Penerapan Algoritma Genetika Untuk Optimasi Pengelolaan Air Lahan Padi Sawah Rendah Emisi Gas Metana (CH4)

Yulvin Marhamah Putri, Chusnul Arif

Abstract


Conventional paddy field with continuous flooding irrigation produces a lot of greenhouse gases (GHG) emissions, especially methane gas (CH4). Effective water management is important to reduce methane gas emissions from paddy fields. This study aimed to determine optimum water level and soil moisture in each plant growth stage by genetic algorithms (GA) with system of rice intensification (SRI) practices. Research was conducted with three irrigation regimes i.e, continuous flooding regime (FR), moderate regime (MR), and dry regime (DR). Observation data were used to simulate the optimum water level and soil moisture. Based on the optimum water level scenario of the GA model, methane gas emissions could reduce 63.54% and optimum soil moisture can reduce methane gas emission up to 58.12%.

Full Text:

PDF

References


Ai NS, Tondais SM, Butarbutar R. 2010. Evaluasi indikator toleransi cekaman kekeringan pada fase perkecambahan padi (Oryza sativa L.). Jurnal Biologi. 14(2):50-54.

Arif C. 2013. Optimizing water management in system of rice intensification paddy fields by field monitoring technology [disertasi]. Tokyo (JP): The University of Tokyo.

Arif C, Setiawan BI, Mizoguchi M. 2014. Penentuan kelembaban tanah optimum untuk budidaya padi (system of rice intensification) menggunakan algoritma genetika. Jurnal Irigasi. 9(1):29-40.

Arif C, Setiawan BI, Munarso DT, Nugraha MD, Simarmata PW, Ardiansyah, Mizoguchi M. 2016. Potensi pemanasan global dari padi sawah system of rice intensification (SRI) dengan berbagai ketinggian muka air. Jurnal Irigasi. 11(2):81-90.

Arif C, Setiawan BI, Widodo S, Rudiyanto, Hasanah NAI, Mizoguchi M. 2015. Pengembangan model jaringan syaraf tiruan untuk meduga emisi gas rumah kaca dari lahan sawah dengan berbagai rejim air. Jurnal Irigasi. 10(1):1-10.

Ennouri K, Ayed RB, Triki MA, Ottaviani E, Mazzarello M, Hertelli F, Zouari N. 2017. Multiple linear regression and artificial neural networks for delta-endotoxin and protease yields modelling of Bacillus thuringiensis. 3 Biotech. 7(3):187.

Hasanah NAI, Setiawan BI, Arif C, Widodo S. 2017. Muka air optimum pada system of rice intensification (SRI). Jurnal Irigasi. 12(1):55-64.

Hashimoto Y. 1997. Applications of artificial neural networks and genetic algorithms to agricultural systems. Comput Electron Agr. 18:71-72.

[IAEA] International Atomic Energy Agency. 1993. Manual on Measurement of Methane and Nitrous Oxide Emission from Agriculture. Vienna (AUT): IAEA.

Janzen H. 2004. Carbon cycling: a measure of ecosystem – a soil science perspective. Agricultural Ecosystem Environment. 104:399-417.

Jumin HB. 1992. Ekologi Tanaman Suatu Pendekatan Fisiologi. Jakarta (ID):

Rajawali Press.

Lintangrino MC, Boedisantoso R. 2016. Inventarisasi emisi gas rumah kaca pada sektor pertanian dan peternakan di kota Surabaya. Jurnal Teknik ITS. 5(2):53-57.

Mosier AR. 2001. Exchange of gaseous nitrogen compound between agricultural system and the atmosphere. Plant Soil. 228:17-27.

Olori VE, Brotherstone S, Hill WG, McGuirk BJ. 1999. Fit of standard models of the lactation curve to weekly records of milk production of cows in a single herd. Livestock Production Science. 58(1):55-63.

Paludi S. 2009. Identifikasi dan pengaruh keberadaan data pencilan (outlier) (studi kasus jumlah kunjungan wisman dan pengunjung asing ke Indonesia melalui pintu masuk Makasar antara bulan januari 2007 s.d. juli 2008). Panorama Nusantara. 6:56-62.

Paustian K, Babcock B, Kling C, Hatfield J, Lal R, McCarl B, Maclaughin S, Post WM, Mosier A, Rice C, Robertson GP, Rosenberg NJ, Rosenzweig C, Schlesinger WH, Zilberman D. 2004. Agricultural mitigation of greenhouses gases: science and policy options. CAST Report. 141:3-18.

Rufako U. 2015. Produktivitas lahan dan air pada padi sawah dengan berbagai sistem irigasi [skripsi]. Bogor (ID): Institut Pertanian Bogor.

Setyanto P, Makarim AK, Fagi AM, Wassman R, Buendia LV. 2000. Crop management affecting methane emissions from irrigated and rainfed rice in Central Java (Indonesia). Nutrient Cycling in Agroecosystems. 58:85-93.

Simarmata PW. 2016. Penentuan air irigasi optimal untuk mitigasi emisi gas rumah kaca dari padi sawah [skripsi]. Bogor (ID): Institut Pertanian Bogor.

Smith KA, Conen F. 2004. Impact of land management on fluxes of trace greenhouse gases. Soil Use Manag. 20:255-263.

Snyder CS, Bruulsema TW, Jensen TL. 2007. Best management practices to minimize greenhouse gas emissions associated with fertilizer use. Better Crops. 91(4):16-18.

Sujono J. 2011. Koefisien tanaman padi sawah pada sistem irigasi hemat air. AGRITECH. 31(4):344-351.

Tyagi L, Kumari B, Singh SN. 2010. Water management – A tool for methane mitigation from irrigated paddy fields. Sci Total Environ. 408:1085-1090.

Whalen SC, Reeburgh WS. 1990. Consumption of atmospheric methane by tundra soils. Nature. 346:160-162.

Wihardjaka A. 2015. Mitgasi emisi gas metana melalui pengelolaan lahan sawah. Jurnal Litbang Pertanian. 34(3):95-104.