Web-Based Decision Support System for Japonica Rice Cultivation in West Java Province, Indonesia

Authors

  • Taufiq Yuliawan  M.Sc. IT, Natural Resource Management Study Program, Bogor Agricultural University, Bogor, Indonesia
  • Handoko  Department of Geophisic and Meteorology, Bogor Agricultural University, Bogor, Indonesia
  • Impron  Department of Geophisic and Meteorology, Bogor Agricultural University, Bogor, Indonesia
  • Hiroki Oue  The United Graduate School of Agricultural Sciences, Ehime University, Ehime Prefecture, Japan Corresponding Author : taufiq.yuliawan@gmail.com1

DOI:

https://doi.org//10.32628/IJSRSET196297

Keywords:

Decission Support System, Crop Modelling, Yield Prediction, Japonica Rice

Abstract

Indonesia has a potency for planting Nikomaru, a japonica rice cultivar that has a capability for tolerating a high air temperature due to a chance for international trading, mainly to Japan. Developing a crop model to know the potency of Nikomaru in Indonesia based on the climate condition is an easier step than doing direct planting. A Decision Support System (DSS) was expected to help Indonesian farmers to decide their plantation. A field experiment was needed to develop and evaluate a crop model for predicting rice production. A web-based DSS developed for simulating some scenarios to know the potency of Nikomaru in West Java Province, Indonesia. Bogor Regency and Bandung Regency were selected area due to a higher rice production than the other places. Both of them would face dry periods. Bandung Regency will face the worst dry period in the first scenario.

References

  1. Mohanty S. 2013. “Trend in global rice consumption” in Bouman B, Dionora M J, Dobermann A, Koyama O, Lokolo E M, Sharma P K, Wopereis M, Zorrilla G, editor. Rice Today. 12(1): 44-45. Manila (PH): IRRI.
  2. Kaye F, Susanna H A H, Janette C B. 2002. International table of glycemic index and glycemic load values: 2002, Am J Clin Nutr. 76(1): 5–56. DOI: 10.1093/ajcn/76.1.5.
  3. Fiona S A, Kaye F, and Jennie C B. 2008. International tables of glycemic index and glycemic load values: 2008. Diabetes Care, 31(12): 2281-2283. DOI: 10.2337/dc08-1239.
  4. Deepa G, Singh V, and Naidu K A. 2010. A comparative study on starch digestibility, glycemic index and resistant starch of pigmented (‘Njavara’ and ‘Jyothi’) and a non-pigmented (‘IR 64’) rice varieties. J Food Sci Technol. 47(6): 644-649. DOI: 10.1007/s13197-010-0106-1.
  5. Widowati S, Santosa B A S, Astawan M, and Akhyar. 2010. Reducing glycemic index of some rice varieties using parboiling process. Indonesian Jour. of Agr. 3(2): 104-111.
  6. Fiona S A, Kaye F, and Jennie C B. 2008. International tables of glycemic index and glycemic load values: 2008. Diabetes Care, 31(12): 2281-2283. DOI: 10.2337/dc08-1239.
  7. Anhar A, Sumarmin R, and Zainul R. 2016. Measurement of glycemic index of West Sumatera local rice genotypes for healthy food selection. J. Chem. Pharm. Res., 8(8): 1035-1040.
  8. Yoshida S. 1981. Fundamentals of Rice Crop Science. Los Banos (PH): IRRI.
  9. Sakai M, Okamoto M, Tamura K, Kaji R, Mizobuchi R, Hirabayashi H, Fukaura S, Nishimura M, and Yagi T. 2007. 'Nikomaru', a new rice (Oryza sativa) variety with excellent palatability and grain appearance developed for warm region of Japan. Breeding Research. 9: 67-73.
  10. Sakai M. 2014. “Nikomaru” and “Kinumusume”, rice varieties for western region of Japan with fine palatability, grain quality, stable productivity, and tolerance to high temperature during ripening period, Breeding Research 16(4): 180-185.doi: 10.1270/jsbbr.16.180.
  11. Hasegawa W, Shiraishi M, Onari S, Yasui T, Emoto K, Sato Y, Kitazono K, Jufuku K, Yamasaki A, Nagamoto Y, Ono K, Ninomiya Y, Goto S, and Kawazu K. 2009. Characteristics of rice varieties 'Nikomaru' and 'Akimasari' urgently introduced for global warming in Oita Prefecture. Bulletin of Oita Prefectural Agriculture. 3: 27-44.
  12. Tanamachi K, Miyazaki M, Matsuo K, Suriyasak C, Tamada A, Matsuyama K, Iwaya-Inoue M, and Ishibashi Y. 2016. Differential responses to high temperature during maturation in heat-stress-tolerant cultivars of Japonica rice. Plant Production Science. 19 (2): 300-308. DOI: 10.1080 / 1343943X. 2016.1140007.
  13. Handoko. 1994. Dasar penyusunan dan aplikasi model simulasi komputer untuk pertanian. Bogor (ID): Jurusan Geofisika dan Meteorologi, Bogor Agricultural University.
  14. Ginardi R V H, Handoko I, and Seminar K B. 2002. Direct NOAA imagery extraction system for data acquisition of rice growth modeling. J. of GIS, Remote Sens. and Dynamic Modelling. 2002(2): 1-23.
  15. Bahar Y. 2012. Rice crop monitoring system. J. IT for NRM. 3(2).
  16. Yuliawan T and Handoko I 2016. The effect of temperature rise to rice crop yield in indonesia uses shierary rice model with geographical information system (GIS) feature. Procedia Environ. Sci. 33: 214-220. DOI: 10.1016/j.proenv.2016.03.072.
  17. Pambengo W and Suwarto. 2012. Model simulasi pertumbuhan dan produksi tanaman tebu. JATT. 1(1): 33-45.
  18. Salwati, Handoko I, Las I, and Hidayati R. 2013. Model simulasi perkembangan, pertumbuhan, dan neraca air tanaman Kentang pada dataran tinggi di Indonesia. J. Informatika Pertanian. 22(1): 53 – 64. DOI: 10.21082/ip.v22n1.2013.
  19. Angstrom A. 1924. Solar and terrestrial radiation. Q. J. Royal Meteorol. Soc 50 pp 121–125.
  20. Sekihara K and Suzuki M. 1966. Solar radiation and duration of sunshine in Japan. Pap. Meteor. Geophys. 17 pp 190-199.
  21. Monteith J L. 1977. Climate and the efficiency of crop production in Britain. Philos. Trans. R. Soc. London B 281: 277-294.
  22. Sinclair T R and Muchow R C. 1999. Radiation use efficiency. Advances in Agronomy 65:215-265. DOI: 10.1016/S0065-2113(08)60914-1.
  23. Monsi S and Saeki T. 1953 Uber den Lichtfaktor in den Pflanzengesellschaften und seine Bedeutung fur die Stoffproduktion Jpn. J. Bot 14 pp 22-52.
  24. Zhang L, Hu Z, Fan J, Zhou D, and Tang F. 2014. a meta-analysis of the canopy light extinction coefficient in terresterial Ecosystems. Front. Earth Sci. 8(4) pp 599-609. DOI: 10.1007/s11707-014-0446-7
  25. Runyon J, Waring R H, Goward S N, and Welles J M. 1994. Environmental limits on net primary production and light-use efficiency acros the oregon transect. Ecological App 4(2): 226-237.
  26. Liu S, Riekerk H, Gholz H L. 1997. Leaf litterfall, leaf area index, and radiation transmittance in cypress wetlands and pine plantations in north-central Florida. Wetlends Ecology and Management 4:257-271.
  27. Sampson D A, Allen H L. 1998. Light attenuation in a 14-year-old loblolly pine stand as influenced by fertilization and irrigation. Tree 13:80-87.
  28. Reamur R A F. 1735. Temperature observations in Paris during the year 1735, and the climatic analogue studies of i’isle de France, Algeria and some islands of America. Memoirs Acad. Sci. Paris 1735:545.
  29. Tang L, Zhu Y, Hannaway D, Meng Y, Liu L, Chen L, and Cao W. 2009. RiceGrow: a rice growth and productivity model. NJAS. 57(1): 83-92. DOI: 10.1016/j.njas.2009.12.003.
  30. Moldenhauer K, Wilson C E Jr, Counce P, and Hardke J. “Rice Growth and Development” in Goforth L (editor) Arkansas Rice Production Handbook 9-22. Arkansas (USA): University of Arkansas Division of Agriculture. Available on https://www.uaex.edu/publications/mp-192.aspx.

International Journal of Scientific Research in Science, Engineering and Technology

Downloads

Published

2019-04-30

Issue

Volume 6, Issue 2, March-April-2019

Section

Research Articles

License

Copyright (c) IJSRSET

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Taufiq Yuliawan, Handoko, Impron, Hiroki Oue, " Web-Based Decision Support System for Japonica Rice Cultivation in West Java Province, Indonesia, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 6, Issue 2, pp.363-372, March-April-2019. Available at doi : https://doi.org/10.32628/IJSRSET196297