Submitted to: Experiment Station Bulletins
Publication Type: Experiment Station
Publication Acceptance Date: June 15, 2006
Publication Date: July 13, 2006
Citation: Kepiro, J.L., Fjellstrom, R.G., McClung, A.M. 2006. Studying the inheritance of high milling yield in Cypress. Texas Rice, Highlighting Research in 2006. pp.VII-IX.. Technical Abstract: Milling yield, also called ‘head rice yield’, is the percentage of whole grain obtained from rough rice (paddy rice) after milling. Milling yield is a critically important trait in rice because it is a major factor determining the price farmers are paid for their crop. Developing genetic markers that are associated with milling yield will help breeders to develop new rice cultivars that have high farm gate value. Data are collected for each step of the milling process, hulling, milling and separating, and these measurements are reported as brown rice (BR) recovery, milled rice (MR) recovery, and head rice (HR) recovery, respectively. However, rice milling yield is a complex trait with multiple components, each under the control of numerous genes that may be affected by environmental (non-genetic) factors, including the weather conditions prior to harvest and post-harvest handling of the grain. Breeding for improved milling yield is difficult because of the numerous sub-component traits, their quantitative inheritance and the difficulty of duplicating commercial farming practices and environments on a small scale. Milling yield was evaluated in a segregating population (F11) derived from a Cypress x Panda cross. Although both are early maturing long grain tropical japonica cultivars, Cypress has gained wide recognition for having high and stable milling yield (~64%) over a variety of harvest moisture levels, whereas Panda is characterized by low milling yield (~52%). The offspring of this cross were evaluated for traits regularly used as selection criterion by breeders of Southern US long grain rice, such as grain chemistry, appearance, BR, MR, and HR. Development of innovative techniques allowed us to identify molecular markers for kernel length, width and thickness; as well as percentage of chalkiness and green area in kernels, factors associated with immature grains. Statistical analysis uncovered 14 different traits affecting milling yield. We have identified molecular markers for these traits and are developing statistical regression models to prioritize each trait’s usefulness in selecting superior high milling cultivars.