Document Type : Original Article

Authors

• Maryam Harati Rad ¹
• Nafiseh Mahdinezhad ²
• Reza Darvishzadeh ³
• Barat Ali Fakheri ⁴
• Mitra Jabbari ⁵
• Sorour Arzangh ⁶

¹ Ph.D student of Plant Breeding, Department of Plant Breeding and Biotechnology, Faculty of Agriculture, University of Zabol, Zabol, Iran

² Associated Professor, Department of Plant Breeding and Biotechnology, Faculty of Agriculture, University of Zabol, Zabol, Iran

³ Professor, Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran

⁴ Professor, Department of Plant Breeding and Biotechnology, Faculty of Agriculture, University of Zabol, Zabol, Iran

⁵ Assistant Professor, Department of Plant Production and Genetic Engineering, Faculty of Agriculture, Higher Education Complex of Saravan. Saravan, Iran

⁶ Ph.D., Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran

Abstract

Introduction
Maize is a fast-growing plant that absorbs a lot of nutrients from the soil, indicating the need for the availability of various nutrients, including micronutrients, during its growth and development. Zinc is one of the most important micronutrient elements for this plant, which has many roles. Zinc deficiency is one of the global problems for grain production, and the genotype of the plant has a major effect on the absorption of zinc from the soil or the use of zinc inside the plant. Investigations have shown that the accumulation of micronutrients in seeds is genetically controlled. Knowledge on genetic diversity, in addition to preserving the genetic reserves of plants, also helps to use them effectively and better in plant breeding programs. Study of genetic diversity is a process that reveal any difference or similarity among species, populations or individuals using special statistical methods and models on molecular or morphological traits.
Materials and methods
In this research, 95 maize inbre lines were assessed under optimum and zinc deficiency conditions using α-lattic design with two replications in Zabol Agriculture and Natural Resources Research Center, during 2 successive crop years (2020 and 2021). The investigated lines were obtained from Razi University of Kermanshah, Khorasan Razavi Agricultural and Natural Resources Research and Education Center and Seed and Plant Improvement Institute. Zinc treatment was applied before the beginning of the reproductive stage at the stage of 4, 6 and 10 leaf stages. Zinc fertilizer was added to the ground along with water in the early hours of the day (due to low air temperature). For each genotype in each experimental unit, five plants were randomly selected and the desired traits were measured. The number of 29 traits, including phenological, morphological and yield-related traits were measured. Combined analysis of variance of the studied traits in both environments was performed using SAS 9.4 software. Stepwise regression was performed using the "olsrr" package based on Pearson's correlation coefficient; calculated using the "corrplot" package in R software. Also, in order to more accurately interpret the results of correlation and stepwise regression, path analysis was performed on the traits entered into the final regression model in PATH 2 software. Hierarchical clustering by Ward's method on standardized data was conducted by "cluster and factoexta" packages. Factor analysis were conducted in R software by means of the "FactoMineR" package.
Results and discussion
The results showed that there is a significant statistical difference among the studied lines in terms of all the investigated traits. Based on step-by-step regression and path analysis, the number of seeds in the cob and the weight of 100 seeds were determined as important traits affecting economic yield under optimum and zinc deficiency conditions. By cluster analysis, the studied lines were classified into 5 and 4 clusters, respectively, in terms of the investigated traits under zinc deficiency and optimal (normal) conditions. Based on mean comparison of investigated traits in clusters of the hierarchical method, 11 lines in the first cluster (Ma001, Ma023, Ma039, Ma043, Ma044, Ma057, Ma062, Ma065, Ma100, Ma112, Ma117) were identified as sensitive genotypes to zinc deficiency conditions. Thirty lines with the highest values for most of studied traits including Ma004, Ma006, Ma015, Ma017, Ma018, Ma019, Ma020, Ma021, Ma027, Ma030, Ma031, Ma032, Ma035, Ma038, Ma049, Ma055, Ma064, Ma072, Ma075, Ma096, Ma098, Ma104, Ma105, Ma107, Ma108, Ma111, Ma114, Ma123, G703, Simon in the fourth cluster were identified as tolerant genotypes to zinc deficiency conditions. The results of principal component analysis confirmed the results of cluster analysis. In optimum conditions, the first seven components and in zinc deficiency conditions, the first eight components had eigenvalues greater than one, which in total explained 79.77% and 78.99% of proportion of total variance, respectively.
Conclusion
The results showed that there is a wide diversity among the studied lines in terms of traits related to the seed yield in both optimum and zinc deficiency conditions, which is valuable for the exploitation of these lines in order to developing tolerant lines to withstand zinc deficiency stress. In this regard, in order to obtain hybrids tolerant to zinc deficiency with higher economic performance, it is suggested to cross tolerant lines in this research to benefit from the phenomena of transgressive segregation and heterosis.
Acknowledgement
The authors would like to express their sincere gratitude to the esteemed Research Deputy of Zabol University for the financial support provided for this project, identified by the code UOZ-GR-158-3014.

Keywords

• Biplot
• Cluster analysis
• Path analysis
• Micronutrient elements

Main Subjects

• Breeding plants for stress conditions