Submitted to: Agricultural Experiment Station Publication
Publication Type: Experiment Station
Publication Acceptance Date: 6/3/2011
Publication Date: 6/28/2011
Citation: Ratnaprabha, R., Pinson, S.R., Salt, D.E., Tarpley, L. 2011. Can seedling mineral composition predict rice grain nutritional value? Texas Rice Special Section, Highlighting Research in 2011. p. XIII-XIV. Interpretive Summary:
Technical Abstract: This study investigated the possibility of using the mineral (ionomic) composition of rice (Oryza sativa L.) seedling leaves to predict varieties that accumulate large amounts of specific minerals in their grain. This information will be used for genetically improving the nutritional value of rice grain and for improving our understanding of mineral uptake, transport, and accumulation in rice. In 2007 and 2008, flooded and unflooded field trials were conducted on a core subset of 1,640 rice varieties from the USDA National Small Grains Collection to identify varieties with varying levels of grain mineral composition. The present study investigates correlation between seedling leaf and grain mineral contents of 16 minerals within this diverse set of varieties to determine if seedling leaf data could be used to predict grain content. Such correlations could greatly accelerate breeding efforts aimed at developing rice varieties with improved grain mineral composition (nutritional value). The 40 rice accessions selected for their extreme grain mineral composition were grown in an outdoor potted plant study in 2010. All 40 varieties were planted in 7-to-10 day intervals to provide, on a single sampling date, plants of a wide range of developmental stages. Leaf tips (2 inches) were collected from the most recently fully emerged leaf per plant for ionomic analysis. The results for Molybdenum (Mo) illustrate the potential of this approach to comparing leaf and grain mineral contents. Rice accessions from Malaysia (GSOR accessions 310354, 310355, 310356, 311643, and 311743) with high grain Mo also displayed high leaf Mo, indicating that seedling leaf data can be used to predict grain Mo.