Climate Change Mitigation and Adaptation in Dairy Production Systems of the Great Lakes

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To better understand how dairy agriculture can reduce its impact on climate change, the USDA has supported a large, transdisciplinary research project to examine dairy production systems across the Great Lakes region of the United States. The goals of the Sustainable Dairy Coordinated Agricultural Project are to identify where in the life cycle of a dairy system can beneficial management practices (BMP) be applied to reduce greenhouse gases (GHG) without sacrificing productivity or profit to the farmer. Since 2013, a team of 70 researchers has been collaborating across institutions and disciplines to conduct the investigations.

What did we do?

Experimental data were collected at the cow, barn, manure, crop and soil levels from 2013-2016 by agricultural and life scientists. Modelers continue to conduct comparative analyses of process models at the animal, field and farm scales. Atmospheric scientists have down-scaled global climate models to the Great Lakes region and are integrating climate projections with process modeling results. The Life Cycle Assessment team is evaluating select beneficial management practices to identify where the greatest reduction of greenhouse gases (GHG) may occur. Results of focus groups and farmer surveys in Wisconsin and New York will help us understand how producers currently farm and what types of changes they may be willing to implement, not just to reduce emissions but to adapt to long-term changes in climate.

What have we learned?

Through the Dairy CAP grant, researchers have developed and refined the best ways to measure GHG emissions at the cow, barn, manure, crop and soil levels, and these data are archived through the USDA National Sustainable Dairy LogoAgricultural Library. Results show that the greatest levels of methane produced on a farm come from enteric emissions of the cow and changes in the diet, digestion and genetics of the cow can reduce those emissions. Another significant source of methane—manure production, storage and management—can be substantially reduced through manure management practices, particularly when it is processed through an anaerobic digester. Changes in timing of nitrogen application and use of cover crops practices are found to improve nitrogen efficiency and reduce losses from the field.

A comparative analysis of process models showed multiple differences in their ability to predict GHG emissions and nutrient flow (particularly nitrogen dynamics) at the animal, farm, and field scales. Field data collected were used to calibrate and refine several models. The Life Cycle Assessment approach shows that a combination of BMPs can reduce GHG emissions without sacrificing milk production. The application of down-scaled climate data for the Great Lakes region is being used in conjunction with the suite of BMPs to develop mitigation and adaptation scenarios for dairy farming in the Upper Midwest.

Research findings are shared through a series of fact sheets available on the project website, and a web-based, virtual farm that presents educational materials for 150- and 1500-cow operations to a variety of audiences, ranging from high school students to academics.

Future Plans

The Dairy CAP grant sunsets in 2018, but research questions remain relative to the efficacy of beneficial management practices at different stages in the life cycle of a farm. Challenges revolve around the complexity of farming practices, the individuality of each farm and how it is managed, and uncertainty associated with the predictive capabilities of models. Mitigation and adaptation strategies will be shared with the dairy industry, educators and extension partners who will be responsible for working with farmers at the field level. Implementation of these strategies will make dairy farming in the Great Lakes region more resilient.

Corresponding author, title, and affiliation

Carolyn Betz, Research Project Manager, University of Wisconsin-Madison. Department of Soil Science

Corresponding author email

Other authors

Matt Ruark and Molly Jahn

Additional information


This material is based upon work that is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, under award number 2013-68002-20525.

A Model Comparison of Daily N2O Flux with DayCent, DNDC, and EPIC

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Process-based models are increasingly used as tools for studying complex agroecosystem interactions and N2O emissions from agricultural fields. The widespread use of these models to conduct research and inform policy benefits from periodic model comparisons that assess the state of agroecosystem modeling and indicate areas for model improvement. The increasingly broad application of models requires an assessment of model performance using datasets that span multiple biogeophysical contexts. While limited in the capacity to identify specific areas for model improvement, general evaluations provide a critical perspective on the use of model estimates to inform policy and also identify necessary model improvements that require further evaluation.

What did we do? 

The objectives of this model comparison were to i) calibrate and validate three process-based models using a large dataset; ii) evaluate the performance of a multi-model ensemble to estimate observed data; and iii) construct a linear model to identify and quantify possible model bias in the estimation of soil N2O flux from agricultural fields. We selected three models that have been used to evaluate N2O emissions from agricultural fields: DayCent, DNDC, and EPIC. Using data from two field experiments over five years, we calibrated and validated each model using observations of soil temperature (n = 887), volumetric soil water content (VSWC) (n = 880), crop yield (n = 67), and soil N2O flux (n = 896). Our model validations and comparisons consisted of commonly conducted statistical evaluations of root mean squared error, correlation, and model efficiency. Additionally, the large sample sizes used here allowed for more robust linear regression models that offered additional insight into relationships between model estimations and observations of N2O flux. We hypothesized that such a linear model would indicate if there was model bias in estimations of soil N2O flux. Ensemble modeling can reduce the error associated with climate projections and has recently been applied to the estimation of N2O flux from agroecosystems. Thus, we also constructed a multi-model ensemble to evaluate the use of multiple models to improve estimates of soil N2O flux.

What have we learned? 

In a comparison of three process-based models, calibration to a large dataset produced favorable estimations of soil temperature, VSWC, average yield, and N2O flux when the models were evaluated using RMSE, R2, and the Nash-Sutcliffe E-statistic. However, an evaluation of linear regression models revealed a consistent bias towards underestimating high-magnitude daily N2O flux and cumulative N2O flux. Observations of soil temperature and VSWC were unable to significantly explain model bias. Calibration to available data did not result in consistent model estimation of additional system properties that contribute to N2O flux, which suggests a need for additional model comparisons that make use of a wide variety of data types. The major contribution of this work has been to identify a potential model bias and future steps required to evaluate its source and improve the simulation of nitrogen cycling in agroecosystems. Process-based models are powerful tools, and it is not our objective to undermine their past and future application. However, more work is left to be done in understanding the biogeophysical system that produces soil N2O and in harmonizing the process-based models that simulate that system and which are used to evaluate management and generate policy.

Future Plans 

Future work should test our findings in additional agroecological contexts to determine the extent to which a bias towards underestimating peak N2O flux persists. A meta-analysis of published data may be the most direct method for doing so. New datasets will need to be collected that represent simultaneous observations of multiple system properties (e.g. soil NO3-, soil NH4+, and heterotrophic respiration) from different soil layers and at increased temporal frequencies. Model developers should use these rich datasets to identify the source of N2O estimation bias and improve the structure and function of process-based models.

Corresponding author, title, and affiliation      

Richard K. Gaillard, Graduate Student, University of Wisconsin-Madison

Corresponding author email

Other authors  

Curtis D. Jones, Assistant Research Professor, University of Maryland; Pete Ingraham, Research Scientist, Applied Geosolutions;

Additional information       


This material is based upon work that is supported by the National Institute of Food and Agriculture, U.S. Department of Agriculture, under award number 2013-68002-20525. Any opinions, findings, conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the view of the U.S. Department of Agriculture.

Additional Authors:

Sarah Collier, Research Associate, University of Wisconsin-Madison;

Roberto Cesar Izaurralde, Research Professor, University of Maryland;

William Jokela, Research Scientist (retired), USDA-ARS;

William Osterholz, Research Associate, University of Wisconsin-Madison;

William Salas, President and Chief Scientist, Applied Geosolutions;

Peter Vadas, Research Scientist, USDA-ARS;

Matthew Ruark; Associate Professor; University of Wisconsin-Madison

The authors are solely responsible for the content of these proceedings. The technical information does not necessarily reflect the official position of the sponsoring agencies or institutions represented by planning committee members, and inclusion and distribution herein does not constitute an endorsement of views expressed by the same. Printed materials included herein are not refereed publications. Citations should appear as follows. EXAMPLE: Authors. 2017. Title of presentation. Waste to Worth: Spreading Science and Solutions. Cary, NC. April 18-21, 2017. URL of this page. Accessed on: today’s date.

Are Models Useful for Evaluating or Improving the Environmental Impact of Pork Production?

green stylized pig logo

Models are basically equations that are based on real-world measurements. Measurements are made in different situations and/or different times. Models are used to make comparisons between different choices or look at “what if” scenarios without having to implement each possible option.

Generally, models that are created with large, diverse (but still compatible) data sets containing relevant information are going to be more reliable than models with smaller data sets with smaller data sets. Models can then be used to predict performance or evaluate changes in a system.

There are very good reasons to use models when looking at the environmental footprint of pork production:

  1. Efficiency. It is expensive and impractical to measure actual emissions from every farm or barn.
  2. Decision-making. Models allow farmers and their advisers to look at “what if?”. Prior to making an expensive decision, farmers can evaluate the location, type of building, manure storage, manure treatment, feed ration, etc. and select the best option.
  3. Measure progress trends. Models can be applied at different points in time to see if a farm or industry is making progress in reducing their impacts.

Are there limitations to models?

Yes. By their very nature, models are a simplified representation of a complex system. Modeling is a balance between complexity (how much information does the user need and how much time will it take?) and accuracy (how much is gained by including additional variables?).  The results must be evaluated in their appropriate context. As an example, many TV weather forecasters look at several sources of information, including models when formulating their forecast. While on a given day the forecast may be off (either due to inaccurate analysis or results) it is safe to say that overall, weather forecasting is greatly enhanced by the use of models.

Do you have an example of a model used on pig farms?

One example of a model that is currently looking at the environmental footprint of pork production is the Pork Production Environmental Footprint Calculator.  It currently estimates greenhouse gas (GHG) emissions and the day to day costs of the activities that generate those emissions, but research is underway to expand the model to include land,  and water footprints–leading to a more comprehensive “environmental footprint” model.

The model referenced above can be used for estimating the GHG emissions from the various operations on a pig farm in order to calculate the farm’s cumulative emissions. It shows where the major contributions arise, and provides a test bed for identifying strategies that reduce emissions at least cost. The model requires input information that most producers will know about their operation such as the type of barn, animal throughput, type and quantity of feed ration used, a physical description of the facilities (size of barn, insulation, fans etc.), the time in the barn, temperature profile for that area, type of manure management system (lagoon, dry lot, pit, etc.).  Sample costs for day to day farm activities are provided in the model, but can be updated by the user. The model output includes a summary of feed and energy usage for the simulation, including energy estimates for temperature control (both heating and ventilation) as well as costs.

Authors: Jill Heemstra, University of Nebraska and Rick Fields, University of Arkansas

Reviewers: Dr. Jennie Sheerin Popp, University of Arkansas, Dr. Karl Vandevender, University of Arkansas

For More Information:


This information is part of the program “Integrated Resource Management Tool to Mitigate the Carbon Footprint of Swine Produced In the U.S.,” and is supported by Agriculture and Food Research Initiative Competitive Grant no. 2011-68002-30208 from the USDA National Institute of Food and Agriculture. Project website.

Modeling water movement in beef cattle bedded manure pack

Why Examine Moisture Content of a Manure Pack?

Bedded manure is a valuable fertilizer source because it contains essential macronutrients (nitrogen (N), phosphorus (P), and potassium (K)) for crop production. Previous research with beef cattle bedded manure packs demonstrated that water-soluble macronutrients accumulated toward the bottom of the packs with water movement. Thus, predicting water movement in bedded manure helps to estimate nutrient composition throughout the bedded pack. This work presents a development of a process-based model of vertical water movement that considers percolation and diffusion as the main processes of water and vapor movements in bedded manure packs. Evaporation from the top zone to the atmosphere was considered a process of convective mass transfer. The model predicts the change in moisture content of the different zones in the bedded manure and assists in estimating nutrient composition.

cattle loafing on a bed pack in their barnWhy Study Moisture Movement In a Bedded Pack?

Beef cattle producers that raise cattle in complete confinement, such as mono-slope or hoop barns, may apply bedding material to manage moisture and improve the environment for the animals. Some producers let the manure and bedding accumulate to form a bedded manure pack, which is compacted by cattle activity. The bedded manure contains valuable nitrogen (N), phosphorus (P), and potassium (K) that are essential for crop production and soil sustainability. Depending on temperature, bedding material, and storage time of the bedded pack, the concentration of water-soluble N, P and K compounds may increase in the bottom of the bedded pack where water accumulates. Thus, understanding and predicting water movements within the bedded manure is important to estimate fertilizer N-P-K content and distribution in the bedded manure.

What did we do?

The processes considered in this process-based model include evaporation, percolation, diffusion of water vapor and diffusion of liquid water for vertical water movement. The model by Seng et al. (2012) for static compost piles and a modified version of the Integrated Farm System Model (not yet released) by Rotz et al. (2014) for bedded manure were reviewed and compared. Ultimately, the model needs to be adaptable to estimate the water content of the pack over time for different environmental conditions, bedding materials, and storage times at varying depths within the bedded pack. Data for model calibration and validation were gained through laboratory-scale experiments by Ayadi et al. (in review).

What have we learned?

Percolation and liquid water diffusion are considered the main processes for vertical water movement between layers in the bedded manure. Evaporation occurs from the surface of the top zone of the bedded pack. The rates of percolation and liquid water diffusion are depth-specific and their rates therefore vary. The modified version of the Integrated Farm System Model (IFSM) is more adaptable to data gained through laboratory-scale experiments. Overall, IFSM is more applicable to producer-available data and thus more applicable to predict water movement for bedded manure packs in real-life conditions.

Future Plans

After predicting water movements in the bedded manure, the model will be used to estimate N, P and K movement through the different zones of the bedded manure pack as well as gaseous emission (ammonia and nitrous oxide) from the bedded pack surface. The final overall model will be a calculator that estimates fertilizer N-P-K content and value and ammonia and nitrous oxide emissions of the bedded manure packs from confined beef cattle facilities with respect to temperature, bedding material, storage time and depth of the bedded pack.


Erin Cortus, Ph. D., Assistant Professor, South Dakota State University, Brookings, SD

Ferouz Ayadi, M.S., Graduate Student, South Dakota State University, Brookings, SD; Mindy Spiehs, Ph. D., Animal Scientist, USDA‐ARS Meat Animal Research Center, Clay Center, NE

Additional information


Ayadi, F. Y., M. J. Spiehs, E. L. Cortus, and D. N. Miller. In review. Physical, chemical and biological properties of different depths and ages of simulated beef bedded manure packs. Transactions of the ASABE.

Rotz, C.A., Corson, M.S., Chianese, D.S., Montes, F., Hafner, S.D., Bonifacio, H.F., Coiner, C.U., 2013a.

The Integrated Farm System Model Reference Manual, Version 4.1. USDA-Agricultural Research Service. Avaialble at:

Seng, B., H. Kaneko, K. Hirayama, and K. Katayama-Hirayama. 2012. Development of water movement model as a module of moisture content simulation in static pile composting. Environmental Technology 33(15):1685-1694.


The support and assistance of Henry F. Bonifacio with the simulation of water movements in the bedded pack manure is very much appreciated. This project and all associated reports and support materials were supported by the Sustainable Agriculture Research and Education (SARE) program, which is funded by the U.S. Department of Agriculture- National Institute of Food and Agriculture (USDA-NIFA). Any opinions, findings, conclusions or recommendations expressed within do not necessarily reflect the view of the SARE program or the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer. The mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA.

The authors are solely responsible for the content of these proceedings. The technical information does not necessarily reflect the official position of the sponsoring agencies or institutions represented by planning committee members, and inclusion and distribution herein does not constitute an endorsement of views expressed by the same. Printed materials included herein are not refereed publications. Citations should appear as follows. EXAMPLE: Authors. 2015. Title of presentation. Waste to Worth: Spreading Science and Solutions. Seattle, WA. March 31-April 3, 2015. URL of this page. Accessed on: today’s date.