DOI: https://doi.org/10.18517/ijods.2.2.69-76.2021

Does ENSO Significantly Affect Rice Production In Indonesia? A Preliminary Study Using Computational Time-Series Approach

Sandy H. S. Herho (1) , Ferio Brahmana (2) , Katarina E. P. Herho (3) , Dasapta E. Irawan (4)
(1) Department of Geology, College of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, MD, United States
(2) Pacmann Academy, Algoritma Cerdas Indonesia Inc., South Jakarta, Special Capital Region of Jakarta, Indonesia
(3) Geological Engineering Study Program, Faculty of Earth Technology and Energy, Trisakti University, West Jakarta, Special Capital Region of Jakarta, Indonesia
(4) Applied Geology Research Group, Faculty of Earth Sciences and Technology, Bandung Institute of Technology (ITB), West Java, Indonesia
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Abstract

ENSO is a phenomenon that is suspected to influence rice production in Indonesia. In this study, we try to find direct correlation between ENSO and rice production in this region by using various latest computational time series methods, such as Dynamic Time Warping, Wavelet Coherence, and Bayesian Structural Time Series to quantify the statistical relationship between the Multivariate ENSO Index on annual rice production in 1961- 2019. We did not find a direct correlation between these two variables, which may be due to the local influence of ENSO on different rice production areas in Indonesia. This study would also point out the importance of shifting the theme of research in Indonesia from mapping to monitoring and freely share the data. This step would bring science to progress further and faster.

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How to Cite
[1]
S. H. S. Herho, F. Brahmana, K. E. P. Herho, and D. E. Irawan, “Does ENSO Significantly Affect Rice Production In Indonesia? A Preliminary Study Using Computational Time-Series Approach”, Int. J. Data. Science., vol. 2, no. 2, pp. 69-76, Dec. 2021.
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References

A. Grinsted, J. C. Moore, and S. Jevrejeva, “Application of the cross wavelet transform and wavelet coherence to geophysical time series,” Nonlinear Processes in Geophysics, vol. 11, no. 5/6, pp. 561–566, 2004.

A. H. Malian, S. Mardianto, and M. Ariani, “Faktor-Faktor yang Mempengaruhi Produksi, Konsumsi dan Harga Beras serta Inflasi Bahan Makanan,” Jurnal Agro Ekonomi, vol. 22, pp. 119–146, Sep 2016.

C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. del Río, M. Wiebe, P. Peterson, P. Gérard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, and T. E. Oliphant, “Array programming with NumPy,” Nature, vol. 585, pp. 357–362, Sept. 2020.

C. Torrence and G. P. Compo, “A practical guide to wavelet analysis,” Bulletin of the American Meteorological Society, vol. 79, no. 1, pp. 61–78, 1998.

D. J. Berndt and J. Clifford, “Using dynamic time warping to find patterns in time series,” in Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, AAAIWS’94, p. 359–370, AAAI Press, 1994.

D. R. Panuju, K. Mizuno, and B. H. Trisasongko, “The dynamics of rice production in indonesia 1961–2009,” Journal of the Saudi Society of Agricultural Sciences, vol. 12, no. 1, pp. 27–37, 2013.

D. S. Bloomfield, R. T. J. McAteer, B. W. Lites, P. G. Judge, M. Mathioudakis, and F. P. Keenan, “Wavelet phase coherence analysis: Application to a quiet-sun magnetic element,” The Astrophysical Journal, vol. 617, no. 1, pp. 623–632, 2004.

FAO, “Food and Agriculture Organization of the United Nations. FAOSTAT Statistical Database,” 1997. [Online; accessed 5. Jul. 2021].

G. van Rossum, “Python tutorial,” Tech. Rep. CS-R9526, Centrum voor Wiskunde en Informatica (CWI), Amsterdam, May 1995.

H. Sakoe and S. Chiba, “Dynamic programming algorithm optimization for spoken word recognition,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 26, no. 1, pp. 43–49, 1978.

J. D. Hunter, “Matplotlib: A 2d graphics environment,” Computing in Science & Engineering, vol. 9, no. 3, pp. 90–95, 2007.

K. Wolter and M. S. Timlin, “Measuring the strength of enso events: How does 1997/98 rank?,” Weather, vol. 53, no. 9, pp. 315–324, 1998.

M. Ambarinanti, “Analisis faktor-faktor yang mempengaruhi produksi dan ekspor beras Indonesia,” IPB (Bogor Agricultural University), 2007.

M. G. Roberts, D. Dawe, W. P. Falcon, and R. L. Naylor, “El niño–southern oscillation impacts on rice production in luzon, the philippines,” Journal of Applied Meteorology AND Climatology, vol. 48, pp. 1718–1723, 2009.

Mahananto, S. Sutrisno, and C.-F. Ananda, “FAKTOR-FAKTOR YANG MEMPENGARUHI PRODUKSI PADI Studi Kasus di Kecamatan Nogosari, Boyolali, Jawa Tengah,” Wacana Journal of Social and Humanity Studies, vol. 12, no. 1, pp. 179–191, 2009.

N. V. Nguyen, “Global climate changes and rice food security,” 2005. [Online; accessed 14. Jul. 2021].

N. W. Ismail and S. M. Chan, “Impacts of the el niño-southern oscillation (enso) on paddy production in southeast asia,” Climate and Development, 2019.

R Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2017.

R. L. Naylor, W. P. Falcon, D. Rochberg, and N. Wada, “Using el niño/southern oscillation climate data to predict rice production in indonesia,” Climatic Change, vol. 50, pp. 255–265, 2001.

“Read Rectangular Text Data [R package readr version 2.0.1],” Aug 2021. [Online; accessed 18. Aug. 2021].

S. L. Scott, bsts: Bayesian Structural Time Series, 2020. R package version 0.9.5.

S. Salvador and P. Chan, “Toward accurate dynamic time warping in linear time and space,” Intelligent Data Analysis, vol. 11, pp. 70–80, 2004.

S. Scott and H. Varian, “Predicting the present with bayesian structural time series,” International Journal of Mathematical Modelling and Numerical Optimisation, vol. 5, pp. 4 – 23, 2014.

S. Wright, “Largest Rice-Producing Countries,” WorldAtlas, Jul 2020.

T. C. Gouhier, A. Grinsted, and V. Simko, R package biwavelet: Conduct Univariate and Bivariate Wavelet Analyses, 2021. (Version 0.20.21).

T. Giorgino, “Computing and visualizing dynamic time warping alignments in r: The dtw package,” Journal of Statistical Software, vol. 31, no. 7, pp. 1–24, 2009.

T. pandas development team, pandas-dev/pandas: Pandas, Feb. 2020.

X. Deng, J. Huang, F. Qiao, R. L. Naylor, W. P. Falcon, M. Burke, S. Rozelle, and D. Battisti, “Impacts of el niño-southern oscillation events on china’s rice production,” Journal of Geographical Science, vol. 20, no. 1, pp. 3–16, 2010.