J. Dairy Sci. 103:3760–3773
https://doi.org/10.3168/jds.2019-16576© 2020, The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Greenhouse gas, water, and land footprint per unit of productionof the California dairy industry over 50 years
A. Naranjo,1
1
2

A. Johnson,2

H. Rossow,2

and E. Kebreab1*

Department of Animal Science, University of California, Davis 95616
School of Veterinary Medicine, University of California, Davis 95616

ABSTRACT

Food production including dairy has been associatedwith environmental impacts and resource use that has
been steadily improving when adjusted per unit ofproduct. The objective of this study was to conduct a
cradle-to-farm gate environmental impact analysis andresource inventory of the California dairy production
system to estimate the change in greenhouse gas emissions and water and land use over the 50-yr period
between 1964 and 2014. Using a life cycle assessment
according to international standards and the Food andAgriculture Organization of the United Nations guidelines, we analyzed contributions from dairy production
in California to global environmental change. Production of 1 kg of energy- and protein-corrected milk(ECM) in California emitted 1.12 to 1.16 kg of CO2
equivalents (CO2e) in 2014 compared with 2.11 kg ofCO2e in 1964, a reduction of 45.0 to 46.9% over the last
50 yr, depending on the model used. Greater reductions
in enteric methane intensity (i.e., methane production
per kilogram of ECM) were observed (reduction of
54.1 to 55.7%) compared with manure GHG (reduction of 8.73 to 11.9%) in 2014 compared with 1964.
This was mainly because manure management in thestate relies on lagoons for storage, which has a greater
methane conversion factor than solid manure storage.
Water use intensity was reduced by 88.1 to 89.9%, withwater reductions of 88.7 to 90.5% in crop production,
55.3 to 59.2% in housing and milking, and 52.4 to
54% in free water intake. Improved crop genetics andmanagement have contributed to large efficiencies inwater utilization. Land requirements for crop production were reduced by 89.4 to 89.7% in 2014 comparedwith 1964. This was mainly due to dramatic increases
in crop yields in the last 50 yr. The increases in milk

Received March 5, 2019.
Accepted December 11, 2019.
*Corresponding author: ekebreab@​ucdavis​.edu

production per cow through genetic improvements andbetter nutrition and animal care have contributed toreductions in greenhouse gas emissions and land andwater usage when calculated per unit of production(intensity) basis.
Key words: dairy, environment, life cycle assessment,
greenhouse gas
INTRODUCTION

Milk production is the third largest agricultural industry in the United States, with California being the
top dairy-producing state (USDA, 2014). Milk is themost important agricultural commodity produced in
the state by farm revenue (Sumner et al., 2015). The
California dairy industry, including milk production on
farms and milk processing, supported about 190,000
jobs and contributed about $21 billion in economic
value to the gross state product in 2014 (Sumner et al.,2015). Relative to the 1960s, total milk production in
the state has increased by about 500% (von Keyserlingk
et al., 2013). The dramatic rise in production was due
to increases in the number of cows, from about 790,000
to 1.78 million, and in milk production per cow, fromabout 4,850 to about 10,600 kg/cow per year (Sumner
et al., 2015). Over the last 50 yr, dairy production in
California has undergone significant improvements andadvancements in animal husbandry, feeding and housing practices, and in animal and plant genetics andcrop production methods.
The dairy industry has been scrutinized regarding its
environmental impact. Several studies have indicatedthat the livestock sector in general contributes to environmental change (e.g., de Vries and De Boer, 2010),
including greenhouse gas (GHG) emissions, water usage, and land resources. An estimated 70% of global
freshwater use by humans is attributed to agriculture,including 38% of freshwater withdrawals in the UnitedStates (USGS, 2009). In dairy production, the impact
on the environment is mainly from (1) direct GHG
emissions from enteric fermentation, manure storage
and field application, and crop production; (2) use of3760

Naranjo et al.: DAIRY PRODUCTION AND THE ENVIRONMENT IN CALIFORNIA

water resources for feed production and impact on water quality due to excretion of nitrogen and phosphorus;and (3) land requirement for feed production.
Increased animal productivity reduces the environmental impact of the dairy industry when calculated
on an emission intensity (GHG emission per unit ofproduct) basis. For example, Capper et al. (2009) calculated that compared with 1944, the 2007 US dairy
industry required 21% of the dairy cattle, 23% of thefeedstuffs, 10% of the land, and 35% of the water toproduce 1 unit of milk. Thoma et al. (2013) summarized several life cycle assessment (LCA) studies on
the dairy sector and noted that each followed slightly
different methodologies with some differences in systemboundaries, and allocation of impacts to milk and beef.
Some considered only carbon footprint (e.g., Gerber
et al., 2010), whereas others included inventories onwater and other resources (e.g., Capper et al., 2009).
California has unique attributes in its milk production
system that have not been analyzed previously, andseveral advancements in the methodology of calculating
emissions have been published since the latest studies
were conducted. Our objective was to conduct a cradleto-farm gate analysis of the California dairy production system to estimate changes in GHG emissions andwater and land use over the 50-yr period between 1964
and 2014 using the most up-to-date LCA methodologies.

3761

Because the objective was to estimate changes in GHG
and water and land use over 50 yr, we selected the reference years 1964 and 2014, primarily based on available
inputs for the LCA. The milk production system wasdivided into 4 processes: feed production, enteric methane, manure storage, and farm management (Figure1). The LCA model considered “upstream” activities
such as the extraction of raw materials for fuels, andintermediate products required in the system such as
diesel, electricity, and fertilizer. The system boundary
considered these processes up to the farm gate. “Downstream” activities, including milk processing, distribution, retail, or consumption, were not considered.
Functional Unit

Milk varies in its nutrient composition; therefore, thequality of milk must be standardized to allow for aconsistent, equitable comparison. The functional unit
used for analysis was defined as 1 kg of ECM at thefarm gate. The ECM was calculated by multiplying
milk production by the ratio of the energy content of
the milk to the energy content of standard milk with
4% fat and 3.3% true protein according to NRC (2001)
as follows:
ECM = milk production × (0.0929 × fat% + 0.0588
× true protein% + 0.192)/[0.0929 × (4%)

MATERIALS AND METHODS+ 0.0588 × (3.3%) + 0.192],

The LCA was conducted using GaBi software version 6 (PE International AG, 2015) and conformed to
ISO 14040 and 14044 standards (ISO, 2006a,b; Finkbeiner, 2014) and Food and Agriculture Organizationof the United Nations (FAO) Livestock Environmental
Assessment Protocol guidelines (FAO, 2016a,b). The
software includes GaBi content databases providing thecosts, energy, and environmental impacts of sourcing
and refining every raw material or processed component of a manufactured item. Where information fora product was missing, literature values were sourced
and integrated into the GaBi software. The life cycle
inventory for feed ingredients was taken from the foodand feed extension database of GaBi (http:​/​/​www​.gabi​-software​.com/​deutsch/​databases/​gabi​-databases/​food​-feed/​) and implemented as reported by Liedke et al.
(2014). The feed ingredients life cycle inventory was
also updated using primary data from California.

where fat% and protein% are fat and protein percentages in milk, respectively. All processes in the systemwere calculated based on 1 kg of ECM. Two scenarios
were considered for data collection. Model 1 was basedon primary data from 5 commercial dairies located in
Tulare and Kings Counties, California (Rossow andAly, 2013), and these were used to determine the average milk production and composition for this model.
The average milk production representing 2014 model 1
levels was 39.8 kg/d, with milk fat and protein percentages of 4 and 3.3%, respectively. Model 2 was based on
average diets collected by the California Department ofFood and Agriculture (CDFA) from 2013, 2014, and2015 (CDFA, 2013, 2014, 2015). The average milk production for this model was 36.4 kg/d, with similar milk
fat and protein as model 1. The average milk yield in
1964 was an average value of California milk productionin 1963, 1964, and 1965 from the USDA National Agricultural Statistical Service (USDA-NASS, 1960–2010)at 15.9 kg/d, and milk fat and protein percentages were
3.85 and 3.40%, respectively, calculated from Capper etal. (2009) and assuming an even distribution between
large and small breeds.

System Boundary

The animal production system analyzed in this study
represents the milk production system in California.
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Naranjo et al.: DAIRY PRODUCTION AND THE ENVIRONMENT IN CALIFORNIA

Data Sources

Data were collected from multiple sources including the USDA National Agricultural Statistical Service (USDA-NASS) and Economic Research Service(USDA-ERS), CDFA, peer-reviewed literature related
to multiple aspects of milk production, and other published literature such as extension reports, particularlyfor 1964. When literature numbers were not available,
the next best data were used or an assumption was
made. Upstream unit processes associated with fuels,
fertilizers, and transportation as well as some feedbased databases were modeled using the GaBi 6 software (PE International AG, 2015). Two different dietswith respective milk production were considered for2014 using IPCC assessment report 5 characterization
factors (AR5; IPCC, 2014). Model 1 was based on highproducing cows in Rossow and Aly (2013), whereas
model 2 was the average cow based on CDFA cost of
milk production reports from 2013 to 2015.
Feed Production

California representative diets were collected frompublished data for 1964 and 2014 (NRC, 1958; Meadand Ronning 1961; Palmquist et al., 1964; Hutton andBath, 1967a,b; Rauch et al., 2012, 2014; Rossow and
Aly 2013; Swanepoel et al., 2014; Havlin et al., 2015;
CDFA, 2014). In 1964, separate diets were used forcalves, heifers, pregnant heifers, close-up heifers, andlactating and dry cows (Table 1). For model 1, several
representative diets were collected based on the different stages of the life cycle for 2014, including calf (summary of 6 stages from birth to 12 mo), heifer, pregnant
heifer, close-up heifer, fresh and high-producing lactating cows, far-off dry cows, and close-up dry cows (Table2). Model 2 diets (Table 3) were based on reports fromCDFA (2013, 2014, 2015). Because the compositionof feed varied between different stages of production,
the feed for each reference year was a representative
weighted average over the whole production cycle tosimplify the calculations. The average lifespan of dairy
cows in California was assumed to be 4 lactations (H.
Rossow), which was used for the normalization of calculations (Tables 1 and 2).
Crop Production

The crop production process included the farm
practices to produce the feed, land, water, fertilizers,
pesticides/herbicides, energy used for irrigation, andtransport from field to farm including in-state andfrom-out-of-state transport. Data on crop yield/acreand the proportion of irrigated fields were obtained at
the state level using a combination of sources, including
USDA-NASS Quick Stats (USDA-NASS, 2017), USDA
farm and ranch irrigation reports (USDA, 2013), andFigure 1. Overview of the milk production system boundary considered in the study.
Journal of Dairy Science Vol. 103 No. 4, 2020

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Naranjo et al.: DAIRY PRODUCTION AND THE ENVIRONMENT IN CALIFORNIA

Table 1. Ingredient and nutrient composition of 1 kg of diet for calves, heifers, and lactating cows in the California dairy system in 19641
Calf
Item
Ingredient (%)
Alfalfa hay
Oat hay
PastureBarley grains
Oat grains
Beet pulp
Hominy feed
Linseed meal
Molasses
Urea
Wheat bran
Wheat mixed feed
Wheat mill run
Composition
DMI (kg/d)
Days in pen
DM (%)
NDF (% of DM)
ADF (% of DM)
Ether extract (% of DM)
CP (% of DM)

Up to 6 mo

38.4
26.3

Heifer
6–12 moWeighted
averageUp to calving

Close up

Lactating

Dry

20.0

47.8
20.5

71.4

70.0
30.0

54.1
2.58
23.2
14.1
0.459
1.95
0.236
0.159
0.054
0.007
0.459
2.51
0.158

15.0
180
78.7
47.4
34.3
2.10
17.3

17.8

100

79.9
0.18

21.7
8.34
12.2

9.10

2.89

2.76
0.38

26.3

3.61
180
90.5
30.3
12.7
3.50
15.7

Cow

15.1
180
24.0
51.8
30.0
3.30
21.3

25.4
290
28.0
50.0
30.6
3.00
21.2

0.00
8.15

4.05

24.0
30
85.3
41.7
27.1
2.40
16.9

18.8
1,220
85.7
31.9
26.6
2.10
18.6

72.1
37.5
26.9
2.45
18.8

1

Data from NRC (1958), Mead and Ronning (1961), Palmquist et al. (1964), and Hutton and Bath (1967a,b).

California-specific agricultural reports (Burt et al.,
2003; Johnson and Cody, 2015). For fertilizer use,
USDA reports were used (Lin et al., 1995; USDA-ERS,2011). Pesticide and herbicide usage were based on costand return studies conducted in California and archived
by the UC Agricultural Issues Center (2016). Emission
factors for farm operations were based on Lal (2004)
and Camargo et al. (2013). When detailed data were
not available, University of California crop cost andreturn studies (UC Agricultural Issues Center, 2016)
were used as representative crop production models.
Furthermore, some fuel and electricity values requiredfor crop production were extracted from published literature (Liedke and Deimling, 2015). Because of thelimited data for 1964, government reports were used
when available (US Bureau of the Census, 1967). Whendata were not available for 1964 crops, 2014 crop inputs
were used with 1964 yields. Allocation of emissions forco-products were according to the Livestock Environmental Assessment Partnership (LEAP) guidelines(FAO, 2016a). By-products were treated as residues
and carried no emissions burden for production, buttransportation to the animal facility was counted toward emissions from the dairy sector.
Enteric Methane

Several mathematical models have been developed
to predict enteric methane emissions from cattle (e.g.,
Journal of Dairy Science Vol. 103 No. 4, 2020

Mills et al., 2003; IPCC, 2006; Ellis et al., 2007; Moraes
et al., 2014). Although most LCA studies use the IPCC
(2006) Tier II model to estimate enteric methane emissions, it can overestimate emissions by about 12.5% inNorth American dairy cattle (Kebreab et al., 2008).
Recently, Appuhamy et al. (2016a) evaluated 40 empirical extant models using region-specific independentdata. The authors reported that for North America,
the best performing model was the one they modified
based on Nielsen et al. (2013), which included DMI,
fatty acid, and digestible NDF concentration in thediet (Supplemental Table S1; https:​/​/​doi​.org/​10​.3168/​jds​.2019​-16576). However, because digestible NDF
was not available in our data set, the original modelof Nielsen et al. (2013), which performed second bestof all models in North America, was used to estimate
enteric methane emissions in this study.
Farm Management

Farm management activities include the use of energy
and water required on farm. Dairy farms use energy andwater for many purposes, including crop production;
animal consumption; cow comfort through cooling ofanimals and barns; cooling milk; sanitation operations,
including animal hygiene and cleaning of facilities; andcollection and transport of wastes. Water intake wasestimated using recently published equations based on
California cows (Appuhamy et al., 2016b). Separate

Journal of Dairy Science Vol. 103 No. 4, 2020

Distillers dried grains with solubles.

1

Ingredient (%)
Alfalfa hay
Wheat hay
Oat hay
Corn silage
Wheat silage
Triticale silage
Corn grains
Barley grains
Almond hulls
Canola meal
Citrus, wetCorn gluten meal
Cottonseed meal
DDGS1
Molasses
Soybean hulls
Soybean meal
Soybean oil
Wheat mill run
Wheat straw
Whey
Composition
DMI (kg/d)
Days in pen
DM (%)
NDF (% of DM)
ADF (% of DM)
Ether extract (% of DM)
CP (% of DM)

Item

4.11
360
74.3
25.0
15.9
3.65
19.1

0.305

1.30
6.24
1.52

7.72

10.5

18.6

8.11
3.67
0.850
35.4
4.97

5.76
75
61.7
41.9
28.5
4.2
17.8

22.4
2.07

14.1

7.44

35.4

AI

17.0
2.45

Up to 12 moCalves

11.15
240
50.1
49.6
32.9
3
12.2

19.1
5.65

9.43

38.0

20.3
7.46

Pregnant

Heifer

12.07
30
55.3
42.5
28.6
3.3
16.6

5.18

9.40

26.2
1,080
67.1
34.3
21.1
3.9
20.6

6.59
11.3

9.73

22.6
84
62.0
36.8
23.5
3.7
26.1

1.65

1.37
11.8
1.97
5.27
6.63
7.69
0.10

20.2

21.0
0.35

4.15

High

2.43

7.78
7.78
8.76

12.2

27.6

10.3
38.3
3.99

23.8

Fresh

32.9

Close up

Lactating

12.1
90
51.2
48
32.3
2.7
12.9

18.5
4.69

5.47

37.4

28.5
5.41

Far off

Dry

14.1
90
59.6
34.2
23.4
3.2
14.4

8.73
7.73

23.4

30.7

29.4

Close up

Table 2. Ingredient and nutrient composition of 1 kg of diet for different stages of a dairy cow in California in 2014 based on Rossow and Aly (2013) (model 1)

64.8
35.6
22.6
3.66
18.8

18.3

9.00
0.76
0.10
23.0
0.43
0.12
17.5
0.18
1.49
10.3
1.48
4.71
5.40
7.44
0.35
0.06
1.37
0.01
5.46
10.4
0.54

Weighted
average

Naranjo et al.: DAIRY PRODUCTION AND THE ENVIRONMENT IN CALIFORNIA

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Naranjo et al.: DAIRY PRODUCTION AND THE ENVIRONMENT IN CALIFORNIA

Table 3. Ingredient and nutrient composition of 1 kg of diet for different stages of a dairy cow in California in 2014 based on CDFA (2018)(model 2)
Calves
Item

HeiferUp to 12 moAI

Pregnant

Close up

Lactating

Dry

17.0

35.4

20.3

32.9

15.8
1.68
0.34
0.30
18.3
3.59

25.5
0.37
2.55
4.23
15.5
24.1

0.72
0.27
17.6
0.34
8.48
0.64
0.72
8.96
3.50
5.88
0.99
1.85
4.99
0.65

3.24
0.51
3.54
0.11
8.15
0.14
0.09
1.55
1.73
1.05
1.63
0.04
0.13
0.93

0.91
0.62

0.23
0.04

1.28
0.40
1.18

0.45
4.00
0.22

22.6
1,164
62.5
34.9
23.1
4.20
18.7

12.6
180
50.6
47.4
32.0
2.80
14.3

Ingredient (%)
Alfalfa hay
Alfalfa haylage
Wheat hay
Oat hay
Corn silage
Wheat silage
Triticale silage
Sorghum silage
PastureCorn grains
Barley grains
Almond hulls
Beet pulp
Bakery waste
Canola meal
Corn gluten
DDGS1
Fruits and vegetables
Cottonseed meal
Whole cottonseed
Green chop
Molasses
Soybean hulls
Soybean meal
Soybean oil
Wheat mill run
Wheat straw
Whey
Composition
DMI (kg/d)
Days in pen
DM (%)
NDF (% of DM)
ADF (% of DM)
Ether extract (% of DM)
CP (% of DM)

2.45
8.11
3.67
0.85
35.4
4.97

10.5
7.72
1.30

6.24
1.52

7.46
18.6

38.0

10.3
38.3

7.44
3.99
14.09
9.40
22.38

9.43

5.18

2.07

0.30
19.1
5.65
4.11
360
74.4
25.0
15.9
3.65
19.1

5.76
75
61.7
41.9
28.5
4.20
17.8

11.15
240
50.1
49.6
32.9
3.00
12.2

12.1
30
55.3
42.5
28.6
3.30
16.6

Weighted
average
17.32
1.34
1.11
0.64
19.4
4.63
0.14
0.78
0.25
15.7
0.47
7.39
0.51
0.57
7.68
3.23
5.84
0.89
1.45
3.93
0.57
0.30
0.80
0.49
0.01
1.03
2.11
1.39
16.4
62.0
36.3
24.0
3.83
17.6

1

Distillers dried grains with solubles.

equations were used to estimate water intake by calves
and lactating and dry cows, as suggested by Appuhamy
et al. (2016b) (Supplemental Table S1). The equation
to predict free water intake by lactating dairy cows requires data on sodium and potassium concentrations in
the diet. Data were collected from 43 California dairy
farms by Castillo et al. (2013) to calculate the mineralcontent of the diets and used to estimate free water
intake.
Values from CARB (2015), PE International AG(2015), and Capareda et al. (2010) were used to determine energy emission factors for on-farm energy use(Supplemental Table S2; https:​/​/​doi​.org/​10​.3168/​jds​.2019​-16576). Farm water and fuel usage reports do notdifferentiate between water and energy used for milking
or other on-farm practices.
Journal of Dairy Science Vol. 103 No. 4, 2020

Manure Management

Estimates of methane emissions from manure storage and field application were based on IPCC (2006)
guidelines. However, calculation of some variables weremodified. The IPCC (2006) equation requires volatilesolid (VS) output from dairy cows to be estimated before estimation of emissions. Appuhamy et al. (2016c)
reported that the VS model they developed based on
extensive North American data performed better thanthat recommended by IPCC (2006) guidelines. Therefore, VS was calculated using the Appuhamy et al.
(2016c) model (Supplemental Table S1) and used as
an input to the IPCC (2006) equation to determine
manure methane emissions. The maximum methaneproducing capacity for manure was 0.24 m3/kg of VS.

Naranjo et al.: DAIRY PRODUCTION AND THE ENVIRONMENT IN CALIFORNIA

The methane conversion factor (MCF) is an estimate of
the manure carbon (in energy terms) that is converted
to methane. The MCF is based on several factors, suchas manure storage type and temperature. The averagetemperature in the state was taken to be 16°C in 2014
and 14°C in 1964 (NOAA, 2014). Based on these average temperatures, the MCF for manure spread daily, in
dry lot, solid storage, liquid with natural crust cover,
and anaerobic lagoon were 0.5, 1.5, 4, 18, and 75%,
respectively for 2014 and 0.1, 1.0, 2, 15, and 73% for1964 (IPCC, 2006). The following management systems
were assumed. For heifers, 11% of manure was spreaddaily and 88% was in dry lot. For lactating cows, 11%
was spread daily, 9% in solid storage, 20% in liquidwith crust cover, and 59% in anaerobic lagoon (CARB,
2016). Due to paucity of data, we assumed that manure in 1964 was deposited onto pasture for all stages,
except during lactation, where manure was assumed tobe managed as liquid slurry (D. Meyer, University ofCalifornia, Davis, personal communication).
Nitrous oxide (N2O) emissions were calculated using
the IPCC (2006) guidelines. However, a separate methodology was used to calculate total nitrogen output,
which is one of the inputs in the IPCC (2006) equation.
Johnson et al. (2016) evaluated 45 nitrogen excretion
equations for lactating cows, heifers, and nonlactating cows. The authors reported that the equations
developed by Reed et al. (2015) for nitrogen excretion
performed best. Therefore, the Reed et al. (2015) models were used in this study to estimate total nitrogenexcretion instead of using the IPCC nitrogen excretion
equation. Combining the Reed et al. (2015) equation
for nitrogen excretion with the IPCC (2006) equation,
N2O emissions were predicted (Supplemental Table S1).
The total available nitrogen in manure was calculatedusing the IPCC (2006) guidelines and by determining
the amount of nitrogen from synthetic fertilizers. The
N2O emissions from the applied manure and fertilizer
were calculated using the IPCC (2006) direct and indirect N2O emission equations. The direct N2O emission
factor (kg of N2O-N/kg of N excreted) was negligible(assumed to be zero) for daily spread manure, 0.02 fordry lot, 0.005 for solid storage, 0.005 for liquid withnatural crust, and 0 for anaerobic lagoon for both reference years (IPCC, 2006). The indirect N2O emissions,which include ammonia emissions converted to N2O,
were also calculated based on IPCC (2006) guidelines.
Co-Product Allocation

Meat and milk are produced simultaneously in thedairy industry; therefore, the environmental impact
burden must be allocated between the 2 products. AlJournal of Dairy Science Vol. 103 No. 4, 2020

3766

though there are several ways to allocate environmental impact, the International Dairy Federation (IDF,
2015) recommends a biophysical allocation approach.
The allocation factor depends on the amount of milk
produced by the cow in her lifetime and the live weightof the cow at the time of slaughter. For 2014 and 1964,
slaughter weights of 650 and 600 kg, respectively, wereassumed. The milk produced for each reference year
was used to determine the respective allocation factors. For 2014, the calculated allocation to milk was
91.9%; for 1964, it was 81.1%. The allocation usedin this study is on the higher end of values reported
in various LCA studies summarized by Thoma et al.
(2013). The allocation factor was applied to categories
where the burden could be split between meat andmilk. For this model, crop production, enteric methane emissions, and the manure management emission
categories were allocated to both products. The farmmanagement category was not allocated between meatand milk because the data available for farm energy
usage did not differentiate between on-farm milking
and non-milking energy. Therefore, milk production
carried the full burden of that category.
Assumptions and Limitations

The results of this assessment are limited to the defined goal and scope, and exclusion of certain life cycle
impact categories may result in an incomplete pictureof the overall performance of the studied products.
For instance, social and economic indicators were notcovered in this LCA, so trade-offs between environmental, social, and economic factors were not evaluated.
Although there is natural animal-to-animal variability
in performance, this study assumed an average performance. The data used for the analysis were taken
mostly from existing data sets and may be improvedby expanding the data through targeted surveys at different locations in the state. Some data were missing
for the 1964 analysis, so assumptions were made. Careshould be taken in comparing results from this study
with those of other studies due to differing methodologies. Specifically, calculations for enteric fermentationand manure storage contain elements that have been
published recently so other studies might have reliedmore on IPCC (2006) methodologies or used information published before 2016. The LCA would be considerably improved if information on current manure
management practices is updated and data on soil
carbon dynamics and biogenic carbon are incorporated
as the research results become available. Due to a lackof data from 1964, we assumed the lifespan of a cow has
not changed much over the last 50 yr.

Naranjo et al.: DAIRY PRODUCTION AND THE ENVIRONMENT IN CALIFORNIA

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Figure 2. Comparison of global warming potential (GWP) in 1964 and 2014 by emission source for model 1 (using farm sampled diets) andmodel 2 (based on CDFA, 2013, 2014, 2015). GHG = greenhouse gas; CO2e = CO2 equivalents.

RESULTS AND DISCUSSION

The LCA was conducted to estimate the change inenvironmental impact of California dairy production
over the last 50 yr by taking reference years 1964 and2014. This section discusses the main results for GHG,
water use and land use, using IPCC AR5 and taking
into consideration the different diets and milk production inputs.
GHG Emissions

Producing 1 kg of ECM in California in 2014 was
associated with 1.12 to 1.16 kg of CO2e emissions compared with 2.11 kg of CO2e for 1964 (Figure 2). The
use of different dietary inputs (model 1 farm-based andmodel 2 using diets from CDFA) had minor differencesin this impact category, with model 2 inputs showing
slightly lower GHG emissions than model 1. The 2014
GHG emissions in this study were lower than thoseof previous LCA values reported. For example, for acradle-to-farm gate LCA, Thoma et al. (2013) reported
the average GHG emissions to be 1.23 kg of CO2e/
kg of ECM, and Gerber et al. (2011) reported 1.20 kg
of CO2e/kg of ECM for North America. In contrast
with several other studies (e.g., Thoma et al., 2013),
the largest contributor to GHG emissions in California
was manure methane, making up about 41% of total
emissions in both 2014 models, but only 24.5% in 1964.
Enteric methane emissions were the second largest
Journal of Dairy Science Vol. 103 No. 4, 2020

contributor, accounting for 38.5 to 38.7% of emissions
in 2014 and 46.1% in 1964. About 15.4% of emissions
were attributed to crop production for feed in 2014
and 22.6% in 1964. Farm management contributed toabout 5.1 to 5.4% of emissions in 2014 and 6.8% in 1964
(Figure 2). The total amount of emissions went up fromabout 8.2 Mt of CO2e/yr in 1964 to 21.8 Mt of CO2e/yr in 2014. However, if production had remained thesame as in 1964, it would take 39.7 Mt of CO2e/yr to
produce the same amount of milk currently producedin California per year.
Emissions from Crop Production. The emissions
from crop production were 62.6 to 63.9% lower in 2014
than in1964. The main reasons for the considerable difference in emissions from crop production were (1) the
differences in crop yield between the 2 reference years(Supplemental Table S3; https:​/​/​doi​.org/​10​.3168/​jds​.2019​-16576); (2) energy used for transport, farm operations, seed production, pesticide application, fertilizer
application and irrigation; and (3) feed conversion ratio(Supplemental Figures S1 and S2; https:​/​/​doi​.org/​10​.3168/​jds​.2019​-16576). The contribution of emissions
from crop production in 2014 was lower in this study
than in other LCA (e.g., Thoma et al., 2013), mainly
because California dairy farms typically use greater
amounts of by-products compared with other regions in
the United States. Russomanno et al. (2012) calculated
the average by-product content of diets in 10 states (including California) to be 31.3%. In our analysis, which
considered commercial diets from California farms, theNaranjo et al.: DAIRY PRODUCTION AND THE ENVIRONMENT IN CALIFORNIA

by-product use in the average feed in a cow’s lifetime
was about 49%. This is in contrast to by-product useof about 3% in 1964 (Table 2). California is unique inusing by-products such as almond hulls, which replace
other feeds and their associated emissions. If by-products were not fed to dairy cattle, they would have to bedisposed of by composting, combusting, tilling back tosoil, and landfilling (Russomanno et al., 2012). The full
effects of the avoided alternate treatment of hulls is notaccounted for in this study because it is attributional
LCA rather than consequential LCA.
Farm Management Emissions. In agreement withanother LCA on dairy production (e.g., Thoma et al.,2013), farm management contributed the least to total
global warming potential. Compared with 1964, emissions related to farm management in 2014 were reducedby 57.7 to 59.2%. The main activities contributing toemissions from this category were electricity and diesel
used to operate equipment on the farm (Supplemental
Table S4; https:​/​/​doi​.org/​10​.3168/​jds​.2019​-16576).
Enteric Methane Emissions. Only minor differences were found between models 1 and 2 in estimatedenteric methane emissions. Compared with 1964, theproduction of 1 kg of ECM in 2014 led to a 54.1 to55.7% reduction in enteric methane emissions, mainly
due to efficiency gains. In 1964, a cow consumed about
1.93 kg of feed to produce 1 kg of ECM (normalized toa lifetime basis), whereas in 2014, the feed conversion
ratio was 0.79 to 0.81 kg of feed/kg of ECM. On a per
daily cow·kg ECM basis, in 1964, each cow emitted
0.98 kg of CO2e of enteric methane compared with 0.43
to 0.45 kg of CO2e in 2014. Due to the low productionof milk in 1964, proportionally more enteric emissions
would be attributed to meat production. California
dairies produced about 19 Mt of milk in 2014 (Sumner et al., 2015). To produce the same amount of milk
using 1964-equivalent cows, an additional 1.3 million
cows would be needed. Contributors to the reduction inemission intensity due to increased milk production efficiency include improved genetics, nutrition, cow comfort, and overall management. Dairy cows in California
are among the highest milk producers in the world. For
example, since 1984, milk per cow in California has
increased about 50%, from 7,105 to about 10,900 kg per
cow per year (Sumner et al., 2015). However, the variation in the amount of milk produced per lactation andper cow over her lifetime remains large, indicating thehigh potential for genetic selection to further increasemilk production efficiency while decreasing management costs and GHG emissions (Knapp et al., 2011).
The selection of models will make a difference inthe absolute amount of enteric methane emissions estimated. The California Air Resources Board (CARB,
2017) calculated enteric methane emissions from dairy
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cows in California in 2014 to be 8.24 Mt of CO2e. The
number from CARB (2017) is likely to be about 5%
overestimated based on recent work that recalculated
methane emissions in California accounting for differences in feed intake and emissions factors (Appuhamy
and Kebreab, 2018). CARB (2017) follows methodology from the US Environmental Protection Agency(EPA) that uses a methane emission factor (Ym) of
4.8% of gross energy intake. The average Ym for North
America has consistently been reported to be 5.7%
(SE = 0.9; Kebreab et al., 2008; Appuhamy et al.,2016a; Jayasundara et al., 2016), and using this Ym
would yield 9.53 Mt of CO2e, which is very close to ourestimate of 9.65 Mt of CO2e based on IPCC AR5 anddiets for high-genetic-merit cows. Thoma et al. (2013)used the model from Ellis et al. (2007), which fitted
best to their national database, and if that equation
had been used in this study, the estimate would be
an adjusted 8.66 Mt of CO2e. The Ellis et al. (2007)
model was not used in this study because it was notin the top 10 models for North American dairy cattle
in the evaluation of models conducted by Appuhamy
et al. (2016a).
Manure Management Emissions. The GHG
emissions from manure management decreased by 8.73
to 11.9%, depending on model used, in 2014 comparedwith 1964. The main contributing factor for emissions
in 2014 was manure management per unit of milk. Although the feed conversion ratio was lower in 2014 thanin 1964, most manure in 2014 was stored in lagoons,
which has much a greater MCF (solid storage at 15–
25°C has an MCF of 4%, whereas an uncovered anaerobic lagoon has an MCF of 74–79%). Our estimate ofemissions from manure was very close to that reported
by CARB (2017) (9.17 to 12.3 vs. 11.2 Mt of CO2e,
respectively). This was expected because the only difference was the calculation of VS that was based on a
new equation from Apphuamy et al. (2016c), developed
from cows in North America. Our analysis agrees withthat of Thoma et al. (2013), specifically region 5 intheir paper, which corresponds to the US West Coastof dairies with farms over 500 head. This region had asignificant amount of emissions from manure management. However, there is large uncertainty in estimating
emissions from manure because currently there are no
GHG emissions measurements published to predict
emissions from manure management or land application of dairy manure in California. There are wide
variations in GHG emission estimates from manure(e.g., Borhan et al., 2011; Leytem et al., 2011). Someof the variation could be explained by the manure storage method (open, dry lot, wastewater pond, manurelagoon, composted). In addition, temperature plays amajor role; in southern Idaho, methane emissions wereNaranjo et al.: DAIRY PRODUCTION AND THE ENVIRONMENT IN CALIFORNIA

3769

Figure 3. Blue water use to produce 1 kg of ECM in 2014 (blue bars) and 1964 (red bars) using diets from Rossow and Aly (2013; model 1)
or California Department of Food and Agriculture (model 2; CDFA, 2013, 2014, 2015).

greatest from open lots in the spring and from manurelagoons for the rest of the year (Leytem et al., 2011).
There is also no reported field and laboratory researchon GHG sources and sinks and model parameters suchas MCF for California. Therefore, default IPCC (2006)
values with limited modification as described by CARB
(2016) were used. New surveys of manure management
practices in California are urgently needed to supplement the work of Meyer et al. (2011). These data would
be needed to improve GHG emission estimates andidentify best management practices.
A primary source of manure GHG emissions in California is anaerobic lagoons, which represent the mostcommon on-farm dairy manure storage practice in thestate. Anaerobic lagoons have more than 10 times theglobal warning potential of solid manure piles (CARB,
2017). Gerber et al. (2011) assumed anaerobic lagoon
and solid storage to represent 12 and 31% of manure
management systems in the United States, respectively,
whereas in California, it is 59 and 9%, respectively(CARB, 2017). This has considerable implications for
GHG emissions because of the dramatically different
MCF for solid and liquid stored manure.
Water

Dairy farms use water for many purposes, including crop production, animal consumption, cow comfort through cooling of animals, milk and barns, andcleaning operations. In this study, only blue water
Journal of Dairy Science Vol. 103 No. 4, 2020

was considered (i.e., water that has been sourced fromsurface or groundwater resources). Water from rainfall(green water) and water needed to dilute pollutants tomeet quality standards (gray water) were not considered in this study. The amount of blue water used in
California dairy farms to produce 1 kg of ECM in 2014
decreased 88.1 to 89.9% compared with 1964 (Figure3). The main categories that affect water usage in dairy
farms are crop production, housing and milking–relateduse, and free water intake by the animals. The largest
contributor in both 1964 and 2014 was that used forcrops, which was estimated to be 98.2% of total water
use in the 1964 model and slightly less, 92.5 to 93.2%,
in 2014. The results agree with values calculated by
Mekonnen and Hoekstra (2012), who suggested thatfeed was responsible for 98% of total blue water use for
global animal production.
To estimate water use for crop production in 1964,a California-specific water-use life cycle inventory wasdeveloped using cost analysis conducted by the University of California Agricultural Extension Service fordifferent crops (Fischer and Yeary, 1963; Kearney andParsons, 1964). The main crops used with their water
footprint are given in Supplemental Figure S3 (https:​/​/​
doi​.org/​10​.3168/​jds​.2019​-16576). Water usage for crops
in 2014 was only 9.4 to 11.3% of the amount used in1964 per 1 kg of ECM. The main reason for this reduction was the improved yield and water use efficiency
in the last 50 yr (Supplemental Table S3; https:​/​/​doi​.org/​10​.3168/​jds​.2019​-16576). Improved crop genetics

Naranjo et al.: DAIRY PRODUCTION AND THE ENVIRONMENT IN CALIFORNIA

3770

Figure 4. Comparison of calculated land use for crop production for a diet in 1964 and 2 diets in 2014. Model 1 was based on Rossow andAly (2013) and model 2 on California Department of Food and Agriculture (CDFA, 2013, 2014, 2015).

and management have contributed to large efficiencies
in water utilization. Blue water use for irrigation has
declined steadily since the 1960s, even though irrigatedacreage has increased (USGS, 2009). Irrigation practices have been adopted to improve uniformity of water
distribution and efficiency of water delivery with a net
benefit of less water needed per unit of crop farmed(CAST, 2012).
On-farm water use values were taken from a study
conducted by Meyer et al. (2005). The main activities that required on-farm water use were the milking
parlor, sprinkler pens, udder hygiene, milk equipment,
sanitation, parlor cleaning, plate coolers, and ice makers. There was a 55.3 to 59.2% reduction in on-farm use(per unit of ECM) in 2014 compared with 1964. This
reduction in water use was mainly due to the improvement in milk production per cow in 2014 and improvements in milk equipment such as vacuum pumps andplate coolers, which allow a producer to use the samewater for multiple functions. Drinking water contributed only 1.3 to 1.6% in 2014 and 0.34% in 1964. These
values agree with Mekonnen and Hoekstra (2012), whosuggested that drinking water contributed only 1.1% of
the total water footprint.
Land

About 19.5% of land in the United States was classified as cropland in 2002 (USDA-ERS, 2007). One of
Journal of Dairy Science Vol. 103 No. 4, 2020

the main uses of cropland for animal agriculture is to
produce grain, hay, and silages for cattle. The amountof land required to produce each ingredient used in
the reference years is given in Supplemental Table S5
Although(https:​/​/​doi​.org/​10​.3168/​jds​.2019​-16576).
production of barley grains and soy oil had the greatest
land requirement per 1 kg of dry crop weight, their inclusion rate in the diet was low. Corn grains and silages
have relatively greater contribution to the diet and to
the land requirement to produce 1 unit of ECM. About
72% of the land requirement for 2014 was from out ofstate and 28% was in state (Supplemental Table S5).
Figure 4 shows the cropland used to produce 1 kg of
ECM in the reference years. Compared with 1964, 89.4
to 89.7% less land was required to produce 1 kg of
ECM in 2014.
Improvements in crop genetics and production practices have dramatically increased crop yields (USDANASS, 2011). Similarly, soil erosion has been reduced
through sustained research, education, extension, andpolicy development (USDA-NRCS, 2010). Several
strategies for further decreased water use and degradation as well as more efficient use of land have beensuggested, including improving irrigation efficiency,
waste management, and water productivity; better dietformulation; use of enzymes; improved manure collection, storage, treatment, and utilization; improved
land management; and improved livestock distribution(CAST, 2012).

Naranjo et al.: DAIRY PRODUCTION AND THE ENVIRONMENT IN CALIFORNIACONCLUSIONSThis study covered a cradle-to-farm gate agricultural
LCA for California dairy production and evaluatedchanges in GHG emissions and water and land use in1964 and 2014. Two diet input scenarios were considered, which had minor differences in the estimates of
the impact categories. The average farm-gate GHG
emission for 2014 in the study ranged from 1.12 to 1.16
kg of CO2e/kg of ECM, depending on the modeled
diet. The results are on the lower side compared withother LCA conducted for the US dairy sector. Relative sources of emissions differed, with lower emissions
from feed due to its high utilization of by-products, andhigher emissions from manure due to anaerobic storage systems. Manure and enteric methane emissions
present opportunities to mitigate emissions throughinnovation. Although total emissions increased due tothe volume of milk production, compared with 1964,
enteric methane (per kg of ECM) was reduced 54.1 to55.7%, and manure GHG decreased by 8.73 to 11.9%
in 2014 compared with 1964. Total water use was reduced 88.1 to 89.9%, with water reductions of 88.7 to
90.5% in crop production, 57.7 to 59.2% in housing andmilking, and 52.4 to 54% in free water intake. Landrequirements were also reduced 89.4 to 89.7% in 2014
compared with 1964. As milk production per cow continues to be increased through genetic improvements
and better nutrition and animal care, feed conversion
efficiency will also improve, leading to further reductions (measured in emission intensity) in environmental
impact. More emissions data and improved models suchas soil carbon dynamics are needed to accurately quantify this achievement and optimize energy, crop, andeconomic returns from manure management.
ACKNOWLEDGMENTS

This research was supported by California Dairy
Research Foundation (Davis, CA). The authors also acknowledge the Sesnon Endowment fund of University ofCalifornia, Davis. The authors have stated no conflictsof interest.
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ORCIDS
A. Naranjo https:​/​/​orcid​.org/​0000​-0002​-6135​-6583
A. Johnson https:​/​/​orcid​.org/​0000​-0002​-6617​-9328
H. Rossow https:​/​/​orcid​.org/​0000​-0002​-3753​-4263
E. Kebreab https:​/​/​orcid​.org/​0000​-0002​-0833​-1352