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Economic Potential of Oil Palm Plantation Using Remote Sensing-Based Technology in Indonesia

Shinta Rahma Diana, Farida Farida

Abstract


Abstract. The productivity of Indonesia's palm oil was considered low when referring to the 14.6 million ha land area in 2019, with the production of national palm oil only reaching 3.2 tons of CPO/ha/year. The uses of remote sensing technology as a means of monitoring and supervising, were expected to increase oil palm production in line with productivity. The purpose of this study was to determine the economic potential based on oil palm plantation productivity, with and without using remote sensing-based technology, as well as other variables likely to affect productivity. Primary and secondary data collection methods were also used in this research. There were three quantitative methods being used in this study, namely (i) Multiple regression model with panel data, (ii) Data Envelopment Analysis (DEA) tool, and (iii) Multinomial logistic regression technique. The results showed that the generated economic potential from the utilization of the remote sensing model, had efficient opportunity value of 10.48, which was higher than the non-usage of the technology.  Therefore, the main variables that affected productivity in this study, were fertilizer and labour. 

Keywords: Efficiency, oil-palm, remote sensing (spot 6), policy, binomial logistic

Keywords


Efficiency; Oil-palm; Remote Sensing (SPOT 6); Policy; Bonomial Logistic

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References


Acil Allen (2013).The economic value of earth observation from space: A review of the value to Australia of Earth observation from space. Acil Allen. Retrieved from http://www.acilallen.com.au/cms_files/ACILAllen_Earth2013.pdf.

Alston, J. M., Babcock, B. A., & Pardey, P. G. (2010). The shifting patterns of agricultural production and productivity worldwide. Midwest Agribusiness Trade Research and Information Center.

Anurogo, W., Silaban, R. D., Nugroho, C. B., Mufida, M. A. K., & Pamungkas, D. S. (2019, October). Pixel-based Remote Sensing Data Processing for Estimating Rubber Plantations Productivity. In 2019 2nd International Conference on Applied Engineering (ICAE) (pp. 1-5). IEEE.

Ardana, I.K., Kariyasa, K. (2016).Influence of Technological Innovation and Use of Production Input on Productivity of Oil Palm in West Kalimantan Province. Jurnal Penelitian Tanaman Industri, Jurnal Littri 22(3), 125-134.

Bakir, L.H. (2007). Kinerja perusahaan inti rakyat kelapa sawit di Sumatera Selatan: analisis kemitraan dan ekonomi rumah tangga petani. Eprint.unsri.ac.id

Bappenas. (2010). Kebijakan dan Strategi dalam Meningkatkan Nilai Tambah dan Daya Saing KelapaSawit Indonesia secara Berkelanjutan dan Berkeadilan. Naskah Kebijakan (Policy Paper). Direktorat Pangan dan Pertanian, Kementerian Perencanaan Pembangunan Nasional/Badan Perencanaan Pembangunan Nasional (BAPPENAS). Desember 2010.

Bpdpks. (2018). Sawit Kontributor Utama PDB Indonesia. Bpdpks. Retrived from https://www.bpdp.or.id/Sawit-Kontributor-Utama-PDB-Indonesia

BPS. (2019). Indonesia Oil Palm Statistics. Retrived frrom https://www.bps.go.id/publication/2020/11/30/36cba77a73179202def4ba14/statistik-kelapa-sawit-indonesia-2019.html

Case, E. K., & Fair, C. (2004). Prinsip-Prinsip Ekonomi Makro. Edisi Kelima, Cetakan Kesatu. Jakarta: PT. Indeks.

Christiani, E., Mara, A., & Naenggolan, S. (2013). Peranan Perkebunan Kelapa Sawit dalam Pembangunan Ekonomi Wilayah di Kabupaten Muaro Jambi. Jurnal Sosio Ekonomika Bisnis, 16(2), 63-73.

Carolita, I., Sitorus, J., Manalu, J., & Wiratmoko, D. (2017). Growth profile analysis of oil palm by using SPOT 6 the case of North Sumatra. International Journal of Remote Sensing and Earth Sciences (IJReSES), 12(1), 21-26.

Chong, K. L., Kanniah, K. D., Pohl, C., & Tan, K. P. (2017). A review of remote sensing applications for oil palm studies. Geo-spatial Information Science, 20(2), 184-200.

Comte, I., Colin, F., Whalen, J. K., Grünberger, O., & Caliman, J. P. (2012). Agricultural practices in oil palm plantations and their impact on hydrological changes, nutrient fluxes and water quality in Indonesia: a review. Advances in Agronomy, 116, 71-124.

Demircan, V., Binici, T., & Zulauf, C. R. (2010). Assessing pure technical efficiency of dairy farms in Turkey. Agricultural Economics-Czech, 56(3), 141-148.

Diana, S. R., Purnama, S. M., Dharma, G., Sutrisnanto, A., Perwitasari, I., & Farida, F. (2019a). Estimation the Amount of Oil Palm Production Using Artificial Neural Network and NDVI SPOT-6 Imagery. International Journal of Innovative Science and Research Technology, 4(11), 548-554.

Diana, S. R., Hidayat, A., Rafikasari, A., Ibrahim, I. M., & Farida, F. (2019b). Economic Assesstment of Satellite Remote Sensing Data in Indonesia: A Net Present Value Approach. International Journal of Economics and Financial Issues, 9(1), 140-146.

Danoedoro, P. (2012). Pengantar Penginderaan Jauh Digital. [Introduction to Digital Remote Sensing]. Yogyakarta: Penerbit Andi.

Elachi, C., & Van Zyl, J. J. (2006).Introduction to the Pyhsics and Techniques of Remote Sensing. New York: Wiley & Sons.

Farida, F., R Osman, I., Kurniawan Lim, A., & Wahyuni, N. (2018). The efficiency of formal microfinance in Indonesia: using data envelopment analysis application. Iranian Economic Review, 22(3), 787-810.

Gao, T., Zhu, J., Deng, S., Zheng, X., Zhang, J., Shang, G., & Huang, L. (2016). Timber production assessment of a plantation forest: An integrated framework with field-based inventory, multi-source remote sensing data and forest management history. International journal of applied earth observation and geoinformation, 52, 155-165.

Goh, K. J., Teo, C. B., Chew, P. S., & Chiu, S. B. (1999). Fertiliser management in oil palm: Agronomic principles and field practices. In: Fertiliser management for oil palm plantations, 20-21, September 1999, ISP North-east Branch, Sandakan, Malaysia: 44 pp.

Gordon, D., & Vaughan, R. (2011). The Historical Role of the Production Function in Economics and Business. American Journal of Business Education, 4(4), 25-30.

Hansen, S. B., Padfield, R., Syayuti, K., Evers, S., Zakariah, Z., & Mastura, S. (2015). Trends in global palm oil sustainability research. Journal of cleaner Production, 100, 140-149.

Howard, J. A. (1996). Remote Sensing of Forest Resources: Theory and Application, London: Chapman & Hall.

Hossain, M. Z., & Al?Amri, K. S. (2010). Use of Cobb?Douglas production model on some selected manufacturing industries in Oman. Education, Business and Society: Contemporary Middle Eastern Issues, 3(2), 78-85.

Ismail, A. (2013). The effect of labour shortage in the supply and demand of palm oil in Malaysia. Oil Palm Industry Economic Journal, 13(2), 15-26.

Ismiasih, I. (2018). Technical efficiency of palm oil production in West Kalimantan. Habitat, 28(3), 91-98.

Kelly, E., Shalloo, L., Geary, U., Kinsella, A., & Wallace, M. (2012). Application of data envelopment analysis to measure technical efficiency on a sample of Irish dairy farms. Irish Journal of Agricultural and Food Research 51(1), 63-77.

Liu, K. (2015). Application of DEA method in the evaluation of agriculture economic efficiency. Journal of Chemical and Pharmaceutical Research, 7(3), 997-1000.

LPEI (2018). Analisa Rantai Pasok (Supply Chain) Komoditas Unggulan Ekspor Indonesia: Minyak Sawit. Indonesia Eximbank Institute, Lembaga Pembiayaan Ekspor Indonesia (LPEI).

Mahsun, I., & Soejoeti, Z. (1976). Proyek Pemanfaatan Satelit Tele Deteksi Sumberdaya alam/Proyek Telsa. Jakarta: LAPAN.

McGuckin, R. H., Streitwieser, M. L., & Doms, M. (1998). The effect of technology use on productivity growth. Economics of Innovation and New Technology, 7(1), 1-26.

Mulyadi, K. (2009). Pemanfaatan Data Inderaja untuk Pemantauan Sumberdaya Alam dan Lingkungan. Jakarta: Massma Publishing.

Nuryartono, N., Pasaribu, S. H., & Panggabean, P. N. K. (2016). Total factor productivity analysis of oil palm production in Indonesia. Journal of Economics and Financial Issues, 6(4), 1570-1577.

Pardamaen, M. (2017). Kupas tuntas agribisnis kelapa sawit: Mengelola kebun dan pabrik kelapa sawit secara efektif dan efisien. [Exploring oil palm agribusiness: Managing oil palm plantations and mills effectively and efficiently]. Jakarta: Penebar Swadaya.

PPKS (2016a).Pedoman Norma Kerja Perkebunan Kelapa Sawit Pada Lahan Mineral, Buku 1.

PPKS (2016b). Pedoman Norma Kerja Perkebunan Kelapa Sawit Pada Lahan Mineral, Buku 2.

Rizeei, H. M., Shafri, H. Z., Mohamoud, M. A., Pradhan, B., & Kalantar, B. (2018). Oil palm counting and age estimation from WorldView-3 imagery and LiDAR data using an integrated OBIA height model and regression analysis. Journal of Sensors, 2018, 1-13.

Saragih, B. (2017). Produktivitas sumber pertumbuhan minyak sawit yang berkelanjutan. Palm Oil Technical Meeting (PTKS). Retrived from http://www.iopri.org/wp-content/uploads/2017/07/I-01.-Makalah-Prof-Bungaran-Saragih-Produktivitas.pdf

Saraswati, D., Sari, D. K., & Hapsari, D. (2019, May). The applications of Cobb-Douglas Production Function in remanufacturing industry. In IOP Conference Series: Materials Science and Engineering (Vol. 528, No. 1, p. 012055). IOP Publishing.

Setyowati, H. A., & Murti BS, S. H. (2015). Aplikasi Citra Spot-6 Berbasis Transformasi Indeks Vegetasi untuk Estimasi Produksi Kelapa Sawit (Elaeis Guineensis Jacq) (Kasus Perkebunan Kelapa Sawit PT. Tunggal Perkasa Plantations, Air Molek, Kabupaten Indragiri Hulu, Propinsi Riau, Sumatera). JurnalBumi Indonesia, 4(4), 1-7.

Shanmugapriya, P., Rathika, S., Ramesh, T., & Janaki, P. (2019). Applications of remote sensing in agriculture-A Review. International Journal of Current Microbiology and Applied Sciences, 8(1), 2270-2283.

Sharma, A., Liu, X., Yang, X., & Shi, D. (2017). A patch-based convolutional neural network for remote sensing image classification. Neural Networks, 95, 19-28.

Sishodia, R. P., Ray, R. L., & Singh, S. K. (2020). Applications of remote sensing in precision agriculture: A review. Remote Sensing, 12(19), 3136.

Souder, W. E. (1989). Improving productivity through technology push. Research-Technology Management, 32(2), 19-24.

Space.tec partners (2012). Assessing the Economic Value of Copernicus: “European Earth Observation and Copernicus Downstream Services Market Study", Retrived from http://www.copernicus.eu/sites/default/files/library/GMES_GIO _LOT3_PublishableExecutiveSummary_final.pdf.

Srestasathiern, P., & Rakwatin, P. (2014). Oil palm tree detection with high resolution multi-spectral satellite imagery. Remote Sensing, 6(10), 9749-9774.

Suratin, A., Karuniasa, M., & Utomo, S. W. (2018). Is sustainable oil palm production possible for smallholders?. Journal of Environmental Science and Sustainable Development, 1(1), 25-39.

Trisasongko, B. H., & Paull, D. (2020). A review of remote sensing applications in tropical forestry with a particular emphasis in the plantation sector. Geocarto International, 35(3), 317-339.

USGS. (2013).What is the Economic Value of Satellite Imagery? Retrieved from https://pubs.usgs.gov/fs/2013/3003/fs2013-3003.pdf.

Wiranto A. (1985). LAPAN. Special Edition of LAPAN Journal.

Wiratmoko, D., Hartono, H., & Murti, S. H. (2016). Worldview-2 Imagery Vegetation Index Calculation for Oil Palm Yield Estimation. Jurnal Penelitian Kelapa Sawit, 24(3), 143–156.

Vijay, V., Pimm, S. L., Jenkins, C. N., & Smith, S. J. (2016). The impacts of oil palm on recent deforestation and biodiversity loss. PloS one, 11(7), e0159668.

Vîlcu, G. E. (2011). A geometric perspective on the generalized Cobb–Douglas production functions. Applied Mathematics Letters, 24(5), 777-783.




DOI: http://dx.doi.org/10.12695/ajtm.2021.14.1.2

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