf
  2. FAOSTAT. Food and Agriculture Organization of the United Nations [Internet]. 2019 [cited 2010 May 24]. Available from: https://www.fao.org/faostat/en/#data/QCL/visualize
  3. FAOSTAT. FAOSTAT [Internet]. 2019 [cited 2010 Mar 30]. Available from: https://www.fao.org/faostat/fr/#compare
  4. MAPMDREF. Agriculture en chiffres [Internet]. Ministry of Agriculture, Fisheries, Rura Development, Water and Forests; Le Maroc Vert. 2019. Available from: http://www.agriculture.gov.ma/pages/publications/agriculture-en-chiffres-2018-edition-2019
  5. Idrissi O, Houasli C, Amamou A, Nsarellah N. Lentil genetic improvement in Morocco: State of art of the program, major achievements and perspective. Moroccan J Agric Sci. 2020;1(2). https://techagro.org/index.php/MJAS/article/view/816
  6. Ramírez-Ojeda AM, Moreno-Rojas R, Cámara-Martos F. Mineral and trace element content in legumes (lentils, chickpeas and beans): Bioaccesibility and probabilistic assessment of the dietary intake. J Food Compos Anal. 2018;73:17-28. https://doi.org/10.1016/j.jfca.2018.07.007
  7. Viadel B, Barberá R, Farré R. Uptake and retention of calcium, iron and zinc from raw legumes and the effect of cooking on lentils in Caco-2 cells. Nutr Res. 2006;26(11):591-96. https://doi.org/10.1016/J.NUTRES.2006.09.016
  8. FAOSTAT. FAOSTAT [Internet]. 2022 [cited 2010 Oct 15]. Available from: https://www.fao.org/faostat/en/#data/FS
  9. Goudia BD, Hash CT. Breeding for high grain Fe and Zn levels in cereals. Int J Innov Appl Stud ISSN. 2015;12(2):2028-9324. http://www.ijias.issr-journals.org/
  10. Sarker A, Aydogan A, Chandra S, Kharrat M, Sabaghpour S. Genetic enhancement for yield and yield stability. In: Erskine W, Muehlbauer FJ, Sarker A, Sharma B (eds) The Lentil: Botany, Production and Uses. CABI, Wallingford. 2009;4:88-100 p. https://www.cabidigitallibrary.org/doi/10.1079/9781845934873.0102
  11. Gupta S, Das S, Dikshit HK, Mishra GP, Aski MS, Bansal R et al. Genotype by environment interaction effect on grain iron and zinc concentration of Indian and mediterranean lentil genotypes. Agronomy. 2021;11(9). https://doi.org/10.3390/agronomy11091761
  12. Cardona-Ayala CE, Aramendiz-Tatis H, Camacho MME. Adaptability and stability for iron and zinc in cowpea by ammi analysis. Rev Caatinga [Internet]. 2021;34(3):590-98. Available from: http://dx.doi.org/10.1590/1983-21252021v34n310rc
  13. Wardofa GA, Asnake D, Mohammed H. GGE biplot analysis of genotype-by-environment interaction and grain yield stability of bread wheat genotypes in South Tigray, Ethiopia. Commun Biometry Crop Sci. 2015;10(1):17-26. https://doi.org/10.33687/pbg.007.02.2846
  14. Omrani A, Omrani S, Khodarahmi M, Shojaei SH, Illés Á, Bojtor C et al. Evaluation of grain yield stability in some selected wheat genotypes using AMMI and GGE biplot methods. 2022; https://doi.org/10.3390/agronomy12051130
  15. Habib Shojaei S, Mostafavi K, Reza Bihamta M, Omrani A, Mohammad Nasir Mousavi S, Illés Á et al. Stability on maize hybrids based on GGE biplot graphical technique. 2022. https://doi.org/10.3390/agronomy12020394
  16. Yan W. GGEbiplot - A windows application for graphical analysis of multienvironment trial data and other types of two-way data. Agron J. 2002;93(5):1111-18. https://doi.org/10.2134/agronj2001.9351111x
  17. Taghouti M, Bennani S, Gaboun F, Rochdi A. New insights for combining grain yield and quality gains in modern durum wheat varieties across various environmental conditions. Plant Cell Biotechnol Mol Biol. 2019;20(15-16):700-09. https://www.ikprress.org/index.php/PCBMB/article/view/4719
  18. Bhartiya A, Aditya JP, Kumari V, Kishore N, Purwar JP, Agrawal A et al. GGE biplot and AMMI analysis of yield stability in multi-environment trial of soybean [Glycine max (L.) merrill] genotypes under rainfed condition of North Western Himalayan hills. Indian J Genet Plant Breed. 2018;78(3):342-47. https://doi.org/10.31742/IJGPB.78.3.6
  19. Yue H, Gauch HG, Wei J, Xie J, Chen S, Peng H et al. Genotype by environment interaction analysis for grain yield and yield components of summer maize hybrids across the Huanghuaihai region in China. 2022;1-17. https://doi.org/10.3390/agriculture12050602
  20. Negash K, Tumsa K, Amsalu B, Gebeyehu S. Grouping of environments for testing Navy bean in Ethiopia common bean is one of the grain legume crops grown in Ethiopia and is being. 2017;27(2):111-30. https://www.ajol.info/index.php/ejas/article/download/156185/145803
  21. Baethgen WE, Alley MM. A manual colorimetric procedure for measuring ammonium nitrogen in soil and plant kjeldahl digests. Commun Soil Sci Plant Anal [Internet]. 1989;20(9-10):961-69. https://doi.org/10.1080/00103628909368129
  22. Hugh G, Gauch J. Model selection and validation for yield trials with interaction author ( s ): Hugh G Gauch Jr. Published by: International Biometric Society Stable references Linked references are available on JSTOR for the Biometrics. 1988;44(3):705-15. http://www.jstor.org/stable/2531585 https://doi.org/10.2307/2531585
  23. Yan W, Kang MS, Ma B, Woods S, Cornelius PL. GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci. 2007 [cited 2010 May 25];47(2):643-53. Available from: https://onlinelibrary.wiley.com/doi/full/10.2135/cropsci2006.06.0374 https://doi.org/10.2135/cropsci2006.06.0374
  24. Yan W, Hunt LA, Sheng Q, Szlavnics Z. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci. 2000;40(3):597-605. https://doi.org/10.2135/cropsci2000.403597x
  25. Yan W, Kang MS. GGE biplot analysis?: A graphical tool for breeders, geneticists and agronomists. GGE Biplot Anal. 2002. https://www.taylorfrancis.com/books/mono/10.1201/9781420040371/gge-biplot-analysis-weikai-yan-manjit-kang https://doi.org/10.1201/9781420040371
  26. Yan W, Tinker NA. Biplot analysis of multi-environment trial data: Principles and applications. Can J Plant Sci. 2006;86(3):623-45. https://doi.org/10.4141/P05-169
  27. Eberhart SA, Russell WA. Stability parameters for comparing varieties 1. Crop Sci. 1966;6(1):36-40. https://doi.org/10.2135/cropsci1966.0011183X000600010011x
  28. Jeberson MS, Shashidhar KS, Wani SH, Singh AK, Dar SA. Identification of stable lentil (Lens culinaris Medik) genotypes through GGE biplotand AMMI analysis for North Hill Zone of India. 2019;42(4):467-72. https://doi.org/10.18805/LR-3901
  29. Darai R, Dhakal KH, Pandey MP. Effect of genotype by environment interaction (GEI), correlation and GGE biplot analysis for high concentration of grain Iron and Zinc biofortified lentils and their agronomic traits in multi-environment domains of Nepal. (IJOEAR). 2020;6(6):ISSN:2454-1850. https://doi.org/10.5281/zenodo.3931172
  30. Gupta S, Das S, Dikshit HK, Mishra GP, Aski MS, Bansal R et al. Genotype by environment interaction effect on grain iron and zinc concentration of Indian and mediterranean lentil genotypes. Agronomy. 2021;11(9). https://doi.org/10.3390/agronomy11091761
  31. Yan W, Rajcan I. Biplot analysis of test sites and trait relations of soybean in Ontario. Crop Sci. 2002;42(1):11-20. https://doi.org/10.2135/cropsci2002.1100
  32. Nleya T, Vandenberg A, Araganosa G, Warkentin T, Muehlbauer FJ, Slinkard AE. Produce quality of food legumes: Genotype (G), environment (E) and (GxE) considerations. 2000;173-82. https://doi.org/10.1007/978-94-011-4385-1_15
  33. Thavarajah D, Thavarajah P, Wejesuriya A, Rutzke M, Glahn RP, Combs GF et al. The potential of lentil (Lens culinaris L.) as a whole food for increased selenium, iron and zinc intake: Preliminary results from a 3 year study. Euphytica. 2011;180(1):123-28. https://doi.org/10.1007/s10681-011-0365-6%0AThe
  34. Kumar H, Dikshit HK, Singh AM, Singh D, Kumari J, Singh A et al. Characterization of elite lentil genotypes for seed iron and zinc concentration and genotype × environment interaction studies. Indian J Genet Plant Breed. 2013;73(2):169-76. https://doi.org/10.5958/j.0975-6906.73.2.024
  35. Singh D, Singh CK, Taunk J, Tomar RSS, Chaturvedi AK, Gaikwad K et al. Transcriptome analysis of lentil (Lens culinaris Medikus) in response to seedling drought stress. BMC Genomics. 2017;18(1):1-20. https://doi.org/10.1186/s12864-017-3596-7
  36. Sabaghnia N, Sabaghpour SH, Dehghani H. The use of an AMMI model and its parameters to analyse yield stability in multi-environment trials. J Agric Sci. 2008;146(5):571-81. https://doi.org/10.1017/S0021859608007831
  37. Joshi AK, Crossa J, Arun B, Chand R, Trethowan R, Vargas M et al. Genotype × environment interaction for zinc and iron concentration of wheat grain in eastern Gangetic plains of India. F Crop Res. 2010;116(3):268-77. http://dx.doi.org/10.1016/j.fcr.2010.01.004
  38. Phuke RM, Anuradha K, Radhika K, Jabeen F, Anuradha G, Ramesh T et al. 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. 2017;8:712. https://doi.org/10.3389/fpls.2017.00712
  39. Singhal T, Satyavathi CT, Kumar A, Sankar SM, Singh SP, Bharadwaj C et al. Genotype×environment interaction and genetic association of grain iron and zinc content with other agronomic traits in RIL population of pearl millet. Crop Pasture Sci [Internet]. 2018;69(11):1092-102. https://www.publish.csiro.au/cp/CP18306 https://doi.org/10.1071/CP18306
  40. Desai K, Tank CJ, Gami RA, Patel AM. G X E Interaction and stability analysis in chickpea (Cicer arietinum L.). Int J Agric Environ Biotechnol. 2016;9(4):479. https://doi.org/10.5958/2230-732X.2016.00063.2
  41. Bänziger M, Long J. The potential for increasing the iron and zinc density of maize through plant-breeding. 2000; 10.1177/156482650002100410 https://doi.org/10.1177/156482650002100410
  42. Phuke RM, Anuradha K, Radhika K, Jabeen F, Anuradha G, Ramesh T et al. 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. 2017 [cited 2010 Jun 15];8:712. https://doi.org/10.3389/FPLS.2017.00712/BIBTEX
  43. Oladosu Y, Rafii MY, Abdullah N, Magaji U, Miah G, Hussin G et al. Genotype × environment interaction and stability analyses of yield and yield components of established and mutant rice genotypes tested in multiple locations in Malaysia*. 2017;67(7):590-606. http://dx.doi.org/101080/0906471020171321138.
  44. Dehghani H, Ebadi A, Yousefi A. Biplot analysis of genotype by environment interaction for barley yield in Iran. Agron J. 2006;98(2):388-93. https://doi.org/10.2134/agronj2004.0310
  45. Benbrahim N, Mona T, Baggar A, Rachid M, Joseph MM, Fatima G. Grain yield stability of lentil released varieties in moroccan climatic contrasted environments using AMMI-GGE-biplot analysis. 2021;22:551-66. https://www.ikprress.org/index.php/PCBMB/article/view/7353
  46. Choukri H, Hejjaoui K, El-Baouchi A, El haddad N, Smouni A, Maalouf F et al. Heat and drought stress impact on phenology, grain yield and nutritional quality of lentil (Lens culinaris Medikus). Front Nutr. 2020;7(November):1-14. https://doi.org/10.3389/fnut.2020.596307
  47. El haddad N, Rajendran K, Smouni A, Es-Safi NE, Benbrahim N, Mentag R et al. Screening the FIGS set of lentil (Lens culinaris Medikus) germplasm for tolerance to terminal heat and combined drought-heat stress. Agronomy. 2020;10(7):1-27. https://doi.org/10.3390/agronomy10071036
  48. Taghouti M, Gaboun F, Nsarellah N, Rhrib R, El-Haila M, Kamar M et al. Genotype x environment interaction for quality traits in durum wheat cultivars adapted to different environments. African J Biotechnol. 2010;9(21):3054-62. https://doi.org/10.5897/AJB2010.000-3142
  49. Asbai Z, Zain F. Evaluation de différents génotypes de Vicia faba L. en condition de stress hydrique. 2022;212(0).
  50. Mukendi RT, Kayenga AL, Baboy LL, Bugeme DM, Kalonji AM, Munyuli TM. Genotype-environment interactions and yield stability of cowpea (Vigna Unguiculata L. Walp) in Lomami Province, Central Part of Democratic Republic of Congo. Int J Sustain Agric Res. 2019;6(1):33-46. https://doi.org/10.18488/journal.70.2019.61.33.46
  51. Singh A, Sharma V, Dikshit HK, Aski M, Kumar H, Thirunavukkarasu N et al. Association mapping unveils favorable alleles for grain iron and zinc concentrations in lentil (Lens culinaris subsp. culinaris). PLoS One. 2017;12(11):1-25. https://doi.org/10.1371/journal.pone.0188296
  52. Dehghani H, Sabaghpour SH, Ebadi A. Study of genotype × environment interaction for chickpea yield in Iran. Agron J. 2010;102(1):1-8. https://doi.org/10.2134/AGRONJ2009.0156
  53. Kaya Y, Akçura M, Taner S. GGE-biplot analysis of multi-environment yield trials in bread wheat. 2006 [cited 2010 Jun 11]; Available from: https://journals.tubitak.gov.tr/agriculture/vol30/iss5/3
  54. Shah SH, Shah SM, Khan MI, Ahmed M, Hussain I, Eskridge KM. Nonparametric methods in combined heteroscedastic experiments for assessing stability of wheat genotypes in Pakistan nonparametric methods in combined heteroscedastic experiments for assessing. 2009;(February 2015). http://142.54.178.187:9060/xmlui/handle/123456789/17494
  55. Baggar A, Safi A, Gaboun F, Taghouti M, Benbrahim N. Identification of stable lentil genotypes through genotype by environment interactions on yield potential in Morocco. Plant Sci Today. 2022;x(x). https://horizonepublishing.com/journals/index.php/PST/article/view/1814 https://doi.org/10.14719/pst.1814
  56. Yan W, Tinker NA. Biplot analysis of multi-environment trial data: Principles and applications. Can J Plant Sci. 2006; https://doi.org/10.4141/P05-169

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