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Pigeonpea [ (L.) Millspaugh] is a widely grown pulse with high seed protein content that contributes to food and nutritional security in the Indian subcontinent. The majority of pigeonpea varieties cultivated in India are of medium duration (<180 days to maturity), which makes it essential for breeders to focus on the development of stable high-yielding varieties. The diverse agroecological regime in the Indian subcontinent necessitates an efficient multi-environment study by taking into consideration genotype (G) × environment (E) interaction (GEI) that has a significant impact on traits like grain yield (GY) in developing high-yielding and widely adaptable varieties. In the present study, 37 pigeonpea genotypes were evaluated during the 2021 rainy season at ARS Badnapur, ARS Tandur, BAU Ranchi, GKVK Bengaluru, and ICRISAT Patancheru. The GEI was significant on the grain yield ( < 0.01), and hence, genotype + genotype × environment (GGE) and additive main effects and multiplicative interaction (AMMI) biplots along with AMMI stability value (ASV) and yield relative to environmental maximum (YREM) statistics were used to identify stable high-yielding genotypes. The interaction principal component analysis 1 and 2 (IPC1 and IPC2) explained 40.6% and 23.3% variations, respectively. Based on the rankings of genotypes, G37 (ICPL 20205), G35 (ICPL 20203), G8 (ICPL 19404), G17 (ICPL 19415), and G9 (ICPL 19405) were identified as ideal genotypes. Discriminativeness vs. representativeness identified GKVK Bengaluru as an ideal environment for comprehensive evaluation of test genotypes. However, ICPL 19405 was identified as the potentially stable high-yielding genotype for further testing and release across the test environments based on its mean grain yield (1,469.30 kg/ha), least ASV (3.82), and low yield stability index (YSI) of 13.
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http://dx.doi.org/10.3389/fpls.2024.1396826 | DOI Listing |
Plant Genome
September 2025
Agronomy Department, University of Florida, Gainesville, Florida, USA.
Multi-environment trials are routinely conducted in plant breeding to capture the genotype-by-environment interaction (G × E) effects. Significant G × E could alter the response pattern of genotypes (the change in rankings of genotypes), subsequently complicating the selection process. Four genomic prediction (GP) models were assessed in three groundnut yield-related traits: pod yield (PY), seed weight (SW), and 100 seed weight (SW100), across four environments.
View Article and Find Full Text PDFBMC Plant Biol
July 2025
Department of Botany and Microbiology, College of Science, King Saud University, Saudi Arabia, 11451, Riyadh.
Background: Powdery mildew (PM), caused by Blumeria graminis f. sp. tritici, and yellow rust (YR), caused by Puccinia striiformis f.
View Article and Find Full Text PDFSci Rep
July 2025
ICAR-National Bureau of Plant Genetic Resources Regional Station, Shimla, India.
Chickpea productivity remains low due to limited genetic variability and susceptibility to biotic and abiotic stresses. To address these challenges, the introgression of novel genes from wild relatives and multi-environment evaluations are essential for identifying high-yielding, stable interspecific derivatives (ISDs). Despite the availability of several statistical tools, few chickpea studies have used a comprehensive multi-model approach for stability analysis.
View Article and Find Full Text PDFPlant Genome
June 2025
Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, Michigan, USA.
For important food crops such as the common bean (Phaseolus vulgaris, L.), global demand continues to outpace the rate of genetic gain for quantitative traits. In this study, we leveraged the multi-environment trial (MET) dataset from the cooperative dry bean nursery (CDBN) to investigate the use of ensemble models for genomic prediction.
View Article and Find Full Text PDFPLoS One
June 2025
Crop and Horticultural Science Research Department, South Kerman Agricultural and Natural Resources Research and Education Center, AREEO, Jiroft, Iran.
Plant breeders are increasingly utilizing stability parameters as valuable tools for selecting cultivars in the context of genotype × environment interaction (GEI). Neglecting GEI in multi-environment trials (MET) can significantly heighten the risk of making inaccurate cultivar recommendations to farmers. Consequently, breeders must strive to find an optimal balance between yield and stability, favoring varieties that minimize the risk of extremely low yields.
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