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Assessing the Potential Economic Value of Seasonal Climate Forecasts for Corn-based Farming Systems in the Philippines PowerPoint PPT Presentation


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Assessing the Potential Economic Value of Seasonal Climate Forecasts for Corn-based Farming Systems in the Philippines. Canesio Predo 1 , Peter Hayman 2 , Jason Crean 2 , John Mullen 2 , Kevin Parton 2 ,Celia Reyes 3 , Eva Monte 1 ,and Ian Mina 3. Malaybalay, Bukidnon Dec 1, 2005.

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Assessing the Potential Economic Value of Seasonal Climate Forecasts for Corn-based Farming Systems in the Philippines

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Assessing the Potential Economic Value of Seasonal Climate Forecasts for Corn-based Farming Systems in the Philippines

Canesio Predo1, Peter Hayman2, Jason Crean2, John Mullen2, Kevin Parton2,Celia Reyes3, Eva Monte1,and Ian Mina3

Malaybalay, Bukidnon

Dec 1, 2005

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

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


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Location of Farm Level Case Study Areas

Isabela

Leyte

Cebu

Bukidnon

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


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The Philippine Climate Classification

(Modified Coronas)

Avg annual rainfall:

l,777 mm

Type IV Climate

Rainfall more or less evenly distributed throughout the year.

2,556.3mm

Type III Climate

Type III Climate

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

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


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Background 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


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Impacts 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


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Impacts 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


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Application of SCF to Cope Climate Variability

  • Crop choice

  • Timing of cropping periods

  • Levels of input use


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Objectives

  • General:

  • 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.


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Farm Level Case Study Areas

Mahaplag and Matalom, Leyte (Visayas)

Argao, Cebu (Visayas)

Malaybalay/Manolo Fortich/Lantapan, Bukidnon (Mindanao)

Isabela province (Luzon)

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


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

Source: BAS


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

ENSO Intensities:

Weak La NiñaWeak El Niño

Moderate La NiñaModerate El Niño

Strong La NiñaStrong El Niño

Sources:

Volume of corn production - BAS

ENSO years - PAGASA


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Climate 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


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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

    • 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 elevationcorncorn

    • Higher elevationcorncorn

      vegetablescorn

2nd crop

1st crop


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Climate and Cropping Pattern

  • Yield: Local variety (1.0 – 2.5 t/ha)

  • OPVs (2.0 – 4.0 t/ha)

  • Hybrid (3.0 – 6.0 t/ha)


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Cropping Choice Decision

Representation of the farmers’ cropping decision problem

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

2nd crop

1st crop

Corn

Vegetable

Fallow

Crop choice

Corn

Vegetable

Fallow

Crop

choice

Corn

Vegetable

Fallow

Crop choice

Corn

Vegetable

Fallow

Crop choice


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Cropping Choice Decision

Native corn

Hybrid corn (2)

OPV (improved) (3)

Sowing date (3)

Corn

Sowing date (3)

N fert rate (2)

Sowing date (3)

N fert rate (2)


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Conceptual SCF Valuation Framework

*Biophysical

secondary data

*Experts’ judgment

*Observed farmers’

practice

*Material Input

reqmts

*Labor reqmts

*Input/Output

prices

Corn-based

Farming Systems

(DSSAT v4)

Calibration

Input

parameters

Seasonal Climate

Forecasts

*Amt of rainfall

*Timing of rainfall

events

*Freq of rainfall

CROP YIELD and

CROP YIELD DISTN

EXPECTED PAYOFFS/RETURNS (NPV)

Cost-loss ratio analysis

Stochastic dominance (FSD, SSD, TSD)


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Economic 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


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Data Sources

  • Secondary data (published and unpublished)

  • Expert’s judgement

  • Personal communication

  • Observed farmers practice (focus group)

  • DSSAT simulation output


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Preliminary Results

  • Relationship of Seasonal Climate Forecasts and Rainfall (using Rainman)


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Chance of Rainfall in Malaybalay

Analysis of historical data (1919 to 2004) using SST Phase forecast in Sep for

Rainfall period: Oct to Dec (leadtime of 0 months).

The SST phases/rainfall relationship for this season is statistically significant

Because KW test is above 0.9 and Skill Score (13.3) is above 7.6 (p=0.97).

Source: Rainman


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Thank you for your attention!!!


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The Leyte State University

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


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