Location: Livestock Nutrient Management
Title: Process models vs empirical models Author
Submitted to: Western Dairy Air Quality Symposium
Publication Type: Proceedings
Publication Acceptance Date: March 25, 2013
Publication Date: April 16, 2013
Citation: Waldrip, H. 2013. Process models vs empirical models. Western Dairy Air Quality Symposium. 1:1. Interpretive Summary: The dairy industry is growing in the western and south-central parts of the United States, and farms in these regions are usually bigger than farms in the eastern parts of the nation. On these large farms it is hard to manage nutrients and control losses of gases that can harm human health and the environment, such as ammonia. More regulation on dairies and other animal farms means that tools are needed to help to figure out how much gas, and what kinds, are produced. To meet these needs, the National Research Council found three possible ways to predict gas losses from animal farms: emission factors, empirical models, and process-based models. Emission factors are pulled from published papers where gas losses were measured on farms that were considered average for the area and type of animal being raised. Emission factors are often used to make rules and figure out how much gases are being produced by different kinds of farms. One big problem with emission factors is that they do not account differences is gas production that are caused by weather or farm management. Empirical models are made by using statistics to figure out how things are related. Empirical models can be good for predicting gas losses or creating information summaries, but they are only useful for the type of farm and area for which they were made. Right now there is very little information on gas losses from dairies in the western and south-central states, so it is important to be careful when making predictions with empirical models made for dairies in other area. Process-based models are based on scientific information about how a system works. Dynamic, process-based computer models that predict gas losses based on classical scientific principles are an inexpensive way to estimate gas losses and figure out how changing climate and management practices could influence gas losses from animal farms. Two examples of models that can predict gas emissions from dairies are Manure-DNDC and the Integrated Farm Systems Model. These models use a step-by-step approach to follow compounds of interest, such as nitrogen and carbon, from beginning to end. These models have been evaluated for predicting gas losses from many kinds of animal farms, including dairies. They are sensitive to changes in farm management and weather in a manner similar to measured emissions. In conclusion, emission factors, empirical models, and process-based models are available to predict gas losses from animal farms. Process-based models are flexible and are the best way to predict gas losses. Process-based models can help to figure out what kind of gases, and how much, gas, are being lost from farms. They can also help predict what will happen to gas losses when there is a change in climate or how the farm is being managed.
Technical Abstract: The dairy industry is rapidly expanding in the western and south-central U.S., with a trend towards larger, high-density, intensive production systems. Increased production intensity creates challenges for effective management of nutrients and control of emissions of ammonia, hydrogen sulfide, greenhouse gases (methane, nitrous oxide), and volatile organic compounds: compounds that are potentially harmful to human health and the environment. Increased legislation regarding emissions from dairies and other livestock production systems has prompted a call for tools to assist with quantifying the impact of animal agriculture on the environment. To meet the need for valid inventory estimates, the National Research Council has identified three possible approaches to estimate emissions from livestock production, emission factors, empirical models, and process-based models. Emission factors (EFs) are derived from the literature by selecting data from studies that measured emissions from “average” operations assumed representative of production facilities for a specific livestock type and region. These EFs are often used in setting regulatory policy and inventory of emissions; however, they do not account for temporal and spatial differences in management practices and climatic conditions. Empirical models are constructed by statistically determining relationships between dependent variables and factors that are hypothesized to influence them. Empirical models are often suitable for interpolating data or when a convenient data summary is the objective; however, they must be used within the constraints of the particular system for which they were designed. Due to very limited data on emissions from dairies in the western states, much care should be exercised when making predictions with empirical equations developed for dairies in the eastern U.S. and Europe. Process-based models are based on scientific understanding of causal relationships of the system being modeled and include ideas of how the system works, and what the important elements are and how they relate to each other. Dynamic, process-based computer simulation models that quantify emissions based on classical principles of thermodynamics and kinetics provide a cost-effective method of estimating emissions and determining how changing climate and management practices affect emissions from animal agriculture. Two examples of whole-farm models that can simulate emissions of ammonia and greenhouse gases from dairy production systems are Manure-DNDC and the Integrated Farm Systems Model (IFSM). These models employ a stepwise approach to track the fate of compounds of interest, such as nitrogen and carbon, from beginning (e.g. animal feeding) to end (e.g. ammonia volatilization). These models have been evaluated for emissions from numerous livestock systems, including dairies, and exhibit sensitivity to changes in management and climatic conditions in a manner similar to measured emissions. In conclusion, emission factors, empirical models, and process-based models are available to estimate emissions from animal production systems; however, valid process-based models offer the most flexible approach. Process-based models can assist with the production of high-resolution gas inventories, forecast future trajectories of the impact of livestock production on the environment, and evaluate the effects of changing management on farm nutrient balances.