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United States Department of Agriculture

Agricultural Research Service

Research Project: SOIL ORGANIC MATTER AND NUTRIENT CYCLING TO SUSTAIN AGRICULTURE IN THE SOUTHEASTERN USA Title: Simulating field-scale soil organic carbon dynamics using EPIC

Authors
item Causarano, Hector - USDA-ARS
item Shaw, J - AUBURN UNIVERSITY
item Franzluebbers, Alan
item Reeves, Donald
item Donoghue, Ann
item Balkcom, Kipling
item Norfleet, M - USDA-NRCS
item Izaurralde, R - JOINT GLOBAL CNGE RES INS

Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: April 12, 2007
Publication Date: June 7, 2007
Citation: Causarano, H.J., Shaw, J.N., Franzluebbers, A.J., Reeves, D.W., Raper, R.L., Balkcom, K.S., Norfleet, M.L., Izaurralde, R.C. 2007. Simulating field-scale soil organic carbon dynamics using EPIC. Soil Science Society of America Journal. 71:1174-1185.

Interpretive Summary: Computer simulation models can be useful tools to predict changes in crop yields and environmental consequences from soil management practices. However, these models need to be checked or validated against data from long-term field experiments in order to have confidence in model predictions and improve their usefulness. A collaboration among scientists from the USDA-Agricultural Research Service in Watkinsville Georgia and Auburn Alabama, Auburn University, USDA-Natural Resources Conservation Service in Temple Texas, and Joint Global Change Research Institute in College Park Maryland tested the performance of a highly technical environmental model (EPIC v. 3060) against five years of crop yield and soil data collected from a corn–cotton rotation in central Alabama. The cropping system had additional variables of dairy bedding manure and conventional and conservation tillage systems. The model accounted for 88% of the variation in corn grain and cotton lint yields during the five years. Model predictions were sensitive to landscape position. Predictions of soil organic carbon at the end of five years of the different management schemes were very reasonable, although distribution with depth and within various fractions of organic matter were not wholly adequate. This research demonstrated that EPIC modeling has challenges to overcome, but could be a reasonably accurate tool to predict yield and environmental consequences for the greater than 10 million acres of corn and cotton land in the southeastern USA.

Technical Abstract: Simulation models integrate our understanding of soil organic C (SOC) dynamics and are useful tools for evaluating impacts of crop management on soil C sequestration, yet, they require local calibration. Our objectives were to calibrate the Environmental Policy Integrated Climate (EPIC) model, and evaluate its performance for simulating SOC fractions as affected by landscape and management system. An automated parameter optimization procedure was used to calibrate the model for a site-specific experiment in the Coastal Plain of central Alabama. Model performance in predicting corn (Zea mays L.) and cotton (Gossypium hirsutum L.) yields and SOC dynamics was evaluated on different soil landscapes (Typic, Oxyaquic and Aquic Paleudults) during the initial period of conservation tillage adoption (5 years), using regression and mean squared deviations (MSD). Simulated yield explained 88% of measured yield variation, with greatest disagreement on the sideslope position and highest agreement in the drainageway. Simulations explained approximately 23, 27 and 40% of the total variation in microbial biomass C (MBC), particulate organic C (POC) and total organic C (TOC), respectively. Lowest errors on TOC simulations (0-20 cm) were found on the sideslope and in the drainageway. We conclude that the automated parameterization was successful, although further work is needed to fine tune the POC and MBC fractions, and to improve EPIC predictions of SOC dynamics with depth. Overall, EPIC was sensitive to spatial differences in C fractions that resulted from differing soil landscape positions. With correct parameterization, EPIC is a valuable tool for simulating field-scale SOC dynamics affected by short-term management.

Last Modified: 10/25/2014
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