Greenhouse Gas Emission Intensities for the Livestock Sector in Indonesia, Based on the National Specific Data

Nugrahaeningtyas, Eska; Baek, Chun-Youl; Jeon, Jung-Hwan; Jo, HyunBin; Park, Kyu Hyun

doi:10.3390/su10061912

Cited by 19 publications

(15 citation statements)

References 8 publications

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“…Reduction of greenhouse gas emission is considered as an arduous and urgent task for the Chinese livestock industry [18]. Despite of different methods of IPCC Tier 1 (constant EFs) and Tier 2 (dynamic EFs) [19,20], the present study obtained similar temporal patterns of annual CH4 emissions (10-36 Gg) (Figure 3a). However, the annual CH4 emissions in this study derived from the lower dynamic EFs were approximately 13-19% lower than the previous results [12] based on the constant EF before 2010 (Figure 3).…”

Section: Temporal Ch4 Emission Of Livestock and Poultry Farmingsupporting

confidence: 61%

Inventory of Spatio-Temporal Methane Emissions from Livestock and Poultry Farming in Beijing

et al. 2019

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Livestock and poultry farming sectors are among the largest anthropogenic methane (CH4) emission sources, mainly from enteric fermentation and manure management. Previous inventories of CH4 emission were generally based on constant emission factor (EF) per head, which had some weaknesses mainly due to the succession of breeding and feeding systems over decades. Here, more reliable long-term changes of CH4 emissions from livestock and poultry farming in Beijing are estimated using the dynamic EFs based on the Intergovernmental Panel on Climate Change (IPCC) Tier 2 method, and high-resolution spatial patterns of CH4 emissions are also estimated with intensive field survey. The results showed that the estimated CH4 emissions derived by dynamic EFs were approximately 13–19% lower than those based on the constant EF before 2010. After 2011, however, the dynamic EFs-derived CH4 emissions were a little higher (3%) than the constant EF method. Temporal CH4 emissions in Beijing had experienced four developing stages (1978–1988: stable; 1989–1998: slow growth; 1999–2004: rapid growth and reached hot moments; 2005–2014: decline) during 1978–2014. Over the first two decades, the contributions of pigs (45%) and cattle (46%) to annual CH4 emission were similar; subsequently, the cattle emitted more CH4 compared to the pigs. At a spatial scale, Shunyi, Daxing, and Tongzhou districts with more cattle and pigs are the hotspots of CH4 emission. In conclusion, the dynamic EFs method obviously improved the spatio-temporal estimates of CH4 emissions compared to the constant EF approach, and the improvements depended on the period and aquaculture structure. Therefore, the dynamic EFs method should be recommended for estimating CH4 emissions from livestock and poultry farming in the future.

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Section: Temporal Ch4 Emission Of Livestock and Poultry Farmingsupporting

confidence: 61%

Inventory of Spatio-Temporal Methane Emissions from Livestock and Poultry Farming in Beijing

et al. 2019

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“…Usually, countries where CH 4 emissions are predominant have a relatively high proportion of animal production; thus, they have high CH 4 emissions due to enteric fermentation. The exception is Indonesia, where the main cereal produced is rice, whose cultivation accounts for most of the CH 4 emissions; a relatively small amount of CH 4 emissions comes from animal production [ 61 ].…”

Section: Resultsmentioning

confidence: 99%

Greenhouse gas emissions intensity of food production systems and its determinants

et al. 2021

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It is estimated that about 1/4th of all greenhouse gas (GHG) emissions may be caused by the global food system. Reducing the GHG emissions from food production is a major challenge in the context of the projected growth of the world’s population, which is increasing demand for food. In this context, the goal should be to achieve the lowest possible emission intensity of the food production system, understood as the amount of GHG emissions per unit of output. The study aimed to calculate the emission intensity of food production systems and to specify its determinants based on a panel regression model for 14 countries, which accounted for more than 65% of food production in the world between 2000 and 2014. In this article, emission intensity is defined as the amount of GHG emissions per value of global output. Research on the determinants of GHG emissions related to food production is well documented in the literature; however, there is a lack of research on the determinants of the emission intensity ratio for food production. Hence, the original contribution of this paper is the analysis of the determinants of GHG emissions intensity of food production systems. The study found the decreased of emission intensity from an average of more than 0.68 kg of CO2 equivalent per USD 1 worth of food production global output in 2000 to less than 0.46 in 2014. The determinants of emission intensity decrease included the yield of cereals, the use of nitrogen fertilizers, the agriculture material intensity, the Human Development Index, and the share of fossil fuel energy consumption in total energy use. The determinants of growth of emission intensity of food production systems included GDP per capita, population density, nitrogen fertilizer production, utilized agriculture area, share of animal production, and energy use per capita.

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“…In the emission inventory for South, South East and East Asia for 2000 [11], cattle was the largest emitter for CH 4 , N 2 O and NH 3 emissions, with a share of 56%, 30% and 33% respectively. Study of Reference [32] in Indonesia has also shown cattle as the major contributor to GHG emissions in the 2005-2015 period. Our results provide a reminder that regarding agricultural sector emission mitigation, policies in the Red River Delta should not be copied from other countries.…”

Section: Discussionmentioning

confidence: 99%

Methane, Nitrous Oxide and Ammonia Emissions from Livestock Farming in the Red River Delta, Vietnam: An Inventory and Projection for 2000–2030

et al. 2018

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Livestock farming is a major source of greenhouse gas and ammonia emissions. In this study, we estimate methane, nitrous oxide and ammonia emission from livestock sector in the Red River Delta region from 2000 to 2015 and provide a projection to 2030 using IPCC 2006 methodologies with the integration of local emission factors and provincial statistic livestock database. Methane, nitrous oxide and ammonia emissions from livestock farming in the Red River Delta in 2030 are estimated at 132 kt, 8.3 kt and 34.2 kt, respectively. Total global warming potential is estimated at 5.9 MtCO2eq in 2030 and accounts for 33% of projected greenhouse gas emissions from livestock in Vietnam. Pig farming is responsible for half of both greenhouse gases and ammonia emissions in the Red River Delta region. Cattle is another major livestock responsible for greenhouse gas emissions and poultry is one that is responsible for ammonia emissions. Hanoi contributes for the largest emissions in the region in 2015 but will be surpassed by other provinces in Vietnam by 2030.

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Greenhouse Gas Emission Intensities for the Livestock Sector in Indonesia, Based on the National Specific Data

Cited by 19 publications

References 8 publications

Inventory of Spatio-Temporal Methane Emissions from Livestock and Poultry Farming in Beijing

Inventory of Spatio-Temporal Methane Emissions from Livestock and Poultry Farming in Beijing

Greenhouse gas emissions intensity of food production systems and its determinants

Methane, Nitrous Oxide and Ammonia Emissions from Livestock Farming in the Red River Delta, Vietnam: An Inventory and Projection for 2000–2030

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