International Journal of Innovative Research and Knowledge


Volume-4 Issue-3, March 2019




Title: PREDICTIVE ANALYSIS OF RICE PRODUCTION IN RELATION TO CLIMATIC PARAMETERS: A SUPERVISED MACHINE LEARNING APPROACH

Author: Myelinda A. Baldelovar, MIT

Abstract

Climate change will adversely affect the agricultural production. The changes in climatic parameters such as rainfall, temperature, pressure and humidity would implicate to food security worldwide. In this study, using the five year data collected from Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA) and Philippines Statistics Authority (PSA), the relationship between the production of irrigated rice, rainfed rice and climatic parameters such as maximum temperature, minimum temperature, rainfall, humidity and pressure was analyzed. Using Rapidminer Studio version 9.1.0, a multiple linear regression analysis was applied to the data. Results revealed that for rainfed rice production, maximum temperature, minimum temperature and rainfall are significant. Maximum temperature has a negative relationship for both irrigated and irrigated rice production. A positive relationship for both irrigated and irrigated rice production with minimum temperature was noted. It was also revealed that rainfall has a positive significant relationship with rainfed rice production.


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IJIRK DESCRIPTION

ISSN: 2213-1356

Publisher: Scholar Touch Publishers

Area/Scope: Business, Economics & Management; Social Science, Literature, Arts & Humanities; Engineering & Technology; Life Science & Physical Science, Health & Medical Science

Frequency: Monthly

Format: Online & Print

Language: English

Review Process: Double Blinded

Access: Open Access