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Harry Rukavina 1, Randy Johnson 2 and Harrison Hughes 1 1 Colorado State University, Department of Horticulture and Landscape Architecture 2 USDA Forest.

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Presentation on theme: "Harry Rukavina 1, Randy Johnson 2 and Harrison Hughes 1 1 Colorado State University, Department of Horticulture and Landscape Architecture 2 USDA Forest."— Presentation transcript:

1 Harry Rukavina 1, Randy Johnson 2 and Harrison Hughes 1 1 Colorado State University, Department of Horticulture and Landscape Architecture 2 USDA Forest Service, Pacific Northwest Research Station Introduction -Because of population growth in semi-arid U.S. west there is interest in developing alternative turfgrass species that are water efficient and tolerate poor quality water (Kopec and Marcum, 2001). -Colorado State University is currently evaluating saltgrass (Distichlis spicata) [Fig.1] and other U.S. native grasses for their potential as turf. -Efficient selection in the saltgrass breeding program necessitates characterization of germplasm in relation to environmental (climatic and geographic) factors (Ram et al., 2004). -Determination of environmental factors that are related to variation in specific trait can be useful in identifying environments that may be the best for the trait of interest (St Clair et al., 2005). Objective of study This study was initiated to characterize variation in growth (morphology) and time of leaf browning in fall (cold hardiness indication) among 53 saltgrass from 42 locations clones relative to geographic and climatic factors at locations of clones’ origin. Materials and Methods -Traits of growth (morphology) and time of leaf browning in fall were measured on 53 saltgrass clones from 42 locations. -Analysis of variance was performed to determine the effect of location of clones’ origin on measured traits. -Geographic factors (latitudes, longitudes and elevations) were obtained for each location of clones origin (http://www.topozone.com).http://www.topozone.com -Climatic parameters, monthly and annual precipitation and temperature (mean, minimum and maximum) and several seasonal rate variables (summer drying and fall cooling) for each locations were obtained by the PRISM statistical geographical model. -Principal component analysis was performed on the set of morphological traits followed by correlation and multiple regression of factor score (PC-1) and time of leaf browning on the environmental (geographic and climatic) factors. Results - Canopy height, internode length and leaf length were significantly influenced by location of clone’s origin, while time of leaf browning in fall had borderline significance (Table 1). -The first principal component (PC-1) from principal component analysis explained 78% of variability in traits of growth. PC-1 was therefore retained for further analysis and represented growth in saltgrass clones. -Saltgrass growth was negatively correlated with spring and early summer precipitation (Fig. 2A). -Time of leaf browning in fall had strong negative correlations with spring and summer precipitation, but correlation was positive with winter precipitation (Fig. 2B). -Multiple regression models suggest that most of the variation (50%) in saltgrass growth was explained by seasonal climatic variables, summer drying (June mean precipitation – August mean precipitation) and fall cooling (September maximum temperature – October maximum temperature) [Fig. 3]. -Time of leaf browning in fall was strongly related to longitude and winter months’ minimum temperature. Longitude and February minimum temperature explained 60% of the variability in that trait (Fig. 4). Figure 2B. Correlations of monthly climatic data* with time of saltgrass leaf browning in fall. Figure 2A. Correlations of monthly climatic data* with saltgrass growth. Levels of statistical significance are r = 0.304 for a p value of 0.05, and r = 0.393 for a p value of 0.01. * Key to monthly climatic data: min temp = minimum temperature, max temp = maximum temperature, ppt = precipitation. Figure 3. Geographical variation in saltgrass growth. Map presents multiple regression model where growth is a function of summer drying and fall cooling. Figure 4. Geographical variation in saltgrass time of leaf browning in fall. Map presents multiple regression model where time of leaf browning is a function of longitude and February minimum temperature. Color groupings are in the units of a standard deviation of the 42 original points of saltgrass clones’ origin. Summary - Seasonal climatic variables were better than geographic variables in explaining variation in growth of saltgrass clones. -Variation in time of leaf browning in fall was function of both climate (minimum winter temperatures) and geography (longitude). -Traits of saltgrass growth and time of leaf browning in fall may be of adaptive importance because they showed relation with environmental factors at source location. -A saltgrass breeding program should maintain geographical structure with regards to those traits analyzed in this study. References Kopec, D.M., and K. Marcum. 2001. Desert saltgrass: a potential new turfgrass species. USGA Green Section Record, 6-8. Reid, S. 2001. Chromosome races and polyploid cytoforms in Distichlis spicata. M.S. thesis, Colorado State University. Ram, A., M. Zaccai, D. Pasternak, and A. Bustan. 2004. Analysis of phenotypic and genetic polymorphism among accessions of saltgrass (Distichlis spicata). Genetic Resources and Crop Evolution 51: 687-699. St Clair, J.B., N.L. Mandel, and K.W. Vance-Borland. 2005. Genecology of Douglas fir in western Oregon and Washington. Annals of Botany 96: 1199:1214. TraitP value Canopy height0.012 Internodes length0.001 Leaf length0.022 Time of leaf browning in fall 0.055 Leaf width0.38 Table 1. The effect of locations of clones origin on the measured traits. Level of statistical significance is a p value of 0.05. Figure 1. Saltgrass clones from different U.S. areas. Source: S. Reid’s M.S. thesis.


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