Calibration, validation and application of CERES-Maize model for climate change impact assessment in Abuan Watershed, Isabela, Philippines
Received: 08 March 2016 / Accepted: 16 September 2016/ Published online: 12 November 2016
- This study developed a Decision Support System for Agrotechnology Transfer - Crop Environment Resource Synthesis (DSSAT-CERES) Maize model for local cultivar of maize. The model output parameter was the set of genetic coefficient for the cultivar which was successfully calibrated and validated in the field with a good degree of accuracy.
- The model was used to project yield of corn as affected by change in temperature and rainfall amounts brought about by extreme weather conditions.
- Assessment of climate change projections published by the Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA) showed a significant reduction of corn yield by up to 44% by 2020 and 35% in 2050.
Recently, corn farmers in Abuan Watershed and Isabela Province are experiencing declining crop yields caused by insufficient amount of rainfall. To increase crop yields and reduce production risks, research on better use of available rainfall and better understanding on effects of climate variability, and soil and field management on crop production is imperative. Simulation models, driven by daily climatic data, can be used to predict the impact of long-term climate variability on the probability of success of a range of crop, and water and soil management strategies. Calibration of the model was based on field data from Cagayan Valley Research Center at Ilagan, Isabela during two growing seasons in 2013-2015. Model validation of phenology, yield and biomass was undertaken on-station and at farmer fields. Results showed acceptable accuracy between actual and simulated data. Specifically, the model yields high efficiency of 0.86 in predicting yield of participating farmers. It also demonstrated the possibility of simulating production of other hybrid varieties. Projected yield as affected by temperature and rainfall changes indicated a sharp reduction by up to 44% in 2020 and 35% in 2050. The model can be used as important input in developing decision support system by the Department of Agriculture and local government units.
CERES-Maize Model, calibration, validation, Abuan Watershed
Lanie A. Alejo
Isabela State University