Development of a functional model for predicting rice yield in Ilocos Norte [Philippines]
2013
Maloon, J.M. | Vives, M.J.C. | Castro, R.C. | Alibuyog, N.R.
Monitoring the growth of rice and forecasting its yield before harvest season is very important for crop and food management. Remote sensing images are capable of identifying crop health as well as predicting its yield. This study was conducted to develop a functional model for predicting rice yield based from remotely sensed data. The Normalized Difference Vegetation Index (DNVI) calculated from remote sensing images has been widely used to monitor crop growth and related it to crop yield. The study used 16-day Composite TERRA MODIS (Moderate Resolution Imaging Spectroradiator) data, a free satellite images downloaded from the internet from November 2010 to April 2011 (covering the entire cropping season) to predict rice yield in the province of Ilocos Norte. The satellite images have a resolution of 250 m, which means that at ground level each pixel had an area of 6.25 ha. The NDVI values were extracted thru GIS software, and the values were imported to MS Excel worksheet for tabulation and graphical representation. Eight yield prediction models were developed through linear regression analysis (Stepwise method) between NDVI and observed yield. Among the eight models developed, Model 3 had the highest potential. It can predict way ahead before harvesting. The R2 value of the models ranged from 0.36-0.75 which means that about 36-75% of the yield variability could be explained by the models. The variability between predicted and actual yield are due to the factors not considered in the model such as the type of soil, varieties planted, weather and other cultural management practices, such as water, nutrient and pest managements done for the standing crop studied.
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