P1290
Automated High-Throughput Phenotyping to Study Salinity Tolerance in Rice

Nadia Al-Tamimi , King Abdullah University of Science and Tecnology, Thuwal, Saudi Arabia
Bettina Berger , University of Adelaide, Urrbrae SA, Australia
Chris Brien , University of South Australia, Mawson Lakes, Australia
Stephanie Saade , King Abdullah University of Science and Tecnology, Thuwal, Saudi Arabia
Helena Oakey , King Abdullah University of Science and Tecnology, Thuwal, Saudi Arabia
Yung Shwen Ho , King Abdullah University of Science and Tecnology, Thuwal, Saudi Arabia
Sandra M Schmoeckel , King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Mark Tester , King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Sónia Negrão , King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
Automated high-throughput phenotyping to study salinity tolerance in rice

Nadia Al-Tamimi, Bettina Berger, Chris Brien, Stephanie Saade, Helena Oakey, Yung Shwen Ho, Sandra Schmöckel, Mark Tester and Sónia Negrão

Soil salinity is a major constraint to agriculture. Rice (O. sativa), one of the most important crop species, is a very salt-sensitive cereal. With rising population, it is projected that rice yields need to increase by 26% in the next 25 years to meet global demands (Rice Almanac, 2013). Hence, there is a vital requirement to significantly increase rice productivity in salinized lands.

Here we present a high-throughput phenotypic study on two diversity panels of rice for a Genome Wide Association Study (GWAS) with the aim of identifying novel loci contributing to the early responses of rice to soil salinity. These diversity panels include the “Phenomics of Rice Adaptation and Yield Potential” (“PRAY”) panel, which has been phenotyped for several traits in plants grown in a diversity of conditions within the Global Rice Phenotyping Network (http://ricephenonetwork.irri.org/). We phenotyped these panels, comprising the rice groups Indica (297 genotypes) and AUS (256 genotypes), using the non-invasive automated imaging technology in The Plant Accelerator®. Rice plants were phenotyped daily for 13 days after application of salt (150 mM NaCl, applied to the flood water in which pots were immersed). Mathematical models were used to analyze the temporal phenotypic data. The main traits measured at The Plant Accelerator® included relative growth rate (RGR), which is inferred from the RGB pixel counts of the shoot, and transpiration rate (TR), which is deduced from automated daily weighing and watering of the plants. Combining these, were able to also calculate the transpiration use efficiency (TUE), a key index determined by the ratio of biomass produced per unit of water transpired. We observed a clear reduction in RGR, TR and TUE within a few days of salt imposition, which is due to an early response of salt application, termed “osmotic stress” (sensu Munns & Tester, 2008). RGR, TR and TUE were found to be higher, on average, in Indica accessions compared to AUS accessions. Ninety six indices for each panel were derived from the measured traits. Association analyses are currently ongoing using two different sets of genotypic data (the 44K and 700K SNP chips developed in the group of Prof. Susan McCouch). By comparing different genotypic data and association models, we expect to dissect the genetic architecture of RGR, TR and TUE and the effects of salinity on these parameters.

GRiSP (Global Rice Science Partnership). 2013. Rice almanac, 4th edition. Los Baňos (Philippines):  International Rice Research Institute. 283

Back to: Genome Mapping, Tagging & Characterization: Rice
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