Mitigation potential and costs for global agricultural greenhouse gas emissions1
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AGRICULTURAL
ECONOMICS
Agricultural Economics 38 (2008) 109–115
Mitigation potential and costs for global agricultural
greenhouse gas emissions1
Robert H. Beacha,∗ , Benjamin J. DeAngelob , Steven Roseb , Changsheng Lic , William Salasd ,
Stephen J. DelGrossoe
a Foodand Agricultural Policy Research Program, RTI International, 3040 Cornwallis Road, Research Triangle Park, NC 27709-2194, USA
b U.S. Environmental Protection Agency, 1200 Pennsylvania Ave NW (6207J),Washington, DC 20460, USA
c Complex Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH 03824, USA
d Applied Geosolutions, LLC, 10 Newmarket Road, Durham, NH 03824-2808, USA
e Natural Resource Ecology Laboratory, Colorado State University, Campus Mail 1499, Fort Collins, CO 80523-1499, USA
Received 30 November 2005; received in revised form 30 April 2006; accepted 16 August 2006
Abstract
Agricultural activities are a substantial contributor to global greenhouse gas (GHG) emissions, accounting for about 58% of the world’s
anthropogenic non-carbon dioxide GHG emissions and 14% of all anthropogenic GHG emissions, and agriculture is often viewed as a potential
source of relatively low-cost emissions reductions. We estimate the costs of GHG mitigation for 36 world agricultural regions for the 2000–2020
period, taking into account net GHG reductions, yield effects, livestock productivity effects, commodity prices, labor requirements, and capital
costs where appropriate. For croplands and rice cultivation, we use biophysical, process-based models (DAYCENT and DNDC) to capture the
net GHG and yield effects of baseline and mitigation scenarios for different world regions. For the livestock sector, we use information from the
literature on key mitigation options and apply the mitigation options to emission baselines compiled by EPA.
JEL classification: Q18, Q52
Keywords: Greenhouse gas; Mitigation; Methane; Nitrous oxide; Abatement costs
1. Introduction and background As an important source of global emissions, the agricultural
sector also potentially offers relatively low-cost opportunities
Agricultural activities generate the largest share, 58%, of the for GHG mitigation. However, there are unique challenges
world’s anthropogenic non-carbon dioxide (non-CO2 ) emis- to developing estimates of mitigation costs over large spatial
sions (84% of nitrous oxide [N2 O], 47% of methane [CH4 ]), scales, which are needed to assess agriculture’s potential role in
and make up roughly 14% of all anthropogenic GHG emissions reducing global GHG emissions relative to other sectors of the
(U.S. Environmental Protection Agency [EPA], 2006; Prentice economy. First, there is a high degree of spatial and temporal
et al., 2001; Scheehle and Kruger, 2006). In many developing heterogeneity in biophysical and management conditions that
countries, the agricultural sector is the largest source of GHG affect both production and emissions. Second, many agricul-
emissions. In addition, GHG emissions from agriculture are tural activities emit multiple GHGs, and there are often complex
projected to increase significantly over the next 20 years, espe- interactions between these emissions. Third, there is a paucity
cially in Asia, Latin America, and Africa, due to increased of regionally specific cost data from which one can estimate the
demand for agricultural products as a result of population economic implications of implementing GHG mitigation prac-
growth, rising per capita caloric intake, and changing dietary tices, in terms of changes in inputs, revenue, and labor. Fourth,
preferences. estimating the expected level of implementation of the miti-
gation options in response to financial incentives (e.g., carbon
1 Contributed paper and 2006 winner of the T. W. Schultz award presented
price) is difficult given the implementation barriers that exist in
at the International Association of Agricultural Economists Conference, Gold
Coast, Australia, August 12–18, 2006.
different regions.
∗
Corresponding author. Tel: +1-919-485-5579. Fax: +1-919-541-6683. Nonetheless, agricultural net GHG and non-CO2 mitigation
E-mail address: rbeach@rti. org (R. H. Beach). analyses have been developed for several countries and the
c 2008 International Association of Agricultural Economists110 R. H. Beach et al. / Agricultural Economics 38 (2008) 109–115
world. Some include a relatively comprehensive set of GHG N2 O emitted by cropland soils is typically the largest source
mitigation options with dynamic economic feedbacks (see EPA of GHG from agricultural systems. It is produced naturally
[2005a] and McCarl and Schneider [2001] for the United through the processes of nitrification and denitrification. Appli-
States), whereas others have targeted individual agricultural cation of nitrogen (N)-based fertilizers is a key determinant of
emission sources with static engineering mitigation estimation N2 O emissions, as excess N not used by the plants is subject to
methods (see Kroeze and Mosier [1999] for global cropland gaseous emissions, as well as leaching and runoff. Other soil
N2 O and enteric CH4 ; Riemer and Freund [1999] for global management activities such as irrigation, drainage, tillage prac-
rice emissions; see also Table 3.27 in Moomaw et al. [2001] tices, and fallowing of land, can also affect N2 O fluxes as well
of IPCC Working Group III). Our previous analysis, DeAngelo as soil C and fossil fuel CO2 emissions.
et al. (2006) developed global mitigation estimates for cropland Methane is produced as part of the normal enteric fermen-
N2 O, livestock enteric and manure CH4 , and rice CH4 ; these tation process in animals. During digestion, microbes in an
values were used by many participants of the Energy Modeling animal’s digestive system ferment food consumed by the ani-
Forum-21 study to represent the agricultural sector in global mal. This process produces CH4 as a by-product, which can be
multisector, multigas mitigation scenarios (Van Vuuren et al., exhaled or eructated by the animal. The amount of CH4 emit-
2006). ted by an animal depends primarily on the animal’s digestive
This analysis improves on DeAngelo et al. in a number system and the amount and type of feed it consumes. Ruminant
of areas. Biophysical, process-based models (DAYCENT and animals (e.g., cattle, buffalo, sheep, goats, and camels) are the
DNDC) are used to better capture the net GHG effects of crop- major emitters because of their unique digestive system.
land and rice emissions (i.e., soil carbon, CH4 , N2 O) under Most rice in Asia and the rest of the world is grown in flooded
baseline and mitigation scenarios. The previous analysis often paddy fields, where aerobic decomposition of organic material
assumed uniform changes in emissions and/or yields between gradually depletes the oxygen present in the soil and floodwa-
baseline and mitigation scenarios (based on literature studies) ter, causing anaerobic conditions. Anaerobic decomposition of
across regions. They also focused on a single gas for each miti- soil organic matter by methanogenic bacteria generates CH4 .
gation option. Although process-based models account for het- The water management system under which rice is grown is
erogeneous and dynamic emission and yield effects of adopting therefore one of the most important factors affecting CH4 emis-
mitigation practices, while at the same time tracking multiple sions. In addition, other practices (e.g., tillage, fertilization,
GHG fluxes, they require detailed data on local conditions in manure amendment) alter soil conditions and therefore soil C
order to accurately simulate site-specific yields and daily emis- and N driving processes such as decomposition, nitrification,
sion fluxes. Rather than attempt to calibrate to site-specific data and denitrification.
for regions around the world, we use these models to capture The management of livestock manure can produce both CH4
the broad trends between baseline and mitigation scenarios over and N2 O emissions. Methane is produced by the anaerobic
large temporal and spatial scales, adjusting for regional condi- decomposition of manure. N2 O is produced through the nitri-
tions. Using process-based models to generate both baseline fication and denitrification of the organic nitrogen in livestock
and mitigation scenarios provides consistency in underlying manure and urine. When manure is stored or treated in systems
assumptions. We emphasize the estimated differences between that promote anaerobic conditions (e.g., as a liquid or slurry in
baseline and mitigation scenarios rather than absolute values. lagoons, ponds, tanks, or pits), the decomposition of materials
In addition, this analysis provides results for individual major in the manure tends to produce CH4 . In addition, a small portion
crops (rice, maize, wheat, and soybeans) under both irrigated of the total nitrogen excreted in manure and urine is expected to
and rain-fed conditions, and we estimate results for 36 different convert to N2 O. The production of N2 O from livestock manure
regions of the world, which is more spatially disaggregated than depends on the composition of the manure and urine, the type
previous studies in the literature. of bacteria involved in the process, and the amount of oxygen
and liquid in the manure system (EPA, 2005b).
2. Emissions characterization
3. Methods
The majority of agricultural non-CO2 GHG emissions are
from one of four sources: cropland soil management (primar- We apply a set of mitigation options identified in the lit-
ily N2 O), ruminant livestock enteric fermentation (primarily erature for each emission category: croplands (N2 O and soil
CH4 ), rice cultivation (primarily CH4 for flooded rice paddies, C); livestock enteric fermentation (CH4 ); rice cultivation (CH4 ,
though N2 O is also important under certain growing condi- N2 O, and soil C); and livestock manure management (CH4 and
tions), and livestock manure management (both CH4 and N2 O, N2 O). Emissions, yields, productivity changes, labor require-
with CH4 from anaerobic manure management systems dom- ment changes, and other factors from the mitigation scenarios
inating). Changes in soil carbon (C) are also important deter- are compared with baseline conditions for years 2000, 2010, and
minants of net GHG emissions for soil management and rice 2020, and for all major world agricultural regions. If a mitiga-
cultivation. tion option is considered technically feasible for a given region,R. H. Beach et al. / Agricultural Economics 38 (2008) 109–115 111
it is assumed to be adopted immediately, that is, in data year The mitigation options are simulated with DAYCENT and
2000, and the change in management is continuous for the entire those results are compared with the baseline to calculate the
2000–2020 period. Mitigation estimates therefore represent the impacts of each mitigation option on fertilizer use, crop yields,
technical potential for GHG reductions, with associated costs, and net GHG emissions. Revenue changes are estimated by
without accounting for implementation barriers that would slow using the percentage yield changes, from DNDC applied to
adoption of technically feasible options. Each set of mitigation baseline yields and crop prices from IFPRI’s IMPACT model.
options for each emission category in each region is assumed to Labor requirements to implement the mitigation options are
be implemented simultaneously but without any overlap among based on a review of relevant literature and associated cost
the options. For example, six mitigation options are applied to implications are calculated using regional agricultural wages
rice emissions in China; each option is therefore applied to from IMPACT data.
one-sixth of applicable baseline emissions. This is a simplistic
method that avoids double counting among options, but likely
3.2. Rice GHG mitigation options
underestimates potential penetration of low-cost options and
overestimates potential penetration of high-cost options. How-
The DNDC model (DNDC 8.6; Li et al., 2004) is used to es-
ever, it is used in the absence of region-specific data on adoption
timate baseline and mitigation option emissions of CH4 , N2 O,
feasibility.
and soil carbon, as well as yield and water resource changes,
The break-even price for each mitigation option is calculated
for Asian rice systems. Greenhouse gas emissions from non-
according to Eq. (1), which sets total benefits equal to total
Asian rice systems, which represent about 10% of the world’s
costs, and solves for the present-value, break-even price (P),
total rice area (Wassmann et al., 2000), are excluded. The mit-
expressed in 2000 US$/tCO2 eq.
igation options involve a change in water management (to re-
T T duce anaerobic conditions), fertilizers or amendments (to in-
(P • E) + R (CA )
= C K + (1) hibit methanogenesis), or switching from flooded to upland
t=1
(1 + δ)t t=1
(1 + δ)t rice. Revenue changes are estimated by using the percentage
yield changes from DNDC applied to baseline yield and rice
E is the absolute net GHG emission reduction. R is the revenue prices from IFPRI’s IMPACT model. Some options require soil
effect as a result of the mitigation option (e.g., yield changes, amendments (e.g., phosphogypsum) or an alternative fertilizer
electricity generation). CK is capital cost for each option. CA (e.g., ammonium sulfate instead of urea) (Denier van der Gon
includes annual costs (scaled to different regions based on agri- et al., 2001). These additional input costs are included by using
cultural labor wages) and input costs such as fertilizers. T is the baseline application rates from Wassmann et al. (2000) to cal-
assumed useful life of capital equipment used for mitigation, culate incremental requirements for amendments or fertilizer
which applies only to the small number of options identified switching and regional prices from FAOSTAT. Labor require-
that involve purchases of capital (primarily manure manage- ments to implement the mitigation options are estimated using
ment options involving the purchase of anaerobic digesters). δ a study on labor requirements for adopting rice intensification
is the real discount rate, assumed to be 5% in all cases. practices (Barrett et al., 2004). The cost implications of any la-
bor requirement changes are calculated using agricultural wages
for each region from IMPACT data.
3.1. Cropland soil GHG mitigation options
Options have been identified that could decrease N2 O while 3.3. Enteric CH4 mitigation options
maintaining yields (Mosier et al., 2002). The mitigation options
used in this analysis involve more efficient or simply reduced N- Enteric CH4 mitigation options examined fall into four gen-
based fertilizer application (e.g., N-inhibitors, split fertilization, eral categories: (1) improvements to food conversion efficiency
reducing N-fertilization to 70, 80, or 90% of baseline levels) by increasing energy content and digestibility of feed; (2) in-
and adoption of no-till cultivation. creased animal productivity through the use of natural or syn-
Baseline N2 O emissions, soil carbon levels, and crop yields thetic compounds that enhance animal growth and/or lactation
are estimated for all world regions with the DAYCENT model (e.g., bovine somatotropin [bST], antibiotics); (3) feed supple-
(Del Grosso et al., 2001; Parton et al., 1998) for years 2000, mentation to combat nutrient deficiencies that prevent animals
2010, and 2020. Underlying data for the DAYCENT simula- from optimally using the potential energy available in their
tions include global data sets of weather, soils, cropland area, feed; and (4) changes in herd management (e.g., use of inten-
and native vegetation, mapped to an approximate 2◦ × 2◦ resolu- sive grazing). In each case, the production of CH4 per unit of
tion. Current and historic nitrogenous fertilization data for each product (meat, milk, or work [for draft animals]) is decreased,
region and country are from FAOSTAT (2004) and the Interna- but emissions of CH4 per animal may increase. Thus, emis-
tional Fertilizer Industry Association (IFA) (2002). Projected sion reductions at the national or regional level are calculated
fertilization rates are taken from FAO (2000) and FAOSTAT assuming that total production of meat, milk, or work remain
(2004). constant after implementation of the mitigation options.112 R. H. Beach et al. / Agricultural Economics 38 (2008) 109–115
Table 1
Distribution of net GHG 2000–2020 emissions reductions across major river basins in China from conversion to shallow water flooding
Average annual proportion Average annual Proportion of Proportion of Average annual change in
Waterbasin of baseline change in emissions national paddy average annual emissions per
name emissions reduced (1,000 kg CO2 eq) rice acreage national reduction hectare (kg CO2 eq ha−1 )
Inland 0.52 –415,395 0.00 0.00 –9,213
Haihe 0.58 –2,346,338 0.01 0.01 –11,248
Songliao 0.46 –13,522,372 0.10 0.08 –7,116
Huaihe 0.55 –30,545,538 0.13 0.18 –12,729
Huanghe 0.58 –1,674,906 0.01 0.01 –8,349
ZhuJiang 0.58 –78,540,207 0.17 0.46 –25,232
Southeast 0.53 –26,681,240 0.08 0.16 –17,640
Changjian 0.56 –16,825,005 0.48 0.10 –1,899
Southwest 0.44 –996,033 0.02 0.01 –3,257
Source: Li et al. (2006).
These mitigation options are applied to EPA (2006) baseline the use of captured CH4 for either heat or electricity on the farm.
emission projections for the 2000–2020 period. The percent- Revenues are scaled to other regions based on a U.S. Energy
age emission reductions, and incremental net cost per tCO2 eq, Information Agency (EIA, 2003) electricity price index.
for each option in each region are drawn directly from, or cal-
culated based on, either Gerbens (1998), Bates (2001), John-
son et al. (2003a, 2003b), or personal communication with 4. Results
Johnson.
We generate estimates of net GHG mitigation potential and
3.4. Manure CH4 mitigation options costs for 36 different world regions for each agricultural emis-
sion category, and construct marginal abatement cost curves
All manure CH4 mitigation options included involve the cap- for each region and for the world for 2000, 2010, and 2020.
ture and use of CH4 through anaerobic digesters. Anaerobic di- Owing to space constraints, we present a limited number of
gesters are currently in limited use, though primarily as a means representative global results here. We find major differences in
of controlling odor and pathogens. The types of digesters are these curves across regions and time due to differing baseline
aggregated based on a categorization from EPA’s AgStar pro- emissions, biophysical soil and crop conditions, and potential
gram. These options are applied to the estimated share of base- for mitigation, confirming the importance of considering het-
line emissions from swine and dairy cattle. Methane reduction erogeneity.
efficiencies and capital costs are taken from Bates (2001) for Even within a country, the baseline emissions per hectare and
non-U.S. regions. Revenues (or cost savings) are generated from percentage and absolute reductions in emissions from adoption
25,000
GWP, kgCO 2 equivalent/ha/yr
20,000
15,000
10,000
5,000
0
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Year
Baseline Continuous flooding Midseason drainage
Shallow flooding Upland rice Off-season straw
Sulfate fertilizer Slow-release fertilizer
Source: Li et al. (2006).
Fig. 1. Impacts of alternatives on net GWP for rice cultivation in China, 2000–2020.R. H. Beach et al. / Agricultural Economics 38 (2008) 109–115 113
Figure 2. Global marginal abatement cost curve for soil management.
200
150
100
$/tCO2eq
2000
2010
50 2020
0
0% 5% 10% 15% 20% 25% 30%
-50
Percent Net GHG Emissions Reduction
Fig. 3. Global marginal abatement cost curve for rice cultivation.
of a single mitigation option can differ significantly, as shown ing large potential agricultural mitigation from “no-regrets”
in Table 1 for shallow flooding in nine regions of China. Re- options, but the fact that farmers are not adopting options that
ductions in net GHG emissions per hectare range from 1,899 seemingly would increase profitability indicates that there may
kgCO2 eq in Changjian to 25,232 kgCO2 eq in ZhuJiang. Fig. 1 be costs and barriers to adoption that are not being captured in
shows, again for China, changes in the baseline over time as this analysis.
well as in emissions attributable to the mitigation options. In Fig. 3 provides similar curves for rice cultivation. There is
some cases, mitigation options are estimated to increase emis- a large outward shift between 2000 and 2010. This primarily
sions relative to the baseline in some regions. This is generally reflects the large decrease in net emissions over the first decade
due to offsetting interactions among GHGs; these options are after implementation of several options having dynamic impacts
removed for construction of the marginal abatement cost curves on soil C. Changes in baseline emissions, commodity prices,
unless they temporarily increase emissions and then lead to a labor rates, and other factors also contribute to this shift over
decrease. time. Total global mitigation for rice cultivation is estimated to
Fig. 2 shows the globally aggregated marginal abatement be above 20% in 2010 and 2020 at about $10/tCO2 eq. After
curve for soil management GHG mitigation. Over 25% of base- that level, costs rise very rapidly.
line emissions are mitigated at less than $40/tCO2 eq, but costs Total global mitigation for livestock management is esti-
begin to rise rapidly beyond that point. Negative costs result mated to be about 10–12% at $50/tCO2 eq (Fig. 4). If other GHG
from options with cost savings due to more efficient fertilizer benefits were included (e.g., soil C increases, cropland N2 O re-
application that more than offset any revenue losses from lower ductions for less feed), the mitigation estimates would be higher,
yields, whereas high-cost options are those for which yields fall but no model was identified to allow estimation of multigas im-
substantially in response to suboptimal fertilizer applications. pacts for livestock analogous to the DNDC and DAYCENT
Negative cost options are consistent with previous studies find- models used for soil management and rice cultivation.114 R. H. Beach et al. / Agricultural Economics 38 (2008) 109–115
200
150
$/tCO2eq 100 2000
2010
50 2020
0
0% 5% 10% 15% 20% 25% 30%
-50
Percent Net GHG Emissions Reduction
Fig. 4. Global marginal abatement cost curves for livestock management.
5. Conclusions Food and Agriculture Organization (FAO), 2000. Fertilizer Requirements
for 2015 and 2030. UN Food and Agriculture Organization, Rome,
Italy.
This study contributes estimates of the potential for and costs
FAOSTAT, 2004. FAO fertilizer yearbook 2003, v. 53. FAO Statistics Se-
of global agricultural GHG mitigation. There are many chal- ries (FAO). No. 183. UN Food and Agriculture Organization, Rome,
lenges in developing these estimates due to the high degree of Italy.
heterogeneity in management practices and resultant emissions Gerbens, S., 1998. Cost-effectiveness of methane emission reduction from
in the agricultural sector. Our unique combination of biophys- enteric fermentation of cattle and buffalo. Draft Report.
International Fertilizer Industry Association (IFA), 2002. Fertilizer
ical process-based models, consistent global data sets (to the
Use by Crop (5th ed.). Available at http://www.fertilizer.org/ifa/
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