Free Standard AU & NZ Shipping For All Book Orders Over $80!
Register      Login
Crop and Pasture Science Crop and Pasture Science Society
Plant sciences, sustainable farming systems and food quality
RESEARCH ARTICLE

Genotype × environment interaction and genetic association of grain iron and zinc content with other agronomic traits in RIL population of pearl millet

Tripti Singhal A B , C. Tara Satyavathi C F , Aruna Kumar B , S. Mukesh Sankar A , S. P. Singh A , C. Bharadwaj A , J. Aravind D , N. Anuradha E , M. C. Meena A and Nirupama Singh A
+ Author Affiliations
- Author Affiliations

A ICAR—Indian Agriculture Research Institute, Pusa, New Delhi 110012, India.

B Amity Institute of Biotechnology, Amity University Campus, Sector 125, Noida 201303, Uttar Pradesh, India.

C ICAR—All India Coordinated Research Project on Pearl Millet, Jodhpur 342 304, Rajasthan, India.

D ICAR—National Bureau of Plant Genetic Resources, Pusa, New Delhi 110012, India.

E Acharya NG. Ranga Agricultural University, Vizianagaram 535003, Andhra Pradesh, India.

F Corresponding author. Email: csatyavathi@gmail.com

Crop and Pasture Science 69(11) 1092-1102 https://doi.org/10.1071/CP18306
Submitted: 14 June 2018  Accepted: 29 September 2018   Published: 12 November 2018

Abstract

Biofortification of lines of pearl millet (Pennisetum glaucum (L.) R.Br.) with increased iron (Fe) and zinc (Zn) will have great impact because pearl millet is an indispensable component of food and nutritional security of inhabitants of arid and semi-arid regions. The aim of the present study was to assess the stability of Fe and Zn content in recombinant inbred lines (RILs) developed for grain Fe and Zn content, and to use these lines in developing micronutrient-rich pearl millet hybrids. A mapping population consisting of 210 RILs along, with parents and checks, was assessed in three consecutive years (2014–16) under rainfed conditions at the same experimental location in an alpha design with two repetitions. Significant differences were observed in genotype, environment and genotype × environment interaction mean squares for all variables, particularly grain micronutrients. The first two principal components of an interaction principal component analysis cumulatively explained 100% of the total variation; respective contributions of the first and second components were 64.0% and 36.0% for Fe, and 58.1% and 41.9% for Zn. A positive and moderately high correlation (0.696**) between Fe and Zn contents suggests good prospects of simultaneous improvement for both micronutrients. Among the 210 RILs, RIL 69, RIL 186, RIL 191, RIL 149 and RIL 45 were found to be more stable with higher mean micronutrient content, additive main effects and multiplicative interaction stability value (ASV) and genotype selection index (GSI) under rainfed condition. These RILs are promising and can be tested further for their combining ability for yield as well as grain micronutrient content for developing superior biofortified, heterotic pearl millet hybrids.

Additional keywords: AMMI, GGE, malnutrition, stability.


References

Allard RW, Bradshaw AD (1964) Implications of genotype–environmental interactions in applied plant breeding 1. Crop Science 4, 503–508.
Implications of genotype–environmental interactions in applied plant breeding 1.Crossref | GoogleScholarGoogle Scholar |

Anuradha N, Satyavathi CT, Meena MC, Sankar SM, Bharadwaj C, Bhat J, Singh O, Singh SP (2017) Evaluation of pearl millet [Pennisetum glaucum (L.) R. Br.] for grain iron and zinc content in different agro climatic zones of India. Indian Journal of Genetics and Plant Breeding 77, 65–73.
Evaluation of pearl millet [Pennisetum glaucum (L.) R. Br.] for grain iron and zinc content in different agro climatic zones of India.Crossref | GoogleScholarGoogle Scholar |

Anuradha N, Satyavathi CT, Bharadwaj C, Sankar M, Singh SP, Pathy TL (2018) Pearl millet genetic variability for grain yield and micronutrients in the arid zone of India. Journal of Pharmacognosy and Phytochemistry 7, 875–878.

Arshadi A, Karami E, Sartip A, Zare M, Rezabakhsh P (2018) Genotypes performance in relation to drought tolerance in barley using multi-environment trials. Agronomy Research 16, 5–21.

Ashok Kumar A, Anuradha K, Ramaiah B, Grando S, Frederick H, Rattunde W, Virk P, Pfeiffer WH (2015) Recent advances in sorghum biofortification research. Plant Breeding Reviews 39, 89–124.

Bänziger M, Long J (2000) The potential for increasing the iron and zinc density of maize through plant-breeding. Food and Nutrition Bulletin 21, 397–400.
The potential for increasing the iron and zinc density of maize through plant-breeding.Crossref | GoogleScholarGoogle Scholar |

Bashir EM, Ali AM, Ali AM, Ismail MI, Parzies HK, Haussmann BI (2014) Patterns of pearl millet genotype-by-environment interaction for yield performance and grain iron (Fe) and zinc (Zn) concentrations in Sudan. Field Crops Research 166, 82–91.
Patterns of pearl millet genotype-by-environment interaction for yield performance and grain iron (Fe) and zinc (Zn) concentrations in Sudan.Crossref | GoogleScholarGoogle Scholar |

Cakmak I, Kutman UB (2018) Agronomic biofortification of cereals with zinc. a review. European Journal of Soil Science 69, 172–180.
Agronomic biofortification of cereals with zinc. a review.Crossref | GoogleScholarGoogle Scholar |

Darai R, Sarker A, Sah RP, Pokhrel K, Chaudhary R (2017) AMMI biplot analysis for genotype × environment interaction on yield trait of high Fe content lentil genotypes in Terai and Mid-Hill environment of Nepal. Annals of Agricultural and Crop Sciences 2, 1026

de Leon N, Jannink JL, Edwards JW, Kaeppler SM (2016) Introduction to a special issue on genotype by environment interaction. Crop Science 56, 2081–2089.
Introduction to a special issue on genotype by environment interaction.Crossref | GoogleScholarGoogle Scholar |

Dhyan S, Chhonkar PK, Dwivedi BS (2005) ‘Manual on soil, plant and water analysis.’ (Westville Publishing House: Delhi) 10.5539/jas.v3n2p97

Ebdon JS, Gauch HG (2002a) Additive main effect and multiplicative interaction analysis of national turfgrass performance trials I. Interpretation of genotype × environment interaction. Crop Science 42, 489–496.
Additive main effect and multiplicative interaction analysis of national turfgrass performance trials I. Interpretation of genotype × environment interaction.Crossref | GoogleScholarGoogle Scholar |

Ebdon JS, Gauch HG (2002b) Additive main effect and multiplicative interaction analysis of national turfgrass performance trials II. Cultivar recommendations. Crop Science 42, 497–506.
Additive main effect and multiplicative interaction analysis of national turfgrass performance trials II. Cultivar recommendations.Crossref | GoogleScholarGoogle Scholar |

FAO (2015) The State of Food Insecurity in the World. Food and Agriculture Organization of United Nations report. FAO, Rome. http://www.fao.org/3/a-i4646e.pdf

Feil B, Moser SB, Jampatong S, Stamp P (2005) Mineral composition of the grains of tropical maize varieties as affected by pre-anthesis drought and rate of nitrogen fertilization. Crop Science 45, 516–523.
Mineral composition of the grains of tropical maize varieties as affected by pre-anthesis drought and rate of nitrogen fertilization.Crossref | GoogleScholarGoogle Scholar |

Gauch HG (1992) ‘Statistical analysis of regional yield trials: AMMI analysis of factorial designs.’ (Elsevier: Amsterdam) 10.1016/0308-521x(96)86769-2

Gauch HG (2013) A simple protocol for AMMI analysis of yield trials. Crop Science 53, 1860–1869.
A simple protocol for AMMI analysis of yield trials.Crossref | GoogleScholarGoogle Scholar |

Gauch HG, Zobel RW (1996) AMMI analysis of yield trials. In ‘Genotype-by-environment interaction’. (Eds MS Kang, HG Gauch) pp. 85–122. (CRC Press: Boca Raton, FL, USA) 10.1590/S0103-90162013000100005

Gollob HF (1968) A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33, 73–115.
A statistical model which combines features of factor analytic and analysis of variance techniques.Crossref | GoogleScholarGoogle Scholar |

Gomez-Becerra HF, Yazici A, Ozturk L, Budak H, Peleg Z, Morgounov A, Fahima T, Saranga Y, Cakmak I (2010) Genetic variation and environmental stability of grain mineral nutrient concentrations in Triticum dicoccoides under five environments. Euphytica 171, 39–52.
Genetic variation and environmental stability of grain mineral nutrient concentrations in Triticum dicoccoides under five environments.Crossref | GoogleScholarGoogle Scholar |

Gregorio GB (2002) Progress in breeding for trace minerals in staple crops. The Journal of Nutrition 132, 500S–502S.
Progress in breeding for trace minerals in staple crops.Crossref | GoogleScholarGoogle Scholar |

Gregory MA, Silva BS, Narlon C, Gill DP, McGowan CL, Liu-Ambrose T, Shoemaker JK, Hachinski V, Holmes J, Petrella RJ (2017) Combined dual-task gait training and aerobic exercise to improve cognition, mobility, and vascular health in community-dwelling older adults at risk for future cognitive decline 1. Journal of Alzheimer’s Disease 57, 747–763.
Combined dual-task gait training and aerobic exercise to improve cognition, mobility, and vascular health in community-dwelling older adults at risk for future cognitive decline 1.Crossref | GoogleScholarGoogle Scholar |

Hassani M, Heidari B, Dadkhodaie A, Stevanato P (2018) Genotype by environment interaction components underlying variations in root, sugar and white sugar yield in sugar beet (Beta vulgaris L.). Euphytica 214, 79
Genotype by environment interaction components underlying variations in root, sugar and white sugar yield in sugar beet (Beta vulgaris L.).Crossref | GoogleScholarGoogle Scholar |

Jalaja N, Maheshwari P, Naidu KR, Kavi Kishor PB (2016) In vitro regeneration and optimization of conditions for transformation methods in pearl millet, Pennisetum glaucum (L.). International Journal of Clinical and Biological Sciences 1, 34–52.

Kanatti A, Rai KN, Radhika K, Govindaraj M, Sahrawat KL, Rao AS (2014) Grain iron and zinc density in pearl millet: combining ability, heterosis and association with grain yield and grain size. SpringerPlus 3, 763
Grain iron and zinc density in pearl millet: combining ability, heterosis and association with grain yield and grain size.Crossref | GoogleScholarGoogle Scholar |

Karimizadeh R, Mohammadi M, Shefazadeh MK, Mahmoodi AA, Rostami B, Karimpour F (2012) Relationship among and repeatability of ten stability indices for grain yield of food lentil genotypes in Iran. Turkish Journal of Field Crops 17, 51–61.

Kumar S, Hash CT, Thirunavukkarasu N, Singh G, Rajaram V, Rathore A, Senapathy S, Mahendrakar MD, Yadav RS, Srivastava RK (2016) Mapping quantitative trait loci controlling high iron and zinc content in self and open pollinated grains of pearl millet [Pennisetum glaucum (L.) R. Br.]. Frontiers of Plant Science 7, 1636
Mapping quantitative trait loci controlling high iron and zinc content in self and open pollinated grains of pearl millet [Pennisetum glaucum (L.) R. Br.].Crossref | GoogleScholarGoogle Scholar |

Kumssa DB, Joy EJ, Ander EL, Watts MJ, Young SD, Walker S, Broadley MR (2015) Dietary calcium and zinc deficiency risks are decreasing but remain prevalent. Scientific Reports 5, 10974
Dietary calcium and zinc deficiency risks are decreasing but remain prevalent.Crossref | GoogleScholarGoogle Scholar |

Long JK, Bänziger M, Smith ME (2004) Diallel analysis of grain iron and zinc density in southern African-adapted maize inbreds. Crop Science 44, 2019–2026.
Diallel analysis of grain iron and zinc density in southern African-adapted maize inbreds.Crossref | GoogleScholarGoogle Scholar |

Mallikarjuna MG, Thirunavukkarasu N, Hossain F, Bhat JS, Jha SK, Rathore A, Agrawal PK, Pattanayak A, Reddy SS, Gularia SK, Singh AM (2015) Stability performance of inductively coupled plasma mass spectrometry-phenotyped kernel minerals concentration and grain yield in maize in different agro-climatic zones. PLoS One 10, e0139067
Stability performance of inductively coupled plasma mass spectrometry-phenotyped kernel minerals concentration and grain yield in maize in different agro-climatic zones.Crossref | GoogleScholarGoogle Scholar |

Manwaring HR, Bligh HF, Yadav R (2016) The challenges and opportunities associated with biofortification of pearl millet (Pennisetum glaucum) with elevated levels of grain iron and zinc. Frontiers of Plant Science 7, 1944
The challenges and opportunities associated with biofortification of pearl millet (Pennisetum glaucum) with elevated levels of grain iron and zinc.Crossref | GoogleScholarGoogle Scholar |

Mohammadi R, Abdulahi A, Haghparast R, Armion M (2007) Interpreting genotype× environment interactions for durum wheat grain yields using nonparametric methods. Euphytica 157, 239–251.
Interpreting genotype× environment interactions for durum wheat grain yields using nonparametric methods.Crossref | GoogleScholarGoogle Scholar |

Morgounov A, Gómez-Becerra HF, Abugalieva A, Dzhunusova M, Yessimbekova M, Muminjanov H, Zelenskiy Y, Ozturk L, Cakmak I (2007) Iron and zinc grain density in common wheat grown in Central Asia. Euphytica 155, 193–203.
Iron and zinc grain density in common wheat grown in Central Asia.Crossref | GoogleScholarGoogle Scholar |

Ndhlela T, Herselman L, Magorokosho C, Setimela P, Mutimaamba C, Labuschagne M (2014) Genotype × environment interaction of maize grain yield using AMMI biplots. Crop Science 54, 1992–1999.
Genotype × environment interaction of maize grain yield using AMMI biplots.Crossref | GoogleScholarGoogle Scholar |

Oikeh SO, Menkir A, Maziya‐Dixon B, Welch R, Glahn RP (2003a) Genotypic differences in concentration and bioavailability of kernel‐iron in tropical maize varieties grown under field conditions. Journal of Plant Nutrition 26, 2307–2319.
Genotypic differences in concentration and bioavailability of kernel‐iron in tropical maize varieties grown under field conditions.Crossref | GoogleScholarGoogle Scholar |

Oikeh SO, Menkir A, Maziya-Dixon B, Welch R, Glahn RP (2003b) Assessment of concentrations of iron and zinc and bioavailable iron in grains of early-maturing tropical maize varieties. Journal of Agricultural and Food Chemistry 51, 3688–3694.
Assessment of concentrations of iron and zinc and bioavailable iron in grains of early-maturing tropical maize varieties.Crossref | GoogleScholarGoogle Scholar |

Oikeh SO, Menkir A, Maziya-Dixon B, Welch RM, Glahn RP, Gauch G (2004) Environmental stability of iron and zinc concentrations in grain of elite early-maturing tropical maize genotypes grown under field conditions. The Journal of Agricultural Science 142, 543–551.
Environmental stability of iron and zinc concentrations in grain of elite early-maturing tropical maize genotypes grown under field conditions.Crossref | GoogleScholarGoogle Scholar |

Ortiz-Monasterio JI, Palacios-Rojas N, Meng E, Pixley K, Trethowan R, Pena RJ (2007) Enhancing the mineral and vitamin content of wheat and maize through plant breeding. Journal of Cereal Science 46, 293–307.
Enhancing the mineral and vitamin content of wheat and maize through plant breeding.Crossref | GoogleScholarGoogle Scholar |

Paltridge NG, Palmer LJ, Milham PJ, Guild GE, Stangoulis JC (2012) Energy-dispersive X-ray fluorescence analysis of zinc and iron concentration in rice and pearl millet grain. Plant and Soil 361, 251–260.
Energy-dispersive X-ray fluorescence analysis of zinc and iron concentration in rice and pearl millet grain.Crossref | GoogleScholarGoogle Scholar |

Phuke RM, Anuradha K, Radhika K, Jabeen F, Anuradha G, Ramesh T, Hariprasanna K, Mehtre SP, Deshpande SP, Anil G, Das RR (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). Frontiers of Plant Science 8, 712
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).Crossref | GoogleScholarGoogle Scholar |

Prasanna BM, Mazumdar S, Chakraborti M, Hossain F, Manjaiah KM, Agrawal PK, Guleria SK, Gupta HS (2011) Genetic variability and genotype × environment interactions for kernel iron and zinc concentrations in maize (Zea mays) genotypes. Indian Journal of Agricultural Sciences 81, 704–711.

Puranik S, Kam J, Sahu PP, Yadav R, Srivastava RK, Ojulong H, Yadav R (2017) Harnessing finger millet to combat calcium deficiency in humans: Challenges and prospects. Frontiers of Plant Science 8, 1311
Harnessing finger millet to combat calcium deficiency in humans: Challenges and prospects.Crossref | GoogleScholarGoogle Scholar |

Purchase JL, Hatting H, Van Deventer CS (2000) Genotype × environment interaction of winter wheat (Triticum aestivum L.) in South Africa: II. Stability analysis of yield performance. South African Journal of Plant and Soil 17, 101–107.
Genotype × environment interaction of winter wheat (Triticum aestivum L.) in South Africa: II. Stability analysis of yield performance.Crossref | GoogleScholarGoogle Scholar |

Rai KN, Yadav OP, Rajpurohit BS, Patil HT, Govindaraj M, Khairwal IS, Rao AS (2013) Breeding pearl millet cultivars for high iron density with zinc density as an associated trait. Journal of SAT Agricultural Research 11, 1–7.

Rao PP, Birthal PS, Reddy BV, Rai KN, Ramesh S (2006) Diagnostics of sorghum and pearl millet grains-based nutrition in India. International Sorghum and Millets Newsletter 47, 93–96.

Sabaghnia N, Sabaghpour SH, Dehghani H (2008) The use of an AMMI model and its parameters to analyse yield stability in multi-environment trials. The Journal of Agricultural Science 146, 571–581.
The use of an AMMI model and its parameters to analyse yield stability in multi-environment trials.Crossref | GoogleScholarGoogle Scholar |

Satyavathi CT, Sankar SM, Singh SP, Bhowmick P, Bhat J, Singh O, Anuradha N (2015) Stability analysis of grain iron and zinc content in pearl millet (Pennisetum glaucum (L.) R. Br). International Journal of Tropical Agriculture 33, 1387–1394.

Sumathi P, Govindaraj M, Govintharaj P (2017) Identifying promising pearl millet hybrids using AMMI and clustering models. International Journal of Current Microbiology and Applied Sciences 6, 1348–1359.
Identifying promising pearl millet hybrids using AMMI and clustering models.Crossref | GoogleScholarGoogle Scholar |

Suwarto , Nasrullah (2011) Genotype × environment interaction for iron concentration of rice in central Java of Indonesia. Rice Science 18, 75–78.
Genotype × environment interaction for iron concentration of rice in central Java of Indonesia.Crossref | GoogleScholarGoogle Scholar |

Tako E, Reed SM, Budiman J, Hart JJ, Glahn RP (2015) Higher iron pearl millet (Pennisetum glaucum L.) provides more absorbable iron that is limited by increased polyphenolic content. Nutrition Journal 14, 11
Higher iron pearl millet (Pennisetum glaucum L.) provides more absorbable iron that is limited by increased polyphenolic content.Crossref | GoogleScholarGoogle Scholar |

WHO (2002) ‘The World Health Report.’ (World Health Organization: Geneva) 10.3389/fpls.2017.00412

Yan W, Kang MS (2003) ‘GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists.’ (CRC Press: Boca Raton, FL, USA) 10.2135/cropsci2006.06.0374

Zobel RW, Wright MJ, Gauch HG (1988) Statistical analysis of a yield trial. Agronomy Journal 80, 388–393.
Statistical analysis of a yield trial.Crossref | GoogleScholarGoogle Scholar |