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Genomic Prediction of Sweet Sorghum Agronomic Performance under Drought and Irrigated Environments in Haiti

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  • Title:
    Genomic Prediction of Sweet Sorghum Agronomic Performance under Drought and Irrigated Environments in Haiti
  • Author: Charles, Jean Rigaud ; Dorval, Darline Marie ; Morris, Geoffrey ; Pressoir, Gael
  • Subjects: Environmental Sciences
  • Description: Over the last decade, genomic selection (GS) has gained momentum as a tool for predicting phenotypic performance in plant breeding populations and accelerating the development of new cultivars with the potential to adapt to climate change. Different statistical models and approaches have been developed to implement GS in plant breeding, and strategies that promote accurate and resource-efficient prediction are of increasing interest. Since its establishment in 2010, the sweet sorghum breeding program at “CHIBAS” has led efforts to develop and release cultivars resilient to abiotic and biotic stress. Among the abiotic constraints, drought stress is the most limiting since growers depend on erratic rainfall for sorghum production. The goal of this study was to evaluate the predictive ability of genomic prediction (GP) models across contrasting environments in Haiti using RRBLUP as statistical method. We evaluated twelve sorghum traits and performed GP within and across irrigated and water stress. Prediction accuracy (PA) was higher for within-environment (0.31 to 0.73) than across-environment (0.06 to 0.67). The lowest PAs (0.06 & 0.08) were obtained for grain yield with the scenario in which water stress 1 treatment was used to train the model and water stress 2 treatment was used to validate the model and vice versa. The highest PA for grain yield was obtained for the scenario in which the model was trained and validated in the irrigation treatment. Additionally, PA varied substantially for all traits, with brix showing the highest mean value (0.73), and maturity date showing the lowest mean value (0.31).
  • Creation Date: 2023
  • Language: English
  • Source: Hyper Article en Ligne (HAL) (Open Access)
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