Can Paddy Growing Phase Produce an Accurate Forecast of Paddy Harvested Area in Indonesia? Analysis of the Area Sampling Frame Results

Kadir, Kadir and Prasetyo, Octavia Rizky (2023): Can Paddy Growing Phase Produce an Accurate Forecast of Paddy Harvested Area in Indonesia? Analysis of the Area Sampling Frame Results. Published in: Proceeding of International Conference on Data Science and Official Statistics , Vol. 2023, No. 1 (29 December 2023): pp. 746-755.

Abstract

Our study aims to evaluate the accuracy of the forecasts produced based on the paddy growing phase obtained from the results of the Area Sampling Frame (ASF) Survey and, as a comparison, proposes an alternative forecast method taking into account the seasonal pattern and hierarchical structure of the national paddy harvested area estimation obtained from the ASF to improve the accuracy. In doing so, we calculated the MAPE by comparing the realization of paddy harvested area during the period January to September 2022 with their forecasts produced from the area of generative, late vegetative, and early vegetative phases. We also implemented a Hierarchical forecasting method on monthly data of the harvested area from January 2018 to August 2022 for all provinces. Specifically, we applied the bottom-up method for the reconciliation and the rolling window method to produce a three-consecutive month forecast for the period January to September 2022. We found that the accuracy prediction based on the paddy growing phase is moderately accurate. The combination of the bottom-up reconciliation method and the SARIMA model produces a much better accuracy for the national figure of paddy harvested area as shown by a lower MAPE. Our findings suggest that the Hierarchical forecasting method could be an alternative for the prediction of harvested area based on the ASF results other than the prediction obtained from the standing crops.

• Item Type: MPRA Paper
• Language: English
• Keywords: ASF, Hierarchical, forecasting, paddy, SARIMA
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C18 - Methodological Issues: General; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C40 - General; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q1 - Agriculture; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q1 - Agriculture > Q10 - General

References

[1] Ruslan K 2019 Improving Indonesia’s Food Statistics through the Area Sampling Frame Method (Center for Indonesian Policy Studies). https://doi.org/10.35497/287781 [2] Sawit MH 2014 Analisa Hasil Sensus Penggilingan Padi 2012. Jurnal PANGAN. 23 3 208-219 [3] Maksum C and Sastrotaruno S 1998 Evaluasi Luas Tanaman Padi Dengan Pendekatan Rumah Tangga (Badan Pusat Statistik) [4] Rosner LP and McCulloch N 2008 A Note on Rice Production, Consumption and Import Data in Indonesia. Bulletin of Indonesian Economic Studies. 44 1 81-91 [5] Khudori 2019 Kaji Ulang Kebijakan Perberasan. Jurnal PANGAN. 28 1 57-72 [6] Kadir K & Prasetyo OR 2021 Peramalan Luas Panen Padi Indonesia Dengan Model ETS (Error, Trend, Seasonal). Jurnal MSA (Matematika Dan Statistika Serta Aplikasinya). 9 1 715. https://doi.org/10.24252/msa.v9i1.19666 [7] Mubekti & Sumargana L 2016 Pendekatan Kerangka Sampel Area untuk Estimasi dan Peramalan Produksi Padi. Jurnal PANGAN. 25 2 71 – 82 [8] Badan Pusat Statistik 2018 Pedoman Teknis Pendataan Statistik Pertanian Tanaman Pangan Terintegrasi dengan Metode Kerangka Sampel Area (KSA) 2018 (Badan Pusat Statistik) [9] Amalia RR and Kadir 2019 Improving Paddy Statistics in Indonesia: Developing Crop Cutting Survey Using Area Sampling Frame (pre-print) [10] Gandharum L, Mulyani ME, Hartono DM, Karsidi A, & Ahmad M 2021 Remote sensing versus the area sampling frame method in paddy rice acreage estimation in regency, West Java province, Indonesia. International Journal of Remote Sensing. 42 5 1738- 1767 DOI: 10.1080/01431161.2020.1842541 [11] Raharjo M, Kurnia A, & Wijayanto H 2022 A Study on Accuracy of Paddy Harvest Area Estimation on Area Sampling Frame Method. Indonesian Journal of Statistics and Its Applications. 6 1 41–49. https://doi.org/10.29244/ijsa.v6i1p41-49 [12] Prasetyo OR, Kadir, and Amalia RR 2019 Evaluasi Akurasi Data Potensi Luas Panen Padi di Indonesia (pre-print) [13] Lewis C D 1982 Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting (Butterworth-Heinemann) [14] Tashman LJ 2000 Out-of-sample tests of forecasting accuracy: an analysis and review International Journal of Forecasting. 16 4 437–450 [15] Athanasopoulos G, Ahmed RA, and Hyndman RJ 2009 Hierarchical forecasts for Australian domestic tourism. International Journal of Forecasting. 25 1 146–166 [16] Kahn KB 1998 Revisiting top-down versus bottom-up forecasting. The Journal of Business Forecasting. 17 2 14–19 [17] Hyndman RJ and Athanasopoulos G 2018 Forecasting: principles and practice. 2nd ed (Melbourne, Australia: OTexts. OTexts.org/fpp2/) [18] Hyndman RJ, Ahmed RA, Shang HL, Wang E 2014 hts: Hierarchical and grouped time series. R package version 4.00, URL http://CRAN.R-project.org/package=hts