2003.a country-specific, high-resolution emission inventory for methane from livestock in asia in...

7/31/2019 2003.a Country-specific, High-resolution Emission Inventory for Methane From Livestock in Asia in 2000

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Atmospheric Environment 37 (2003) 43934406

A country-specific, high-resolution emission inventory for

methane from livestock in Asia in 2000

Kazuyo Yamajia,*, Toshimasa Oharaa,b, Hajime Akimotoa

aFrontier Research System for Global Change, 3173-25 Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, JapanbFaculty of Engineering, Shizuoka University, 3-5-1 Johoku, Hamamatsu, Shizuoka 432-8561, Japan

Received 6 January 2003; received in revised form 26 June 2003; accepted 3 July 2003

Abstract

Methane emissions from livestock in South, Southeast, and East Asia were estimated to be about 29.9 Tg CH4 in

2000 using the Food and Agriculture Organization database and district-level data on regional activity and emission

factors, considering regional specificities. These emissions consisted of 25.9 Tg CH4 from enteric fermentation and

4.0Tg CH4 from livestock manure management systems. India had the greatest production, with 11.8 Tg CH4 from

livestock, primarily cattle and buffaloes. China was also a high-emission country, producing about 10.4 Tg CH4. To

determine their spatial distribution, emissions at the country and district levels were plotted on a 0.5 0.5 grid

according to weight, using high-resolution land cover/use datasets. This gridded database shows considerable emissions

throughout the Ganges basin, with peak emissions exceeding 30 Gg CH4 grid1 in the Ganges River delta. The total

methane emissions from livestock increased by an average of 2% per annum from 1965 to 2000. The recent increase in

methane emissions in China was especially remarkable.

r 2003 Elsevier Ltd. All rights reserved.

Keywords: Methane emission; Gridded database; Spatial distribution; Livestock; South, Southeast, and East Asia

1. Introduction

Atmospheric methane (CH4) is one of the most

important trace gases from the perspective of the global

environment and climate change. The radiative forcing

of CH4 from the pre-industrial era to the present is

second to that of carbon dioxide. In addition to

greenhouse forcing, CH4 plays another important rolein the atmospheric photochemistry of the troposphere

and stratosphere. Atmospheric CH4 is mainly decom-

posed by its reaction with hydroxyl radicals in the

troposphere and contributes to the photochemical

production of tropospheric ozone. In the stratosphere,

CH4 affects the ozone layer by removing chlorine atoms

and producing water vapor. Therefore, accurate infor-

mation regarding the sources and sinks of atmospheric

CH4 is necessary to understand the present global

environment, and to make future projections.

According to the Intergovernmental Panel on Climate

Change (IPCC), the global abundance of atmospheric

CH4 was about 4,850 Tg CH4 and the calculated global

annual emissions were 598 Tg CH4 in 1998 (Houghton

et al., 2001). Total annual CH4 emissions from all

sources have been estimated at 500600 Tg CH4globally. The major CH4 sources are wetlands, livestock,

and paddy fields, and each contributes more than 60 Tg

CH4 yr1 (more than 10% of the total). Biomass

burning, landfills, natural gas, and coalmines are also

significant CH4 sources. Enteric fermentation in live-

stock is the largest CH4 source, and its production is

estimated at 80115 Tg CH4 yr1 globally. Some authors

have estimated the CH4 emissions from livestock on

country and regional levels (Lerner et al., 1988; Olivier

et al., 1999). These studies have shown that India has the

highest CH4 emissions, while China, Pakistan, and

Bangladesh make considerable contributions to global

ARTICLE IN PRESS

AE International Asia

*Corresponding author. Tel.: +81-45-778-5719; fax: +81-

45-778-5496.

E-mail address:[email protected] (K. Yamaji).

1352-2310/$- see front matterr 2003 Elsevier Ltd. All rights reserved.

doi:10.1016/S1352-2310(03)00586-7


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CH4 production. These results suggested that Asia is the

most important area for CH4 production from livestock.

Nevertheless, in many Asian countries, research on CH4emissions from livestock lags behind research in devel-

oped countries where livestock are bred commercially.

The IPCC (Houghton et al., 1997) has published

useful methods for determining CH4 emission factors forlivestock using a simplified approach that relies on

default emission factors drawn from previous studies

(Tier 1) and a more complex approach that requires

country-specific information on livestock characteristics

(Tier 2). Recently, the governments of many countries in

East and Southeast Asia have been working aggressively

to deal with CH4 emissions from livestock, as reported

at the Institute for Global Environmental Strategies

(IGES) and National Institute for Environmental

Studies (NIES) workshop on greenhouse gas (GHG)

inventories for the Asian Pacific region in 2000. In

determining CH4 emissions from enteric fermentation inmajor livestock species in China and Japan, new studies

have determined country-specific emission factors for

different sub-categories (e.g., age, sex, breed, and

purpose) on theoretical and experimental bases. Esti-

mates of CH4 emissions using these original factors have

also been reported for each country (Saito, 1988;

Shibata et al., 1993; Braatz et al., 1996; Dong et al.,

1996, 2000; Terada, 2000). Other countries have

determined CH4 emissions from major livestock species

using the IPCC Tier 1 and other global uniform default

emission factors (Braatz et al., 1996; Lantin and Villarin,

2000). India is a leader in emission studies in South Asia,

and has determined the CH4 emission rates for sub-

categories of major livestock species based on both

IPCC Tier 1 and Tier 2, and experimentally. Country

emission inventories using these factors have also been

reported (Mitra, 1992, 1996; Singh and Mohini, 1996;

Garg et al., 2001). In Bangladesh, a preliminary national

emission inventory was reported using the emission

factors given in Indian reports (Braatz et al., 1996). For

many other Asian countries, however, there have been

insufficient emissions studies.

To study the spatial distribution of emissions, Lerner

et al. (1988) estimated the global CH4 emissions from

enteric fermentation in animals and reported a griddedemission database with a 1.0 1.0 latitudelongitude

resolution. Recently, the Emission Database for Global

Atmospheric Research (EDGAR) provided a GHG

global emission inventory database with a 1.0 1.0

grid (e.g., Olivier et al., 1999), including CH4 from

livestock. EDGAR version 3.2 (EDGAR 3.2) is now

available, and its base year is 1995 (Olivier and

Berdowski, 2001).

In this study, we examined the geographic distribution

of CH4 emissions from livestock in South, Southeast,

and East Asia. As livestock breeding has expanded with

the growth of the human population and land use has

changed due to rapid economic growth in this area, it is

worth constructing a more accurate database incorpo-

rating emission factors with sufficient regional specificity

and detailed geographic information. CH4 emissions

resulting from enteric fermentation in livestock (dairy

cattle, non-dairy cattle, buffaloes, goats, sheep, pigs,

horses, asses, mules, and camels) and livestock manuremanagement systems (the same animals plus poultry)

were estimated. For some countries, we obtained

country-specific emission factors for major livestock

species, such as cattle and buffaloes, to determine

emissions more accurately. The geographic distribution

of emissions was mapped with a resolution of 0.5 0.5

latitudelongitude. Finally, the historic trends in CH4emissions from livestock in this area were also

determined.

2. Methodology

This study targeted Asia from 55N (Mongolia or

China) to 10S (Indonesia) latitude and from 60E

(Afghanistan) to 150E (Japan) longitude. CH4 emis-

sions from livestock were evaluated on country or

district levels within this area. For three countries, India,

China, and Japan, we considered livestock populations

in smaller administrative regions, 26 states and union

territories, 31 provinces and cities, and 47 prefectures,

respectively. First, we prepared gridded databases, with

a resolution of 0.5 0.5 latitudelongitude, for several

species of livestock in each country or smaller district

from finer gridded digital information on land area and

land cover/land use. Then, CH4 emissions from enteric

fermentation in livestock, Ei (kg CH4 yr1), in each grid

square were determined using Ei EFi LPi; where EFi(kg CH4 head

1 yr1) are the emission factors for

livestock species i; and LPi is the number of individuals

in livestock species i: We applied Asian country-specific

emission factors for several types (e.g., their age, sex,

and purpose) whenever possible. For CH4 emissions

from manure management systems, Ei (kg CH4 yr1)

was calculated from Ei P

EFit LPit using a gridded

temperature dataset (New et al., 2000). Here, EFit (kg

CH4 head

1 yr

1) are the emission factors and LPit(head) is the livestock population for species i and

climate region t: The climate regions were consistent

with climate types: cool (less than 15C), temperate

(1525C), and warm (greater than 25C) (Houghton

et al., 1997). Finally, the CH4 emissions from each

country were obtained by integrating the gridded

emissions for that country.

2.1. Spatial allocation of livestock populations

To prepare gridded datasets of livestock for each

country or smaller district, we used a methodology that

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is similar to that used to determine the geographic

distribution of animals (Lerner et al., 1988) with a

database that was used previously by some authors

(Bouwman et al., 1995, 1997; Olivier et al., 1999). The

basic method allocates livestock populations into all

grids with vegetation or land cover, where livestock seem

to be able to live, considering the proportion of the areaof each grid square occupied by each land type and

district/country level. For the spatial allocation of

livestock populations in Japan, we used a land use

database containing digital geographic information

including an administration code, the total land area,

and the area of each of 11 land types in each grid with a

45 3000 latitudelongitude resolution, or about

1 1 km2 (Geographical Survey Institute (GSI), 1991).

Each livestock population on a prefectural level was

allocated into a 0.5 0.5 grid using the proportional

area of four farm types to the total land area in each

grid. For the spatial allocation of livestock populationsin other countries, we used two databases: the area grid

database that contains the land area for each 2.5 2.50

grid cell in the Gridded Population of the World version

2 (GPW v2) dataset, and the land cover gridded

database in the 3000 Land Cover dataset. The former

dataset was provided by the International Earth Science

Information Network of Columbia University, and is

available from the web page of the Socioeconomic Data

and Applications Center (http://sedac.ciesin.org/). This

dataset gives the land area in each grid occupied by each

country and each smaller district for large countries

(e.g., India and China). The latter is provided by the

Land Cover Working Group (LCWG) of the Asian

Association on Remote Sensing (AARS) and the Center

for Environmental Remote Sensing (CEReS) at Chiba

University (CEReS, 1999). Each grid is classified

according to the main land use pattern, based on 59

classes including 47 classes for vegetation, 8 classes for

non-vegetation, and 4 classes for water. Using these two

databases, each livestock population at the country

level, or the district level for India and China, wasplotted into a 0.5 0.5 grid using the relative area

based on land use types: crops, grasslands (including

grass crops), mixed-vegetation, etc.

2.2. Emission factors

2.2.1. Enteric fermentation

Studies of emission factors for livestock began with

Crutzen et al. (1986), who presented a comprehensive

assessment of CH4 production from enteric fermenta-

tion in each animal based on the type and weight of the

animal, the kind and quality of feedstuffs, and theenergy expenditure of the animal. Recently, the IPCC

(Houghton et al., 1997) summarized the emission factors

that are thought to be the most appropriate for the

livestock of each country. Some countries in the Asian

Pacific region have developed emission factors for their

major CH4-producing livestock based on actual experi-

ments and reported the results at the IGES and NIES

workshop on GHG inventories for the Asia-Pacific

region, and the emission factors are available from the

IGES web page at http://www.iges.or.jp/. However,

such emission factors based on observed data have not

been prepared for all countries. Therefore, we divided

our study area into four regions: (A) the countries of

South Asia, (B) the countries of Southeast Asia,

ARTICLE IN PRESS

Fig. 1. Domain in present study. All countries were divided into four groups to set the emission factors for CH4 emissions.

K. Yamaji et al. / Atmospheric Environment 37 (2003) 43934406 4395
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(C) China, Mongolia, and North Korea, and (D) Japan,

South Korea, and Taiwan (Fig. 1). We selected India,

Thailand, China, and Japan as representative of each

region, and their emission factors were applied to the

corresponding region. India is the largest CH4-emitting

country in South Asia. Indian emission factors for major

CH4-producing livestock were based on actual experi-ments and the IPCC Tier 2 methodology (Mitra, 1992;

Singh and Mohini, 1996). We used emission factors for

cattle, buffaloes, goats, and sheep based on actual

experimental data for India (Singh and Mohini, 1996).

For Thailand, which is representative of Southeast

Asian countries, we applied the CH4 emission factors

for cattle and buffaloes from the IPCC Tier 2 level

reported at the IGES and NIES workshop. China is the

most important country in East Asia with respect to

CH4 emissions from livestock. Chinese CH4 emission

factors for major livestock species based on the IPCC

Tier 2 methodology have been reported in the IGES andNIES workshop and elsewhere (Braatz et al., 1996;

Dong et al., 1996, 2000). We used the emission factors

for cattle, buffaloes, goats, and sheep from Dong et al.

(2000). In addition, in Japan, the CH4 emission factors

for cattle, goats, sheep, and pigs have been obtained

using chamber, facemask, and indicator methods. In this

study, we used the Japanese emission factors reported by

Saito (1988), Shibata et al. (1993), and Terada (2000).

For livestock species other than dairy cattle, we used the

emission factors from Crutzen et al. (1986). These

emission factors are summarized in Table 1.

For countries other than India, Thailand, China, and

Japan, we applied the emission factors shown in Table 2.

For the livestock species for which region-specific

emission factors were estimated in a representative

country of each region, averaged factors weighted by

the relative population of each livestock type were used.

For dairy cattle, there is a good correlation between

milk production (kg head1 yr1) and the default IPCC

CH4 emission factor for each country. The correlation

can be written as

EF 1:485 106mp2 2:314 102mp

29:53r2 0:9631; 1

where EF (kg CH4 head1 yr1) is the emission factor

for dairy cattle, and mp (kg head1 yr1) is the milk

production per cow. For the countries in Regions B, C,

and D, an emission factor was calculated using the mp

value obtained from the Food and Agriculture Organi-

zation (FAO) web page using Eq. (1) (Table 3). For

Region A, we used the preferential emission factors for

dairy and nondairy cattle in India.

2.2.2. Manure management systems

The emission factors for manure management systems

are summarized in Table 4. The IPCC guidelines

ARTICLE IN PRESS

Table1

Emissionfactorsformethane(kgCH4

head

1

yr

1)fromentericfermentationforIndia,

Thailand,

China,andJapan

Country

Cattle

Buffaloes

Goats

Sheep

Pigs

Horses

Mules&Asses

Camels

Refs.

India

Indigenous

Male

29.8

3.9

4.7

1.0

a

18.0

a

10a

58a

Singhan

dMohini(1996)

Male

31.1

Female

25.8

Female

37.2

Crutzen

etal.(1986)

Crossbred

Male

36.0

Female

37.8

Dairy

Non-dairy

Thailand

Milking

78.8

Beef&work

Male

54.9

5.0

a

5.0

a

1.0

a

18.0

a

10a

58a

Webpag

esoftheIGES/NIES

Dry

78.8

Female

46.0

Female

51.6

worksho

p(2000)

Male

41.3

Other

41.3

Crutzen

etal.(1986)

Other

38.3

Fatting

41.3

China

Breedable

70.4

Breedable

51.3

Breedable

67.5

Breadable

7.1

Breadable7.1

1.0

a

18.0

a

10a

58a

Donget

al.(2000)

Young

38.4

Young

28.5

Young

38.4

Other3.6

Other3.6

Crutzen

etal.(1986)

Other

56.5

Other

53.1

Other

56.5

Japan

Lactating

116.4

Breeding

59.6

4.1

4.1

1.1

18.0

a

Saito(19

98)

Dry

66.6

Fatting

Shibataetal.(1993)

o2years

69.7

>1yea

r

65.0

Terada(

2000)

o1yea

r

47.4

Crutzen

etal.(1986)

Dairyb

reed

81.4

a

UniformvaluesquotedfromCrutzenetal.(1986).



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(Houghton et al., 1997) also provided sectoral emission

factors that are dependent on environmental tempera-

ture and the management systems for livestock manure(Safley et al., 1992; Woodbury and Hashimoto, 1993).

As there is less information on the emission factors for

manure management systems compared with those for

enteric fermentation in this area, we estimated the CH4emissions from livestock manure management systems

using these default emission factors.

2.3. Activity data selection

To estimate CH4 emissions from livestock, we

included major CH4-producing livestock in this study:

for enteric fermentation, non-dairy cattle, dairy cattle,

buffaloes, goats, sheep, pigs, horses, asses, mules, and

camels; for manure management systems, the same

animals plus poultry. The populations of each type of

livestock are available from databases on the FAO web

page (http://www.fao.org). Milk production by dairycattle in each country was obtained from the same web

page. For India, Thailand, China, Taiwan, and Japan,

we obtained country- and district-level statistics, which

also divided some livestock populations into subtypes

(e.g., age, sex, and purpose) (Samnakngan Sathiti haeng

Chat, 1996; Department of Animal Husbandry and

Ministry of Agriculture (AH&D), 1999; Census and

Statistics Department, 2000; Directorate of Economics

and Statistics, 2000; Council for Agriculture, 2001;

Council for Economic Planning and Development

(CEPD), 2001; National Bureau of Statistics of China

(NBS), 2001; Ministry of Agriculture, Forestry, and

Fisheries (MAFF), 2001, 2002). However, the data for

India and Thailand were for 1992 and 1993, respectively,

and were too old to use. Therefore, only the ratios of

these data were used to distribute the 2000 FAO

livestock population into livestock subcategories.

3. Results and discussion

3.1. Overview

The total CH4 emissions from livestock in South,Southeast, and East Asia were estimated to be about

29.9Tg CH4 yr1 in 2000, of which 87% (25.9 Tg

CH4 yr1) was produced via enteric fermentation and

13% (4.0 Tg CH4 yr1) by manure management systems

(Table 5). The total emissions accounted for 56% of

the global CH4 emissions from all sources during the

1990s (Houghton et al., 2001). Compared with the

global estimates, 80 Tg CH4 yr1 (enteric fermentation)

and 14T g CH4 yr1 (manure management system)

(Mosier et al., 1998), the CH4 emissions in the targeted

area accounted for 32% and 28% of global emissions,

respectively. Overall, these Asian CH4 emissions

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Table 2

Regional emission factors for methane (kgCH4 head1 yr1) from enteric fermentation

Region Non-dairy cattle Buffaloes Goats Sheep Pigs Horses Mules & Asses Camels

A 29.0 35.7 3.9 4.7 1.0a 18.0a 10a 58a

B 44.9 53.2 5.0a 5.0a 1.0a 18.0a 10a 58a

C 33.0 56.3 5.4 5.6 1.0a

18.0a

10a

58a

D 55.0 56.3b 4.1 4.1 1.1 18.0a 10a 58a

aUniform values quoted from Crutzen et al. (1986).bSubstituting emission factor of Region C.

Table 3

Milk production (mp) (kg head1 yr1) of dairy cattle and the

emission factor (EF) for methane (kg CH4 head1 yr1) from

enteric fermentation for each country

Country mp EF

Region A 29

Region B

Cambodia 170 33

Indonesia 1,435 60

Laos 200 34

Malaysia 477 40

Myanmar 392 38

Philippines 2,618 80

Viet Nam 802 47

Region C

North Korea 2,308 75

Mongolia 312 37

Region D

South Korea 8,833 118

Taiwan 5,414 111

Note: Region A includes Afghanistan, Bangladesh, Bhutan,

Nepal, Pakistan, and Sri Lanka, where EF was obtained from

EF for cattle in India. EF values in the other countries were

calculated from Eq. (1).

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amounted to 32% of global emissions from livestock.

Therefore, CH4 emissions from livestock in Asia

contribute close to one-third of the amount of global

emissions. Table 5 lists the CH4 emissions from livestock

by country. India, which has been reported to have the

largest CH4 production by livestock (Lerner et al.,

1988), had total estimated livestock emissions of 11.8 Tg

CH4, in 2000, or about 40% of the total CH4 emissions

from the study area. Cattle and buffaloes produced 7.0

and 3.8Tg CH4 yr1, respectively, and accounted for

more than 90% of the CH4 emissions from all livestock

in India. In particular, the contribution of buffaloes was

very large, accounting for more than half of the

emissions from all buffaloes in Asia. The second largest

producer was China, where livestock emitted about

10.4Tg CH4 yr1. Cattle produced 5.6 Tg CH4 yr

1 or

about 54% of the CH4 emissions from all livestock in

China. Moreover, in China, the emissions from manure

management systems, especially for pigs, were very high.

Pakistan was the third largest contributor, with a level ofabout 1.8 Tg CH4 yr

1. The emissions from Pakistan

were one order of magnitude lower than those from

India and China. These three countries contributed

more than two-thirds of the CH4 emissions from South,

Southeast, and East Asia. These countries were followed

by Bangladesh and Indonesia.

3.2. Spatial distribution of CH4 emissions from large-

emission livestock

Cattle, which are bred widely in the study area,

produced about 16.8 Tg CH4 yr

1

, 56% of the emissions

by all livestock in the target area. Of this, 15.5Tg

CH4 yr1 was produced by enteric fermentation. Fig. 2

shows the spatial distribution of CH4 emissions from

cattle. Very high levels of emission were seen throughout

India. Emissions exceeding 20 Gg CH4 grid1 were seen

in the Ganges River delta in northeast India and

Bangladesh, where cattle are bred mainly as draft and

dairy animals. The north China plain, where cattle are

bred for meat, also includes areas of high CH4 emission.

In particular, CH4 from the China plain (Shandong and

Henan) and southern China (Guangxi and Hainan)

contributes greatly to the high levels of emissions in

China. According to a report by the Livestock Industry

Information network in Japan (http://www.lin.go.jp),

there are plans to enlarge the meat cattle sector rapidly

in some of these regions (Zhongyuan, Donbei, and

Henan) and to develop the meat cattle sector in the Red

Basin and the Jianghan Plain (fork of the Yangtze River

and Han Shui). Therefore, it is possible that CH4

emissions from cattle will increase in these regions ofChina. Comparatively high CH4 emissions are also seen

in the lower Irrawaddy in Myanmar.

Buffaloes, which are mainly bred in southern Asia,

produced about 7.1 Tg CH4 yr1, 24% of the emissions

by all livestock. As shown in Fig. 3, almost all of the

CH4 from buffaloes was emitted from areas south of

35N. Comparatively high-emission regions appeared

throughout India and Pakistan. The higher CH4emission areas were in the Ganges basin, in north India,

where buffaloes are used mainly as draft and dairy

animals. In particular, grids exceeding 10 Gg CH4 grid1

were found in the north Indian state of Uttar Pradesh.

ARTICLE IN PRESS

Table 4

Emission factors for methane (kg CH4 head1 yr1) from manure management systems of livestock in each climate region

Region Climate regiona Cattle Buffaloes Goats Sheep Pigs Horses Mules & Asses Camels Poultry

Dairy Non-dairy

A Cool 5 2 4 0.11 0.10 3 1.1 0.60 1.3 0.012

Temp. 5 2 5 0.17 0.16 4 1.6 0.90 1.9 0.018

Warm 6 2 5 0.22 0.21 6 2.2 1.2 2.6 0.023

B Cool 7 1 1 0.12 0.10 1 1.1 0.60 1.3 0.012

Temp. 16 1 2 0.18 0.16 4 1.6 0.90 1.9 0.018

Warm 27 2 3 0.23 0.21 7 2.2 1.2 2.6 0.023

C Cool 7 1 1 0.12 0.10 1 1.1 0.60 1.3 0.012

Temp. 16 1 2 0.18 0.16 4 1.6 0.90 1.9 0.018

Warm 27 2 3 0.23 0.21 7 2.2 1.2 2.6 0.023

D Cool 7 1 1 0.12 0.19 1 1.4 0.76 1.6 0.078

Temp. 16 1 2 0.18 0.28 4 2.1 1.14 2.4 0.117

Warm 27 2 3 0.23 0.37 7 2.8 1.51 3.2 0.157

Refs. Safly et al. (1992), Woodbury and Hashimoto (1993), and Houghton et al. (1997).aTerms of annual average temperature: Cool=o15C; Temp.=1525C; Warm=>25C.

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Table 5

Methane emissions (Gg CH4) from livestock in each country

Country Cattle Buffaloes Goats Sheep Pigs Horses Mules & Asses Cam

ef mm Total ef mm Total ef mm Total ef mm Total ef mm Total ef mm Total ef mm Total ef

Region A

Afghanistan 76 9 85 0 0 0 23 1 24 66 2 68 0 0 0 2 + 2 9 1 10 17

Bangladesh 695 62 757 30 4 34 133 7 140 5 + 6 0 0 0 0 0 0 0 0 0 0

Bhutan 13 1 14 + + + + + + + + + + + + 1 + 1 + + + 0

India 6,381 574 6,955 3,345 466 3,812 480 25 505 272 11 284 17 94 111 14 2 16 12 1 13 60

Nepal 204 17 221 126 16 142 25 1 26 4 + 4 1 3 4 0 0 0 0 + 0 0

Pakistan 642 67 707 810 111 92 0 + 0 113 4 117 0 0 0 5 1 6 40 4 44 46

Sri Lanka 20 4 24 25 3 28 2 + 2 + + + + + + + + + 0 0 0 0

Region B

Brunei + + + + + + + + + 0 0 0 + 0 + 0 0 0 0 0 0 0

Cambodia 132 9 142 37 2 39 0 0 0 0 0 0 2 14 15 + + 1 0 0 0 0

Indonesia 517 28 546 128 7 135 63 3 66 37 1 39 5 33 39 9 1 10 0 0 0 0

Laos 51 2 53 54 3 56 1 + 1 0 0 0 1 7 9 1 + 1 0 0 0 0Malaysia 31 3 34 8 + 9 1 + 1 1 + 1 2 11 13 + + + 0 0 0 0

Myanmar 473 44 517 130 6 136 7 + 7 2 + 2 4 23 26 2 + 2 + + + 0

Philippines 111 4 115 161 8 169 35 1 36 + + + 11 63 74 4 + 5 0 0 0 0

Singapore + + + + + + + + + 0 0 0 + 1 2 0 0 0 0 0 0 0

Thailand 271 17 287 101 6 107 1 + 1 + + + 7 44 50 + + + + + + 0

Viet Nam 184 7 192 154 7 161 3 + 3 0 0 0 20 107 127 2 + 3 0 0 0 0

Region C

China 5,074 488 5,562 1,281 41 1,322 849 20 869 746 14 759 447 1,174 1,621 158 11 168 14 9 22 19

North Korea 19 1 20 0 0 0 12 + 13 1 + 1 3 3 6 1 + 1 0 0 0 0

Mongolia 98 10 108 0 0 0 55 1 57 78 1 79 + + + 50 3 53 0 0 0 21

Region D

Japan 355 16 372 0 0 0 + + + + + + 11 19 30 + + + 0 0 0 0

South Korea 118 4 121 0 0 0 2 + 2 + + + 9 8 17 + + + + 0 + 0Taiwan 7 1 8 + + + 1 + 1 0 + 0 8 27 35 + + + 0 0 0 0

Total 15,473 1,368 16,843 6,391 680 7,072 1,692 61 1,753 1,326 34 1,360 548 1,627 2,180 251 18 269 76 15 90 163

Note: + means a little emission, less than 1 Gg. ef and mm is emissions from enteric fermentation and manure management system of liv


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Moreover, the CH4 emissions from the southern

provinces of China (Guangxi and Hainan) and the

Philippines were also comparatively high.

The contribution of pigs to CH4 emissions is

considerable as pigs are important domestic livestock

in East Asia, although pigs are non-ruminants and the

emissions from enteric fermentation are low. In parti-

cular, in China, CH4 emissions from pigs amounted to

more than 15% of the total. The CH4 emissions from

pigs were estimated at 2.2 Tg CH4 yr1, of which 1.6 Tg

CH4 yr1 was emitted through manure management

systems. Unlike another livestock, the contribution of

pigs due to manure management systems was large. As

shown in Fig. 4, high levels of emission occur widely

throughout China. In particular, there were high CH4emission grids, 1 Gg CH4 grid

1, in eastern China.

Many parts of the Philippines, Vietnam, and Taiwan

also produced high levels of CH4 emissions due to their

relatively high pig populations.

Fig. 5(a) illustrates the spatial distribution of CH4emissions from all livestock in the study area. High CH4emissions occur in a wide area of India. The higher

CH4 emission grids are concentrated in the northern

Indian states of West Bengal, Bihar, and Uttar Pradesh,

which are located in the Ganges basin. These high

emissions in India are due mainly to cattle and

buffaloes, which are bred as dairy and draft animals.

This area is divided into two: in the upper and middle

Ganges, CH4 is mainly produced by buffaloes, and in

the lower Ganges, CH4 is mainly produced by cattle. In

China, the emissions are concentrated in the lower

Yellow River basin (Shandong and Henan) and the

mouth of the Xi River. In northern China, CH4 is

produced mainly by cattle, goats, and sheep, while insouthern China, it is produced predominantly by cattle

and buffaloes.

3.3. Comparison with previous studies

Our results are compared with those of other studies

in Table 6 (Lerner et al., 1988; Singh and Mohini, 1996;

Dong et al., 1996; Garg et al., 2001; Olivier and

Berdowski, 2001). Compared with the report of Lerner

et al. (1988), our values are substantially lower for India,

Pakistan, and Bangladesh, considering the difference in

base years. This may be due to their use of old uniform

ARTICLE IN PRESS

Fig. 2. Spatial distribution of methane from cattle in 2000.

Fig. 3. Spatial distribution of methane from buffaloes in 2000.

Fig. 4. Spatial distribution of methane from pigs in 2000.



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emission factors. In contrast, the gap for China may be

less of a discrepancy after considering the growth of

livestock populations during the interval. Although the

comparison with EDGAR 3.2 (Olivier and Berdowski,

2001) shows excellent agreement for total emissions in

Asia, there are considerable differences in country-level

emissions. Our total emissions for each South Asian

country are 622% lower than the EDGAR data

because we used smaller emission factors for ruminant

animals. In particular, for buffaloes, the current global

default emission factor is 55 kg CH4 head1, but we used

country-specific values of 29.837.2 kg CH4 head1.

Conversely, our emissions for China and Japan, typical

countries in Regions C and D, were about 10% and

30% higher than the EDGAR values, respectively. This

is also due to differences in the emission factors because

we used country-specific values and metabolism, body,

and production information for each sub-type of major

livestock, while EDGAR used regional default values.Our estimate for Indonesia is similar to that of EDGAR

3.2, because for Region B we could not obtain as much

country- and region-specific data as for the other

regions. There have been two other recent estimates of

CH4 emissions from livestock in India (Singh and

Mohini, 1996; Garg et al., 2001). While Singh and

Mohini (1996) considered only emissions from rumi-

nants, we used the same emission factors, so the gap

between our results is due to the difference in base year.

In contrast, the differences in emission factors made our

estimate 37% higher than that of Garg et al. (2001),

because their estimate was based on the IPCC Tier 2

method for enteric fermentation and the IPCC Tier 1

method for manure management systems. For China,

we compared our results with those of the study by

Dong et al. (1996). Their estimate was considerably

smaller than that in the present study due to the

difference in base year, which is ascribed to differences

in livestock populations and the basic parameters used

to obtain emission factors.

The global spatial distribution of CH4 emissions from

livestock was reported by Lerner et al. (1988), and more

recently by the EDGAR group (Olivier and Berdowski,

2001) with a 1 1 resolution. Allocations of livestock

by the EDGAR group were based on gridded maps by

Lerner et al. (1988), so the spatial distributions in these

digital maps were similar. Here, we compared our results

(Fig. 5(a)) with EDGAR (Fig. 5(b)). Although our map

has a higher 0.5 0.5 resolution, it is broadly

consistent with the previous results. For example, the

highest emission areas are from north India toBangladesh. In contrast, the distribution patterns in

China do not correspond. Our map contains high-

emission areas in the north China plain (lower Yellow

River basin) and southern China, while the EDGAR

map is uniform and does not distinguish high-emission

areas. The largest cause may be the difference in the

gridded databases used to allocate livestock; a land

cover dataset with a 3000 resolution (LCWG/CEReS,

1999) used in this study, versus an animal population

density map with 1 resolution (Lerner et al., 1988)

based on the vegetation/land use map (Matthews, 1983)

used by the EDGAR group.

ARTICLE IN PRESS

Fig. 5. (a) Spatial distribution of methane from all livestock in 2000. (b) Spatial distribution of methane from all livestock in 1995

(Olivier and Berdowski, 2001).



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3.4. Historic trends in CH4 emissions from livestock

We attempted to estimate CH4 emissions for every

period of 5 years from 1965 to 2000 using FAO data and

the emission factors discussed above (Figs. 6(a) and (b)).

This increase reflects the recent increase in livestock

populations, because we did not consider changes in theemission factors. CH4 emissions from livestock for the

period 19652000 tended to increase gradually, from

18.6 Tg CH4 yr1 in 1965 to 29.9 Tg CH4 yr

1 in 2000.

The total increase from 1965 to 2000 was about 2%

yr1. The reported global increase in CH4 from livestock

was 2% yr1 for 19651993 (Stern and Kaufmann,

1996) and around 1% yr1 for 19601990 (Van

Aardenne et al., 2001). Our results were similar to the

global results. The rate of increase for 19651985 was

about 0.2 Tg CH4 yr1 and was smaller than the value of

0.4Tg CH4 yr1 for the period 19852000. Although the

average increase was around 1% yr

1

for 19651980, the

rate grew to around 2% yr1 for 19801995 and then

decreased to 1% yr1 for 19952000. These trends reflect

the average increase rate of CH4 from cattle, although

buffaloes and pigs also contributed to the strong upward

tendency.

Fig. 6(b) shows an important association between

the average increase in total CH4 emissions andthose for China. In China, the rate of increase

was about 0.1 Tg CH4 yr1 for 19651985, and it

increased to 0.2 Tg CH4 yr1 for 19852000. This rapid

increase in China caused the marked increase for the

entire study area. In contrast, in India, the increase in

CH4 emissions was about 0.1 Tg CH4 yr1 (around 1%

yr1), which made a relatively small contribution to the

overall increase. The CH4 emissions from the other

countries in the period 19652000 also increased

gradually. However, the changes in emissions for each

livestock species, country, and sub-region were not

uniform.

ARTICLE IN PRESS

Table 6

Comparison of estimated methane emissions (Tg CH4) with previous estimations

Country Source

category

This study Lerner

et al.

(1988)

Singh and

Mohini

(1996)a

Dong

et al.

(1996)a

Garg

et al.

(2001)

EDGAR 3.2

(Olivier and

Berdowski, 2001)

Base year 1995 2000 1984 1993 1990 1995 1995

India ef 10.0 10.6 10.3 9.0 10.8

mm 1.1 1.2 1.0

Total 11.1 11.8 8.1 11.8

China ef 8.1 8.6 4.4 5.8 7.7

mm 1.4 1.8 1.0

Total 9.5 10.4 8.7

Pakistan ef 1.6 1.7 1.5 2.1

mm 0.2 0.2 0.2

Total 1.8 1.8 2.3

Bangladesh ef 0.8 0.9 1.4 0.9mm 0.1 0.1 0.1

Total 0.9 0.9 1.0

Indonesia ef 0.8 0.8 0.8

mm 0.1 0.1 0.1

Total 0.8 0.9 0.9

Japan ef 0.4 0.4 0.2

mm 0.1 0.1 0.1

Total 0.4 0.4 0.3

Totalb ef 25.0 25.9 25.9

mm 3.3 4.0 2.8Total 28.3 29.9 28.7

Note: ef and mm is emission from enteric fermentation and manure management system of livestock, respectively.aCH4 emissions from ruminants (cattle, buffaloes, sheep, goats, and camels).bTotal emission from this objective area.



11/14

In most countries in the study area, livestock are

raised under worse conditions than their European and

American counterparts: i.e., livestock are given less food

and it is of low quality and less digestible, so they emit

less CH4 per head. In India, which had the largest

emissions, most livestock are raised in small rural

holdings; they therefore weigh less and eat less than

their European and American counterparts, so they

must emit less CH4 (Mitra, 1992, 1996; Singh and

Mohini, 1996). For example, for enteric fermentation,

the annual emission rate of cattle in India is 8.536.0 kg

CH4 head1 (Mitra, 1992; Singh and Mohini, 1996)

versus 47118 kg CH4 head1 for European and North

American cattle (Houghton et al., 1997). However, the

study area will inevitably become a greater source of

CH4 as these conditions are improved. Indeed, in Japan,

which imports high quality grain from the USA andother countries, and where livestock are adequately fed,

the emission factor for cattle is much higher than in

other Asian countries. China has relied on feed grain

imports since the mid-1990s, so CH4 emission factors for

China are also likely to increase substantially. Therefore,

it is important to consider such detailed evaluations in

future scenarios of CH4 emissions from livestock.

3.5. Limitations and uncertainties

It is inevitable that our study also includes uncertain-

ties, as mentioned in many previous studies (e.g.,

Crutzen et al., 1986; Lerner et al., 1988; Penman et al.,

2001; Olivier et al., 2002). First, the emission factors,

which are the basis of any estimate of emissions, include

significant uncertainty due to the lack of appropriate

observations. According to recent uncertainty estimates

by the IPCC, the uncertainty for CH4 emissions from

enteric fermentation is likely 73050% with the Tier 1method and of the order of720% with the Tier 2

method (Penman et al., 2001). From these uncertainties,

the emission factors used in our inventory were classified

into six groups (S, NS, T2, NT2, ST1, and T1): S is a

country-specific emission factor based on experiments

(uncertainty 720% or less); NS is an emission factor

extrapolated from a neighboring country with an S level

(uncertainty 720% to 50%); T2 is a Tier 2 level

emission factor (uncertainty720%); NT2 is an emission

factor extrapolated from a neighboring country with a

T2 level (uncertainty720% or more); ST1 is a country-

specific emission factor based on the milk production ofdairy cattle (uncertainty 730% to 50% or less); and T1

is a Tier 1 level emission factor (uncertainty 730% to

50%) (Table 7). About 83% of our total estimate,

29.9 Tg CH4, was calculated using emission factors that

were assumed to be more accurate than the IPCC Tier 1

method. In particular, about 64% of the total estimated

CH4 emissions used emission factors with an accuracy of

Tier 2 or better. This is more accurate than other

inventories, e.g., the EDGAR group reported medium

uncertainty (50%) for CH4 emission factors from

animals (Olivier et al., 1999, 2002). Nevertheless, the

difference between our total estimated CH4 emissions

(29.9 Tg CH4) and those using the default IPCC Tier 1

emission factors (33.0 Tg CH4) is less than 10%. This

difference is mainly due to the emission factors for large

ruminants, such as cattle and buffaloes, in Regions A

and C. Second, errors included in the data cause

significant uncertainty, as pointed out by Lerner et al.

(1988). Note that the cattle populations in the FAO

database contain some uncertainties. The data include

an uncertainty of a few percent that we cannot improve

or verify. For example, the FAO web page estimates that

there were 359, 340, and 353 million cattle in this area in

1995 in data reported at the end of 2000, the beginning

of 2001, and November 2001, respectively. However,these uncertainties are small compared with uncertain-

ties in the emission factors under specialized feeding or

management conditions, as pointed out in the IPCC

guidelines (Houghton et al., 1997). Third, the reliability

of the spatial distribution of CH4 depends on the land

cover database. Lerner et al. (1988) compiled a

1.0 1.0 animal density map using a 1.0 digital

database of land use practices (Matthews, 1983), which

was used by the EDGAR project (e.g., Bouwman et al.,

1995, 1997; Olivier et al., 1999). As we used a more

detailed, newer 3000 land cover dataset (LCWG/CEReS,

1999) to complete a higher resolution 0.5 0.5

digital

ARTICLE IN PRESS

Fig. 6. Trends of methane emission from livestock (19652000).



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dataset, our study appears to provide an emission

database of Asia with a finer resolution, although the

method used the same data as used by Lerner et al.

(1988). Nevertheless, it is difficult to quantify the

distribution errors.

4. Conclusions

This study compiled an inventory of CH4 emissions

from livestock in South, Southeast, and East Asia in

2000. This inventory provides CH4 emissions by country

and livestock type. Estimates were based on detailed

activity data at the country/district level, using factors

for livestock types and country-/region-specific emission

factors as much as possible. Our inventory gave a high-resolution 0.5 0.5 gridded database of CH4 emissions

according to source livestock.

In 2000, CH4 emissions from livestock were 29.9 Tg

CH4, of which 87% (25.9 Tg CH4 yr1) was produced

via enteric fermentation, and 13% (4.0 Tg CH4 yr1)

from manure management systems. Ruminants, such as

cattle and buffaloes, produced particularly high levels of

CH4, 16.8 and 7.1 Tg CH4, respectively, which exceeded

80% of the total CH4 emissions from Asian livestock. In

India, the country with the highest levels of CH4emission, livestock produced 11.8 Tg CH4. High-emis-

sion grids were concentrated in the Ganges basin. Most

of the emissions in the lower Ganges were from cattle,

whereas buffaloes made the greatest contribution in the

middle and upper reaches. Grids producing more than

30Tg CH4 grid1 were seen in the Ganges delta. China,

the second largest producer, produced 10.4 Tg CH4 from

livestock. The north China plain and southern China

were comparatively high-emission regions. Our results

for China differed markedly from those of EDGAR,

mainly due to the different land use/cover datasets used.

Our inventory still includes large uncertainties due to

a lack of experimental data. We evaluated the un-

certainties from three perspectives: emission factors,

statistical data, and digital area databases. Emission

factors included the greatest uncertainty: about 64% of

our estimate used country-specific emission factors,

which were IPCC Tier 2 level or better (uncertainty720% or less). Nevertheless, the differences between

our results and those using the default Tier 1 emission

factors for the entire target area were less than 10%,

although clear differences were seen for some countries

in East Asia.

In addition, we evaluated the trend in CH4 emissions

for 19652000. In this period, emissions increased by 2%

yr1, although it is necessary to focus on significant

uncertainties in the estimate due to the emission factors

used for each livestock type. The increase in China was

most marked. Due to changes in the livestock popula-

tions and the intricate interactions between the needs of

ARTICLE IN PRESS

Table 7

Accuracy level of each emission factor

Country Enteric fermentation MMS

Cattle Dairy

Cattle

Non-dairy

Cattle

Buffaloes Goats Sheep Pigs Horses Mules

& Asses

Camels

Region A

India S S S S T1 T1 T1 T1 T1

Other countries NS NS NS NS T1 T1 T1 T1 T1

Region B

Thailand T2 T2 T2 T1 T1 T1 T1 T1 T1 T1

Other countries ST1 NT2 NT2 T1 T1 T1 T1 T1 T1 T1

Region C

China T2 T2 T2 T2 T2 T1 T1 T1 T1 T1

Other countries ST1 NT2 NT2 NT2 NT2 T1 T1 T1 T1 T1

Region D

Japan S S S S S T1 T1Other countries ST1 NS NT2 NS NS NS T1 T1 T1 T1

Note: MMS means manure management system. Susing country-specific factors based on experiments is likely to be in the order of the

uncertainty,"720%. NS using extrapolation from neighbouring countrys values (country-specific factors), is likely to be in the order

of the uncertainty, E720% to 750%. T2 using the IPCC Tire 2 level is likely to be in the order of the uncertainty, E720%. NT2

using extrapolation from neighbouring countrys values (IPCC Tire 2 level) is likely to be in the order of the uncertainty, h720%.

ST1 using the IPCC Tire 1 level with country-specific information of milk production is likely to be in the order of the uncertainty,

"730% to 750%. T1 using the IPCC Tire 1 level is likely to be in the order of the uncertainty, 730% to 750%.



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livestock, human population growth, and changing land

use, a future increase cannot be ruled out. Changes in

the feeding of livestock in the targeted area will also

raise CH4 emissions further.

Our results suggest the importance of using empirical

parameters to calculate CH4 emissions and geographic

gridded datasets to allocate these emissions to gridswhen the emission inventory is completed. These

considerations will also invite further empirical study

in this important area of CH4 emissions.

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