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Genotype × Environment Interaction Effects for Identifying Stable Genotypes with High Iron and Zinc Content in Mung Bean (Vigna radiata)

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Iron (Fe) and zinc (Zn) are crucial micronutrients for growth and development. Mung bean, a key source of plant-based protein, is largely cultivated in South Asia. Enhancing micronutrient levels through biofortification offers a promising strategy to combat deficiencies. This study evaluated the genotype × environment interaction (G × E) and stability of Fe, Zn, and hundred-seed weight (HSW) in 33 mung bean genotypes across three locations in Tamil Nadu. Fe and Zn levels were estimated using atomic absorption spectroscopy (AAS). The combined analysis of variance revealed a significant (p < 0.05) G × E interaction for Fe, Zn, and HSW. (Additive Main Effects and Multiplicative Interaction) AMMI and (Genotype and Genotype × Environment) GGE. Biplot analyses identified stable genotypes with superior micronutrient profiles. COGG 22–03, COGG 23–021, and VBN 7 exhibited enhanced stability and micronutrient content. Stability indices, including AMMI stability index (ASI) and multi-trait stability index (MTSI), identified VGG 20–157, VBN 7, and COGG 23–006 as potential lines for biofortified mung bean breeding programs.

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No datasets were generated or analyzed during the current study.

References

  1. Ajay B, Aravind J, Fiyaz RA (2019) Ammistability: R package for ranking genotypes based on stability parameters derived from AMMI model. Indian J Genet Plant Breed 79(02):460–466

    Google Scholar 

  2. Asfaw A, Gurum F, Alemayehu F, Rezene Y (2012) Analysis of multi-environment grain yield trials in mung bean Vigna radiata (L.) Wilczek based on GGE bipot in Southern Ethiopia. J Agric Sci Technol 14(2):389–398

    Google Scholar 

  3. Aski MS, Rai N, Reddy VRP, Gayacharan DHK, Mishra GP et al (2021) Assessment of root phenotypes in mungbean mini-core collection (MMC) from the World Vegetable Center (AVRDC) Taiwan. PLoS ONE 16(3):e0247810

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Bautista-Diaz J, Cruz-Alvarez O, Hernández-Rodríguez OA, Sánchez-Chávez E, Jacobo-Cuellar JL, Preciado-Rangel P et al (2021) Zinc sulphate or zinc nanoparticle applications to leaves of green beans. Folia Hortic 33(2):365–375

    Article  Google Scholar 

  5. Blair MW, Izquierdo P, Astudillo C, Grusak MA (2013) A legume biofortification quandary: variability and genetic control of seed coat micronutrient accumulation in common beans. Front Plant Sci 4:275

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Cornelius P, Crossa J, Seyedsadr M (1996) Statistical tests and estimators of multiplicative models for genotype-by-environment interaction. Genotype-by-environment interaction CRC Press, Boca Raton, FL, pp 199–234

    Google Scholar 

  7. Crossa J, Cornelius PL (1997) Sites regression and shifted multiplicative model clustering of cultivar trial sites under heterogeneity of error variances. Crop Sci 37(2):406–415

    Article  Google Scholar 

  8. Crossa J, Cornelius PL, Yan W (2002) Biplots of linear-bilinear models for studying crossover genotype× environment interaction. Crop Sci 42(2):619–633

    Google Scholar 

  9. Dahiya P, Linnemann A, Van Boekel M, Khetarpaul N, Grewal R, Nout M (2015) Mung bean: technological and nutritional potential. Crit Rev Food Sci Nutr 55(5):670–688

    Article  PubMed  CAS  Google Scholar 

  10. Dhaliwal SS, Sharma V, Shukla AK (2022) Impact of micronutrients in mitigation of abiotic stresses in soils and plants—a progressive step toward crop security and nutritional quality. Adv Agron 173:1–78

    Article  Google Scholar 

  11. Ebdon J, Gauch JH (2002) Additive main effect and multiplicative interaction analysis of national turfgrass performance trials: I. interpretation of genotype× environment interaction. Crop Sci 42(2):489–496

    Google Scholar 

  12. Erdemcı İ (2018) Investigation of genotype× environment interaction in chickpea genotypes using AMMI and GGE biplot analysis. Turk J Field Crops 23(1):20–26

    Article  Google Scholar 

  13. Garg M, Sharma N, Sharma S, Kapoor P, Kumar A, Chunduri V et al (2018) Biofortified crops generated by breeding, agronomy, and transgenic approaches are improving lives of millions of people around the world. Front Nutr 5:12

    Article  PubMed  PubMed Central  Google Scholar 

  14. Gauch JHG, Zobel RW (1997) Identifying mega-environments and targeting genotypes. Crop Sci 37(2):311–326

    Article  Google Scholar 

  15. Gebeyehu C, Bulti T, Dagnachew L, Kebede D (2019) Additive main effect and multiplicative interactions (AMMI) and regression analysis in sorghum [Sorghum bicolor (L). Moench] varieties. J Appl Biosci 136:13877–13886

    Article  Google Scholar 

  16. Gupta S, Das S, Dikshit HK, Mishra GP, Aski MS, Bansal R et al (2021) Genotype by environment interaction effect on grain iron and zinc concentration of Indian and mediterranean lentil genotypes. Agron 11(9):1761

    Article  CAS  Google Scholar 

  17. Hou D, Yousaf L, Xue Y, Hu J, Wu J, Hu X et al (2019) Mung bean (Vigna radiata L.): Bioactive polyphenols, polysaccharides, peptides, and health benefits. Nutrients 11(6):1238

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Jeberson MS, Shashidhar KS, Wani SH, Singh AK, Dar SA (2019) Identification of stable lentil (Lens culinaris Medik) genotypes through GGE biplotand AMMI analysis for North Hill Zone of India. Legume Res 42(4):467–472

    Google Scholar 

  19. Kang MS (1993) Simultaneous selection for yield and stability in crop performance trials: consequences for growers. Agron J 85(3):754–757

    Article  Google Scholar 

  20. Lambrides C, Godwin I (2007) Mungbean. Pulses, sugar and tuber crops. Springer, pp 69–90

    Chapter  Google Scholar 

  21. Madhan A, Arumugam Y, Dharmalingam K, Chinnusamy M, Narayanan MB, Ramasamy J et al (2025) Fostering nutritional wealth: unveiling iron and zinc enhancement in pulse crops—mechanisms, achievements and future frontiers. J Plant Nutr 48(5):751–772

    Article  CAS  Google Scholar 

  22. Madhusudhana R, Hariprasanna K, Aruna C, Sajjanar GM, Hanamaratti N, Sameera S et al (2023) Genetic variability, G× E interaction and stability for iron and zinc content in sorghum grains in advanced breeding lines. J Cereal Sci 110:103653

    Article  CAS  Google Scholar 

  23. Malosetti M, Ribaut J-M, van Eeuwijk FA (2013) The statistical analysis of multi-environment data: modeling genotype-by-environment interaction and its genetic basis. Front Physiol 4:44

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Mekonnen TW, Mekbib F, Amsalu B, Gedil M, Labuschagne M (2022) Genotype by environment interaction and grain yield stability of drought tolerant cowpea landraces in Ethiopia. Euphytica 218(5):57

    Article  CAS  Google Scholar 

  25. Nair RM, Thavarajah P, Giri RR, Ledesma D, Yang R-Y, Hanson P et al (2015) Mineral and phenolic concentrations of mungbean [Vigna radiata (L.) R. Wilczek var. radiata] grown in semi-arid tropical India. J Food Compos Anal 39:23–32

    Article  CAS  Google Scholar 

  26. Nair RM, Yang RY, Easdown WJ, Thavarajah D, Thavarajah P, Hughes JdA et al (2013) Biofortification of mungbean (Vigna radiata) as a whole food to enhance human health. J Sci Food Agric 93(8):1805–1813

    Article  PubMed  CAS  Google Scholar 

  27. Olivoto T, Lúcio AD, da Silva JA, Marchioro VS, de Souza VQ, Jost E (2019) Mean performance and stability in multi-environment trials I: combining features of AMMI and BLUP techniques. Agron j 111(6):2949–2960

    Article  Google Scholar 

  28. Olson R, Gavin-Smith B, Ferraboschi C, Kraemer K (2021) Food fortification: the advantages, disadvantages and lessons from sight and life programs. Nutrients 13(4):1118

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Pandey MK, Roorkiwal M, Singh VK, Ramalingam A, Kudapa H, Thudi M et al (2016) Emerging genomic tools for legume breeding: current status and future prospects. Front Plant Sci 7:455

    Article  PubMed  PubMed Central  Google Scholar 

  30. Pfeiffer WH, McClafferty B (2007) HarvestPlus: breeding crops for better nutrition. Crop Sci 47:S-88-S−105

    Article  Google Scholar 

  31. Piepho H-P (1994) Best linear unbiased prediction (BLUP) for regional yield trials: a comparison to additive main effects and multiplicative interaction (AMMI) analysis. Theor Appl Genet 89:647–654

    Article  PubMed  CAS  Google Scholar 

  32. Pratap A, Gupta S, Rathore M, Basavaraja T, Singh CM, Prajapati U et al (2021) Mungbean. The beans and the peas: From Orphan to Mainstream Crops. Elsevier, pp 1–32

    Google Scholar 

  33. Rana C, Sharma A, Sharma K, Mittal P, Sinha BN, Sharma VK et al (2021) Stability analysis of garden pea (Pisum sativum L.) genotypes under North Western Himalayas using joint regression analysis and GGE biplots. Genet Resour Crop Evol 68:999–1010

    Article  Google Scholar 

  34. Rao A, Prabhakaran V (2005) Use of AMMI in simultaneous selection of genotypes for yield and stability. J Indian Soc Agric Stat 59:76–82

    Google Scholar 

  35. Sahrawat K, Kumar GR, Murthy K (2002) Sulfuric acid–selenium digestion for multi-element analysis in a single plant digest. Commun Soil Sci Plant Anal 33(19–20):3757–3765

    Article  CAS  Google Scholar 

  36. Sahrawat K, Rego T, Wani S, Pardhasaradhi G (2008) Stretching soil sampling to watershed: evaluation of soil-test parameters in a semi-arid tropical watershed. Commun Soil Sci Plant Anal 39(19–20):2950–2960

    Article  CAS  Google Scholar 

  37. Singh R, Van Heusden AW, Bala S, Batra K, Visser RG (2018) Stability Analysis in Mungbean (Vigna radiata L.) for Micronutrients (Fe & Zn) and Seed Yield. Int J Curr Microbiol App Sci 7(4):419–423

    Article  Google Scholar 

  38. Singh UM, Sareen P, Sengar RS, Kumar A (2013) Plant ionomics: a newer approach to study mineral transport and its regulation. Acta Physiol Plant 35:2641–2653

    Article  CAS  Google Scholar 

  39. Singh A, Sharma V, Meena J, Gupta S, Dikshit H, Aski M et al (2017) Genetic variability for grain iron and zinc concentration in lentil. Chem Sci Rev Lett 6:1327–1332

    CAS  Google Scholar 

  40. Singh R, Van Heusden AW, Kumar R, Visser RG (2018) Genetic variation and correlation studies between micronutrient (Fe and Zn), protein content and yield attributing traits in mungbean (Vigna radiata L.). Legume Res 41(2):167–174

    Google Scholar 

  41. Singh V, Yadav N, Singh J (2017) Role of genomic tools for mungbean [Vigna radiata (L.) Wilczek] improvement. Legume Res 40(4):601–608

    Google Scholar 

  42. Smith A, Cullis BR, Thompson R (2005) The analysis of crop cultivar breeding and evaluation trials: an overview of current mixed model approaches. J Agric Sci 143(6):449–462

    Article  Google Scholar 

  43. Srivastava J. (2020). Boron toxicity effects on growth, biochemical, nutrient and yield parameters of mung bean [Vigna radiata (L). Wilczek]: Banaras Hindu University Varanasi.

  44. Stevens GA, Finucane MM, De-Regil LM, Paciorek CJ, Flaxman SR, Branca F et al (2013) Global, regional, and national trends in haemoglobin concentration and prevalence of total and severe anaemia in children and pregnant and non-pregnant women for 1995–2011: a systematic analysis of population-representative data. Lancet Glob Health 1(1):e16–e25

    Article  PubMed Central  Google Scholar 

  45. Taunk J, Yadav NR, Yadav R, Kumar R. (2011). Molecular markers for assessment of genetic diversity for zinc content among green gram [Vigna radiata (L.) Wilczek] genotypes. the zinc crops.

  46. Taunk J, Yadav NR, Yadav RC, Kumar R (2012) Genetic diversity among greengram [Vigna radiata (L.) Wilczek] genotypes varying in micronutrient (Fe and Zn) content using RAPD markers. IJBT. 11(1):48–53

    CAS  Google Scholar 

  47. Tiwari A, Shivhare A. (2016). Pulses in India: retrospect and prospects. Published by Director, Govt of India, Ministry of Agri & Farmers Welfare (DAC&FW), Directorate of Pulses Development, VindhyachalBhavan, Bhopal, MP.

  48. Waters BM, Sankaran RP (2011) Moving micronutrients from the soil to the seeds: genes and physiological processes from a biofortification perspective. Plant Sci 180(4):562–574

    Article  PubMed  CAS  Google Scholar 

  49. Welch RM, Graham RD (2004) Breeding for micronutrients in staple food crops from a human nutrition perspective. J Exp Bot 55(396):353–364

    Article  PubMed  CAS  Google Scholar 

  50. Yan W, Kang MS. (2002). GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists: CRC press.

  51. Yan W, Kang MS, Ma B, Woods S, Cornelius PL (2007) GGE biplot vs. AMMI analysis of genotype-by-environment data. Crop Sci 47(2):643–653

    Article  Google Scholar 

  52. Yan WeiKai YW. (2011). GGE biplot vs. AMMI graphs for genotype-by-environment data analysis.

  53. Zafar M, Ahmed S, Munir MK, Zafar N, Saqib M, Sarwar MA et al (2023) Application of zinc, iron and boron enhances productivity and grain biofortification of mungbean. Phyton 92(4):983–999

    Article  Google Scholar 

  54. Zuffo AM, Steiner F, Aguilera JG, Teodoro PE, Teodoro LPR, Busch A (2020) Multi-trait stability index: a tool for simultaneous selection of soya bean genotypes in drought and saline stress. J Agron Crop Sci 206(6):815–822

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Genetics and Plant Breeding, National Pulses Research Centre, Tamil Nadu, Vamban, Pudukkottai, 622 303, India

    Madhan Arunkumar & Arumugam Yuvaraja

  2. Department of Rice, Center for Plant Breeding and Genetics, Tamil Nadu Agricultural University, Tamil Nadu, Coimbatore, 641003, India

    Dharmalingam Kumaresan

  3. Department of Biotechnology, Centre for Plant Molecular Biology and Biotechnology, Tamil Nadu Agricultural University, Tamil Nadu, Coimbatore, 641003, India

    Narayanan Manikanda Boopathi

  4. National Pulses Research Centre, Tamil Nadu Agricultural University, Tamil Nadu, Vamban, 622303, India

    Chinnusamy Menaka

  5. Department of Soil Science and Agricultural Chemistry, Tamil Nadu Agricultural University, Tamil Nadu, Coimbatore, 641003, India

    Ramasamy Jagadeeswaran

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Contributions

MA conducted the experiment, analyzed the data, and wrote the original manuscript. AY designed the experiment. DK, CM, NMB, and RJ provided technical comments during the experiment. DK, NMB, and RJ did supervision during the experimental analysis. AY improved and revised the manuscript. All authors read and approved the final manuscript.

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Correspondence to Arumugam Yuvaraja.

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Arunkumar, M., Yuvaraja, A., Kumaresan, D. et al. Genotype × Environment Interaction Effects for Identifying Stable Genotypes with High Iron and Zinc Content in Mung Bean (Vigna radiata). Agric Res (2025). https://doi.org/10.1007/s40003-025-00907-x

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