Sains Malaysiana 50(9)(2021): 2791-2817

http://doi.org/10.17576/jsm-2021-5009-24

 

Diversification of Agricultural Areas in Indonesia using Dynamic Copula Modeling and K-Means Clustering

(Pempelbagaian Kawasan Pertanian di Indonesia menggunakan Pemodelan Copula Dinamik dan Pengelompokan K-Min)

 

ATINA AHDIKA1*, MUJIATI DWI KARTIKASARI1, SEKTI KARTIKA DINI1 & INTAN RAMADHANI2,3

 

1Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Indonesia, Yogyakarta, Indonesia

 

2Alumnus of Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Indonesia, Yogyakarta, Indonesia

 

3PT Sigma Cipta Caraka (TelkomSigma), Tangerang, Indonesia

 

Received: 20 July 2020/Accepted: 15 January 2021

 

ABSTRACT

Agriculture is one of the main pillars of economic growth in Indonesia. Failure in this sector can result in faltering economic stability of the country. Thus, to minimize these failures, mapping of areas with particular commodity potential is needed. One of the main factors affecting the growth of crops is rainfall. Therefore, this paper aims to model the potential distribution of commodity growth based on rainfall precipitation using dynamic copula. The modeling results are then used as a basis for grouping the potential of food crop commodities in Indonesia. The determination of the group was carried out using the k-means clustering method. We expect that the result of the modeling can provide an overview for farmers or the government to make policies related to the optimization of Indonesia's agricultural sector. This result will enable the government to offer facilities that can minimize agricultural losses, such as superior seeds that are resistant to weather changes and the provision of training for enhancing farming skills. In addition, it is also suggested to diversify farm areas to reduce the failures due to dependence on a single agricultural product.

 

Keywords: Agriculture; diversification; dynamic copula; k-means clustering

 

ABSTRAK

Pertanian adalah satu daripada tonggak utama yang mendorong ekonomi di Indonesia. Kegagalan dalam sektor ini boleh mengakibatkan kestabilan ekonomi di negara ini merosot. Oleh sebab itu, untuk mengurangkan kegagalan ini, diperlukan pemetaan kawasan dengan potensi komoditi tertentu. Satu daripada faktor utama yang mempengaruhi pertumbuhan tanaman adalah hujan. Oleh sebab itu, makalah ini bertujuan untuk memodelkan potensi penyebaran pertumbuhan komoditi berdasarkan curahan hujan menggunakan model copula dinamik. Hasil pemodelan kemudian digunakan sebagai dasar untuk mengelompokkan potensi komoditi tanaman makanan di Indonesia. Penentuan kelompok dilakukan dengan kaedah pengelompokan k-min. Penulis mengharapkan hasil pemodelan dapat memberikan gambaran umum kepada petani atau kerajaan untuk membuat polisi yang berkaitan dengan pengoptimuman sektor pertanian Indonesia. Kerajaan dapat menawarkan kemudahan yang dapat meminimumkan kerugian dalam pertanian, seperti benih unggul yang tahan terhadap perubahan cuaca dan pemberian latihan kepada petani untuk meningkatkan kemahiran mereka. Sebagai tambahan, dicadangkan juga supaya petani mempelbagaikan kawasan pertanian untuk mengurangkan kegagalan akibat kebergantungan pada satu produk pertanian sahaja.

 

Kata kunci: Copula dinamik; pempelbagaian; pengelompokan k-min; pertanian

 

REFERENCES

Adimassu, Z., Kessler, A. & Stroosnijder, L. 2014. Farmers' strategies to perceived trends of rainfall and crop productivity in the Central Rift Valley of Ethiopia. Environmental Development 11: 123-140.

Agusta, Y. 2007. K-Means - Penerapan, permasalahan dan metode terkait. Jurnal Sistem dan Informatika 3: 47-60.

Alidoost, F., Su, Z. & Stein, A. 2019. Evaluating the effects of climate extremes on crop yield, production and price using multivariate distributions: A new copula application. Weather and Climate Extremes 26: 100227.

Antwi-Agyei, P., Fraser, E.D., Dougill, A.J. Stringer, L.C. & Simelton, E. 2012. Mapping the vulnerability of crop production to drought in Ghana using rainfall, yield and socioeconomic data. Applied Geography 32(2): 324-334.

Ausin, M.C. & Lopes, H.F. 2010. Time-varying joint distribution through copulas. Computational Statistics and Data Analysis 54(11): 2383-2399.

Bandara, J.S. & Cai, Y. 2014. The impact of climate change on food crop productivity, food prices, and food security in South Asia. Economic Analysis and Policy 44(4): 451-465.

Bezabih, M. & Di Falco, S. 2012. Rainfall variability and food crop portfolio choice: Evidence from Ethiopia. Food Security 4(4): 557-567.

Chand, S. 2020. Cultivation of Rice: Suitable Conditions Required for the Cultivation of Rice (6 Conditions). https://www.yourarticlelibrary.com/cultivation/cultivation-of-rice-suitable-conditions-required-for-the-cultivation-of-rice-6-conditions/25491#:~:text=Rainfall%3A, water%20than%20any%20other%20crop.&text=Although%20the%20regions%20are%20having,weeks%20of%20the%20growing%20period. Accessed on October 20, 2020.

Charrad, M., Ghazzali, N., Boiteau, V. & Niknafs, A. 2014. NbClust: An R package for determining the relevant number of clusters in a data set. Journal of Statistical Software 61(6): 1-36.

Cong, R.G. & Brady, M. 2012. The interdependence between rainfall and temperature: Copula analyses. The Scientific World Journal 2012: Article ID. 405675.

Dash, P., Nayak, M. & Das, G.P. 2014. Principal component analysis using singular value decomposition for image compression. International Journal of Computer Applications 93(9): 21-27. https://doi.org/10.5120/16243-5795.

Han, J., Kamber, M. & Pei, J. 2012. Data Mining Concepts and Techniques. Massachusetts: Morgan Kaufmann Publishers.

Hyndman, R.J. & Athanasopoulos, G. 2018. Forecasting: Principles and Practice. 2nd ed. OTexts: Melbourne.

Haraty, R.A., Dimishkieh, M. & Masud, M. 2015. An enhanced k-Means clustering algorithm for pattern discovery in healthcare data. International Journal of Distributed Sensor Networks 11(6).

James, G., Witten, D., Hastie, T. & Tibshirani, R. 2013. An Introduction to Statistical Learning with Applications in R. New York: Springer.

Johnson, R.A. & Wichern, D.W. 2007. Applied Multivariate Statistical Analysis. 6th ed. New Jersey: Pearson Prentice Hall.

Jondeau, E. & Rockinger, M. 2006. The Copula-GARCH model of conditional dependencies: An international stock market application. Journal of International Money and Finance 25(5): 827-853.

Juaeni, I. 2014. Impact of principal component analysis (PCA) implementation on rainfall clustering over Java, Bali, and Lombok Islands. Jurnal Sains Dirgantara 11(2): 97-108.

Li, Y., Gu, W., Cui, W., Chang, Z. & Xu, Y. 2015. Exploration of copula function use in crop meteorological drought risk analysis: A case study of winter wheat in Beijing, China. Natural Hazards 77(2): 1289-1303.

Maheswari, K. 2019. Finding best possible number of clusters using k-means algorithm. International Journal of Engineering and Advanced Technology 9(1S4).

Manner, H. & Reznikova, O. 2012. A survey on time-varying copulas: Specification, simulations, and application. Econometric Reviews 31(6): 654-687.

Ministry of Agriculture. 2018. Data Lima Tahun Terakhir. pertanian.go.id Accessed on October 20, 2020.

Nguyen-Huy, T., Deo, R.C., Mushtaq, S., An-Vo, D.A. & Khan, S. 2018. Modeling the joint influence of multiple synoptic-scale, climate mode indices on Australian wheat yield using a vine copula-based approach. European Journal of Agronomy 98(May): 65-81.

Patton, A.J. 2006. Modelling asymmetric exchange rate dependence. International Economic Review 47(2): 527-556.

Rencher, A.C. 2001. Method of Multivariate Analysis. 2nd ed. New York: A Wiley-Interscience Publication.

Ribeiro, A.F., Russo, A., Gouveia, C.M. & Pascoa, P. 2019. Copula-based agricultural drought risk of rainfed cropping systems. Agricultural Water Management 223: 105689.

Sklar, A. 1959. Distribution functions of n dimensions and margins. Publications of the Institute of Statistics of the University of Paris 8: 229-231.

Tao, F., Yokozawa, M. & Zhang, Z. 2009. Modelling the impacts of weather and climate variability on crop productivity over a large area: A new process-based model development, optimization, and uncertainties analysis. Agricultural and Forest Meteorology 149(5): 831-850.

Team of SUTAS2018. 2018. Result of Inter-Censal Agricultural Survey 2018. Jakarta: BPS-Statistics, Indonesia.

Turvey, C.G. 1991. Regional and farm-level risk analyses with the single-index model. Northeastern Journal of Agricultural and Resource Economics 20(2): 181-188.

Vogel, E., Donat, M.G., Alexander, L.V., Meinshausen, M., Ray, D.K., Karoly, D., Meinshausen, N. & Frieler, K. 2019. The effects of climate extremes on global agricultural yields. Environmental Research Letters 14: 054010.

Xu, W., Filler, G., Odening, M. & Okhrin, O. 2010. On the systemic nature of weather risk. Agricultural Finance Review 70(2): 267-284.

 

*Corresponding author; email: atina.a@uii.ac.id

 

 

 

previous