Forecasting the Impact of Climate Change on Rice Crop Yields under RCP4.5 and RCP8.5 Scenarios in Central Luzon, Philippines, Using Machine Learning Algorithms

Rizza G Baltazar


Climate change poses a significant threat to the agricultural sector around the world, and it could impact the rice crop yields of the Philippines's major rice-producing provinces in Central Luzon; thus, there is a need to investigate the possible influence, both in the near and long term, of climate change on rice crop yields in these major rice-producing provinces. This paper presents the use of random forest, gradient boosting, regression analysis, and artificial neural network (ANN) methods for modeling climate factors (pressure, temperature, relative humidity, rainfall, wind, cloudiness, and sunshine) and rice crop yield (RCY) data from Central Luzon, Philippines, covering the years 2009 to 2018. The projected temperature and rainfall for Representative Concentration Pathways 4.5 and 8.5 (RCP4.5 and RCP8.5, respectively) scenarios were used as input to the models to estimate the possible influence of climate change on the rice crop yields in the rice-producing provinces in Central Luzon. The results suggest an increase in temperature of 1.14 °C in RCP4.5, which surges to 1.62 °C in RCP8.5, and an increase in rainfall by a mean of 3.70 mm in RCP4.5 and 12.28 mm in RCP8.5. Furthermore, three of the models: ANN, random forest (RF), and gradient boosting (GB) showed an increase in RCY, while linear regression (LR) showed a decrease in RCY for both the RCP4.5 and RCP8.5 scenarios compared to the current RCY. Therefore, it is uncertain whether climate change, as characterized by rainfall and temperature, will have a positive or negative impact on rice crop yields in Central Luzon, Philippines.


Artificial neural network, climate change, gradient boosting, machine learning, Philippines, RCP8.5, RCP4.5, random forest, rice crop yield

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