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Title: Use of gross income as a measure of productivity in rice breeding

Author
item SAMONTE, STANLEY OMAR P - TAES
item WILSON, LLOYD - TAES
item TABIEN, RODANTE - TAES
item McClung, Anna

Submitted to: Canadian Journal of Plant Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/22/2008
Publication Date: 9/1/2008
Citation: Samonte, S.B., Wilson, L.T., Tabien, R.E., McClung, A.M. 2008. Use of gross income as a measure of productivity in rice breeding. Canadian Journal of Plant Science 88:1015-1022.

Interpretive Summary: Rice breeders primarily usually consider high grain yield and grain quality in the development of new cultivars. This study was conducted to determine if statistical methods including path analysis and genotype-genotype by environment biplot analysis were useful in identifying rice cultivars that had high economic value in specific growing environments. Field yield and milling quality data from experimental trials conducted in five southern US locations, across three years and involving 47 long grain cultivars were analyzed. Grain yield had the highest positive effect on gross income but whole grain and total milling yield also had a positive effect. In addition, grain yield was not correlated with milling quality indicating that both of these traits need to be improved to optimize economic value of a cultivar. The statistical analysis revealed that the highest yielding cultivar with the highest yield was not the same as the cultivar with the highest gross income in 9 out of 13 environments. This study demonstrated that both rice grain yield and milling quality are important in estimating gross income at the farm level and the use of GGE biplot analysis is helpful in identifying cultivars that are produce the highest economic value for a specific location.

Technical Abstract: Rice breeders consider high grain yield and grain quality in the development of new cultivars, but usually do not go a step further and consider gross income per se. The objectives of this study were to determine the direct effects of whole and total milled rice percentages on gross income using path analysis and to determine the genotypes that produced high and stable expected gross income using GGE biplot analysis. Data from the Uniform Rice Regional Nursery (URRN) on main crop grain yield and percentages of whole and total milled rice of 47 long grain genotypes grown at five locations (AR, LA, MO, MS, and TX) during three years (2001 to 2003) were used in this study. Gross income of each genotype was estimated based on rough rice grain yield, whole and total milled rice percentages, market prices of milled rice, and direct and counter-cyclical payments. Path analysis was used to estimate the path coefficients (p) of the direct effect of grain yield and percentages of whole and total milled rice on gross income. Genotype plus genotype x environment interaction (GGE) biplot analysis was used to identify the highest yielding and highest income-grossing genotype(s) for each location-year combination. Grain yield had the highest positive direct effect on gross income (p = 0.87) although whole (p = 0.35) and total (p = 0.14) milled rice percentages also had significant positive direct effects. Grain yield was not correlated with either whole or total milled rice percentages indicating that all three traits need to be improved to develop a high gross income genotype. The highest yielding genotype was not the highest income-grossing genotype in 9 out of 13 environments (location-year combinations) based on GGE biplot analysis. RU0103184 was the highest income grossing genotype in 6 environments, Francis was highest in 3, while RU0003178 and Banks were highest in 2. In LA, only one genotype (R0103184) was consistently identified as the high income-grossing genotype across years whereas the other locations had different high income-grossing genotypes in each of the three years. This study demonstrated the importance of both grain yield and percentages of whole and milled grain in estimating rough rice gross income and the use of GGE biplot analysis in identifying the highest income-grossing genotype for a specific location.