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Title: IDENTIFYING OPTIMAL PHENOTYPIC TRAIT SETS USING PHYSIOLOGICALLY-BASED MODELING

Author
item WILSON, L - TEXAS A&M UNIV
item WU, G - TEXAS A&M UNIV
item SAMONTE, O - TEXAS A&M UNIV
item McClung, Anna
item PARK, W
item Pinson, Shannon

Submitted to: Rice Technical Working Group Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 12/1/1997
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: Over the last 5 yrs, we have developed a physiologically-based crop simulation model for rice which provides a mechanism for identifying which combination of phenotypic traits would result in maximal grain yield for the TX Gulf Coast environment. The resulting rice simulation model, captures essential physiological processes for a cultivar or breeding line through incorporation of key parameters, and then predicts yield and time course of growth and maturation for any year or location. Using this model, we can explain over 91 percent of the yield variability. In a second analysis the simulated response of rice to rice water weevil injury was determined. In this analysis 95 percent of the variation in grain yield and above ground biomass was explained using the model. Simulation analyses strongly suggested that the reductions in yields for the mid-season maturing lines was largely due to carbohydrate depletion caused by high day yand night time temperatures during the middle of the 95 summer months. Subsequent biochemical analysis to total non-structural carbohydrate levels showed a 23 percent reduction during a year when grain filling was stressed. RECEPSMs accuracy at predicting the growth, maturation, and yield of a wide range of cultivars and lines for a number of years of data is highly encouraging and supports its use as part of a marker assisted selection program to determine which phenotypic traits to select to most effectively improve grain yield. In this paper, a sensitivity analysis is presented which simulates the effect of four phenotypic traits on crop maturation and yield. Results will be used in a prototype DNA marker and population model assisted breeding program to define which combination of phenotypic traits and genes to select when breeding for higher grain yield.