Dairies emit roughly half of total methane (CH4) emissions in
California, generating CH4 from both enteric fermentation by ruminant
gut microbes and anaerobic decomposition of manure. Representation of these
emission processes is essential for management and mitigation of CH4
emissions and is typically done using standardized emission factors applied
at large spatial scales (e.g., state level). However, CH4-emitting
activities and management decisions vary across facilities, and current
inventories do not have sufficiently high spatial resolution to capture
changes at this scale. Here, we develop a spatially explicit database of
dairies in California, with information from operating permits and
California-specific reports detailing herd demographics and manure
management at the facility scale. We calculated manure management and
enteric fermentation CH4 emissions using two previously published
bottom-up approaches and a new farm-specific calculation developed in this
work. We also estimate the effect of mitigation strategies – the use of
mechanical separators and installation of anaerobic digesters – on CH4
emissions. We predict that implementation of digesters at the 106 dairies
that are existing or planned in California will reduce manure CH4
emissions from those facilities by an average of 26 % and total state
CH4 emissions by 5 % (or ∼36.5 Gg CH4/yr). In
addition to serving as a planning tool for mitigation, this database is
useful as a prior for atmospheric observation-based emissions estimates,
attribution of emissions to a specific facility, and validation of CH4
emissions reductions from management changes. Raster files of the datasets
and associated metadata are available from the Oak Ridge National Laboratory
Distributed Active Archive Center for Biogeochemical Dynamics (ORNL DAAC;
Marklein and Hopkins, 2020; 10.3334/ORNLDAAC/1814).
Introduction
Methane (CH4) is a greenhouse gas with a large influence on the rate of
short-term warming due to its high global warming potential, roughly 85
times that of CO2 in a 20-year time frame (Dlugokencky et al., 2011).
Climate mitigation policy in California targets a reduction in CH4
emissions by 40 % below 2013 inventory levels by 2030 (State of
California, 2016). Dairies provide a major opportunity for CH4
reduction, as roughly half of state-total CH4 emissions come from
nearly equal contributions of enteric fermentation by ruminant gut microbes
and anaerobic decomposition of dairy manure (Charrier, 2016). The primary
method by which California currently plans to reduce dairy CH4
emissions is through installation of anaerobic digesters, which capture
manure CH4 emissions for subsequent use as a renewable biofuel (State
of California, 2016). However, facility-level measurements of both the
magnitude of total emissions and relative contributions of enteric
fermentation versus manure management are only available for a few dairies in
the state (Arndt et al., 2018). Indeed, uncertainty in CH4 emissions
from the dairy industry in California and globally makes it difficult to
optimize mitigation actions at the spatial scales relevant to policy and to
establish an emissions baseline against which mitigation efforts can be
measured.
CH4 emissions are often estimated by bottom-up (calculated
activity-based) or top-down (atmospheric observation-based) methods
(NASEM, 2018). Bottom-up
inventories, including those used by the US Environmental Protection
Agency (US EPA, 2017) and the California Air Resources Board (Charrier,
2016), estimate dairy emission rates at the state level based on the total
number of cows and herd demographics and on the average statewide manure
management approach, CH4 emissions factor, and climate. However,
livestock emissions, especially from dairies, remain one of the largest
uncertainties in these inventories (Maasakkers et al., 2016), as there is no
comprehensive information source for the number of cows or manure management
strategies. In addition, the lack of spatial and temporal detail in these
inventories makes it difficult to verify their accuracy with observational
data, particularly given high levels of spatial variability observed for CH4
emissions (NASEM, 2018).
Top-down estimates of emissions measure atmospheric CH4 enhancements at
farm to regional scales using one or a combination of ground, aircraft, and
satellite observations (Arndt et al., 2018; Cui et al., 2017; Wecht et al.,
2014). Top-down studies often report CH4 emissions for dairies up to
2 times higher than bottom-up measurements (Cui et al., 2017; Jeong et
al., 2016; Miller et al., 2013; NASEM, 2018; Trousdell et al., 2016; Wolf et al., 2017). However,
these comparisons are complicated by uncertainties in source attribution,
atmospheric transport models, and the spatial and temporal mismatch that
commonly exists between top-down estimates and bottom-up inventories.
Previous bottom-up inventories have estimated national (e.g., US EPA 2017
Greenhouse Gas Inventory, US EPA, 2017) and statewide (e.g., CARB
Greenhouse Gas Inventory, Charrier, 2016) emissions based on the number of
cows at the state and county levels, respectively. These inventories have
been downscaled to 0.1×0.1∘ gridded inventories of
CH4 emissions using a combination of California Regional Water Quality
Control Board data of dairy-specific herd size and county-level livestock
data in the CALGEM inventory (Jeong et al., 2016, 2012) or county-level
dairy cow counts from the US Environmental Protection Agency (EPA)
Inventory of US Greenhouse Gas Emissions and Sinks alone (Maasakkers et
al., 2016; USEPA, 2017). While these gridded products provide finer spatial
detail than statewide inventories, there are limitations to the livestock
maps that distribute dairies within a county. For example, some gridded
products estimate CH4 production from dairies in the Sierra Nevada
range (Maasakkers et al., 2016), while in reality these animals exist
further west in the Central Valley. In another example, although
regional-scale top-down studies (Cui et al., 2017; Jeong et al., 2016)
suggest bottom-up inventories underestimate dairy CH4 emissions, a
comparison of bottom-up and top-down CH4 emissions at the facility
scale (two dairies) was much more comparable (Arndt et al., 2018). This
facility-scale comparison suggests the discrepancy might be due to spatial
scale. Dairy-level inventories of CH4 emissions are also needed to be
relevant to management and mitigation actions that are implemented at the
facility level.
To improve the spatial distribution of CH4 emissions from dairies, we
describe a new, farm-level database called Vista-California (CA) Dairies, based on the existing database Vista-CA (Rafiq et al., 2020). In
this analysis, we disaggregate the CARB inventory to the facility level by
(1) developing a spatially explicit map of dairy locations, (2) applying
facility-level information from regulatory permit data and county-level
animal inventories to estimate herd sizes; and (3) estimating enteric and
manure CH4 emissions from dairy facilities based on manure management
from permit data and regional norms. Vista-CA Dairies is hence the first
spatially explicit inventory at the scale at which management and mitigation
decisions are made. Compared to previous inventories, we significantly
improve (1) spatial resolution of dairy CH4 emissions using more
accurate farm-level herd demographics and (2) spatial variation in
partitioning of emissions between enteric and manure sources by
incorporating information on manure management practices at a finer scale
than used in typical inventories. These improvements are critical for
accurately attributing local- to regional-scale CH4 emissions to their
sources, identifying high-priority areas for mitigation management, and
assessing progress towards achieving mitigation goals (e.g., State of
California, 2016).
To demonstrate the utility of this facility-scale product in monitoring
mitigation outcomes, we apply the inventory to address the effectiveness of
mechanical separators and anaerobic digesters – two climate mitigation
strategies that the state is pursuing – in reducing manure methane emissions
(CDFA, 2020a, b). Mechanical separators separate out larger-sized
solid particles from the liquid manure pathway, reducing the amount of
manure entering lagoon treatment systems that are the major source of manure
methane (CDFA, 2020a). Digesters, as described above, promote the production
of methane from liquid manure waste through anaerobic conditions but
capture it for use as a fuel. First, we perform a sensitivity analysis on
the efficiency of mechanical separators in removing solids and quantify the
uncertainty in their reduction in emissions. Second, we quantify the
projected effect of anaerobic digesters on total CH4 emissions and on
the ratio of enteric CH4 to manure CH4 at the farm and regional
scale. Since 2015, cap and trade funds have supported 106 anaerobic
digesters in an effort to reduce manure CH4 emissions (CDFA, 2020b).
This dataset provides the facility-level inventory of methane emissions,
critical for attributing methane plumes to dairy sources and for monitoring
methane reduction strategies.
Methods
We determined the locations of dairy farms in California and estimated the
herd numbers for each farm. We estimated the enteric and manure CH4
emissions in three different ways each and the uncertainty in each parameter
affecting emission estimates at the facility and state scales. These data
were compiled in the database Vista-CA and compared to other methane
emission maps in the same domain. Finally, we evaluated the efficacy of two
manure management CH4 mitigation strategies that are currently being
implemented in California: mechanical separators and anaerobic digesters
(Meyer, 2019).
Dairy locations
We used Google Earth satellite imagery to determine the locations of 1330
dairy farms in California, by identifying metal-topped shelters alongside
manure lagoons and corrals (further details given in Duren et al., 2019). We
used addresses to determine the approximate location of each dairy and
manually adjusted the location to the center of a dairy farm using satellite
imagery in Google Earth (Duren et al., 2019; Rafiq et al., 2020). These dairy
locations are publicly available as part of the Vista-CA methane mapping
project on the Oak Ridge National Laboratory Distributed Active Archive
Center for Biogeochemical Dynamics (https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1726, last access: 26 January 2021).
We determined which facilities are still operational by checking the data
against the list of facilities that paid 2019 State Water Resources Control
Board Confined Animal Facility fees (CAF fees; California State Water Quality Control Board, personal communication,
22 November 2019). We assumed
that all dairies with permits and/or paying CAF fees are currently
operational. This assumption is untrue, as there are an unknown number of
facilities with cattle that do not pay fees (likely <20 facilities;
new facilities since 2007). It is not currently possible to confidently
confirm which dairies are functioning and which are not, since dairy closures
are tracked by a variety of agencies, with some lag time, but this
information is not accessible or consistent. Milk production statistics show
that there are roughly 1400 commercial dairies in CA, including 162 dairies
in northern California (CDFA, 2018).
We grouped dairies into three geographic categories by county (Table S3),
North Coast (153 dairies), Central Valley (1102 dairies), and Southern
California (75 dairies), to account for differences in climate, animal
housing, and primary manure management styles among these three regions
(Meyer, 2019).
Herd populations and demographics
We determined herd population sizes primarily from the 2019 CAF fees list.
However, some dairies did not pay a fee in 2019, but still have animals, so
for these facilities we integrated data from three sources to estimate herd
numbers and demographic categories at each dairy: Regional Water Quality
Control Board (RWQCB) permits, San Joaquin Valley Air Pollution Control District (SJVAPCD) permits, and individual facility
documentation. The RWQCB permits are required for dairies that existed in
October 2007 (California Regional Water Quality Control Board, 2013), and we
used a collection of permit lists from 2014–2018 to determine the number of
lactating cows. Some dairies in the Central Valley that are either new or
expanded since 2007 have inaccurate or incomplete data, so we determined the
number of lactating animals from the SJVAQPCD and reading individual
facility documentation. The SJVAPCD permits include the maximum number of
cattle in each class at a given facility in 2011, rather than the number of
animals, and these dairies may have expanded since then. These data
represent our best estimates, but they represent specific points in time
that are not consistent between data sources. Based on milk shipments, we
know that at the time of this publication, there are roughly 1.7 million lactating
cows in California (Ross, 2019). Additionally, we compared our list with the
2017 United States Department of Agriculture (USDA) National Agricultural
Statistics Survey (USDA NASS, 2017), which provides the number of farms and
the number of cows in different dairy size classes in each county, though
the NASS Census data include farms that are not commercial dairies.
For dairies with RWQCB reports, we use the number of milk cows, dry cows,
heifers, and calves as the number of cattle in each class. Given that we are
calculating an annual CH4 emission rate for each farm, we assume the
population and demographics of each farm are constant in time, though in
reality these fluctuate as cattle are sold or born. The Central Valley RWQCB
assumes the population size of the lactating and dry cows varies by 15 %
or less (California Regional Water Quality Control Board, 2013).
We also estimate the populations of non-lactating animals, though these data
are less reliable than data for lactating animals. The RWQCB reports provide
the number of dry cows, bred heifers, heifers, calves 0–3 months, and calves
4–6 months (California Regional Water Quality Control Board, 2013). From
these data, we determine the median ratio of dry cows to the number of milk
cows to estimate the number of dry cows for dairies without RWQCB reports.
Calf and heifer populations are less reliable than mature cow populations
(lactating + dry cows), as these replacement animals may or may not be at
the same facility as the animals they will replace. We assume that
replacement animal populations are 10 % higher than the mature cow
populations (Deanne Meyer, personal communication, 7 February 2020) and
are evenly distributed among the 0–23-month-old animals. For this analysis,
we assume that the replacements are on the same dairies as the lactating
cows in order to not double count the heifer ranches; these animals do exist
but may not be present on the dairies. We also estimated the effect of this
assumption on overall emissions. Enteric fermentation emissions equations
also distinguish between replacement heifers <227 kg (calves) and
replacement heifers >227 kg. We assume the populations are split
equally between the size classes.
Enteric fermentation emissions
We estimated enteric fermentation in three ways, which have previously been
used to estimate emissions at the state or national levels: (E1) according
to the method used in California's greenhouse gas emission inventory
(Charrier, 2016), (E2) a method used for estimating emissions for the
continental US (Hristov et al., 2017) and (E3) a method suggested by
recent research done in California (Appuhamy, 2018). These three
methods increase in their complexity: method E1 is based solely on the
population and a statewide emissions factor, E2 is based on a statewide
emission factor and diets, and E3 is based on diet as well as the quality of
milk provided. We performed each of these calculations with lactating cows
only (subscript l) and total cattle, including calves, replacement heifers,
and dry cows (subscript t).
The first method, E1, is based on the calculations used by CARB for the
official statewide greenhouse gas emission inventory (Charrier, 2016), which
is in turn based on the IPCC Tier 1 Guidelines (IPCC, 2006). For this method,
we estimate total enteric emissions (CH4,e1) based on the number of
cattle (n) and a standard emission factor for each cattle type (Eq. 1).
Method E1 assumes enteric fermentation emissions (ef1) are 114.61 kg CH4 per lactating dairy cow per year (ef1,l; Table 3).
CH4,e1l=ef1l⋅nl
For all cattle, the total enteric emissions are the sum of the product of
the number of cattle (n) and the emission factor (Eq. 2). Method E1
assumes that the emissions factors are 11.63 kg CH4 per dairy calf per
year (ef1c), 43.53 kg CH4 per replacement heifer aged 7–12 months
per year, and 65.71 kg CH4 per replacement heifer aged 12–24 months per
year (Charrier, 2016). We use a weighted mean of 58.32 kg CH4 per
replacement heifer per year (ef1h). Here i represents the classes of
cattle, including milk cows, calves, and replacement heifers. The CARB
inventory does not provide an emission factor for dry cows, so we exclude
those from this analysis (Charrier, 2016).
CH4,e1t=∑ief1i⋅ni
The second method, E2, is based off of calculations in Hristov et al. (2017). For this method, we estimate the total enteric emissions
(CH4,e2) as the product of the number of cattle (n), dry matter
intake (DMI), and an emission factor (ef2; Eq. 3) (Hristov et al., 2017).
Method E2 assumes DMI is 22.9 kg per head per day for lactating cows, 12.7 kg per head per day for dry cows, 8.5 kg per head per day for dairy replacement heifers, and
3.7 kg per head per day for calves, and emission factors are 436, 280, 161, and 70 g per head per day for lactating cows, dry cows, dairy replacement heifers, and
calves, respectively (Table 3).
CH4,e2t=∑ini⋅DMIi⋅ef2e,i
The third method, E3, is based on calculations by Appuhamy (2018).
For this method, we estimate the total enteric emissions including the
number of cattle (n), dry matter intake (DMI), neutral detergent fiber
(NDF) in the diet, and milk fat (mf) (Appuhamy and Kebreab, 2018; Table 3). We also
include factors for DMI (fDMI), NDF (fNDF), and milk fat (fmf).
Here, emissions are the sum of emissions due to DMI, neutral detergent, and
milk fat content (Eq. 4).
CH4,e3l=nl⋅(fDMI,l⋅DMIl+fNDF,l⋅NDFl+fmfl⋅mfl)
Note that Appuhamy (2018) consider mature cows to be dry cows for 60 d of the year (16.4 %) and lactating cows the remainder of the year,
while we count the dry and lactating cows separately. For the other cattle
classes (i, including dry cattle, replacement heifers, and calves), the E3
emissions are the product of DMI and a DMI factor (fDMI), times 365 d per year as in E2 (Eq. 5).
CH4,e3t=nl⋅fDMI,l⋅DMIl+fdNDF,l⋅NDFl+fmf⋅mf⋅365+∑i(ni⋅fDMI,i⋅DMIi)⋅365
Diagram of manure flows on a dairy farm. Dashed lines indicate
North Coast dairies only. Modified from Owen and Silver (2014) and Meyer et
al. (2011). Types of manure management include anaerobic digester, large
containment system or covered lagoon designed for capturing methane and
carbon dioxide for use as fuel; anaerobic lagoon, designed storage system
for stabilizing waste; daily spread, the collection of manure that is spread
onto field or pasture within 24 h of deposition, drylot: an open
confined area where manure may be removed occasionally; pasture land
covered in grass that the animals eat; solid storage dried manure stored in
unconfined stacks; liquid/slurry manure stored with some water added, with
a typical residence time of less than 1 year (IPCC, 2006). Fractions of manure
entering each management type are shown in Tables 1 and S1.
Manure management emissions
We estimated manure emissions for each dairy three ways: (M1) according to
the method used in California's greenhouse gas emission inventory (Charrier,
2016), (M2) a method used for estimating emissions for the continental US
(Hristov et al., 2017), and (M3) a method suggested by recent manure
management research done in California (Meyer, 2019; Fig. 1).
Methods M1 and M2 are based on average statewide manure management, while
method M3 is based on facility-level or regional manure management. We
perform each of these calculations first with milk cows only and then
including calves, dry cows, and heifers. All three methods follow the same
general equation, though they have differences in the specific variables used in
Eq. (6).
CH4,m,l=nl⋅ρCH4⋅VSprod⋅Bo⋅∑iMCFsystem⋅fsystem
In this equation, n is the number of cows, ρCH4 is the density of
CH4, a conversion factor of m3CH4 to kg CH4, which is
a constant 0.662 kg CH4/m3 as reported by CARB and the IPCC (IPCC,
2006). VSprod is the total amount of volatile solids (VSs) produced per
animal (kg per head per day), Bo is the maximum methane production capacity
per unit of VS in dairy manure (0.24 m3CH4/kg VS), MCF is the
methane conversion factor for each system, and fsystem is the fraction
of manure going into each manure management system (Table 4). The different
systems include pasture, daily spread, solids, liquid/slurry, lagoon, and
drylot (Fig. 1; IPCC, 2006).
The first method, M1, is based on the method used in the CARB greenhouse gas
inventory. For M1, methane emissions from manure management are calculated
for each dairy facility based on the fraction of manure in each management
system, the total VS production, the CH4 density, Bo, and the
methane conversion factor for each system (CARB, 2014; IPCC, 2006; US
EPA, 2017). For method M1, we assume that a constant proportion of manure is
in each management type on each dairy according to statewide proportions,
which are described in Fig. 1 (CARB, 2014). The methane conversion factor
for each system is shown in Table 4 (Charrier, 2016). VS production is an
animal-specific constant among management types.
Fraction of manure entering each management type for dairy cows for
M1, M2, and M3. For M3, the fraction is different for San Joaquin Valley
with and without freestalls, the North Coast, and the Southern dairies.
The second method, M2, is based on the methodology used by Hristov and
colleagues (Hristov et al., 2017). These are the product of the VSs excreted,
the methane generation potential, the waste management system distribution
in the state, the methane conversion factor (MCF) for the state, and the
methane density (Eq. 7). The percentages of waste entering daily spread,
solid storage, liquid slurry, and anaerobic lagoon are shown in Table 1,
with corresponding MCFs shown in Table 4. VS excreted and Bo are defined in
Appendix A.
For the third method, M3, we estimate manure management based on data from
the SJVAPCD air quality permits and regional differences in manure
management as follows below (Eq. 7) and shown in Fig. 1. CH4
emissions for each manure management system were determined according to
CARB emission factors described above and summed for each farm. As described
previously, only dairies in the San Joaquin Valley with >500 cows in 2011 have SJVAPCD permits. For these dairies, we estimate manure
emissions based on the reported dairy management practices documented in
permits, though this information represents facilities inconsistently. These
permits report the presence of corrals or freestalls as housing types;
flush, scrape, or vacuum systems for manure collection; and mechanical
separator, settling basin, or weeping wall as solid–liquid separator systems
(Table S1 in the Supplement). Housing type typically determines the fraction of manure that is
processed by the manure handling system, which can be quantified as the
percentage of time cows spend on concrete. For dairies with corrals or
freestalls present, we assume time on concrete to be 70 % (Meyer, 2019).
For dairies without freestalls, we assume time on concrete to be 30 %
(Meyer, 2019). We assume that time in the milking parlor is 12.5 % of
total time, which is almost always flushed or hosed out into a liquid manure
handling system (i.e., liquid/slurry or lagoon). For the remainder of the
time on concrete, we assume that for facilities with scrape or vacuum
systems reported, the manure is stored as solids; for facilities with only
flush systems reported, we assume that this manure is flushed into lagoons.
We assume that the remaining manure (time not spent in housing) is not
collected and remains as solids in the open lot or pasture. For dairies with
solid–liquid separator systems reported, manure that is flushed to lagoon is
diverted to solid storage based on the mechanical separator efficiency (0.05
for mechanical separator; 0.225 for settling basin; 0.25 for weeping wall).
We also estimate the effect of using manure solids as bedding. The majority
of manure solids are used as bedding, as it is a cost-effective and easily
available option to keep the animals comfortable, though some solids are
land applied or removed off farm (Chang et al., 2004). Previous research
suggests that solid manure loses roughly 33 % of its C as CO2 in the
first month (Ahn et al., 2011); we assume that on dairies with lagoons, solid
manure remains in the manure pile for at least 1 month to dry out and
that half of the remaining 67 % of the manure C returns to the housing
facility and ultimately ends up in the lagoon. The fraction of manure
entering the lagoon, fbed, is therefore 33 %. We assume that all
heifer manure is scraped, though in reality some heifer lanes may be flushed
(Table S2).
CH4,m3,l=nl⋅ρCH4⋅VSprod⋅Bo⋅[(flagoon+fsolid⋅fbed)⋅MCFlagoon+fsolid⋅(1-fbed)⋅MCFsolid+fliquid⋅MCFliquid+fpasture⋅MCFpasture]
Given that air district data only exist for the San Joaquin Valley, we made
assumptions about housing and manure management in the other regions in
California for method M3. For the remaining Central Valley dairies without
air quality permits, we used the mean partitioning of solid vs. liquids from
permitted dairies in each county. In the Southern California dairies, open-lot-style farms are predominant (Deanne Meyer, personal communication,
7 February 2020), and most do not even flush the feed lane. On these
dairies, we assume that only the milking parlor is flushed, at 12.5 % of
the time, and the rest of the manure is either dry scraped or remains in the
open lot. In the North Coast, pasture dairies are prevalent, though many
dairies have some housing for cows. Here, we assume time on concrete is
39 %: on average 2 months inside in the winter and 30 % of the rest of
the year. During the winter months, the manure is scraped into pits. In the
summer, the manure is dried and stacked. In the North Coast, we assumed that
only the milking parlor was flushed (12.5 %). Nevertheless, even with
accurate accounting, the different climatic, animal housing, manure
management, and biogeochemical factors in each dairy affect the actual
CH4 emissions at any given time (Hamilton et al., 2006).
Enteric and manure CH4 emissions and standard error at the facility
and statewide scales.
Mean per dairyFacilityStatewide estimateStatewideStatewide estimate(milk cows)level(milk cows)SE(all cattle)kg CH4/yrSEGg CH4/yr(milk cows)Gg CH4/yrCH4,E1158.221.3 %210.57.4 %310.0CH4,E2173.833.5 %231.18.3 %376.7CH4,E3161.935.6 %215.320 %354.8CH4,M1233.851.0 %310.99.7 %315.1CH4,M2251.847.8 %334.932.7 %340.0CH4,M3270.573.5 %329.630 %333.8Uncertainty and sensitivity analysis
We estimated facility-level uncertainty in the number of cows as 20 %, as
suggested by the IPCC (IPCC, 2006, Sect. S1 in the Supplement). We
estimated facility-scale uncertainty for enteric fermentation emissions for
each of the three methods (Table 2, Sect. S1). The methods for
calculating the standard errors of each variable are shown in the
Supplement. For E1, we calculated the standard error in
ef1 and n. For method E2, we calculated the standard error in DMI, n,
and ef2. For E3, we calculated the standard error in DMI, NDF, milk fat,
fDMI, fNDF, and fmf for lactating cows and DMI only for
nonlactating animals. We propagated the standard error of each variable
through the emissions calculation equations, assuming the errors were
uncorrelated (Sect. S1.1).
We estimate the facility-scale uncertainty in manure management emissions by
propagating uncertainty in the terms ncows, fraction of time on
concrete, VSprod, methane conversion factor (MCF), Bo, and
fbed. We did not address uncertainty in ρCH4, as it is
considered to be a constant (US EPA, 2017). Uncertainty for time on concrete
was determined from variance observed in a recent study (Meyer, 2019) that
describes four Central Valley dairies: two with freestalls and two without
freestalls. We assume for our analysis that the time on concrete is equal to
the fraction of manure produced that passes through the lagoon
(flagoon). We also assume that the remainder of the manure
(1-flagoon) is stored as a solid in the Central Valley, in pasture in
the North Coast, and drylot in the Southern dairies. We assumed that the North
Coast dairies had freestalls or loafing barns for the winter, and the
Southern dairies had no barn housing; however, there are exceptions to these
generalizations we did not consider as we have little systematic data on
dairies outside of the Central Valley apart from expert knowledge. We
estimated the uncertainty in the VS production rate based on the variability
reported for lactating cattle and heifers over 13 years (2000–2012) in the
CARB inventory (CARB, 2014). We calculated the mean and standard error for VS
production for each of these two populations. We estimated the uncertainty
of the MCFs using data reported by Owen and Silver (Owen and Silver, 2014).
We estimated the uncertainty of Bo, the theoretical maximum methane
production, using data from a meta-analysis (Miranda et al., 2016). We
estimated the error uncertainty of fbed to be 100 %, as this value
may range from including no manure as bedding to including all solid manure
as bedding. To propagate the errors in total for the manure management
system, we rearranged Eq. (8) with two factors to be as follows, where
MCFx is the MCF for either solids, pasture, or drylot, and given that
flagoon+fx=1.
CH4,m=nl⋅VSprod⋅Bo⋅ρCH4⋅(flagoon⋅MCFlagoon-flagoon⋅MCFx+MCFx)
We used the sum of the squared partial derivatives of each variable times
the variance of that variable to propagate the uncertainty in facility-scale
manure emissions (Sect. S1.1). To determine the relative
effect of manure and enteric emissions from E3 and M3 on facility-level
emissions, we propagated the uncertainty associated with the two emissions
in quadrature.
Due to the large number of dairies, propagating the facility-level
uncertainty to the state level using standard methods produces
unrealistically low statewide uncertainty estimates (<1 %). This
suggests that the uncertainties at the facility level are not independent.
Therefore, we used previously published estimates for state-scale
uncertainties for each of the six methods, from the EPA (E1, M1; US EPA,
2017), Hristov et al. (2017) (E2, M2), and the IPCC
(E3, M3, IPCC, 2006).
We performed a sensitivity analysis on each of the methods. We calculate
sensitivity δx|y of emissions (x) to each parameter (y) as
δx|y=∂x∂y⋅σy,
where ∂x∂y is the partial derivative of emissions
(x) with respect to each variable (y) in the emissions equation and σy is the uncertainty in each parameter y (i.e., fractional uncertainty times the value). We calculate fractional uncertainty as each uncertainty divided by
the sum of all uncertainties, as in Eq. (12).
δ=δx|y∑iδx|y
We also determined the relative sensitivity of total emissions to manure and
enteric emissions.
Spatial patterns of CH4 emissions and
comparison with existing spatial inventories
We converted the Vista-CA dairy database into a raster image using R (R Core
Team, 2013). We then convert the image to a 0.1∘× 0.1∘ grid in WGS84 to match CALGEM (Jeong et al., 2012) and
the Spatial EPA (Maasakkers et al., 2016) inventories. We subtract the
values from the CALGEM, Hristov, and Maasakkers emission inventories from
the Vista-CA map to observe spatial variations between inventories.
Alternative manure management strategy assessmentSolid separators
Solid separators, including mechanical separators, weeping walls, and
settling basins, are an alternative methane mitigation manure management
practice in California (CDFA, 2020a). Separating out solids from liquid
manure reduces CH4 emissions by removing a fraction of the carbon
content by aerobic decomposition prior to entering anaerobic storage.
Mechanical separators, settling basins, and weeping walls remove
approximately 5 %, 22.5 %, and 25 % of volatile solids, respectively
(Meyer et al., 2011).
Anaerobic digesters
We determined the 106 dairies that have installed or are planning to install
anaerobic digesters from reports from the CDFA Dairy Digester Reports in
2017–2019 (CDFA, 2020b). We used our database to estimate the effects of
anaerobic digesters on CH4 emissions from these 100 dairies in the
Central Valley. We assumed a 75 % efficiency of CH4 capture in
anaerobic digesters (Charrier, 2016; US EPA, 2017).
Results and discussionHerd populations and demographics
The 2017 USDA Dairy Census reports the number of milk cows in California to
be 1 750 329. We report a total of 1 712 229 mature cows in
Vista-CA, including 1 455 395 lactating cows and 217 400 dry cows,
distributed across 1330 dairy farms. We also report a total of 1 380 040
heifers, and 1 639 966 calves. We assume a 20 % error in our uncertainty in
the number of cattle, as recommended by the IPCC (2006).
The data regarding the number of cows are proprietary information that are
not consistently reported by any one agency, and the agencies do not
communicate with each other. Regulatory agencies should strive to identify
facilities and the number of animals in a timely manner, and they should
communicate with each other. Further, the number of milk cows varies
interannually (as they only lactate for part of the year and are considered
dry cows the remainder of the year), and the animals are sold and traded.
These factors make this information surprisingly difficult to estimate.
Total state (a) enteric and (b) manure CH4 emissions for each
of the three calculations. Dark bars include all cattle, while light bars
include only milk cows. The lack of significant difference between the three
methods supports the validity of the farm-scale method.
Enteric fermentation
Total enteric emissions for all cattle are 310.0±22.9 Gg CH4/yr
for method E1, 376.7±31.2 Gg CH4/yr for method E2, and 354.8±71.0 Gg CH4/yr for method E3. We did not find statistically
significant differences between the three methods of calculations of enteric
CH4 emissions for either milk cows or all cattle in the state (Table 2,
Fig. 2a). Statewide enteric emissions for milk cows only are 210.5±15.6 Gg CH4/yr for method E1, 231.1±19.2 Gg CH4/yr for
method E2, and 215.3±43.1 Gg CH4/yr for method E3. We found
relatively consistent proportions of enteric fermentation CH4 emissions
of milk cows to total cattle. Milk cows account for 71 %, 67 %, and
61 % of total enteric emissions based on methods E1, E2, and E3,
respectively. The difference in enteric emissions between the milk cows and
total cows is due to the fact that non-milk cows produce significant amounts
of enteric methane emissions.
Manure management emissions
Total manure management emissions for all cattle are 315.1±30.6 Gg CH4/yr based on M1, 340.0±111.2 Gg CH4/yr based on
M2, and 368.0±110.4 Gg CH4/yr based on M3, the farm-specific
method. We did not find statistically significant differences in manure
management emissions between the methods of calculations for either milk
cows or all cattle (Table 2, Fig. 2b). Total manure management emissions
for milk cows only are 310.9±30.2 Gg CH4/yr based on M1, 334.9±109.5 Gg CH4/yr based on M2, and 359.8±107.9 Gg CH4/yr based on M3. The fraction of manure emissions that comes from
the milk cows is greater than 98 % for all three methods. This is because
the manure of non-milk cows is primarily managed in ways with very low
methane emissions, including daily spread, on drylots, or on pasture. The
difference between the emissions from milk cows alone and emissions from the
total dairy herd is smaller than the uncertainties in manure emissions.
Estimated input variables and standard error as a percentage of the mean
for each of the methods to calculate enteric fermentation methane emissions at the farm scale,
along with sensitivity to each input variable.
VariableMean value (%SE*)SensitivitySourceE1 (Eq. 2)nLactating cows1287 cows (20 %)88.0 %ef1Lactating cows144.61 kg CH4 per head per day (7.4 %)12.0 %CARB (2014), US EPA (2017)E2 (Eq. 4)nLactating cows1287 cows (20 %)35.6 %DMILactating cows22.9 kg per head per day (18 %)28.8 %Hristov et al. (2017)Heifers8.5 kg per head per day (15 %)Calves3.7 kg per head per day (15 %)ef2Lactating cows19 g/kg DMI (20 %)35.6 %HeifersCalvesE3 (Eq. 6)nLactating cows1287 cows (20 %)29.1 %DMILactating cows22.9 kg per head per day (38.2 %)32.4 %Appuhamy (2018)Dry cows13.5 kg per head per day (30.5 %)37.1 %dNDFLactating cows15.1 % DM (35.6 %)0.5 %mfLactating cows3.6 % (6.0 %)0.2 %fDMILactating cows22.1 (3.5 %)0.3 %fNDFLactating cows2.18 (36.7 %)0.5 %fmfLactating cows32.2 (13.0 %)0.8 %
* Description of SE
calculations are provided in Sect. S1.
Sensitivity analysis
We report the statewide uncertainty in enteric emissions to be 7.4 %,
8.3 %, and 20 % for E1, E2, and E3, respectively (Table 2). The
facility-level standard errors for enteric fermentation we calculated are
21.3 % for E1, 33.5 % for E2, and 35.6 % for E3. We find that
sensitivities in enteric fermentation differ between the three methods
(Table 3). E1 is most sensitive to the number of cows (n) at a facility. E2
is equally sensitive to n and ef2, followed by the DMI of lactating
cows. E3 is most sensitive to DMI, followed by n.
Estimated input variables and standard error as a percentage of the mean
for each of the methods to calculate manure methane emissions at the farm
scale, along with sensitivity to each input variable.
VariableMean value (%SE*)SensitivitySourceM1n (head)Lactating cows1287 (20 %)13.5 %IPCCVSprod (kg per head per year)Lactating cows2654 (1.4 %)0.1 %CARB (2014)Nonlactating cows1219 (0.9 %)Bo (m3CH4/kg VS)Lactating cows0.24 (23 %)4.6 %Miranda et al. (2016)MCF (unitless)Pasture0.15 (245 %)0.6 %CARB, Owen and Silver (2014)Daily spread0.005 (245 %)0.0 %Solid storage0.04 (86.2 %)0.2 %Liquid/slurry0.323 (47.1 %)0.9 %Lagoon0.748 (52.3 %)80.2 %Drylot0.04 (86.2 %)M2n (head)Lactating cows1287 (20 %)15.3 %IPCCVSprod (kg per head per year)Lactating cows2799 (1.4 %)0.1 %Hristov et al. (2017), CARB dataHeifer1251 (0.9 %)Calves370 (0.9 %)Bo (m3CH4/kg VS)Lactating cows0.24 (23 %)5.2 %Miranda et al. (2016)MCF (unitless)Pasture0.15 (245 %)CARB, Owen and Silver (2014)Daily spread0.005 (245 %)0.0 %Solid storage0.04 (86.2 %)0.0 %Liquid/slurry0.323 (47.1 %)1.4 %Lagoon0.748 (52.3 %)78.0 %Drylot0.04 (86.2 %)M3n (head)1720 cows per dairy1287 (20 %)7.2 %IPCCVSprod (kg per head per year)Lactating cows2654 (1.4 %)0.0 %CARB dataNonlactating cows1219 (0.9 %)TOC (flagoon) (unitless)Freestall74 % (5.7 %)3.3 %Meyer (2019)Nonfreestall34 % (8.8 %)Nonlactating26 % (12.3 %)Bo (m3CH4/kg VS)Lactating cows0.24 (23 %)2.5 %Miranda et al. (2016)MCF (unitless)Pasture0.15 (245 %)0.2 %CARB, Owen and Silver (2014)Daily spread0.005 (245 %)Solid storage0.04 (86.2 %)0.1 %Liquid/slurry0.323 (47.1 %)0.3 %Lagoon0.748 (52.3 %)45.0 %Drylot0.04 (86.2 %)fbed (unitless)Fraction bedding0.33 (100 %)41.5 %Ahn et al. (2011)
* Description of SE
calculations are provided in the Supplement.
We report the statewide uncertainty in manure emissions to be 9.7 %,
32.7 %, and 30 % for M1, M2, and M3, respectively (Table 4). The
facility-level standard errors for manure emissions we calculated are
51.0 % for M1, 47.8 % for M2, and 73.5 % for M3. While the uncertainty
for M1 is smaller than M2 and M3, this is due to the relative simplicity of
the equation, with fewer propagated errors, rather than being the inherently
best model. A recent report determined that the CARB methodology
underestimates manure methane emissions (NASEM, 2018). Here, all three of
our methods are most sensitive to the lagoon MCF (45.0 %–80.2 %),
followed by n cows (7.2 %–15.3 %), except for M3, which is sensitive to
the fraction of manure allocated to bedding (41.5 %) (Table 4). Our data
on MCF for lagoons are only based on nine observational studies from outside
California (Owen and Silver, 2014), so more measurements are needed to
reduce this uncertainty. Further, there is little information on the amount
of manure used for bedding. Overall, our uncertainty analysis is based on
limited data from very few dairies.
Total uncertainty in CH4 emissions at the facility scale (E3 + M3) is
35.6 %; 81.9 % of the uncertainty is due to uncertainty in manure
emissions, while 18.1 % of the uncertainty is due to enteric emissions.
The higher uncertainty in the manure emissions than enteric emissions is due
primarily to our uncertainty in facility-level manure management practices
and the limited information on lagoon MCF.
Spatial patterns of CH4 emissions
Using the farm-specific method (E3 and M3), the two largest sources of CH4 from
California dairy farms are manure emissions from lagoons (34.8 %) and
enteric fermentation (51.5 %) statewide. Of manure management CH4
emissions, 96.3 % came from lagoons statewide, 1.8 % from solid storage,
0.4 % from liquid/slurry, 0.2 % from pasture, and 0.0 % from drylot
and solid spread. Of the three geographic regions, the majority of manure
management CH4 emissions came from the Central Valley (94.9 %), with
only 1.7 % of manure emissions from the North Coast and 3.4 % from
Southern California. Per-cow manure management emissions were also highest
in the Central Valley (0.24 Tg CH4 per milk cow per year) due to the
predominance of lagoons as manure management practice, compared to the North
Coast (0.12 Tg CH4 per milk cow per year) and Southern regions (0.15 Tg CH4 per milk cow per year). In the 153 North Coast dairies, the 46 931 cows
encompassed 1.7 % of calculated manure emissions and 3.2 % of calculated
enteric emissions. The 75 dairies with a total of 77 122 cows in the
Southern dairies made up 3.4 % of calculated manure emissions and 5.3 %
of calculated enteric emissions.
With these emissions data, we also calculated enteric–manure ratios, which
can be useful for methane mitigation planning. Mitigation strategies for
dairy methane generally target either enteric or manure emissions, affecting
this ratio. Manure management emissions per cow are much more variable than
enteric emissions regionally, as manure practices vary more than feeding
regimes. Therefore, differences in enteric–manure emissions are likely due
to differences in manure management. The enteric–manure ratio of CH4
emissions in the North Coast is the highest, at 2.0, the enteric–manure
ratio in the Southern dairies is 1.7, and in the Central Valley the ratio is 1.0
(Fig. 4). These differences are primarily due to the differences in manure
management and cow housing type across regions: the Central Valley primarily
uses flush systems, storing a large percentage of manure in lagoons, while
North Coast and Southern California dairies tend to have scrape systems and
drylots, respectively. Because lagoons have the highest MCF, the Central
Valley has the highest per-cow emissions and lowest enteric–manure CH4
ratios. The CARB inventory also shows a statewide enteric–manure ratio of
1.08, which is primarily influenced by the large number of dairies in the
Central Valley (CARB, 2014). The enteric–manure ratio also has implications
for verifying mitigation effectiveness, as strategies that reduce either
enteric or manure emissions should alter this ratio. If emission signatures
of enteric fermentation differ from those of manure management, such as the
δ13C–CH4 isotopic signature, it may be possible to use downwind or
regional measurements of these signatures and their changes with mitigation
to quantify enteric–manure ratios.
Comparison with existing spatial inventories
We compare this spatially explicit facility-level database with three other
existing bottom-up spatial inventories, the spatially explicit EPA model
(Maasakkers et al., 2016; comparable to E1 + M1), the Hristov model (Hristov
et al., 2017, comparable to E2 + M2), and the CALGEM model (Jeong et al.,
2012, 2016), by aggregating these estimates to
0.1∘× 0.1∘ resolution to match the spatial
scale of these other products (Fig. 4). The EPA model and the Hristov
model were both developed for the contiguous United States, while CALGEM was
developed for California only. First, we note that there are no significant
differences in the statewide total methane emissions or methane emissions on
a per-cow basis amongst the three products. However, there are differences
in how manure is treated. CARB estimates that 76 % of manure is stored as
a liquid, in either lagoon or liquid/slurry, while Hristov assumes that all
manure is in lagoon or liquid/slurry, which are the manure treatments with
the two highest emissions factors (Hristov et al., 2017). Thus the Hristov
estimates are consistently higher than those of CARB and this farm-scale
estimate.
We determined Pearson's correlation coefficients using R to test differences
in spatial patterns between inventories. CALGEM is the closest to Vista-CA
emissions (E3 + M3), with a Pearson's correlation coefficient of 0.77.
The estimate of Hristov et al. (2017) is the second closest, with a Pearson's correlation
coefficient of 0.58, but it tends to overestimate emissions in the Central
Valley, including hotspots of methane emissions. The estimate of Maasakkers et al. (2016) matches
the least, with a Pearson's correlation coefficient of 0.25, and tends to
underestimate the hotspots of methane emissions in the Central Valley. The
other models also have emissions in areas where Vista-CA does not have
dairies (shown in gray in Fig. 4). The estimate of Hristov et al. (2017) includes the
largest emissions area where Vista-CA does not show dairies, mostly in the
lower Central Valley and Southern regions, though also in the North Coast.
The estimate of Maasakkers et al. (2016) follows, with additional emitting areas primarily
in the lower Central Valley. CALGEM has the fewest areas that are not in
Vista-CA, mostly in the North Coast and Southern regions of California.
Map of (a) total methane emissions and (b) ratio of enteric
fermentation emissions to manure emissions in California. In panel (a), red
indicates high total methane emissions and blue indicates low total methane
emissions. In panel (b), red indicates relatively high enteric fermentation
emissions, while blue indicates relatively high manure management emissions.
Map of the difference between facility-scale (M3) measurements and
(a) M1 (Masakkers et al., 2017), (b) M2 (Hristov et al., 2017), and (c) CALGEM
(Jeong et al., 2016) in California. Positive (red) numbers indicate M1, M2,
and CALGEM are higher than M3 measurements, while negative (blue) values
indicate M3 is higher than M1, M2, or CALGEM. Gray values show where M1, M2,
and CALGEM show dairy emissions but M3 does not. The color bar represents
absolute differences between the methods in gigagrams per square kilometer, where red indicates M3 is lower than the other method, and blue indicates that M3 is larger
than the other method.
Total methane emissions of California San Joaquin Valley in
gigagrams per square kilometer (a, b) before and (c) after installation of anaerobic digesters.
Darker red shows higher emissions. The box in panel (a) is expanded in
panels (b) and (c).
Alternative manure management strategy assessment
We found that existing solid separators reduce statewide manure CH4
emissions by 26.2 Gg/yr (8.0 %). This estimate assumes that half of all
separated solids are used as bedding, and one-third of the C of separated
solids is emitted as CO2, rather than CH4, as with other solids.
We estimated the effects of anaerobic digesters on CH4 emissions at 100
dairies in the Central Valley that have or are scheduled to have anaerobic
digesters in 2017–2019 (CDFA, 2020b, Fig. 5). Following the USEPA, we
assume a 75 % efficiency in anaerobic digesters (Lory et al., 2010;
Charrier, 2016). We predict a total reduction of CH4 emissions by 36.5 Gg CH4/yr. This represents a 51.9 % decrease in manure emissions
and a 25.9 % reduction in total (manure + enteric) emissions from
dairies with these digesters, resulting in a 10.9 % decrease in statewide
manure emissions and a 5.3 % decrease in total (enteric + manure)
statewide dairy emissions. However, limited data exist on farm-scale
emissions before and after digesters, or on the efficiency of digesters.
Our estimate provides a baseline against which the effectiveness of digester
systems to reduce CH4 emissions can be assessed. Current top-down
measurements of CH4 emissions in California are associated with large
uncertainty and are not likely to capture signals of this magnitude. Jeong
et al. (2016) inversion modeling posteriors suggest a 25 % error in
CH4 emissions in the California Central Valley, but grid-scale
error is much higher. The 95 % confidence intervals for the Central Valley
are 1020–1740 Gg CH4/yr (Jeong et al., 2016), where the uncertainty in emissions is an order of magnitude larger than the reduction we expect to see from the digesters.
Data availability
Raster files at 0.1∘ resolution of methane emissions from
the Vista-CA Dairy dataset and associated metadata are open access and are
available in the Oak Ridge National Laboratory Distributed Active Archive
Center for Biogeochemical Dynamics (ORNL DAAC) (Marklein and Hopkins, 2020;
10.3334/ORNLDAAC/1814).
Conclusions
The farm-specific Vista-CA Dairies emission product is the first
spatially explicit database of CH4 emissions from dairies at the farm
scale. By separately mapping enteric fermentation emissions and manure
management emissions, our product is valuable for source attribution and for
determining the effects of changes to management on greenhouse gas budgets.
At the state level, manure and enteric fermentation CH4 emissions from
the farm-specific method were not significantly different than previous
analyses (Appuhamy, 2018; CARB, 2014; Hristov et al., 2017; Maasakkers et
al., 2016), which supports the validity of the farm-specific methodology.
However, at the facility scale, state- or county-level assumptions by EPA and
CARB often do not match on-farm reality (Arndt et al., 2018), particularly
given that they use statewide average emissions factors that cannot capture
regional differences in climate or management.
The farm-specific data also explicitly include manure management practices,
which can vary with climate, geography, and regional policy. The spatial
differences in per-cow emissions are particularly pronounced because of
regional patterns in manure management strategies. Because the Central
Valley primarily uses flush systems, storing a large percentage of manure in
lagoons, while North Coast and Southern California dairies tend to have
scrape systems and open lots, the Central Valley has higher per-cow
emissions and lower enteric–manure CH4 emissions (Fig. 3).
We are most confident in the estimates in the San Joaquin Valley region,
where air quality permits and water board reports exist, providing
facility-level information on the herd sizes and manure management
practices. Major uncertainties exist in both bottom up and top down
estimates of CH4 emissions from dairies, including methane conversion
factors, the number of cows, the amount of manure entering different waste
streams, the time on concrete for the cattle, the functionality and
efficiency of solid-separator systems, and the amount of manure solids used
as bedding. Further, manure management strategies were not defined
consistently in the reports, so permit information may not be directly
comparable between dairies.
Nevertheless, this dataset is the first comprehensive, facility-scale
inventory of CH4 emissions and can be easily updated as more data
become available. This includes addition or removal of dairies, updated
information on herd demographics, and information on manure management. We
can also update the database with new estimates for CH4 emissions as
more data emerge and models become more accurate. More facility-scale
information could be gained through either policy initiatives that require
more detailed reports or thorough data mining of spatial images. For
example, including an account of different types of feed will improve
enteric fermentation emission predictions (NRC report 2018 24987-2).
Mitigation activities including digesters, diet changes, and manure
management are implemented at the facility scale. With emissions detail at
the facility and process level, the Vista-CA database is therefore useful
for predicting and verifying the effects of mitigation activities.
Glossary
BoMaximum methane production capacity (0.24, 0.17, and 0.17 m3CH4/kg VS for dairy cows, replacement heifers, and calves, respectively)Calf0–6-month-old cattle (n=1637339)CH4MethaneDMIDry matter intake (22.9 kg per head per day for lactating cows, 12.7 for dry cows, 8.5 for dairy replacement heifers, and 3.7 for calves)dNDFDigestible neutral detergent fiberDry cowCattle that have calved that are not lactating; includes mature cows 15 % of the time (n=216155)ef1Emission factor associated with method E1 (114.61 kg CH4 per head per year for lactating cows, 58.32 kg CH4 per replacement heifer per year, and 11.63 kg CH4 per dairy calf per year)ef2emission factor associated with method E2 (436 g per head per day for lactating cows, 280 g per head per day for dry cows, 161 g per head per day for replacement heifers, and 70 g per head per day for calves)fbedFraction of manure that is used as bedding (0.33)fDMIEmissions factor for dry matter intake (22.1)flagoonFraction of manure that enters the lagoonfmfFactor associated with milk fat (32.2)fNDFFactor associated with dNDF (2.18)Heifer6–24-month-old cattle (n=1372160)iClass of animalLactating cowCattle that have calved that are lactating; includes mature cows 85 % of the time (n=1447088)Mature cowCattle that have calved that may or may not be lactating (n=1702456)mfMilk fat content (3.6 %)MCFMethane conversion factor for manure emissions (0.15 for pasture, 0.005 for daily spread, 0.04 for solid storage, 0.323 for liquid/slurry, 0.748 for lagoon, 0.04 for drylot)nAQPopulation of cattle included in air quality (AQ) permitsniPopulation of animal class inUSDAPopulation of cattle in USDA NASS censusnWBPopulation of cattle included in RWQCB reportsTOCTime on concreteTOMPTime in milking parlorVSVolatile solidVSprodVolatile solids produced by each animal class (kg per head per day)ρCH4Density of methane (0.662 kg CH4/m3)
The supplement related to this article is available online at: https://doi.org/10.5194/essd-13-1151-2021-supplement.
Author contributions
FMH conceived of the presented idea. ARM developed the
methods and analyzed the data with input from FMH, DM, SJ, and MLF. ARM, SJ, and
MLF performed the statistics. MC and TR compiled the data. DM provided
guidance on the methods and all other aspects of the manuscript. ARM prepared
the manuscript with contributions from all authors. FMH supervised the
project.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors acknowledge the dairy farmers who provided
information on the permits and reports, Patricia Price for helping obtain
the dairy locations and herd populations, and the San Joaquin Valley
and Santa Ana Air Quality Control Boards, California Integrated Water
Quality System, and Regional Water Quality Control Boards.
Financial support
This research has been supported by the UCOP grant (grant no. LFR-18-548581) and NASA's Advancing Collaborative Connections for Earth System Science (ACCESS) Methane Source Finder (grant no. 15-ACCESS15-0034).
Review statement
This paper was edited by Nellie Elguindi and reviewed by two anonymous referees.
ReferencesAhn, H. K., Mulbry, W., White, J. W., and Konrad, S. L.: Pile mixing increases greenhouse gas emissions during composting of dairy manure, Bioresource Technol., 102, 2904–2909, 10.1016/j.biortech.2010.10.142, 2011.Appuhamy, R. and Kebreab, E.: Characterizing California-specific cattle feed rations and
improved modeling of enteric fermentation for California's greenhouse gas
inventory 2018, 1–41, available at: https://ww2.arb.ca.gov/sites/default/files/classic//research/apr/past/16rd001.pdf (last access: 4 March 2021), 2018.Arndt, C., Leytem, A. B., Hristov, A. N., Zavala-Araiza, D., Cativiela, J.
P., Conley, S., Daube, C., Faloona, I., and Herndon, S. C.: Short-term
methane emissions from 2 dairy farms in California estimated by different
measurement techniques and US Environmental Protection Agency inventory
methodology: A case study, J. Dairy Sci., 101, 11461–11479,
10.3168/jds.2017-13881, 2018.California Air Resources Board: 2014 Edition California's 2000–2012
Greenhouse Gas Emissions Inventory Technical Support Document State of
California Air Resources Board Air Quality Planning and Science Division,
1–168, available at: https://ww3.arb.ca.gov/cc/inventory/doc/methods_00-14/ghg_inventory_00-14_technical_support_document.pdf (last access: 4 March 2021), 2014.California Department of Food and Agriculture: Annual Statistics Report
2017–2018, available at: https://www.cdfa.ca.gov/statistics/PDFs/2017-18AgReport.pdf (last access: 12 March 2020), 2018.California Department of Food and Agriculture: Alternative Manure Management
Program, available at: https://www.cdfa.ca.gov/oefi/AMMP/
(last access: 12 March 2020), 2020a.California Department of Food and Agriculture: Dairy Digester Research and
Development Program, available at: https://www.cdfa.ca.gov/oefi/ddrdp/ (last access: 12 March 2020), 2020b.California Integrated Water Quality System: California Integrated Water
Quality System Regulated Facility Reports, available at:
https://ciwqs.waterboards.ca.gov/ciwqs/readOnly/CiwqsReportServlet?inCommand=reset&reportName=RegulatedFacility (last access: 4 March 2021), 2019.California Integrated Water Quality System: Regulated Facility Reports,
https://ciwqs.waterboards.ca.gov/ciwqs/readOnly/CiwqsReportServlet?inCommand=reset&reportName=RegulatedFacility, 2017.California Regional Water Quality Control Board: Reissued waste discharge
requirements general order for existing milk cow dairies, 1–167, available at: https://www.waterboards.ca.gov/centralvalley/board_decisions/adopted_orders/general_orders/r5-2013-0122.pdf (last access: 4 March 2021), 2013.Chang, A., Harter, T., Letey, J., Meyer, D., Meyer, R. D., Mastthews, M. C.,
Mitloehner, F., Pettygrove, S., Robinson, P., and Zhang, R.: Managing Dairy
Manure in the Central Valley of California, available at: http://groundwater.ucdavis.edu/files/136450.pdf (last access: 4 March 2021), 2004.Charrier, J.: 2016 Edition California's 2000–2014 Greenhouse Gas Emission
Inventory Technical Support Document, State of California Air Resources Board, Air Quality Planning and Science Division, September 2016, 1–174, available at: https://ww3.arb.ca.gov/cc/inventory/pubs/reports/2000_2014/ghg_inventory_00-14_technical_support_document.pdf (last access: 4 March 2021), 2016.Cui, Y. Y., Brioude, J., Angevine, W. M., Peischl, J., McKeen, S. A., Kim,
S.-W., Neuman, J. A., Henze, D. K., Bousserez, N., Fischer, M. L., Jeong,
S., Michelsen, H. A., Bambha, R. P., Liu, Z., Santoni, G. W., Daube, B. C.,
Kort, E. A., Frost, G. J., Ryerson, T. B., Wofsy, S. C., and Trainer, M.:
Top-down estimate of methane emissions in California using a mesoscale
inverse modeling technique: The San Joaquin Valley, J. Geophys. Res.-Atmos.,
122, 3686–3699, 10.1002/2016JD026398, 2017.Dlugokencky, E. J., Nisbet, E. G., Fisher, R., and Lowry, D.: Global
atmospheric methane: budget, changes and dangers, Philos. Trans. R. Soc.
London A, 369, 2058–2072, 10.1098/rsta.2010.0341, 2011.Duren, R. M., Thorpe, A. K., Foster, K. T., Rafiq, T., Hopkins, F. M.,
Yadav, V., Bue, B. D., Thompson, D. R., Conley, S., Colombi, N. K.,
Frankenberg, C., McCubbin, I. B., Eastwood, M. L., Falk, M., Herner, J. D.,
Croes, B. E., Green, R. O., and Miller, C. E.: California's methane
super-emitters, Nature, 575, 180–184, 10.1038/s41586-019-1720-3, 2019.
Hamilton, D. W., Fathepure, B., Fulhage, C. D., Clarkson, W., and Lalman, J.: Treatment lagoons for animal agriculture, in: Animal Agriculture and the Environment: National Center for Manure and Animal Waste Management White Papers, edited by: Rice, J. M., Caldwell, D. F., and Humenik, F. J., ASABE, St. Joseph, Michigan, Pub. Number 913C0306, 547–574, 2006.Hristov, A. N., Harper, M., Meinen, R., Day, R., Lopes, J., Ott, T.,
Venkatesh, A., and Randles, C. A.: Discrepancies and Uncertainties in
Bottom-up Gridded Inventories of Livestock Methane Emissions for the
Contiguous United States, Environ. Sci. Technol., 51, 13668–13677,
10.1021/acs.est.7b03332, 2017.IPCC: IPCC Guidelines for National Greenhouse Gas Inventories. Volume 4 –
Agriculture, Forestry and Other Land Use, Chapter 10: Emissions from
Livestock and Manure Management, available at:
https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock.pdf (last access: 4 March 2021), edited by: Eggleston, H. S., Buendia, L., Miwa, K., Ngara, T., and Tanabe, K., IGES, Japan, 2006.Jeong, S., Zhao, C., Andrews, A. E., Bianco, L., Wilczak, J. M., and Fischer,
M. L.: Seasonal variation of CH4 emissions from central California, J. Geophys. Res., 117, D11306, 10.1029/2011JD016896, 2012.Jeong, S., Newman, S., Zhang, J., Andrews, A. E., Bianco, L., Bagley, J.,
Cui, X., Graven, H., Kim, J., Salameh, P., LaFranchi, B. W., Priest, C.,
Campos-Pineda, M., Novakovskaia, E., Sloop, C. D., Michelsen, H. A., Bambha,
R. P., Weiss, R. F., Keeling, R., and Fischer, M. L.: Estimating methane
emissions in California's urban and rural regions using multitower
observations, J. Geophys. Res.-Atmos., 121, 13031–13049,
10.1002/2016JD025404, 2016.Lory, J. A., Massey, R. E. and Zulovich, J. M.: An Evaluation of the USEPA
Calculations of Greenhouse Gas Emissions from Anaerobic Lagoons, J.
Environ. Qual., 39, 776–778, 10.2134/jeq2009.0319, 2010.Maasakkers, J. D., Jacob, D. J., Sulprizio, M. P., Turner, A. J., Weitz, M.,
Wirth, T., Hight, C., DeFigueiredo, M., Desai, M., Schmeltz, R., Hockstad,
L., Bloom, A. A., Bowman, K. W., Jeong, S., and Fischer, M. L.: Gridded
National Inventory of U.S. Methane Emissions, Environ. Sci. Technol.,
50, 13123–13133, 10.1021/acs.est.6b02878, 2016.Marklein, A. R. and Hopkins, F. M.: Dairy Sources of Methane Emissions in
California, ORNL DAAC, Oak Ridge, Tennessee, USA,
10.3334/ORNLDAAC/1814, 2020.Meyer, D.: Characterize Physical and Chemical Properties of Manure in
California Dairy Systems to Improve Greenhouse Gas Emission Estimates,
1–70, available at: https://ww2.arb.ca.gov/sites/default/files/classic/research/apr/past/16rd002.pdf (last access: 4 March 2021), 2019.Meyer, D., Price, P. L., Rossow, H. A., Silva-del-Rio, N., Karle, B. M.,
Robinson, P. H., DePeters, E. J., and Fadel, J. G.: Survey of dairy housing and manure management practices in California, J. Dairy Sci., 94,
4744–4750, 10.3168/jds.2010-3761, 2011.Miller, S. M., Wofsy, S. C., Michalak, A. M., Kort, E. A., Andrews, A. E.,
Biraurd, S. C., Dlugokencky, E. J., Eluszkiewicz, J., and Fischer, M. L.:
Anthropogenic emissions of methane in the United States, P. Natl. Acad. Sci. USA, 575, 180–184, 10.1073/pnas.1314392110, 2013.
Miranda, N. D., Granell, R., Tuomisto, H., and Mcculloch, M. D.: Meta-analysis of methane yields from anaerobic digestion of dairy cattle manure, Biomass Bioenerg., 86, 65–75, 10.1016/j.biombioe.2016.01.012, 2016.
NASEM (National Academies of Sciences, Engineering, and Medicine): Improving
Characterization of Anthropogenic Methane Emissions in the United States,
National Academies Press, Washington, D.C., 2018.Owen, J. J. and Silver, W. L.: Greenhouse gas emissions from dairy manure
management: a review of field-based studies, Glob. Change Biol., 21,
550–565, 10.1111/gcb.12687, 2014.
Rafiq, T., Duren, R. M., Thorpe, A. K., Foster, K., Patarsuk,
R., Miller, C. E., and Hopkins, F. M.: Attribution of Methane
Point Source Emissions using Airborne Imaging Spectroscopy and the
Vista-California Methane Infrastructure Dataset, Environ. Res.
Lett., 15, 2020.R Core Team: A language and environment for statistical computing. R
Foundation for Statistical Computing, Vienna, Austria, available at: http://www.R-project.org/ (last access: 4 March 2021), 2013.Ross, K.: California Agricultural Statistics Review, 1–121, available at: https://www.nass.usda.gov/Statistics_by_State/California/Publications/Annual_Statistical_Reviews/2019/2018cas-all.pdf (last access: 4 March 2021), 2019.State of California: Senate Bill 1383, available at: https://leginfo.legislature.ca.gov/faces/billNavClient.xhtml?bill_id=201520160SB1383 (last access: 4 March 2021), 2016.Trousdell, J. F., Conley, S. A., Post, A., and Faloona, I. C.: Observing entrainment mixing, photochemical ozone production, and regional methane emissions by aircraft using a simple mixed-layer framework, Atmos. Chem. Phys., 16, 15433–15450, 10.5194/acp-16-15433-2016, 2016.USDA NASS: Census of Agriculture, 1–20, available at:
http://www.nass.usda.gov/AgCensus (last access: 4 March 2021), 2017.US EPA, O. C. C. D.: Inventory of U.S. Greenhouse Gas Emissions and Sinks:
1990–2015 – Annexes, 1–475, available at: https://www.epa.gov/sites/production/files/2017-02/documents/2017_all_annexes.pdf (last access: 4 March 2021), 2017.Wecht, K. J., Jacob, D. J., Sulprizio, M. P., Santoni, G. W., Wofsy, S. C., Parker, R., Bösch, H., and Worden, J.: Spatially resolving methane emissions in California: constraints from the CalNex aircraft campaign and from present (GOSAT, TES) and future (TROPOMI, geostationary) satellite observations, Atmos. Chem. Phys., 14, 8173–8184, 10.5194/acp-14-8173-2014, 2014.Wolf, J., Asrar, G. R., and West, T. O.: Revised methane emissions factors
and spatially distributed annual carbon fluxes for global livestock, Carbon
Balance and Management, 12, 16, 10.1186/s13021-017-0084-y, 2017.