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A reliable laboratory index of N 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 combining indices for predicting potentially mineralizable N ([N.sub.0]). Soils (0-5 and 5-15 cm) from nine tillage studies across the southern USA were used in the evaluations. Long-term incubation data were fit to a first-order exponential equation to determine [N.sub.0], k (mineralization rate), and [N.sub.0.sup.*] ([N.sub.0] estimated with a fixed k equal to 0.054 [wk.sup.-1]). Out of 13 indices, five [total C (TC), total N (TN), N mineralized by hot KC1 (Hot_N), anaerobic N (Ana_N), and N mineralized in 24 d (Nmin_24)] were strongly correlated to NO (r > 0.85) and had linear regressions with [r.sup.2] > 0.60. None of the indices were good predictors of k. Correlations between indices and [N.sub.0.sup.*] improved compared with [N.sub.0], ranging from r = 0.90 to 0.95. Total N and flush of C[O.sub.2] determined after 3 d (Fl_C[O.sub.2]) produced the best multiple regression for predicting [N.sub.0] ([R.sup.2] = 0.85) while the best combination for predicting [N.sub.0.sup.*] ([R.sup.2] = 0.94) included TN, Fl_CO2 Cold_N, and NaOH_N. Combining indices appears promising for predicting potentially mineralizable N, and because TN and Fl_CO2 are rapid and simple, this approach could be easily adopted by soil testing laboratories. Abbreviations: Ana_N, anaerobic N mineralization; TC, total carbon; Ca_hypcl, calcium hypochlorite; Cold_N, KCl extractable N[O.sub.3]--N; CT, conventional tillage; Fl_CO2, flush of C[O.sub.2] during 3 d; Hot_N, hot KCl extractable N[H.sub.4]- -N; Hyd_N, hydrolyzable N; k, mineralization rate constant; TN, total nitrogen; NaOH_N, sodium hydroxide distillable N; [N.sub.0], potentially mineralizable N; [N.sub.0.sup.*], value of [N.sub.0] determined using a fixed value for k, Nmin_24, N mineralization during 24 d; NP, not plowed (prairie soil); NT, no-Tillage; NT+SS, no-tillage with non-inversion subsurface deep tillage; PB_N, phosphate- berate distillable N; POMC, particulate organic matter C; POMN, particulate organic matter N; SM, stubble mulch tillage (sweeps to undercut weeds); ST, strip tillage (in-row subsoil for disruption of subsurface pan and coulters for preparation of narrow strip of tilled soil).

Poultry litter (PL) can supply N, P, K, and other plant nutrients; however, excessive application may cause environmental problems, depending on management and crop nutrient demand. Changes in soil test (ST) nutrient content in a Cecil soil (a fine, kaolinitic, thermic Typic Kanhapludult) during a 10-yr period of PL use was evaluated at the USDA-ARS J. Phil Campbell, Sr., Natural Resource Conservation Center, Watkinsville, GA. During the cotton (Gossypium hirsutum L.) cropping phase (1995-2000), 4.4 Mg PL [ha.sup.-1] [yr.sup.-1] resulted in small changes in ST nutrient content in the surface 15 cm. Differences were observed between tillage treatments, with less accumulation of Ca, Mg, and Mn and greater accumulation of Zn for no-till (NT) than conventional tillage (CT). During the corn (Zea mays L.) cropping phase (2001-2005), average annual PL inputs (11.2 Mg [ha.sup.-1]) increased P and Zn contents, with changes being similar for CT and NT. After 10 yr, ST nutrient contents in the surface 15 cm reflected 25, 4, 45, 26, 17, and 97% of the input from PL for P, K, Ca, Mg, Mn, and Zn, respectively. Changes in soil profile nutrient content (to a depth of 60 cm) from 1997 to 2005 were predominantly at 0 to 15 cm, where P and Zn increased >200%. Accumulation of Ca, K, P, and Zn at lower depths was also observed. Strategies for increasing nutrient removal following repeated long-term application of PL should be considered to avoid excessive levels of nutrients. Abbreviations: CE conventional fertilizer; CT, conventional tillage; FS, fertilizer source; NT, no-till; PL, poultry litter; ST, soil test.

The use of urea fertilizers in grasslands is likely to increase in areas with concentrated animal feeding operations as restrictions on manure applications are implemented. Concerns have been raised about the economic and environmental impacts of N[H.sub.3] loss from these urea fertilizers. This study evaluated N[H.sub.3] losses from Nitamin (a urea polymer), urea-N[H.sub.4]N[O.sub.3] (UAN), and granular urea applied to tall fescue (Festuca arundinacea Schreb.) plots at 50 kg N [ha.sup.-1] in fall and spring for 2 yr. Fertilizers were applied to circular plots (30-m diameter) and N[H.sub.3] loss was measured by the modified passive flux method for 69 to 120 d after application. In a separate laboratory study, Nitamin, UAN, and urea were surfaced applied to rescue thatch at 100 kg N [ha.sup.-1] and treatments were incubated at 24[degrees]C and 90% relative humidity for 31 d. In fall applications, urea lost more N[H.sub.3] (19% in 2004, 46% in 2005) than UAN or Nitamin, which were not different from each other (6% in 2004, 34% in 2005). In contrast, there were no differences among fertilizers in spring applications, with average losses of 13% in 2005 and 17% in 2006. In the laboratory study, urea lost significantly more N[H.sub.3] (24%) than UAN or Nitamin, which were not different from each other (average 9% loss). These results indicate that Nitamin and UAN undergo similar N[H.sub.3] losses, and that both fertilizers may lose less N[H.sub.3] than urea under conditions favorable to volatilization. Abbreviations: CRH, critical relative humidity; RH, relative humidity; UAN, urea--ammonium nitrate.

Previous work showed that simulated rainfall applied immediately after urea application to a pine forest floor decreased N[H.sub.3] losses to negligible levels. The same work also showed that unless rain occurs before urea is dissolved, it might not be effective at leaching urea into the forest floor and decreasing N[H.sub.3] losses. This study evaluated the effects of diurnal humidity cycles and rainfall amounts on the proportion of urea leached from loblolly pine (Pinus taeda L.) forest floor fractions, and on subsequent ammonia volatilization. New and old pine needles, as well as the partially decomposed ([O.sub.e]) horizon, were collected from a loblolly pine forest floor in Georgia, USA. Control samples and samples treated with urea at 200 kg N [ha.sup.-1] were exposed to zero, two, four, or eight simulated humidity cycles, after which they were leached with nine 20-mm increments of simulated rain. Rainfall (180 mm) applied immediately after urea application (0 humidity cycles) leached >98% of the urea from the three forest fractions. Increasing the number of simulated humidity cycles from zero to eight decreased the percentage of urea leached from new pine needles from 98 to 49%, but did not have a major effect on the amount of urea leached from old needles or partially decomposed fraction. All of the urea not leached from new pine needles by 180 mm of simulated rainfall was extractable by water when the new needles were ground. When new needles treated with urea were leached with 180 mm of simulated rainfall and then incubated at 25[degrees]C and 95% relative humidity (RH) for 15 d, N[H.sub.3] losses amounted to 8% of the urea applied. These results suggest that urea retained by recently dropped pine needles is found inside the needles, where it can be hydrolyzed and subsequently lost as N[H.sub.3].

Quantification of nitrogen mineralization at the field scale is necessary to improve current N fertilizer recommendations. The objective of this study was to identify a suitable set of explanatory variables and an appropriate modelling framework to estimate the average daily amount of N mineralized (DANM) during a corn (Zea mays L.) growing season. In six field experiments in Flat Inland Pampas, Argentina, DANM was evaluated in three different homogeneous zones (HZ), which were defined based on terrain elevation. Mixed‐effect generalized linear models were developed that included growing season as random effect as well as HZ, sand, clay, silt, soil organic carbon, and normalized difference vegetation index (NDVI) as fixed effects. Results showed that DANM ranged from 0.19 to 1.25 kg N ha−1 day−1 with a mean of 0.64 kg N ha−1 day−1, which is equivalent to 110 kg N ha−1 for the corn growing season. The best model included HZ, sand, and early NDVI measurements, and predicted DANM with an RMSE of 0.19 kg N ha−1 day−1. Thus, the model should be improved and tested in the Inland Flat Pampas before it could be used to assess N fertilizer application rates. [ABSTRACT FROM AUTHOR]

Legume cover crops can supply a significant amount of nitrogen (N) for cash crops, which is particularly important for organic farmers. Because N mineralization from cover crop residue depends on the amount of biomass, cover crop quality, as well as environmental conditions such as soil moisture and temperature, predicting the amount of N mineralized and the timing of release has been difficult. We have developed a Cover Crop Nitrogen Calculator based on the N subroutine of the CERES crop model and evaluated the use of the predicted N credits on yields of fall broccoli [Brassica oleracea L. (Italica group)] at a research farm and two working farms. Research farm trials consisted of a cowpea (Vigna unguiculata L. Walp.) cover crop and no cover crop treatments, each at four N rates (0N, 0.5N, 1N and 1.5N, with 1N the target N rate of 112 kg N ha−1 in 2013 and 168 kg N ha−1 in 2014 and 2015) in a randomized complete block design. On-farm trials consisted of a cowpea or sunn hemp (Crotolaria juncea L.) cover crop at four N rates (0N, 0.5N, 1N and 1.5N) and no cover crop treatment at the 1N rate in a completely randomized design. Cover crop biomass and quality (N%, carbohydrates%, cellulose% and lignin%) were measured and used with a 5-yr average soil moisture and soil temperature from a local weather station to predict an N credit. In the cover crop treatments, the N rate was modified by the predicted credit, while the no cover crop treatment received the full N fertilizer rate either as feathermeal (certified organic fields) or as urea (conventional field). At the research farm, broccoli yield increased up to the 0.5N rate, and there was no difference in yield between the no cover 0.5N rate and the cover crop 0.5N rate in 2013, 2014 and 2105. On-farm, we saw an N response in two site-years. In these site-years, there was no difference between the no cover 1N rate and the cover crop 1N rate. At the third site-year, no N response was seen. Overall, our results showed using the cover crop credit predicted by the Calculator did not reduce yields. The use of a decision support tool such as the Calculator may help farmers better manage N fertilizer when cover crops are used, and increase cover crop adoption. [ABSTRACT FROM AUTHOR]