ACIAR Project: Bridging the gaps between SCFs and decision makers in agriculture Assessing the Potential Economic Value of Seasonal Climate Forecasts for.

ACIAR Project: Bridging the gaps between SCFs and decision makers in agricultureACIAR Project: Bridging the gaps between SCFs and decision makers in agriculture

Assessing the Potential Economic Value of Assessing the Potential Economic Value of Seasonal Climate Forecasts for Corn-based Seasonal Climate Forecasts for Corn-based

Farming Systems in the PhilippinesFarming Systems in the Philippines

Canesio Predo1, Peter Hayman2, Jason Crean2, John Mullen2, Kevin Parton2,Celia

Reyes3, Eva Monte1,and Ian Mina3

1/ LSU team; 2/ Australian team; 2/ PIDS team

Malaybalay, BukidnonDec 1, 2005

Location of Farm Level Case Study AreasLocation of Farm Level Case Study Areas

Leyte

Cebu

Bukidnon


Isabela

ACIAR Project: Bridging the gaps between SCFs and decision makers ACIAR Project: Bridging the gaps between SCFs and decision makers

The Philippine Climate ClassificationThe Philippine Climate Classification(Modified Coronas)(Modified Coronas)

Type III Climate

Type IV ClimateRainfall more or less evenly distributed throughout the year.

Type III Climate

Seasons not very pronounced; relatively dry from Dec to Apr & wet during the rest of the year.

Avg annual rainfall:l,777 mm

2,556.3mm

Seasons not very pronounced; relatively dry from Dec to Apr & wet during the rest of the year.


Background InformationBackground Information

Importance and Uses of Corn• In the Phil. corn is 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


Impacts of Climate VariabilityImpacts of Climate Variability

• Agriculture sector (e.g., corn) • Occurrence of El Niño-induced drought (1997/98)

greatly reduced corn yield and production (PCARRD 2000)

• Implications of Climate Variability• Corn production always remains at risk to climate

variability associated with ENSO events• Corn farming is a risky investment decision


Impacts of Climate VariabilityImpacts of Climate Variability

• Corn productivity constraints associated with climatic extremes

• flooding during wet season cropping, especially for low lying areas

• drought during dry season cropping• drought at any stage of crop development affects production,

but maximum damage when it occurs around flowering stage– replanting if drought occurs at planting stage– mitigation is irrigation only at flowing stage

• Skillful seasonal climate forecasts information important to corn farmers’ production decision


Application of SCF to Cope Climate Application of SCF to Cope Climate VariabilityVariability

• Crop choice • Timing of cropping periods• Levels of input use


ObjectivesObjectivesGeneral:

To estimate the potential economic value of seasonal climate forecasts (SCF) for the corn-based farming systems in the Philippines.

Specific: • To present a brief description of dominant cropping patterns and

corn production practices in the study areas; • To review and present a valuation framework for estimating the

economic benefits of SCFs information under various assumptions of risks and uncertainty;

• To quantity the potential economic value of SCF to corn farmers in the Philippines; and

• To conduct policy analysis and draw policy implications on the usefulness of SCF to corn farmers and determine when and where it can be best ignored.


Farm Level Case Study AreasFarm Level Case Study Areas

Mahaplag and Matalom, Leyte (Visayas)

Argao, Cebu (Visayas)

Malaybalay/Manolo Fortich/Lantapan, Bukidnon (Mindanao)

Isabela province (Luzon)



Table 1.Table 1. Volume of corn production (‘000 MT) & % share Volume of corn production (‘000 MT) & % share of top 10 corn-producing provinces in the of top 10 corn-producing provinces in the Philippines, 2004Philippines, 2004

Rank ProvinceProduction ('000 MT)

% Share

1 Isabela 850 15.72 Bukidnon 608 11.233 South Cotabato 409 7.564 Maguindanao 359 6.635 North Cotabato 333 6.166 Lanao del Sur 295 5.457 Lanao del Norte 219 4.058 Cagayan 189 3.499 Sultan Kudarat 172 3.1810 Pangasinan 131 2.13

Source: BAS


0

100

200

300

400

500

600

700

1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000

ENSO Year All Types White Yellow

ENSO Intensities:

Weak La Niña Weak El Niño

Moderate La Niña Moderate El Niño

Strong La Niña Strong El Niño

Sources:

Volume of corn production - BAS

ENSO years - PAGASA

Figure 1.Figure 1. Volume of Corn Production (in ‘000 MT) Volume of Corn Production (in ‘000 MT), , by typeby type Bukidnon, 1970-2000 Bukidnon, 1970-2000


Climate and Cropping PatternClimate and Cropping Pattern

Climate:• Type III classification: seasons not very

pronounced, dry from Nov-April and wet during rest of the year

• Average annual rainfall: 2,556.3 mm• Average temp = 23.9oC



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

• 1st cropping: Feb/Mar and harvested in Jun/Jul

• 2nd cropping: Jul/Aug and harvested in Nov/Dec

• 3rd cropping in upland sloping, rolling to hilly envt: Nov/Dec and harvested in Feb/Mar/Apr (corn or legumes)

• Cropping pattern by elevation

• Lower elevation corn corn

• Higher elevation corn corn

vegetables corn

1st crop 2nd crop



Yield: Local variety (1.0 – 2.5 t/ha)

OPVs (2.0 – 4.0 t/ha)

Hybrid (3.0 – 6.0 t/ha)


Cropping Choice Decision Cropping Choice Decision

Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan

1st crop 2nd crop

Cropchoice

Corn

Vegetable

Fallow

CornVegetableFallow

Crop choice

Representation of the farmers’ cropping decision problem

CornVegetableFallow

Crop choice

CornVegetableFallow

Crop choice


Corn

Native corn

Hybrid corn (2)

OPV (improved) (3)

Sowing date (3)

Sowing date (3)

Sowing date (3)

N fert rate (2)

N fert rate (2)

Cropping Choice Decision Cropping Choice Decision

*Biophysical secondary data

*Experts’ judgment*Observed farmers’

practice

Seasonal ClimateForecasts

*Amt of rainfall*Timing of rainfall

events*Freq of rainfall

Corn-based Farming Systems

(DSSAT v4)Calibration

*Material Inputreqmts

*Labor reqmts*Input/Output

pricesInputparameters

CROP YIELD andCROP YIELD DISTN

EXPECTED PAYOFFS/RETURNS (NPV)

Cost-loss ratio analysis

Stochastic dominance (FSD, SSD, TSD)

Conceptual SCF Valuation Framework


Economic Valuation FrameworkEconomic Valuation Framework

• Opportunity Cost Approach (With and Without Seasonal Climate Forecast Information) Outcomes: rainfall, yield and gross margin or payoff

(NPV terms) VCF = NR(wc) - NR(woc)

where: NR(wc) = net returns with climate forecast

NR(woc) = net returns without climate forecast

• Methods of Assessment Cost-loss ratio model Stochastic dominance analysis


Data SourcesData Sources

• Secondary data (published and unpublished)• Expert’s judgement• Personal communication• Observed farmers practice (focus group)• DSSAT simulation output


Preliminary ResultsPreliminary Results

Relationship of Seasonal Climate Forecasts and Rainfall (using Rainman)

Percent Chance of Rainfall All years 992 mm 6800 mm 27700 mm 37600 mm 52500 mm 73400 mm 89332 mm 94

% yrs above median 627 mm 49

Chance of Rainfall in Malaybalay

Source: Rainman

Analysis of historical data (1919 to 2004) using SST Phase forecast in Sep forRainfall period: Oct to Dec (leadtime of 0 months).The SST phases/rainfall relationship for this season is statistically significantBecause KW test is above 0.9 and Skill Score (13.3) is above 7.6 (p=0.97).


Thank you for your attention!!!

The Leyte State UniversityThe Leyte State UniversityURL: URL: http://www.lsu-visca.edu.ph

http://www.lsu-visca.edu.ph/

ACIAR Project: Bridging the gaps between SCFs and decision makers in agriculture Assessing the Potential Economic Value of Seasonal Climate Forecasts for.

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