Research has verified the occurrence of veterinary antibiotics in manure, agricultural fields, and surface water bodies, yet little research has evaluated antibiotic runoff from agricultural fields. The objective of this study was to evaluate the potential for agricultural runoff to contribute antibiotics to surface water bodies in a worst-case scenario. Our hypothesis was that there would be significant differences in antibiotic concentrations, partitioning of losses between runoff and sediment, and pseudo-partitioning coefficients (ratio of sediment concentration to runoff concentration) among antibiotics. An antibiotic solution including tetracycline (TC), chlortetracycline (CTC), sulfathiazole (STZ), sulfamethazine (SMZ), erythromycin (ERY), tylosin (TYL), and monensin (MNS) was sprayed on the soil surface 1 h before rainfall simulation (average intensity = 60 mm h(-1) for 1 h). Runoff samples were collected continuously and analyzed for aqueous and sediment antibiotic concentrations. MNS had the highest concentration in runoff, resulting in the highest absolute loss, although the amount of loss associated with sediment transport was <10%. ERY had the highest concentrations in sediment and had a relative loss associated with sediment >50%. TYL also had >50% relative loss associated with sediment, and its pseudo-partitioning coefficient (P-PC) was very high. The tetracyclines (TC and CTC) had very low aqueous concentrations and had the lowest absolute losses. If agricultural runoff is proven to result in development of resistance genes or toxicity to aquatic organisms, then erosion control practices could be used to reduce TC, ERY, and TYL losses leaving agricultural fields. Other methods will be needed to reduce transport of other antibiotics.

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 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 fi rst-order exponential

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