Agroecosystems /
Carbon Sequestration
Research Focus
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Iowa Final Report
Modeling Soil Organic Matter
The Century EcoSystem Soil Organic Matter Computer Model used in this study was first developed for grassland systems (Parton et al., 1987) but has subsequently been updated and enhanced (Metherall, et al., 1993; Parton et al., 1994; Paustian et al., In prep.) and has been used extensively to simulate organic matter and nutrient dynamics in agricultural cropping systems (e.g., Paustian et al., 1992, 1996, 2001b; Carter et al., 1993; Parton and Rasmussen, 1994). Century simulates long-term dynamics of carbon, nitrogen, phosphorus and sulfur in the top 20 cm of soil on a monthly basis and has proven to provide reliable estimates of soil C changes. Soil organic carbon and nitrogen stocks are represented by two plant litter pools and three soil organic matter pools (termed active, slow, and passive). The crop growth submodel simulates crop growth, dry matter production and yield to estimate the amount and quality of residue returned to the soil, as well as plant influence on soil water, nutrients and other factors affecting soil organic matter turnover. The soil water balance submodel calculates water balance components and changes in soil water availability, which influence both plant growth and decomposition/nutrient cycling processes. A variety of management options may be specified including crop type, tillage, fertilization, organic matter addition (e.g., manuring), harvest (with variable residue removal), drainage, irrigation, burning and grazing intensity. Specifying crop type and management options in the management schedule file simulates the desired cropping sequence. Figure 9 provides an overview of the Century model illustrating the main components of the model. Only carbon and nitrogen dynamics were addressed in this research. Model simulations did not include the occurrence of soil erosion.
Figure 9
To evaluate the model under conditions representative for the Corn Belt Region of the U.S., the model was used to simulate long-term continuous corn and corn-soybean cropping systems at five different locations involving various soil types and climate regimes, involving a total of 29 separate treatments for tillage and fertilization management (Paul et al., 1997) (Lafayette, IN; Lexington, KY; Hoytville, OH; Wooster, OH; and Arlington, WI). To test the model’s ability to estimate soil carbon levels and changes due to management without using site-specific information on initial soil C levels, we initialized and executed the model using only climate, soil physical properties, and management driving variables. The model first estimated pre-cultivation soil carbon contents under native vegetation using a stochastic weather generator (based on long-term mean climate) and the physical description for the site, including soil texture and soil hydric properties. We assumed the vegetation to be a tall grass prairie that was moderately grazed in the summer months with a fire frequency of three years, and the model was run for 6000 years to approximate steady-state conditions. Next, representative historical practices, as reported by the managers of each of the long-term sites and/or based on published literature, were simulated for the period from initial cultivation (mid to late 1800s) to the start of the field experiment. Observed weather data from the nearest weather station were used for the period of record. Finally, the field experimental period was simulated using the actual management practices for multiple treatments per site, as reported by the site managers (Paul et al., 1997). Most of the experiments have been in place for 20-30 years. Model simulations were run based on these data and compared to measured soil C and crop yields reported for each site. The model explained 85% of the variability across all treatments, sites, and time periods, using all published data from the studies and explained 82% of the variability when looking at only soil C data obtained in 1992 from a cross-site sampling which we conducted (Figures 10 and 11).
Figure 10
Figure 11
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