DOI: https://doi.org/10.14710/geoplanning.5.2.237-250

SPATIAL PATTERN OF RICE FIELD PRODUCTIVITY BASED ON PHYSICAL CHARACTERISTICS OF LANDSCAPE IN CITARUM WATERSHED, WEST JAVA

Nugroho Purwono  -  Geospatial Information Agency, Indonesia
*Arif Aprianto  -  Master of Geography Science Program, University of Indonesia / Geospatial Information Agency, Indonesia

Citation Format:
Abstract

This research to analyze the pattern of rice field productivity that is identified through landscape perspective. Identification of productivity pattern has been done partially based on each typology of land components into several segment of the Citarum watershed, West Java Province, Indonesia. Spatial autocorrelation through GIS tool is used as the method in this research. By using moran’s I (index) measurement, degree of dependency of these variables are generated to find the spatial pattern. The result of this study is separated the value of productivity based on segments of watershed, the values of the average of productivity are upstream (6,39 ton/Ha), middle stream (6,52 ton/Ha), and downstream (7,17 ton/Ha), sequentially. The highest productivity is in the downstream area (9,83 ton/Ha) and the lowest is in the upstream area (4,55 ton/Ha). In accordance with physiographic typology showed the rice field in the middle stream has more variation than the upstream or the downstream area. The highest of average rice field productivity is on alluvial plain. Overall, the rice field productivity on the hills is higher rather that other types of landform the structural formation is more dominant, in addition. The spatial pattern shows the distribution of rice field productivity most likely to clustered based on the similarity of physiographic type. Statistically, it has p-value <0,01 and z-score >2,58 (239,26) correspond to Spatial Autocorrelation (Moron’s I). This positive value means a less than 1% likelihood that this clustered pattern could be result of random choice, which the rice field productivity value has similar pattern to others. Thus, it can be generated that the pattern of rice field productivity has a very close relation with the physical characteristics which associated of each typology of land components.

Fulltext View|Download
Keywords: Rice field productivity; Spatial patern; Land component; Landform; Watershed

Article Metrics:

Article Info
Section: Original Research
Language : EN
Recent articles
THE EFFECT OF MINIMUM NOISE FRACTION ON MULTISPECTRAL IMAGERY DATA FOR VEGETATION CANOPY DENSITY MODELLING POTENTIAL OF BIG DATA FOR PRO-ACTIVE PARTICIPATORY LAND USE PLANNING THE INFLUENCE OF SPRAWL DEVELOPMENT, INCOME, AND INFRASTRUCTURE ON ENERGY CONSUMPTION IN CENTRAL JAVA PROVINCE, INDONESIA More recent articles
Most cited articles
POTENTIAL OF BIG DATA FOR PRO-ACTIVE PARTICIPATORY LAND USE PLANNING THE PERFORMANCE OF LAND USE CHANGE CAUSATIVE FACTOR ON LANDSLIDE SUSCEPTIBILITY MAP IN UPPER UJUNG-LOE WATERSHEDS SOUTH SULAWESI, INDONESIA LAND USE CHANGE IN SUBURBAN AREA: A CASE OF MALANG CITY, EAST JAVA PROVINCE PENGARUH KEBERADAAN PERGURUAN TINGGI TERHADAP PERUBAHAN MORFOLOGI KAWASAN SEKITARNYA DETEKSI PERUBAHAN PENGGUNAAN LAHAN DENGAN CITRA LANDSAT DAN SISTEM INFORMASI GEOGRAFIS: STUDI KASUS DI WILAYAH METROPOLITAN BANDUNG, INDONESIA More cited articles

  1. Álvarez-López, C. J., Riveiro-Valiño, J. A., & Marey-Pérez, M. F. (2008). Typology, classification and characterization of farms for agricultural production planning. Spanish Journal of Agricultural Research, 6(1), 125–136. [https://doi.org/10.5424/sjar/2008061-299">Crossref]

  2. Anselin, L. (1995). Local Indicators of Spatial Association - LISA. Geographical Analysis, 27(2), 93–115. [https://doi.org/10.1111/j.1538-4632.1995.tb00338.x">Crossref]

  3. Arul Kumar, C., & Manimannan, G. (2014). Spatial pattern of agriculture productivity of Crops in Cauvery Delta Zone of Tamilnadu. Journal of Agriculture and Veterinary Science (IOSR-JAVS), 7(11), 01–07. [https://doi.org/10.9790/2380-071120107">Crossref]

  4. Diao, X., You, L., Alpuerto, V., & Folledo, R. (2012). Assessing Agricultural Potential in South Sudan – A Spatial Analysis Method. Intech, 139–152. [https://doi.org/10.5772/47938">Crossref]

  5. Diniz-Filho, J. A. F., Oliveira, G. de, Lobo, F., Ferreira, L. G., Bini, L. M., & Rangel, T. F. L. V. B. (2009). Agriculture, habitat loss and spatial patterns of human occupation in a biodiversity hotspot. Scientia Agricola, 66(6), 764–771. [https://doi.org/10.1590/S0103-90162009000600007">Crossref]

  6. Donfouet, H. P. P., Barczak, A., Détang-Dessendre, C., & Maigné, E. (2017). Crop Production and Crop Diversity in France: A Spatial Analysis. Ecological Economics, 134, 29–39. [https://doi.org/10.1016/j.ecolecon.2016.11.016">Crossref]

  7. Dorner, B., Lertzman, K., & Fall, J. (2002). Landscape pattern in topographically complex landscapes: Issues and techniques for analysis. Landscape Ecology, 17(8), 729–743. [https://doi.org/10.1023/A:1022944019665">Crossref]

  8. ESRI. (2010). GIS Best Practices: Environmental Management.

  9. FAO. (2014). a Regional Rice Strategy for Sustainable Food Security in Asia and the Pacific.

  10. Iimi, A., You, L., Wood-Sichra, U., & Humphrey, R. M. (2015a). Agriculture Production and Transport Infrastructure in East Africa: An Application of Spatial Autoregression. Policy Research Working Papers 7281, (June).

  11. Iimi, A., You, L., Wood-Sichra, U., & Humphrey, R. M. (2015b). Agriculture Production and Transport Infrastructure in East Africa: An Application of Spatial Autoregression, (June).

  12. Jiang, X. jian, Tang, L., Liu, X. jun, Cao, W. xing, & Zhu, Y. (2013). Spatial and Temporal Characteristics of Rice Potential Productivity and Potential Yield Increment in Main Production Regions of China. Journal of Integrative Agriculture, 12(1), 45–56. [https://doi.org/10.1016/S2095-3119(13)60204-X">Crossref]

  13. Jones, J. W., Antle, J. M., Basso, B., Boote, K. J., Conant, R. T., Foster, I., … Wheeler, T. R. (2017). Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science. Agricultural Systems, 155, 269–288. [https://doi.org/10.1016/j.agsy.2016.09.021">Crossref]

  14. Legendre, P. (1993). Spatial Autocorrelation : Trouble or New Paradigm ? Author ( s ): Pierre Legendre Published by : Ecological Society of America Stable URL : http://www.jstor.org/stable/1939924 REFERENCES Linked references are available on JSTOR for this article : You may. Ecology, 74(6), 1659–1673. []

  15. Mahananto, S. S. dan C. F. A. (2009). Faktor- Faktor Yang Mempengaruhi Produksi Padi Studi Kasus di Kecamatan Nogosari, Boyolali, Jawa Tengah. Wacana, 12 No.1(1), 179–191.

  16. Mennis, J., & Guo, D. (2009). Spatial data mining and geographic knowledge discovery-An introduction. Computers, Environment and Urban Systems, 33(6), 403–408. [https://doi.org/10.1016/j.compenvurbsys.2009.11.001">Crossref]

  17. Mokarram, M., & Hojati, M. (2017). Morphometric analysis of stream as one of resources for agricultural lands irrigation using high spatial resolution of digital elevation model (DEM). Computers and Electronics in Agriculture, 142, 190–200. [https://doi.org/10.1016/j.compag.2017.09.001">Crossref]

  18. Nurliani, & Rosada, I. (2016). Rice-field Conversion and its Impact on Food Availability. Agriculture and Agricultural Science Procedia, 9, 40–46.[https://doi.org/10.1016/j.aaspro.2016.02.121">Crossref]

  19. Ogale, S., & Nagarale, V. (2014). Agricultural Productivity of the Baramati Tahsil, Pune District n(Maharashtra).n. IOSR Journal of Agriculture and Veterinary Science (IOSR-JAVS), 7(5), 25–30.

  20. Ord, J. K., & Getis, A. (1995). Local Spatial Autocorrelation Statistics: Distributional Issues and an Application. Geographical Analysis, 27(4), 286–306. [https://doi.org/10.1111/j.1538-4632.1995.tb00912.x">Crossref]

  21. Santoso, P. B. K., Sabiham, S., & Wayan Rusastra, I. (2017). Analisis Pola Konversi Lahan Sawah dan Struktur Hubungan Penyebab dan Pencegahannya (Studi Kasus Kabupaten Subang, Provinsi Jawa Barat). Jurnal Pengelolaan Sumberdaya Alam Dan Lingkungan, 7(2), 184–194. [https://doi.org/10.19081/jpsl.2017.7.2.184">Crossref]

  22. Saroinsong, F., Harashina, K., Arifin, H., Gandasasmita, K., & Sakamoto, K. (2007). Practical application of a land resources information system for agricultural landscape planning. Landscape and Urban Planning, 79(1), 38–52. [https://doi.org/10.1016/j.landurbplan.2006.03.002">Crossref]

  23. Suwartha, N., Maulani, I., Priadi, C. R., Felaza, E., Tjahjono, T., & Putri, G. L. (2017). Mapping land use suitability for development of recharge wells in the ciliwung watershed, Indonesia. Water Practice and Technology, 12(1), 166–178. [https://doi.org/10.2166/wpt.2017.022">Crossref]

  24. Ziadat, F. M. (2007). Land suitability classification using different sources of information: Soil maps and predicted soil attributes in Jordan. Geoderma, 140(1–2), 73–80. [https://doi.org/10.1016/j.geoderma.2007.03.004">Crossref]


Last update:

  1. Strategi Pengendalian Konversi Lahan Sawah untuk Mempertahankan Swasembada Pangan di Kabupaten Toba

    Tugma Jaya Manalu, Dyah Retno Panuju, Untung Sudadi. Jurnal Ilmu Tanah dan Lingkungan, 24 (2), 2022. doi: 10.29244/jitl.24.2.96-102

Last update: 2024-12-23 17:53:38

No citation recorded.