DOI : https://doi.org/10.18517/ijaseit.4.3.387

Implementation of Information System on the Planting Time Prediction Based on Climate Modelling

Armi Susandi (1), Mamad Tamamadin (2), Irsal Las (3), Erizal Jamal (4)
(1) Department of Meteorology, Institut Teknologi Bandung, Labtek XI Building floor 1, Jalan Ganesa 10 Bandung 40132, Indonesia
(2) Department of Meteorology, Institut Teknologi Bandung, Labtek XI Building floor 1, Jalan Ganesa 10 Bandung 40132, Indonesia
(3) Indonesian Agroclimate and Hydrology Research Institute, Ministry of Agriculture, Jl. Tentara Pelajar No. 1A Bogor 16111, Indonesia
(4) Indonesian Institute for Agricultural Technology Transfer (IIATT), Ministry of Agriculture Jl. Salak 22 Bogor, Indonesia
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How to cite (IJASEIT) :
Susandi, Armi, et al. “Implementation of Information System on the Planting Time Prediction Based on Climate Modelling”. International Journal on Advanced Science, Engineering and Information Technology, vol. 4, no. 3, June 2014, pp. 140-2, doi:10.18517/ijaseit.4.3.387.
Citation Format :
Changes in rainfall patterns due to climate change have resulted in losses of farmers in Indramayu. Farmers has loss of economic due to failing of planting and harvesting every year in this location. Therefore, farmers need information on future rainfall prediction to plant rice accurately and on time. The information system should be able to be easily accessed by farmers and extension. This paper discuss research result on rainfall prediction system and planting rice with high resolution in dasarian (10-days) scale. The information system includes predictive technology using Geographic Information System (GIS) with input from Smart Climate Modelling using stochastic approach. Information delivery system was developed in line with the predictions of rainfall information and feedback planting season comes to evaluate the predicted results. Such information will be dynamically run through overlaying with Google maps on the web server. This system can be accessed by the public and was designed to automatically generate maps of rainfall and planting prediction that can be viewed directly in the Google maps application. This research result will be effort to adapt to climate change that has impacted to agriculture sector in Indonesia.

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