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ARS Home » Plains Area » Lincoln, Nebraska » Wheat, Sorghum and Forage Research » Research » Publications at this Location » Publication #214365
Title: Evaluating a hybrid soil temperature model in a corn-soybean agroecosystem and a tallgrass prairie in the Great Plains

Author
item FENG, S. - UNIVERSITY OF NEBRASKA
item SALVAGIOTTI, F. - UNIVERSITY OF NEBRASKA
item Schmer, Marty
item WINGEYER, A.B. - UNIVERSITY OF NEBRASKA
item WEISS, A. - UNIVERSITY OF NEBRASKA

Submitted to: Great Plains Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/8/2010
Publication Date: 10/14/2010
Citation: Feng, S., Salvagiotti, F., Schmer, M.R., Wingeyer, A., Weiss, A. 2010. Evaluating a hybrid soil temperature model in a corn-soybean agroecosystem and a tallgrass prairie in the Great Plains. Great Plains Research 20 (Fall 2010):249-60.

Interpretive Summary: Soil temperature is an important factor that regulates chemical, physical and biological processes within soils. Approaches to simulate soil temperature, with different degrees of complexity, are implemented in different crop and land surface models. The objective of this research was to compare a relatively simple hybrid soil temperature model that was developed for a forest region and apply it to a no-tillage corn and soybean rotation cropping system and a native grassland. The hybrid model did a reasonable job in predicting soil temperature at various soil depths in both the cropping system and grassland system. Hybrid model advantages are the model’s relative simplicity using easily available data, ability to be incorporated into a larger model where soil temperatures are required, and to evaluate surface litter impacts on soil temperatures. Given the applied nature of this hybrid model, it would be well suited to simulate soil temperatures in the first 50 cm (20 inches) of soil over a vegetated surface for processes related to soil respiration, soil organic matter decomposition and soil-borne pests.

Technical Abstract: Simulation models of soil related biological processes usually require soil temperature data. Frequently these soil temperatures are simulated and the soil temperature algorithms cannot be more complicated than the original process model. This situation has led to the use of semi-empirical type relationships in these process models. The objective of this study was to evaluate a hybrid soil temperature model, which combines empirical and mechanistic approaches, in an agroecosystem and a tallgrass prairie in the Great Plains. The original hybrid soil temperature model was developed and verified for a temperate forest system. This model simulated soil temperatures on a daily basis from meteorological inputs (maximum and minimum air temperatures) and soil and plant properties. This model was modified using different extinction coefficients for the plant canopy and ground litter. The agroecosystem consisted of a no-till corn (Zea mays L.) and soybean (Glycine max Merr. (L)) rotation system. Soil temperatures were measured at different depths in multiple years (three and two and a half years in the agroecosystem and tall grass prairie, respectively. In the agroecosystem, the root mean square error of the modified model simulation varied from 1.41 to 2.05 oC for the four depths (0.1, 0.2, 0.3 and 0.5 m). The mean absolute error varied from 1.06 to 1.53 oC. The root mean square error and mean absolute error of the modified model were about 0.1-0.3 oC less than the original model at the 0.2-0.5m depths. For the tallgrass prairie, the mean absolute errors of the simulated soil temperatures were slightly greater than the agroecosystem varying from 1.48-1.7 oC for all years and from 1.09-1.37 oC during the active growing seasons for all years.