Exploring potential GxE interactions in the lifetime performance of suckling ewes

Session 1. Integrating mitigation and adaptation breeding strategies

International audience

英语. Prolific ewes produce more lambs per time and are often preferred by breeders. Yet, in grazing systems, those ewes might not be able to cope with feed resource limitations that are increasing in frequency, severity and duration with climate change. To explore the effects of possible feed constraints at different prolificacy potentials, we simulated the ewe’s lifetime lamb production in two contrasting pasture scenarios using a mechanistic model of energy acquisition and allocation between functions. Each ewe genotype was defined by several parameters controlling energy acquisition and allocation, including the prolificacy potential. In the favorable scenario, grass regrowth was maintained high during summer, whereas it was strongly impacted by drought in the unfavorable scenario. In both scenarios, ewes with higher prolificacy potential weaned on average more lambs over their life. Most ewes living long also weaned more lambs. There was no clear trade-off between ewe longevity and prolificacy. In very prolific ewes, the number of lambs weaned during lifetime was more strongly correlated between environments (r = 0.69 to 0.78) compared to less prolific ewes (r = 0.26 to 0.43). Those correlations suggest that ranking of ewes in one scenario did not match well the ranking in the other, especially at lower prolificacy potential. However, a high prolificacy potential was not enough to ensure a high and robust lifetime performance. The best prolific ewes in the favorable scenario showed a wide range of performance in the unfavorable scenario. Generally, the best ewe genotypes combined a high prolificacy potential with a balanced energy acquisition and allocation strategy that allowed them to both maintain their body reserves and to mitigate the higher lamb mortality observed in large litters. Although our model suggests the existence of GxE interactions, further analyses integrating real data will be needed to assess the genetic component of energy allocation strategies.