As a major contributor in food production, beef production provides a major service to our economy that must be maintained. Production of cattle and the associated feed crops required also impact our environment, and this impact is not well understood. Several studies have determined the carbon footprint of beef, but there are other environmental impacts that must be considered such as fossil energy use, water use, and reactive nitrogen loss to the environment. Because of the large amount of data available to support model evaluation, production systems of the U.S. Meat Animal Research Center were simulated with the Integrated Farm System Model for the purpose of evaluating the environmental impact of the beef cattle produced.
What Did We Do?
The environmental footprints of beef produced at the U.S. Meat Animal Research Center (MARC) in Clay Center, Nebraska were determined with the objective of quantifying improvements achieved over the past 40 years. Relevant information for MARC operations was used to establish parameters representing their production system with the Integrated Farm System Model. The MARC farm, cow calf and feedlot operations were each simulated over recent historical weather to evaluate performance, environmental impact and economics. The current farm operation included 2,078 acres of alfalfa and 2,865 acres of corn to produce feed predominately for the beef herd of 5,500 cows, 1200 replacement heifers and 3,724 cattle finished per year. Spring and fall cow calf herds were fed on 24,000 acres of pastureland supplemented through the winter with hay and silage produced by the farm operation. Feedlot cattle were backgrounded 3 mo on hay and silage and finished over 7 mo on a diet high in corn grain and wet distiller’s grain.
What Have We Learned?
Model simulated predictions for weather year 2011 were within 1% of actual records for feed production and use, energy use, and production costs. A 25-year simulation of their current production system gave a carbon footprint of 10.9 lb of CO2 equivalent units per lb body weight (BW) sold, and the energy required to produce that beef was 11,400 Btu/lb BW. The total water required was 2,560 gallon/lb BW sold, and the water footprint excluding that obtained through precipitation was 335 gallon/lb BW. Reactive N loss was 0.09 lb/lb BW, and the simulated total cost of producing their beef was $0.96/lb BW sold. Simulation of the production practices of 2005 indicate that the use of distiller’s grain in animal diets has had a small impact on environmental footprints except that reactive N loss has increased 10%. Compared to 1970, the carbon footprint of beef produced has decreased 6% with no change in the energy footprint, a 3% reduction in the reactive N footprint, and a 6% reduction in the real cost of production. The water footprint, excluding precipitation, has increased 42% due to greater use of irrigated corn production.
Future Plans
Now that the modeling approach has been shown to appropriately represent beef production systems, further simulation analyses are planned to evaluate beef production systems on a regional and national scale.
Authors
C. Alan Rotz, Agricultural Engineer, Pasture Systems and Watershed Management Research Unit, USDA/ARS al.rotz@ars.usda.gov
B.J. Isenberg, Research Assistant, The Pennsylvania State University
K.R. Stackhouse-Lawson, Director of Sustainability Research, National Cattlemen’s Beef Association
E.J. Pollak, Director, Roman L. Hruska U.S. Meat Animal Research Center, USDA / ARS
Funded in part by The Beef Checkoff and the USDA’s Agricultural Research Service
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. 2013. Title of presentation. Waste to Worth: Spreading Science and Solutions. Denver, CO. April 1-5, 2013. URL of this page. Accessed on: today’s date.
Much of the greenhouse gases (GHG) generated from the poultry industry is primarily from feed production. The poultry producer does not have control over the production and distribution of the feed used on the farm. However, they can control other emissions that occur on the farm such as emissions from the utilization of fossil fuels and from manure management. A series of studies were conducted to evaluate on-farm greenhouse gas emissions from broiler, breeder and pullets houses in addition to an in-line commercial layer complex. Data was collected from distributed questionnaires and included; the activity data from the facility operations (in the form of fuel bills and electricity bills), house size and age, flock size, number of flocks per year, and manure management system. Emissions were calculated using GHG calculation tools and emission factors from IPCC. The carbon dioxide, nitrous oxide and methane emissions were computed and a carbon footprint was determined and expressed in tonnes carbon dioxide equivalents (CO2e).
The results from the study showed that about 90% of the emissions from the broiler and pullet farms were from propane and diesel gas use, while only 6% of the total emissions from breeder farms were from propane and diesel gas use. On breeder farms, about 29% of GHG emissions were the result of electricity use while the pullet and broiler farms had only 3% emissions from electricity use. Emissions from manure management in the layer facility were responsible for 53% of the total emission from the facility, while electricity use represented 28% of the total emissions. The results from these studies identified the major sources of on-farm of GHG emissions. This will allow us to target these areas for abatement and mitigation strategies.
Why Study Greenhouse Gases on Poultry Farms?
Human activities, including modern agriculture, contribute to greenhouse gas (GHG) emissions (IPCC, 1996). Agriculture has been reported to be responsible for 6.3% of the GHG emissions in the U.S., of this 53.5% were a result of animal agriculture. Of the emissions from animal agriculture, poultry was responsible for only 0.6%. Much of the CO2e that is generated from the poultry industry is primarily from feed production, the utilization of fossil fuels and manure management (Pelletier, 2008; EWG, 2011). While the poultry producer does not have control over the production of the feed that is used on the farm, there are other GHG emissions that occur on the farm that are under their control. These emissions may be in the form of purchased electricity, propane used for heating houses and incineration of dead birds, diesel used in farm equipment which includes generators and emissions from manure management.
What Did We Do?
A series of studies were conducted to examine the GHG emissions from poultry production houses and involved the estimation of emissions from; broiler grow-out farms, pullet farms, breeder farms from one commercial egg complex. Data collection included the fuel and electricity bills from each farm, house size and age, flock size and number of flocks per year and manure management. The GHG emissions were evaluated using the IPCC spreadsheets with emission factors based on region and animal type. We separated the emissions based on their sources and determined that there were two main sources, 1. Mechanical and 2. Non-mechanical. After we determined the sources, we looked at what contributed to each source.
What Have We Learned?
When all GHG emissions from each type of operation was evaluated, the total for an average broiler house was approximately 847 tonnes CO2e/year, the average breeder house emission was 102.56 tonnes CO2e/year, pullet houses had a total emission of 487.67 tonnes CO2e/year, and 4585.52 CO2e/year from a 12 house laying facility. The results from this study showed that approximately 96% of the mechanical emissions from broiler and pullet houses were from propane (stationary combustion) use while less than 5% of these emissions from breeder houses were from propane use. The high emission from propane use in broiler and pullet houses is due to heating the houses during brooding and cold weather. Annual emissions from manure management showed that layer houses had higher emissions (139 tonnes CO2e/year) when compared to breeder houses (65.3 tonnes CO2e/year), broiler houses (59 tonnes CO2e/year) and pullet houses (61.7tonnesCO2e/year). Poultry reared in management systems with litter and using solid storage has relatively high N2O emissions but low CH4 emissions.We have learned that there is variability in the amount of emissions within each type of poultry production facility regardless of the age or structure of houses and as such reduction strategies will have to be tailored to suit each situation. We have also learned that the amount of emissions from each source (mechanical or non-mechanical) depends on the type of operation (broiler, pullet, breeder, or layer).
Future Plans
Abatement and Mitigation strategies will be assessed and a Poultry Carbon Footprint Calculation Tool is currently being developed by the team and will be made available to poultry producers to calculate their on-farm emissions. This tool will populate a report and make mitigation recommendations for each scenario presented. Best management practices (BMP) can result in improvements in energy use and will help to reduce the use of fossil fuel, specifically propane on the poultry farms thereby reducing GHG emissions, we will develop a set of BMP for the poultry producer.
Authors
Claudia. S. Dunkley, Department of Poultry Science, University of Georgia; cdunkley@uga.edu
Brian. D. Fairchild, Casey. W. Ritz, Brian. H. Kiepper, and Michael. P. Lacy, Department of Poultry Science, University of Georgia
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. 2013. Title of presentation. Waste to Worth: Spreading Science and Solutions. Denver, CO. April 1-5, 2013. URL of this page. Accessed on: today’s date.
Dr. Karl VanDevender – University of Arkansas:Cooperative Extension Service and Dr. Brian Richert – Purdue University
Sub-Project Overview
The research and modeling work encompassed by this project offers great potential for assisting researchers, policy makers, and especially farm managers to make informed management and facilities decisions regarding GHG emissions while maintaining production and profitability. However for this potential to be realized the swine production community needs to be aware of both its abilities and availability. To this end existing relationships with the National Pork Board , U.S. Pork Center of Excellence, and eXtension are being enhanced.
The focus audience of this training is technical service providers (professionals, consultants, engineers, nutritionists, extension swine specialist and educators) who are involved in assisting swine producer’s making both strategic and tactical management decisions. The goal is to develop and implement education and outreach programs focused on the interaction between climate and swine science.
Dr. Richert serves as the nutrition domain editor for the US Pork Center of Excellence and US Pork Information Gateway which also serves as the swine domain for National eXtension. Through this relationship with domain editor for the Environmental section, Erin Cortus, we have initiated the rewriting of the factsheet “Pork production and greenhouse gas emissions” and have started authoring a factsheet on “Pork production, what might your carbon footprint be?”
Karl VanDevender, the project liaison with eXtension has a long history with the Livestock and Poultry Environmental Learning Center (LPELC) which has become the eXtension community of practice working in the area of livestock and poultry manure management. In response to the project needs he has transitioned from a topic area leadership role to a member of the LPELC leadership team, alongside other eXtension professionals. This provides the capacity to ensure that the project and LPELC take full advantage of the synergy of multiple funded projects without undesirable duplication of effort and resources.
The benefits of integration of extension efforts with research and education is that more rapid translation of research results into actionable knowledge will be achieved. Frequent team meetings are established to ensure that there is continual interaction and information exchange.
The working relationship between our project’s Extension component and the LPELC has helped facilitate the recent production of the webcast “Life Cycle Assesment Modeling for the Pork Industry“, which overviews the LCA goals and provides details in the sub-project areas dealing with concepts and research of feed management. Our project will also be hosting a special session featuring project leads during the LPELC hosted Waste to Worth: Spreading Science and Solutions conference in Denver Colorado, April 2013. The special session will feature a showcasing of the project’s individual and collective works; topics discussed will include: LCA concepts, research and modeling challenges, model results, and economics.
Dr. VanDevender recently published an article in PIG Progress Magazine entitled “A Swine Carbon Footprint Model as a decision aid tool”, in which the efforts of this project were presented. The article stressed the imoportance of continued management of greenhouse gasses in the livestock industry through improvements in efficiencies.
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Sub-Project Objectives
Developing the necessary system and protocols for sharing and vetting information and educational materials
Strengthening the relationship between this project and eXtension
Development and delivery of information both internally and externally.
Contact Information
Dr. Karl VanDevender kvan@uaex.edu
Phone: (501) 671-2244
Dr. Marty Matlock and Dr. Jennie Popp – University of Arkansas
Sub-Project Overview
A summer research program for undergraduate students has been developed with the focus of carbon footprint mitigation from agriculture. The goal of this USDA REU is to support 10 students each summer for a 10 week period throughout the duration of this project. The REU supported by this project is offered parallel to the National Science Foundation Research Experience for Undergraduates (NSF REU), and students are recruited from the same pool as the NSF REU programs. The REU students are selected through an application process informed by an established recruitment network. We are actively recruiting from under-represented minorities, including: Native Americans, African Americans and Hispanics.
REU Students spend the first week in a skills development workshop where they learn to develop and test hypotheses, analyze data and write reports and manuscripts. They then spend eight weeks working with their faculty mentors, followed by a one week report of results in poster and presentation format.
The expected outcomes of the REU include; inspiring students to continue their education through graduate school, and fostering an interest and competency in agricultural research.
Sub-Project Objectives
To expose undergraduate students to the scientific research process through the steps of developing testable hypotheses, data collection and analysis, and scientific communication in the form of oral presentations and written manuscripts.
Dr. Marty Matlock and Dr. Thomas Costello – University of Arkansas
Sub-Project Overview
Algal biomass offers many advantages over traditional energy crops; algal biomass generates higher yields and requires smaller land area than other energy crops. In addition to biomass production for potential biofuel feedstock generation, algal growth systems can also act as tertiary treatment systems for wastewater. Algal growth can dramatically reduce nitrogen and phosphorus from wastewater. Unlike conventional open pond and photo-bioreactor systems, periphytic systems (e.g., algal turf scrubbers) generally involve the polyculture of micro-algae, which does not require specialized conditions. While algal turf scrubber systems are traditionally used for water treatment, they are also capable of generating high biomass yields.
The Algal Nutrient Removal Team has focused on installation of the test bed for the research. This has included construction of a precision graded base for the 20-ft wide by 200-ft long flow way. Our working hypothesis is that operation of an algal flow-way to treat swine manure will remove nutrients, produce a harvestable biomass residue, and add dissolved oxygen which will decrease potential for nitrous oxide emissions (and possibly methane emissions) during manure storage. Wastewater from the swine finisher unit at the University of Arkansas will provide nutrient input to the Algae Flow-way. The flow way will be tested with manure output from pigs fed conventional diets as well as the custom rations intended to reduce manure nitrogen. Impacts on nutrient removal and algal biomass productivity, as a function of diet formulation, will be measured. Nutrient removal will be documented and data collected will be used in the DNDC model to represent the waste treatment performance of the algal systems
Algal growth systems not only provide a method for nutrient removal from animal waste, but also provide biomass production as feedstock for biofuels which can improve the carbon footprint of swine production and other animal production systems in the U.S. This project will provide field scalable data on life cycle impacts of the technology. Design and construct algal turf scrubber concluded in late summer of 2012, and the system is currently undergoing callibrations in preperation for full-scale trials.
Sub-Project Objectives
Measure algal productivity.
Quantify impact of algal nitrogen uptake on swine system GHG emissions.
Dr. Thomas Costello tac@uark.edu
Phone: (479) 575-2847
Solids Separation
Dr. Karl VanDevender – University of Arkansas. Cooperative Extension Service
Sub-Project Overview
Many technologies being considered incorporate some type of manure separation to concentrate manure solids, nutrients, and energy content. An LCA study in Denmark showed that energy recovery (incineration, gasification, and anaerobic digestion) had lower GHG emissions than traditional land application of swine manure. Other studies point to the complexities of manure management system design options in relation to GHG emissions. This portion of the project will quantify the effect of various solid separation approaches on the chemical composition of the manure generated by the feed trials at the University of Arkansas facilities during this project ,and generate the necessary manure solids for the thermo-chemical conversion portion of this project. Design and construct a pilot scale mobile solids separation system (see image below) is currently approaching completion and anticipated to be ready to begin trial calibrations soon.
Sub-Project Objectives
Capture and separation of manure from the UA animal experiments.
This unit contains systems to allow for various combinations of mechanical screen and filter bag separation, with and without chemical treatment; and is designed to operate in a batch mode with a capacity of 1000 gallons per batch.
Determine overall characteristics for the feed trial manure samples to provide additional validation data for the animal physiology sub-model.
Contact Information
Dr. Karl VanDevender kvan@uaex.edu
Phone: (501) 671-2244
Auger Reactor Gasification
Dr. Sammy Sadaka – University of Arkansas. Cooperative Extension Service
Sub-Project Overview
Due to the high moisture content, it is not economical to transport raw swine manure over long distances. As a result, manure is spread on land close to the source at high application rates. The energy content of dry manure is in the range of 12 to 18 GJ/ton, about half that of coal. In recent years, wet and dry gasification of algal biomass has been investigated by several researchers. Fluidized bed and downdraft gasification of algal biomass showed various challenges due to the nature of algae biomass. An auger gasification system (see image below) developed in the University of Arkansas, Bioenergy Laboratory, may help to simplify the air gasification process for this type of biomass. Algal biomass was gasified using the auger system during preliminary tests. Several improvements to the system took place during the first year to ensure smooth operation. Our long-term goal is to provide technology to convert swine manure and/or algal biomass to biofuel via a continuous gasification process. Energy conversion technology could provide a revenue stream of about $23 billion/year to the livestock industry.
Sub-Project Objectives
Modify the existing gasification unit to handle swine manure and/or algal biomass.
Test the performance of the gasifier
Optimize the operating parameters to maximize producer gas quality.
Study the effect of reactor temperature on the process yields (gas, char, and tar), as well as on the process efficiency.
Dr. Charles Maxwell, Dr. Jennie Popp, and Dr. Richard Ulrich – University of Arkansas; Dr. Scott Radcliffe – Purdue University, and Dr. Mark Hanigan – Virginia Tech
Why study crude protein and carbon footprint?
Maximizing feed grade amino acid (FGAA) use and reducing dietary crude protein in swine diets has been shown to dramatically reduce nitrogen excretion in both nursery and growing/finishing swine which could substantially reduce nitrous oxide (N2O) emissions associated with manure management in swine production. The global warming potential of N2O is about 298 times that of CO2 (carbon dioxide). Reducing the crude protein (CP) content of grower and finisher diets has also been repeatedly shown to enhance carcass quality by elevating intramuscular fat. While some crystalline amino acids are already commonly used in pork production the maximum level of CP reduction, in conjunction with the optimum amino acid inclusion rate, has not been sufficiently determined for widespread acceptance by the swine industry.
Project Objectives:
Hypothesis: Reducing dietary CP while maintaining amino acids (AA) at equivalent Standardized Ileal Digestibility (SID) ratios by supplementing feed grade AA will reduce nitrogen (N) excretion and greenhouse gas (GHG) emissions (N2O from manure) without impacting swine performance or carcass yield.
Determine the practical limits of reducing CP in diets of nursery and finishing pigs.
Validate the effectiveness of reduced dietary nitrogen as a mitigation strategy for greenhouse gases.
Provide data for validation of animal physiology model capable of predicting swine performance and relevant manure characteristics (quantity and composition for Manure DNDC).
Compare ME vs. NE formulation strategies on lean tissue deposition and fat accretion.
Determine the impact of dietary amino acid levels on signaling in regulation of tissue growth.
[2014 & 2015 annual reports indicated another objective was added] Estimate manure reductions in N excretion via a N balance trial.
Studies were conducted at multiple sites. One was the University of Arkansas wean-to-finish facilities and the second at the Purdue Swine Environmental Research Building (SERB). See the videos above for more on these facilities). The data generated was utilized in modelling work at Virginia Tech. The model was ultimately incorporated into the Swine Environmental Footprint Calculator.
Nursery Studies
What Did We Do? (Methods or Experimental Design)
Experiment 1
To evaluate maximum replacement of CP with FGAA, 320 weaned pigs were allotted to gender-balanced pens in a wean-to-finish facility (8 pigs/pen). Within blocks, pens were randomly assigned to 1 of 5 dietary treatments. Diets were formulated to maintain constant ME and SID Lys across treatments with SID Lys set at 95% requirement (PIC Nutrient Specification Manual, 2011). Diets were formulated to meet the SID AA ratio recommendations for other indispensable AA (SID) for nursery pigs through the 6th limiting AA (PIC Nutrient Specifications Manual, 2011). For each phase, Ctrl diets were devoid of FGAA, whereas Lys HCl was added in equal increments at the expense of CP (SBM, fish meal [FM], and poultry meal [PM] in phase 1, FM and PM in phase 2, and SBM in phase 3). This formulation procedure resulted in diets that were below the His and Phe/Tyr SID requirement for the highest level of CP reduction. CP and Lys inclusion levels were analyzed. Experiment 2
A second three phase nursery study was conducted with pigs weaned at 21 d to further evaluate limits of CP reduction in nursery diets and compare performance in pigs fed diets based on formulation on an ME vs. NE basis. The study involved 7 pigs/pen and 7 replicates/treatment. Dietary ingredients were similar to those used in experiment 1, except soy protein concentrate was used to replace fish meal. Dietary treatments were: 1), Control diet formulated on an ME basis and with FGAA used to meet the “Trp Set Point” without adding feed grade Trp in phase 1 and 2 and 0.02 % added Trp in phase 3; 2) Diet formulated on an ME basis and to meet the “His Set Point” without added feed grade His; 3) As 2 with diets formulated on a NE basis.
Nursery What Did We Learn (Results)?
Experiment 1
Pigs fed RCP 1, RCP 2, and RCP 3 diets in phase 1, 2 and 3, and for the overall study had similar ADG and BW but growth performance declined for pigs fed RCP 4 diets (Table 5; Quadratic effect, P < 0.01). A similar response was observed in ADFI in all time periods except phase 1 where ADFI was similar among treatments. In phase 1, G:F ratio followed a similar response (Quadratic effect, P < 0.01), but decreased linearly in phase 2 (P < 0.08), 3, and overall (P < 0.01). It should be noted that the RCP 4 diet was below requirement for SID His:Lys and Phe/Tyr:Lys which might explain the decrease in performance. The results of this study establishes that a high inclusion of feed grade Lys at the expense of intact proteins can be fed without decreasing ADG and ADFI except at the highest level of FGAA where the requirement for all IDAA was not met. However, G:F was generally reduced at the higher inclusion rates of FGAA, particularly in phase 3. Experiment 2
No differences were observed in ADG, ADFI, or G:F (Table 6) in any phase or overall in pigs fed diets formulated on an aggressive FGAA inclusion (His Set Point) based on ME (Trt.2) or NE (Trt. 3) compared to pigs fed AA inclusion levels currently used in swine industry (Trt. 1). These results indicate that in nursery pigs, one should be able to use a His Set Point in formulating AA based diets without concern for pig performance.
The previous nursery experiment (Experiment 1; Bass et al., 2013) conducted to evaluate feeding reduced CP diet with the highest levels of FGAA to nursery pigs resulted in poor growth performance, especially G:F ratio in phase 3 and the overall nursery period. In the previous study, experimental diets were formulated to meet the 95% of SID Lys requirement for nursery pigs. Also, RCP 4, which was formulated with the highest level of FGAA, did not meet the His and Phe requirement Lys/NE.
Conclusion
In conclusion, unlike the previous study, growth performance of nursery pigs was not affected by the higher level of FGAA and lower dietary CP. This may be due to different SID His:Lys and SID Phe+Tyr:Lys ratios used in diet formulation or different protein source used in each study. In the second nursery study, all diets were formulated based on 100 % or excess of SID Lys requirement for nursery pigs, and were formulated to meet the His and Phe+Tyr requirement. In addition, soy protein concentrate (SPC) was used in the second study during phase 1 and 2, replacing menhaden fish meal used in nursery study one.
Publications
2013 Midwest American Society of Animal Science (ASAS) meeting (Experiment 1) Abstract P042, page 102
Grow-Finish Studies
What Did We Do? (Methods)
Each experiment was conducted following a five phase grow-finish protocol. Pigs were fed 1 of 4 or 5 diets and 10 ppm of Paylean was fed during the final 3-week finishing phase-Phase 5. During phase 1 through 5, individual pig BW, and pen feed disappearance were measured over each phase to allow calculation of ADG, ADFI and G:F by phase. Tenth rib, ¾ midline back fat measurements and loin muscle area were estimated at study initiation and at the end of each phase via ultrasound to allow estimation of carcass lean gain. When the average of all blocks was 129-134 kg all pigs were individually weighed, tattooed, transported to, and harvested at a commercial pork packing plant according to industry accepted procedures. Longissimus muscle (LM) and fat depths at the 10th rib were measured on-line with a Fat-O-Meater probe and individual hot carcass weight was recorded. Experiment 1
A total of 420 pigs were blocked within gender and randomly allotted to pens with 6 pigs/pen. Within blocks, pens of pigs were randomly assigned to 1 of 5 dietary treatments (7 reps/treatment/gender). Diets were formulated by incrementally increasing levels of Lys with corresponding reductions in CP. Pigs were randomly allotted to the following diets:
Ctrl: Corn-SBM-DDGS diets devoid of FGAA,
RCP 1 (reduced crude protein 1)
RCP 2
RCP3
RCP 4 – was balanced on the requirement of the 7th limiting AA, His (PIC Nutrient Specifications Manual, 2011) which was considered the practical limit of the highest level of RCP because of availability constraints.
RCP 1 to 4 were then formulated to have stepwise and equally spaced increased Lys with corresponding reductions in CP between RCP 1 and 4. Diets 2-4 were supplemented with FGAA as needed to meet AA needs based on AA minimum ratios. Dietary CP and Lys inclusion levels were analyzed.
Diets were formulated to 95% of the average SID Lys requirement for barrows and gilts (PIC Nutrient Specifications Manual, 2011), and exceeded the SID AA/Lys ratio recommendations for other IDAA by 2 percentage points.
Distillers dried grains with solubles (DDGS) were included in all diets at the 20% level, with the exception of phase-5 finishing diets which was devoid of DDGS. Experiment 2
In experiment 1, diets were formulated on an ME basis and as soybean meal was reduced in diets, the calculated Lys/NE decreased which may explain some of the increase in fat deposition in pigs fed ME based diets formulated by decreasing soybean meal and including high levels of FGAA. Therefore, experiment 2 was conducted to establish the efficacy of using a “Set Point SID requirement” of sequentially reducing CP by adding FGAA to meet the SID IDAA/Lys ratio as a means of establishing the practical limits of CP reduction and AA replacement without impacting growth performance, carcass composition or quality in growing and finishing pigs fed NE based RCP diets. Diets were formulated starting with a Ctrl diet that approximates acceptable inclusion levels of FGAA currently used in industry, followed by sequentially formulating three additional dietary treatments, each based on the next limiting AA. Diets in this study were formulated on a constant NE basis within phase. DDGS was included in all diets. The SID His requirement in the highest RCP diet was met in each phase without added feed grade His.
There were a total of 9 replicates/treatment with pigs housed 6 pigs/pen. Sex within pen was balanced. Diets were formulated as in experiment 1 which were:
Treatment 1, Ctrl: Conventional phase 1 through 5 diets that approximates acceptable levels of FGAA currently used in industry. The assumption is that most in the industry are comfortable utilizing feed grade Thr and Met to meet the suggested SID Thr/Lys and Met/Lys ratio in diets formulated to meet the SID Trp/Lys requirement without added feed grade Trp. This is referred to as the Trp Set Point.
Treatment 2, RCP 1: Diets were formulated to meet the next limiting AA. In phase 1 and 5, the next limiting AA was Val while Ile was next limiting in phases 2, 3 and 4. This is referred to as the “Val or Ile Set Point”. Note that neither feed grade Val nor Ile were added in any phase.
Treatment 3, RCP 2: Diets were formulated to meet the next limiting AA. In phase 1 and 5, the next limiting AA was Ile while Val was next limiting in phases 2, 3 and 4. This is referred to as the “Val and Ile Set Point”. Note that feed grade Val but not Ile was added in phases 1 and 5, and Ile but not Val was added in phase 2, 3, and 4.
Treatment 4, RCP 3: Diets were formulated to meet the next (7th) limiting AA, His. This is referred to as the “His Set Point”.
All diets were supplemented with FGAA to meet IDAA recommended levels. Note that feed grade His was not added to any diet.
Grow-Finish Studies – What Did We Learn?
Experiment 1
Body weights of pigs decreased linearly with decreasing dietary CP during phase 1, 2, and 3 (P < 0.01; Table 7). Additionally, BW increased and then decreased quadratically during phase 3 (P = 0.09), 4 (P < 0.04), and 5 (P < 0.01) with BW decreasing significantly in pigs fed RCP 4. When Paylean was included in the Phase 5 diets, barrows fed the Ctrl diet had greater ADG than Ctrl-fed gilts, but RCP 1-, RCP 2-, and RCP 3-fed gilts had greater ADG than their castrated male counterparts (Quadratic gender × reduced CP diet, P = 0.08; Figure 1A) Both ADG and G:F decreased linearly (P ≤ 0.06) during phase 1 and 2. Furthermore, gain efficiency increased 4.6 % in gilts between Ctrl and RCP 2 before decreasing to similar G:F values between Ctrl and RCP 4; however, G:F remained relatively unchanged in barrows across the 5 dietary treatments (Quadratic gender × reduced CP diet, P = 0.04; Figure 1B).
Across the entire feeding trial, ADG increased only 2 % between Ctrl and RCP3, but dropped 6 % between RCP3 and RCP4 (Quadratic, P < 0.01). On the other hand, ADFI tended to decreased linearly (P = 0.09) as CP was reduced in swine diets. Gain efficiency increased 4.6 % in gilts between Ctrl and RCP2 before decreasing to similar values between Ctrl and RCP4; however, G:F remained relatively unchanged in barrows across the 5 dietary treatments (Quadratic gender × reduced CP diet, P = 0.04; Figure 1C).
Reducing dietary CP and optimizing the use of FGAA had limited (P ≥ 0.21) effects on HCW, dressing percentage, or LM depth; however, 10th rib fat depth increased linearly (P < 0.01), and fat free lean percentage at study termination decreased linearly as CP was reduced in swine diets (P < 0.02; Figure 1D). Experiment 2
Effects of dietary treatment (Trt.) indicated that ADG decreased linearly with increasing dietary FGAA in phase 3 (Table 8, P < 0.05), 4 (P < 0.10), 5 (P < 0.01) and overall (P < 0.01). Similarly, ADFI decreased linearly in phase 4 (P < 0.05), 5 (P < 0.01) and overall (P < 0.01) with increasing FGAA. Compared to pigs fed the control diet (Trt. 1), G:F in phase 1 increased in pigs fed increasing levels of FGAA at the lower inclusion rates (Trt. 2 and 3) before decreasing to the control level at the highest level of inclusion (Trt. 4, Quadratic effect, P < 0.05). During phase 3, a small, but significant, decrease in G:F was observed with increasing levels of FGAA (Linear effect, P < 0.05). For the overall study, however, a trend for increased G:F was observed (Linear effect, P < 0.06). BW increased at the end of phase 2 with increasing level of FGAA (Quadratic effect, P < 0.06). However, consistent with ADG, BW decreased with increasing dietary FGAA at the end of phase 3, 4 and 5 (Linear effect, P < 0.05, P < 0.01 and P < 0.01, respectively).
As might be expected based on BW, HCW decreased with increasing inclusion of dietary FGAA (Linear effect, P < 0.01). Tenth rib backfat was lower in pigs fed diets formulated to the Val or Ile Set Point (Trt. 2) or the His Set Point (Trt. 4) when compared to those fed diets formulated to the Val and Ile Set Point (Trt. 3).
Publications – G/F (Trial 1)
2013 Midwest American Society of Animal Science (ASAS) meeting Abstract 0224, page 73 (performance and carcass composition)
2013 Midwest American Society of Animal Science (ASAS) meeting Abstract P027, page 97 (LM – longissimus muscle quality)
Significance. This information is timely since the cost of soybean meal is approaching record levels which make substitution of synthetic amino acids for intact protein more economically feasible.
Nitrogen (N) Balance Study
What Did We Do? (Methods)
Thirty-two barrows were used to evaluate the effect of feeding reduced CP, AA supplemented diets, on nutrient and VFA excretion. Pigs were randomly allotted to the following diets:
Control: Corn-SBM-DDGS diets with no FGAA,
1X reduction in CP,
2X reduction in CP, and
3X reduction in CP. This diet was balanced on the 7th limiting AA in each phase.
Diets 2 and 3 were formulated to have stepwise and equally spaced reductions in CP between diets 1 and 4. Diets 2-4 were supplemented with FGAA as needed to meet AA needs based on NRC 2012 AA minimum ratios. Four nursery phases and 5 grow-finish phases (21d phases) were fed. Pigs were housed in stainless-steel metabolism pens equipped with a nipple waterer and stainless steel feeder. Collections started with nursery phase 3 and during nursery phases pigs were allowed an eight day adjustment period to the diets followed by a 3 d total collection of feces, urine, and orts. During the grow-finish phases, pigs were acclimated to diets for the first 10 d of each phase, and then feces, urine, and orts were collected for 3 days.
Nitrogen Balance – What Did We Learn? (Results)
Overall, from d 14-147 post-weaning ADFI was linearly increased as dietary CP was reduced, but no effect of dietary CP concentration on ADG or G:F (Table 9) was observed. Fecal excretion (DM) tended to respond in a quadratic (P = 0.08) fashion with decreasing fecal excretion (DM) up to 2X reduction in CP, but then increasing in 3X fed pigs. Both DE and ME (kcal/kg) were linearly (P < 0.01) reduced as dietary CP was reduced. The linear (P < 0.01) decrease in N intake for pigs fed reduced CP diets was accompanied by linear (P < 0.01) decreases in both urinary and total N excreted. Nitrogen digestibility (%) linearly decreased (P < 0.01) and N retention linearly (%) increased (P < 0.01) with reductions in dietary CP. Overall, there was a linear (P < 0.03) reduction in fecal ammonium as dietary CP was reduced. Total carbon (C) intake and total fecal C excreted tended (P = 0.06) to respond quadratically with an increase in both C intake and C excretion up to the 1X reduced CP diets, followed by a decrease in C intake and increasing C excretion to the 3X diet creating a linear (P < 0.05) decrease in C digestibility as dietary CP was reduced.
Significance. The ability to add total fecal and urine collections of the nitrogen manipulation diets across all body weights tested at the University of Arkansas before the diets were tested for greenhouse gas emissions at Purdue University improved the estimation of compounds excreted in fresh manure and then the conversion of these excreted nutrients and compounds during storage in typical deep pit manure structures under the swine facilities (as were measured during the Purdue 12 room study). This research project provided data for a critical link between excretion, storage, and land application.
Validating the Nursery, Grow-Finish, and GHG Measurement Studies
Large-scale trials are in progress to validate the nursery and grow-finish trials done at Arkansas. The Purdue facility is scaled more similarly to a commercial production system. Gas measurement and monitoring, and separate manure handling systems for each room allowed direct measurements with which to compare previous estimations.
Treatment structure for the experiment was:
Conventional diet containing ~ 0.15% Lys-HCl
1X reduction in CP with additional synthetic amino acids
2X reduction in CP with additional synthetic amino acids
All diets were supplemented with Ractopamine during the final 3-4 weeks of the trial, and amino acid concentrations were increased based on estimated increases in lean tissue accretion. However, the overall treatment structure will remain the same. The Experiment involved 24 pens of 10 pigs each per treatment, with 8 manure pits/trt, and 4 rooms per trt. Pen was the experimental unit for all growth performance, feed intake, and carcass data. Pit was the experimental unit for manure excretion data and room is the experimental unit for emission data.
Real-time monitoring of air temperatures included relative humidity, carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), ammonia (NH3), and hydrogen sulfide (H2S). Total suspended particulates were monitored using gravimetric samplers. The emission and animal performance data were significantly more accurate in this replicated, environmentally-controlled building than is possible commercially, yet still simulate commercial conditions.
Manure from each pit was completely removed to outside storage for volume determination, thorough agitation and sampling at the end of each trial. In addition, core manure samples based on a grid system were obtained at the end of each growth phase. Manure volume in each pit was determined at diet phase changes by taking 6 gridded depth measurements between the slatted floor. Manure samples were analyzed for pH, dry matter, ash, total nitrogen (N), ammonium N, phosphorus (P), carbon (C), and sulfur (S). Data defining the relationship between the reduction in dietary CP with a reduction in manure N and other changes in manure characteristics are essential to model development of the impact of this mitigation strategy on GHG emissions. An additional benefit will be defining the limits of this strategy without impacting animal growth performance under conditions where industry stakeholders have input.
Validation study – What did we learn (results)?
Coming Soon! (first half 2016)
Modeling the Data
[See also: Virginia Tech modeling efforts] Current nutritional requirement models for swine are focused on partitioning of dietary energy and amino acids to maintenance, growth, gestation, and lactation. Little focus is placed on predicting nutrient excretion, and thus these models cannot be used to provide inputs to an emissions model. Models that predict excretion of energy, protein, and phosphorus have been developed, but have not been evaluated for the accuracy of predictions; and evaluations that have been undertaken focus on predictions of growth and body composition, not nutrient excretion.
GHG emissions from manure storage facilities can be predicted from manure composition, underscoring the need for a robust animal model capable of predicting both animal performance and nutrient excretion. Prediction of GHG emissions from swine manure requires knowledge of N and volatile solids content, neither of which are provided by current NRC predictions.
Data from the nitrogen mitigation growth and nitrogen excretion studies were utilized in enhancement of an animal model capable of predicting swine performance and excretion of nitrogen, carbon, and volatile solids (inputs required by the DNDC model). The growth model developed by the workers at UC-Davis and the recently released Nutrient Requirements of Swine (NRC) eleventh revised edition will be used as a starting point for model development. Equations describing the response to dietary protein and amino acid additions will be evaluated for accuracy, and the effects of Paylean on animal performance and manure nutrient output will be encoded and tested for accuracy. This work is scheduled to precede and utilize data from the UA trial and literature sources. The model will be further evaluated in the 5th year using the Purdue data set.
Why Study Health of Pigs In Relation to Greenhouse Gas (GHG) Emissions?
Our working hypothesis is that immune activation, from clinical and subclinical disease, reduces growth performance and concomitantly increases nutrient excretion and subsequent GHG emissions from manure management.
Findings from this project will provide validated information for incorporation into the animal physiology models.
Project Objectives
Vaccination/PRRSV Trials
Evaluate impact of Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) exposure and vaccination on animal performance, manure output and composition, and greenhouse gas (GHG) emissions from stored manure.
Antibiotics and Antibiotic Alternatives
Evaluate the effects of health status on GHG emissions and carbon and water footprints
Salmonella
Evaluate effects of health status (Salmonella) on animal performance, manure output and composition, and GHG emissions from stored manure.
Research Summary: What Have We Done? What Have We Learned?
Manure reactors installed at Virginia Tech for the pilot study. The manifold on the wall distributes a constant stream of air to each bioreactor, and forces gas through the exhaust lines into the gas analyzers
Thermo Scientific analyzers for carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), mono-nitrogen oxides (NOx), ammonium (NH4), and hydrogen sulfide (H2S). A programmable manifold switch will sample gas from each reactor sequentially throughout the day.
Vaccination/PRRSV
Experimental Design:
Pigs were randomly assigned to a 2 x 2 factorial design investigating the interaction of Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) vaccination (with or without) and exposure to the PRRSV. One week after weaning, pigs were treated intramuscularly with 2.0 mL of a commercial PRRSV vaccine (Ingelvac PRRS MLV) or sterile saline and housed 4/pen (2 barrows and 2 gilts). Pigs were inoculated 3 weeks after vaccination with sterile medium or PRRSV (MN184). This vaccination protocol has provided high protection against experimental PRRSV infection. (Thacker et al., 2000). Blood samples were collected from pigs upon arrival to the biosafety laboratory to corroborate PRRSV-naïve status. Pigs were given antibiotic-free diets that meet or exceed all nutrient recommendations (NRC, 1998). Growth performance, manure output and composition, and GHG emissions were determined from manure collected from each pen. The study was carried-out for 4 weeks after PRRSV inoculation and used a total of 3 experimental units (i.e., pens) per treatment (i.e., 48 pigs in total). The mitigation strategy tested was the effect of vaccination on pig growth and GHG emissions.
Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) infection caused significant reductions in feed intake which led to reductions in rates of gain and body weight. The infection also caused a reduction in diet digestibility leading to greater manure nutrient output per unit of feed intake and increased greenhouse gas (GHG) production from the stored manure. The impact on GHG production is particularly striking when the data are expressed as litters of gas per kilogram of body weight gain. The increased gas production combined with reduced rates of gain results in more than a tripling of gas production per unit of gain for all of the gases. Vaccination against PRRSV appeared to offer little benefit in terms of animal performance, manure nutrient output, or gas production from manure. Results of the gas production data are presented in Table 1.
Antibiotics and Antibiotic Alternatives
Experimental Design: Seven hundred twenty-four, mixed sex pigs were placed in 11 rooms at the SERB to determine the effects of rearing pigs without antibiotics on growth performance. Pigs were blocked by (body weight) BW and gender and allotted to room and pen with 10/11 mixed-sex pigs/pen. Control pigs consumed diets (Table 2) containing antibiotics and were treated with injectable antibiotics when deemed necessary. Antibiotic-free animals consumed diets with alternatives to antibiotics and received no injectable antibiotics. If sick animals did not respond to antibiotic alternatives, they were removed from the experiment.
Pigs were weighed at the start and end of each dietary phase, and mortality and morbidity were recorded daily. Data were analyzed using the general linear model (GLM) procedure in SAS (statistical software). During the nursery phase, control pigs grew faster (P<0.02; 0.449 vs 0.426 kg/d), and consumed more feed (P<0.05; 0.694 vs. 0.660 kg/d) than antibiotic free animals, resulting in similar (gain to feed ratio) G:F.
Similar average daily gain (ADG), average daily feed intake (ADFI) and G:F were observed throughout the grower phases, and therefore the increased BW of control-fed pigs was maintained and tended (P=0.06) to be heavier at the start of the finisher phases (86.0 vs. 84.5 kg). However, antibiotic-free animals grew 3% faster (P<0.01) and had 6% better G:F (P<0.001) in the finisher phases.
As a result, there was no overall effect (P>0.10) of treatment on ADG, but there was a trend (P = 0.08) for increased ADFI (2.11 vs. 2.07 kg) and reduced (P<0.05) G:F (0.518 vs. 0.527) in control pigs compared to antibiotic-free. Thirty antibiotic-free animals (8.3%) were removed from the study compared to 11 control (3.0%). In conclusion, antibiotic-free management can yield a similar growth performance to conventional systems, but the limited disease treatment options may limit the number of pigs marketed under this management system.
Salmonella Trial
Experimental Design:
The objective of the study was to determine the impacts of a dirty environment leading to increased pathogen load and salmonella infection on animal performance, manure output and composition, and GHG production from the stored manure. 24 3-week old pigs were transported from the VT swine facility to the Biosafety Laboratory on campus and randomly allotted to one of the following health statuses: 1) High (clean room); 2) Medium (replicated clean on-farm environment); 3) Low (replicated “dirty” farm environment); or 4) Low + Salmonella challenge. Pigs were housed in individual metabolism stalls and fed the same antibiotic free diet as for the VT PRRSV trial. All pigs were assessed for fecal salmonella shedding which is indicative of an active infection upon arrival and found to be negative, and the feed was checked for salmonella contamination and found to be negative. After 10 days of adjustment (31 d of age), pigs allotted to the infected group were orally inoculated with 1×10^9 CFU of Salmonella enterica serotype enterica serovar Typhimurium strain DT104 (ATTC; BAA-185, Manassas, Virginia), and pigs were monitored for growth rate and fecal output and composition for an additional 24 d. Manure was collected each day and the loaded into the manure storage containers. Gas production from the storage containers was assessed continuously throughout the day every other day for the duration of the experiment plus an additional 11 days after the animal trial ended.
Pig inoculated with salmonella exhibited elevated rectal temperatures for 4 d post-innoculation, and shed salmonella in feces for the full 19 days that fecal shedding was monitored. Maintaining pigs in a dirty environment (heavy fecal contamination of the pens) and salmonella infection resulted in equal reductions in the rate of gain and numerical reductions feed efficiency as compared to control animals housed in a clean environment. Emissions of methane from stored manure per unit of weight gain was increased for both the dirty group and the salmonella group by more than 3 fold; and emissions of CO2 and N2O were increased by almost 50% for the dirty group and by 2 fold for the salmonella group.
The effect of Salmonella infection on the gut microbiome are currently being determined. Correlations between greenhouse gas production and key microbial population members will be determined.
The impact of PRRSV and salmonella infections on feed intake was modeled as a time dependent process relative to initial infection and incorporated into the NRC growth model. Although PRRSV vaccination is not completely effective, it did provide partial protection which modified the time course of the infection. The PRRSV vaccination effect was also modeled as a time dependent process which was additively applied to the disease equation. The model predicted intake and growth depressions for both pathogens and the effect of vaccination with minimal mean and slope bias indicating the model represented the data well. Surprisingly the salmonella equation also did well in describing the negative effects of an e-coli infection suggesting that it could be used to predict the effects of other digestive pathogens. The reductions in feed intake explained all of the changes in animal performance, and thus no additional equations were required to simulate potential decreases in diet digestibility or increases in animal maintenance requirements.
The modified model was incorporated into the grower submodel of the overall barn model to allow simulations of PRRSV and salmonella infections and PRRSV vaccination.
The Swine Environmental Research Building (SERB)
The Swine Environmental Research Building (SERB) is set up at a scale that can validate the results of pilot scale studies done elsewhere. It houses 720 pigs in 12 rooms with 6 pens per room and 10 pigs per pen. Manure is quantitatively collected and stored in a deep pit under each side of the room (3 pens of 10 pigs each). The two manure pits in each room are divided by a wall under the central walkway. The building is equipped with a centralized laboratory capable of monitoring GHG emissions from each independently ventilated room. Pigs will be supplied by Purdue or obtained from a commercial source at weaning, blocked by weight and sex and randomly assigned to treatments.
Dr. Greg Thoma, Dr. Richard Ulrich and Dr. Jennie Popp – University of Arkansas, Dr. William Salas and Dr. Chengsheng Li – DNDC Applications, Research and Training
Why Develop Models for Pork Production and Environmental Footprint?
Change in complex systems can occur either systemically, for example by government policy or regulation, or by adoption of new practices by individuals followed by wider adoption where the new practice is effective. This is costly and early adopters incur high risk of failure. This risk can be reduced through good decision support systems to aid in the selection of optimal practices – in effect, with a good model of the system, adoption of management techniques or technology can be tested by simulation before physical implementation.
This is the fundamental utility of models: they provide an inexpensive low risk alternative to experimental trial and error. The swine production model being developed for this project is based on the National Pork Board (NPB) Pig Production Environmental Footprint Calculator written at the University of Arkansas and first released in May 2011.
The National Academy of Sciences reported that EPA methodology should be improved by replacing emission factors with “process-based” models.” The tradeoff is that process-based models are more complex. Our team worked with the National Pork Board to create a process based emission model for swine production to serve as the foundation for a decision support system. This combined emission and cost model, the Pig Production Environmental Footprint Calculator (V2), was released in June 2013, and V3 will be released in Fall, 2015.
This model estimates GHG emissions, water use, land occupation and day-to-day costs from multiple farm operations to identify major contributions and provide a test bed for evaluating potential reduction strategies. The model requires readily available input information such as the type of barn, animal throughput, ration used, the time in the barn, weather for the area, type of manure management system as well as energy and feed prices. The model output includes a summary GHG emissions, water consumption, land occupation and costs by source, of as well as feed and energy usage for the simulation.
Project Objectives
Integrate process-models of swine production with coupled life cycle assessment (LCA) and economic models to create a decision support tool to identify economical swine production system options which minimize GHG emission and increase sustainability of production systems.
Improve existing process algorithms to capture effects of barn climate control, feed phases, water distribution, solar insolation, and manure application technology on GHG emissions.
Expand and improve the user interface, making it more intuitive and user-friendly.
Expand the feed ingredient list and improve estimations of important feed characteristics needed for the model.
Develop economic algorithms and compile relevant cost databases to capture the costs of day-to-day activities that entail water use and generate GHGs on farm.
Research Summary: What Have We Done? What Have We Learned?
Scale of the farm and manure systems
The model was converted from barn-level to a farm-level tool by integrating the barns and manure systems together through the model input procedures. In this way the emissions from the various on-farm operations can be compared on the same basis and put into perspective with regard to emission sources. There can be up to ten barns, each with its own associated manure system (subfloor, deep pit and, added in year 4, dry bedding) and 10 separate downstream manure handling systems (lagoon, outside storage and, recently added, a digester). Each barn can have its manure stream routed to any downstream manure system enabling streams to be combined for processing before going to the fields. An algal turn scrubber option can be added as an adjunct to any downstream system.
All of the manure handling systems, both those associated with a barn and those downstream of the barns, were written at the University of Arkansas and all but the digester are process-based. A digester option was added with options for burning the produced methane as barn heat or for producing electricity. Emissions are calculated for the transport of manure to the fields but not for emissions after application.
Growth, performance and amino acid inclusion in rations
The National Research Council (NRC) growth and performance model was integrated into the full farm level model in order to link ration characteristics and growth performance. We have closed the mass balance over the farm for carbon, nitrogen, phosphorous, water and manure solids. Addition of the NRC model also brought in the effects of ractopamine and immunocastration management options.
Testing of the revised growth equations with respect to the effects of individual amino acids (AA) was completed and a manuscript has been partially drafted. A revised equation predicting the effects of heat stress on feed intake was derived and incorporated into the model resulting in much better predictions of these effects than provided by the native NRC equations. Equations describing the impact of heat and cold stress on energy maintenance costs were also constructed, but have yet to be incorporated into the model. These latter 2 efforts were carried out primarily by a postdoctoral student employed on the National Animal Nutrition Program (NRSP-9) with the resulting equations made available to the project. Two manuscripts describing this work have been drafted and will be submitted in Fall, 2015. Finally, a method of deriving model settings to match observed rates of daily gain and feed conversion efficiency was devised and recently passed onto the barn model team for incorporation into the barn model. This will allow the model to be easily calibrated to observed gain and feed efficiency as input by the user.
Weather information
We updated the model weather files from the MERRA database for each of the 3102 counties in the U.S. These files have, in addition to temperature and humidity, other useful information such as precipitation, solar insolation, subsurface temperatures at various depths, and snow cover. The additional MERRA information facilitated addition of a solar panel option and will be used to estimate rainwater contribution to outside manure handling facilities and of solar insolation on inside barn temperatures.
What Is the DNDC Model?
The DNDC (DeNitrification and DeComposition) model was developed for quantifying N2O emissions from agricultural soils in the late 1980s (US EPA, 1995). By including fundamental bio-geochemical processes of carbon and nitrogen transformations, DNDC was extended to model soil C sequestration and other trace gases (e.g., methane, nitric oxide, ammonia etc.) in the early 1990s.
DNDC consists of two components. The first component entails three sub-models and converts primary drivers (i.e., climate, soil, vegetation and anthropogenic activity) to soil environmental factors (i.e., temperature, moisture, pH, Eh and substrate concentration gradient). The second component consists of nitrification, denitrification and fermentation sub-models; and simulates production/consumption of N2O, NO, N2, NH3 and CH4 driven by the modeled soil environmental conditions [see graphic below]. With the bio-geochemical reactions embedded in the model framework, DNDC can predict the turnover of soil organic matter and the consequent trace gas emissions and nitrate leaching losses.
Feed ingredients
With input from industry and academic experts, our feed ingredient database was revised to better capture the expected range of ingredients typically available to producers in the US. Carbon, water and land footprint data as well as nutritional characteristics for the NRC growth equations were compiled for each feed ingredient. Economic models, that estimate the cost of feed, manure handling, utilities (water, electricity, gas, propane and diesel), dead animal disposal and immunocastration were integrated into the model. Capital costs are not considered. We are conducting cost benefit analyses on combinations of operations, manure management and dietary feeding systems aimed at reducing GHG emissions. These will help identify incentives to minimize mitigation strategy cost. Routines have been developed that will allow the user to download a set of updated prices for utilities and major feed ingredients.
DeNitrification and DeComposition (DNDC) Model (Soil)
The DeNitrification and DeComposition (DNDC) model requires numerous weather and site inputs, many of which are output from the environmental calculator, and others which require site-specific geographical characteristics (e.g., soil type). We analyzed the agricultural area of each continental-US county using agricultural classes from the 2013 NASS Cropland Data Layer. For each county, we assigned the mean latitude/longitude of all agricultural pixels as the agriculture-weighted centroid from which representative weather data will be extracted.
County soils data are derived from the NRCS General Soils Map (STATSGO). We derived the spatial intersection of STATSGO soil polygons with county boundaries. We summarized top soil data for each soil polygon from 0 to 10cm depth for clay fraction (a proxy for soil texture), bulk density, organic matter fraction (to estimate soil organic carbon, SOC), and pH. Modeled results will be based on either the comprehensive set of soil polygon attributes or a representative distribution of soil attributes for each county (depending on timing and available computing power).
Summary
The model will enable the user to find hot spots in their emissions profile, evaluate the effects of operational changes, and estimate the emissions from facilities during the design stage. The further addition of an operational economic model will provide the ability to perform cost/benefit analyses of practices that can change impact GHG emissions (see video).
Work will continue on this project through Spring, 2016.
Figure 1. Diagram of the DeNitrification and DeComposition (DNDC) model
Why Does This Matter?
The environmental footprint model, with improved algorithms for manure management, economics, and animal performance provide high resolution and flexible decision support for the swine industry. The model enables users to identify hot spots in their emissions and water/land use profiles, evaluate the effects of operational changes, and estimate the emissions from facilities during the design stage. The further addition of an operational economic model enables cost/benefit analyses.
These enhancements support evaluations of dietary energy, protein, and amino acid content for much of the life cycle and immunocastration and the use of ractopamine during the growth cycle. They also allow assessment of the performance, economic, and environmental impact of transient health events during the growth cycle with respect to whole farm operation.
The use and impacts on land and soils, air, water, and greenhouse gases all make up the environmental footprint of pork production. This section highlights many different aspects of pork production and how those impact emissions of greenhouse gases and other aspects of environmental impact.
Thermal Conversion of Animal Manure to Biofuel – Go to archive… (February, 2014)
Life Cycle Assessment Modeling for the Pork Industry – Go to archive…. (July, 2012)
Producer Association Efforts to Address Carbon Footprint (Pork and Poultry) – Go to archive… (June, 2012)
Research Summaries
a five-year project examining different aspects of the environmental footprint of pork production was recently completed. This project looked at feed rations, animal health, and manure management to provide data for integration into a comprehensive
A carbon footprint is the total greenhouse gas emissions for a given person, place, event or product.
Carbon footprints are created using a process called life cycle assessment. Life cycle assessment or LCA is a method of resource accounting where quantitative measures of inputs, outputs and impacts of a product are determined.
Life cycle assessment is commonly used to:
find process or production improvements
compare different systems or products
find the ‘hot spots’ in a product’s life cycle where the most environmental impacts are made
help businesses or consumers make informed sourcing decisions
LCA Methods
There are several standard approaches for developing a life cycle assessment including the International Dairy Federation, the U.S. EPA, and two European standards: ISO 14040, and PSA 2050. While this can be completed with a simple spreadsheet, there are several software packages available to help complete the LCA
Steps to a Life Cycle Analysis:
Define the goal & scope of the LCA. This includes determining the purpose for the analysis.
Set the boundaries of the system: each higher tier provides a more complete picture of the product’s impacts, however requires more time and resources to complete.
Gate to Gate (LCA Tier I) – inventories the direct emissions for a single product of process
Cradle to Gate (Tier II) – inputs are taken back to the initial extraction as natural resources up to a certain point in the product’s life such as its sale from the farm, i.e. farm gate. This will include both direct and indirect emissions from the product.
Cradle To Grave (Tier III) – the product is followed through the consumer to its eventual recycling or disposal.
Determine how the impacts will be measured, also known as the functional unit. This can be expressed as the net sum of all impacts per unit of product, or the opposite: for a given amount of product, the amount of impact e.g. pounds of greenhouse gas emissions produced per pound of energy corrected milk.
Example impacts: greenhouse gas emissions, water use, land use, health impact
Example livestock products: Pound of meat, dozen eggs, energy corrected milk production, nutritional content.
Inventory the needed data. Information is gathered to identify and quantify energy, water and materials usage and the environmental releases associated with each step of the process. These data are collected through research and modeling for many different inputs, from coal mining to equipment manufacturing, and are available through worldwide databases. However, some of the needed data may not yet be available so research articles, models and assumptions must be used to fill in the final informaion.
Allocate resources and impacts to co-products. For example in dairy production, feeder cattle for meat production are also grown. The impacts of dairy feeder production can be included in the milk LCA, because calves are necessary for milk production, or a portion of the impacts can be allocated to beef production. This allocation can be made several ways, with the most common being economic, i.e. the calves are 10% of the value of dairy enterprise, or mass, i.e. the calves are 1% of the mass leaving the farm.
Impact assessment. This is where all the impacts are totaled and summarized. If the purpose of the LCA was to produce a carbon footprint, then only greenhouse gas impacts need totaled. However, multiple impacts can be compared and given different weighting if an overall score for a product is part of the purpose for the LCA.
Sources of variation
Different researchers may get different results when performing a LCA on the same product. This can happen for many reasons:
System boundary definition
Inclusion/exclusion of secondary/ indirect sources
Inclusion/exclusion of biogenic carbon (stored in organisims)
Inclusion/exclusion of carbon dioxide from fuel combustion
Functional relationships used
Global warming potential indexes
Inclusion/exclusion of carbon sequestration
Additional Resources
Additional Animal Agriculture and Climate Change Resources
Author: Crystal A. Powers – UNL
Reviewers: J. Harrison – WSU, J. Heemstra – UNL, S Mukhtar – TAMU, D. Smith – TAMU
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Waste to Worth home | More proceedings….
The research and modeling work encompassed by this project offers great potential for assisting researchers, policy makers, and especially farm managers to make informed management and facilities decisions regarding GHG emissions while maintaining production and profitability. However for this potential to be realized the swine production community needs to be aware of both its abilities and availability. To this end existing relationships with the
A summer research program for undergraduate students has been developed with the focus of carbon footprint mitigation from agriculture. The goal of this USDA REU is to support 10 students each summer for a 10 week period throughout the duration of this project. The REU supported by this project is offered parallel to the National Science Foundation Research Experience for Undergraduates (NSF REU), and students are recruited from the same pool as the NSF REU programs. The REU students are selected through an application process informed by an established recruitment network. We are actively recruiting from under-represented minorities, including: Native Americans, African Americans and Hispanics.
Algal biomass offers many advantages over traditional energy crops; algal biomass generates higher yields and requires smaller land area than other energy crops. In addition to biomass production for potential biofuel feedstock generation, algal growth systems can also act as tertiary treatment systems for wastewater. Algal growth can dramatically reduce nitrogen and phosphorus from wastewater. Unlike conventional open pond and photo-bioreactor systems, periphytic systems (e.g., algal turf scrubbers) generally involve the polyculture of micro-algae, which does not require specialized conditions. While algal turf scrubber systems are traditionally used for water treatment, they are also capable of generating high biomass yields.
Many technologies being considered incorporate some type of manure separation to concentrate manure solids, nutrients, and energy content. An LCA study in Denmark showed that energy recovery (incineration, gasification, and anaerobic digestion) had lower GHG emissions than traditional land application of swine manure. Other studies point to the complexities of manure management system design options in relation to GHG emissions. This portion of the project will quantify the effect of various solid separation approaches on the chemical composition of the manure generated by the feed trials at the University of Arkansas facilities during this project ,and generate the necessary manure solids for the thermo-chemical conversion portion of this project. Design and construct a pilot scale mobile solids separation system (see image below) is currently approaching completion and anticipated to be ready to begin trial calibrations soon.
Maximizing feed grade amino acid (FGAA) use and reducing dietary crude protein in swine diets has been shown to dramatically reduce nitrogen excretion in both nursery and growing/finishing swine which could substantially reduce nitrous oxide (N
Our working hypothesis is that immune activation, from clinical and subclinical disease, reduces growth performance and concomitantly increases nutrient excretion and subsequent 
Project Objectives
