Combined Monitoring and Modeling Indicate the Most Effective Agricultural Best Management Practices

Abstract

Although water quality problems associated with agricultural nonpoint source (NPS) pollution have prompted the rapid and widespread adoption of a variety of so called "best management practices" (BMPs), there have been few realistic eff orts to assess their combined eff ectiveness in reducing NPS pollution. Th is study used the Variable Source Loading Function (VSLF) model, a distributed watershed model, to simulate phosphorus (P) loading from an upstate New York dairy farm before and after the implementation of a suite of BMPs. With minimal calibration, the model calculates the dissolved P (DP) losses from impervious surfaces (e.g., barnyards), the plant/soil complex, fi eld-applied manure, and loads associated with basefl ow conditions. Th e simulated DP loads agreed well with measured loads for both the pre-BMP and post-BMP periods. More importantly, results showed that BMPs reduced DP loads by 35%, which is over half of the expected reduction if all manure was removed from the watershed, i.e., ~50% reduction. Th e model results indicate that had no BMPs been installed DP loads would be ~37% greater than observed at the watershed outlet. Th e most eff ective BMPs were those that disassociated pollutant loading areas from areas prone to generating runoff , i.e., hydrologically sensitive areas. By contrast, attempts to reduce P content in manure were somewhat less eff ective. Th is study demonstrates that a combination of distributed, mechanistic modeling and long-term monitoring provides better insights into the eff ectiveness of water quality protection eff orts than either individually.

Measuring conservation program best management practice implementation and maintenance at the watershed scale

“…Official records have been utilized to compare the impacts of different densities of BMP use on measured water quality parameters at the subwatershed level (Yates et al 2007) and to compare water quality conditions at the farm and watershed scale between pre-versus post-BMP imple- mentation time periods (Brannan et al 2000;Currens 2002;Gitau et al 2008). Although standard watershed process models such as SWAT, AnnAGNPS, and GLEAMS have mainly been used to simulate the effects of hypothetical patterns of BMP usage (Arnold et al 1998;Heathman et al 2008; Thomas et al 2007), a growing number of researchers have worked to incorporate information about the types and locations of actual BMP behaviors into these process models at both the field and watershed levels and to compare simulated versus measured water quality outcomes associated with these real-world conservation behaviors (Bracmort et al 2006, Easton et al 2008 Rao et al 2009;.While a valuable resource, there are several potential limitations to using official conservation program records. Initially, researchers have found agency staff hesitant to release details of individual conservation contract files to nonagency personnel, largely because of legal requirements to protect the confidentiality of contract information.…”

Assessment of Agricultural Best Management Practices Using Models: Current Issues and Future Perspectives

“…Though modelling methods have been widely used, very few of them have been verified [87]. Part of the reason is the lack of detailed monitoring data.…”

Real-Time Forecast of Hydrologically Sensitive Areas in the Salmon Creek Watershed, New York State, Using an Online Prediction Tool

“…Additionally, the current NYS P-Index transport factor is more or less static, restricting the application of manure within a fixed-width 30 m buffer to the stream [33], which has been shown to not effectively abate dissolved phosphorus transport to streams [4,8,9,13]. Recognizing that the location where runoff occurs varies both spatially and temporally and depends on the amount of rainfall and antecedent moisture conditions in the watershed [14,34], the HSA prediction tool provides a means to support the adaptation of variable-width buffers (e.g., derived with the soil topographic index [35]) that capture more realistic runoff generating areas [25,26,35,54] and dissolved nutrient transport via shallow subsurface storm flow [34,77]. Furthermore, the spatial maps provided in the VSA prediction tool support the development of other land management decisions (e.g., riparian buffers) that can also be effective of keeping pollutants from being directly introduced to streams.…”