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Published on: August 6, 2018
Stability yield indices on different sweet corn hybrids based on AMMI analysis.
S M N Mousavi1,2, A Illés1, A Szabó1
1University of Debrecen, Faculty of Agricultural and Food Sciences and Environmental Management, Institute of Land Use, Engineering and Precision Farming Technology, Debrecen, Hungary.
Eight sweet corn hybrids were evaluated for yield and nutritional traits. Hybrids DB, NO, GS, and GB demonstrated stable and high performance, with stable fructose and glucose levels, indicating suitability for cultivation.
Area of Science:
- Agricultural Science
- Plant Breeding
- Agronomy
Background:
- Sweet corn is valued for its high sugar and low starch content, with key sugars including sucrose, fructose, glucose, and maltose.
- Understanding yield indices and their correlation is crucial for consumer satisfaction and agricultural productivity.
Purpose of the Study:
- To evaluate yield indices and their correlations across eight sweet corn hybrids.
- To identify stable and high-performing sweet corn hybrids based on yield and nutritional parameters.
Main Methods:
- A small plot experiment was conducted with a strip plot design and four replications.
- Analysis of yield indices, sugar content (fructose, glucose, sucrose), and mineral content (iron, zinc) using AMMI biplot analysis (IPCA1 and IPCA2).
Main Results:
- Hybrids DB, NO, GS, and GB exhibited stable and high yield performance (IPCA1).
- Fructose and glucose levels were stable across hybrids.
- Hybrid ME showed stability and high performance for iron and zinc (IPCA1).
- Hybrids DE, GB, and GS demonstrated stability and top performance in yield parameters (IPCA2).
- Fructose, glucose, and sucrose were stable across hybrids (IPCA2).
- Hybrids DB and SE showed desirable performance for Lutein and Zeaxanthin.
- Hybrid DE achieved maximum performance for iron and zinc.
Conclusions:
- Specific sweet corn hybrids (DB, NO, GS, GB, DE, SE) show promising stability and high performance for yield and nutritional traits.
- AMMI biplot analysis is effective in identifying superior hybrids for diverse agronomic and quality attributes.
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