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
Recommended Citation
Palad, Janina Gaile M., "Prediction of sugarcane (Saccharum offcinarum L.) yield in Balayan Mill District, Batangas, Philippines using MODIS NDVI." (2016). Undergraduate Theses. 4538.
https://www.ukdr.uplb.edu.ph/etd-undergrad/4538
Document Type
Thesis