Open access peer-reviewed chapter

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

Written By

Shilpi Kumari, Moonmoon Hiloidhari, Satya Narayan Naik and Raj Pal Dahiya

Submitted: September 3rd, 2018 Reviewed: September 27th, 2018 Published: June 5th, 2019

DOI: 10.5772/intechopen.81713

Chapter metrics overview

1,111 Chapter Downloads

View Full Metrics

Abstract

Indian livestock farming is one of the significant anthropogenic sources of methane (CH4) in the world. Here, CH4 emission from Indian livestock and climate change impact in terms of two climate metrics, global surface temperature change potential (GTP) and absolute GTP (AGTP), to assess the surface temperature changes for 20 and 100 year time frame have been studied. CH4 emission from Indian livestock was 15.3 Tg in 2012. GTP20 and GTP100 for livestock-related CH4 emission in India in 2012 were 1030 and 62 Tg CO2e, respectively. The study also illustrates that CH4 emissions can cause a surface temperature increase of up to 0.7–0.036 mK over the 20 and 100 year time periods, respectively. Thus, the negative climate change impact is global in nature, not only restricted to India. GTP and AGTP can be used in climate change impact study and as a more policy relevant tool.

Keywords

  • CH4 emission
  • climate change
  • global temperature change potential (GTP)
  • absolute GTP (AGTP)

1. Introduction

With the growing awareness toward the detrimental impacts of climate change, identifying and controlling of potential sources of greenhouse gas (GHG) emission have become a universal priority. Livestock farming is one of the most prominent anthropogenic sources of GHGs [1, 2, 3]. The total global GHG emission from livestock is 7.1 gigatonnes CO2e year−1, which accounts for 14.5% of all anthropogenic emissions [4, 5]. India, China, Brazil, and the USA are major regional contributors of GHG emission from livestock [6]. The growing economy and increasing demand for livestock products such as meat and dairy products increase challenges on livestock production and thus risk for climate change [7]. Therefore, it is very important in the coming future to reduce GHG emissions from livestock and promote sustainable livestock farming [8].

For sustainable livestock farming, climate change impact assessment of GHG emission and effective climate mitigation policies development are needed. For 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 potential impact on surface temperature rise.

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, 16, 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 change impact mitigation [13, 15].

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 livestock-mediated 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 emission sources in future.

Advertisement

2. Methodology

The methodology is divided into three sections as presented in flow chart (Figure 1).

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.

2.1 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.

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).

Category Enteric fermentation Manure management
Cattle Dairy cattle 58 5
Non-dairy 27 2
Buffalo 55 4
Sheep 5 2
Goat 5 0.22

Table 1.

Specific CH4 emission factor* (kg CH4 head−1 year−1) of different livestock categories.

IPCC 2006 guidelines.


Equations with their description
E d = i = 1 z p i × EF i (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−1; 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−1 year−1
GTP dt = E d × GTP t (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]
AGTP CH 4 t = A CH 4 × j = 1 2 α × c j α d j × e t α e t d j (3)
AGTP CH 4 t is the absolute global temperature potential of CH4, K kg−1, and t is 20 or 100 year time horizon; A CH 4 is radiative forcing of CH4, 2.1 × 10−13 W (kg m2)−1; α is perturbation life or e-folding time of CH4, 12 years; c j is climate sensitive parameters and d j response times [11]. c 1 and c 2 are 0.631 and 0.429, respectively; d 1 and d 2 are 8.4 and 409.5, respectively; e t α is known as an impulse radiative flux (IRF), i.e., changes in instantaneous radiative flux due to pulse emission of GHGs
ΔT t = E d × AGTP CH 4 t (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−1

Table 2.

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

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. 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.

Advertisement

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.

3.1 CH4 emission

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 contributed by sheep (as shown in Table 3).

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

Table 3.

National level CH4 (Tg year−1) emission from different categories of livestock.

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

Figure 2.

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

State Cattle Buffalo Sheep Goat Total
Andhra Pradesh 383 627 185 47 1242
Arunachal Pradesh 17 0 0 2 19
Assam 403 26 4 32 465
Bihar 508 446 2 63 1019
Chhattisgarh 373 82 1 17 473
Goa 2 0 0 0 2
Gujarat 417 613 12 26 1068
Haryana 78 359 3 2 442
Himachal Pradesh 93 42 6 6 147
Jammu and Kashmir 120 44 24 11 199
Jharkhand 328 70 4 34 436
Karnataka 410 205 67 25 707
Kerala 60 6 0 7 73
Madhya Pradesh 783 483 2 42 1310
Maharashtra 622 330 0 44 996
Manipur 10 4 0 0 14
Meghalaya 35 1 0 2 38
Mizoram 1 0 0 0 1
Nagaland 9 0 0 1 10
Orissa 442 43 0 34 519
Punjab 112 304 1 2 419
Rajasthan 586 766 64 113 1529
Sikkim 6 0 0 1 7
Tamil Nadu 392 46 34 43 515
Tripura 37 1 0 3 41
Uttar Pradesh 848 1807 9 81 2745
Uttarakhand 84 58 3 7 152
West Bengal 662 35 8 60 765
UTs 10 11 0 0 21

Table 4.

State-wise livestock category-wise CH4 emission, Gg year−1 in the year 2012.

As there are significant variations in terms of livestock populations up to district level, CH4 emission pattern also shows wide variations in India as shown in Figure 3. Banas Kantha, Gujarat (112 Gg CH4); Paschim Medinipur, West Bengal (103 Gg CH4); and Jaipur, Rajasthan (102 Gg CH4) are top three districts in terms of livestock-related CH4 emission. Furthermore, out of the total 15.3 Tg CH4 emission in India, about 50% of the emission is contributed by 153 districts alone out of total 649 total districts. Within 153 districts, of the 4 livestock groups, maximum CH4 emission (more than 50%) is contributed by buffalo in 84 districts followed by cattle (55 districts). Thus, this detailed GIS-based representation of the spatial distribution of CH4 emission from livestock reveals that the highest emitting districts (emission >50% of total CH4 emission) are located in the states of Uttar Pradesh, Gujarat, West Bengal, Rajasthan, Andhra Pradesh, and Maharashtra.

Figure 3.

CH4 emission (Gg year−1) from different categories of livestock at district levels in India, (a) emission from cattle, (b) emission from buffalo, (c) emission from sheep, and (d) emission from goat.

3.2 Climate metric assessment

The above estimation of livestock CH4 emission is estimated further used to estimate its role in climate change using climate metrics in terms of GTP and AGTP. These are further elaborated to estimate surface temperature response (ΔT) from CH4 emission due to Indian livestock. The results obtained from using Eqs. (2) –(4) (see Table 2) indicate significant contribution to GHG effect in global warming.

3.2.1 GTP of CH4 emission

The estimated CH4 emission data is used to calculate GTP at 20 and 100 year time horizon as GTP20 and GTP100. GTP due to livestock CH4 emission at 20 year time horizon is 1030 Tg CO2e (GTP20) while for 100 year time horizon 62 Tg CO2e (GTP100). Among the livestock categories, cattle and buffalo are the major sources of CH4 emission and hence for GTP. The GTP of cattle and buffalo together is worked out to more than 953.9 Tg CO2e (GTP20) and 56.9 Tg CO2e (GTP100), respectively, as given in Figure 4. The results also indicate that enteric fermentation is the major contributor (more than 90%) to GTP.

Figure 4.

Livestock category-wise GTP estimate for CH4 emission at different time horizons (a) GTP20 and (b) GTP100.

Similarly, at state level, GTP20 and GTP100 vary between 0.01–184 Tg CO2e (GTP20) and 0.007–18.0 Tg CO2e (GTP100), respectively, with the lowest in Mizoram and highest in Uttar Pradesh (Table 5 and Figure 5b and d). At district level, GTP20 and GTP100 vary between 0.009–7.5 Tg CO2e (GTP20) and 3.75 × 10−6–0.3 Tg CO2e (GTP100) (Figure 5a and c).

State GTP20 GTP100
Andhra Pradesh 80.03 4.78
Arunachal Pradesh 1.29 0.08
Assam 31.09 1.86
Bihar 68.31 4.08
Chhattisgarh 31.65 1.89
Goa 0.17 0.01
Gujarat 71.30 4.26
Haryana 29.54 1.76
Himachal Pradesh 9.71 0.58
Jammu and Kashmir 12.86 0.77
Jharkhand 29.15 1.74
Karnataka 46.18 2.76
Kerala 4.87 0.29
Madhya Pradesh 87.75 5.24
Maharashtra 66.75 3.98
Manipur 0.98 0.06
Meghalaya 2.64 0.16
Mizoram 0.12 0.01
Nagaland 0.64 0.04
Odisha 34.75 2.07
Punjab 28.09 1.68
Rajasthan 101.29 6.05
Sikkim 0.44 0.03
Tamil Nadu 33.83 2.02
Tripura 2.72 0.16
Uttar Pradesh 183.79 10.97
Uttarakhand 10.12 0.60
West Bengal 51.12 3.05
UTs 1.54 0.09

Table 5.

State-wise GTP20 and GTP100 of CH4 emission.

Figure 5.

GTP estimate of CH4 emission in India: GTP20 of CH4 in Tg CO2e at (a) district and (b) state level; GTP100 of CH4 in Tg CO2e at (c) district and (d) state level.

The GTP is a common unit of climate impact assessment per unit of GHG emissions. The results and findings of the climate metrics allow policymakers to develop GHG emission mitigation policies for different anthropogenic GHG emission sectors and for other non-CO2 GHG gases [23]. The different time horizon for GTP measurement (e.g., 20 and 100 years) allows comparisons of the global warming impacts of a gas over a period of time [24, 25]. The larger the value of GTP, the higher will be the potential for temperature change by a given non-CO2 GHG gas [15, 16, 26]. In the study, it is observed that climate change impact of CH4 in GTP100 timeframe is smaller as compared to GTP20, indicating that as the time horizon becomes longer, short-lived non-CO2 GHG gases have less impact on GTP [10, 12]. This also suggests immediate requirements of mitigation measures for CH4.

3.2.2 AGTP and surface temperature response (ΔT)

Similarly, climatic metric AGTP is also estimated, and it is worked out 4.56 × 10−14 and 2.28 × 10−15 K kg−1, for 20 and 100 year time frames, respectively. The AGTP can be used to explore more about climate change impact assessment than GWP [27]. The AGTP value is further used to estimate surface temperature response (ΔT). The surface temperature response (ΔT) of CH4 emission from the country for 20 year time frame is 0.70 mK (milli-Kelvin), and 100 year time frame is 0.036 mK.

At the state level, the highest global surface temperature response is observed resulting from CH4 emission in Uttar Pradesh, with the lowest response resulting from CH4 emission in Mizoram. CH4 emission from livestock from different states can contribute to the surface temperature response (ΔT20), ranging between 8.5 × 10−5 and 1.25 × 10−1 mK in 20 year time horizon. While in 100 year time horizon, ΔT100 varies from 4.23 × 10−5 to 6.50 × 10−3 mK for different states.

Potential rise in surface temperature due to Indian livestock sector that results from the annual CH4 emission at district level is also evaluated here. At 20 year time horizon, the ΔT20 varies from 1.53 × 10−7 to 0.005 mK due to Indian livestock sector. However, at 100 year time horizon, the ΔT100 varies from 7.66 × 10−9 to 0.0002 mK.

In addition to the above, the AGTP is also used to estimate the year-by-year response from a single year’s CH4 emission from livestock. The continuous analysis of AGTP is used to calculate the climate change impact on surface temperature using the annual AGTP calculation. The surface temperature change by the year (ΔT) is shown in Figure 6.

Figure 6.

Year-by-year surface temperature response (ΔT) due to constant rate of CH4 emission, Tg year−1.

It is estimated that the surface temperature will keep rising till 2021 reaching the peak temperature rise (ΔT) 0.937 mK and would start decreasing thereafter. After few years of span beyond the year 2084, the surface temperature response would asymptotically attain steady state. The continuous AGTP calculation is useful for policy makers when comparing multiple greenhouse gases. Due to high radiative forcing, CH4 can cause large impacts on climate change on short time scales, but due to its short lifetime, that impact decreases more quickly than for longer-lived GHG gases. Although the potential rise in surface temperature due to different livestock size in states and districts is global in nature, their contribution from livestock is significantly variable with respect to different livestock sizes. Hence, estimating contribution from each state and each district will be useful for policy makers to develop decentralized mitigation policy. Thus, the surface temperature response gives significant information that CH4 emission from livestock sector, even at small scale, can lead to significant climate change impact.

3.2.3 Comparison between GTP and GWP

Here, CH4 emission values are used to compare its GTP results with GWP values using GWP of CH4, i.e., 34 [11]. The different values of GTP and GWP are given in Table 6. It is found that the results from GTP20 (1030 Tg CO2e) to GTP100 (62 Tg CO2e) drop off quickly compared to GWP20 (1292 Tg CO2e) and GWP100 (430 Tg CO2e). Both the climate metrics, GWP and GTP, are worked out in “CO2 equivalents” but fundamentally different by construction, and therefore different numerical values can be expected [11]. If we look at the findings of GWP and GTP over the same period of time, GWP100 is higher than that of GTP100 due to the integrative nature of the GWP [11]. Also in the case of GTP20 and GTP100, the GTP20 is 17 times higher than that of GTP100, while GWP20 is only 3 times higher than that of GWP100. The GTP calculation is based on assumptions about the climate sensitivity and heat uptake by the ocean and significantly varies with the change in these assumptions [11]. GTP is a metric which is used with reference to CO2, and it is equal to the ratio of AGTP of reference gas and AGTP of CO2. AGTP is the absolute GTP that gives temperature change per unit of GHG emission. As already discussed, GTP is an endpoint metric therefore for short GHG having half-life less than CO2; its climate metric, taken for large time horizon, is less than that of climate metric calculated for short time horizon [11]. The differences in GTP and GWP could be due to the fact that the GTP accounts the atmospheric adjustment time scale of the component and the response time scale of the climate system, which is not considered in the GWP. Climatic impact assessment has been facing difficulties when comparing the effect of short- and long-lived GHGs. The GWP and GTP of long-lived gases are the same [10]. However, for short-lived GHGs, the GWP does not account the radiative forcing for a short period.

Category Enteric fermentation Manure management
GTP20 GTP100 GWP20 GWP100 GTP20 GTP100 GWP20 GWP100
Cattle 485.55 28.99 608.75 202.92 39.21 2.34 49.16 16.39
Buffalo 400.23 23.89 501.78 167.26 28.97 1.73 36.32 12.11
Goat 45.32 2.71 56.82 18.94 1.97 0.12 2.47 0.82
Sheep 20.30 1.21 25.45 8.48 8.69 0.52 10.90 3.63
Total 951.40 56.80 1192.80 397.60 78.84 4.71 98.85 32.95

Table 6.

Comparison between GTP20, GTP100, GWP20, and GWP100 of estimated CH4 emission from livestock.

Therefore, the GTP has been proposed for the comparison of the impact of GHG emissions on temperature changes at a specific time in future rather than the radiative forcing over a period of time [23]. Hence, we can say that the GTP compares temperatures at the end of a given time period due to GHG emissions. In comparison to GWP, GTP extends the information from radiative forcing to rise in the surface temperature relative to that of CO2 [11]. The GTP further extends the cause-effect chain by adding the temperature impact assessment in comparison with GWP and hence more relevant by comparing temperature changes [28]. The GTP is a function of time and used for analyzing the economic benefits from emission reduction. Therefore, it is useful to develop cost-effective policy for mitigation policies targeting temperature reduction.

Overall the results estimated here are compiled in Table 7 in which the minimum, the maximum, and average are given.

CH4 (Tg year−1) GWP (Tg CO2e) GTP20 (Tg CO2e) GTP100 (Tg CO2e) ΔT20 (mK) ΔT100 (mK)
Country level 15.3 523 1030 61.51 0.70 0.036
State level
Minimum 0.12 4.06 0.01 0.00 0.00 0.00
Maximum 2.74 93.35 183.79 10.97 0.13 0.006
Average 0.43 14.93 29.22 1.74 0.02 0.001
District level
Minimum 0.00 0.00 0.00 0.00 0.00 0.000
Maximum 0.11 3.82 7.53 0.45 0.002 0.003
Average 0.02 0.81 1.59 0.10 0.0005 0.0006

Table 7.

Results of CH4 emission and other climate metrics at national, state, and district levels.

3.3 Uncertainty analysis

The CH4 emission estimation depends mainly on two factors, i.e., livestock population and CH4-specific emission factors of different types of livestock categories. Both the factor could be a source of uncertainty. For the livestock population database, we rely on livestock census taken from the reports published by the Government of India [29], and emission factors are collected from the IPCC report [20]. During livestock census, the database collection based on only 5% of the total livestock population is used for sampling purposes during the census, which is then aggregated into 100% data. This creates uncertainty in the methodology. Also, in IPCC guidelines 2006, three types of estimation methodology are proposed, i.e., basic method IPCC Tier 1, intermediate method IPCC Tier 2, and complex method IPCC Tier 3. As the method becomes advance, uncertainty related to methodology decreases. As found by Patra [30], Tier 1 method overestimates the CH4 emission by 15% compared to Tier 2 estimate. But, IPCC Tier 1 is readily available which covers for national or international level in combination with default emission factors. Therefore, it is feasible for all countries. But, country-specific or even smaller region-specific emission factors would bring more precise information. However, such issues could not be considered in the present work and would require further investigation.

Advertisement

4. Conclusions

The findings of the study are CH4 emission, high GTP and surface temperature response at district level, state level, and national level in India. The total CH4 emission in India is 15.3 Tg in 2012, with the highest almost 92% of the emission occurring via enteric fermentation. The GTP due to CH4 emission at 20 and 100 year time horizon in India is 1030 Tg GTP20 CO2e and 62 Tg GTP100 CO2e, respectively. The livestock emission in India has the potential to cause the surface temperature rise up to 0.69 mK and 0.036 mK over 20 and 100 year time period, respectively. At a state level, the emission can cause the surface temperature response (ΔT) to vary from 8.49 × 10−5 to 1.25 × 10−1 mK in 20 year time horizon and from 4.23 × 10−5 to 6.25 × 10−2 mK in 100 year time horizon. On the other hand, at district level, the ΔT varies from 1.53 × 10−7 to 0.005 mK in 20 years and from 7.66 × 10−9 to 0.0002 mK in 100 years’ time frame. The GTP values of CH4 for 20 and 100 years are 67 and 4, respectively. The AGTP values for the same time horizons are 4.6 × 10−14 and 2.3 × 10−15 K kg−1. GTP is a metric, which is used in comparing multiple gases with reference to CO2, whereas AGTP is the absolute GTP giving temperature change per unit of GHG emission. Temperature indices like GTP and AGTP both give the surface temperature change and response using pulse emission. GTP of any greenhouse gas is equal to the ratio of AGTP of the given gas and AGTP of CO2. The AGTP measures the temperature change over the period of time after the GHG emission. It depends upon some factors such as climate sensitivity and ocean uptake of heat by the ocean. All of these factors response vary with the time horizon and may substantially modify climate metrics GTP and AGTP.

So, it follows a decreasing trend with an increase over the period of time from 20 to 100 years. GTP and AGTP follow the same pattern and also decrease with the year. These temperature indices GTP and AGTP both can be used to study the impact on surface temperature due to GHG emission with time. This finding helps to study the climate change impact on surface temperature from CH4 emission, which can cause climate damage over a short period of time, even emitted in small quantity.

Advertisement

Acknowledgments

Shilpi Kumari is thankful to the University Grant Commission, Government of India for UGC-SRF, for providing research fellowship (JRF) (Sr. No. 2121120406 and Ref. No: 18-12/2011(ii) EU-V).

References

  1. 1. Casey JW, Holden NM. The relationship between greenhouse gas emissions and the intensity of milk production in Ireland. Journal of Environmental Quality. 2005;34(2):429-436. DOI: 10.2134/jeq2005.0429
  2. 2. Garnett T. Livestock-related greenhouse gas emissions: Impacts and options for policy makers. Environmental Science & Policy. 2009;12(4):491-503. DOI: 10.1016/j.envsci.2009.01.006
  3. 3. Kumari S, Dahiya RP, Kumari N, Sharawat I. Estimation of methane emission from livestock through enteric fermentation using system dynamic model in India. International Journal of Environmental Research and Development. 2014;4:347-352
  4. 4. Gerber PJ, Steinfeld H, Henderson B, Mottet A, Opio C, Dijkman J, et al. Tackling Climate Change through Livestock: A Global Assessment of Emissions and Mitigation Opportunities. Food and Agriculture Organization of the United Nations (FAO). Rome; 2013
  5. 5. Kumari S, Dahiya RP, Naik SN, Hiloidhari M, Thakur IS, Sharawat I, et al. Projection of methane emissions from livestock through enteric fermentation: A case study from India. Environmental Development. 2016;20:31-44. DOI: 10.1016/j.envdev.2016.08.001
  6. 6. IPCC. Climate Change: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the IPCC. Cambridge and New York: Cambridge University Press; 2013
  7. 7. Steinfeld H, Gerber P. Livestock production and the global environment: Consume less or produce better? Proceedings of the National Academy of Sciences. 2010;107(43):18237-18238. DOI: 10.1073/pnas.1012541107
  8. 8. Kipling RP, Bannink A, Bellocchi G, Dalgaard T, Fox NJ, Hutchings NJ, et al. Modeling European ruminant production systems: Facing the challenges of climate change. Agricultural Systems. 2016;147:24-37. DOI: 10.1016/j.agsy.2016.05.007
  9. 9. Fuglestvedt JS, Berntsen TK, Godal O, Sausen R, Shine KP, Skodvin T. Metrics of climate change: Assessing radiative forcing and emission indices. Climatic Change. 2003;58(3):267-331
  10. 10. Huntingford C, Lowe JA, Howarth N, Bowerman NH, Gohar LK, Otto A, et al. The implications of carbon dioxide and methane exchange for the heavy mitigation RCP2.6 scenario under two metrics. Environmental Science & Policy. 2015;51:77-87. DOI: 10.1016/j.envsci.2015.03.013
  11. 11. IPCC (Intergovermental Panel on Climate Change). Climate change 2014. In: IPPC Fifth Assessment Report. Geneva, Switzerland: IPCC; 2014
  12. 12. Shine KP, Fuglestvedt JS, Hailemariam K, Stuber N. Alternatives to the global warming potential for comparing climate impacts of emissions of greenhouse gases. Climatic Change. 2005;68(3):281-302
  13. 13. Sarofim MC. The GTP of methane: Modeling analysis of temperature impacts of methane and carbon dioxide reductions. Environmental Modeling and Assessment. 2012;17(3):231-239. DOI: 10.1007/s10666-011-9287-x
  14. 14. Shine KP, Fuglestvedt JS, Hailemariam K, Stuber N. Alternatives to the global warming potential for comparing climate impacts of emissions of greenhouse gases. Climatic Change. 2005;68(3):281-302
  15. 15. Shine KP, Berntsen TK, Fuglestvedt JS, Skeie RB, Stuber N. Comparing the climate effect of emissions of short-and long-lived climate agents. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences. 2007;365(1856):1903-1914. DOI: 10.1098/rsta.2007.2050
  16. 16. Peters GP, Aamaas B, Berntsen T, Fuglestvedt JS. The integrated global temperature change potential (iGTP) and relationships between emission metrics. Environmental Research Letters. 2011;6(4):044021. DOI: 10.1088/1748-9326/6/4/044021
  17. 17. Persson UM, Johansson DJ, Cederberg C, Hedenus F, Bryngelsson D. Climate metrics and the carbon footprint of livestock products: Where’s the beef? Environmental Research Letters. 2015;10(3):034005
  18. 18. Government of India. 19th Livestock Census 2014. New Delhi: Ministry of Agriculture, Govt. of India, Department of Animal Husbandry, Dairying and Fisheries; 2014
  19. 19. BAHS. Ministry of Agriculture and Farmers Welfare Department of Animal Husbandry, Dairying and Fisheries. India. 2012
  20. 20. IPCC. In: Eggleston HS, Buendia L, Miwa K, Ngara T, Tanabe K, editors. IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas Inventories Programme. Japan: IGES; 2006
  21. 21. Farquharson D, Jaramillo P, Schivley G, Klima K, Carlson D, Samaras C. Beyond global warming potential: A comparative application of climate impact metrics for the life cycle assessment of coal and natural gas based electricity. Journal of Industrial Ecology. 2017;21(4):857-873. DOI: 10.1111/jiec.12475
  22. 22. Giuntoli J, Agostini A, Caserini S, Lugato E, Baxter D, Marelli L. Climate change impacts of power generation from residual biomass. Biomass and Bioenergy. 2016;89:146-158. DOI: 10.1016/j.biombioe.2016.02.024
  23. 23. Manning M, Reisinger A, Bodeker G. Global Warming Potentials and Alternate Metrics. New Zealand Climate Change Research Centre; 2009
  24. 24. Boucher O, Friedlingstein P, Collins B, Shine KP. The indirect global warming potential and global temperature change potential due to methane oxidation. Environmental Research Letters. 2009;21(4):4, 044007. DOI: 10.1088/1748-9326/4/4/044007
  25. 25. Joos F, Roth R, Fuglestvedt JS, Peters GP, Enting IG, Bloh WV, et al. Carbon dioxide and climate impulse response functions for the computation of greenhouse gas metrics: A multi-model analysis. Atmospheric Chemistry and Physics. 2013;13(5):2793-2825. DOI: 10.5194/acp-13-2793-2013
  26. 26. Kumari S, Hiloidhari M, Kumari N, Naik SN, Dahiya RP. Climate change impact of livestock CH4 emission in India: Global temperature change potential (GTP) and surface temperature response. Ecotoxicology and Environmental Safety. 2018;147:516-522. DOI: 10.1016/j.ecoenv.2017.09.003
  27. 27. Fagodiya RK, Pathak H, Kumar A, Bhatia A, Jain N. Global temperature change potential of nitrogen use in agriculture: A 50-year assessment. Scientific Reports. 2017;7:44928. DOI: 10.1038/srep44928
  28. 28. Shine KP. The global warming potential—The need for an interdisciplinary retrial. Climatic Change. 2009;96(4):467-472. DOI: 10.1007/s10584-009-9647-6
  29. 29. BAHS 2014. Ministry of Agriculture and Farmers Welfare Department of Animal Husbandry, Dairying and Fisheries. India
  30. 30. Patra AK. Prediction of enteric methane emission from buffaloes using statistical models. Agriculture, Ecosystems & Environment. 2014;195:139-148. DOI: 10.1016/j.agee.2014.06.006

Written By

Shilpi Kumari, Moonmoon Hiloidhari, Satya Narayan Naik and Raj Pal Dahiya

Submitted: September 3rd, 2018 Reviewed: September 27th, 2018 Published: June 5th, 2019