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Physiological and genetic control of transpiration efficiency in African rice, Oryza glaberrima Steud.

Pablo AffortitBranly Effa-EffaMame Sokhatil NdoyeDaniel MoukouangaNathalie LuchaireLlorenç Cabrera-BosquetMaricarmen PerálvarezRaphaël PilloniClaude WelckerAntony ChampionPascal GantetAbdala Gamby DiedhiouBaboucarr MannehRicardo ArocaVincent VadezLaurent LaplazePhilippe CubryAlexandre Grondin
Published in: Journal of experimental botany (2022)
Improving crop water use efficiency, the amount of carbon assimilated as biomass per unit of water used by a plant, is of major importance as water for agriculture becomes scarcer. In rice, the genetic bases of transpiration efficiency, the derivation of water use efficiency at the whole-plant scale, and its putative component trait transpiration restriction under high evaporative demand remain unknown. These traits were measured in 2019 in a panel of 147 African rice (Oryza glaberrima) genotypes known to be potential sources of tolerance genes to biotic and abiotic stresses. Our results reveal that higher transpiration efficiency is associated with transpiration restriction in African rice. Detailed measurements in a subset of highly contrasted genotypes in terms of biomass accumulation and transpiration confirmed these associations and suggested that root to shoot ratio played an important role in transpiration restriction. Genome wide association studies identified marker-trait associations for transpiration response to evaporative demand, transpiration efficiency, and its residuals, with links to genes involved in water transport and cell wall patterning. Our data suggest that root-shoot partitioning is an important component of transpiration restriction that has a positive effect on transpiration efficiency in African rice. Both traits are heritable and define targets for breeding rice with improved water use strategies.
Keyphrases
  • genome wide
  • cell wall
  • climate change
  • copy number
  • wastewater treatment
  • risk assessment
  • gene expression
  • genome wide association
  • machine learning