Notice: Undefined offset: 1 in /var/www/ on line 103
Downscaling Seasonal Climate Forecasts for Risks Management of Rice Production in the Philippines
  • P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2020, Volume: 13, Issue: 1, Pages: 23 – 39

Original Article

Downscaling Seasonal Climate Forecasts for Risks Management of Rice Production in the Philippines


Objectives: This study is centered on the potential use of a dynamic seasonal climate forecast for informing climate risk management in Central Luzon, Philippines to improve rice productivity and resilience. Specifically, we seek to test the downscalibility of the seasonal climate forecasts in the region using a multi-variate spatio-temporal downscaling technique, understand and assess the predictability of rice yield at selected growing areas in the Philippines, and provide guidance on how to develop agricultural risk management strategies.

Methods/statistical analysis: The coupled Global Circulation Model (GCM) CFSv2 was used to evaluate the utility of MJJA (May-June-JulyAugust) rainfall forecasts for risk management of rice production in Central Luzon, Philippines. We used a non-homogeneous hidden Markov model (NHMM) to downscale and simulate the GCM forecasts to selected weather stations in the region. On the other hand, we evaluated the skill of the climate forecasts for predicting crop yields. The simulated rainfall was used to drive the rice models set up in DSSATv4.5. Other weather variables needed by DSSAT were generated and conditioned on the occurrence of rainfall based on NHMM rainfall simulation. Simulated rice yields obtained from these models using observed (i.e., simulated by observed weather) and conditioned rainfall (i.e., NHMM downscaling) serve as a baseline for evaluating yields. We also performed a sensitivity analysis to assess appropriate planting windows for the target season for risk management.

Findings: Inter-annual variability of rainfall is moderately simulated, with a skill (r) of 0.41, suggesting that NHMM was fairly successful downscaling rainfall from the regional scale given the predictive nature of the predictor, at three months lead time. The observed MJJA mean rainfall from the six stations falls at around 56% within the interquartile ranges of simulated rainfall, which indicates reasonable skill of the NHMM downscaling CFSv2’s MJJA rainfall hindcasts. Simulated yield was found comparable with the observed at 5% level of significance using t-test (p > 0.05). The correlations between observed and predicted yields are equal to 0.56. This indicates that the models can represent about 31.36% of the inter-annual variability of the yields of rice, albeit of the three months lead-time of the CFSv2 hindcasts. It suggests a reasonable performance of the models in simulating rice yield using the NHMM generated climate information. Climatologically, the best planting and sowing windows for rice in the study area is on the first week of May. This can be adjusted by using seasonal climate forecasts information. Harvest period should not cross over in the month of September to avoid exposure to heavy typhoons.

Application/ improvements: Sensitivity analysis showed that planting rice earlier than the usual planting windows practiced by the farmers could improve resilience to climate risks. Managing the variance of this management window, however, is of paramount importance, which can be informed by skillful climate forecasts.

Keywords: Seasonal Climate Forecast, Downscaling, Nonhomogenous Hidden Markov Model, Crop Model, Climate Risks Management, Yield Prediction.


Subscribe now for latest articles and news.