Adventure Dairy: Educational and Technical Tools for Carbon Cycle Modeling on a Virtual Dairy Farm

Conceptualizing, modeling, and controlling carbon flows at the farm scale can improve efficiency in production, reduce costs, and promote beneficial products and byproducts of agricultural processes through best management practices. On dairy farms, opportunities exist for farmers to control factors affecting greenhouse gas (GHG) emissions and diversions from production and operations. Complex programs to model the effects of different carbon management strategies on net emissions are very useful to farmers, but lack visualization of flows through a user interface to show effects of different management choices in real time.

Previously, a collaborative research team at Penn State University and the University of Wisconsin-Madison has developed a Virtual Dairy Farm website to share information about how dairy farms incorporate best management practices and other on-farm production choices to reduce environmental impacts. The website is organized in two model farm configurations, a 150-cow and a 1500-cow modern dairy farm. Website users can find information on different components of the farm by exploring locations on the farm. Links to information about farm operations are structured in multiple levels such that information is understandable to the general public but also supported by technical factsheets for agriculture professionals.

What did we do?

Building on the strengths of the Penn State Virtual Dairy Farm interactive website, the Team has developed a concept for an “Adventure Dairy” package for users to explore how management choices affect total on-farm GHG emissions for a model Pennsylvania dairy farm (Figure 1). Five main categories serve as management portals superimposed on the Virtual Dairy interface, including Cow Life Cycle, Manure Management, Crop Production, Energy Use on Farm, and Feed on Farm.

Figure 1. Example landing page for the proposed Adventure Dairy website. Different regions to explore to access the GHG emissions calculator are highlighted in different colors.
Figure 1. Example landing page for the proposed Adventure Dairy website. Different regions to explore to access the GHG emissions calculator are highlighted in different colors.

For each category, users can select from multiple options to see how these decisions increase or decrease emissions. Along the bottom of the webpage, a calculator displays the net carbon balance for the model system and change emissions estimates as users choose feed composition, land use strategies, and other important components (Figure 2). Under each category, users can make choices about different management practices that affect on-farm carbon cycling. For example, different choices for feed additives change total net GHG emissions, and, in turn, can affect total manure production. A change in management and operational choices, such as storage, is visually communicated through interactions and on the interface (Figure 3). These management portals can be seamlessly integrated with the Virtual Dairy Farm as an addition to the right sidebar. The click-through factsheets currently a part of the interface can be preserved through new informational “fast facts” overlays with accompanying infographics and charts. Pathways to optimizing carbon flows to ensure maximum production and minimum environmental impact will be featured as “demo” examples for users.

Figure 2. Example Adventure Dairy user interface for manure management. User choices to increase and decrease total GHG emissions are included in the right sidebar. Along the bottom of the page, a ribbon shows the total emissions from each category.
Figure 2. Example Adventure Dairy user interface for manure management. User choices to increase and decrease total GHG emissions are included in the right sidebar. Along the bottom of the page, a ribbon shows the total emissions from each category.
Figure 3. A different user choice for manure storage type on the Adventure Dairy interface. Total GHG emissions decrease by 14% because of the switch from an uncovered to a covered anaerobic lagoon because of reduced volatilization of methane.
Figure 3. A different user choice for manure storage type on the Adventure Dairy interface. Total GHG emissions decrease by 14% because of the switch from an uncovered to a covered anaerobic lagoon because of reduced volatilization of methane.

What have we learned?

This model offers a novel platform for more interactive software programs and websites for on-farm modeling of carbon emissions and will inform future farm management visualizations and data analysis program interfaces. The Team envisions the Adventure Dairy platform as an important tool for Extension specialists to share information with dairy professionals about managing carbon flows on-farm. Simultaneously, consumers increasingly seek information on the environmental impacts of agriculture. This interactive website is a valuable educational and technical tool for a variety of audiences.

Uniquely, a multidisciplinary team of agriculture and engineering graduate students from multiple institutions are leading this project, as facilitated by faculty. This Cohort Challenge model allows for graduate students to engage with complex food-energy-water nexus problems at the level of faculty investigators in a virtual educational resource center. Future INFEWS-ER teams and “wicked problems” challenge projects will continue to develop this model of learning and producing novel research products.

Future plans

The Cohort Challenge Team is entering a peer/faculty review process of the simplified carbon model for the Virtual Dairy Farm website. The user interface for the Adventure Dairy calculator is not currently a part of the Penn State Virtual Dairy Farm. The Team will be working with software developers to integrate our model in the existing interface. Additional components under consideration for inclusion in the Adventure Dairy calculator include cost comparisons for different best management practices, an expanded crop production best management calculator, and incorporation of

Authors

Student Team: Margaret Carolan,1 Joseph Burke,2 Kirby Krogstad,3 Joslyn Mendez,4 Anna Naranjo,4 and Breanna Roque4

Project Leads: Deanne Meyer,4 Richard Koelsch,3 Eileen Fabian,5 and Rebecca Larson6

 

  1. Department of Civil and Environmental Engineering, University of Iowa, Iowa City, IA crln@uiowa.edu
  2. Texas A&M University
  3. University of Nebraska-Lincoln
  4. University of California-Davis
  5. Penn State University
  6. University of Wisconsin-Madison

Acknowledgements

Funding for the INFEWS-ER was provided by the National Science Foundation #1639340. Additional support was provided by the National Institute for Food and Agriculture’s Sustainable Dairy CAP and the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign.

Useful resources

  1. Penn State Virtual Dairy Farm: http://virtualfarm.psu.edu/
  2. INFEWS-ER: http://infews-er.net/
  3. Sustainable Dairy CAP: http://www.sustainabledairy.org/Pages/home.aspx

 

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. 2019. Title of presentation. Waste to Worth. Minneapolis, MN. April 22-26, 2019. URL of this page. Accessed on: today’s date.

Real-Time Data Collection:  Lessons Learned from the Dead Cow Tool and Hurricane Harvey

Provide leaders with information to develop a well-planned crowd-sourced data application that improves communication and speed response times in disasters. Recognize the potential benefits of crowd-sourced and employee-sourced real-time geospatial data during emergency response.  Present steps taken by United States Department of Agriculture – Natural Resources Conservation Service (NRCS) Texas State Office for the deployment of the “dead cow tool” and share lessons learned.  Provide a framework of questions and items that need to be addressed for the successful deployment of a real-time data collection tool.

What Did We Do?

In response to Hurricane Harvey in the fall of 2017, the Texas NRCS GIS staff developed on-line reporting tools to collect real-time data related to damages and animal mortalities that could be used by employees and the public.  ESRI’s ArcGIS Collector Application was selected for its ease of use, ability to be used when off-line, and staff familiarity with the tool’s programming language. In this case, NRCS already had the necessary licensing for ArcGIS Online accounts.  

The “dead cow tool” is a near real-time reporting tool for the public to identify locations, types and magnitude of agricultural losses.  This provides NRCS and other agencies with data to request funding for emergency response and recovery funds to assist the local agricultural producers.    However, significant concerns were raised relative to releasing the application for public use, so the data collection applications were then limited to a handful of NRCS employees within the disaster areas.  

The Dead Cow Tool (displayed as Hurricane Harvey Data Collector Map) was designed to collect the following parameters:  Damage Type; Livestock Type; Number of Livestock Lost; Number of Livestock in Need; Accessibility; and Comments. There was also an option to add or take images and add the location from a map previously downloaded onto a user’s mobile device (if network connectivity was lacking).  Here are a few screenshots to serve as an example from an iPhone (Figures 1 – 6):

 


Figure 1. Data collection maps developed in response to Hurricane Harvey Figure 2.  Main data collection screen for the “Dead Cow Tool” aka Hurricane Harvey Collector Map
Figure 3. Options for damage types in the “dead cow tool”                                                                                                                                    Figure 4. Completed Livestock Damage Assessment ready to be submitted.  There is includes the option to add a photo.

 

Texas NRCS developed and deployed another tool for employees to complete Damage Survey Reports while in the field.  “In Hurricanes Ike and Rita, staff went out in the field, took handwritten notes about the damage, wrote down the location, took pictures and then had to return to the office, to download and enter the information on their computer. They had to look up the latitude and longitude points from their notes to document the exact location and then save all that information in several different locations.  It was a long process for our staff,” says NRCS State Soil Scientist Alan Stahnke. “I knew there had to be a way to make it more efficient for them.” Stahnke had been working with Steven Diehl, GIS technician, and others on his staff for several months on an ArcGIS application, ArcCollector, based on ESRI map data. They had the basics down and when Hurricane Harvey showed up on the radar, they knew they had to work fast to get the application ready for staff in the wake of Harvey’s wrath. The resulting smart phone device field tool – the Hurricane Harvey Damage Reporter – is a method to record the damage and collect information on all the points into a central database. (Littlefield, 2017)  

The Damage Survey Report tool reduced the time needed by field engineers by approximately 50% from the previous method.  The data was available to others with access as it was entered – thereby providing timely data to managers and leadership.  Additionally, it allowed the final reports to be developed by state office personnel further reducing the time required by the field – allowing them to take care of other pressing matters.

Figure 5.  Screenshot of Data Collected and viewed through ESRI’s ArcGISOnline Portal
Figure 5.  Screenshot of Data Collected and viewed through ESRI’s ArcGISOnline Portal

 

Figure 6.  Screenshot of visual map of data collected through ESRI’s ArcGIS Online Portal
Figure 6.  Screenshot of visual map of data collected through ESRI’s ArcGIS Online Portal

What Have We Learned?

Several lessons were learned:  First, approve policies on data collection prior to the disaster – these need buy-in and flexibility.  Second, decide how data will be released and identify typical reports. Third, develop Data Collection Applications in advance – allowing testing, training, familiarity, and formatting needed for user-friendliness. Fourth, select the correct Data Collection Tool.  Fifth, identify data collection alternatives if the application cannot be realistically utilized – power outages, lack of network connection, closed roads, flooded areas, etc.

It is important to prepare, plan, and train prior to a disaster to allow time to adjust and/or develop policies and reduce knee-jerk reactions.  

Data collection can have negative impacts if not properly administered and protected.  Several identified concerns during Hurricane Harvey were protecting the data collected, preventing submittal of inappropriate language and/or photos, potential for someone submitting the data to believe that they had applied or requested assistance, and data distribution.  Our NRCS GIS specialists (Texas and across the US) worked with ESRI developers to overcome some of the data protection and prevention of inappropriate material. However, obtaining clearance from leadership for public-use of the application was not obtainable in a timely matter.  

Real-time data collection is a useful tool for both internal customers and the public when faced with a disaster and allows the timely coordination of resources for rapid response and recovery.  

Disasters such as Hurricane Harvey require significant resources for response and recovery.  Real-time data collection can aid in allocating resources. With animal mortalities, it is important that animals in sensitive environmental areas are properly disposed of in a timely manner.  NRCS has provided technical and financial assistance for proper carcass disposal following natural disasters to reduce the associated environmental risk.

Generating Reports and Maps with Information Collected — Recognize the market impacts of sharing reported losses.  NRCS must follow applicable federal rules and regulations to related to personally identifiable information. Generally, a report could be published with information grouped by county based on collected data provided there is more than one producer in the county with that type of livestock or commodity.  For example, if there is only one farm in a county with emus, and the producer reported their losses of 50% of their emus, USDA Agencies could not share that data, as the producer could then be identified.

Policies are needed to address data collection with public interfaces.  Consider modifications to existing policies or creating new policies to allow the use of crowd-source data.  

The intent of the app and intended use of the data must be clearly conveyed to users.  USDA Agencies raised concerns the public would believe that the tool indicated that they were applying for assistance, not simply reporting. (Stahnke, Jannise, & Northcut, 2018)

Prior to collecting data, appropriate policies should be written that address, how, when, where and why the information is needed and how it will be used in accordance with federal data collection requirements.  The policies should be reviewed internally by a variety of users to ensure that the policy is clear and provides adequate accountability. Buy-in from all levels is needed prior to launching a data collection system to the public.  Depending upon the type of organization that is collecting the data – a variety of controls may need to be established to protect the data.

  • How the data will be collected and shared– this area should allow flexibility.  Allowing public to enter data using their own devices may be necessary to obtain the data in a timely manner.  What will public users gain by sharing their information?
  • Will the data be shared with other agencies, non-governmental organizations (NGOs), etc.?  
  • If employees are allowed to use their own device, is there a possibility of a litigation hold on the personal device? (USDA – Forest Service, Mobile Geospatial Advisory Group, 2015)
  • When does the data need to be collected?  This may vary – for example, the number and type of livestock lost in a sensitive area may need to be reported as soon as the livestock are found; flooded fields and associated losses, road closures, or areas with downed power lines may have some lag-time in reporting over a period of several weeks as roads and properties become available for inspection
  • Where does the data that has been submitted get collected?
  • Who is going to oversee the data collection and create needed reports?  
  • Will the data be adequately protected? As mentioned, some of the data collected, particularly with potential images and audio embedded with file attributes, will likely include personal or sensitive data that must be protected.  
  • Why is the data being collected?  
  • Will it serve a purpose and be used?  
  • Can the data be potentially abused?
  • What level of data integrity is required?  
  • Will certification or training be required for various users?  
  • Will additional weight be placed on data from “authenticated” or “certified” users?
  • Who will be required to review of the proposed data collection system?  
  • Should these vary based on the scope of the project?  Setting the review levels and identifying who is authorized for deployment of the tool in advance is helpful to know what rules need to be followed.  Some flexibility should be provided to allow modifications and adaptations as needed during an emergency.

Selecting the appropriate data collection tool and platform is critical to success.  There is an organization “Principles for Data Collection” that has created a guidance document for mobile data collection (MDC).  Additionally, they host a “Digital Principles Forum — an online meeting place for peer learning, connection building, and debate on the Principles for Digital Development. Together with you, we aim to build a community that connects ICT4D, information technology, and international aid and humanitarian development practitioners with thoughtful curated content, relevant conversation and quality opportunities to improve their work.”

“How to Choose a Mobile Data Collection Platform” is a guidance document prepared by the Principal for Data Collection group.  Below are some of the considerations that they have identified:

  1. Consider data and security needs including personal or sensitive data.
  2. Consider the ecosystem – following a disaster, internet and wireless connections may be intermittent or non-existent.  
  3. Identify and prioritize selection criteria
  1. Short-term and long-term costs
  2. Number of users, surveys, and items
  3. Devices and data requirements for enumerators
  4. Security and privacy compliance
  5. Integration with other technology
  6. Offline collection
  7. Short Message Service (SMS) integration
  8. Unstructured Supplementary Service Data Integration
  9. Authentication and user roles
  10. Skip logic and data parameters
  11. Data analysis
  12. GIS and mapping
  13. Language
  14. Photos, audio and video
  15. Ease of setup and use
  1. Research MDC platform options
  2. Rank options
  3. Consider whether to customize an MDC platform
  4. Select and test your platform.

 

TIP: Be sure to test several devices in your context before making a final selection.

(Principles for Digital Development, 2018)

When developing and testing applications, consider the following:

  • Users accessibility to AGOL, i.e., do they need a login in their company’s Enterprise ESRI platform or is the application in the public domain on AGOL.
  • Amount of training required for user to input
  • Ease of navigation and number of clicks required to complete form
  • Varying size of screens on user devices (small screens vs. tablets)
  • Test with a variety of different levels of users
  • Types of reports and training required for the administrator
  • Duration of the application and availability

 

Creating sample reports and identifying who can see specific data in advance will aid when a disaster does occur.  In Texas, this type of data might be useful to the Texas Animal Health Commission, and other agencies involved in Emergency Support Function #11 – Agriculture and Natural Resources Annex (ESF-11).

The following items should be further investigated for disaster related activities:

  • FEMA’s National Incident Management System
  • How to share some information with users that have input data – allows them to know that their data is being utilized for a worthy cause
  • Identifying other agencies that are working on recovery efforts with the same groups
  • Setting up mechanisms to share data automatically rather than relying on an individual to send out reports
  • Methodologies for ground-truthing and screening data quickly

 

Explore the possibilities of utilizing ESRI’s WorkForce application to track locations of employees for safety and workflow coordination.

It is important to consider that even with the best tools developed and ready for deployment – they might not be able to be used in the field if there is no power to charge the mobile data collection device or ability to transmit the data back to the database.  Considerations of solar chargers for the mobile devices for employees might be helpful. Establishing alternate methods of communication such as, but not limited to, land lines, postal mail, drop off locations, leaving surveys at the gates, and 800 phone numbers should be implemented.

Future Plans

There are infinite possibilities for the collection and use of real-time data in a disaster.  It is the opinion of the authors that the potential benefits greatly outweigh the risks of not obtaining and utilizing the data.  We will continue to share the lessons learned to help others implement solid data collection tools.

Authors

Cherie LaFleur, P.E., Environmental Engineer, USDA – Natural Resources Conservation Service, Central National Technical Service Center, Fort Worth, Texas. Cherie.lafleur@usda.gov

Catherine Stanley, E.I.T., Water Quality Specialist, USDA – Natural Resources Conservation Service, Weatherford, Texas. Catherine.stanley@usda.gov

Additional Information

NRCS Develops New Web App to Expedite Agency Response to Harvey. .

Citations:

Collins, C. (2017, October 27). Retrieved from Texas Observer: https://www.texasobserver.org/agriculture-losses-estimated-200-million-harvey/

Fannin, B. (2017, October 27). Texas agricultural losses from Hurricane Harvey estimated at more than $200 million. Retrieved from AgriLife Today — Texas Agrilife Extension: https://today.agrilife.org/2017/10/27/texas-agricultural-losses-hurricane-harvey-estimated-200-million/

Littlefield, D. A. (2017, September). NRCS Develops New Web App to Expedite Agency Response to Harvey. Retrieved from USDA-NRCS: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/tx/newsroom/stories/?cid=nrcseprd1351676

Principles for Digital Development. (2018, May 8). How to Choose a Mobile Data Collection Platform. Retrieved from Digital Principles: https://digitalprinciples.org/wp-content/uploads/PDD_HowTo_ChooseMDC-v3.pdf

Stahnke, A., Jannise, P., & Northcut, M. (2018, 05 15). USDA NRCS Texas Personnel. (C. Stanley, Interviewer)

The Weather Company. (2017, September 2). Historic Hurricane Harvey’s Recap. Retrieved from The Weather Company: https://weather.com/storms/hurricane/news/tropical-storm-harvey-forecast-texas-louisiana-arkansas

USDA – Forest Service, Mobile Geospatial Advisory Group. (2015, August). Internal Document: Collector for ArcGIS Field Data Collection Pilot for Enterprise GIS Using ArcGIS Online.

Acknowledgements

Alan Stahnke, State Soil Scientist, NRCS, Temple, TX.

Pam Jannise, State GIS Specialist, NRCS, Temple, TX.

Steven Diehl, Cartographic Technician, NRCS, Temple, TX.

Mark Northcut, Landscape and Planning Staff Leader, NRCS, Temple, TX.

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. 2019. Title of presentation. Waste to Worth. Minneapolis, MN. April 22-26, 2019. URL of this page. Accessed on: today’s date.

Innovative Use of Solar Energy to Mitigate Heat Stress in Sows

Food retailers and consumers worldwide are pressuring producers to reduce the use of fossil fuels and the carbon footprint of swine production systems. The primary objective of this study was to evaluate a solar-powered system designed to cool sows that might reduce the use of fossil fuels in farrowing rooms and improve performance of lactating sows.

What did we do?

Two mirror-image, farrowing rooms equipped with 16 farrowing stalls each were used for this study.  Each farrowing stall in the COOL room was equipped with a cooled flooring insert (Cool Sow, Nooyen Manufacturing) under the sow and a single nipple drinker delivering chilled drinking water.  Circulating water cooled by a water-source heat pump powered by a 20 kW photovoltaic solar array cooled the floor inserts (60 to 65 °F) and chilled the drinking water (55 to 60 °F). Warm water (110 to 119 °F) was circulated through pads in the piglet creep area.  The CONTROL room was nearly identical to the COOL room except there was no cooling of floor inserts or drinking water and supplemental heat for piglets was provided by one heat lamp (125 W) per farrowing stall (Group 1) or an electric heating pad (Hog Hearth, Innovative Heating Technologies; Group 2).  Groups (n = 28 CONTROL sows and 28 COOL sows) were studied during summer months and room heaters were operated to keep rooms above 75 °F to ensure sows were heat stressed. Electric consumption for all systems (ventilation, piglet heating, lights, and cooling system) was measured and performance of sows and piglets were recorded over lactation.

What have we learned?

The COOL room consistently used more electricity than the CONTROL room (Figures 1 and 2).  For Group 1, the COOL room used 93.0 kWh/day while the CONTROL room used 35.3 kWh/day. Similarly in Group 2, the COOL and CONTROL rooms required 71.5 and 19.7 kWh/day, respectively.  Production of electricity from the solar panels totaled 95.3 and 86.7 kWh/day, respectively. Sows housed in the COOL room were more comfortable as indicated by a lower respiration rate (64.4 vs 96.8 breaths/min; P < 0.01), higher feed intake (11.39 vs 9.25 lb/d; P < 0.01) and reduced lactation body weight loss (35.1 vs. 54.2 lbs; P < 0.06) compared with sows housed in the CONTROL room.  Litter size at birth and weaning as well as piglet weaning weights were not different across rooms.

The cooling systems (cooled floor and cooled drinking water) and piglet heating systems studied effectively mitigated heat stress of lactating sows but did not enhance pig performance.  Furthermore, these systems required over 2.5 times more total electrical energy than a traditional lactation housing system without sow cooling.

Future Plans:

Effects of cooled floors and cooled drinking water were confounded in this study.  Cooled floors are expensive and difficult to install in existing facilities. The effects of cooled drinking water will be assessed independent of cooled floors in future studies.  Cooled drinking water will be easier to install in existing barns. Future analyses will consider the economic feasibility of various components of the sow cooling and piglet heating systems.  

Corresponding authors, titles, and affiliations:

B. M. Lozinski1, M. Reese1, E. Buchanan1, A. M. Hilbrands1, K. A. Janni2, E. Cortus2, B. Hetchler2, J. Tallaksen1, Y. Li1, and L. J. Johnston1

1West Central Research and Outreach Center, University of Minnesota, Morris, and

2Department of Biosystems and Biological Engineering, University of Minnesota, St. Paul

Acknowledgements:

The authors would like to express gratitude to the Minnesota Environment and Natural Resources Trust Fund for their financial support of this project.

 

Figure 1. Total energy use by room (kWh) and total solar energy produced (kWh) per day for Group 1.
Figure 1. Total energy use by room (kWh) and total solar energy produced (kWh) per day for Group 1.

 

Figure 2. Total energy use by room (kWh) and total solar energy produced (kWh) per day for Group 2.
Figure 2. Total energy use by room (kWh) and total solar energy produced (kWh) per day for Group 2.

 

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. 2019. Title of presentation. Waste to Worth. Minneapolis, MN. April 22-26, 2019. URL of this page. Accessed on: today’s date.

Feed Manipulation, Manure Treatment and Sustainable Poultry Production

This study examined the effects of different treatments of poultry faecal matter on potential greenhouse gas emission and its field application and also evaluated dietary manipulation of protein on the physico-chemical quality of broiler faeces and response of these qualities to 1.5% alum (Aluminium sulphate) treatment during storage.

Poultry litters were randomly assigned to four treatments: salt solution, alum, air exclusion and the control (untreated). Chicks were allotted to corn-soy diets for 42d. The diets were 22 and 20% CP with methionine + lysine content balance and, 22 and 20% CP diets with 110% NRC recommendation of methionine and lysine.

Alum treated faeces had higher (p<0.05) nitrogen retention than other treatments. Treated faecal samples retained more moisture (p < 0.05) than control. The pH tended to be acidic in treated samples (alum, 6.03, p<0.05) and alkaline in the control (7.37, p<0.05). Mean faecal temperature was lower for alum treated faecal samples (28.58oC, p<0.05) and highest for air-tight (29.4oC, p<0.05). Nitrogen depletion rate was significant lower (p<0.05) in alum treated faecal samples. Post-storage, samples treated with alum increased substantially (≥ 46.51%) in total microbial count, while total viable count was lower (p>0.05; 2.83×106 cfu/ml) in air-tight treatment. Maize seeds planted on alum, air-excluded and control litter soils had average germination percentage range of 65–75%, 54–75% and 74-75%, respectively. In Sorghum plots, GP was 99%, and 89%, respectively for alum and air-tight treated soil 2WAP. Average maize height 21DAP was 48 cm and 23 cm for alum and air-tight treatment, respectively. Salt treated faecal samples did not support germination. Faecal pH of broiler fed low protein diets was acidic (4.76-4.80) while treatment with alum (1.5%) led to further reduction in pH (4.78 to 4.58) faecal nitrogen and organic matter compared with control faeces in a 7 days storage. Faecal minerals were generally lower. In conclusion, feeding low level of dietary protein with or without methionine and lysine supplementation in excess of requirement is a suitable mitigation for nitrogen emission and mineral excretion in broiler production. Alum treated poultry litter will mitigate further nitrogen loss in storage because it lowered nitrogen depletion rate, pH, weight, temperature and supports potential agronomic field application index.

On-farm Demonstration of the application of these results to assist farmers to produce poultry sustainably.

Further reading

https://scholar.google.com/citations?hl=en&user=NZGTKC8AAAAJ#d=gs_md_cita-d&u=%2Fcitations%3Fview_op%3Dview_citation%26hl%3Den%26user%3DNZGTKC8AAAAJ%26citation_for_view%3DNZGTKC8AAAAJ%3AW7OEmFMy1HYC%26tzom%3D-60  

*BOLU, Steven Abiodun, ADERIBIGBE, Simeon Adedeji  OLAWALE, Simon, Malomo, G. A., Olutade, S.G and Suleiman, Z.G. Department of Animal Production, University of Ilorin, Ilorin, Kwara State, Nigeria.
*Corresponding Author: Department of Animal Production, University of Ilorin, Ilorin, Kwara State, Nigeria.
Email: bolusao2002@yahoo.co.uk Phone: +234 8060240049

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. 2019. Title of presentation. Waste to Worth. Minneapolis, MN. April 22-26, 2019. URL of this page. Accessed on: today’s date.

Energy Consumption in Commercial Midwest Dairy Barns

Consumer interest and concern is growing in regards to sustainability of livestock production systems. Demand for reduced carbon emissions within agricultural systems has been growing along with increasing demand for food. Baseline fossil fuel consumption within agricultural systems, including dairy production, is scarce. Therefore, there is a need to discern where and how fossil energy is being used within dairy production systems. Determining baseline energy use is the first step in investigating the demand for a reduced carbon footprint within dairy production systems. The objective of this study was to measure total electricity use and determine specific areas of high energy consumption in commercial dairy barns located in the Upper Midwest of the United States.

What did we do?

Four commercial dairy barns representative of typical Midwest dairy farms and located in west central Minnesota were evaluated in the study. The dairy farms were: 1) a 9,500 cow cross-ventilated barn with a rotary milking parlor (Farm A), 2) a 300 cow naturally-ventilated barn with stirring fans for air movement and 6 automatic milking systems (Farm B), 3) a 200 cow naturally-ventilated barn with stirring fans for air movement and a parabone milking parlor (Farm C), and 4) a 400 cow naturally-ventilated barn with stirring fans for air movement and a parallel milking parlor (Farm D).

Electricity use was monitored from July 2018 to December 2018 with a goal of collecting two years of total energy usage. Two-hundred ninety-two  electric loads across the four farms were monitored on the farm side of the electric utility meter to evaluate areas of highest energy usage (Figure 1). Some of the monitored electric loads included freestall barn fans, water heaters, compressors, chillers, manure pumps, and pressure washers. The electric loads were monitored by data loggers (eGauge, Boulder, CO) and electric current sensors at the circuit panels. Electrical use data (kWh) of each load were collected and analyzed on a monthly basis. In addition, monthly inventory of cows on farm, cows milked per day, and milk production was recorded. Bulk tank production records (milk, fat percentage, protein percentage, and somatic cell count) were also recorded.

Figure 1. Data loggers with electric current sensors installed on farm circuit panel boxes.
Figure 1. Data loggers with electric current sensors installed on farm circuit panel boxes.

What have we learned?

Based on preliminary results, fans were the largest electrical load across all four dairy farms. Fan usage during the summer ranged from 36 to 59% of the total electricity measured (Figure 2). Regular maintenance, proper control settings, design, sizing, location, selecting energy efficient fans and motors, and other factors all could influence the efficiency of these ventilation/cooling systems. Farms B, C, and D had greater electricity usage across all months for milk cooling (compressors and chillers) than Farm A. This is likely due to the fact that Farm A does not utilize bulk tanks to store milk, but instead, milk is directly loaded onto bulk milk trucks. Lighting use ranged from 7 to 21% of the total electricity use measured across the four farms, which suggests there is potential to reduce energy usage by upgrading to more efficient lighting systems such as LEDs. For heating, energy usage includes water heating, heating units in the milking parlor or work rooms, waterer heating elements, and generator engine block heaters. Average monthly heating use ranged from 5% of electricity used in Farm A to 32% of electricity used in Farm C.

Figure 2. The average monthly electrical use measured by data loggers and the percent used by each electrical load category. The average monthly total electricity in kWh is displayed at the top of each bar.
Figure 2. The average monthly electrical use measured by data loggers and the percent used by each electrical load category. The average monthly total electricity in kWh is displayed at the top of each bar.

Future plans

Based on the preliminary analysis, clean energy alternatives and energy-optimized farms will be modeled as clean energy alternatives for Minnesota dairy facilities. An economic analysis will also be conducted on the clean energy alternatives and farms. Potential on-site renewable electric generation may supply some or the entire electric load allowing the buildings to approach net-zero (producing as much energy as is used).

The results of this study provide recent energy usage for farm energy benchmarks, agricultural energy policy, economic evaluations, and further research into dairy farm energy studies. The data will also be useful to producers who are searching for areas for reduced energy usage in their own production systems. Improving the efficiency of electrical components in dairy operations could provide opportunities to improve the carbon footprint of dairy production systems.

Authors

Kirsten Sharpe, Animal Science Graduate Research Assistant, West Central Research and Outreach Center (WCROC), Morris, MN, sharp200@umn.edu

Bradley J. Heins, Associate Professor, Dairy Management, WCROC, Morris, MN

Eric Buchanan, Renewable Energy Scientist, WCROC, Morris, MN

Michael Cotter, Renewable Energy Researcher, WCROC, Morris, MN

Michael Reese, Director of Renewable Energy, WCROC, Morris, MN

Additional information

The West Central Research and Outreach Center (WCROC) has developed a Dairy Energy Efficiency Decision Tool to help provide producers a way to estimate possible energy and costs savings from equipment efficiency upgrades. The tool can be used to evaluate areas of a dairy farm that may provide the best return on investment for energy usage. Furthermore, a guidebook has been developed for Optimizing Energy Systems for Midwest Dairy Production. This guidebook provides additional information about energy usage issues as well as a decision tool. More information may be found at https://wcroc.cfans.umn.edu/energy-dairy

Acknowledgements

The funding for this project was provided by the Minnesota Environment and Natural Resources Trust Fund as recommended by the Legislative-Citizen Commission on Minnesota Resources (LCCMR).

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. 2019. Title of presentation. Waste to Worth. Minneapolis, MN. April 22-26, 2019. URL of this page. Accessed on: today’s date.

Regional Runoff Risk Tools for Nutrient Reduction in Great Lakes States

One method to reduce the impacts of excess nutrients leaving agricultural fields and degrading water quality across the Nation is to ensure nutrients are not applied right before a runoff event could occur.  Generally nutrient management approaches, including the 4-Rs (“right” timing, rate, placement, and source), include some discussion about the “right time” for nutrient applications, however that information is static guidance usually centered on the timing of crop needs.  What has been missing, and what will be discussed in this talk, will be the development and introduction to runoff risk decision support tools focused on providing farmers and producers real-time guidance on when to not apply nutrients in the next week to 10 days due to the risk of runoff capable of transporting those nutrients off their fields.  The voluntary adoption and use of runoff risk in short-term field management decisions could provide both environmental and economic benefits.

In response to the need for real-time nutrient application guidance and a request from states in the Great Lakes region, the National Weather Service (NWS) North Central River Forecast Center (NCRFC) has helped develop these runoff risk tools in collaboration with multiple state agencies and universities and with support from the Great Lakes Restoration Initiative (GLRI).  There are currently four active runoff risk tools in the Great Lakes region: Michigan, Minnesota, Ohio, and Wisconsin.  It is possible to develop similar tools for Illinois, Indiana, and New York if willing state partners are identified.  

What did we do?

Studies have shown that a few large runoff events per year contribute a majority of the annual load leaving fields.  In addition applications generally occur during the riskiest times of year for runoff (fall through spring) when fields experience the least vegetative cover and soils are vulnerable.  Knowing this information, real-time NWS weather and hydrologic models were evaluated to identify conditions that correlated with runoff observed at edge-of-field (EOF) locations.  The runoff risk algorithm identifies daily runoff events and stratifies the events by magnitude respective to each grid cell’s historical behavior.  The events are then classified into risk categories for the farmers and producers. In general, high risk events are larger magnitude events that don’t happen as often and also have a higher accuracy rate.  On the other end, low risk events are smaller magnitude events that have a higher chance of being a false alarm yet are also less likely to be associated with significant nutrient loss.

NWS models are run twice daily and simulate soil temperature, soil moisture, runoff, and snowpack conditions continuously.  The runoff risk algorithm is applied against the model output to produce runoff risk guidance which is sent to the state partners.  Each state has a working group and a lead agency or organization that manages the effort to produce and maintain the runoff risk websites as well as promote the tools and educate the users on how to interpret and use the guidance.  

What have we learned?

At this point there are four regional runoff risk tools available.  Response has been positive from both state agencies and when farming groups are asked about the runoff risk concept during post-presentation surveys and small focus groups.  There is a strong desire from the farming community to make the best decision during stressful times of the year when farming schedules and the weather are often in conflict.  

At this point, it is universally accepted among the runoff risk collaborators that there is a need to provide free, easily obtainable forecast guidance to the farming community so they can make the best nutrient application decisions for their operations and the environment.

Runoff risk tools are strictly for decision support and not meant to be a regulatory tool in nature.  This is due to the limitations in hydrologic models, weather forecasting, spatial scale issues, and that the tools have no way of incorporating farmer specific practices into the risk calculations.  Although model improvements will occur in the future, ensuring users understand the limitations but also the benefits they can provide are important components in the States’ outreach and education functions.  

Future Plans

Based on feedback from the states employing runoff tools, there is a second round of enhancement planned for the runoff risk algorithm in the summer of 2019.  Other improvements from the states’ perspective deal with updating webpages and building on and enhancing push notification capabilities such as text message and email alerts.

The next major step forward begins in spring 2019 with the start of version 3 runoff risk.  This 2-year development will transition runoff risk guidance from the current model over to the new NWS National Water Model (NWM).  The NWM framework will allow finer resolution guidance (1km or smaller) for numerous models runs per day all with full operational support.  Moving to the NWM also allows continuous improvement and future collaboration opportunities with universities to improve the underlying WRF-Hydro model as well as runoff risk and other derived decision support guidance.

Authors

Dustin Goering, Senior Hydrologist, North Central River Forecast Center, National Weather Service
Andrea Thorstensen, Hydrologist, North Central River Forecast Center, National Weather Service

Corresponding Author email
dustin.goering@noaa.gov

Additional Information

For further information on runoff risk background please visit this page: https://vlab.ncep.noaa.gov/web/noaa-runoff-risk/runoff-risk-background  (Still under construction)

 

To visit the state tools see the following links:

    

Michigan  

Minnesota 

Ohio  

Wisconsin  

Acknowledgements

There are many individuals across a wide spectrum of agencies, industry, and universities that have been instrumental in the development of runoff risk to this point.

Support for the development of runoff risk across the Great Lakes and the upcoming version 3 runoff risk from the National Water Model has been provided by multi-year grants from the Great Lakes Restoration Initiative.

 

 

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. 2019. Title of presentation. Waste to Worth. Minneapolis, MN. April 22-26, 2019. URL of this page. Accessed on: today’s date.

Thermal and Electrical Energy and Water Consumption in a Midwest Dairy Parlor

The typical dairy farm uses a large amount of energy during milking activities. This is due to the frequency of milking and the energy intensive nature of harvesting milk, keeping it cool, and cleaning the equipment with hot water. Renewable energy systems generally become more economically efficient as the amount of energy used increases, making dairy farms a great place to incorporate renewable energy.

Dairy farms have not typically been set up with energy efficiency in mind and often use relatively expensive fuel sources like heating oil or propane to heat water. One of the difficulties encountered with renewable energy systems is the intermittent generation of wind and solar energy, whereas the energy load on a dairy farm is very consistent since cows are typically milked twice or three times every day (very large dairies may milk continuously). An efficient way to store energy has long been sought to tie energy production and consumption together. A dairy farm’s need for both electricity and heat provides an ideal situation to generate electrical energy on-site to meet current electrical load requirements, displace conventional thermal fuels with electrical energy, and evaluate thermal storage as a solution to the time shifting of wind and solar electrical generation.

What did we do?

The dairy operation at the University of Minnesota West Central Research and Outreach Center in Morris milks between 200 and 275 cows twice daily and is representative of a mid-size Minnesota dairy farm. The cows are split almost evenly between a conventional and a certified organic grazing herd, and all cows spend the winter outside in lots near the milking parlor. The existing dairy equipment is typical for similarly sized dairy farms and includes none of the commonly recommended energy efficiency enhancements such as a plate cooler, refrigeration heat recovery, or variable frequency drives for pump motors. The WCROC dairy provides an ideal testing opportunity to evaluate and demonstrate the effect of on-site renewable energy generation and energy efficient upgrades on fossil fuel consumption and greenhouse gas emissions (Figure 1).

Heat pumps, electric water heaters, and thermal storage at the University of Minnesota Morris
Figure 1. Renewable energy upgrades that include new heat pumps, electric water heaters, and thermal storage tank at the University of Minnesota WCROC Dairy in Morris, MN.

A data logger was installed in the utility room of the milking parlor in August 2013 to monitor 18 individual electric loads, 12 water flow rates, 13 water temperatures, and two air temperatures. Average values were recorded every 10 minutes for the last 4 years. The milking parlor has gas and electric meters that measure the total consumption of natural gas and electricity within the parlor. The data helped us evaluate energy and water usage of various milking appliances. Some small energy loads were not measured in unused parts of the barn, or for equipment not directly related to the milking operation. These small and miscellaneous loads were estimated by subtracting monitored energy use from the total energy use.

Baseline measurements were collected at the WCROC dairy and overall, the milking parlor currently consumes about 250 to 400 kWh in electricity and uses between 1,300 and 1,500 gallons of water per day (Figures 2 and 3). The parlor currently uses about 110,000 kWh per year (440 kWh per cow per day) in electricity and 4,500 therms per year in natural gas. A majority of the electricity (26 percent) is used for cooling milk , with ventilation, fans and heaters  utilizing 16 percent. The dairy uses about 600 gallons of hot water per day, with a majority used for cleaning and sanitizing milking equipment (57%), followed closely by cleaning the milking parlor (27%). Energy and water usage fluctuates throughout the year; the dairy calves 40 percent of the cows from September to December and 60 percent from March to May. Therefore, water and energy use escalates dramatically during April.

The first energy efficiency upgrade was the installation of a variable frequency drive for the vacuum pump in September 2013. Prior to the upgrade, the vacuum pump used 55 to 65 kWh per day. Following installation, electrical consumption by the vacuum pump decreased by 75% to just 12 kWh per day. This data provides a vivid example of the significant energy savings that can be achieved with relatively simple upgrades.

Because the dairy operates both  organic and conventional systems, two bulk tank compressors are used: one scroll and one reciprocating. The scroll compressor is the newest and uses 15 kWh per day versus 40 kWh per day for the reciprocating compressor. Based on milk production, the scroll compressor costs $0.73 per kWh per cwt. versus $1.08 per kWh per cwt. for the reciprocating compressor, indicating that the scroll compressor is more efficient. In terms of fossil fuel consumption, milk harvesting consumed more energy than feeding and maintenance.  

Pie Chart: Electrical usage by equipment component for 2016.
Figure 2. Electrical usage by equipment component for 2016.
Pie chart: Hot water usage by activity during 2016
Figure 3. Hot water usage by activity during 2016

During the fall of 2016, a TenKSolar Reflect XTG 50 kW DC array was installed. The annual production from this solar PV system was projected to be 70,000 kWh. At a total cost of $138,000 ($2.77/W) for the solar system,  a 19.7-year simple payback without incentives was predicted. Adding the “Made in Minnesota” incentives would reduce the payback period to 8.6 years.

In 2017, two 10-kW VT10 wind turbines from Ventera were installed. These turbines are a three blade, downwind turbine model, each with an annual predicted generation of 22,400 kWh. The wind system cost was $156,800 ($78,400 per tower) with a 35-year simple payback without incentives. With the 30% federal credit, each turbine would have a 24.5-year payback.

What we have learned?

Our study suggests that fossil energy use per unit of milk could be greatly reduced by replacing older equipment with new, more efficient technology or substituting renewable sources of energy into the milk harvesting process. To improve energy efficiency, begin with an audit to gather data and identify energy-saving opportunities. Some energy efficiency options that may be installed on dairy farms include refrigeration heat recovery, variable frequency drives, plate coolers, and more efficient lighting and fans. A majority of these upgrades have immediate to two- to five-year paybacks. Make all electrical loads as efficient as possible, yet practical. Consider converting all thermal loads to electricity by the use of heat pumps that allow for cooling of milk. In the future, we have plans to harvest energy from our manure lagoon and store electricity as heat by use of heat pumps. Renewable energy options also can improve energy efficiency.

Solar panels
Figure 4. 50 kW solar array at the University of Minnesota WCROC Dairy, Morris, MN

Future Plans

We will continue to monitor the WCROC dairy and make renewable energy upgrades. We have begun monitoring the two 10-kW wind turbines, and installed a new 30-kW solar array in the WCROC pastures for renewable energy production. Additionally, we will evaluate the cow cooling potential of solar systems in the grazing dairy system at the WCROC. This study is the first step toward converting fossil fuel-based vehicles used in dairy farms to clean and locally produced energy. The knowledge and information generated will be disseminated to agricultural producers, energy professionals, students, and other stakeholders.

Authors

Brad Heins, Associate Professor, Dairy Management, hein0106@umn.edu

Mike Reese, Director of Renewable Energy

Eric Buchanan, Renewable Energy Scientist

Mickey Cotter, Renewable Energy Junior Scientist

Kirsten Sharpe, Research Assistant, Dairy Management

Additional information

We have developed a Dairy Energy Efficiency Decision Tool to help provide producers a quick way to estimate possible energy and costs savings from equipment efficiency upgrades. The tool can be used to quickly see what areas of a dairy operation may provide the best return on investment. Furthermore, we have developed a U of MN Guidebook for Optimizing Energy Systems for Midwest Dairy Production. This guidebook provides additional information about the topics that were discussed in this article, as well as the decision tool. More information may be found at https://wcroc.cfans.umn.edu/energy-dairy

Acknowledgements

To complete our goals, we have secured grants from the University of Minnesota Initiative for Renewable Energy and the Environment (IREE), the Minnesota Rapid Agricultural Response Fund, and the Xcel Energy RDF Fund.

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. 2019. Title of presentation. Waste to Worth. Minneapolis, MN. April 22-26, 2019. URL of this page. Accessed on: today’s date.

Environmental Impacts of Dairy Production Systems in the Changing Climate of the Northeast

To meet the nutritional needs of a growing population, dairy producers must increase milk production while minimizing farm environmental impacts. As we look to the future, management practices must also be adapted to maintain production under projected climate change. To plan for the future, better information is needed on practices that can reduce emissions from the farm and adapt to changes in the climate while maintaining or improving production and profitability.

What did we do?

We conducted a comprehensive assessment of the effects of climate change on both the productivity and environmental performance of farms as influenced by strategies to reduce emissions and adapt to the changing climate. Production systems were evaluated using three representative northeastern dairy farms: a 1500-cow farm in New York, a 150-cow farm in Wisconsin and a 50-cow farm in southern Pennsylvania. A cradle-to-farm gate life cycle assessment was conducted using farm-scale process-based modeling and climate projections for high and low emission scenarios. Environmental considerations included the carbon footprint of the milk produced and reactive N and P losses from the farms.

What we have learned?

We found that the environmental impact of the three representative dairy farms generally increased in the near future (2050) climate if no mitigation measures were taken. Overall, feed production was maintained as decreases in corn grain yield were compensated by increases in forage yields. Adaptation of the cropping system through changes in planting and harvest dates and corn variety led to a smaller reduction in corn grain yield, but the detrimental effects of climate change could not be fully negated. Considering the increased forage yield, total feed production increased except for the most severe projected climate change. Adoption of farm-specific beneficial management practices substantially reduced the greenhouse gas emissions and nutrient losses of the farms in current climate conditions and stabilized the environmental impact in future climate conditions, while maintaining feed and milk production (See Figure 1 for example results).

Figure 1. Carbon footprint, reactive nitrogen footprint and P loss in recent (2000) and future (2050) climate conditions (RCP4.5 and RCP8.5) for a 1500-cow farm in New York with baseline and Best Management Practice (BMP) scenarios, with and without crop system adaptions in 2050. Error bars represent the standard deviation of IFSM simulations for 3 climate scenarios per RCP. Unadapt = not adapted cropping system. Adapt = adapted cropping system.
Figure 1. Carbon footprint, reactive nitrogen footprint and P loss in recent (2000) and future (2050) climate conditions (RCP4.5 and RCP8.5) for a 1500-cow farm in New York with baseline and Best Management Practice (BMP) scenarios, with and without crop system adaptions in 2050. Error bars represent the standard deviation of IFSM simulations for 3 climate scenarios per RCP. Unadapt = not adapted cropping system. Adapt = adapted cropping system.

The take-home message is that with appropriate management changes, our dairy farms can become more sustainable under current climate and better prepared to adapt to future climate variability.

Future plans

A more comprehensive life cycle assessment is being done by linking the output of the farm model with life cycle assessment software. The process level simulation of the farm provides inventory information for an inclusive life cycle assessment with multiple environmental considerations. This integrated software will provide a more complete sustainability assessment of the potential benefits of alternative management strategies for both now and the future.

Authors

Karin Veltman, University of Michigan; C. Alan Rotz, USDA-ARS; Larry Chase, Cornell University; Joyce Cooper, Washington State University; Chris Forest, Penn State University; Pete Ingraham, Applied GeoSolutions; R. César Izaurralde, University of Maryland; Curtis D. Jones, University of Maryland; Robert Nicholas, Penn State University; Matt Ruark, University of Wisconsin; William Salas, Applied GeoSolutions; Greg Thoma, University of Arkansas; Olivier Jolliet, University of Michigan.

Additional information

Information on the Integrated Farm System Model is available in the reference manual:

Rotz, C., Corson, M., Chianese, D., Montes, F., Hafner, S., Bonifacio, H., Coiner, C., 2018. The Integrated Farm System Model, Reference Manual Version 4.4. Agricultural Research Service, USDA. Available at: https://www.ars.usda.gov/northeast-area/up-pa/pswmru/docs/integrated-farm-system-model/#Reference.

Information on the analysis of Best Management Practices on northeastern dairy farms is available in:

Veltman, K., C. A. Rotz, L. Chase, J. Cooper, P. Ingraham, R. C. Izaurralde, C. D. Jones, R. Gaillard, R. A. Larsson, M. Ruark, W. Salas, G. Thoma, and O. Jolliet. 2017. A quantitative assessment of beneficial management practices to reduce carbon and reactive nitrogen footprints and phosphorus losses of dairy farms in the Great Lakes region of the United States. Agric. Systems 166:10-25.

Acknowledgements

This work was 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 authors and do not necessarily reflect the view of the U.S. Department of Agriculture. USDA is an equal opportunity provider and employer.

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. 2019. Title of presentation. Waste to Worth. Minneapolis, MN. April 22-26, 2019. URL of this page. Accessed on: today’s date.

Talking Climate with Animal Agriculture Advisers


Proceedings Home W2W Home w2w17 logo

Purpose             

The Animal Agriculture in a Changing Climate (AACC) project was established to leverage limited Extension expertise across the country in climate change mitigation and adaptation, with the goal of building capacity among Extension professionals and other livestock advisers to address climate change issues.

What did we do? 

The Animal Agriculture in a Changing Climate project team created a suite of educational programs and products to build capacity across the United States. Key products of the project:

  • Online courses: 363 participants registered with a 35% completion rate (Whitefield et al., JOE, 2016)
  • National and regional symposia and workshops: 11 face-to-face conferences with approximately 1,350 attendees.
  • Website: Over 5,900 users with over 21,100 total views. Project videos have received nearly 8,900 views.
  • Social media: AACC weekly blog (990 subscribers); daily Southeast Climate Blog (38,506 site visits); regional newsletters (627 subscribers); Facebook & Twitter (280 followers)
  • Ready-to-use videos, slide sets, and fact sheets
  • Educational programming: 390 presentations at local, regional, and international meetings
  • Collaboration with 14 related research and education projects

What have we learned? 

A survey was sent out to participants in any of the project efforts, in the third year of the project and again in year five. Overall, participants found the project resources valuable, particularly the project website, the online course, and regional meetings. We surveyed two key measures: abilities and motivations. Overall, 60% or more of respondents report being able or very able to address all eight capabilities after their participation in the AACC program. A sizeable increase in respondent motivation (motivated or very motivated) existed after participation in the program, particularly for helping producers take steps to address climate change, informing others about greenhouse gases emitted by agriculture, answering client questions, and adding new information to programs or curriculum.

The first challenge in building capacity in Extension professionals was finding key communication methods to engage them. Two key strategies identified were to: 1) start programming with a discussion of historical trends and agricultural impacts, as locally relevant as available, and 2) start the discussion around adaptation rather than mitigation. Seeing the changes that are already apparent in the climatic record and how agriculture has adapted in the past and is adapting to more recent weather variability and climatic changes often were excellent discussion starters.

Another challenge was that many were comfortable with the science, but were unsure how to effectively communicate that science with the sometimes controversial discussions that surround climate change. This prompted us to include climate science communication in most of the professional development opportunities, which were then consistently rated as one of the most valuable topics.

Future Plans    

The project funding ended on March 31, 2017. All project materials will continue to be available on the LPELC webpage.

Corresponding author, title, and affiliation        

Crystal Powers, Extension Engineer, University of Nebraska – Lincoln

Corresponding author email    

cpowers2@unl.edu

Other authors   

Rick Stowell, University of Nebraska – Lincoln

Additional information

lpelc.org/animal-agriculture-and-climate-change

Acknowledgements

Thank you to the project team:

Rick Stowell, Crystal Powers, and Jill Heemstra, University of Nebraska – Lincoln

Mark Risse, Pam Knox, and Gary Hawkins, University of Georgia

Larry Jacobson and David Schmidt, University of Minnesota

Saqib Mukhtar, University of Florida

David Smith, Texas A&M University

Joe Harrison and Liz Whitefield, Washington State University

Curt Gooch and Jennifer Pronto, Cornell University

This project was supported by Agricultural and Food Research Initiative Competitive Grant No. 2011-67003-30206 from the USDA National Institute of Food and Agriculture.

 

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.