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Identifying Adaptable Varieties of Sorghum (Sorghum bicolor L) in Tidal Swamplands and Sandy Soils by MGIDI and GGE Biplots
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Identifying Adaptable Varieties of Sorghum (Sorghum bicolor L) in Tidal Swamplands and Sandy Soils by MGIDI and GGE Biplots
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Journal Article
Identifying Adaptable Varieties of Sorghum (Sorghum bicolor L) in Tidal Swamplands and Sandy Soils by MGIDI and GGE Biplots
2025
Overview
Background Sorghum has potential as a source of material for food, bioenergy, and animal feed, making it a worthy candidate for promotion. This cereal thrives in regions characterized by low moisture and dry conditions. To address the diminishing availability of arable dry land, it may be necessary to explore the cultivation of sorghum in tidal swamplands and sandy soils. Methods Twelve sorghum varieties were evaluated in tidal swamplands during the rainy and dry seasons, as well as in sandy soil during the dry season, using two levels of organic fertilizers to create six test environments. The experiments were arranged in a completely randomized block design with three replications. To choose sorghum varieties with features that closely resemble an idealized sorghum variety, the Multi-trait Genotype-Ideotype Distance Index (MGIDI) was utilized. Simultaneously, genotype plus genotype-environment interaction (GGE) biplots were employed to determine the best circumstances for choosing broadly adaptable varieties that exhibit desirable features, as well as to find varieties that thrive environmental contexts. Results Based on the MGIDI ranking on the average across environment, two varieties, i.e., Numbu and Kawali were selected. However selected varieties in each environment were differed due to significant variety-environment interaction. In terms of grain weight, the Soper 7 Agritan variety exhibits adaptability across diverse environments, while the Numbu variety likewise demonstrates versatility in various environmental conditions. When evaluating forage yield, several adaptable varieties have emerged. Tidal swamplands treated with a high application of organic fertilizer, as well as sandy soils, provide optimal environments for selecting broadly adaptable varieties that focus on both grain and forage yields. Conclusion Adaptable varieties differ for various groups of environments and different traits under consideration. Optimal environments for identifying broadly adaptable varieties varied by trait. The MGIDI proves to be a valuable tool for selecting varieties based on multiple traits. In parallel, the GGE biplots effectively identifies adaptable varieties based on individual traits.
Publisher
F1000 Research Ltd
Subject
