Sains Malaysiana 48(7)(2019): 1325–1332

http://dx.doi.org/10.17576/jsm-2019-4807-02

Artificial Intelligence Projection Model for Methane Emission from Livestock in Sarawak

(Unjuran Model Kecerdasan Buatan untuk Pelepasan Metana daripada Ternakan di Sarawak)

PENG ENG KIAT¹*, MARLINDA ABDUL MALEK² & SITI MARIYAM SHAMSUDDIN³

¹Department of Civil Engineering, Universiti Tenaga Nasional, 43600 Kajang, Selangor Darul Ehsan, Malaysia

²Institute of Sustainable Energy (ISE), Universiti Tenaga Nasional, 43600 Kajang, Selangor Darul Ehsan, Malaysia

³UTM Big Data Centre, Ibnu Sina Institute for Scientific and Industrial Research, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor Darul Takzim, Malaysia

Diserahkan: 2 Februari 2019/Diterima: 25 April 2019

ABSTRACT

Artificial Intelligence is a topical trend employed to solve engineering and industrial problems by virtue of its abilities to deal with data uncertainty such as methane emissions. Hard computing methods are not suitable for determining the optimal emission in a methane emission data set. Instead, soft computing solutions should be considered in an effort to obtain better optimal solutions for industrial problems. This paper utilized the Guidelines provided in the 2006 Intergovernmental Panel on Climate Change (IPCC) to calculate and project methane emissions from selected six livestock in Sarawak, Malaysia. A particle swarm optimization (PSO) model was developed to project future methane emission by using number of livestock as the input parameter. The total CH4 inventory from the enteric fermentation of cattle, buffaloes, goats, sheep, swine and deer in Sarawak decreased from 1.860 to 1.856 Gg when calculation was carried out using the Tier 1 method. This decrease was due to population growth and the emission factors employed. Three statistical measures, root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were employed for evaluation. PSO has been shown to be able to give an accurate projection. The results of this study provide a benchmark information which can be used by the Sarawak government to develop appropriate policies and mitigation strategies to reduce future carbon footprint in the Sarawak livestock sector.

Keywords: Enteric fermentation; livestock; manure management; methane inventory; Tier 1

ABSTRAK

Kecerdasan Buatan adalah tren topikal yang digunakan untuk menyelesaikan masalah kejuruteraan dan perindustrian berdasarkan kemampuannya untuk menangani ketidakpastian data seperti pelepasan metana. Kaedah pengkomputeran keras tidak sesuai untuk menentukan pelepasan optimum dalam set data pelepasan metana. Sebaliknya, penyelesaian pengkomputeran lembut perlu dipertimbangkan dalam usaha untuk mendapatkan penyelesaian optimum yang lebih baik untuk masalah perindustrian. Kertas ini menggunakan Garis Panduan yang disediakan dalam Panel Antara Kerajaan tentang Perubahan Cuaca (IPCC) 2006 untuk menghitung dan mengunjurkan pelepasan metana daripada enam jenis ternakan terpilih di Sarawak, Malaysia. Model Particle Swarm Optimization (PSO) telah dibangunkan untuk mengunjurkan pelepasan metana masa depan dengan menggunakan bilangan ternakan sebagai parameter input. Keseluruhan inventori CH4 daripada penternakan lembu, kerbau, kambing, biri-biri, khinzir dan rusa di Sarawak menurun daripada 1.860 hingga 1.856 Gg apabila pengiraan dilakukan menggunakan kaedah Tier 1. Penurunan ini disebabkan oleh pertumbuhan penduduk dan faktor pelepasan yang digunakan. Tiga langkah statistik, iaitu kesilapan akar min kesilapan (RMSE), bermakna ralat mutlak (MAE), dan kesilapan peratusan mutlak (MAPE) digunakan untuk penilaian. PSO telah terbukti dapat memberikan unjuran yang tepat. Hasil kajian ini memberikan maklumat penanda aras yang boleh digunakan oleh kerajaan Sarawak untuk membangunkan dasar dan strategi mitigasi yang sesuai untuk mengurangkan jejak karbon pada masa hadapan dalam sektor ternakan di Sarawak.

Kata kunci: Fermentasi enterik; inventori metana; pengurusan baja; ternakan; Tier 1

RUJUKAN

Al-Sulttani, A.O., Ahsan, A., Hanoon, A.N., Rahman, A., Daud, N.N.N. & Idrus, S. 2017. Hourly yield prediction of a double-slope solar still hybrid with rubber scrapers in low-latitude areas based on the particle swarm optimization technique. Applied Energy 203: 280-303.

Alwee, R., Shamsuddin, S.M., Aziz, F.A., Chey, K.H. & Hameed, H.N.A. 2009. The impact of social network structure in particle swarm optimization for classification problems. International Journal of Soft Computing 4(4): 151-156.

Amon, B., Kryvoruchko, V., Amon, T. & Zechmeister- Boltenstern, S. 2006. Methane: Nitrous oxide and ammonia emissions during storage and after application of dairy cattle slurry and influence of slurry treatment. Agriculture, Ecosystems & Environment 112(2-3): 153-162.

Aneja, V.P., Schlesinger, W.H. & Erisman, J.W. 2009. Effects of agriculture upon the air quality and climate: Research, policy and regulations. Environmental Science & Technology 43(12): 4234-4240.

Bell, M., Eckard, R., Moate, P.J. & Yan, T. 2016. Modelling the effect of diet composition on enteric methane emissions across sheep, beef cattle and dairy cows. Animals 6(9): 1-16.

Chadwick, D., Sommer, S., Thorman, R., Fangueiro, D., Cardenas, L., Amon, B. & Misselbrook, T. 2011. Manure management: Implications for greenhouse gas emissions. Animal Feed Science and Technology 166-167: 514-531.

Chairul, S., Dzakiyullah, N.R. & Nugroho, J.B. 2016. Carbon Dioxide Emission Prediction using Support Vector Machine. IOP Conference Series: Materials Science and Enginering. United Kingdom: IOPscience. pp. 1-8.

Chhabra, A., Manjunath, K., Panigrahy, S. & Parihar, J. 2009. Spatial pattern of methane emissions from Indian livestock. Current Science 96: 683-689.

Clark, H., Brookes, I. & Walcroft, A. 2003. Enteric Methane Emissions from New Zealand Ruminants 1999-2001 Calculated using an IPCC Tier 2 Approach. New Zealand: Ministry of Agriculture and Forestry.

Crutzen, P.J., Aselmann, I. & Seiler, W. 1986. Methane production by domestic animals, wild ruminants, other herbivorous fauna, and humans. Tellus B: Chemical and Physical Meteorology 38(3-4): 271-284.

Grainger, C. & Beauchemin, K.A. 2011. Can enteric methane emissions from ruminants be lowered without lowering their production? Animal Feed Science and Technology 166-167: 308-320.

Intergovernmental Panel on Climate Change (IPCC). 2006. 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Volume 4. Japan: Institute for Global Environmental Strategies.

Jha, A.K., Singh, K., Sharma, C., Singh, S.K. & Gupta, P.K. 2011. Assessment of methane and nitrous oxide emissions from livestock in India. Journal of Earth Science & Climatic Change 2(1): 1-12.

Johnson, K., Huyler, M., Westberg, H., Lamb, B. & Zimmerman, P. 1994. Measurement of methane emissions from ruminant livestock using a SF6 Tracer technique. Environmental Science and Technology 28(2): 359-362.

Knapp, J.R., Laur, G.L., Vadas, P.A., Weiss, W.P. & Tricarico, J.M. 2014. Invited Review: Enteric methane in dairy cattle production: Quantifying the opportunities and impact of reducing emissions. Journal of Dairy Science 97(6): 3231- 3261.

Le Goff, G., Dubois, S., Van Milgen, J. & Noblet, J. 2002. Influence of dietary fibre level on digestive and metabolic utilisation of energy in growing and finishing pigs. Animal Research 51(3): 245-259.

Leytem, A.B., Dungan, R.S., Bjorneberg, D.L. & Koehn, A.C. 2010. Emissions of ammonia, methane, carbon dioxide, and nitrous oxide from dairy cattle housing and manure management systems. Journal of Environmental Quality 39: 1-12.

Marai, I.F.M., El-Darawany, A.A., Fadiel, A. & Abdel-Hafez, M.A.M. 2007. Physiological traits as affected by heat stress in sheep - A review. Small Ruminant Research 71(1-3): 1-12.

Marino, R., Atzori, A.S., D’Andrea, M., Lovane, G., Trabalza- Marinucci, M. & Rinaldi, L. 2016. Climate change: Production performance, health issues, greenhouse gas emissions and mitigation strategies in sheep and goat farming. Small Ruminant Research 135: 50-59.

Martin, C., Morgavi, D.P. & Doreau, M. 2010. Methane mitigation in ruminants: From microbe to the farm scale. Animal 4: 3141-3150.

Ming, M, Niu, D. & Shang, W. 2014. A small-sample hybrid model for forecasting energy-related CO2 emissions. Energy 64: 673-677.

Malaysia’s Second National Communication (NC2) to the UNFCCC. http://unfccc.int/resource/docs/natc/malnc2.pdf. Accessed on 18 August 2016.

Moeletsi, M.E. & Tongwane, M.I. 2015. 2004 methane and nitrous oxide emissions from manure management in South Africa. Animals 5: 193-205.

Moraes, L.E., Strathe, A.B., Fadel, J.G., Casper, D.P. & Kebreab, E. 2014. Prediction of methane emission from cattle. Global Change Biology 20: 2140-2148.

O’Mara, F.P. 2011. The significance of livestock as a contributor to global greenhouse gas emissions today and in the near future. Animal Feed Science and Technology 166-167: 7-15.

Ogle, S.M., Buendia, L., Butterbach-Bahl, K., Breidt, F.J., Hartman, M., Yagi, K., Nayamuth, R., Spencer, S., Wirth, T. & Smith, P. 2013. Advancing national greenhouse gas inventories for agriculture in developing countries: Improving activity data, emission factors and software technology. Environmental Research Letters 8: 1-8.

Philippe, F.X. & Nicks, B. 2015. Review on greenhouse gas emissions from pig houses: Production of carbon dioxide, methane and nitrous oxide by animals and manure. Agriculture, Ecosystems & Environment 199: 10-25.

Pragna, P., Chauhan, S.S., Sejian, V., Leury, B.J. & Dunshea, F.R. 2018. Climate change and goat production: Enteric methane emission and its mitigation. Animals 8(12): 1-17.

Pratt, C., Redding, M., Hill, J. & Jensen, P. 2015. Does manure management affect the latent greenhouse gas emitting potential of livestock manures? Waste Management 46: 568-576.

Sevi, A. & Caroprese, M. 2012. Impact of heat stress on milk production, immunity and udder health in sheep: A critical review. Small Ruminant Research 107(1): 1-7.

Sneath, R.W., Phillips, V.R., Demmers, G.M., Burgess, L.R., Short, J.L. 1997. Long Term Measurements of Greenhouse Gas Emissions from UK Livestock Buildings. Livestock Environment: Proceedings of the Fifth International Symposium, Bloomington MN, May 29-31. Wrest Park, Silsoe, Bedford: Silsoe Research Institute.

Tang, N. & Zhang, D.J. 2011. Application of a load forecasting model based on improved grey neural network in the smart grid. Energy Procedia 12: 180-184.

Tauseef, S.M., Premalatha, M., Abbasi, T. & Abbasi, S.A. 2013. Review: Methane capture from livestock manure. Journal of Environmental Management 117: 187-207.

United Nations Climate Change Conference, 15th Conference of Parties (COP 15). https://www.najibrazak.com/en/speeches/ u-n-climate-change-conference-2009-15th-conference-of-parties-cop-15/. Accessed on 26 November 2016.

United Nations Framework Convention on Climate Change, List of Non-Annex I Parties to the Convention http://unfccc.int/ parties_and_observers/parties/non_annex_i/items/2833.php. Accessed on 20 December 2017.

Yusuf, R.O., Noor, Z.Z., Abba, A.H., Hassan, M.A. & Din, M.F. 2012. Methane emission by sectors: A comprehensive review of emission sources and mitigation methods. Renewable and Sustainable Energy Reviews 16(7): 5059-5070.

*Pengarang untuk surat-menyurat; email: pengek@gmail.com