Multi-Traits Selection of Maize Hybrids under Sole-Crop and Multiple-Crops with Soybean

Abstract

Multi-trait selection is very helpful in plant breeding programs. This technique can select genotypes for specific traits. Yield performance of maize hybrids is influenced by genotype-by-environment interactions (GEIs). This requires multi-environmental testing to identify superior hybrids. The aims of this study were to: (1) select maize hybrids within favorable traits such as grain yield and yield attributes; (2) identify genotype and environment interactions (GEIs) within maize yield; (3) evaluate maize hybrids adaptability to four different locations in West Java; and (4) identify a representative environment for testing and developing new maize hybrids. Twenty-two maize hybrids were evaluated using a randomized block design repeated three times in each environment. The genotype by traits (GT) biplot showed that G3 maize hybrid was superior to ear weight (EW), ear weight without corn husk (EwoH), ear weight per plot (EWP), plant height (PH), ear diameter (ED), and 1000 seed weight (OSW) traits. The G22 maize hybrid was superior to ear weight without corn husk per plot (EwoP), ratio seed weight and ear weight without corn husk (RSEW), grain yield per ha (Y), seed weight (SW), and seed weight per plot (SWP) traits. Combined analysis of variance showed that genotype (G), environment (E), and their interactions (GEIs) had a significant effect on yield (p < 0.01) with contributions of 85.63%, 2.24%, and 12.13%, respectively. The combined stability measurement results and the GGE biplot reveal that G2 is a stable high-yield hybrid, while G4 and G16 are unstable high-yield hybrids. The most representative environment is Arjasari, with the sole-cropping system (ARJ M). This study reveals that the resulting maize hybrid can be useful as a substitute for commercial hybrids currently used. The stable high yield of maize hybrids can be recommended as new varieties with wide adaptation, while unstable high-yield hybrids can be recommended as varieties adapted to specific environments.

Stability analysis and nutritional quality of soybean (Glycine max (L).Merrill.) genotypes for feed in southwestern Ethiopia

“…These findings are congruent with the study conducted by Ref. [ 34 ], who used the GGE biplot to identify represented environments.

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“…This finding is consistent with the study carried out by Refs. [ [34] , [45] ] using GGE biplot. A genotype that is connected to a vertex of a polygon when no environmental indicator is present in the sector may not be operating as well as it could [ 48 ].…”

Selection for representative environment to identify high yielding and sweetness levels of sweet potato (Ipomoea batatas L.) in Indonesia

“…This environment was the most representative environment for sweet potato yield test. This environment was suitable for selecting high-yielding genotypes because of its high discriminating power and representativeness and its small angle with the abscissa (Pobkhunthod et al 2022;Ruswandi et al 2023;Tabu et al 2023). The Bandung dry field and Sumedang dry field environments are Type III environments because they have long vectors but large angles to the abscissa.…”

“…Environment has an impact on changes in crop yields and also the quality of the results (Solihin et al 2018;Maulana et al 2023). In addition to sweet potatoes, it also affects other crops' yield (Ruswandi et al 2023) such as rice (Utami et al 2023) and soybeans (Wijaya et al 2022). Environmental selection needs to be done to obtain information on locations that have the potential as a development area for a variety of some environmental factors are considered very specific in influencing crop yields, such as temperature, rainfall, altitude, and soil properties (Markos et al 2022;Maulana et al 2022;Solihin et al 2023).…”

“…Therefore, to determine a representative location, environmental selection is carried out. Some researchers use the biplot approach to determine the favorable environment (Maulana et al 2022;Ruswandi et al 2023;Utami et al 2023). The use of biplots in representative environment selection is considered more informative because it provides a visualization of the environment.…”