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Title: SELECTION FOR STABLE HIGH INCOME GROSSING RICE GENOTYPES

Author
item SAMONTE, STANLEY - TEXAS AGRI. EXP. STN
item WILSON, LLODY - TEXAS AGRI. EXP. STN
item McClung, Anna
item TABIEN, RODANTE - TEXAS AGRI. EXP. STN.

Submitted to: Rice Technical Working Group Meeting Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 1/1/2006
Publication Date: 2/15/2006
Citation: Samonte, S.O., Wilson, L.T., McClung, A.M., Tabien, R.E. 2006. Selection for stable high income grossing rice genotypes. Rice Technical Working Group Meeting Proceedings, February 29-March 1, 2006, Houston, Texas. 2006 CDROM.

Interpretive Summary:

Technical Abstract: Rice breeders aim to develop new cultivars with high grain yield, resistance to stresses, and acceptable grain quality. On the other hand, rice producers aim for maximum income. The objective of this study was to determine the direct effect of whole and total milled rice percentages on gross income using path analysis and to determine the genotypes that produce stable and high expected gross income using GGE biplot analysis. Data on main crop grain yield and the percentages of whole and total milled rice from the 53 genotypes that were common across 5 locations (AR, LA, MO, MS, and TX) and 3 cropping seasons (2001 to 2003) of the Uniform Rice Regional Nursery trials were used in this study. Rough rice gross income of each genotype was estimated based on rough rice grain yield, milling yield percentages, market prices of milled rice, and direct and counter-cyclical payments. Path analysis was used to estimate the path coefficient (p) of the direct effect of grain yield and the percentages of whole and total milled rice on rough rice gross income. Genotype and genotype x environment interaction (GGE) biplot analysis was used to identify the highest yielding and income grossing genotype for each location. The highest yielding genotype was not the highest income grossing genotype in 6 out of 13 environments (location-season combinations). Based on path analysis, grain yield had the highest positive direct effect on gross income (p = 0.86) but whole (p = 0.32) and total (p=0.13) milled rice percentages also had significant positive direct effects. Based on GGE biplot analysis, RU0002146 was both the highest yielding and income grossing genotype for the TX, LA, and MS locations. The highest yielding and income grossing genotype for AR was Francis and RU0003178, respectively, while that for MO (based on one year of milling yield percentage data) was Francis and RU0002146, respectively. This study demonstrated the importance and method of estimating rough rice gross income and using GGE biplot analysis in identifying the highest income grossing genotype for each location.