Interaction Between Philippine Met Services and Corn Farmers in the Southern Philippines

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1 Interaction Between Philippine Met Services and Corn Farmers in the Southern Philippines C. Predo, R. de Guzman, E. Juanillo, P. Hayman, C. Reyes, E. Monte, K. Gonzales, R. Patindol, R. Gravoso, J. Liguton, F. Hilario, B. Alexander, D. Ortega and T. Cinco VMO International Workshop on the Content, Communication and Use of Climate Product and Services for Sustainable Agriculture May 2009 University of Southern Queensland Toowoomba City, Queensland Australia

2 Mandate Philippine Atmospheric, Geophysical & Astronomical Services Administration, Department of Science & Technology (PAGASA-DOST) To provide public weather forecasts and advisories, typhoon and flood warnings, meteorological /climatological/ and other specialized services primarily for the protection of life and property, and in support of economic productivity

3 Background Information In the Philippines corn is the most important crop next to rice Highly valued as human food, animal feed, and raw materials for industry 1.7 million corn growers covering 2.7 M ha (1991) 41% of grower in Mindanao (53% of the area) 27% in Visayas (18% of the area) P27 billion corn industry employs about 30% of farmers 20% of popn depends on corn as staple food, especially in Visayas, Cagayan Valley and Mindanao 27% of corn production used as staple food (white corn); 70% as feed (yellow corn) Corn also used in the manufacture of starch, gluten and alcohol During the El Nino 565,240 metric tons of corn was damage amounting to about P 3 billion pesos

4 Climate Type Classification of the Philippines Climate Classifications: Type I Two pronounced season dry from November to April and wet for the rest of the year. maximum rain period coincides with the peak of the southwest monsoon (July to September). Type II No dry season with a very pronounced maximum rainfall from November to April and wet for the rest of the year. Type III Season not very pronounced relatively dry November to April and wet for the rest of the year Type IV Rainfall more or less evenly distributed through out the year

5 Annual normal rainfall ( ) Highest 24 hr rainfall =1085.mm a natural disasterprone country large number of islands (>7,100); vast, irregular coastline (17,000 kms); an average of 19.6 tropical cyclones a year (with 8 to 9 crossing the country triggering floods/landslides); abundant rainfall, yet some areas are semi-arid ENSO is a major source of climate variability.

6 El Niño o Vulnerability Maps Rice Corn

7 Losses in Corn Production Annual Corn Production Losses in the Philippines ( ) Production Losses (Millions Pesos) Year Source: Bureau of Agricultural Statistics

8 Major Causes of Damages Major Causes of Damages in Corn Production in the Philippines ( ) Drought Typhoon/Flashflood Pest & Disease Others 4% 1% 35% 60% Source: Bureau of Agricultural Statistics

9 Services & Products to Agriculture & Other Sectors

10 Climate Outlook Fora

11 Proposed SCF dissemination program

12 Linking climate information and corn farmers decision making

13 Understanding Corn Farming Systems Conducted focus group discussions Cebu Leyte Bukidnon, Mindanao

14 Conducted farm and household survey

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17 Climate and Cropping Pattern Farming Systems/Cropping Pattern Farming is the dominant activity Major crops: corn, sugarcane, pineapple, rice and bananas For most parts of Bukidnon: corn follows a twice-a-year planting pattern; in elevated areas sometimes 3x a year 1 st cropping: Feb/Mar and harvested in Jun/Jul 2 nd cropping: Jul/Aug and harvested in Nov/Dec 3 rd cropping in upland sloping, rolling to hilly envt: Nov/Dec and harvested in Feb/Mar/Apr (corn or legumes) Cropping pattern by elevation 1 st crop 2 nd crop Lower elevation corn corn Higher elevation corn corn Yield: Local variety ( t/ha) vegetables corn OPVs ( t/ha) Hybrid ( t/ha)

18 Farmers Response and Use of SCF Cropping choice decision Timing of cropping periods Levels of N use

19 Farmers Cropping Decision Problem Decisions Affected by SCF for Malaybalay, Bukidnon Feb Mar Apr May Jun Jul Aug Sep 1 st crop Oct Nov Dec Jan 2 nd crop Crop choice Corn Vegetable Fallow Crop choice Crop choice Crop choice Corn Vegetable Fallow Corn Vegetable Fallow Corn Vegetable Fallow

20 Native corn Corn Hybrid corn with N fertilizer application (using farmer s practice)

21 *Biophysical secondary data *Experts judgment *Observed farmers practice Farm Decision Analysis Framework Calibration Crop simulation model (DSSAT) Input parameters *Material Input reqmts *Labor reqmts *Input/Output prices Seasonal Climate Forecasts *Amt of rainfall *Timing of rainfall events *Freq of rainfall With SCF El Niño Neutral La Niña Without SCF Crop Yield and Crop Yield Distribution Expected Payoff (Gross Margin/NPV) Good, Average, Poor Decision tree analysis Stochastic dominance/ Efficiency analysis

22 Valuing SCF Information With and Without Seasonal Climate Forecast Information: Outcomes: rainfall, yield and gross margin or payoff V(SCF) = E(GM) (wcf) E(GM) (wocf) where: E(GM) (wcf) = expected gross margins with climate forecast E(GM) (wocf) = expected gross margins without climate forecast

23 Stochastic Dominance/Efficiency with Respect to a Function and Bayesian approach

24 Stochastic Dominance Analysis with Respect to a Function The efficient sets are not the same for both RAC values. This result suggests that the efficient set changes between the two RACs. Use SERF analysis to determine the RAC(s) where the efficient set changes.

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27 Feedback Mechanism Conducted farmers training on awareness and use of SCF in farm decision making

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30 Conducted series of seminar-workshops Leyte Cebu Bukidnon

31 Conclusions: What did we learn? Identifying the dominant farming decisions affected by climate variability is an important consideration in determining the SCF value The economic value of SCFs were found to be P194.50/ha per season using SOI, P303/ha per season using SST, and P1, /ha under perfect forecast SCFs were found to be valuable to farmers in better managing cropping systems Forecast was found to be valuable in deciding what crop/variety to plant during the growing season

32 Under risk neutrality assumptions, farmers prefer to grow hybrid variety for Mar-Jun season. However, risk-averse farmers prefer native variety Value of a whole forecasting system is often driven by just one or two forecast types Some forecast types do not have a sufficient influence on expected yields and returns to change a decision (no value). Growers need to be conscious of when to apply and when to disregard the information provided by SCFs.

33 Attempts to Popularize SCF Design and production of a handbook on SCF entitled, Understanding Seasonal Climate Forecast and Its Application: A Handbook for Extension Workers Aims to develop extension workers capability to provide advice to the farmers Focuses on: definition of SCF, potential users of SCF, uses of SCF, valuing SCF, nature of probabilistic forecasts, what to do during El Nino/La Nina forecasts, and where to obtain SCF A product of multidisciplinary team from PhilRice, PAGASA, VSU, PIDS

34 Thank you!!!