A process-based model of ammonia emissions from dairy cows: improved temporal and spatial resolution
Introduction
Ammonia is an important atmospheric pollutant that plays a key role in several air pollution problems. When combined with nitric acid, ammonia forms aerosol nitrate, which contributes significantly to total particulate matter (PM) (McNaughton and Vet, 1996). These particles have serious impacts on human health (Dockery et al., 1993) and influence climate (Charlson et al., 1992). When deposited in relatively pristine areas, an abundance of ammonia can cause degradation of aquatic (Jenkinson, 2001; Howarth et al., 2002) and terrestrial (Rennenberg and Gessler, 1999) ecosystems. While deposition of other atmospheric pollutants in the United States has been decreasing, concentrations of ammonia in precipitation have increased over the past 20 years (Nilles and Conley, 2001).
The sensitivity of ammonium nitrate aerosol concentrations to ammonia varies seasonally and geographically (West et al., 1999). However, previous inventories of ammonia emissions assume uniform emission factors for livestock across all locations and seasons (Pain et al., 1998; Hutchings et al., 2001). More accurate emission inventories require emission factors that are geographically and temporally resolved.
In both Europe and the United States, the largest source of ammonia emissions is livestock, estimated to account for 70–90% of total emissions, and dairy cows are one of the largest livestock sources (Battye, 1994; USEPA, 2000; Pain et al., 1998; Hutchings et al., 2001). These emissions arise from urine patches on grazed pastures, excreta deposited onto the floor of housing facilities, manure held in storage, and volatilization during the application of manure onto fields (Sommer and Hutchings, 1997).
Variation in ammonia emission factors results from the dependence of ammonia volatilization on meteorological conditions and seasonal and regional differences in farming practices. In field studies, high temperatures and wind speeds have been shown to increase the volatilization of ammonia (Sommer et al., 1991; Demmers et al., 1998). Heavy rains cause emissions to decrease to near zero (Sommer and Olesen, 2000). In cooler climates, cows are confined in housing units for the duration of winter; manure stored during this period is not applied to the fields until spring. In warmer climates, the cows may graze throughout the year. However, the amount of variation we can explain with current scientific understanding is limited. Because of the vast number of farm types and climate conditions, all of the experimental results to date cover only a small subset of possible emission scenarios.
We have addressed this need by developing the Farm Emissions Model (FEM), an integrated model of a dairy farm similar to that presented by Hutchings et al. (1996). The FEM predicts per cow emissions given a specific set of manure management practices and a temporal profile of temperature, precipitation, and wind speed. The FEM can be combined with the geographic distribution of manure management practices, animal populations, and climate data to produce an emission inventory. The results of such an application to the United States are discussed in a future paper.
To determine their model parameters, Hutchings et al. (1996) surveyed the literature and calculated the parameters based on the best available data. A major difference in our approach is that we use a formal technique to estimate model parameters. We apply Bayesian parameter estimation and experimental data to estimate the model parameters and uncertainty. The resulting parameters reflect the range of experimental conditions found in the literature. Where possible, we then validate our model with independent experimental results. As an example application of the FEM, we present monthly emission factors for four different farms in the United States.
Section snippets
Overview
Most experiments that measure ammonia emissions collect samples at a particular phase of the manure management process over a limited period of time. Such experiments generally focus on a subset of the factors that affect emissions. The FEM is designed to use these experimental results to generalize over the set of possible farming practices and conditions required for a large-scale emission inventory. In order to explain the variability present in emission factors, the FEM explicitly models
Results
Fig. 4 shows emissions from a confined and a grazing farm in two counties: Tulare County, California and Lancaster County, Pennsylvania. The confined California farm is typical of a large confined animal feeding operation, with manure stored in lagoons and applied seasonally. The grazing California farm has the same manure management system, but the animals graze seasonally. The Pennsylvania dairy with grazing is typical for a small farm with manure handled in solid form and applied daily,
Conclusions
We have presented an integrated model for a dairy farm that predicts monthly ammonia emission factors for a variety of farming practices and climate conditions. Comparison with independent test data has shown good agreement. Our results suggest that farming practices are the most important determinant of annual average emissions. However, differences in the seasonal temperature, wind speed, and precipitation profile also significantly affect emissions. When calculating emission inventories, it
Disclaimer
Data included in some parts of this analysis were provided by the U.S. Department of Agriculture, Animal and Plant Health Inspection Service, Veterinary Services, National Animal Health Monitoring System (NAHMS). However, the analysis and conclusions described in this article are independent of Veterinary Services and NAHMS.
Acknowledgements
This work has been supported by the Mid-Atlantic Regional Air Management Association (MARAMA), the Northeast States for Coordinated Air Use Management (NESCAUM), and a graduate student fellowship from the National Science Foundation.
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