OPTIMIZATION OF RICE COMMODITY BASED ON SPATIAL DATA: A CASE STUDY OF MAGELANG REGENCY
Keywords:
natural resource balance, downscaling, optimization, agricultureAbstract
The use of spatial data in regional planning is essential, particularly to identify the distribution of agricultural land for each commodity—whether passive/actual or active/potential land—and to support effective decision-making, especially in relation to land management and food security. One of the key agricultural commodities that plays an important role in Indonesia’s food security is rice. Food security is a strategic issue in regional development, particularly in agrarian areas such as Magelang Regency. With the limited availability of agricultural land, optimizing land use has become crucial to ensure an adequate food supply.
In this study, spatial data–based analysis offers a solution for understanding the distribution of land resources and productivity potential more comprehensively. The research aims to analyze the optimization—in this case, the reserves—of the rice commodity, both physically and monetarily, based on spatial data in Magelang Regency. The analytical methods used include the Natural Resource Balance (NRB) and downscaling analysis.
The results show that Magelang Regency does not have available land reserves for the expansion of rice cultivation. However, the reserve volume can still be maintained through high productivity. This indicates that although the current reserve volume is sufficient to meet present needs, the absence of land reserves raises concerns about reliance on high productivity, which may be affected by various external factors. This study emphasizes the importance of commodity diversification and sustainable land management to ensure food security in the future.
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References
Badan Pusat Statistik. 2024. Statistik Indonesia 2024. Diakses pada tanggal 15 Okotober 2024 (https://www.bps.go.id/)
Badan Pusat Statistik. 2024. Provinsi Jawa Tengah Dalam Angka 2024. Diakses pada tanggal 15 Oktober 2024 (https://jateng.bps.go.id/)
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Jin, Y., Ge, Y., Wang, J., Heuvelink, G. B. M., Wang, L. 2018. Geographically Weighted Area to Point Regression Kriging for Spatial Downscaling in Remote Sensing. MDPI Journals. Vol 10(579): 1-22
Pramono, R. W. D. 2021. Modul Teknik Analisis dan Perencanaan Wilayah. Yogyakarta. Deeppublish (Grup Penerbitan Cv Budi Utama).
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