Forecasting and pattern analysis of Philippine regions' palay and corn production


College of Computer Studies


Information Technology

Document Type

Conference Proceeding

Source Title

IOP Conference Series: Materials Science and Engineering





Publication Date



Inadequate food production has been an immediate concern that the country is trying to address. Where farmers used fertilizers in their farms in the hope of increasing their agricultural production. Though, if fertilizer application is poorly managed, instead of having an increased production it will result in the contrary. Therefore, the efficient use of fertilizer is critical and has a great impact on crop production. Thus this study aims to show the pattern of Philippine' regions crop production, specifically in rainfed and irrigated palay; white and yellow corn. This will also show which fertilizer will maximize their crop production and to seek for the most applicable association model in forecasting future crop production. Three predictive techniques were used namely, canopy clustering, Apriori association rule mining and time series forecasting models. Results reveal that all regions have a low volume of production for rainfed rice. The canopy clustering shows the pattern leading to the high production of irrigated rice for Region III. Also, Region II, Region X, and Region XII have a high volume of production of yellow corn and lastly clustering results on white corn shows Region VII has a Mid area harvested but shows Low volume of production, while Region X though have a low area harvested managed to have a Mid volume of production. The association of fertilizers to the volume of production shows that low Ammophos leads to a lower volume of production and the low Ammosul is not associated with a low volume of production hence a combination of low Ammosul and low Ammophos leads to a low volume of production. The forecasting methods' linear regression, Gaussian processes, and SMOreg are all applicable in predicting the regions' volume of production, whereas the SMOreg has the least MAE of 8.90% for Region VI. © Published under licence by IOP Publishing Ltd.


Digitial Object Identifier (DOI)



Agricultural productivity--Philippines—Forecasting; Rice--Philippines—Forecasting; Corn--Philippines—Forecasting

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