Impacts of Coffee Agroforestry System on Social-Economic Performance of Upper

Sekampung Watershed in Sumatra-Indonesia

Bustanul Arifin, University of Lampung (UNILA), IndonesiaKatsuya Tanaka, Shiga University and RIHN, JapanRyohei Kada, Research Institute for Humanity and Nature (RIHN), JapanHanung Ismono, University of Lampung (UNILA), Indonesia

World Congress on Agroforestry “Trees For Life: Accelerating the Impact of Agroforestry”, 10-14 February 2014, New Delhi, India

“Notorious” Sekampung Watershed• The main watershed, major food baskets

and agric exports area of the province,• Land area: 484 ha (49% degraded, 34%

potential to degrade 17% non-degraded)• Soil erosion rate: 67.5 ton/ha/year, far

higher the 25 ton/ha tolerable rate• Land use: Coffee agroforestry system

• Identify the characteristics of coffee agroforestry system in Upper Sekampung Watershed in the Province of Lampung Sumatra-Indonesia;

• Interview 408 households of coffee farmers practicing the multi-strata agroforestry system in two subdistricts of Pulau Panggung and Pugung in the Watershed.

• Apply quasi-experimental impact evaluation method using a propensity score matching (PSM) technique by analyzing some relevant variables that shape the characteristics of coffee agroforestry system.

Approach and Methods

Map of Land Use at Sekampung Watershed

Study Sites

Multi-Strata Coffee Agroforestry System

Shaded coffee Multistrata coffee

• In addition to securing household income, coffee-agroforestry system (shaded & multi-strata) could be seen as both forest stewardship and later as reward for environmental services.

• Treatment on coffee agroforestry system in the state-protection forest differ from that in the private and communal land, especially on land-use decision and “optimal level” with the tree crops-MPTS.

Coffee in Protection Forest: Rights to Use

1. Length of main contract: 5 years probation, + 25 years2. Density of non-coffee trees (MPTS): 400 trees per hectare3. Tree compositions: At least 30% timber trees 4. No rights to cut and sell the timber trees, except for own use5. Currently, no annual fees to be paid to the state6. Establish farmers’ group and written rules and regulations

Scenario 1: MPTS 400--Demographic Data

Coffee Farmers

SampleMan Woman

Age

Farm Years

DependenceAdults Children

Non-adopter 346 306 40 44.5 18.9 1.8 1.0

Adopter 62 55 7 41.9 16.7 2.1 1.1

Total 408 361 47 44.2 18.6 1.8 1.0

Scenario 1: Number of MPTS 400 is the cut-off point of agroforestry adopters and non-adopters (This is based on the government policy of community-based forestry management)

Scenario 1: Land Holding and MPTS

Coffee Farmers Sample Holding (ha)

Coffee Tree

Number of MPTS

Non-adopter346 1.40 2,370 113

Adopter62 1.02 1,953 768

Total408 1.34 2,306 213

Scenario 1: Number of MPTS 400 is the cut-off point of agroforestry adopters and non-adopters (This is based on the government policy of community-based forestry management)

Scenario 2: MPTS 100—Demographic Data

Coffee Farmers

SampleMan Woman

Age

Farm Years

DependenceAdults Children

Non-adopter(47%)

192169 23 43.1

17.1 1.8 1.1

Adopter(53%)

216192 24 45.0

18.8 1.9 1.0

Total 408 361 47 44.2 18.6 1.8 1.0

Scenario 2: Number of MPTS 100 is the cut-off point of agroforestry adopters and non-adopters (This is based on the argument that one MPTS provide shade for 4 coffee trees).

Scenario 2: Land Holding and MPTS

Coffee Farmers Sample Holding (ha)

Coffee Tree

Number of MPTS

Non-adopter192 1.22 2,405 295

Adopter216 1.42 2,292 364

Total408 1.34 2,306 213

MPTS: Average Number of TimberTrees

Timber Trees Dadap Medang Cempaka Albisia Sengon Teak-wood

Lamtoro Others

Timber Tree 49 1 13 113 16 2 7 19

MPTS: Average Number of Non-Timber Trees Coffee Farmers Pete Durian Avocado Clove Jengkol Others

Non-Timber 3 1 0.5 3 14 1

Scenario 1: 400 MPTS--Farm Income

Elements of Farm Income Unit Non-Adopters AdoptersTotal Sample

Coffee Production Kg 385.1 362.5 394.2Coffee Price Rp/kg 16,728 16,137 16,638Revenue from coffee Rp 6,442,670 5,849,663 6,557,932Total Production Costs Rp 1,683,581 2,029,699 1,933,977Farm Income from Coffee Rp 4,759,088 3,819,963 4,623,954Revenue from MPTS Rp 2,128,857 4,586,500 2,481,462 Revenue from other crops Rp 6,674,786 12,546,500 7,646,923 Total Farm Income Rp 13,562,731 20,952,963 14,752,339Revenue to Cost (R/C) Ratio--Coffee 3.83 2.88 3.39Benefit to Cost (B/C) Ratio--Total Farm 8.06 10.32 7.63

Elements of Farm Income Unit Non-Adopters Adopters Total Sample

Coffee Production Kg 391.2 381.5 394.2Coffee Price Rp/kg 16,521 16,732 16,638Revenue from coffee Rp 6,463,015 6,383,258 6,557,932

Total Production Costs Rp 1,831,311 2,134,141 1,933,977

Farm Income from Coffee Rp 4,631,704 4,249,117 4,623,955

Revenue from MPTS Rp 3,113,133 3,524,242 2,481,462

Revenue from other crops Rp 7,131,413 8,131,183 7,646,923

Total Farm Income Rp 14,876,250 15,904,542 14,752,339

Revenue to Cost (R/C) Ratio--Coffee 3.53 2.99 3.39Benefit to Cost (B/C) Ratio--Coffee 8.12 7.45 2.39

Scenario 2: 100 MPTS--Farm Income

Farmer’s Perception on Risks

Type of Risks Not Important Important Very important

Resp % Resp % Resp %

1. Land tenure insecurity 1 0.2 30 7.4 377 92.42. Decreasing production 20 4.9 71 17.4 317 77.73. Land degradation-erosion 8 2.0 164 40.2 236 57.84. Decreasing water quality 7 1.7 158 38.7 243 59.65. Increasing water shortage 2 0.5 95 23.3 311 76.26. Loss of biodiversity 57 14.0 202 49.5 149 36.57. Global warming 10 2.5 113 27.7 285 69.9

Next: Strategic issues in impact evaluation• Impact is the difference between the outcome with and without the

program (policy or project);• Impact evaluation before-and- after

– Ex ante evaluation: predicts program impacts before the program implementation (anticipated impacts of the program)

– Ex post evaluation: examines program impacts after programs have been implemented (based on past impacts using the data)

• You may want to compare:– Participants and non-participants in terms of the program outcome– Or, performance of the participants before-and-after the program

• Those are not a good idea because:– Participants and non-participants are not the same individuals and should be

different in number of factors affecting program outcome– Outcomes before-and-after the program are influenced by a number of factors,

not just the program

Counterfactual: with-without comparison

• What would have happened if program had not existed?–A beneficiary’s outcome in the absence of intervention (e.g.

policy implementation) is counterfactual–Finding a good counterfactual (control group) is a key of

successful evaluation (i.e. how to create good control group)

• Different evaluation methods make different assumptions on counterfactual (control group)

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Next Steps: Propensity Score Matching• PSM constructs a statistical comparison group by modeling the

probability of participating (propensity score) in the program on the basis of observed characteristics unaffected by the program– Control group: matched non-participants

• Participants are then matched to non-participants based on the propensity score

• The average treatment effect of the program is calculated as the mean difference in outcomes across these two groups (matching)

• Participants and non-participants must be representative and highly comparable groups– They are typically constructed from large survey– In general, the more observations, the better the results– Data quality is a key for good comparison among two groups

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Using PSM to estimate the impact18

• First stage: regress treatment on observables

• Second stage: form individual probabilities of treatment and save observations where there is overlap (region of common support)

• Third stage: compare outcomes of treated observations to similar non-treated observations (average treatment effect = the effect of agroforestry).

i.e., the weight a comparison (between treated vs. controlled units) gets in the analysis decreases as the units get less similar

it it itT X

ˆ ˆPr 1it it itT X p

1ˆ1

0ˆ ˆ

it jti T j T

ij

it jt

ij Y YN N

p p

Steps of using the PSMStep 1

–Construct groups of participants and non-participants

Step 2–Pool the two samples and estimate a logit or probit model of

program participation.–Predicted values are the “propensity scores”

Step 3–Restrict samples to assure common support

• Failure of common support is an important source of bias in observational studies (Heckman et al.)

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Example of common support

Source: Khandker et. al (2010) p.57

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Region of common support (Matched participants and non-participants)

Region of common support (Matched participants and non-participants)

Example of poorly balanced and weak common support

Source: Khandker et. al (2010) p.57

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Steps of using the PSM (continued)

• Step 4 (matching)–For each participant find a sample of non-participants that have

similar propensity scores • Step 5

–Compare the outcome indicators;–The difference is the estimated gain due to the program for that

observation• Step 6 (average treatment effect)

–Calculate the mean of these individual gains to obtain the average overall gain

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Advantages and Disadvantages of PSMAdvantages• Provides good estimates of the impact if observed characteristics

explain program participation well;• Only cross-section data is needed (no panel data required)• Use of propensity score solves dimensionality problem in matchingDisadvantages• Provides poor estimates of the impact if unobserved characteristics

is not negligible in explaining program participation;• Requires good quality data: need to control for all factors that

influence program participation;• Cannot correct for unobserved characteristics influencing outcome

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Poor Upstream Farmers: Severe Sedimentation

No Mechanisms of PES from Downstream Users