Stochastic simulation to optimize rice breeding at IRRI

Seck Fallou, Prakash Parthiban Thathapalli, Covarrubias-Pazaran Giovanny, Gueye Tala, Diédhiou Ibrahima, Bhosale Sankalp, Bartholome Jérôme. 2024. Stochastic simulation to optimize rice breeding at IRRI. Frontiers in Plant Science, 15, 15 p.

Url - autres données associées : https://github.com/fallseck/IRRI-OneRice-Simulation

Résumé : Introduction: Genetic improvement in rice increased yield potential and improved varieties for farmers over the last decades. However, the demand for rice is growing while its cultivation faces challenges posed by climate change. To address these challenges, rice breeding programs need to adopt efficient breeding strategies to provide a steady increase in the rate of genetic gain for major traits. The International Rice Research Institute (IRRI) breeding program has evolved over time to implement faster and more efficient breeding techniques such as rapid generation advance (RGA) and genomic selection (GS). Simulation experiments support data-driven optimization of the breeding program toward the desired rate of genetic gain for key traits. Methods: This study used stochastic simulations to compare breeding schemes with different cycle times. The objective was to assess the impact of different genomic selection strategies on medium- and long-term genetic gain. Four genomic selection schemes were simulated, representing the past approaches (5 years recycling), current schemes (3 years recycling), and two options for the future schemes (both with 2 years recycling). Results: The 2-Year within-cohort prediction scheme showed a significant increase in genetic gain in the medium-term horizon. Specifically, it resulted in a 22%, 24%, and 27% increase over the current scheme in the zero, intermediate, and high genotype-by-environment interaction (GEI) contexts, respectively. On the other hand, the 2-Year scheme based on between-cohort prediction was more efficient in the long term, but only in the absence of GEI. Consistent with our expectations, the shortest breeding schemes showed an increase in genetic gain and faster depletion of genetic variance compared to the current scheme. Discussion: These results suggest that higher rates of genetic gain are achievable in the breeding program by further reducing the cycle time and adjusting the target population of environments. However, more attention is needed regarding the crossing strategy to use genetic variance optimally.

Mots-clés Agrovoc : Oryza sativa, sélection, amélioration génétique, changement climatique, amélioration des plantes, gain génétique, variation génétique, génotype, génétique des populations, essai de variété, variance génétique, riz, variété, modèle stochastique

Classification Agris : F30 - Génétique et amélioration des plantes

Champ stratégique Cirad : CTS 2 (2019-) - Transitions agroécologiques

Agences de financement hors UE : Bill and Melinda Gates Foundation

Auteurs et affiliations

• Seck Fallou, IRRI [International Rice Research Institute] (PHL)
• Prakash Parthiban Thathapalli, IRRI [International Rice Research Institute] (PHL) - auteur correspondant
• Covarrubias-Pazaran Giovanny, IRRI [International Rice Research Institute] (PHL)
• Gueye Tala, Université Iba Der Thiam de Thiès (SEN)
• Diédhiou Ibrahima, Université de Thiès (SEN)
• Bhosale Sankalp, IRRI [International Rice Research Institute] (PHL)
• Bartholome Jérôme, CIRAD-BIOS-UMR AGAP (COL) ORCID: 0000-0002-0855-3828 - auteur correspondant

Source : Cirad-Agritrop (https://agritrop.cirad.fr/612064/)

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