Submitted to: ASA-CSSA-SSSA Annual Meeting Abstracts
Publication Type: Abstract Only
Publication Acceptance Date: 7/28/2014
Publication Date: 11/2/2014
Citation: Jin, V.L., Varvel, G.E., Schmer, M.R., Wienhold, B.J., Del Grosso, S.J., Johnson, J.M. 2014. Long-term tillage studies: what's new and what's surprising?. ASA-CSSA-SSSA Annual Meeting Abstracts. Paper No. 88519..
Technical Abstract: Conservation tillage practices (e.g. reduced or no-tillage) have been promoted for mitigating atmospheric CO2 levels by enhancing soil organic carbon (SOC) storage in agricultural landscapes. Conservation tillage can lead to higher SOC in surface soils (<30 cm), but when the whole soil profile is considered, the net difference in SOC stocks is often equivocal. Six different tillage practices ranging in intensity from no-till to moldboard plow have been evaluated since 1981 in continuous corn, continuous soybean, and corn-soybean systems at a long-term field experiment in eastern Nebraska. During the first 22 years of management (1981-2004), soil organic carbon accrual occurred in surface soils under all tillage treatments. On a whole soil basis (0-150 cm), SOC accrual was evident for soils under no-till and disk tillage, with no change occurring under the most aggressive practice of moldboard plow. Since 2004, however, SOC stocks have declined in surface soils and for the whole soil profile, suggesting that soil disturbance associated with recent changes in fertilizer application methods (e.g. surface broadcast ammonium nitrate to injected urea) have resulted in SOC losses. While results here are specific to this site, coordinated data sets from multiple long-term studies are necessary to evaluate overall management impacts and agroecosystem potential to sequester C. Data networks such as USDA-ARS’s Greenhouse gas Reduction through Agricultural Carbon Enhancement Network (GRACEnet) help fill this critical need by (1) providing empirical data to the scientific community at large, and (2) facilitating the calibration and validation of computer models used to simulate regional and national impacts of agricultural management practices.