Fariz, T. R.
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Journal : Jurnal Pendidikan IPA Indonesia

Greenhouse Gas Emissions and Biogas Potential from Livestock in Rural Indonesia Heriyanti, A. P.; Purwanto, P.; Purnaweni, H.; Fariz, T. R.
Jurnal Pendidikan IPA Indonesia Vol 11, No 1 (2022): March 2022
Publisher : Program Studi Pendidikan IPA Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jpii.v11i1.34465

Abstract

The livestock sector is one of the most significant contributors to greenhouse gas (GHG) emissions. Jetak Village in Indonesia has a large livestock population, so it has the potential to be a reasonably high contributor to GHG emissions. Therefore, research is needed to calculate GHG from the livestock sector and calculate biogas potential. Besides, we also discuss data collection techniques that are important but often forgotten in GHG reduction studies in developing countries. This is useful as an effort and reference to reduce GHG emissions in rural areas, especially in Jetak Village. The GHG calculation uses the Tier-1 method, while the data on the potential for biogas utilization is obtained from manure production calculations and in-depth interviews. The calculation results show that the highest total GHG from livestock management in Jetak Village in 2017 was 1,106.69 tons CO2-eq/year, while the lowest total GHG emissions in 2015 were 1,018.41 CO2-eq Gg/year. Dairy cows are the biggest emitter in livestock management, with 4,919.61 tons of CO2-eq/year, and laying hens are the lowest emitters with 1.39 tons CO2-eq/year. Dairy cows are the largest contributor to GHG emissions in enteric fermentation with 9,680.52 tons CO2-eq/year, and the lowest number of contributors is horses with 20.79 tons CO2-eq/year. The potential of biogas in Jetak Village based on manure production is 137 installations. The positive community's perception supports this. It tends to be less valid regarding livestock population data used for GHG calculations, so we verified it during in-depth interviews. The in-depth interview process used local language to enhance the quality of responses. This research needs to be developed considering our findings that there are only 50 biogas installations, indicating the biogas potential is not being utilized to its full potential.
Comparison of SWAT-based Ecohydrological Modeling in the Rawa Pening Catchment Area, Indonesia Amalia, A. V.; Fariz, T. R.; Lutfiananda, F.; Ihsan, H. M.; Atunnisa, R.; Jabbar, A.
Jurnal Pendidikan IPA Indonesia Vol 13, No 1 (2024): March 2024
Publisher : Program Studi Pendidikan IPA Fakultas Matematika dan Ilmu Pengetahuan Alam (FMIPA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/jpii.v13i1.45277

Abstract

The Soil and Water Assessment Tool (SWAT) is an ecohydrological model widely applied to assess water quality and watershed management. This tool also has the advantage of building watershed models even with limited monitoring data availability. The essential data required by this tool includes digital elevation models (DEM), land use maps, climate data, and soil data. Nonetheless, the availability of spatial data is still often a challenge in developing hydrological models, especially in developing countries such as Indonesia. This research will compare the accuracy of freely available data in Indonesia in facilitating the development of hydrological models from SWAT in the Rawa Pening catchment area. This research is crucial since Rawa Pening Lake is a priority lake for revitalization, so the research results will help provide suggestions regarding presenting data in SWAT modeling. This research compares SWAT models built from different land use and DEM (Digital Elevation Models) data. The land use data being compared is the result of processing from the Google Earth Engine (GEE) platform using machine learning with land use data from government agencies, namely the Ministry of Environment and Forestry, while the DEM data being compared is SRTM and DEMNAS data. The validation results using R, R2, RMSE, and NSE show that, in general, the model built from land use from GEE is the best compared to the other models. In modeling SWAT in Indonesia, we recommend using good-quality land-use data. Utilizing supervised classification through Random Forest (RF) algorithms within GEE can facilitate the acquisition of this data. To reduce computation time, the DEM can be SRTM with a small sacrifice of accuracy.