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Abstract:The aims of this study were to calculate greenhouse gas (GHG) emissions and to identify the trends of GHG emission intensity, based on meat production from the livestock sector in Indonesia, which had not been done before. The total emissions from the livestock sector from 2000 to 2015 in Indonesia were calculated using the 2006 Intergovernmental Panel on Climate Change Guideline (2006 IPCC GL) using Tier 1 and Tier 2, with its default values and some of the country specific data that were found in the grey literature. During 2000 to 2015, the change from the Tier 1 to Tier 2 methods resulted in an approximately 7.39% emission decrease from enteric fermentation and a 4.24% increase from manure management, which resulted in a 4.98% decrease in the total emissions. The shared emission from manure management increased by about 9% and 6% using Tier 1 and Tier 2, respectively. In contrast with the total emissions, the overall emission intensity in Indonesia decreased (up to 60.77% for swine), showing that the livestock productivity in Indonesia has become more efficient. In order to meet the meat demand with less GHG emissions, chicken farming is one option to be developed. The increased emission and share from manure management indicated that manure management system needs to be of concern, especially for beef cattle and swine.

Abstract:The objective of this paper is to develop a simple method for analyzing the parameter uncertainty of the Japanese life cycle inventory database (LCI DB), termed the inventory database for environmental analysis (IDEA). The IDEA has a weakness of poor data quality because over 60% of datasets in IDEA were compiled based on secondary data (non-site-specific data sources). Three different approaches were used to estimate the uncertainty of the brown rice production dataset, including the stochastic modeling approach, the semi-quantitative DQI (Data Quality Indicator) approach, and a modification of the semi-quantitative DQI approach (including two alternative approaches for modification). The stochastic modeling approach provided the best estimate of the true mean of the sample space and its results were used as the reference for comparison with the other approaches. A simple method for the parameter uncertainty analysis of the agriculture industry DB was proposed by modifying the beta distribution parameters (endpoint range, shape parameter) in the semi-quantitative DQI approach using the results from the stochastic modeling approach. The effect of changing the beta distribution parameters in the semi-quantitative DQI approach indicated that the proposed method is an efficient method for the quantitative parameter uncertainty analysis of the brown rice production dataset in the IDEA.

Abstract:The results of an uncertainty analysis are achieved by the statistical information (standard error, type of probability distributions, and range of minimum and maximum) of the selected input parameters. However, there are limitations in identifying sufficient data samples for the selected input parameters for statistical information in the field of life cycle assessment (LCA). Therefore, there is a strong need for a consistent screening procedure to identify the input parameters for use in uncertainty analysis in the area of LCA. The conventional procedure for identifying input parameters for the uncertainty analysis method includes assessing the data quality using the pedigree method and the contribution analysis of the LCA results. This paper proposes a simplified procedure for ameliorating the existing data quality assessment method, which can lead to an efficient uncertainly analysis of LCA results. The proposed method has two salient features: (i) a simplified procedure based on contribution analysis followed by a data quality assessment for selecting the input parameters for the uncertainty analysis; and (ii) a quantitative data quality assessment method is proposed, based on the pedigree method, that adopts the analytic hierarchy process (AHP) method and quality function deployment (QFD). The effects of the uncertainty of the selected input parameters on the LCA results were assessed using the Monte Carlo simulation method. A case study of greenhouse gas (GHG) emissions from a dairy cow system was used to demonstrate the applicability of the proposed procedure.