Search results for “Plant Breeding

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2 articles
Agronomy Research Open Access

Evaluation of selection indices for heat tolerance and their correlation with yield in some chickpea (Cicer Arietinum L.) genotypes of sudan

Feb 2023 DOI 10.14302/issn.2639-3166.jar-22-4403

The major a biotic stresses affecting chickpea production are high and low temperature, drought and salinity. Heat stress is a major a biotic stress factor, constraining chickpea production worldwide. This study was conducted to identifying chickpea genotypes combining heat tolerance and high yield potential and to study correlation among the heat tolerance indices. The genotypes were tested under different field-growing conditions, normal sown (non – heat stress) and late sown (heat stress) at two locations, Merowe and Gezira during winter season 2018/2019. The trials were laid out in alpha lattice design with three replications. Eight heat tolerance indices, which were most frequently used in plant breeding including, geometric mean productivity (GMP), yield index (YI), mean productivity (MP), stress susceptibility index (SSI), stress tolerance index (STI), tolerance index (TOL), sensitivity heat index (SHI) and relative heat index (RHI) were calculated based on seed yield under heat stress (Ys) and non – heat stress (Yp) conditions. Under both environments, the results from combined analysis of variance showed that there were highly significant differences among the genotypes for all traits studies. There were highly significant differences observed among the tested chickpea genotypes response to heat stress. Seed yield in stress (Ys) condition was positive and highly significant correlated with geometric mean productivity, yield index, stress tolerance index and relative heat index. The Ys was negative and highly significant correlated with stress susceptibility index and sensitivity heat index. Seed yield in non-stress (Yp) condition was positive and significant correlated with geometric mean productivity, yield index, stress tolerance index and tolerance index. The results of this research showed that the six indices, GMP, YI, SSI, STI, SHI and RHI can be used as optimal indicators for screening heat tolerant chickpea genotypes. Nine genotypes including four released improved varieties (Shiekh Mohamed, Wad Hamid, Salwa and Hwata) and five genotypes (no. 11, 4, 26, 30 and 34) are most stable chickpea genotypes for heat tolerance and can be further used in breeding program. These genotypes can be used in the chickpea national breeding program to develop varieties with improved heat tolerance in Sudan. Genotype no. 11 (FLIP 08-59 C) was release by Sudan national variety release committee for commercial production under the name Elbarkal in September 2022 to will be grown in Gezira, River Nile and Northern States of Sudan.

Model Based Research Open Access

RETRACTED: Monte Carlo Approach To Genotype By Environment Interaction Models

Mar 2020 DOI 10.14302/issn.2643-2811.jmbr-20-3237

This article has been retracted on 10 February 2021. VIEW THE RETRACTION NOTICE (https://doi.org/10.14302/issn.2643-2811.jmbr-25-5847) Understanding the implication of Genotype-by-Environment (GXE) interaction structure is an important consideration in plant breeding programs. Traditional statistical analyses of yield trials provide little or no insight into the particular pattern or structure of the GXE interaction. In this study, efforts were made to solve these problems under different level of data occurrence. We employed the simulation process of Monte Carlo in generating since use of a real-life data may pose a serious difficulty. In this paper, we simulated for two data Types of Balance and Unbalance designs with different Levels of generations (3X3, 7X7, 10X10, and 3X7, 7X3, 7X10, 10X7 , , respectively). We therefore check the performance of GXE interaction on four different models (AMMI, FW, GGE and Mixed model), and also their stability and adaptability. The findings revealed that, when the assumption was maintained, AMMI outperformed Finlay-Wilkinson model, GGE Biplot model and Mixed model.

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