Tag: beef

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Manure nutrient trends from 2012-2022

Purpose

Livestock manure nutrients can be variable depending on animal species, age, diet, management, housing, climate, and manure storage and handling. Thousands of samples are analyzed every year by agricultural laboratories across the United States (U.S.). While many published manure characteristics are two decades old, this study provides an updated glimpse into more recent manure data from thousands of samples across the country and reviewed possible trends from 2012-2022 by U.S. regions for common animal categories.

What Did We Do?

We collected manure nutrient data from participating U.S. laboratories and this data was aggregated by researchers at the University of Minnesota into ManureDB, a manure nutrient test database. By February 2024, ManureDB included over 490,000 samples from across the U.S. With ManureDB, data was filtered for the time period from 2012-2022 and common U.S. animal manure categories (solid beef, liquid beef, solid dairy, liquid dairy, solid chicken-broiler, solid chicken-layer, solid turkey, and liquid swine manure) to update nutrient summary statistics for total nitrogen (TN), ammonium-N (NH4-N), phosphorus (P2O5), and potassium (K2O) using the approximately 325,000 samples. Samples were divided by designating samples with <10% total solids as liquid manure and samples with >10% total solids as solid manure. Data was also analyzed to assess regional nutrient comparisons and trends for regions with sufficient samples.

What Have We Learned?

Regional differences impacted nutrient concentrations in solid and liquid manures. When comparing regions with at least 500 samples per animal manure category across 2012-2022 we found significant differences in nutrient concentrations in 66% of the individual year comparisons for solid manures and 91% of comparisons for liquid manures for all four analytes.

Between 2012 and 2022, significant increasing or decreasing nutrient (TN, NH4-N, P2O5, K2O) trends were evident in 25% of solid samples and 18% of liquid samples. The only significant trend for solid beef manure was a decreasing trend in the SE region for NH4-N. Both the solid chicken-broiler SE and NE regions had significant decreases in NH4-N, and only the SE had an increasing trend for K2O. The SE region for solid chicken-layer had decreasing trends for NH4-N, P2O5, and K2O. For solid dairy manure, the MW region only had a decreasing trend for P2O5, while the NE region had decreasing trends for N and NH4-N. Solid turkey manure only had significant trends for P2O5, with the MW increasing and the SE decreasing. Liquid beef manure had no significant trends. For liquid dairy manure, only the NE region had significant decreasing trends for all four nutrients. For liquid swine manure, only the SE region had significant increasing trends for NH4-N.

Standardizing nomenclature and increasing manure sample details, especially with animal life stage and manure storage information on manure sample submittal forms, will further improve ManureDB’s usefulness.

Future Plans

We continue to expand and refine ManureDB by adding data each year, additional labs, making the website more user-friendly, and enhancing data quality control. We archived the first set of data with Ag Data Commons in 2024 and plan to do that annually. We also plan to publish several papers regarding the development of the database and analysis of the manure nutrient data.

Authors

Presenting & corresponding author

Nancy L. Bohl Bormann, Researcher, University of Minnesota, nlbb@umn.edu

Additional authors

Melissa L. Wilson, Associate Professor, University of Minnesota

Erin L. Cortus, Associate Professor and Extension Engineer, University of Minnesota

Additional Information

Acknowledgements

ManureDB is supported through USDA NIFA Award 2020-67021-32465 and Cooperative Ecosystem Studies Unit program [grant no. NR253A750008C001] from the U.S. Department of Agriculture — Natural Resources Conservation 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. 2025. Title of presentation. Waste to Worth. Boise, ID. April 7–11, 2025. URL of this page. Accessed on: today’s date.

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The Role of Agriculture in Atmospheric Nitrogen Deposition: Sources, Impacts, and Management

Agriculture is the largest source of ammonia emissions and contributes to nitrogen deposition which can impact ecosystem health. This webinar introduces the topic of nitrogen deposition and provides an overview of the role of the National Atmospheric Deposition Program (NADP) in determining nitrogen deposition sources. In addition, the speakers provide an overview of a region being impacted by agriculture related nitrogen deposition and discusses agricultural management practices that may reduce ammonia emissions and nitrogen deposition. This presentation was originally broadcast on September 20, 2024. Continue reading “The Role of Agriculture in Atmospheric Nitrogen Deposition: Sources, Impacts, and Management”

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Trends in Manure Sample Data

Purpose

Most manure book values used today from the MidWest Plan Service (MWPS) and American Society of Agricultural and Biological Engineers (ASABE) were derived from manure samples prior to 2003. To update these manure test values, the University of Minnesota in partnership with the Minnesota Supercomputing Institute, is working to build a dynamic manure test database called ManureDB. During this database construction, the University of Minnesota collected manure data spanning the last decade from five labs across the country. Trends, similarities, and challenges arose when comparing these samples. Having current manure test numbers will assist in more accurate nutrient management planning, manure storage design, manure land application, and serve agricultural modeling purposes.

What Did We Do?

We recruited five laboratories for this preliminary study who shared some of their manure sample data between 2012-2021, which represented over 100,000 manure samples. We looked at what species, manure types (liquid/solid), labels, and units we had to work with between the datasets to make them comparable. Once all the samples were converted into either pounds of nutrient/ton for solid manure or pounds of nutrient/1000 gallons for liquid manure, we took the medians of total nitrogen, ammonium-nitrogen (NH4-N), phosphate (P2O5), and potassium oxide (K2O) analyses from those samples and compared them to the MWPS and ASABE manure nutrient values.

What Have We Learned?

There is no standardization of laboratory submission forms for manure samples. The majority of samples have minimal descriptions beyond species of animal and little is known about storage types. With that said, we can still detect some general NPK trends for the beef, dairy, swine, poultry manure collected from the five laboratories in the last decade, compared to the published book values. For liquid manure, the K2O levels generally increased in both the swine and poultry liquid manure samples. For the solid swine manure and solid beef manure, total N, P2O5, and K2O levels all increased compared to the published book values. The solid dairy manure increased in P2O5 and K2O levels, and the solid poultry manure increased in total N and K2O. See Figure 1 for the general trends in liquid and solid manure for swine, dairy, beef, and poultry.

Table 1. Manure sample trends 2012-2021 compared to MWPS/ASABE manure book values. (+) = trending higher, (o) = no change/conflicting samples, (-) = trending lower

Liquid Total N NH4–N P2O5 K2O
Swine o o – +
Dairy – o – o
Beef o o o o
Poultry o + – +
Solid Total N NH4–N P2O5 K2O
Swine + o + +
Dairy o o + +
Beef + – + +
Poultry + o o +

Future Plans

The initial data gives us a framework to standardize fields for the future incoming samples (location, manure type, agitation, species, bedding, storage type, and analytical method) along with creating a unit conversion mechanism for data uploads. We plan to recruit more laboratories to participate in the ManureDB project and acquire more sample datasets. We will compare and analyze this data as it becomes available, especially more detailed data for each species. We will be designing ManureDB with statistical and data visualization features for future public use.

Authors

Nancy L. Bohl Bormann, Graduate Research Assistant, University of Minnesota

Corresponding author email address

bohlb001@umn.edu

Additional authors

Melissa L. Wilson, Assistant Professor, University of Minnesota

Erin L. Cortus, Associate Professor and Extension Engineer, University of Minnesota

Kevin Janni, Extension Engineer, University of Minnesota

Larry Gunderson, Pesticide & Fertilizer Management, Minnesota Department of Agriculture

Tom Prather, Senior Software Developer, University of Minnesota

Kevin Silverstein, Scientific Lead RIS Informatics Analyst, University of Minnesota

Additional Information

ManureDB website: http://manuredb.umn.edu/ (coming soon!)

Twitter: @ManureProf, @nlbb

Lab websites:

https://wilsonlab.cfans.umn.edu/

https://bbe.umn.edu/people/erin-cortus

Acknowledgements

This work is supported by the AFRI Foundational and Applied Science Program [grant no. 2020-67021-32465] from the USDA National Institute of Food and Agriculture, the University of Minnesota College of Food, Agricultural and Natural Resource Sciences, and the Minnesota Supercomputing Institute.

 

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. 2022. Title of presentation. Waste to Worth. Oregon, OH. April 18-22, 2022. URL of this page. Accessed on: today’s date.

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Emission of ammonia, hydrogen sulfide, and greenhouse gases following application of aluminum sulfate to beef feedlot surfaces

Purpose

Alum has been successfully used in the poultry industry to lower ammonia (NH3) emission from the barns. However, it has not been evaluated to reduce NH3 on beef feedlot surfaces. Additionally, it is not known how it would affect other common emissions from beef feedlot surfaces. The purpose of this study was to determine the effect of adding aluminum sulfate to beef feedlot surfaces on NH3, hydrogen sulfide (H2S), carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) emissions.

What Did We Do?

Eight feedlot pens (30 animals per pen) at the U.S. Meat Animal Research Center feedlot were utilized. The pens had a central mound constructed on manure and soil and 3 m concrete apron by the feed bunk and cattle were fed a corn-silage based diet. Four pens (30 cattle/pen) had 10% (g g-1) alum applied to the 6 meters immediately behind the concrete bunk apron and four did not receive alum. The amount of alum added to the area was determined on a mass basis for a depth of 5 cm of feedlot surface material (FSM) using the estimated density of feedlot surface material for Nebraska feedlots (1.5 g cm−3). On sampling days, six representative grab samples were collected from the feedlot surface from the six-meter area behind the bunk apron in each pen; samples were combined within pen to make three representative replicates per pen (N=24). Each of the three pooled samples per pen were measured for pH, NH3, H2S, CH4, CO2, and N2O using petri dishes and wind tunnels in an environmental chamber at an ambient temperature of 25°C (77°F) and 50% relative humidity. Flux measurements for NH3, H2S, CH4, CO2, and N2O flux were measured for 15 minutes using Thermo Fisher Scientific 17i, 450i, 55i, 410iQ, and 46i gas analysis instruments, respectively. Samples were analyzed at day -1, 0, 5, 7, 12, 14, 19, 21, and 26.

What Have We Learned?

Addition of alum lowered pH of FSM from 8.3 to 4.8 (p < 0.01) and the pH remained lower in alum-treated pens for 26 days (p < 0.01). Although the pH remained low, NH3 flux was only lower (p < 0.01) at day 0 and day 5 for alum-treated pens compared to the pens with no alum treatment. Nitrous oxide emission was not affected by alum treatment (6.2 vs 5.7 mg m-2 min-1, respectively for 0 and 10% alum treated pens). Carbon dioxide emission was lower for alum-treated pens than non-treated pens from day 5 until the end of the study (p < 0.05), perhaps due to suppressed microbial activity from the lower pH. Hydrogen sulfide emission was higher (p < 0.05) from alum-treated feedlot surface material (0.8 mg m-2 min-1) compared to non-treated feedlot surface material (0.3 mg m-2 min-1), likely due to addition of sulfate with alum. Methane emission was also higher in alum-treated pens (173.6 mg m-2 min-1) than non-treated pens (81.4 mg m-2 min-1). The limited reduction in NH3, along with increased H2S and CH4 emission from the FSM indicates that alum is not a suitable amendment to reduce emissions from beef feedlot surfaces.

Table 1. pH, ammonia (NH3), hydrogen sulfide (H2S), methane (CH4), carbon dioxide (CO2) and nitrous oxide (N2O) emission from feedlot surface material treated with 0 or 10% alum (g g-1 mass basis).
pH NH3
(mg m-2 min-1)		 H2S
(mg m-2 min-1)		 CH4
(mg m-2 min-1)		 CO2
(mg m-2 min-1)		 N2O
(mg m-2 min-1)
Day 0% Alum 10% Alum 0% Alum 10% Alum 0% Alum 10% Alum 0% Alum 10% Alum 0% Alum 10% Alum 0% Alum 10% Alum
-1 8.1 8.3 229.6d 515.9c 0.3 0.4 136.3 x 73.4w 4,542 3,234 3.1 4.2
0 8.3a 4.8b 163.0c 32.4d 0.2 f 1.8 e 43.1 x 193.8w 4,372 5,294 2.9 1.8
5 8.5a 6.3b 279.5c 83.6d 0.4 0.5 84.1 x 309.5w 404y 1,347z 6.0 6.8
7 8.6a 6.7b 120.2 130.0 0.6 f 1.2e 53.4 61.7 468 y 1,903z 15.3 10.9
12 8.6a 7.2b 418.0 320.3 0.3 0.3 104.5 145.7 3,742y 1,939z 3.3 8.0
14 8.9a 7.6b 229.0 145.5 0.2 0.4 25.4x 180.7w 4,203y 2,018z 11.5 9.3
19 8.6a 7.5b 228.0 225.1 0.1 f 1.1e 132.3x 254.7w 5,999y 3,116z 6.9 5.8
21 8.4a 7.2b 232.0 257.0 0.5 0.8 81.9x 250.0w 4,324y 2,477z 2.2 1.9
26 8.6a 8.0b 584.5c 319.9d 0.1f 0.7e 72.2 92.9 5,534y 3,540z 4.7 2.9
Within a parameter and day, different superscripts indicate a significant difference (p < 0.05) between the emissions from the feedlot surface material treated with 0% and 10% alum.

Future Plans

Future research will evaluate the use of aluminum chloride instead of aluminum sulfate to lower pH of FSM and retain nitrogen. Additionally, microbial amendments are being evaluated to determine if they can reduce gaseous emissions from the feedlot surface.

Authors

Presenting author

Mindy J. Spiehs, Research Animal Scientists, USDA ARS Meat Animal Research Center

Corresponding author

Bryan L. Woodbury, Agricultural Engineer, USDA ARS Meat Animal Research Center

Corresponding author email address

bryan.woodbury@usda.gov

Additional Information

For additional information about the use of alum as a feedlot surface amendment, readers are direct to the following: Effects of using aluminum sulfate (alum) as a surface amendment in beef cattle feedlots on ammonia and sulfide emissions. 2022. Sustainability 14(4): 1984 – 2004. https://doi.org/10.3390/su14041984

Acknowledgements

The authors wish to acknowledge USMARC technicians Alan Kruger and Jessie Clark for their assistance with data collection and analysis.

 

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. 2022. Title of presentation. Waste to Worth. Oregon, OH. April 18-22, 2022. URL of this page. Accessed on: today’s date.

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Estimating Routine Beef and Dairy Mortality Masses Based on Systems Operation

Purpose

The day-to-day loss of animals is a fact of life all cattle producers must face and prepare for. Unfortunately, most published data of animal mortalities are for one-time, catastrophic die offs – where all the cattle on a farm must be exterminated because of disease outbreaks or natural disasters. Routine mortalities on cattle farms do not happen all at once, and mortality rates vary greatly between different life stages of animals and types of production systems.

An expert panel was convened by the Agricultural Working Group of the Chesapeake Bay Program to determine annual mortality, nitrogen and phosphorus masses produced by cow-calf, dairy and cattle on feed (feedlot) operations in the watershed. This paper concentrates on the annual mortality masses estimations determined by the panel. Cattle and Dairymen can use these values to plan for disposal of routine losses.

What Did We Do?

The panel looked, at depth, into existing production systems, and combined morality rates at different life stages, the size of animals at time of death, and the carcass composition varying with age to determine mortality and nutrient masses produced by typical cattle farms in the watershed.

The panel chose a 50-cow cow-calf operation as a model system, where cattle are on pasture 95% of the time. Under ideal conditions, each cow will yield one calf per year to be sold by year’s end. Some female calves will be retained to replace culled cows from the herd, maintaining the same general herd size. It was assumed there was no death loss of mother cows in the herd. We used USDA-APHIS (2010) data of average annual death loss of immature cattle combined with the average weight of cattle at different life-stages to determine weight of mortalities produced each year.

A total confinement beef feedlot was used to model mortalities for cattle on feed. Cattle were assumed to grow linearly with cattle placed in the feedlot at 400 to 600 pounds, and leaving at 1,000 to 1,200 pounds with an average time on lot of 120 days. Midwestern data (Vogel et al, 2015) was used to estimate annual deathrates per feedlot space at 30-day increments since placement in the feedlot.

A 100-cow milking herd was used as a reference for dairy systems. The reference farm contained 50 female calves and 50 heifers in development. Heifers are bred at 15 months and give birth around 24 months (2 years) of age. Male calves are exported from the farm as soon as possible for development as lower grade beef cattle. The reference dairy had heifers and dry cows on pasture, with the active milking herd in free-stall barns or alternative confinement for a 300-day lactation. USDA-APHIS (2016) data of average annual death loss of all types of dairy cattle was combined with the average weight of cattle at different life-stages to determine weight of mortalities produced each year.

What Have We Learned?

Figure 1 shows the estimated total weight of mortalities produced by a 50 cow, cow-calf herd each year broken down by age of animal dying.  As can be seen in Figure 1, the greatest weight of mortalities occurred before calves were weaned – assuming no death of mother cows. The values in Figure 1 represent 1.52 calves born dead, 1.92 calves dying before weaning, and 0.87 head dying after weaning. This means a farmer should prepare for the loss of 2 newborn calves, 2 un-weaned calves, and one weaned steer/heifer per 50 mother cows each year.  Dividing the total weight of mortalities by 50 head gives an average per cow annual mortality of 32 pounds per year.

Figure 1. Estimated Total Annual Weight of Mortalities Produced by a 50 Cow, Cow-Calf Herd.

Figure 2 shows the estimated total weight of mortalities produced by a 100-head-space feedlot. The greatest source of mortalities is steers and heifers weighing close to 700 pounds (31 to 60 days after arrival on the feedlot. Dividing the total weight of mortalities by 100 gives an average annual mortality weight of 18 pounds per head-space per year. The feedlot owner should prepare for approximately 3 animals dying each year per 100 head-space.

Figure 2. Estimated Total Annual Weight of Mortalities Produced by a 100 head-space feedlot.

Figure 3 shows the estimated total weight of mortalities produced by a 100-cow dairy.  Dividing the total weight of mortalities by 100 head gives an average annual mortality weight of 90 pounds per milking cow. The greatest source of mortalities is mature cows. Dairies should prepare for as much as 6 mature cows, 3 pre-weaned calves and heifers, and 1 weaned heifer dying each year per 100 mature cows.

Figure 3. Estimated Total Annual Weight of Mortalities Produced by a 100 milking head dairy.

Future Plans

Cattle producers can use the values estimated by this project to determine resources needed to prepare for mortalities. If burial is the preferred option, the space required to bury mortalities for the expected life of the operation; for composting, the area, and weight of carbon source required to compost; and for incineration, an incinerator capable of handling the largest animal housed on the farm.

Authors

Douglas W. Hamilton, Ph.D. P.E., Extension Waste Management Specialist, Oklahoma State University

Corresponding author email address

dhamilt@okstate.edu

Additional authors

Thomas M. Bass, Livestock Environment Associate Specialist, Montana State University; Amanda Gumbert, PhD., Water Quality Extension Specialist, University of Kentucky; Ernest Hovingh, DVM, PhD., Research Professor Extension Veterinarian, Pennsylvania State University; Mark Hutchinson, Extension Educator, University of Maine; Teng Teeh Lim, PhD, P.E., Extension Professor, University of Missouri;  Sandra Means, P.E., USDA NRCS, Environmental Engineer, East National Technology Support Center (Retired); George “Bud” Malone, Malone Poultry Consulting; Jeremy Hanson, WQGIT Coordinator – STAC Research Associate, Chesapeake Research Consortium – Chesapeake Bay Program

Additional Information

Hamilton, D., Bass, T.M., Gumbert, A., Hovingh, E., Hutchinson, M., Lim, T.-T., Means, S., and G. Malone. (2021). Estimates of nutrient loads from animal mortalities and reductions associated with mortality disposal methods and Best Management Practices (BMPs) in the Chesapeake Bay Watershed (DRAFT). Edited by J. Hanson, A. Gumbert & D. Hamilton.  Annapolis, MD: USEPA Chesapeake Bay Program.

USDA-APHIS (2010). Mortality of Calves and Cattle on U.S. Beef Cow-calf Operations: Info Sheet, 2010. Fort Collins, CO: USDA-APHIS.

USDA-APHIS. (2016). Dairy 2014: Health and Management Practices on US Dairy Operations, 2014. Report, 3, 62-77. Fort Collins, CO: USDA-APHIS,.

Vogel, G. J., Bokenkroger, C. D., Rutten-Ramos, S. C., & Bargen, J. L. (2015). A retrospective evaluation of animal mortality in US feedlots: rate, timing, and cause of death. Bov. Pract, 49(2), 113-123.

Acknowledgements

Funding for this project was provided by the US-EPA Chesapeake Bay Program through Virginia Polytechnic and State University EPA Grant No. CB96326201

 

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. 2022. Title of presentation. Waste to Worth. Oregon, OH. April 18-22, 2022. URL of this page. Accessed on: today’s date.

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Industry Initiatives for Environmental Sustainability – a Role for Everyone

This webinar introduces current and future industry-based initiatives for environmental sustainability in the livestock and poultry sector, and how Livestock and Poultry Environmental Learning Community learners can play a critical role in their region. This presentation was originally broadcast on September 17, 2021. Continue reading “Industry Initiatives for Environmental Sustainability – a Role for Everyone”

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Predicting Manure Nitrogen and Phosphorus Characteristics of Beef Open Lot Systems

This project involves the analysis of a new data set for manure characteristics from open lot beef systems demonstrating both average characteristics and factors contribution to variability in manure characteristics among these systems. Defining the characteristics and quantities of harvested manure and runoff from open earthen lot animal systems is critical to planning storage requirements, land requirements for nutrient utilization, land application rates, and logistical issues, such as equipment and labor requirements. Accuracy of these estimates are critical to planning processes required by federal and state permitting programs. Poor estimates can lead to discharges that result in court action and fines, neighbor nuisance complaints, and surface and ground water degradation. Planning procedures have historically relied upon standard values published by NRCS (Stettler et al., 2008), MWPS (Lorimor et al., 2000), and ASABE (2014) for average characteristics.

What Did We Do?

A large data set of analyses from manure samples collected over a 15-year period from 444 independent cattle feedlot pens at a single eastern Nebraska research facility was reviewed to provide insight to the degree of variability in observed manure characteristics and to investigate the factors influencing this variability. No previous efforts to define these characteristics have included data gathered over such a wide range of dietary strategies and weather conditions. This exclusive research data set is expected to provide new insights regarding influential factors affecting characteristics of manure and runoff harvested from open lot beef systems. The objective of this paper is to share a preliminary summary of findings based upon a review of this data set.

What Have We Learned?

A review of this unique data set reveals several important preliminary observations. Standard values reported by ASABE and MWPS for beef manure characteristics in open lot systems are relatively poor indicators of the significant variability that is observed within open lot feeding systems. Our data set reveals significant differences between manure characteristics as a function of feeding period (Table 1) and substantial variability within feeding period, as illustrated by the large coefficients of variation for individual characteristics. Differences in winter and summer conditions influence the characteristics and quantities of solids, organic matter, and nutrients in the harvested manure. The timing of the feeding period has substantial influence on observed differences in nitrogen loss and nitrogen in manure (Figure 1). Nitrogen recovery for the warmer summer feeding periods averaged 51 and 6 grams/head/day in the manure and runoff, respectively, with losses estimated to be 155 grams/head/day.  Similarly, nitrogen recovery in manure and runoff for the winter feeding period was 90 and 4 grams/head/day, respectively, with losses estimated at 92 grams/head/day (Figure 1 and Koelsch, et al., 2018). In addition, differences in weather and pen conditions during and following winter and summer feeding periods impact manure moisture content and the mixing of inorganics with manure (Table 1).

Table 1. Characteristics of manure collected from 216 and 228 cattle feedlot pens during Summer and Winter feeding periods, respectively1.
University of Nebraska Feedlot in East Central Nebraska Standard Values
Summer Winter ASABE NRCS MWPS3
Mean CV2 Mean CV2 Mean Mean
Total Manure (wet basis), kg/hd/d 9.3 99% 13.1 43% 7.5 7.9
DM    % 71% 10% 63.2% 15% 67% Collected 55%
    kg/hd/d 5.4 80% 8.0 41% 5.0 manure 4.3
OM    % 24% 28% 25.3% 41% 30% is not 50%
    kg/hd/d 1.00 52% 1.87 41% 1.5 reported. 2.2
Ash    % 76% 9% 74.7% 14% 70% 50%
    kg/hd/d 4.16 72% 6.10 49% 3.5 2.2
N    % 1.3% 36% 1.19% 23% 1.18% 1.2%
    g/hd/d 51 50% 90 33% 88 95
P    % 0.37% 41% 0.34% 29% 0.50% 0.35%
    k/hd/d 17.7 55% 26.0 42% 37.5 27.7
DM = dry matter; OM = organic matter (or volatile solids)

1    Summer = April to October feeding period, Winter = November to May feeding period

2    Coefficient of variation, %

3    Unsurfaced lot in dry climate with annual manure removal.
two pie charts
Figure 1. Distribution of dietary nitrogen consumed by beef cattle among four possible ed points for summer and winter feeding periods.

Dietary concentration of nutrients was observed to influence the harvested manure P content (Figure 2) but produce minimal impact on harvested manure N content (not shown). Diet was an important predictor in observed N losses, especially during the summer feeding period. However, its limited value for predicting harvested manure N and moderate value for predicting harvesting manure P suggests that other factors such as weather and management may be influential in determining N and P recovered (Koelsch, et al., 2018).

scatter plot with trendlines
Figure 2. Influence of dietary P concentration on harvested manure P.

Significant variability exists in the quantity of total solids of manure harvested with a factor of 10 difference between the observed low and high values when compared on a mass per finished head basis (note large CVs in Table 1). This variability has significant influence on quality of the manure collected as represented by organic matter, ash content, and moisture content.

Although individual experimental trials comparing practices to increase organic matter on the feedlot surface have demonstrated some benefit to reducing nitrogen losses, the overall data set does not demonstrate value from higher pen surface organic matter for conservation of N in the manure (Koelsch, et al., 2018). However, higher organic matter manure is correlated to improved nitrogen concentration in the manure suggesting a higher value for the manure (Figure 3).

scatter plot with trendlines
Figure 3. Influence of pen surface organic matter measured as organic matter in the harvested manure) on nitrogen concentration in the manure.

It is typically recommended that manure management planning should be based upon unique analysis for manure characteristics representative of the manure being applied.  The large variability in harvested manure from open lot beef systems observed in this study further confirms the importance of this recommendation. The influence of weather on the manure and the management challenges of collecting manure from these systems adds to the complexity of predicting manure characteristics.  In addition, standard reporting methods such as ASABE should consider reporting of separate standard values based upon time of the year feeding and/or manure collection period. This review of beef manure characteristics over a 15 year period further documents the challenge of planning based upon typical or standard value for open lot beef manure.

Future Plans

The compilation and analysis of the manure and runoff data from these 444 independent measure of feedlot manure characteristics is a part of an undergraduate student research experience. Final review and analysis of this data will be completed by summer 2019 with the data published at a later time. The authors will explore the value of this data for adjusting beef manure characteristics for ASABE’s Standard (ASABE, 2014).

References

ASABE. 2014.  ASAE D384.2 MAR2005 (R2014):  Manure Production and Characteristics. ASABE, St. Joseph, Ml. 32 pages.

Koelsch, R. , G. Erickson2, M. Homolka2, M. Luebbe. 2018. redicting Manure Nitrogen, Phosphorus, and Carbon Characteristics of Beef Open Lot Systems. Presented at the 2018 ASABE Annual International Meeting. 15 pages.

Lorimor, J., W. Powers, and A. Sutton. 2000. Manure characteristics. Manure Management Systems Series MWPS-18. Midwest Plan Service. Ames Iowa: Iowa State University.

Stettler, D., C. Zuller, D. Hickman. 2008. Agricultural Waste Characteristics.  Chapter 4 of Part 651, NRCS Agricultural Waste Management Field Handbook. pages 4-1 to 4-32.

 

Authors

Richard (Rick) Koelsch, Professor of Biological Systems Engineering and Animal Science, University of Nebraska-Lincoln

rkoelsch1@unl.edu

Megan Homolka, student, and Galen Erickson Professor of Animal Science, University of Nebraska-Lincoln

Additional Information

Koelsch, R. , G. Erickson2, M. Homolka2, M. Luebbe. 2018. Predicting Manure Nitrogen, Phosphorus, and Carbon Characteristics of Beef Open Lot Systems. Presented at the 2018 ASABE Annual International Meeting. 15 pages.

 

 

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.

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The Use of USDA-NRCS Conservation Innovation Grants to Advance Air Quality Improvements

USDA-NRCS has nearly fifteen years of Conservation Innovation Grant project experience, and several of these projects have provided a means to learn more about various techniques for addressing air emissions from animal agriculture.  The overall goal of the Conservation Innovation Grant program is to provide an avenue for the on-farm demonstration of tools and technologies that have shown promise in a research setting and to further determine the parameters that may enable these promising tools and technologies to be implemented on-farm through USDA-NRCS conservation programs.

What Did We Do?

Several queries for both National Competition and State Competition projects in the USDA-NRCS Conservation Innovation Grant Project Search Tool (https://www.nrcs.usda.gov/wps/portal/nrcs/ciglanding/national/programs/financial/cig/cigsearch/) were conducted using the General Text Search feature for keywords such as “air”, “ammonia”, “animal”, “beef”, “carbon”, “dairy”, “digester”, “digestion”, “livestock”, “manure”, “poultry”, and “swine” in order to try and capture all of the animal air quality-related Conservation Innovation Grant projects.  This approach obviously identified many projects that might be related to one or more of the search words, but were not directly related to animal air quality. Further manual review of the identified projects was conducted to identify those that specifically had some association with animal air quality.

What Have We Learned?

Out of nearly 1,300 total Conservation Innovation Grant projects, just under 50 were identified as having a direct relevance to animal air quality in some way.  These projects represent a USDA-NRCS investment of just under $20 million. Because each project required at least a 50% match by the grantee, the USDA-NRCS Conservation Innovation Grant program has represented a total investment of approximately $40 million over the past 15 years in demonstrating tools and technologies for addressing air emissions from animal agriculture.

The technologies that have been attempted to be demonstrated in the animal air quality-related Conservation Innovation Grant projects have included various feed management strategies, approaches for reducing emissions from animal pens and housing, and an approach to mortality management.  However, the vast majority of animal air quality-related Conservation Innovation Grant projects have focused on air emissions from manure management – primarily looking at anaerobic digestion technologies – and land application of manure. Two projects also developed and enhanced an online tool for assessing livestock and poultry operations for opportunities to address various air emissions.

Future Plans

The 2018 Farm Bill re-authorized the Conservation Innovation Grant Program through 2023 at $25 million per year and allows for on-farm conservation innovation trials.  It is anticipated that additional air quality projects will be funded under the current Farm Bill authorization.

Authors

Greg Zwicke, Air Quality Engineer, USDA-NRCS National Air Quality and Atmospheric Change Technology Development Team

greg.zwicke@ftc.usda.gov

Additional Information

More information about the USDA-NRCS Conservation Innovation Grants program is available on the Conservation Innovation Grants website (https://www.nrcs.usda.gov/wps/portal/nrcs/main/national/programs/financial/cig/), including application information and materials, resources for grantees, success stories, and a project search tool.

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.

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A National Assessment of the Environmental Impacts of Beef Cattle Production

Environmental effects of cattle production and the overall sustainability of beef have become national and international concerns. Our objective was to quantify important environmental impacts of beef cattle production throughout the United States. This provides baseline information for evaluating potential benefits of alternative management practices and mitigation strategies for improving the sustainability of beef.

What did we do?

Surveys and visits of farms, ranches and feedlots were conducted throughout seven regions of the United States (Northeast, Southeast, Midwest, Northern Plains, Southern Plains, Northwest and Southwest) to determine common practices and characteristics of cattle production. These data along with other information sources were used to create about 150 representative production systems throughout the country, which were simulated with the Integrated Farm System Model using local soil and climate data. The simulations quantified the performance and environmental impacts of beef cattle production systems within each region. A farm-to-gate life cycle assessment was used to determine resource use and emissions for all production systems including traditional beef breeds and cull animals from the dairy industry. Regional and national totals were determined as the sum of the production system outputs multiplied by the number of cattle represented by each simulated system.

What we have learned?

Average annual greenhouse gas emission related to beef cattle production was determined as 268 ± 29 million tons of carbon dioxide equivalent, which is approximately 3.3% of the reported total U.S. emission. Fossil energy use was 539 ± 50 trillion BTU, which is less than 1% of total U.S. consumption. Non-precipitation water use was 6.2 ± 0.9 trillion gallons, which is on the order of 5% of estimated total fresh water use for the country. Finally, reactive N loss was 1.9 ± 0.15 million ton, which indicates about 15% of the gaseous emissions of reactive N for the nation are related to beef cattle production. Expressed per lb of carcass weight produced, these impacts were 21.3 ± 2.3 lb CO2,e, 21.6 ± 2.0 BTU, 0.155 ± 0.012 lb N and 244 ± 37 gal for carbon, energy, reactive N and water footprints, respectively. Many sources throughout the production system contributed to these footprints (Figure 1). The majority of most environmental impacts was associated with the cow-calf phase of production (Figure 2).

Distribution of the major sources for each environmental footprint.
Figure 1. Distribution of the major sources for each environmental footprint.
Figure 2. Distribution of the sources of each environmental impact across the three major phases in the life cycle of beef cattle production.
Figure 2. Distribution of the sources of each environmental impact across the three major phases in the life cycle of beef cattle production.

Take-home message: This study is the most detailed, yet comprehensive, study conducted to date that provides baseline measures for the sustainability of U.S. beef.

Future plans

These farm-to-gate values are being combined with sources in packing, processing, distribution, retail, consumption and waste handling to produce a full life cycle assessment of U.S. beef considering additional metrics of environmental and economic impact. Further work is ongoing to complete this full LCA and to more fully assess opportunities for mitigating environmental impacts and improving the sustainability of beef.

Authors

Alan Rotz, USDA-ARS; Senorpe Asem-Hiablie, USDA-ARS; Sara Place, National Cattlemen’s Beef Association; Greg Thoma, University of Arkansas.

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. https://www.ars.usda.gov/ARSUserFiles/80700500/Reference%20Manual.pdf.

Further information on the national assessment of the environmental impacts of U.S. cattle production is available in:

Rotz, C. A., S. Asem-Hiablie, S. Place and G. Thoma. 2019. Environmental footprints of beef cattle production in the United States. Agric. Systems 169:1-13.

Acknowledgements

This work was funded in part by The Beef Checkoff and the USDA’s Agricultural Research Service. 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.

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