Session 3.4 arifin coffee agroforestry system in sekampung watershed, sumatra-indonesia
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Session 3.4 arifin coffee agroforestry system in sekampung watershed, sumatra-indonesia

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Session 3.4 arifin coffee agroforestry system in sekampung watershed, sumatra-indonesia Session 3.4 arifin coffee agroforestry system in sekampung watershed, sumatra-indonesia Presentation Transcript

  • Impacts of Coffee Agroforestry System on Social-Economic Performance of Upper Sekampung Watershed in Sumatra-Indonesia Bustanul Arifin, University of Lampung (UNILA), Indonesia Katsuya Tanaka, Shiga University and RIHN, Japan Ryohei Kada, Research Institute for Humanity and Nature (RIHN), Japan Hanung 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 years 2. Density of non-coffee trees (MPTS): 400 trees per hectare 3. Tree compositions: At least 30% timber trees 4. No rights to cut and sell the timber trees, except for own use 5. Currently, no annual fees to be paid to the state 6. Establish farmers’ group and written rules and regulations
  • Scenario 1: MPTS 400--Demographic Data Coffee Farmers Sample Man Woman Age Farm Years Dependence Adults 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-adopter 346 1.40 2,370 113 Adopter 62 1.02 1,953 768 Total 408 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 Sample Man Woman Age Farm Years Dependence Adults Children Non-adopter (47%) 192 169 23 43.1 17.1 1.8 1.1 Adopter (53%) 216 192 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-adopter 192 1.22 2,405 295 Adopter 216 1.42 2,292 364 Total 408 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 Adopters Total Sample Coffee Production Kg 385.1 362.5 394.2 Coffee Price Rp/kg 16,728 16,137 16,638 Revenue from coffee Rp 6,442,670 5,849,663 6,557,932 Total Production Costs Rp 1,683,581 2,029,699 1,933,977 Farm Income from Coffee Rp 4,759,088 3,819,963 4,623,954 Revenue 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 14,752,339 Revenue to Cost (R/C) Ratio--Coffee 3.83 2.88 3.39 Benefit 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 391.2 381.5 394.2 Coffee Price Rp/kg 16,521 16,732 16,638 Revenue 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 3.39 Benefit 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 92.4 2. Decreasing production 20 4.9 71 17.4 317 77.7 3. Land degradation-erosion 8 2.0 164 40.2 236 57.8 4. Decreasing water quality 7 1.7 158 38.7 243 59.6 5. Increasing water shortage 2 0.5 95 23.3 311 76.2 6. Loss of biodiversity 57 14.0 202 49.5 149 36.5 7. 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) 16
  • 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 17
  • Using PSM to estimate the impact 18 • 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 β ε= + ( ) ( )ˆ ˆPr 1it it itT X pβ= = Φ − = ( ) 1ˆ 1 0 ˆ ˆ it jt i T j T ij it jt ij Y Y N N p p δ ω ω ∈ ∉  = − − ∂ ≤ ∂ − ∑∑
  • Steps of using the PSM Step 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.) 19
  • Example of common support Source: Khandker et. al (2010) p.57 20 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 21
  • 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 22
  • Advantages and Disadvantages of PSM Advantages • 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 matching Disadvantages • 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 23
  • Poor Upstream Farmers: Severe Sedimentation
  • No Mechanisms of PES from Downstream Users