Methane emission inventories for enteric fermentation and manure management of yak, buffalo and dairy and beef cattle in China from 1988 to 2009

2014 ◽  
Vol 195 ◽  
pp. 202-210 ◽  
Author(s):  
B. Xue ◽  
L.Z. Wang ◽  
T. Yan
2016 ◽  
Vol 21 (2) ◽  
pp. 101 ◽  
Author(s):  
Yeni Widiawati ◽  
M.N. Rofiq ◽  
B. Tiesnamurti

<p class="abstrak2">Methane emission from enteric is a sub-category considered under the Agriculture sector greenhouse gas emissions by UNFCCC, thus Indonesia developed calculation on enteric CH<sub>4</sub> EF for ruminant using Tier-2 method as country-specific emission factors (EF). Indonesia has huge amount of beef cattle population, which contributes significant amount to national enteric methane emission. The aim of this study was to estimate enteric methane EF for beef cattle in Indonesia using IPCC Tier-2 method.  The EF generated from this study is then used to estimate the methane emitted from beef cattle. Data on beef cattle population was obtained from BPS, data on energy content of feed, feed intake and digestibility were compiled from laboratory analysis and published paper. Equations were adopted and followed the instruction of IPCC 2006. Local cattle has different CH<sub>4</sub> EF among each sub-category, which are  ranging from 18.18 to 55.89 Kg head-1 yr-1, with the average of 36.75  head-1 yr-1. Imported beef cattle has lower  CH<sub>4</sub> EF (25.49 kg head-1 yr-1) than the average for local beef cattle. Overall, the national CH<sub>4</sub> EF of beef cattle calculated by using IPCC Tier-2 method in Indonesia is 33.14 head-1 yr-1. The value is lower than default EF from IPCC for Asia country (47 kg head-1 yr-1). The conclusion is enteric CH<sub>4</sub> EF for beef cattle in Indonesia calculated using Tier-2 method shows the real livestock system in Indonesia condition. Further research needed to be addressed are calculation of EFs for various breeds and feeding systems, since large variations of breeds and types of feed among provinces in Indonesia.</p>


2005 ◽  
Vol 85 (4) ◽  
pp. 501-512 ◽  
Author(s):  
J. A. Basarab ◽  
E. K. Okine ◽  
V. S. Baron ◽  
T. Marx ◽  
P. Ramsey ◽  
...  

This study determined methane emissions from enteric fermentation in Alberta’s beef cattle population by using three methodologies: (1) Intergovernmental Panel on Climate Change (IPCC), Tier 2 guidelines for cattle, (2) actual methane emission factors, expressed as a percentage of gross energy intake, from Canadian research trials and; (3) CowBytes© plus the basic equation developed by Blaxter and Clapperton (1965). Methane emissions, in carbon dioxide equivalents (CO2-E), from Alberta’s beef cattle were determined for 1990, 1996 and 2001. Census of Agriculture numbers for Alberta (Statistics Canada; www.statcan.com) were used and beef cattle were subdivided into 31 distinct categories based on animal type, physiological status, gender, weight, growth rate, activity level and age. Emission of greenhouse gases (GHG) from Alberta ’s beef cattle population, based on IPCC Tier 2 guidelines, were 4.93, 6.57 and 7.01 Mt CO2-E yr-1 in 1990, 1996 and 2001, respectively. Emissions based on methane emission factors from Canadian research trials were 6.23, 8.26 and 8.77 Mt CO2-E yr-1 in 1990, 1996 and 2001, respectively. Estimated methane emissions based on CowBytes© and Blaxter and Clapperton’s (1965) equation were 6.24, 8.35 and 8.94 Mt CO2-E yr-1 in 1990, 1996 and 2001, respectively. The IPCC Tier 2 values were 25.2–26.5% lower than the GHG emissions calculated using emission factors from western Canadian research and 26.7–27.6% lower than GHG emissions calculated from CowBytes© and Blaxter and Clapperton’s equation. IPCC Tier 1 values, which were calculated by multiplying total beef cattle in Alberta by four single value emission factors (beef cows = 72 kg CH4 yr-1; bulls = 75 kg CH4 yr-1; replacement heifers = 56 kg CH4 yr-1; calves, steer and heifer calves for slaughter = 47 kg CH4 yr-1), were 4.83, 6.40 and 6.83 Mt CO2-E in 1990, 1996 and 2001, respectively. Thus, IPCC Tier 1 GHG emissions from enteric fermentation in beef cattle were 2.0–2.7, 28.6–29.1 and 29.2–31.0% lower than those calculated from IPCC Tier 2, western Canadian research trials, and CowBytes© plus Blaxter and Clapperton’s equation, respectively. These results reflect the uncertainty associated with estimating methane emissions from enteric fermentation in cattle and suggest that further research is required to improve the accuracy of methane emissions, particularly for beef cows in their second and third trimester of pregnancy and fed in confinement. They also indicate that a more robust methodology may be to combine CowBytes© predicted dry matter intake with regional specific methane emission factors, where methane loss is expressed as a percentage of gross energy intake. Key words: Cattle, enteric fermentation, greenhouse gas, methane


2011 ◽  
Vol 150 (5) ◽  
pp. 556-569 ◽  
Author(s):  
Y. KARIMI-ZINDASHTY ◽  
J. D. MACDONALD ◽  
R. L. DESJARDINS ◽  
D. E. WORTH ◽  
J. J. HUTCHINSON ◽  
...  

SUMMARYEstimates of uncertainties are essential when comparing the greenhouse gas (GHG) emissions from a variety of sources. Monte Carlo Simulation (MCS) was applied to estimate the uncertainties in methane emissions and the methane emission intensities from livestock in Canada, calculated using the Intergovernmental Panel on Climate Change (IPCC) methodology. National methane emissions from enteric fermentation and manure management in 2008 were 21·2 and 4·3 Teragram CO2 equivalents (Tg CO2e) with uncertainties of 38 and 73%, respectively. The methane emission intensities (kg of CO2e per kg of live animal weight) were 5·9, 0·9 and 4·9 from Canadian beef, swine and lamb, respectively, with overall uncertainties of 44, 99 and 101%, defined as the 95% confidence interval relative to the mean. A sensitivity analysis demonstrated that IPCC default parameters such as the methane conversion rate (Ym), the coefficient for calculating net energy for maintenance (Cfi) and the methane conversion factor (MCF) were the greatest sources of uncertainty. Canadian agricultural methane emissions are usually calculated by province and by animal subcategories. However, the IPCC default parameters can be assumed to be correlated among regions and animal subcategories; therefore values are assigned at the national scale for the main cattle categories (dairy and non-dairy cattle). When it was assumed that these parameters were uncorrelated at the regional scale, the overall uncertainties were reduced to 20 and 48% for enteric fermentation and manure management, respectively, and assuming that parameters were uncorrelated at the animal subcategory scale reduced uncertainties to 13 and 41% for enteric fermentation and manure management, respectively. When the uncertainty is assigned at the most disaggregated level, even doubling the uncertainty of key parameters such as Ym and Cfi, only increased the national uncertainties to 22 and 52% for enteric fermentation and manure management, respectively. The current analysis demonstrated the importance of obtaining parameters specific to regions and animal subcategories in order to estimate GHG emissions more accurately and to reduce the uncertainties in agricultural GHG inventories. It also showed that assumptions made in the calculation of uncertainties can have a large influence on the uncertainty estimates.


2018 ◽  
Vol 61 (3) ◽  
pp. 1121-1131 ◽  
Author(s):  
Yuanqing Zhou ◽  
Hongmin Dong ◽  
Hongwei Xin ◽  
Zhiping Zhu ◽  
Wenqiang Huang ◽  
...  

Abstract. China raises 50% of global live pigs. However, few studies on the carbon footprint (CF) of large-scale pig production based on China’s actual production conditions have been carried out. In this study, life cycle assessment (LCA) and actual production data of a typical large-scale pig farm in northern China were used to assess the greenhouse gas (GHG) emissions or CF associated with the whole process of pig production, including feed production (crop planting, feed processing, and transportation), enteric fermentation, manure management, and energy consumption. The results showed a CF of 3.39 kg CO2-eq per kg of live market pig and relative contributions of 55%, 28%, 13%, and 4% to the total CF by feed production, manure management, farm energy consumption, and enteric fermentation, respectively. Crop planting accounted for 66% of the feed production CF, while feed processing and transportation accounted for the remaining 34%. Long-distance transport of semi-raw feed materials caused by planting-feeding separation and over-fertilization in feed crop planting were two main reasons for the largest contribution of GHG emissions from feed production to the total CF. The CF from nitrogen fertilizer application accounted for 33% to 44% of crop planting and contributed to 16% of the total CF. The CF from the transport of feed ingredients accounted for 17% of the total CF. If the amount of nitrogen fertilizer used for producing the main feed ingredients is reduced from 209 kg hm-2 (for corn) and 216 kg hm-2 (for wheat) to 140 kg hm-2 (corn) and 180 kg hm-2 (wheat), the total CF would be reduced by 7%. If the transport distance for feed materials decreased from 325 to 493 km to 30 km, along with reducing the number of empty vehicles for transport, the total CF would be reduced by 18%. The combined CF mitigation potential for over-fertilization and transport distance is 26%. In addition, the use of pit storage, anaerobic digestion, and lagoon for manure management can reduce GHG emissions from manure management by 76% as compared to the traditional practice of pit storage and lagoon. This case study reveals the impact of planting-feeding separation and over-fertilization on the CF of the pig supply chain in China. The manure management practice of pit storage, anaerobic digestion, and lagoon is much more conductive to reducing the CF as compared to the traditional practice of pit storage and lagoon. Keywords: Greenhouse gas, Life cycle assessment, Mitigation, Pig.


Author(s):  
N. Andy Cole ◽  
Michael S. Brown ◽  
Vince Varel

2019 ◽  
Vol 48 (5) ◽  
pp. 1454-1461 ◽  
Author(s):  
S. M. McGinn ◽  
T. K. Flesch ◽  
K. A. Beauchemin ◽  
A. Shreck ◽  
M. Kindermann

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