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ABSTRACT
About 2,700 land sale figures were used along with individual county soil-association maps to determine average peracre values of South Dakota counties. Annual average precipitation and temperature data were connected to landsale figures using multiple-regression equations. County crop productivity also was related to precipitation and county land sale data. The best prediction formula for explaining average county land values was a fifth-order polynomial-regression with precipitation as the variable. This explained 95% of the variance. Most of the variance (84%) of county cropland productivity was explained with a second-order polynomial-regression having annual-average precipitation as the variable, and 86% of the variance of county land value figures was explained with a second-order polynomial-regression having county cropland productivity as the variable. However, only a little over 54% of the variance of individual farm sales could be explained using regression equations involving climate, slope, and soil-texture as variables.
1 Joint contribution of South Dakota State Univ., Dep. of Plant Science, and USDA Soil Conservation Service, Huron. Authorized for publication as Journal Series no. 1139 South Dakota Agr. Exp. Sta. Appreciation is expressed to the South Dakota Dep. of Revenue and the Directors of Equalization of the 67 South Dakota counties for supplying the Farm and Ranch Sale Data. Presented before Div S-6. Soil Science Society of America, New York City, Aug. 17, 1971.
2 Professor, Plant Science Dep., South Dakota State Univ., Brookings; formerly State Soil Scientist, SCS, Huron, South Dakota, presently Principal Soil Correlator, SCS, Midwest Regional Technical Service Center, Lincoln, Nebraska; State Soil Scientist, SCS, Huron, South Dakota; and Assistant Professor, South Dakota State Univ., Plant Science Dep., Brookings, respectively.
Received for publication October 27, 1972. Accepted for publication February 12, 1973.
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