2.1 Livestock database collection

The livestock population database is taken from the Department of Animal Husbandry and Statistics, India, for the year 2012 [18]. The livestock census covers all the states (28) and 7 union territories (UTs) as well as all the districts (649) of India [19]. Once, the database is collected, it is sorted and categorized into four categories: cattle, buffalo, goat, and sheep. The cattle group is further categorized into two categories: dairy and nondairy cattle. Other livestock categories including population of pigs, horses, mules, and ponies are comparatively small (less than 5% of total livestock population) and therefore not included in the research work here.

Methane Emission Assessment from Indian Livestock and Its Role in Climate Change Using… DOI: http://dx.doi.org/10.5772/intechopen.81713

Figure 1.

climate impact assessment, different climate metrics are being used to assess the climatic impact of non-CO2 GHGs in terms of CO2 equivalent emission [9, 10]. These climate metrics are estimated in tonnes of CO2e per year by multiplying each non-CO2 GHG emission with their absolute value [11]. Different climate metrics are available with different time horizons such as 20, 50, and 100 years, and it can be used for different non-CO2 GHGs [6]. The assessment may be applied instantaneously or may be integrated over a specified period of time [6]. In IPCC first assessment report, global warming potential (GWP) is proposed as a method for comparing the potential climate impact of different non-CO2 GHGs with reference to CO2 [12]. But later on, the use of GWP in climate impact assessment has not been encouraged by many scientists as GWP does not explain the magnitude of climate change, i.e., impact on temperature rise [12, 13]. Thus, [14] proposed the global surface temperature change potential (GTP) as an alternative metric to GWP to assess climate change impact of GHG emission on climate change to assess its

The GTP is the ratio of the change in the global mean surface temperature due to pulse or sustained GHG emission relative to CO2 at a given time period. The GTP is more useful for those GHGs which have lifetime less than CO2 such as short-lived GHG: CH4 [15–17]. In comparison with GWP, the GTP gives climate impact in terms of change in temperature, and so it is a more policy-relevant tool for climate

The negative climate change impact due to CH4 emission is global in nature, not only restricted to India. Thus, the present chapter is focused on livestockmediated CH4 emission estimation in India and also to assess its role in climate change impact in terms of global surface temperature change potential (GTP) and absolute global surface temperature change potential (AGTP) for potential rise in surface temperature to identify the role of Indian livestock in climate change impact. This study focuses to evaluate the impact of livestock-mediated CH4 emission on surface temperature change. Thus, the study helps researchers and scientists to predict climate change impact evaluation in terms of potential rise in global surface temperature using climate metrics due to any anthropogenic

The methodology is divided into three sections as presented in flow chart

The livestock population database is taken from the Department of Animal Husbandry and Statistics, India, for the year 2012 [18]. The livestock census covers all the states (28) and 7 union territories (UTs) as well as all the districts (649) of India [19]. Once, the database is collected, it is sorted and categorized into four categories: cattle, buffalo, goat, and sheep. The cattle group is further categorized into two categories: dairy and nondairy cattle. Other livestock categories including population of pigs, horses, mules, and ponies are comparatively small (less than 5% of total livestock population) and therefore not included in the

potential impact on surface temperature rise.

change impact mitigation [13, 15].

Climate Change and Agriculture

emission sources in future.

2.1 Livestock database collection

2. Methodology

research work here.

154

(Figure 1).

Flow chart of methodology for estimation of CH4 and climate metrics assessment. And results are represented in GIS mapping at district, state, and national level.

### 2.2 Estimation of CH4 emission

Here, in IPCC guidelines, Tier 1 methodology is used for CH4 emission estimation [20]. In IPCC Tier 1 methodology, country-wise livestock category-wise specific emission factors are available for enteric fermentation and manure management as shown in Table 1. The equation followed in CH4 emission estimation is shown in Table 2 as Eq. (1).

#### 2.3 Other climatic metric assessments

The second objective of the present work of the book chapter is climate metric assessment of livestock-related CH4 emission. Two climate metrics, viz., global surface temperature change potential (GTP) and absolute global surface temperature change potential (AGTP) and surface temperature response were applied for the CH4 emission estimation from livestock at district, state, and national level.


#### Table 1.

Specific CH4 emission factor\* (kg CH4 head<sup>1</sup> year<sup>1</sup> ) of different livestock categories.

#### Equations with their description

#### Ed¼∑<sup>z</sup> <sup>i</sup>¼<sup>1</sup> pi � ��EFi (1)

where, Ed is the CH4 emission from enteric fermentation and manure management for the ith category of livestock (e.g., dairy cattle) in kg year�<sup>1</sup> ; pi is the district wise population of ith category of livestock in million; and EFi is the specific emission factor for ith category of livestock in kg CH4 head�<sup>1</sup> year�<sup>1</sup>

metrics, viz., global surface temperature change potential and absolute global surface temperature change potential and surface temperature response, are also estimated here (Eqs. (2)–(4), Table 2) to understand the climate change impact due to

Methane Emission Assessment from Indian Livestock and Its Role in Climate Change Using…

Using specific emission factors and IPCC Tier 1 methodology, the CH4 emission in India was estimated to be 15.3 Tg CH4 in 2012. CH4 emission related to enteric fermentation is 92% of total CH4 emission (14.20 Tg CH4) and the rest 8% (1.16 Tg CH4) of total CH4 emission from manure management, respectively. Among the livestock groups, the highest CH4 emission is contributed by the cattle group which is nearly 51% of total livestock CH4 emission, and the lowest CH4 emission is

Among the 29 states, the top three most emitting states are Uttar Pradesh (2.89 Tg CH4), followed by Rajasthan (1.52 Tg CH4) and Madhya Pradesh (1.30 Tg CH4), and the lowest is in Mizoram (0.018 Tg CH4). The spatial representation of CH4 emission at state level is represented through Figure 2. From the spatial diagram of livestock CH4 emission, it is observed that the major emitting states are distributed across the western and the Indo-Gangetic plains of India. CH4 emission contributions from all the eight northeastern states are only 3.88% of total national emission. The low CH4 emission is due to less livestock population in comparison with the other states. Details of results of different category-wise livestock esti-

Livestock categories Enteric fermentation Manure management Total Cattle 7.25 0.59 7.84 Buffalo 5.97 0.43 0.64 Sheep 0.68 0.03 0.71 Goat 0.3 0.13 0.43

) emission from different categories of livestock.

livestock-related CH4 emission. The results are discussed below.

contributed by sheep (as shown in Table 3).

DOI: http://dx.doi.org/10.5772/intechopen.81713

mated CH4 emission from each state also shown in Table 4.

Spatial distribution of CH4 emission from livestock in India at state level.

3.1 CH4 emission

Table 3.

Figure 2.

157

National level CH4 (Tg year<sup>1</sup>

#### GTPdt¼Ed�GTPt (2)

GTPdt is GTP of livestock-related CH4 emission for dth district at time "t" (20 or 100 years), kg CO2e; Ed is derived from Eq. 1; GTPt is GTP at "t" time scale, which is equivalent to 67 for 20 year (GTP20) and 4 for 100 year time horizon (GTP100) [11]

#### AGTPð Þ CH<sup>4</sup> <sup>t</sup> <sup>¼</sup>ACH4�∑<sup>2</sup> j¼1 α�cj α�dj � �� <sup>e</sup>�t=<sup>α</sup>�<sup>e</sup> �<sup>t</sup> <sup>=</sup>dj h i � � (3)

AGTPð Þ CH4 <sup>t</sup> is the absolute global temperature potential of CH4, K kg�<sup>1</sup> , and t is 20 or 100 year time horizon; ACH4 is radiative forcing of CH4, 2.1 � <sup>10</sup>�<sup>13</sup> W (kg m<sup>2</sup> ) �1 ; α is perturbation life or e-folding time of CH4, 12 years; cj is climate sensitive parameters and dj response times [11]. c1 and c2 are 0.631 and 0.429, respectively; d1 and d2 are 8.4 and 409.5, respectively; e�t=<sup>α</sup> is known as an impulse radiative flux (IRF), i.e., changes in instantaneous radiative flux due to pulse emission of GHGs

### ΔTt¼Ed�AGTPð Þ CH<sup>4</sup> <sup>t</sup> (4)

An annual CH4 emission (kg) is multiplied by the AGTP values to arrive at the potential of temperature change (ΔT) in a given year (annual AGTP). In the equation, ΔTt is temperature change response, K; Ed is CH4 emission attributed by livestock, kg year�<sup>1</sup>

Table 2.

Mathematical expression for CH4 estimation and climate metric assessment used in methodology.

Climate metric GTP (CH4) for two different time horizons, i.e., 20 and 100 years, is estimated as GTP20 and GTP100 as shown in Eq. (2) in Table 2. These two different assessments are highly significant for the GHGs, which have a shorter lifetime than CO2 and more impact in a shorter time period than longer time horizon.

The AGTP estimates the temperature change (in Kelvin, K) at a time (t) associated with GHG emission as shown in Eq. (3) in Table 2 [11, 12, 21]. The instantaneous surface temperature response (ΔT) is estimated by multiplication of annual CH4 emission and AGTP [22]. Annual ΔT is used for evaluation of the direct temperature effects contributed by an annual rate of CH4 emission over time from livestock as shown in Eq. (4) in Table 2.

#### 2.4 GIS map generation

After the estimation of CH4 emission and climate metric assessment from livestock CH4 emission, GIS software, i.e., ArcGIS software, is applied to generation of spatial map for India up to state and district level. The GIS provides better understanding of results in the form of computerized spatial map. For GIS mapping, standard images have been collected from the National Remote Sensing Centre (NRSC), Government of India, for different districts and states of India. Once these standard images of the district level map and state level map of India have been collected, GIS mapping has been prepared. However, district level map could not be prepared for Jammu and Kashmir and represented at state level map, as their standard images up to district level are not available.

#### 3. Results and discussion

The estimation of CH4 emission from four different livestock categories, cattle, buffalo, goat, and sheep, in India are evaluated at districts, state, and national level using Eq. (1) mentioned in Table 2. In addition to CH4 emission estimation, climate metrics, viz., global surface temperature change potential and absolute global surface temperature change potential and surface temperature response, are also estimated here (Eqs. (2)–(4), Table 2) to understand the climate change impact due to livestock-related CH4 emission. The results are discussed below.
