Prediction of sugarcane (Saccharum offcinarum L.) yield in Balayan Mill District, Batangas, Philippines using MODIS NDVI.

Date

6-2016

Degree

Bachelor of Science in Agricultural and Biosystems Engineering

Major Course

Major in Structures and Environment

College

College of Engineering and Agro-Industrial Technology (CEAT)

Adviser/Committee Chair

Ronaldo B. Saludes

Abstract

Due to the increasing demand for sugar and the ASEAN Free Trade Agreement (AFTA), the Philippines is pressured to increase its local sugar production. Prediction of sugarcane yield prior to harvest time can give farmers enough time to apply interventions such as irrigation and fertilizer. This study focused on the use of remotely sensed NDVI and rainfall data to develop sugarcane yield prediction models and generalized NDVI for 2006-2007, 2007-2008, 2010-2011, and 2013-2014 cropping periods of Balayan Mill District, Batangas showed low R square values. However, adding remotely sensed rainfall data in the prediction model reduced the RMSE to 4.35% which is lower than the result of the traditional yield prediction method being used by the Mill District. In addition, NDVI curve characteristics for all cropping periods were obtained and each was correlated with yield. Results showed that length and area under the NDVI curve have significant relationship with yield but with low R square values. A general crop monitoring model with average yield of 66.4637 TC/ha was established and validation showed that the general NDVI curve can inform planters and farmers if yield of current season will be higher or lower than the average yield.

Language

English

Location

UPLB College of Engineering and Agro-Industrial Technology (CEAT)

Call Number

LG 993.5 2016 A2 P36

Document Type

Thesis

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