CALCULATING THE CARBON FOOTPRINT FOR THE UNIVERSITY …

CALCULATING THE CARBON FOOTPRINT FOR THE UNIVERSITY OF GEORGIA

SWINE AND DAIRY FARMS USING SELECTED MODELS

by

LIN MA

(Under the Direction of Mark Risse)

ABSTRACT

Greenhouse gas (GHG) emissions can create environmental problems. The University of

Georgia (UGA) committed to reduce its GHG emissions by 20% by 2020 and established a long-

term goal of carbon neutrality by 2060. The objective of our study was to find appropriate

models for estimating the carbon footprint for the UGA swine and dairy farms. While easy to

use, the current process model does not include many management options that could influence

the carbon footprint calculations significantly. In this study, the carbon footprint estimation from

a suite of models for the UGA swine and dairy farm were compared with each other.

Management options to reduce the carbon footprint were investigated using selected models to

analyze some of the changes each farm could make to reduce its carbon footprint in the future.

INDEX WORDS: Carbon footprint, Greenhouse gas emissions, Model, Dairy farm, Swine

farm



by

LIN MA

B.S., China Agricultural University, China, 2012

A Thesis Submitted to the Graduate Faculty of the University of Georgia in Partial Fulfillment of

the Requirements for the Degree

MASTER OF SCIENCE

ATHENS, GEORGIA

2015

© 2015

Lin Ma

All Rights Reserved



by

LIN MA

Major Professor: Mark Risse

Committee: Miguel Cabrera

Daniel Markewitz

Electronic Version Approved:

Julie Coffield

Interim Dean of the Graduate School

The University of Georgia

May 2015

iv

ACKNOWLEDGEMENTS

I would like to thank everyone whose time and knowledge contributed to my research. First

and foremost is my major professor, Mark Risse. Thank you for all of your help, guidance and

support. I also appreciate my other committee members, Prof. Miguel Cabrera and Prof. Daniel

Markewitz. Thank you for all of your help and advice. I also really thank Drs. Lane Ely and

Robert Dove and the employees and managers at the UGA Swine and Dairy Center for supplying

information and time to us for this effort. Last but not least, I want to thank my family and

friends for their love and support.

v

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS ........................................................................................................... iv

LIST OF TABLES ........................................................................................................................ vii

LIST OF FIGURES ....................................................................................................................... ix

CHAPTER

1 INTRODUCTION ........................................................................................................ 1

Objectives ............................................................................................................... 2

2 LITERATURE REVIEW ............................................................................................. 3

Greenhouse Gas Emissions ..................................................................................... 3

Greenhouse Gas Emissions in Animal Industry ..................................................... 8

Methods to Mitigating Greenhouse Gas Emissions .............................................. 13

Models to Calculate the Carbon Footprint in Animal Industry ............................ 19

3 MATERIALS AND METHODS ................................................................................ 24

Study Area ............................................................................................................ 24

Tools and Procedures ............................................................................................ 33

4 RESULTS AND DISCUSSIONS ............................................................................... 39

Inputs and Time Required for Different Models .................................................. 39

Carbon Footprint for the UGA Swine Farm ......................................................... 42

Carbon Footprint for the UGA Dairy Farm .......................................................... 47

Management Options for Reducing the Carbon Footprint ................................... 53

vi

5 CONCLUSIONS......................................................................................................... 61

REFERENCES ............................................................................................................................. 62

APPENDICES

A THE UGA SWINE FARM DATA RECORD ............................................................ 86

B THE UGA DAIRY FARM DATA RECORD ............................................................ 93

vii

LIST OF TABLES

Page

Table 2.1: Global warming potential over a 100-year time span of several greenhouse gases ...... 4

Table 2.2: Typical absolute and relative CH4 emissions from enteric fermentation and manure 10

Table 4.1: Inputs and time required for different models ............................................................. 40

Table 4.2: Summary of the carbon footprint for the UGA swine farm ......................................... 42

Table 4.3: Summary of the carbon footprint for the UGA dairy farm .......................................... 47

Table 4.4: The carbon footprint for the UGA dairy farm from Farm Smart (Version 1.5) and the

national average reported by Farm Smart (Version 1.5) ................................................... 50

Table 4.5: The carbon footprint for the UGA dairy farm under different tillage using the

COMET-Farm tool........................................................................................................................ 54

Table 4.6: The carbon footprint for the UGA dairy farm under different manure managements

using the COMET-Farm tool ............................................................................................ 57

Table A.1: The UGA swine farm market hog diets in 2014 ......................................................... 86

Table A.2: The UGA swine farm gestation and lactation diets in 2014 ....................................... 87

Table A.3: The UGA swine farm irrigation pumping record ....................................................... 88

Table A.4: The UGA swine farm facility electricity usage .......................................................... 89

Table A.5: The UGA swine farm field electricity usage .............................................................. 90

Table A.6: The UGA swine farm methane digester building electricity usage ............................ 91

Table B.1: The UGA dairy farm feed ........................................................................................... 93

Table B.2: The UGA dairy farm manure application .................................................................. 94

viii

Table B.3: The UGA dairy farm inventory of animals ................................................................. 95

Table B.4: The UGA dairy farm yearly production in 2013 ......................................................... 96

Table B.5: The UGA dairy farm electricity usage ........................................................................ 98

Table B.6: The UGA dairy farm diesel usage ............................................................................... 99

ix

LIST OF FIGURES

Page

Figure 2.1: Relative contribution of greenhouse gas emissions by major emitters in the United

States ................................................................................................................................. 5

Figure 2.2: U.S. agricultural greenhouse gas sources ..................................................................... 5

Figure 2.3: Relative contributions of various sources to CH4 emissions in the United States ....... 7

Figure 2.4: Relative contributions of various sources to N2O emissions in the United States ....... 8

Figure 2.5: Greenhouse gas emissions from livestock in 2008 ...................................................... 9

Figure 3.1: The UGA swine farm center field map ...................................................................... 26

Figure 3.2: The UGA swine farm soil map................................................................................... 27

Figure 3.3: The UGA swine farm buildings map ......................................................................... 28

Figure 3.4: Location of the UGA dairy farm ................................................................................ 31

Figure 3.5: The UGA dairy farm field map .................................................................................. 32

Figure 4.1: Summary of the carbon footprint for the UGA swine farm ....................................... 43

Figure 4.2: The carbon footprint components for the UGA swine farm using Cool Farm Tool

(Version 2.0) ..................................................................................................................... 46

Figure 4.3: The carbon footprint components for the UGA swine farm using Pig Production

Environmental Footprint Calculator (Version 3.X) .......................................................... 47

Figure 4.4: Summary of the carbon footprint for the UGA dairy farm ........................................ 48

Figure 4.5: The carbon footprint components for the UGA dairy farm using Farm Smart (Version

1.5) .................................................................................................................................... 49

x

Figure 4.6: The carbon footprint components for the UGA dairy farm using Cool Farm Tool

(Version 2.0) ..................................................................................................................... 52

Figure 4.7: The carbon footprint for the UGA swine farm under different corn types as feed

using Pig Production Environmental Footprint Calculator (Version 3.X) ....................... 55

Figure 4.8: The carbon footprint for the UGA swine farm under different manure managements

using Pig Production Environmental Footprint Calculator (Version 3.X) ....................... 58

Figure 4.9: The carbon footprint for the UGA dairy farm under different manure managements

using Farm Smart (Version 1.5)........................................................................................ 59

Figure 4.10: The carbon footprint for the UGA dairy farm under different fertilizer types using

Cool Farm Tool (Version 2.0) .......................................................................................... 60

1

CHAPTER 1

INTRODUCTION

Greenhouse gases (GHG) are gases that absorb and emit radiation within the thermal

infrared range. The accumulation of GHG in the atmosphere has been associated with global

climate change. Anticipated and observed impacts of global climate change include increased sea

level (Gregory et al., 2001; Shepherd and Wingham, 2007), changes in rainfall distribution and

increased storm intensity (IPCC, 2007; Lowe et al., 2001) and accelerated species extinction rate

(Thomas et al., 2004). Human activities including modern agriculture contribute to the

production of GHGs, which have increased since the advent of the industrial age (IPCC, 1996).

World Agriculture is currently faced with the challenge of feeding a rapidly increasing

global population, predicted to peak at 9.2 billion by 2075 (FAO, 2006), while meeting an

obligation to reduce GHG emissions. Agriculture releases significant amounts of carbon dioxide

(CO2), methane (CH4) and nitrous oxide (N2O) to the atmosphere (Cole et al., 2007; IPCC, 2001;

Paustian et al., 2004). It is estimated that the agriculture sector contributes around 10-12% (~ 5-6

Gt CO2-equivelents yr-1

in 2005) of total global anthropogenic GHG emissions, which is about

50 and 60% of CH4 and N2O emissions, respectively (IPCC, 2007; Smith et al., 2007, 2008; EPA,

2006).

The University of Georgia (UGA) made a commitment to reduce its GHG emissions. These

emissions are currently calculated using a model called Clean Air-Cool Planet Campus Carbon

Calculator. However this model is limited for agricultural applications because it does not

2

account for many management changes that might reduce GHG emissions. It was necessary to

select a model to account for crop production, dairy production and swine production and it was

desirable for the model to have limited data requirements, be easy to use and allow for a variety

of management options to reduce GHG emissions. Five different models (Clean Air-Cool Planet

Campus Carbon Calculator (Version 6.9), Cool Farm tool, Farm Smart tool (Version 1.5),

COMET-Farm tool and Pig Production Environmental Footprint Calculator (Version3.X)) were

used to calculate GHG emissions on UGA’s swine farm and dairy farm. Carbon footprints and

time and effort required for the model were compared to each other. I also investigated a variety

of proposed management changes on the farms to determine the resulting impacts on carbon

footprints.

Objectives

1. Choose appropriate existing models to calculate GHG emissions on selected the UGA farms.

2. Make recommendations for future work when recalculating GHG emissions as necessary.

3. Make recommendations for management options to reduce GHG emissions on each farm.

3

CHAPTER 2

LITERATURE REVIEW

Greenhouse Gas Emissions

Anthropogenic emissions of the greenhouse gases CO2, CH4 and N2O have increased

significantly during the last century (Lelieveld et al. 1998; Kroeze et al. 1999; Dentener and Raes

2002). Within the agricultural sector, CO2, CH4 and N2O are the gases considered to be of

primary concern (Reicosky et al., 2000; EPA, 2005). Total GHG emission for the United States

in 2004 was estimated as 84.6% from CO2, 7.9% from CH4, 5.5% from N2O and 2% from

hydrochlorofluorocarbons (HFC), chlorofluorocarbons (CFC) and sulfur hexafluorides (SF)

(EPA, 2005). A mole of CO2 is defined to have a global warming potential (GWP) of one. GWP

is relative measure of how much heat a GHG traps in the atmosphere. The GWP of other GHGs

is greater (Table 2.1). Therefore, even though non-CO2 GHGs represent only a small percentage

of the GHG mixture, they can make a sizable contribution to the total GWP (Johnson et al.,

2007a).

4

Table 2.1: Global warming potential over a 100-year time span of several greenhouse gases

(Johnson et al, 2007a)

Source Gas SARa TAR

b

Naturally occurring CO2 1 1

and agricultural related CH4 21 23

N2O 310 296

Strictly anthropogenic - HFC 140-11,700 120-12,000

Non-agricultural sources CFC 6,500-9,200 5,700-11,900

SF 23,900 22,200

Specific HFCs and CFCs have different individual GWP values.

a IPCC Second Assessment Report (SAR; IPCC, 2001).

b IPCC Third Assessment Report (TAR; IPCC, 2007).

In the United States, agriculture is believed to contribute about 6% of total GHG emissions,

with about half of these emissions from livestock and manure sources (Figure 2.1, EPA 2007;

EPA, 2008). Although this contribution represents only a small portion of CO2 emissions,

agriculture is reported as the largest emitter of N2O and second largest emitter of CH4,

accounting for 75 and 30%, respectively of national total emissions (EPA, 2008). Most

agricultural emissions originate from soil management, enteric fermentation (microbial action in

the digestive system), energy use, and manure management (Figure 2.2, Archibeque, S. et al.,

2012).

5

Figure 2.1: Relative contribution of greenhouse gas emissions by major emitters in the United

States (EPA, 2007)

Figure 2.2: U.S. agricultural greenhouse gas sources (Archibeque, S. et al., 2012).

Carbon dioxide (CO2) is released from the burning of fossil fuels in the use of farm

machinery, the production of synthetic fertilizers and pesticides, and from microbial decay or

burning of plant litter and soil organic matter, grain drying and electricity consumption (Green,

1987; Coxworth, 1997; Janzen et al., 2003; Lal, 2004; Smith and Conen, 2004; Dyer and

30%

14% 28%

12%

15%

1% 0%

Cropland soils

Manure management

Enteric fermentation

Grassland soils

Energy use

Rice cultivation

Burning of residue

6

Desjardins, 2004). In 1990, global atmospheric CO2 concentration was 350 ppm (Wood, 1990);

it surpassed 370 ppm at the Scripps Institution of Oceanography monitoring sites in 2004

(Keeling and Whorf, 2005). It is predicted that the CO2 concentration could reach 500 ppm by

the end of the 21st century (IPCC, 1996).

Methane (CH4) is produced when organic materials decompose in oxygen-deprived

conditions, notably from fermentative digestion by ruminant livestock, stored manures and rice

grown under flooded conditions (Mosier et al., 1998; Lelieveld et al., 1998). In the United States,

the greatest contributions to CH4 emission are enteric fermentation (21%) and manure

management (8%) with minor contributions from rice (Oryza sativa L.) paddies and agricultural

burning (Figure 2.3, EPA, 2007). Livestock production of CH4 including from enteric

fermentation and from animal waste storage was estimated to be 20-34% of all CH4 produced

globally (CAST, 1992; IPCC, 2000).

In 2000, the concentration of CH4 in the atmosphere was 1750 ppb, which far exceeded the

concentration (700 ppb) in 1700 AD (Etheridge et al., 1998). The content of CH4 in 2007 was

estimated as 4900 Tg (Lassey, 2007).

7

Figure 2.3: Relative contributions of various sources to CH4 emissions in the United States (EPA,

2007)

Nitrous oxide (N2O) is generated as an intermediate during nitrification and denitrification

by microbial transformation in soils and manures. Duxbury et al. (1993) and Isermann (1994)

estimated that approximately 75% of the global, anthropogenic N2O emissions were derived

from agricultural activities. The reasons for enhanced emission of N2O directly or indirectly are

increased N inputs by mineral fertilizers, symbiotic N2 fixation, animal manure application,

decomposition of crop residues and applications of available N that exceed plant requirements,

especially under wet conditions (Sahrawat and Keeney, 1986; Granli and Bockman, 1994;

Bremner, 1997; Kroeze et al. 1999; Smith and Conon, 2004; Oenema et al., 2005). Application

of nitrogenous fertilizer and cropping practices are estimated to cause 78% of the total N2O

emission in the United States (Figure 2.4, EPA, 2007).

8

Figure 2.4: Relative contributions of various sources to N2O emissions in the United States (EPA,

2007)

Greenhouse Gas Emissions in Animal Industry

The animal industry contributes 9% of anthropogenic CO2, 37% of CH4 and 65% of N2O

emissions (FAO, 2006), and combined emissions expressed in CO2-equivelents are about 18% of

total anthropogenic GHG emissions (FAO, 2009). Within animal production, the largest

emissions are from beef followed by dairy and swine (Figure 2.5, USDA 2011). Generation and

emission of GHGs from animal operations stem from enteric fermentation, housing confinement,

manure storage, manure treatment and land application (Dong et al., 2006). On a typical

livestock farm, N2O is generated from soil (Olesen et al., 2006), manure and bedding materials

(Amon et al., 1998; Fukumoto et al., 2003; Osada et al., 1998; van der Peet-Schwering et al.,

1999), dung heaps (Wolter et al., 2004), slurry storage (Sommer et al., 2000) and wastewater

treatment (Zhang and Zhu, 2006). According to calculations in the FAO (2006) report, manure

induced soil emissions are the largest livestock sources of N2O, but there is still a large amount

of uncertainty associated with this estimate. CH4 emissions are the result of enteric fermentation

9

and manure storage. Globally, livestock manure contributes 3–6% to the total emission of CH4

(Hogan et al., 1991; Rotmans et al., 1992; Lelieveld et al., 1998) and 7% to the emission of N2O

(Khalil and Rasmussen, 1992; Mosier and Kroeze, 1998).

The rumen is the most important site of CH4 production in ruminants. However, in

monogastric animals (like swine), CH4 is mainly produced in the large intestine and released

through flatulence. Animal manures, stored indoors in sub-floor pits or outdoors, are also

relevant CH4 sources, since conditions usually favor methanogenesis in both slurry and solid

manure heaps (Husted, 1994). Monteny et al. (2001) reported CH4 production from enteric

fermentation and from manure, respectively, for various animal species (Table 2.2, Monteny et

al., 2001). The most important source of CH4 for dairy husbandry is enteric fermentation (around

80%), whereas, most (70%) of the CH4 on swine and poultry farms originates from manures.

Differences in diets (feed intake and feed composition), quantity of energy consumed and

housing systems are reasons for wide variations in the total CH4 emissions from ruminant

livestock (Groot Koerkamp and Uenk, 1997; Monteny et al., 2006).

Figure 2.5: Greenhouse gas emissions from livestock in 2008 (USDA, 2011)

10

Table 2.2: Typical absolute and relative CH4 emissions from enteric fermentation and manure

(Monteny et al., 2001)

Total

(kg CH4 per year per

animal)

Contribution of enteric

fermentation (%)

Dairy cows 84-123 75-83

Swine 4.8 30

Poultry 0.26 ~0

Dairy GHG Emissions

Globally, milk production generates 2.7% of GHG emissions, with a further 1.3% caused by

meat from the dairy herd (i.e. culled and fattening animals from dairy production) (Gerber et al.,

2010). GHG emissions are largely dominated by the CH4 produced during dairy digestion and

manure management (Figure 2.5). In the European Union (EU-15), the total emission of GHG in

ruminant livestock systems is estimated as 435 Tg CO2-equivalents in the reference year 1990

(EPA, 2006), which was 9% of the total GHG emissions. In Australia, the carbon footprint of

milk on average for 2009/2010 was calculated to be 1.11 kg CO2-equivalents kg-1

of fat and

protein corrected milk (FPCM) based on the farms sampled (Sebastian Gollnow et al., 2014). In

Ireland, the total GHG emission was estimated at 1.50 kg CO2-equiavlents kg-1

energy corrected

milk (ECM) yr-1

if there is no allocation and 1.3 kg CO2-equivelents kg-1

ECM yr-1

with

economic allocation between milk and meat (Casey andHolden, 2005). In Canada, the carbon

footprint was estimated as 1.1 kg CO2-equivelents L-1

raw milk produced with lower estimates in

western provinces (0.93 kg CO2-equivelents L-1

raw milk) than in eastern provinces (1.12 kg

CO2-equivelents L-1

raw milk) because of differences in climate conditions and dairy herd

management. Most of the carbon footprint estimates of dairy products ranged between 1 and 3 kg

CO2-equivelents kg-1

of product (Verge et al., 2013). In the United States, the annual GHG

11

emission in dairy livestock systems is 1.07 to 1.11 kg CO2-equivelents kg-1

milk (Hope W.

Phetteplace et al., 2001). Rotz et al. (2010) did an evaluation of dairy farms of various sizes and

production strategies found carbon footprints of 0.37 to 0.69 kg CO2-equivelents kg-1

ECM milk,

depending upon milk production level and the feeding and manure handling strategies used.

Recent studies suggest that annual global GHG emissions will have to be cut by up to 80%

(relative to 1990 levels) before 2050 to prevent the worst effects of climate change (Fisher et al.,

2007). However, demand for milk products is projected to double between 2000 and 2050

(Gerber et al., 2010). Thus reducing GHG emissions per unit of milk is becoming a necessity for

milk producers (O’Brien et al., 2014) and numerous scientific studies regarding the GHG

emissions on dairy farm have been conducted (Sebastian Gollnow et al., 2014). Life cycle

assessment (LCA) studies have been conducted on the global level (FAO, 2010; IDF, 2009;

Severster and Jong, 2008) and at a national level (DairyCo, 2012; Lundie et al., 2002; Lundie et

al., 2009; Thoma et al., 2012) to quantify the environmental impacts associated with dairy milk

production. Numerous studies have also been performed on the comprehensive methodologies

associated with life cycle assessment in the dairy sector (Flysjo et al., 2011; Flysjo et al., 2012).

Following these developments, the dairy sector has developed industry specific carbon footprint

guidelines to provide a consistent methodology specific to conducting the carbon footprint for

the dairy sector (IDF, 2010).

Swine GHG Emissions

Swine is the third largest source of GHG emissions in animal production (Figure 2.5). In

Canada, the main GHG was CH4, representing about 40% of the 6.7 Tg CO2-equivalents total in

2001. N2O and CO2 both accounted for about 30% (Verge et al., 2009). In Italy, annual emission

12

factors for CO2 were measured as 5,997 g d-1

LU-1

(livestock unit, 500 kg pv) for the weaning

room, 1,278 g d-1

LU-1

for the farrowing room, 13,636 g d-1

LU-1

for the fattening room and

8,851 g d-1

LU-1

for the gestation room. Annual emission factors for CH4 were measured as

24.57 g d-1

LU-1

for the weaning room, 4.68 g d-1

LU-1

for the farrowing room, 189.82 g d-1

LU-1

for the fattening room and 132.12 g d-1

LU-1

for the gestation room. Annual emission factors for

N2O were measured as 3.62 g d-1

LU-1

for the weaning room, 0.66 g d-1

LU-1

for the farrowing

room, 3.26 g d-1

LU-1

for the fattening room and 2.72 g d-1

LU-1

for the gestation room

(Annamaria Costa and Marcella Gyarino, 2009). In China, the annual mean daily GHG emission

rates, expressed in g d-1

AU-1

(1 animal unit = 500 kg live body mass), for the gestation,

farrowing, nursery and growing-finishing barns were, respectively, 5,920 ±440, 7,490±110,

29,670±1,090 and 16,730±1,060 for CO2; 9.6±1.9, 9.6±3.6, 58.4±21.8 and 32.1±11.7 for CH4;

and 0.75±0.56; 0.54±0.15; 1.29±0.37 and 0.86±0.75 for N2O (Dong et al., 2007).

Sources of GHG on swine farms include many factors. CO2 is produced by pig respiration

and manure fermentation. CH4 is generated from anaerobic bacterial decomposition of organic

compounds present in feed and excreta. N2O is emitted from manure as an intermediate product

of nitrification/denitrification processes under conditions of low oxygen availability. CH4 and

N2O generation and emission in solid manure-based housing systems come from

nitrification/denitrification and degradation of the organic matter processes (IPCC, 2005).

Because the largest GHG emission on swine farms is CH4 produced during manure

management (Figure 2.5), several recent studies on swine farms GHG emissions focus on the

manure management. Wagner-Riddle et al. (2006) measured CH4 and N2O fluxes from outdoor

pig manure storage tanks using micrometeorological mass balance methods. Amon et al. (2007)

reported GHG emissions from pig slurry storage with large open dynamic chambers. GHG

13

emissions from a pilot store of pig manure (Wolter et al., 2004), and from liquid swine manure

stored in a cold climate (Wagner-Riddle et al., 2006), have also been reported. Masse et al. (2008)

measured CH4 emissions from liquid manure stored on two Canadian farms. Osada et al. (2011)

measured the potential reduction of GHG emissions from swine manure by using a low-protein

diet supplemented with synthetic amino acids. Dong (2011) quantified CO2, CH4 and N2O

emissions from swine manure stored at different stack heights using dynamic emission vessels.

Park et al. (2011) measured CH4 and N2O fluxes during composting of solid swine manure using

three aeration systems including forced aeration (FA), wire mesh (WM) and turnover (TO) and

no aeration.

Methods to Mitigating Greenhouse Gas Emissions

Agricultural GHG fluxes are complex and heterogeneous, but many agricultural practices

can potentially mitigate GHG emissions (Smith et al., 2007). It has been estimated on a global

scale that the agricultural sector has the potential to reduce radiative forcing of GHGs by 1.15–

3.3 Pg C-equivalents per year (Cole et al., 1997).

Tillage and Residue Management

Fluxes of CO2 and N2O from agricultural soils are the result of complex interactions

between climate and several biological, chemical and physical soil properties. Tillage systems

may affect all these soil properties and therefore influence the release of these GHGs. (Oorts et

al., 2007).

Reduced tillage (RT) and no tillage (NT) are now increasingly used throughout the world

and it has been widely stated that conversion from conventional tillage (CT) to RT or NT

14

agriculture could have a favorable impact on reducing soil erosion, enhancing agricultural

sustainability and mitigating atmospheric concentrations of GHGs by promoting the storage of

soil carbon (Lal et al., 1998; West and Post, 2002; Cerri et al., 2004; Cole et al., 1997; Paustian

et al., 1997; Schlesinger, 1999). These practices attempt to minimize the disruption of the soil’s

structure, composition and natural biodiversity thereby reducing soil erosion and degradation

(Chatskikh et al., 2007). They may also lead to reduced use of diesel fuel and fossil energy and

increased carbon (C) storage and N accumulation in soils (Smith et al., 1998; Robertson et al.,

2000; Six et al., 2004; Kustermann, 2013). However, this benefit may be at least partly offset by

increased emissions of N2O (Smith et al., 2000; Six et al., 2004), but the net effects are

inconsistent and not well-quantified globally (Cassman et al., 2003; Smith and Conen, 2004;

Helgason et al., 2005; Li et al., 2005). The effects of tillage practices on the net GHG balance

from CO2 and N2O emissions are likely to be influenced not only by the intensity of tillage, but

also by the soil type and climatic conditions.

There is no consensus in the literature on the differences in CO2 emissions between NT and

CT. Some authors reported similar CO2 emissions under both NT and CT (Fortin et al., 1996;

Aslam et al., 2000). Hendrix et al. (1988) measured generally larger CO2 emissions in NT.

However, Ball et al. (1999) and Vinten et al. (2002) reported higher CO2 emissions for some

periods and smaller emissions for other periods under NT. Also, significantly smaller CO2

emissions for NT than in CT were measured during the short period after tillage (Reicosky and

Lindstrom, 1993; Dao, 1998; Kessavalou et al., 1998; Alvarez et al., 2001; Al-Kaisi and Yin,

2005). Differences in CO2 emissions in the different tillage systems were largely a result of the

soil climatic conditions and the amounts and location of crop residues and SOM in CT and NT

(Oorts et al., 2007).

15

There is also a large uncertainty associated with current estimates of the influence of tillage

practices on N2O emissions (Chatskikh et al., 2007). Many have reported higher N2O emissions

under NT than CT (Burford et al., 1981; Linn and Doran, 1984; MacKenzie et al., 1997;

MacKenzie et al., 1998; Ball et al., 1999; Vinten et al., 2002; Baggs et al., 2003). Helgason et al.

(2005) found that N2O emissions were more often reported to be higher for NT compared to CT

in more humid regions (eastern Canada), but the reverse was true in the drier prairie region. The

greater N2O emissions under NT have been attributed to reduced gas diffusivity and air-filled

porosity, often caused by high rainfall, and having the greatest effects on N2O emissions after

fertilizer application. There are also indications that this effect of tillage practice on N2O

emissions diminishes after long-term practice of NT (Elmi et al., 2003; Six et al., 2004). In

contrast, some studies suggested lower or similar N2O emissions under NT comparing to CT

(Lemke et al., 1999; Baggs et al., 2003; Kessavalou et al., 1998; Choudhary et al., 2002).

Retaining crop residues on fields can increase soil C because these residues are the

precursors for soil organic matter, the main store of carbon in the soil. Avoiding the burning of

residues (Cerri et al., 2004) also avoids emissions of aerosols and GHGs generated from fire.

Feeding and Additives

Feeding and additives can greatly impact CH4 emissions, especially in ruminants. CH4

emissions can be reduced by feeding more concentrates, normally replacing forages (Blaxter and

Clapperton, 1965; Johnson and Johnson, 1995; Lovett et al., 2003; Beauchemin and McGinn,

2005). Some researchers have reported that dietary fat seems to be a promising nutritional

alternative to depress ruminal methanogenesis without affecting other ruminal parameters.

Giger-Reverdin et al. (2003) and Eugene et al. (2008) reported a mean decrease in CH4 of 2.2%

16

per percentage unit of lipid added in the diet of dairy cows, independently of the nature of fatty

acid supply.

As dietary additives, ionophores (Benz and Johnson, 1982; Van Nevel and Demeyer, 1995;

McGinn et al., 2004; Beauchemin et al., 2008), organic acids (Newbold and Rode, 2006;

Newbold et al., 2005; Foley et al., 2009; Wallace et al., 2006), plant extracts (Jouany and

Morgavi, 2007; Tavendale et al., 2005; Tiemann et al., 2008; Dijkstra et al., 2007; Guo et al.,

2008), halogentated compounds (Wolin et al., 1964; Van Nevel and Demeyer, 1995), probiotics

(McGinn et al., 2004), propionate precursors (Newbold et al., 2002), vaccines against

methanogenic bacteria (Wright et al., 2004), bovine somatotrophin (bST) and hormonal growth

implants (Bauman, 1992; Schmidely, 1993; Johnson et al., 1991; McCrabb, 2001) can reduce

CH4 emissions mainly because they can suppress methanogenesis. However, the effect of some

of them may be transitory (Rumpler et al., 1986; Koenig et al., 2007; Pen et al., 2006; Goel et al.,

2008) and have side effects (Wolin et al., 1964; Van Nevel and Demeyer, 1995; McSweeney et

al., 2001; Tiemann et al. 2008). Additionally, some require high doses (Newbold et al. 2005)

and may not be permitted in some countries (Martin et al., 2009).

Manure Management

Animal manures can release significant amounts of N2O and CH4 during storage (Smith et

al., 2007). CH4 emissions from manure stored in lagoons or tanks can be reduced by cooling or

covering the sources, or by capturing the CH4 emitted (Clemens and Ahlgrimm, 2001; Monteny

et al., 2001, 2006; Paustian et al., 2004). Anaerobic digesters are an efficient way to reduce GHG

emissions. The manures can be digested anaerobically to maximize retrieval of CH4 as an energy

source, produce value added fertilizers which more closely match crop requirements and the

17

subsequent production of heat and electricity also contribute to lower fossil energy use and thus

lower CO2 emissions (Clemens and Ahlgrimm, 2001; Clemens et al., 2006; R.L.M. Schils et al.,

2007; D.I. Masse et al., 2011). Storing and handling the manures in solid rather than liquid form

can suppress CH4 emissions, but may increase N2O formation (Paustian et al., 2004). CH4

emissions from manure stored in anaerobic lagoons can potentially be reduced by implementing

solid-liquid separation systems (Birchall et al., 2008). N2O emissions will be eliminated by gas-

tight covers because the headspace contains no oxygen, a prerequisite for N2O formation

(Clemens et al., 2006). Preliminary evidence suggests that covering manure heaps can reduce

N2O emissions (Chadwick, 2005). A wooden cover significantly reduced CH4 emissions from

untreated and digested slurry (Clemens et al., 2006).

Nutrient Management

In general, N2O emissions increase with increased nitrogen inputs (Gregorich et al., 2005;

IPCC, 2001). The proportion of applied N emitted as N2O (independent of source of N) has been

estimated as 1.25% (IPCC, 1997).

Nitrogen applied in fertilizers and manures is not always used efficiently by crops (Cassman

et al., 2003; Galloway et al., 2003). Improving this efficiency of nitrogen use including avoiding

excess N applications and adjusting application rates based on estimation of crop need; using

slow-release fertilizer forms or nitrification inhibitors; placing the N more precisely into the soil

to make it more accessible to crops roots (Cole et al., 1997; Dalal et al., 2003; Paustian et al.,

2004; Robertson, 2004; Monteny et al., 2006) can reduce emissions of N2O generated by soil

microbes largely from surplus N and reduce emissions of CO2 from N fertilizer manufacture

indirectly (Schlesinger, 1999).

18

Energy

Energy production is the largest contributor to GHG emissions (Figure 2.1) and also an

important source for agricultural GHG emissions (Figure 2.2). It is essential to control

consumption of fossil fuels in order to reduce GHG emissions. There are numerous energy

alternatives to fossil fuels, such as biogas, biomass, solar, geothermal, wind, ocean thermal, and

tidal, and straight vegetable oil (Clemens et al., 2006; Johnson et al, 2007a; Fore et al., 2011),

which currently represent only a small fraction of the global energy used (Hoffert et al., 2002).

However, there has been a lively debate in the literature on the pros and cons of bioenergy. Some

of them are expensive (Fore et al., 2011; Baquero et al., 2011; Crabtree and Lewis, 2007),

unstable (Hoffert et al., 2002) to use, require advanced technology (Johnson et al., 2007b), and

increase the risk of accelerated soil erosion or loss of soil organic carbon (Graham et al., 2007;

Nelson et al., 2004; Perlack et al., 2005).

Other Methods

There are many other methods to reduce GHG emissions potentially. For example,

converting cropland to another land cover, typically one similar to the native vegetation (Smith

et al., 2007) can reduce GHG emissions. Using more effective irrigation measures can enhance C

storage in soils through enhanced yields and residue returns (Follett, 2001; Lal, 2004). Drainage

perhaps suppresses N2O emissions by improving aeration (Monteny et al., 2006). For rice

management, draining the wetland rice during the growing season (Smith and Conen, 2004; Yan

et al., 2003) and keeping the soil as dry as possible and avoiding waterlogging in the off-rice

season can reduce CH4 efficiently (Cai et al., 2000, 2003; Kang et al., 2002; Xu et al., 2003).

19

Models to Calculate the Carbon Footprint in Animal Industry

With the rapidly increasing global population, productivity must improve and this will

result in increased GHG emissions from agricultural activities. Realizing the importance to

mitigating the GHG emissions, numerous models have been created to help calculate carbon

footprints. These models are used by producers and commodity groups to assess current

emissions and evaluate alternatives for reducing emissions. Crosson et al (2011) identified 31

published whole farm systems models used to estimate GHG emissions from beef and dairy

cattle farm systems. DairySim, FarmGHG, SIMSDIARY and FarmSim are whole farm models

used to simulate GHG emissions from ruminant livestock farm systems (R.L.M. Schils et al.,

2007).

The carbon footprint estimates are created using a process called life cycle assessment. Life

cycle assessment (LCA) is a method of resource accounting where quantitative measures of

inputs, outputs and impacts of a product are determined. It 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, and help businesses or

consumers make informed sourcing decisions. Full life cycle assessment is a “cradle-to-grave”

(Tier III) approach for assessing industrial systems. “Cradle-to-grave” begins with the gathering

of raw materials from the earth to create the product and ends at the point when all materials are

returned to the earth (FAO, 2006). The models I selected all use “Cradle-to-gate” (Tier II). They

produce an assessment of a partial product life cycle from resource extraction (cradle) to the

factory gate (i.e., before it is transported to the consumer). The use phase and disposal phase of

the product are omitted in this case. This will include both direct and indirect emissions from the

product.

20

Clean Air-Cool Planet Campus Carbon Calculator (Version 6.9)

The Clean Air-Cool Planet Campus Carbon Calculator (Version 6.9) (CCC) is the tool the

UGA uses currently. It was originally developed by Adam Wilson as a collaborative project

between the University of New Hampshire's Office of Sustainability and Clean Air-Cool Planet

in 2001 and released to the public in 2004. This is a Microsoft Excel spreadsheet to calculate the

GHG emissions (CO2, CH4, N2O), HFC and PFC, SF6, and others for college campus from

2000 to current year. Although this model is used to estimate the carbon footprint for whole

campus, it also includes a section to calculate emissions from campus farms. It is a very simple

model that applies an emission factor to the number of animals and amount of fertilizer used to

estimate agricultural emissions. It also has an emission factor in the energy section that can

account for fuel and energy use. For this study, we calculated the carbon footprint for the UGA

farms by only using “agriculture” and “electricity” parts of the model.

Farm Smart (Version 1.5)

Farm Smart (Version 1.5) (FS) is a process-based tool designed specifically for agricultural

conditions on dairy farms. Primary financial support for Farm Smart comes from the United

States Department of Agriculture, the Walton Family Foundation, and the David and Lucile

Packard Foundation. The project has received tremendous support from numerous valued

stakeholders including the Sustainability Council, Manomet Center for Conservation Services,

Quantis, University of Arkansas Applied Sustainability Center, and the University of Michigan.

FS is a system of web-based benchmarking and decision support tool and is available at

http://sites.usdairy.com/farmsmart/Pages/Home.aspx. It aims to help dairy producers optimize

their production techniques, identify potential improvements in management practices and

http://sites.usdairy.com/farmsmart/Pages/Home.aspx

21

communicate positive contributions their farm businesses have made. It gives producers easy

access to geographically specific, real-time data that allows producers to assess, analyze,

interpret, and understand data specific to their dairy facility and their fields to identify

opportunities for economic and environmental improvement. It allows producers to model and

test different production practices to determine which ones can deliver the most environmental

benefits while increasing efficiency and lowering costs. Using data from the GHG Life Cycle

Assessment for Fluid Milk, the Farm Smart provides dairy producers with a snapshot of their

dairy farm’s environmental footprint. This tool allows a farm to evaluate its GHG emissions and

water and energy use and compare them to regional and national averages. Some farms have

already used FS as an evaluation and decision-making tool.

Cool Farm Tool (Version 2.0)

Cool Farm Tool (Version 2.0) (CFT) is also a process-based tool designed for general

agricultural operations. It was originally developed by Unilever (Christof Walter) and

researchers at the University of Aberdeen (Jon Hiller, Pete Smith) and the Sustainable Food Lab

(Daniella Malin, Stephanie Daniels, Christina Ingersoll, Jessica Mullan, Hal Hamilton) in 2009

and released in April 2010.

CFT, available at http://www.coolfarmtool.org/CoolFarmTool, is an online, farm-level

GHG emissions calculator based on empirical research from a broad range of published data sets

to help growers measure and understand on-farm carbon footprint for crop and livestock

products. CFT has been tested and adopted by a range of multinational companies who are using

it to work with their suppliers to measure, manage, and reduce GHG emissions in an effort to

mitigate global climate change. It is designed to be intuitive and easy to complete based on

http://www.coolfarmtool.org/CoolFarmTool

22

information that a farmer will have readily available. The tool identifies hotspots and makes it

easy for farmers to test alternative management scenarios and identify those that will have a

positive impact on the total net GHG emissions. Unlike many other agricultural GHG calculators,

CFT includes calculations of soil carbon sequestration, which is a key feature of agriculture that

has both mitigation and adaptation benefits.

COMET-FARM Tool (CarbOn Management and Emissions Tool)

The COMET-Farm Tool (COMET) is a process-based model that was originally developed

to simulate soil carbon levels. The USDA-NRCS promotes this tool to US farmers. As

collaboration among USDA’s Natural Resources Conservation Service (George Bluhm, John

Brenner and others), Colorado State University (Rich Conant, Mark Easter, Keith Paustian and

others) and USDA’s Climate Change Program Office, the first version of COMET-FARM Tool

was released in 2005.

COMET is available at http://cometfarm.nrel.colostate.edu/. Entering information about

location, soil characteristics, annual crop production, livestock and on-farm energy use, farmers

and ranchers can estimate carbon sequestration and GHG emissions using COMET. It provides a

convenient yet rigorous way to evaluate the benefits of various conservation practices in

reducing GHG emissions and help producers decide on the suite of practices that are best for

their operation and their land.

Pig Production Environmental Footprint Calculator (Version 3.X)

The Pig Production Environmental Footprint Calculator (Version 3.X) (PPC) was

developed for the National pork producers association and is designed specifically for pork

http://cometfarm.nrel.colostate.edu/

23

production. It began as part of a carbon life cycle assessment (LCA) conducted by the University

of Arkansas for the National Pork Board in 2010 and funding was from the National Pork board

and the USDA. The authors of the calculator are Drs. Rick Ulrich, Greg Thoma, Jennie Popp and

Hector German Rodriguez.

The purpose of this calculator is to enable the producer, planner or researcher to identify

and quantify the sources of GHG emissions, water consumption and land usage for a production

facility for a single pig barn (grown barn, gestation barn, sow barn and farrowing barn) or for a

whole swine farm. It also calculates the costs or savings associated with changing farm

operations or hardware with regard to GHG emissions and usage of water and land. This

calculator is a predictive model. The user does not have to know how much fuel, electricity, or

feed was consumed and does not have to know the amount of manure produced, the model

estimates all of this. The user enters the size and basic characteristics of the operation being

modeled and the calculator returns the amount of GHGs emitted, water consumed, and the

associated costs for each source.

24

CHAPTER 3

MATERIALS AND METHODS

Study Area

Two UGA farms were selected as our study area.

Double Bridges Swine Center (the UGA Swine Farm)

Double Bridges Swine Center (the UGA swine farm), built in 2010, is a new farrow-to-

finish operation located approximately 9 miles east of Athens, GA (Figure 3.1). The facility is

located in the Upper Oconee Watershed, in gently rolling hills with dominant soil series of Cecil

(CeC) and Hiwassee (HeB) (Figure 3.2). The swine center has an average inventory of 94

breeding animals producing an average inventory of approximately 1235 pigs per year. Average

weight of cull sows are 450 pounds and finished pigs are 270 pounds. The animal buildings

include seven pre-fab modular buildings including two gestation, one farrowing, two nursery and

two finishing barns (Figure 3.3). When culled sows in the gestation barn #1 are birthing, they

move into the farrowing barn and go back to the gestation barn #1 after the piglets are born.

Piglets stay in the farrowing barn for 3 weeks and they grow to 13 pounds. From 4 to 10 weeks,

piglets live in the nursery barn until they reach 50 pounds. Then, piglets go to the finishing barn

#1 and stay until they are 18 weeks old. From 19 weeks of age, 175-pound piglets move to the

finishing barn #2 and then they are shipped to slaughter when their weight reaches 270 pounds at

an the average age of 26 weeks. Genetic sows in the gestation barn #2 stay in gestation barn #2

25

year round. The feeds for each phase of production are different (Appendix Table A.1 and Table

A.2). Each barn comes equipped with a pull plug flush/scrape pit system. When flushed, the pit

contents are piped to a sump, where it is agitated and put into a 41,000 gallon anaerobic digester

that provides approximately 17 days retention time. Digester contents then gravity flow into a

169,407 gallon Slurry Store tank. This slurry storage holds approximately 87 days of storage

based on a 4 week flush interval with 2 inches of flush water plus added manure. Pits are flushed

with rain water harvested from the buildings. Russell Bermuda and winter rye grass were planted

in a 32.5-acre field (field 8, figure 3.1) in April 2010 and harvested three times a year. Pig slurry

is land applied at agronomic rates on this field and timing and rate are changed based on the

weather conditions.

26

Figure 3.1: The UGA swine farm center field map

27

Figure 3.2: The UGA swine farm soil map

28

Figure 3.3: The UGA swine farm buildings map

Gestation Barn #1

30’*69’

Gestation Barn #2

30’*76’

Farrowing Barn

16’*70’

Nursery Barn

14’*57’

Finishing Barn #1

30’*69’

Finishing Barn #2

30’*69’

Pregnant Sows

After birth

Piglets

Shipped to slaughter

Piglets

29

UGA Teaching Dairy Farm (the UGA dairy farm)

The UGA Teaching Dairy Farm is located approximately 9 miles east of Athens, on the

north side of HWY 78 East (Figure 3.4). It maintains a herd of approximately 90 milking cows,

16 dry cows and 106 replacements. Average weight of replacement heifers is 950 pounds, dry

cows are 1600 pounds and milking cows is 1450 pounds. The milking cows are under total

confinement. The dry cows and replacements are on pasture. The feeds for each phase of

production are different. For dry cows and replacements, fifteen percent of nutrition comes from

grazing. The Dry matter intake (DMI) for replacements is 14 pounds per day, for dry cows is 20

pounds per day and for milking cows is 30 pounds per day.

The barns where animals are confined are flushed using recycled water from the second

stage lagoon. A floating pump is used to pump flush water from a three stage lagoon to four

3,000 gallon and one 1,500 gallon storage tanks which are emptied twice daily for barn flush.

The herd barn is flushed twice daily with a total daily volume of 24,000 gallons of recycled

lagoon water. The calf barn is also flushed twice daily with a total daily volume of 3,000 gallons

of recycled lagoon water. The milking parlor is flushed twice daily with a total daily volume of

600 gallons of fresh water. The flush goes through a gravity settling sand trap, into gravity

settling solid separator, then to a first stage lagoon that drains to a second stage. An

agitator/chopper pump located at the north side of the first stage lagoon is used to land apply

lagoon nutrients to the fields. The animals kept on pasture are used to disperse nutrients across

the field. Feed troughs are strategically located where any runoff from feeding areas goes

through well grassed filter strips before it can enter surface water.

The farm has 570.0 acres (Figure 3.5) with 159.0 (field 2, 3, 4, 7, 8, 13, 14, 15 and 17) of

that being in cropland planted to sorghum in June and wheat in October each year. There are also

30

53.3 acres (field 1 and 16) of hayfield and 59.4 acres (field 10, 12 and 19) of pasture. There were

some land use changes on this farm. Field 2 was hay field before 2004 and changed to fallow

between 2004 and 2010, and wheat and sorghum were planted from 2010 up to now. Field 13

was hay field before 2012 and crop field after that year.

Cattle manure solids are used on field 10, field 13, field 15 and field 16 and lagoon slurry is

used on field 15 and field 16. The application timing and rate are based on the weather and

agronomic conditions. From 2010 to 2013, all fields used poultry litter once a year with the

application rate of 2 tons/acre. Some additional fertilizer is used but the amounts, locations, and

timing vary by crop year.

31

Figure 3.4: Location of the UGA dairy farm

32

Figure 3.5: The UGA dairy farm field map

33

Tools and Procedures

Data needed for both farms were based on models selected and were collected by visiting

the farms and with the help of farm managers, experts and references. Some inputs were

calculated and summarized from nutrient management plans and other records and this was done

using best available information. After entering all the inputs in each model, comparisons of

model outputs was used to assess the models. Additionally, some models were used to assess the

impacts of changes in management on carbon footprints for each farm.

Clean Air-Cool Planet Campus Carbon Calculator (Version 6.9)

The Clean Air-Cool Planet Campus Carbon Calculator (Version 6.9) (CCC) is a simple

spreadsheet tool that is very easy to use. This tool needs the input from 1990 to current year. The

inputs of this tool include:

1. Institutional data: population and physical size.

2. On-campus cogeneration plants, direct transportation sources, refrigerants and chemicals,

agriculture sources, purchased electricity, steam and chilled water, commuting, travel, solid

waste, wastewater, paper and offsets.

To calculate GHG emissions from the UGA farms, only the agricultural and electricity part

of this tool were used. The inputs in the agricultural section include fertilizer application and

animal inventory. After entering the amount of synthetic and organic fertilizer used, nitrogen

content in each kind of fertilizer, number of animals and electricity used on the farm, estimates

of CO2, CH4 and N2O and total CO2-equivalents emissions from 2003 to current are given as

well as predicted emissions through 2020.

34

Because the swine farm was built in 2010, all inputs prior to 2010 were left blank. For

animal inventory, the tool only requires total number of each kind of animals. Since no guidance

was available for the size of animal, the total number of piglets, sows and gilts on the swine farm

were added together to calculate the number of total pigs on swine farm and the total number of

calves, heifers, milking cows and dry cows were summed to calculate total dairy cows on the

dairy farm.

Farm Smart (Version 1.5)

Farm Smart (Version 1.5) (FS) calculates annual GHG emissions based on information

provided in that year. The inputs for this tool include:

1. Location of dairy.

2. Crops (produced for feed) including crop yield and number of acres. The crops only include

feed grown on-farm for the dairy herd and replacements and do not include crops grown for cash

or sale.

3. Livestock: herd description (annual average number by age group including dairy animals

raised on and off the dairy farm); ration (including questions about average daily dry matter

intake for the milking herd, percentage of each ingredient in the ration, and percentage of pasture

in the total ration); stored manure (annual amount for each type of manure generated on-farm

and percentage of total manure generated annually for each type); animal export (dairy herd

animals leaving the farm annually); milk production (pounds of milk shipped annually plus

pounds of milk manufactured on-farm into product).

4. Energy use (energy used in the dairy operation including water heaters, barn fans, water

pumps for animals and cleaning, gas and diesel or electricity used to scrape barns).

35


Cool Farm Tool (Version 2.0) (CFT) is also a process-based tool designed for general

agricultural operation. This tool can calculate annual GHG emissions based on information

provided for that year. The inputs of this tool include:

1. General: location; climate.

2. Crop: crop type; crop yield; crop area and soil properties; residue management (residue is

defined as the plant matter from crop production that is not used as a sellable product); fertilizer

and pesticide applications (these operations include for instance all fertilizer and pesticides used,

from preparing the field until harvesting of the crop).

3. Livestock: product weight; number of animals, feed production and manure management for

each life phase.

4. Energy use: includes all energy used for field operations, on-farm storage and any other

production-related or process-related fuels.

5. Off-farm transportation: this does not include transport beyond the farm gate (to retail or

warehouse) but does include transport of inputs required for production. Examples include

transport of fertilizer, pesticides, seeds and feed from the point of purchase to the growing area.

Also the transport of machinery required for field activities. For instance, from farm to field or

from contractor to field as well as transport of the product/crop evaluated.

To use this tool, the information for crop production and livestock production were entered

and run separately and the emissions from each of these were summed to get total emissions.

Care was taken to insure that inputs were not entered for both production phases.

The UGA swine farm has only one field, so the carbon footprint for this field and for

livestock were summed to get the carbon footprint for the whole farm. Because the UGA dairy

36

farm has numerous crop fields, hay fields and pastures, and all of those fields had different crop

and fertilizer management with minimal records, several assumptions were made. Fields were

divided into wheat with no fertilizer use, using cattle manure solid, and using cattle manure solid

and slurry, sorghum using poultry litter, and using poultry litter and cattle manure solid, and

using all three types of manure, perennial grass using poultry litter, and using poultry litter and

cattle manure solid, and using all three types of manure. The nutrient management plan and

existing records were used to make sure that the approximate total manure solids, slurry, and

poultry litter used matched actual amounts available. The carbon footprint from all these fields

were then added to the carbon footprint for livestock to get the carbon footprint for the entire

farm.

COMET-Farm Tool

The field module estimates the carbon footprint using the DayCent dynamic model, which

is the same model used in the official U.S. National Greenhouse Gas Inventory. The livestock

module estimates the carbon footprint using statistical models based on USDA and university

research results and are similar to models used in the U.S. National Inventory.

The inputs for this tool include:

1. General: location (add parcel representing each field in a satellite map).

2. Field management (from pre-2000 to a decade beyond the current year): this model needs

information for each field each year including cropping sequence and approximate planting and

harvest date; tillage operations; rate, timing, type and application method for fertilizer and

manure applications; irrigation method and application rate; liming and residual management. If

multiple crops were planted in a same field, cropping sequence can be entered. Instead of

37

manually inputting data for all applicable years, the model allows an option to complete the

management for one year, and copy that information to other years, allowing the user to make

minor changes for all subsequent years. This effectively saves a substantial amount of time to

complete this model.

3. Livestock management: herd size, weight and composition, manure management, feed quality

and additives and other livestock-specific management. For the UGA swine farm, populations

for market swine and breeding swine by month were entered. Data including ventilation, heating

and lighting were entered for the farrowing, nursery and finishing barn. For the UGA dairy farm,

population for mature and replacement females per month; percentage lactating days per month;

average percent females pregnant by month; average total daily milk production and fat content

of the milk per month were entered.

Energy use was calculated based on information provided for field and livestock

management practices. COMET does not include all energy use for whole farm. Instead, this tool

only has a separate fuel saving model. If the user provides estimates of energy use reductions,

this tool calculates the reduction of the carbon footprint based on fuel savings.

Pig Production Environmental Footprint Calculator (Version 3.X)

The Pig Production Environmental Footprint Calculator (Version 3.X) (PPC) was

developed for the National pork producers association and is designed specifically for pork

production. It is very detailed for the buildings and pig production but limited in terms of manure

management and field operations.

The information was needed for every barn. Inputs for this tool include:

38

1. General: location (it is used for calculating temperatures in the barns, heating and cooling

burdens, and temperatures to be used in calculations for the outside manure systems); source of

water (it is used to calculate direct costs and GHG emissions for water acquisition, and the

emissions from the energy required to distribute water around the farm); feed delivery; transport

of manure to farm field; solar panels and disposal of dead animals.

2. Herd management (information was needed for each barn): herd demographics, barn size,

manure system, heating and cooling system, water and light usage, feed ingredients.

Information needed for herd demographics varied for each type of barn. This model requires

very specific information for feed. For the growing barn, number and timing of phases and the

ingredient mix for the ration fed in each phase are needed. For the farrowing and gestation barn,

feed mixes for sows and gilts pigs are required. For the sow barn, feed mixes for gestating and

lactating pigs are required.

39

CHAPTER 4

RESULTS AND DISCUSSIONS

Inputs and Time Required for Different Models

PPC required the most amount of time to gather, calculate and enter information and also

needed the most data for the UGA swine farm. COMET and CFT took the most amount of time

and needed the most data for the UGA dairy farm. CCC took the least of time for both farms.

Table 4.1 shows the comparison of inputs and time required for each model. The time shown in

table 4.1 only includes entering information and running for each model. Gathering and

calculating input data would require additional time, however, depending on the farm and past

record keeping efforts, this could be highly variable.

Another consideration is the documentation. We found the documentation lacking for all of

the models. None of them have users manuals available and it was difficult to determine what

the requested input parameters were or how they were used. If these models are meant to be

used by farmers, then serious effort is needed to insure that the inputs are better defined.

40

Table 4.1: Inputs and time required for different models

Inputs

Clean Air-

Cool Planet

Campus

Carbon

Calculator

(Version 6.9)

Farm

Smart

(Version

1.5)

Cool Farm

Tool

(Version

2.0)

COMET-

Farm Tool

Pig Production

Environmental

Footprint

Calculator

(Version 3.X)

Number of

inputs

Number

of inputs

Number of

inputs

Number of

inputs

Number of

inputs

Location and

climate 3 5 2

c1 2

Soil Properties 6

Field

management 2

a1 4 3+14

c2

Fertilizer

application 4 7

b1 6

c3

Tillage 4c4

Crop Residue 2

Pesticide

application 2

Water (Irrigation) 6 2 4

Waste water 4

Animal

husbandry 1 22 3+2

b2 25/ 98

c5 ~80

d1

Feed 2+3a2

1+2b3

4 ~10d2

Milk production 3 1 b4

5

Manure

management 2

a3 3

b5 2

c6 ~10

Energy use 1 2a4

3b6

30/ 6c7

~90d3

Time

required

for

swine

farm

~ 1 hour - ~2 hour ~2 hour 4-5 hours

for

dairy

farm

~ 1 hour ~3 hours ~4 hours ~4 hours -

a1 There are 9 choices for feed related crops, each of them has 2 questions.

a2 There are 2 general questions for ration and 12 choices for feed components, each of them

has 3 questions.

41

a3 There are 8 types of manure management that can be chosen and each of them has 2

questions (for anaerobic digester, it has 3questions).

a4 There are 6 types of energy that can be chosen and each of them has 2 questions.

b1 There are more than 40 types of fertilizer that can be chosen and for each fertilizer type, it

has 7 questions.

b2 There are 3 general questions about animal husbandry. It also has 2 questions for each life

phase (3 total life phases).

b3 For each life phase, there is 1 general question and feed components can be selected from

more than 20 choices. For each feed component, there are 2 questions.

b4 For dairy cow, the milk production can be filled in “finished product amount” in animal

husbandry part.

b5 Users can choose manure management type for each life phase from 16 choices and there

are 3 questions for each manure management type.

b6 There are around 30 types of energy sources that can be chosen and for each energy source,

it has 3 questions. It also provides an estimate for usage of diesel and petrol based on

machines used in field operation.

c1 For field management, the location was entered by adding parcel representing each field in a

satellite map. For livestock management, the zip code was required for location.

c2 There are 3 general questions about field management and 14 questions for each crop in

each field.

c3 There are 6 questions about fertilizer for each crop in each field

c4 There are 4 questions about tillage for each crop in each field

42

c5 For the swine farm, the questions about animal husbandry are about 25. For the dairy farm,

the questions about animal husbandry are about 98.

c6 There are 18 choices for manure management type and each type has 1 or 2 questions.

c7 For the swine farm, there are 30 questions about energy. For the dairy farm, the questions

about energy are about 6.

d1 For each barn, the questions about energy were around 12.

d2 Users can choose feed components within more than one hundred choices including energy ,

protein, amino acids, vitamins and minerals, and additives. This model needs feed components

in different phases in each barn.

d3 For each barn, the questions about energy were around 15.

Carbon Footprint for the UGA Swine Farm

Table 4.2: Summary of the carbon footprint for the UGA swine farm

Model Greenhouse Gas Emission (kg/yr) Total

(kg eCO2/yr) CO2 N2O CH4

Clean Air-Cool Planet Campus

Carbon Calculator (Version 6.9) 102,426 132 21,470 678,400

Cool Farm Tool (Version 2.0) 307,996 16 1,067 339,538

COMET-Farm Tool 26,500 111 20,084 561,700

Pig Production Environmental

Calculator (Version 3.X) 294,092 68 490 326,804

43

Figure 4.1: Summary of the carbon footprint for the UGA swine farm

Carbon footprint for the UGA swine farm calculated using four different selected models

are shown in Table 4.2 and Figure 4.1. Estimates for the carbon footprint in 2013 varied from

around 300,000 kg CO2-equivalent using PPC to around 700,000 kg CO2-equivalent using CCC.

CCC estimated the highest carbon footprint for the UGA swine farm in 2013. This tool was

the simplest one to use with only three inputs needed including numbers of animal in the farm,

amount of fertilizer use and electricity use. Hence, the results for the carbon footprint for the

UGA swine farm calculated by this model represent broad average. For example, this tool is the

only one that does not use the information about farm location, which makes it less site

compared with other tools. Different climates and soil properties can influence carbon footprints.

Soil moisture (Water-filled pore space) for example can affect N2O emission (Ruser, et.al, 2006;

Monteny et.al, 2006). Also, average emissions levels for local utility supplied electricity are

different with the change of farm location. Second, this tool only asks the total number of swine

and uses this number to estimate GHG emissions from swine. However, on a swine farm, there

102,426

307,996

26,500

294,092

39,217 4,860

33,099 17,997

536,760

26,681

502,100

14,715

0

100000

200000

300000

400000

500000

600000

Clean Air-Cool

Planet CampusCarbon Calculator

(Version 6.9)

Cool Farm Tool

(Version 2.0)

COMET-Farm

Tool

Pig Production

EnvironmentalCalculator (Version

3.X)

kg eCO2/yr

CO2

N2O

CH4

44

are many different kinds of barns that hold different life phases of swine and use different feed

and manure management. This can have a big impact on the carbon footprint. These differences

are not accounted for in this model. The UGA swine farm uses phased feeding, has an anaerobic

digester, does not use tillage in the fields, and has new, energy efficient buildings. All of these

will reduce the carbon footprint and are not accounted for in CCC.

The second highest estimate of the carbon footprint for the UGA swine farm is the one

using COMET. It is a relatively complex tool. It needed a lot of specific information. For

example, this tool needed average populations for different phases of swine per month. Those

inputs were not completely recorded by farm but had to be estimated.

Output from this tool showed the carbon footprint for each field and each production phase

on the farm. It is convenient for comparing the carbon footprint for each separate field or

livestock phase. This model did not estimate emissions due to energy use. The energy tool is a

separate one that provides only emissions reductions based on reduced fuel usage. By entering

some information about equipment in each type of barn and tillage practice in crop fields, the

model calculated the amount of energy use and carbon footprint for energy use itself. This might

cause some deviations with actual conditions because the inputs needed in this model did not

include all the energy used in the UGA swine farm. It is interesting to note that these energy

related emissions would show up as CO2 emissions and this model had the lowest CO2 emissions

by far. It was also apparent that this model estimated higher CH4 than CFT or PPC. This may be

due to the fact that COMET does not adequately account for the anaerobic digester used on this

farm as it actually estimated higher CH4 emissions than with a lagoon.

CFT calculated a relative small carbon footprint for the UGA swine farm. It showed the

carbon footprint from different sources (Figure 4.2). Feed and energy use are the two biggest

45

sources of the carbon footprint on the UGA swine farm, followed by enteric fermentation,

fertilizer use, crop residue management and pesticides. It is worth noting that the carbon stock

change is a negative number. This negative emission number shows that the model is estimating

an increase in soil carbon. This is likely since the farm uses a no-till system, does not use

synthetic fertilizer, and adds organic matter in the form of digested swine manure. Animal

manures are eventually decomposed by soil microorganisms and contribute to the pool of SOM

(Johnson et al., 2007a). In this case the organic matter content of the applied fertilizer increases

the organic matter percent in the soil and provides some benefit in GHG emissions. Although

adding organic fertilizer can increase soil organic matter and hence decrease the CO2 emission,

excess organic fertilizer inputs can have negative consequences. In general, N2O emission

increases with increased N-inputs (Gregorich et al., 2005; IPCC, 2001). The main cause of

agricultural increases in N2O emission to the atmosphere is the application of N fertilizers and

animal manures (Storey, 1997). It has been suggested that emissions may be greater from manure

sources rather than conventional fertilizers (Petersen et al., 2006).

This tool showed the CO2 emissions from feed, energy use, fertilizer and pesticide use are

partially offset by carbon stock change. All N2O emissions were generated from fertilizer use

and crop residue management. Enteric fermentation was the source of CH4 emissions. Generally,

this result matches the previous research. The GHG emissions from manure management were 0.

Manure management at the UGA swine farm uses an anaerobic digester which should have very

low CH4 emissions since they are burned by the digester.

46

Figure 4.2: The carbon footprint components for the UGA swine farm using Cool Farm Tool

(Version 2.0)

PPC estimated the lowest carbon footprint for the UGA swine farm in 2013. This tool was

the most complex model and difficult to use. It needed information for each barn. In other words,

for the UGA swine farm, we needed to get information for seven barns separately. It had

hundreds of inputs and took several hours to gather all the needed inputs. Some inputs were too

detailed to be recorded by farm managers and we had to estimate them empirically or to research

those inputs from literature.

This tool showed the carbon footprint results for different levels including farm level,

individual barn and per pig. It is easy to use to do an analysis of where most emissions come

from. This tool also showed the carbon footprint from different sources (Figure 4.3). Feed was

the most significant source (74.1%) for the carbon footprint of the UGA swine farm. Energy use

(15.6%) and manure management (10.0%) also contributed to the carbon footprint of the UGA

swine farm.

195,606

0

125,364

266 2,535 26,681

-13,240 2,325

-50000

0

50000

100000

150000

200000

250000

kg eCO2/yr

47

Figure 4.3: The carbon footprint components for the UGA swine farm using Pig Production

Environmental Footprint Calculator (Version 3.X)

CFT and PPC had similar estimates for total CO2-equivalent emissions and both showed

that most of the emissions were in the form of CO2. Since these models accounted for the more

management practices used on the farm and because most of the emissions were in the form of

CO2, we would expect the actual emissions to be most closely simulated by these models..

Carbon Footprint for the UGA Dairy Farm

Table 4.3: Summary of the carbon footprint for the UGA dairy farm

Model

Greenhouse Gas Emission (kg/yr) Total

(kg eCO2/yr) CO2 N2O CH4

Clean Air-Cool Planet Campus

Carbon Calculator (Version 6.9) 245,163 747 42,103 1,520,300

Cool Farm Tool (Version 2.0) 1,006,782 277 22,040 1,640,954

COMET-Farm Tool -94,500 1,705 95,720 2,806,600

Farm Smart (Version 1.5) 1,383,466 1 1,199 1,413,738

74.2%

10.0%

15.6%

0.2%

0.0%

0.2%

Feed

Manure management

Energy use

Water

Dead Animal Disposal

48

Figure 4.4: Summary of the carbon footprint for the UGA dairy farm

The carbon footprint for the UGA dairy farm was calculated using four different selected

models and results are shown in Table 4.3 and Figure 4.4. Estimates for the carbon footprint in

2013 varied 2-fold among the models from around 1,400,000 kg CO2-equivalent using FS to

around 2,800,000 kg CO2-equivalent using COMET.

FS estimated the lowest carbon footprint for the UGA dairy farm in 2013. This tool showed

the carbon footprint from different sources (Figure 4.5). The biggest source of the carbon

footprint for the UGA dairy farm was manure management (38.3%). Energy use (25.9%), enteric

fermentation (19.9%) and feed (13.0%) also contribute large portions to the carbon footprint in

the UGA dairy farm.

245,163

1,006,782

-94,500

1,383,466

222,517 82,492

508,099

299

1,052,578

550,991

2,393,000

29,974

-500000

0

500000

1000000

1500000

2000000

2500000

3000000

Clean Air-Cool

Planet CampusCarbon Calculator

(Version 6.9)

Cool Farm Tool

(Version 2.0)

COMET-Farm

Tool

Farm Smart

(Version 1.5)

kg eCO2/yr

CO2

N2O

CH4

49

Figure 4.5: The carbon footprint components for the UGA dairy farm using Farm Smart (Version

1.5)

FS supplies an estimate of average US values to compare the carbon footprint for the test

farm to national averages. The original results for this model are reported per pound of milk

which is a more comparable approach when comparing results with other farms (Table 4.4).

Except for the fuel use and manure GHGs, values for all other sources are lower than the national

average, but the value the overall for the entire UGA dairy farm was higher than the national

average.

13.0%

19.9%

25.9%

1.8%

1.1%

38.3%

Feed

Enteric fermentation

Energy use

Indirect nitrous

Direct nitrous

Manure management

50

Table 4.4: The carbon footprint for the UGA dairy farm from Farm Smart (Version 1.5) and the

national average reported by Farm Smart (Version 1.5)

UGA Dairy Farm

(kg eCO2/lb milk)

National

(kg eCO2/lb

milk)

Difference

(kg eCO2/lb

milk)

Feed production 0.0839 0.1451 -0.0612

Enteric fermentation 0.1279 0.1950 -0.0671

Fuel use 0.1668 0.0318 0.1351

Indirect Nitrous 0.0115 0.0163 -0.0048

Direct Nitrous 0.0071 0.0460 -0.0390

Total pasture manure 0.0000 0.0079 -0.0079

Total manure GHGs 0.2467 0.1633 0.0834

Total 0.6440 0.6055 0.0384

CCC, the simplest model, estimated the second lowest carbon footprint for the UGA dairy

farm in 2013. Because this model did not have any information about location and did not

distinguish among different life phases of the dairy cow, the results may be questionable.

The third lowest estimate of the carbon footprint for the UGA dairy farm was the one using

CFT. Manure management was the biggest source of the carbon footprint for the UGA dairy

farm based on this model (Figure 4.6). Enteric fermentation, energy use and feed production also

contributed a large portion of the carbon footprint. Fertilizer and pesticide use and crop residue

management also contributed to a small part of the carbon footprint. The carbon stock change

was shown as negative because of organic fertilizer use (cattle solid manure, cattle slurry and

51

poultry litter) and limited tillage at the UGA dairy farm. These results were similar to FS but the

enteric fermentation contributions were higher while the energy use contributions were lower.

CO2 emissions from manure management, energy use, feed and pesticide uses were partially

offset by the soil carbon stock change. All N2O emissions were generated from fertilizer use and

crop residue management. Enteric fermentation was the source of CH4 emissions. O’Brien et al.

(2011) reported that the main sources of GHG emissions from the US dairy system were enteric

CH4 (42%), N2O emissions from manure storage and spreading (17%), CH4 emissions form

manure storage (14%), GHG emissions from imported concentrate feed (12%), and CO2

emissions from electricity generation and fuel combustion (8%). Although the estimation for the

carbon footprint in the UGA dairy farm using CFT matched previous studies generally, there

were still some differences. Monteny et al. (2001) reported that the relative contribution of

enteric fermentation versus manure sources of CH4 was 8:2 but the CFT estimated all CH4

emissions came from enteric fermentation. Also, it was reported that manure storage and

application as fertilizer in dairy farm were the main sources to emit N2O (Verge et al., 2007;

Gollnow et al., 2014), but the CFT estimated that no N2O emission came from manure.

52

Figure 4.6: The carbon footprint components for the UGA dairy farm using Cool Farm Tool

(Version 2.0)

The COMET estimated the highest carbon footprint for the UGA dairy farm in 2013. As a

relatively complex tool, COMET needed a lot of specific information such as average

populations for different phases of dairy cow per month and the average daily weight gain for

different phases of dairy cow per month. Those inputs were not completely recorded by farm but

had to be estimated.

COMET was the only model that estimated negative CO2 emissions. This might be because

this model accounts for carbon stock in soil and did not estimate feed production and energy use

which are the primary sources for CO2 emissions. Compared with other models, this model

estimated the highest CH4 emissions and this may be due to the fact that COMET does not

adequately account for the manure management.

186,986

550,991

655,163

290,560

65,609

-132,792 7,552 16,885

-200000.00

-100000.00

0.00

100000.00

200000.00

300000.00

400000.00

500000.00

600000.00

700000.00kg eCO2/yr

53

Management Options for Reducing the Carbon Footprint

Tillage

For cropped fields at the UGA dairy farm, sorghum and wheat were planted every year with

no tillage for sorghum and intensive tillage for wheat. To investigate the impact of tillage on

emissions, we changed the tillage for wheat from intensive tillage to RT and NT using COMET

(Table 4.5). The carbon footprint for field (CO2 and field N2O emissions) was 298,300 kg CO2-

equivalent using intensive tillage, 276,000 kg CO2-equivalent using RT and 252,200 kg CO2-

equivalent when NT was used. The carbon footprint for field operations were reduced 7.5% and

15.5% compared with intensive tillage respectively, but the carbon footprint for the entire dairy

farm dropped only 0.8% and 1.6% because the carbon footprint for livestock operations was

around 90% of the entire carbon footprint for the UGA dairy farm and this farm has already used

some methods to reduce the carbon footprint for the field operation, such as using no tillage for

sorghum and using organic fertilizers instead of synthetic fertilizers. Based on results for the

COMET, the dairy farm might change the tillage method for wheat from intensive tillage to RT

or NT in order to decrease the carbon footprint. NT and RT can also save energy by reducing

fuel needs (Phillips et al., 1980; Archer et al., 2002). These reductions were not simulated since

COMET does not calculate energy use.

54

Table 4.5: The carbon footprint for the UGA dairy farm under different tillage using the

COMET-Farm tool

CO2a

(kg

eCO2/yr)

Field N2O

(kg

eCO2/yr)

Livestock N2O

(kg eCO2/yr)

CH4b

(kg eCO2/yr)

Total

(kg eCO2/yr)

Intensive

Tillage -94,500 392,800 115,300 2,393,000 2,806,600

Reduced

Tillage -112,700 388,700 115,300 2,393,000 2,784,300

No Tillage -130,700 382,900 115,300 2,393,000 2,760,500 a All CO2 emissions came from field operations

b All CH4 emissions came from livestock operations

Feed

Because UGA swine farm used imported corn (yellow dent and DDGS) as a major feed

input for all phases of swine diets and there were many choices for corn, we changed the corn

types for swine feed using PPC to determine the impact of this choice on the carbon footprint.

The results (Figure 4.7) showed that carbon footprint changed significantly especially in feed

production CO2 and manure N2O based on corn choice. Emission of manure CH4 was also

reduced when using gluten free corn compared with using DDGS corn. Carbon footprint for

DDGS corn was the largest of available options. Using gluten free corn, hominy corn or NO.2

corn as feed ingredient decreased the carbon footprint by 46.2, 45.0 and 45.1% respectively for

the UGA swine farm compared with DDGS corn. If this choice does not impact production and

can be obtained at similar prices, choosing a non-DDGS form of corn might be a feed option for

reducing the carbon footprint.

55

Figure 4.7: The carbon footprint for the UGA swine farm under different corn types as feed

using Pig Production Environmental Footprint Calculator (Version 3.X)

It has reported that adding ionophores into feed can reduce CH4 emissions (Benz et al.,

1982; Van Nevel et al., 1995; McGinn et al., 2004). The ionophore monensin has been used as a

feed additive in cattle to improve feed conversion efficiency and N metabolism, as a bloat

preventative, and in prevention of ketosis post-calving. Duffield et al. (2008) offers the most

thorough analysis of effects of monensin on milk production and dry matter intake (DMI), and

conclude that monensin decreases DMI by 0.3 kg/d, increases milk yield by 0.7 kg/d, and

improves milk production efficiency by 2.5%. Other practices can reduce GHG emissions,

especially for CH4 by adding oils to the diet (Machmuller et al., 2000; Jordan et al., 2004).

COMET provided the choices for including ionophores and edible oils in the feed ration. I ran

alternatives with these additions to the ration but it did not change the carbon footprint for either

farm.

242,530

98,585 106,215 104,510

14,715

10,718 15,848 17,691

17,997

12,163 6,274 5,617

51,562

54,211 51,484 51,695

0

50000

100000

150000

200000

250000

300000

350000

Corn, DDGS Corn, Gluten

free

Corn,

Hominy

Corn, NO.2

kg eCO2/yr

Others

Manure N2O

Manure CH4

Feed production CO2

56

Manure management

Manure management at the UGA swine farm was anaerobic digester and for dairy farm uses

an uncovered anaerobic lagoon. An anaerobic digester is a manure management method where

animal excreta (with or without straw) is collected and anaerobically digested in a large

containment vessel or covered lagoon. Anaerobic digesters are designed and operated for waste

stabilization by the microbial reduction of complex organic compounds to CO2 and CH4, which

is captured and flared or used as a fuel. An uncovered anaerobic lagoon is a liquid storage system

designed and operated to combine waste stabilization and storage. Lagoon supernatant is usually

used to remove manure from the associated confinement facilities to the lagoon. The water from

the lagoon may be recycled as flush water or used to irrigate and/or fertilize fields. The primary

difference between these two manure management measures is that the anaerobic digester can

capture most of the produced CH4 while uncovered anaerobic lagoons just let produced CH4

escape from the lagoon into the atmosphere.

When using COMET, we could not find any change for the carbon footprint for the UGA

swine farm if the manure management changed from anaerobic digester to uncovered anaerobic

lagoon. For the UGA dairy farm, if the manure management changed from uncovered anaerobic

lagoon to anaerobic digester, the carbon footprint increased 339,000 kg CO2-equivalent (equal to

12.1%) with a 26,100 kg CO2-equivalent of livestock N2O emission decrease and a 365,100 kg

CO2-equivalent of manure CH4 emission increase (Table 4.6).

57

Table 4.6: The carbon footprint for the UGA dairy farm under different manure managements

using the COMET-Farm tool

CO2

(kg

eCO2/yr)

Field

N2O

(kg

eCO2/yr)

Livestock

N2O

(kg

eCO2/yr)

Enteric

CH4

(kg

eCO2/yr)

Manure

CH4

(kg

eCO2/yr)

Total

(kg

eCO2/yr)

Anaerobic digester -94,500 392,800 89,200 1,217,400 1,540,700 3,145,600

Uncovered

anaerobic lagoon -94,500 392,800 115,300 1,217,400 1,175,600 2,806,600

In contrast, we observed the opposite result using CFT. The carbon footprint was 867,883

kg CO2-equivalent (equal to 255.6%) lager if we use an uncovered anaerobic lagoon than using

an anaerobic digester for the UGA swine farm. For the UGA dairy farm, I obtained similar result,

as if the manure management changes from uncovered anaerobic lagoon to anaerobic digester,

the carbon footprint will decrease 655,163 kg CO2-equivalent, a 39.9% reduction.

If we change manure management for swine farm from anaerobic digester to uncovered

anaerobic lagoon using PPC, the manure CH4 will skyrocket to 6,082 kg (82,751 kg CO2-

equivalent), which will be equivalent to a 562.4% increase, whereas, emissions of manure N2O

and CO2 will not change. The carbon footprint will increase 25.3% when manure management

changes (Figure 4.8).

58

Figure 4.8: The carbon footprint for the UGA swine farm under different manure managements

using Pig Production Environmental Footprint Calculator (Version 3.X)

If we change manure management for the UGA dairy farm from uncovered anaerobic

lagoon anaerobic digester to anaerobic digester using FS, the carbon footprint will be 38.3%

smaller with no N2O emissions change, 37.8% and 60.8% less CO2 and CH4 emissions,

respectively (Figure 4.9).

14,715

97,466 17,997

17,997

294,092

294,092

0

50000

100000

150000

200000

250000

300000

350000

400000

450000

Anaerobic digester Uncovered anaerobic lagoon

kg eCO2/yr

CO2

Manure N2O

Manure CH4

59

Figure 4.9: The carbon footprint for the UGA dairy farm under different manure managements

using Farm Smart (Version 1.5)

Compared with uncovered anaerobic lagoon, anaerobic digestion not only can reduce CH4

emissions from manure storage, but fossil fuels consumption. Both processes decrease

anthropogenic GHG emissions (Amon et al., 2006). Maranon et al. (2006) reported that the

potential GHG emissions savings per livestock unit would range from 978 to 1,776 kg CO2-

equivalent year-1

if anaerobic digestion was implemented on dairy cattle farms with the main

savings due to avoided CH4 emissions during slurry management. Produced CH4 can be used as

supplement for digester heating needs (35–55% of the total CH4 produced) and on-farm fuel

energy requirement.

Fertilizer

In 2013, poultry litter was used as one of fertilizers for fields at the UGA dairy farm.

Commercial synthetic fertilizer can used in place of poultry litter. To evaluate the difference on

carbon footprints between these two fertilizers, I changed the fertilizer type from poultry litter to

860,770

1,383,466 299

299 11,750

29,974

0

200000

400000

600000

800000

1000000

1200000

1400000

1600000

Uncovered anaerobic lagoon Anaerobic digester

kg eCO2/yr

CH4

N2O

CO2

60

commercial fertilizer (compound NPK 15% N/15% K2O/15% P2O5) using the same rate of

Nitrogen (Figure 4.10). When the fertilizer type was changed from poultry litter to commercial

fertilizer (compound NPK 15% N/15% K2O/15% P2O5), the carbon footprint for fertilizer

application decreased 8,669 kg CO2-equivanlent, while for fertilizer production and carbon stock

change increased 54,374 and 23,418 kg CO2-equivanlent respectively. Commercial fertilizer can

reduce the carbon footprint during application but it decreased the carbon level in soil and also

led to GHG emissions due to fertilizer production and hence the carbon footprint for the entire

UGA dairy farm increased 69,123 kg CO2-equivanlent (equal to 4.2%).

Figure 4.10: The carbon footprint for the UGA dairy farm under different fertilizer types using


65,609 56,940 0 54,374 -132,792 -109,374

1,708,137 1,708,137

-500000

0

500000

1000000

1500000

2000000

Poultry litter Commercial fertilizer

(compoundNPK15%N/15%K2O/15%P2O5)

kg eCO2/yr

Soil fertiliser

Fertiliser production

Carbon stock change

Others

61

CHAPTER 5

CONCLUSIONS

The tools produced widely varying results. The CCC was the easiest to use and required the

fewest inputs but also did not account for many management options. The more complex models

accounted for a wider variety of options and generally provided more useful output of estimates.

CFT and PPC showed very similar estimates for the carbon footprint for the UGA swine

farm and both showed that most of the emissions were in the form of CO2. Since these models

accounted for the more management practices used on the farm and because most of the

emissions were in the form of CO2, we would expect the actual emissions to be most closely

simulated by these models.

The carbon footprint estimates for the dairy were highly variable. COMET estimated higher

CH4 emissions for manure management and lower estimation for CO2 emissions compared to the

other models. FS accounted for emissions from feed and energy better but did not seem to

adequately assess emissions from cropped areas. CFT appeared to adequately account for most

emission sources.

Several management changes such as changing from intensive tillage to reduced tillage or

no tillage, changing manure management from uncovered anaerobic lagoon to anaerobic digester,

or changing the type of corn used in the swine ration were investigated and shown to have

potential to reduce carbon footprints on the UGA farms. This shows the real strength of using

more complex tools, as these management changes could not be considered using CCC.

62

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86

Appendix A

THE UGA SWINE FARM DATA RECORD

Table A.1: The UGA swine farm market hog diets in 2014

Nursery Grow-Finish

Phase I II III G-1 G-2 F-1

Age,weeks 3-4 4-6 6-8 8-12 12-16 16-M

Ingredient, %

Corn 38.2 52.88 64.83 72.64 77.8 81.5

Whey 27.50 10.00 - - - -

Soybean meal 18.75 27.49 30.77 21.05 14.24 12.39

Spray Dired Plasma

Protein 5.00 - - - - -

Spray Dired Blood Cells - 3.00 - - - -

Fat (Poultry) 2.00 0.32 0.45 4.86 4.64 3.15

Fish Meal 5.0 2.5 - - - -

Dicalcium Phosphate 0.23 1.21 1.93 1.86 1.73 1.21

Limestone 0.83 0.71 0.64 0.74 0.74 0.90

Salt 0.00 0.00 0.35 0.35 0.35 0.35

Zinc Oxide 0.38 0.25 - - - -

Vitamin premix 0.25 0.25 0.25 0.15 0.15 0.15

Mineral premix 0.15 0.15 0.15 0.15 0.15 0.15

DL-methionine 0.12 0.18 0.05 - - -

Lysine 0.23 0.20 0.20 0.20 0.20 0.20

Antibiotic (mecadox) 1.00 1.00 1.00 - - -

Crude Protein, % 22.84 21.74 19.86 16.3 13.6 13.0

87

Table A.2: The UGA swine farm gestation and lactation diets in 2014

Gestation Diet Lactation Diet

Ingredient, %

Corn (Yellow dent) 51.79 38.96

SBM (Debulled, Solvent

Extracted) 5.00 17.23

DDGS (DDGS >6% and

9% oil) 40.00 40.00

Poultry Fat - 0.46

Dical 0.26 -

Limestone 1.74 2.16

Salt 0.35 0.35

Vitamins 0.25 0.25

Trace Minerals (NSNG) 0.15 0.15

Sow Pack 0.25 0.25

Lysine 0.14 0.20

Cride Protein, % 18.1 22.4

88

Table A.3: The UGA swine farm irrigation pumping record

Date

Appl.

Time

Flow

Rate

Nutr. Conc.

(lb/acre-in) Volume

Total Nutrients

Pumped (lb) Field

#

(minutes) (gal/m) N P2O5 K2O

(acre-

in) N P2O5 K2O

3/18/11 275 170 122 40 76 1.72 210 12 12 8

5/16/11 155 170 122 40 76 0.97 118 39 74 8

6/2/11 130 170 122 40 76 0.81 99 33 62 8

8/26/11 240 170 219 159 227 1.50 329 239 341 8

10/7/11 150 170 219 159 227 0.94 206 149 213 8

1/30/12 270 170 219 159 227 1.69 370 269 384 8

3/27/12 300 170 358 90 266 1.88 672 169 500 8

6/13/12 300 170 358 90 266 1.88 672 169 500 8

11/20/12 300 170 358 90 266 1.88 672 169 500 8

4/23/13 480 170 358 90 266 3.00 1076 270 799 8

4/24/13 240 170 358 90 266 1.50 538 135 400 8

11/16/13 512 197 358 90 266 3.71 1330 334 988 8

2/19/14 310 210 358 90 266 2.40 858 216 638 8

2/27/14 150 210 358 90 266 1.16 415 104 309 8

5/23/14 290 210 358 90 266 2.24 803 202 597 8

5/27/14 290 210 358 90 266 2.24 803 202 597 8

Total

Pumped 29.54 9,172 2,711 6,,910 8

89

Table A.4: The UGA swine farm facility electricity usage

Service Period (days) Usage

(kWh)

Usage per

Day (kWh) Cost ($)

Cost per

Day ($)

5/31/2012 7/1/2012 31 10,880 351 1,144.00 36.90

7/1/2012 8/1/2012 31 11,480 370 1,174.00 37.87

8/1/2012 10/2/2012 29 11,280 389 1,075.00 37.07

10/2/2012 10/31/2012 29 6,720 232 1,771.00 61.07

10/31/2012 11/31/2012 30 5,560 185 0.00 0.00

11/31/2012 1/1/2013 32 6,200 194 542.00 16.94

1/1/2013 1/30/2013 29 5,600 193 751.00 25.90

1/30/2013 3/1/2013 30 5,120 171 729.00 24.30

3/1/2013 4/1/2013 31 5,560 179 749.00 24.16

4/1/2013 4/30/2013 29 5,160 178 731.00 25.21

4/30/2013 5/30/2013 30 6,720 224 839.00 27.97

Total for 2013 331 80,280 243 9,505

5/30/2013 6/30/2013 31 10,160 328 1,077.00 34.74

6/30/2013 7/30/2013 30 10,120 337 1,052.00 35.07

7/30/2013 8/29/2013 30 10,800 360 1,198.00 39.93

8/29/2013 9/30/2013 32 8,520 266 1,014.00 31.69

9/30/2013 10/30/2013 30 6,800 227 936.00 31.20

10/30/2013 12/1/2013 32 6,320 187 914.00 28.56

12/1/2013 1/1/2014 31 7,160 231 954.00 30.77

1/1/2014 1/30/2014 29 7,160 247 983.00 33.90

1/30/2014 3/2/2014 31 7,000 226 975.00 31.45

3/2/2014 3/31/2014 29 6,400 221 947.00 32.66

3/31/2014 4/30/2014 30 6,560 219 955.00 31.83

4/30/2014 6/1/2014 32 9,240 289 1,153.00 36.03

Total for 2014 367 96,240 262 12,158

6/1/2014 6/30/2014 29 9,400 324 1,160.00 40.00

6/30/2014 7/30/2014 30 10,000 333 1,192.00 39.73

7/30/2014 9/1/2014 33 10,280 312 1,066.00 32.30

9/1/2014 10/1/2014 30 8,640 288 922.00 30.73

10/1/2014 10/30/2014 29 5,800 200 790.00 27.24

Total for 2015

151

44,120

292

5,130

90

Table A.5: The UGA swine farm field electricity usage


(kWh)

Usage per

Day

(kWh)

Cost

($)

Cost per

Day ($)

5/30/2012 7/1/2012 32 120 4 36.00 1.13

7/1/2012 7/31/2012 30 80 3 30.00 1.00

7/31/2012 8/30/2012 30 0 21.00 0.70

8/30/2012 10/1/2012 32 0 21.00 0.66

10/1/2012 10/30/2012 29 0 21.00 0.72

10/30/2012 11/29/2012 30 240 8 54.00 1.80

11/29/2012 1/2/2013 34 0 21.00 0.62

1/2/2013 1/31/2013 29 0 21.00 0.72

1/31/2013 2/28/2013 28 0 21.00 0.75

2/28/2013 4/1/2013 32 280 9 61.00 1.91

4/1/2013 4/31/2013 29 0 21.00 0.72

4/31/2013 5/31/2013 31 0 21.00 0.68

Total for 2013 366 720 2 349

5/31/2013 7/1/2013 31 200 6 49.00 1.58

7/1/2013 7/31/2013 29 120 4 37.00 1.28

7/31/2013 8/29/2013 30 320 11 68.00 2.27

8/29/2013 10/1/2013 33 120 4 36.00 1.09

10/1/2013 10/31/2013 30 120 4 36.00 1.20

10/31/2013 12/1/2013 31 80 3 30.00 0.97

12/1/2013 1/1/2014 31 120 4 36.00 1.16

1/1/2014 1/31/2014 30 80 3 31.00 1.03

1/31/2014 3/2/2014 30 280 9 63.00 2.10

3/2/2014 3/31/2014 29 0 0 23.00 0.79

3/31/2014 4/31/2014 30 0 0 23.00 0.77

4/31/2014 6/1/2014 32 400 13 85.00 2.66

Total for 2014 366 1840 5 517

6/1/2014 7/1/2014 30 120 4 39.00 1.30

7/1/2014 7/30/2014 29 120 4 39.00 1.34

7/30/2014 9/2/2014 34 160 5 46.00 1.35

9/2/2014 9/30/2014 28 120 4 38.00 1.36

9/30/2014 10/31/2014 31 440 14 89.00 2.87

Total for 2015 152 960 6 251

91

Table A.6: The UGA swine farm methane digester building electricity usage

Service Period Usage

(kWh)

Usage per

Day

(kWh)

Cost ($)

Cost

per Day

($)

5/31/2012 7/1/2012 31 7,160 231 822.00 26.52

7/1/2012 7/31/2012 30 6,440 215 826.00 27.53

7/31/2012 8/30/2012 30 6,240 208 795.00 26.50

8/30/2012 10/1/2012 32 7,160 224 794.00 24.81

10/1/2012 10/30/2012 29 7,120 246 792.00 27.31

10/30/2012 11/29/2012 30 6,600 220 768.00 25.60

10/30/2012 11/29/2012 30 6,600 220 768.00 25.60

11/29/2012 1/2/2013 34 7,480 220 800.00 23.53

1/2/2013 1/30/2013 28 7,400 264 811.00 28.96

1/30/2013 2/28/2013 29 6,760 233 782.00 26.97

2/28/2013 4/1/2013 32 7,680 240 824.00 25.75

4/1/2013 5/1/2013 30 6,520 217 771.00 25.70

Total for 2013 365 83,160 228 9,553

5/30/2013 6/30/2013 31 8,080 261 1,859.00 59.97

5/1/2013 5/30/2013 29 9,920 342 974.00 33.59

6/30/2013 7/30/2013 30 4,720 157 0.00 0.00

7/30/2013 8/30/2013 31 5,000 161 385.00 12.42

8/30/2013 9/30/2013 31 4,680 151 644.00 20.77

9/30/2013 10/31/2013 31 3,520 114 583.00 18.81

10/31/2013 12/2/2013 32 2,320 73 393.00 12.28

12/2/2013 1/2/2014 31 5,560 179 686.00 22.13

1/2/2014 1/30/2014 28 5,440 194 700.00 25.00

1/30/2014 3/2/2014 31 7,160 231 779.00 25.13

3/2/2014 3/31/2014 29 6,720 232 759.00 26.17

3/31/2014 5/1/2014 31 6,760 218 761.00 24.55

5/1/2014 6/1/2014 31 7,080 228 833.00 26.87

Total for 2014 396 76,960 194 9,356

6/1/2014 7/1/2014 30 7,080 236 761.00 25.37

7/1/2014 7/30/2014 29 8,160 281 887.00 30.59

7/30/2014 9/1/2014 33 9,840 298 972.00 29.45

9/1/2014 9/30/2014 29 5,200 179 672.00 23.17

9/30/2014 10/30/2014 30 4,720 157 649.00 21.63

Total for 2015 151 35,000 232 3,941

92

For propane usage, for February 2013, the UGA swine farm account paid for 777.3 gallons

at $ 1.9792 per gallon, and then in April 2014 they paid for 207.7 gallons at $ 1.7545 per gallon.

The propane costs for fiscal year 2013 was $ 5,200, for fiscal year 2014 was $ 5,500, and no

propane has been purchased for fiscal year 2015 yet.

93

Appendix B

THE UGA DAIRY FARM DATA RECORD

Table B.1: The UGA dairy farm feed

Milking

Herd Dry Cow Heifer Calf

Corn 581.4 1,202.2 905 750

Corn gluten

feed 348.9 221.7 750.6 0

Soybean meal

48 116.3 0 0 338

Soy hulls 290.8 500.8 275.3 0

CSH 0 0 0 80

Csm 42 0 0 6.6 0

Oats 0 0 0 250

Citrus plup 405.9 0 0 430

Molasses 0 0 0 90

Limestone 7.9 18.1 36.8 0

Dical 17.1 34.9 12.1 37

Slat 6.5 15.1 10 23

Mg ox 0 0 5 0

Bovatech 0 0 0.4 0.4

TM pack 0 0 1 1

Vit ade 0.8 3.6 1 1

Zinpro 0.6 0 0 0

Tmin 0.8 3.6 0 0

Dynamate 7.6 0 0 0

Dried Dist 215.4 0 0 0

Total 2,000 2,000 2,003.8 2,000.4

94

Table B.2: The UGA dairy farm manure application

Field No.

Cattle Solid Manure

Application Rate

(tons/acre)

Cattle Slurry

Application Rate

(inches/acre)

10 20 0

13 26 0

15 6 1

16 22 2

The solids content in cattle solid manure is 0.20%. The moisture of the cattle slurry is

60.19%.

95

Table B.3: The UGA dairy farm inventory of animals

Date

Year of

Birth

1/1/

06

7/1/

06

1/1/

07

7/1/

07

1/1/

08

7/1/

08

1/1/

09

7/1/

09

1/1/

10

7/1/

10

1/1/

11

7/1/

11

1/1/

12

7/1/

12

1/1/

13

7/1/

13

1/1/

14

1996 1 1

1997 4 3 2 1 1

1998 5 5 2 2 2

1999 6 5 5 5 4 4 3 3 1

2000 5 4 3 1 1 1 1 1 1 1 1 1

2001 21 13 12 8 7 5 4 4 3 2

2002 37 32 27 21 19 15 14 13 5 5 4 3 3 2 1 1

2003 38 35 33 30 29 24 20 18 14 11 5 4 3 3 1 1 1

2004 39 39 39 36 35 29 28 27 23 18 14 10 10 6 3 1 1

2005 35 35 35 34 32 26 23 22 16 12 7 7 6 4 1 1 1

2006

19 51 50 49 47 44 44 38 33 19 17 15 10 7 6 4

2007

11 34 34 33 32 29 29 27 27 24 18 14 10 7

2008

20 33 33 33 32 30 26 25 21 15 12 11

2009

22 51 49 49 49 48 44 36 33 26

2010

23 44 42 41 40 37 31 27

2011

22 40 39 39 34 33

2012

18 50 50 50

2013 18 52

Total 191 191 209 199 213 205 203 219 214 215 200 208 215 205 204 198 213

Milking 88 99 108 107 90 109 114 116 109 114 96 106 103 100 89 97 87

Males 18 34 11 6 22 6

96

Table B.4: The UGA dairy farm yearly production in 2013

Test

Date

Days

in

Test

Period

Number

Cows

in Herd

on Test

Day

Test Day

Averages

(Milking

Cows)

Test Day Averages (All

Cows)

Rolling Yearly

Herd Averages

Number

Left Herd Number

Calving

Total

Pregnant

Cows

DIM Milk % in

Milk Milk

%

Fat

%

Protein Milk Fat Protein Died Sold

2-14-13 43 106 198 73.1 88 64.0 4.1 3.1 21,432 859 671 2 5 15 50

3-18-13 32 108 211 67.6 90 60.6 3.7 3.0 21,483 858 673 3 3 12 60

4-16-13 29 110 221 66.5 90 59.8 3.6 3.1 21,452 849 672 0 0 6 66

5-16-13 30 111 229 66.2 90 59.5 3.9 3.1 21,459 844 672 1 3 7 68

6-26-13 41 109 255 55.8 89 49.6 3.6 3.0 21,474 838 673 3 1 3 74

7-25-13 29 108 255 53.0 66 34.9 3.5 2.9 21,399 831 669 0 1 0 76

8-26-13 32 107 282 51.0 63 31.9 3.7 3.0 20,939 811 653 0 1 1 78

9-30-13 35 99 196 59.1 82 48.3 3.8 2.9 20,212 779 628 2 11 37 47

11-5-13 36 102 184 57.3 81 46.5 3.6 3.0 19,777 754 610 2 1 15 42

12-3-13 28 104 162 66.1 79 51.9 3.9 3.0 19,326 732 593 1 4 11 37

1-7-14 35 106 155 68.1 82 55.9 4.1 3.2 18,868 714 577 0 1 14 44

Average 34 106 213 62.2 82 51.2 3.7 3.0 20,711 806 645 14 31 11 58

97

Table B.5: The UGA dairy farm electricity usage


(kWh)

Usage per

Day (kWh) Cost ($)

Cost per

Day ($)

9/3/2008 10/2/2008 29 32,238 1,112 3,061.00 105.55

10/2/2008 11/2/2008 31 31,688 1,022 3,139.00 101.26

11/2/2008 12/2/2008 30 36,251 1,208 3,365.00 112.17

12/2/2008 1/4/2009 33 43,175 1,308 3,750.00 113.64

1/4/2009 4/1/2009 29 40,317 1,390 3,548.00 122.34

3/5/2009 3/5/2009 27 28,149 1,043 6,347.00 235.07

2/2/2009 4/1/2009 31 38,349 1,237 3,475.00 112.10

4/1/2009 5/1/2009 30 30,390 1,013 0.00 0.00

5/1/2009 6/2/2009 32

Total for 2009 272 280,557 1,031 26,685

6/2/2009 6/30/2009 28 32,671 1,167 3,101.00 110.75

6/30/2009 7/31/2009 31 36,600 1,181 3,492.00 112.65

7/31/2009 9/1/2009 32 40,101 1,253 3,587.00 112.09

9/1/2009 10/1/2009 30 36,300 1,210 3,395.00 113.17

10/1/2009 10/29/2009 28 25,432 908 2,824.00 100.86

10/29/2009 11/30/2009 32 26,400 825 2,879.00 89.97

11/30/2009 12/30/2009 30 32,438 1,081 3,183.00 106.10

12/30/2009 1/31/2010 32 40,475 1,265 3,656.00 114.25

1/31/2010 3/2/2010 30 33,751 1,125 3,181.00 106.03

3/2/2010 3/31/2010 29 29,387 1,013 3,163.00 109.07

3/31/2010 4/30/2010 30 30,967 1,032 3,286.00 109.53

4/30/2010 4/31/2010 31 34,886 1,125 3,627.00 117.00

Total for 2010 363 399,408 1,100 39,374

5/31/2010 6/29/2010 29 34,927 1,204 3,618.00 124.76

6/29/2010 7/30/2010 31 36,511 1,178 3,590.00 115.81

7/30/2010 8/30/2010 31 38,194 1,232 3,720.00 120.00

8/30/2010 9/29/2010 30 34,950 1,165 3,423.00 114.10

9/29/2010 10/28/2010 29 27,221 939 2,987.00 103.00

10/28/2010 11/30/2010 33 32,406 982 3,264.00 98.91

11/30/2010 1/3/2011 34 38,878 1,143 3,563.00 104.79

1/3/2011 2/1/2011 29 35,900 1,238 3,715.00 128.10

2/1/2011 3/1/2011 28 29,400 1,050 3,343.00 119.39

3/1/2011 3/30/2011 29 33,000 1,138 3,717.00 128.17

3/30/2011 4/30/2011 31 34,377 1,109 3,740.00 120.65

98

Total for 2011 334 375,764 1,125 38,680

4/30/2011 6/3/2011 34 36,273 1,067 3,980.00 117.06

6/3/2011 6/30/2011 27 33,273 1,232 3,918.00 145.11

6/30/2011 7/31/2011 31 38,645 1,247 4,161.00 134.23

Total for 2012 92 108,191 1,176 12,059

99

Table B.6: The UGA dairy farm diesel usage

Purchased

Date Cost ($)

Avrage Price

($/gallon)

Usage

(gallons)

3/31/2012 9,493.93 4.178 2272.63

4/30/2012 5,194.94 4.167 1246.57

5/31/2012 385.06 4.025 95.66

6/27/2012 905.37 3.797 238.44

8/31/2012 1,765.76 3.975 444.24

9/14/2012 584.65 4.109 142.30

10/31/2012 902.00 4.090 220.54

11/30/2012 2,024.36 4.054 499.38

12/21/2012 517.03 4.041 127.95

1/31/2013 427.27 4.008 106.61

2/28/2013 601.53 4.160 144.60

3/29/2013 1,198.21 4.105 291.91

6/28/2013 1,278.29 3.841 332.78

7/30/2013 1,131.18 3.875 291.93

8/30/2013 1,781.10 3.916 454.83

9/30/2013 1,701.32 3.969 428.61

10/30/2013 807.36 3.902 206.94

11/30/2013 1,316.64 3.861 341.01

12/20/2013 296.08 3.919 75.55

2/28/2014 1,551.05 4.116 376.86

3/31/2014 1,711.63 4.132 414.24

4/25/2014 1,434.47 4.070 352.49

5/30/2014 927.55 4.036 229.80

6/30/2014 2,146.26 3.985 538.58

7/31/2014 581.19 3.944 147.35

8/29/2014 741.40 3.881 191.05

9/26/2014 741.40 3.819 194.15

10/31/2014 1,940.54 3.690 525.86

11/30/2014 1,298.90 3.557 365.19

12/23/2014 575.70 3.416 168.53

*Diesel price reference:

http://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=EMD_EPD2DXL0_PTE_R10_D

PG&f=W

CALCULATING THE CARBON FOOTPRINT FOR THE UNIVERSITY …

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