Strategies for Effective Implementation of the CPAR Program - Building Up From the Gains: Lessons From the Improvements for Effective Implementation of the Community-Based Participatory Action Research Program (SEARCA-DA-BAR Policy Brief 2021)
Skills Needs Anticipation (SNA): Workplace Skills and Satisfaction (WSS) Baseline Survey of Select Employers in the Construction Industry
Strengthening Multi-employer Bargaining: Policies and Practices (Phase II)
Non-Hazardous Activities for Children: The Case of Banana and Sugarcane Supply Chains

PIDS WB 2021-0905
7th Mindanao Policy Research Forum
PIDS WB 2021-0905
Annual Public Policy Conference Webinar 4: Robust and Healthy Workforce and Closing Program
PIDS WB 2021-0904
Annual Public Policy Conference Webinar 3: Green And Inclusive Recovery
PIDS WB 2021-0903
Annual Public Policy Conference Webinar 2: Ethical Business
Publication Detail
PJD 2009 Vol. XXXVI No. 1-f: Incorporating Regional Rice Production Models in a Simulation Model of Rice Importation: a Discrete Stochastic Programming Approach

In the Philippines, importation has remained as one of the most feasible options for the government to meet the growing demand for rice. It is thus imperative for the government to develop a strategy that would ensure adequate supply and minimum importation costs. One of the critical factors in import decisionmaking is rice production. The Inter-Agency Committee on Rice and Corn (IACRC), of which the National Food Authority (NFA) and the Bureau of Agricultural Statistics (BAS) are members, decides on importation when there is an impending production shortfall in the coming season. However, because Philippine agriculture is vulnerable to extreme climate events and climate change is expected to further intensify climate variability, rice production forecast is becoming more uncertain. Inaccurate production forecasts could lead to incorrect volume and ill-timing of rice imports, which in turn, could result in either a waste of resources for the government or a burden to consumers. Contraction of rice imports in the early 1990s and over-importation in 1998 illustrate how inaccurate forecasts of the volume and timing of rice importation, especially during El Nino and La Nina years, could result in substantial economic costs. This paper evaluates the significance of seasonal climate forecast (SCF) in rice policy decisions of the government, particularly on importation. It presents an alternative method of forecasting the level of rice production through regional rice production models. The rice production models systematically incorporate SCF and could be used in support of the current practice of forecasting rice production based on planting intentions. The paper also demonstrates how SCF, together with these production estimates, could be incorporated in the rice import decisions of the government through the Rice Importation Simulation (RIS) model, which was developed using a Discrete Stochastic Programming (DSP) modeling approach. The RIS model, which recommends a set of optimal rice import strategies, could serve as guide for the government in its rice import decisions in the face of seasonal climate variability and could be used in estimating the potential value of SCF.

Philippine Institute for Development Studies
Authors Keywords
de Guzman, Rosalina G.; Mina, Christian D.; Crean, Jason; Parton, Kevin; Reyes, Celia M.; Philippines; El Niño; La Niña; rice; seasonal climate forecast (SCF); importation; production models; Discrete Stochastic Programming (DSP);
Download PDF Number of Downloads
Published in 2010 and available in the PIDS Library or Downloaded 2,195 times since September 18, 2012
Please let us know your reason for downloading this publication. May we also ask you to provide additional information that will help us serve you better? Rest assured that your answers will not be shared with any outside parties. It will take you only two minutes to complete the survey. Thank you.

To use as reference:
If others, (Please specify):
Name: (optional)
Email: (required, but will not display)
If Prefer to self-describe, please specify:
Level of Education:
If employed either part-time or full-time, name of office:
If others, (Please specify):
Would you like to receive the SERP-P UPDATES e-newsletter? Yes No
Use the space below if you have any comment about this publication or SERP-P knowledge resources in general.