Assessing indices for predicting potential nitrogen mineralization in soils under different management systems

Authors: Schomberg, Harry; WIETHOLTER, SIRIO - EMBRAPA TRIGO, BRAZIL; Griffin, Timothy; Reeves, Donald; CABRERA, M - UNIVERSITY OF GEORGIA; Fisher, Dwight; Endale, Dinku; Novak, Jeffrey; Balkcom, Kipling; Raper, Randy; Kitchen, Newell; Locke, Martin; Potter, Kenneth; Schwartz, Robert; Truman, Clinton; TYLER, D - UNIVERSITY OF TENNESSEE

Submitted to: Soil Science Society of America Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/21/2009
Publication Date: 9/1/2009
Citation: Schomberg, H.H., Wietholter, S., Griffin, T.S., Reeves, D.W., Cabrera, M.L., Fisher, D.S., Endale, D.M., Novak, J.M., Balkcom, K.S., Raper, R.L., Kitchen, N.R., Locke, M.A., Potter, K.N., Schwartz, R.C., Truman, C.C., Tyler, D.D. 2009. Assessing indices for predicting potential nitrogen mineralization in soils under different management systems. Soil Science Society of America Journal. 73:1575-1586.

Interpretive Summary: Nitrogen (N) accounted for 57 percent of the 21 million tons of chemical fertilizer nutrients (nitrogen, phosphate, and potash) used by U.S. agriculture in 2006. Nitrogen fertilizer recommendations for producers are usually based on crop needs and may not take into consideration the amount of N that could be available from the soil. Better predictions of N release from soil could reduce over application of N fertilizers which would reduce costs and the potential for N leaching to groundwater and running into surface waters, which are environmental concerns. Past research has identified several laboratory methods that may be used for predicting N availability but none have worked well across a wide range of soils and management conditions. Researchers from USDA’s Agricultural Research Service (ARS) at the J. Phil Campbell, Sr. Natural Resource Conservation Center led a team of ARS, EMBRAPA (Brazil’s federal agricultural research agency) and university scientists to investigate using a combination of select laboratory methods for improving prediction of soil N availability. The various methods were compared over a range of soils from tillage experiments at nine locations across the southern USA. Five of the laboratory methods using soil carbon, total soil nitrogen, and incubation methods provided the best predictions of potentially available N. Total soil N is easily and rapidly determined, but does not distinguish between readily available inorganic N and other organic forms of N with variable release rates to crops. Using combinations of methods improved the ability to predict N availability. We found that a combination of laboratory methods that included total N and an easily determined measurement of microbial growth appeared promising for predicting potentially available N. Use of this approach by soil testing laboratories could help producers save $10 to $20 per acre in reduced fertilizer costs while reducing the amount of N fertilizer lost to ground water. This information is important for producers, soil testing laboratories, extension agents, policy makers and researchers.

Technical Abstract: A reliable laboratory index of nitrogen availability would be useful for making N recommendations but no single approach has received broad acceptance across a wide range of soils. We compared several indices over a range of soil conditions to test the possibility of determining the best combination of indices for predicting potentially mineralizable N (N0). Soils (0-to-5 and 5-to-15 cm) from tillage experiments at nine locations across the southern USA, were used in the study. Long-term incubation data were fit to a first order exponential equation to determine N0, k (mineralization rate), and N0 estimated with a fixed k (N0*). Five indices [total C, total N, N mineralized by hot KCl (Hot_N), anaerobic N (Ana_N), and N mineralized in 24 d (Nmin_24)] were strongly correlated to N0 (r > 0.85) and had linear regressions with r2 > 0.60. No index was a good predictor of k. Correlations between indices and N0* improved compared to N0 ranging from r = 0.71 to 0.95 with the same five indices (above) being strongest. Total N and flush of CO2 determined after 3 d (FL_CO2) produced the best multiple regression for predicting N0 (R2 = 0.85) while the best combination for predicting N0* (R2 = 0.94) included total N, FL_CO2, Cold_N and NaOH_N. Combining indices appears promising for predicting potentially mineralizable N and because total N and FL_CO2 are rapid and simple this approach could be easily adopted by soil testing laboratories.