| Zusammenfassung: | We analyzed the productivity of C<sub>3</sub> and C<sub>4</sub> grasses throughout the Great Plains of the United States in relation to three environmental factors: mean annual temperature, mean annual precipitation, and soil texture. Productivity data were collected from Natural Resource Conservation Service (NRCS) rangeland survey data. Climate data were interpolated from weather stations throughout the region. Soil texture data were obtained from NRCS State Soil Geographic (STATSGO) databases. A geographic information system was used to integrate the three data sources. With a data set of spatially random points, we performed stepwise multiple regression analyses to derive models of the relative and absolute production of C<sub>3</sub> and C<sub>4</sub> grasses in terms of mean annual temperature (MAT), mean annual precipitation (MAP), percentage sand (SAND), and percentage clay (CLAY). MAT, MAP, and soil texture explained 67-81% of the variation in relative and absolute production of C<sub>3</sub> and C<sub>4</sub> grasses. Both measures of production of C<sub>3</sub> grasses were negatively related to MAT and SAND, and positively related to CLAY. Relative production of C<sub>3</sub> grasses decreased whereas absolute production of C<sub>3</sub> grasses increased with MAP. Production of C<sub>4</sub> grasses was positively related to MAT, MAP, and SAND, and negatively related to CLAY. MAP was the most explanatory variable in the model for C<sub>4</sub> absolute production. MAT was the most explanatory variable in the three other models. Based on these regression models, C<sub>3</sub> grasses dominate 35% of the Great Plains under current climatic conditions, mainly north of Colorado and Nebraska. Under a 2<sup>⚬</sup> C increase in MAT, C<sub>3</sub> grasses recede northward and retain dominance in only 19% of the region. MAT, MAP, and soil texture are important variables in explaining the abundance and distribution of C<sub>3</sub> and C<sub>4</sub> grasses in the Great Plains. Accordingly, these variables will be important under changing CO<sub>2</sub> and climatic forcings.
|
|---|