Observed and simulated solute transport under varying water regimes: II. 2,6-Difluorobenzoic acid and dicamba

Significant interest in the fate of agrichemicals in soils has prompted the development of several transport simulation models. Our primary objective was to evaluate the simulation model LEACHM for predicting the fate of dicamba (3,6-dichloro-2-methoxybenzoic acid) and a nonreactive tracer, 2,6- difluorobenzoic acid (2,6-DFBA) in fallow and cropped (barley, Hordeum vulgare L.) systems under different water application levels. A field study (1992) was conducted using in situ soil columns on a Borollic Calciorthid (Brocko silt loam, Gallatin Co., MT). Dicamba (¹⁴C-labeled) and 2,6-DFBA were surface applied at rates of 0.26 and 112 kg ha^-1, respectively. Solute concentrations were measured at three depths (0.36, 0.66, and 0.96 m) using porous cup lysimeters for 70 d following chemical application. Time-moment analysis of observed solute breakthrough curves (BTCs) generally showed increasing travel times with increasing soil depth and decreasing water application. Dicamba transport was similar to 2,6-DFBA, with the exception that 40 to 60% of applied dicamba was degraded during transport. The distribution of ¹⁴C remaining in the soil columns showed that the primary degradate of dicamba, 3,6-dichlorosalicylic acid (DCSA), was confined primarily to surface samples (0-0.2 m) while dicamba was found only at lower depths. This is consistent with a much higher sorption coefficient (K(oc)) determined for DCSA relative to dicamba. Comparison of observed and predicted (LEACHM) solute BTCs suggested that preferential solute transport occurred especially under high and medium water regimes. Finally, data from three field seasons at the same site suggest that the timing of chemical application relative to initial soil water content and plant stage, the presence of root channels, and temporal changes in soil hydraulic properties in the absence of tillage may significantly affect the degree of preferential flow and subsequent agreement between predicted and observed BTCs.