Use of Mechanistic and Empirical Models of Photosynthesis to Explore Genetic Variation in Rice

Meeting agricultural water demands is becoming progressively difficult due to population growth and changes in climate. Breeding stress-resilient crops is a viable solution, as information about genetic variation and their role in stress tolerance is becoming available due to advancement in technology. We used mechanistic and empirical models to explore the genetic variation of photosynthesis across eight diverse rice genotypes under greenhouse conditions. These include the Asian rice (Oryza sativa) genotypes, drought sensitive Nipponbare, and a transgenic line overexpressing the HYR gene in Nipponbare; six genotypes (Vandana, Bengal, Nagina-22, Kaybonnet, Ai Chueh Ta Pai Ku) and an African rice O. glaberrima, all screened for key photosynthetic parameters such as the maximum carboxylation rate (Vcmax), the maximum electron transport rate (Jmax), the maximum gross photosynthesis rate (Pmax), daytime respiration (Rd), quantum yield (phi), and stress on photosystem II. We collected CO₂ and light response curve data under well-watered and simulated drought conditions in greenhouse. Our results suggest that O. glaberrima and Nipponbare were the most sensitive to drought because Vcmax and Pgmax declined under drought conditions; drought tolerant genotypes did not show significant changes in these model parameters. Our integrated approach, combining genetic information and photosynthesis modeling, adds a new dimension to genetic data and provides improved information for plant breeders to improve crop yield under stress conditions.