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Characterization and Trait Association Analysis of 27 Pearl Millet Landraces in Southern Tunisia

Agronomy 2023 9 citations ? Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count. Score: 40 ? 0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Tebra Triki, Leila Bennani, Faiza Boussora, Samir Tlahig, Sihem Ben Ali, Amel Gasmi, Hédi Yahia, Khaled Belhouchette, Mohamed Loumerem, Ferdaous Guasmi

Summary

Not a microplastics paper — this agricultural study characterizes 27 pearl millet varieties grown in Tunisia, evaluating traits such as grain yield and drought tolerance to support crop breeding programs.

Pearl millet (Pennisetum glaucum (L.) R. Br., 2n = 2x = 14, Poaceae), is a cross-pollinated, warm-season crop grown worldwide. To select genotypes for breeding pearl millet cultivars that adapt to drought condition in southern Tunisia, we evaluated the grain yield (GY) and yield-related traits using a set of 27 landraces at two locations in southern Tunisia for two grown seasons (2019 and 2020). The genetic variability, phenotypic and genotypic association, and path coefficient (PC), based on grain yield (GY) and different yield-related agronomic traits, were evaluated. Analysis of variance and BLUPs value revealed a wide range of variability and the possibility of genetic selection for traits that are advantageous. Broad sense heritability (H) for all the traits ranged from 24.10% for grain yield (GY) to 57.11% for spike length (SL), indicating low to moderate inheritability. Genetic advance as a percentage of the mean (GAM) ranged from high (29.56%) for principal panicle weight (PPW) to moderate for all the traits except from plant high (PH) (7.31%). For all the traits, the phenotypic coefficient of variation (PCV) was higher than genotypic coefficient of variation (GCV), indicating the magnitude of environmental conditions. GY was significantly correlated with all the traits at the genotypic and phenotypic level. According to the path coefficient, the traits PPW and SL displayed the highest direct effects on GY. Heatmap analysis demonstrated a clear segregation between the early and late genotypes based on their geographic origin. Based on the cluster analysis and FAI-BLUPS analysis, genotypes G11, G13, G12, G17 and G18 were selected as the best-performing genotypes with the shortest cycle.

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