Microscopic View of Forestry GovernancePresentation Transcript
Microscopic View of Forestry Governance: Lesson Learnt from Banyumas, Central Java Tatsuro Sakano and Farhan Helmy SAKANO Laboratory, Department of Social Engineering School of Decision Science and Technology Tokyo Institute of Technology Presented at Workshop “ Bali Road Map in Perspective: Forestry Governance and the Role of Decision Support Systems (DSS)” Jakarta, 28 March 2008
Research Progress and Findings (as of March 2008)
Contextual Effect of Social Trust on Forest Governance
from Household Survey
Land Cover and Land Use changes, Changes and Patterns/macro
Preliminary Assessment and Exercise at Village Level
Proposed Policy Directions
Land Use Changes Pattern and Controlling
Data Reliability Issues
Outline of Proposed Further Research Theme and Decision Support Systems (DSS)
Number of households in the villages surveyed The Survey was conducted with the cooperation of Dr. Slamet Roshandy and his students in 2006
Primary occupation of household
Primary occupation class
Summary of Previous Analysis on Forest Preservation
Almost all 94.5% recognize the importance of forest to their area
However, awareness of deforestation is not so high 56.0%
There are some perception gaps between up & down stream areas on
(1) seriousness of deforestation
(2) the interdependency
Water shortage is most distinctive damage 55.9%
Opinion on Forest Management Right is splitted:
Perhutani/SFC 49% vs. Local 41%
Demand for community management Question: Who do you think should own the management right ?
Why do people demand local forest management right?
Two Psychological Theories on Motivation:
(1) Driving Force : Dissatisfaction
People behaves to eradicate dissatisfaction
(2) Self-Efficacy :Confidence in one’s own capacity to do something
Lack of Self-Confidence makes people inactive
Working Hypothesis on Demand for Management Right:
Perceived seriousness of deforestation
-> Dissatisfaction with the existing management
-> more demand for self-management
(2) Confidence in community’s capacity to do sustainable forest management -> more demand for self-management
Job dummy (Farmer = 1) Demand for community management Living location (downstream = 1) (middle stream = 2) (upstream = 3) Community efficacy to sustainable forest management Perceived seriousness of deforestation + Causal Model of Demand for Community Forest Management + + + + +
Community efficacy to sustainable forest management Question: Do you think transfer of ownership from SFC to Villager/local community might lead to sustainable forest management ?
Perceived seriousness of deforestation Question:What do you think the level of deforestation in your area ?
Job dummy (Farmer = 1) Demand for community management Living location (downstream = 1) (middle stream = 2) (upstream = 3) Community efficacy to sustainable forest management Perceived seriousness of deforestation 0.289*** Result of Path analysis 0.112* -0.169*** Significance probability p<0.01:***, p<0.05:**, p<0.10:* -0.058 0.192*** 0.152*** 0.205*** 0.374*** -0.070 CFI=0.976 TLI=0.780 RMSEA=0.077 all sample (N=502) R-square=0.123 R-square=0.137 R-square=0.023 R-square=0.087
Result of Path Analysis
More Confidence in Community’s Capacity to Sustainable Management increases demand for community management right
Against the hypothesis, when people the level of deforestation is more serious, expectation to SFC is increased.
Level of deforestation seriousness increases more in upstream area
People in the upstream area demand more community right
Job (Farmer) and Location (upstream) increase Community – Efficacy of Sustainable Forest Management
Structure of Social Trust :Factor analysis on 14 types of people
Factor analysis: trust to 6 types of people
Job dummy (Farmer = 1) Demand for community management Living location (downstream = 1) (middle stream = 2) (upstream = 3) Community efficacy to sustainable forest management Perceived seriousness of deforestation 0.347*** 0.181* -0.418*** Significance probability p<0.01:***, p<0.05:**, p<0.10:* -0.029 0.174** 0.051 0.223** 0.311*** -0.150* CFI=0.959 TLI=0.633 RMSEA=0.110 R-square=0.306 R-square=0.115 R-square=0.003 R-square=0.083 Path Analysis in 2groups Group: High government trust (N=219)
Job dummy (Farmer = 1) Demand for community management Living location (downstream = 1) (middle stream = 2) (upstream = 3) Community efficacy to sustainable forest management Perceived seriousness of deforestation 0.324*** 0.147 -0.065 Significance probability p<0.01:***, p<0.05:**, p<0.10:* 0.000 0.102 0.226*** 0.244*** 0.500*** -0.046 CFI=0.959 TLI=0.633 RMSEA=0.110 R-square=0.134 R-square=0.242 R-square=0.051 R-square=0.081 Path Analysis in 2groups Group: Low government trust (N=234)
Job dummy (Farmer = 1) Demand for community management Living location (downstream = 1) (middle stream = 2) (upstream = 3) Community efficacy to sustainable forest management Perceived seriousness of deforestation 0.312*** 0.184** -0.313*** Significance probability p<0.01:***, p<0.05:**, p<0.10:* -0.041 0.168** 0.102 0.262*** 0.310*** -0.101 CFI=0.971 TLI=0.738 RMSEA=0.089 R-square=0.218 R-square=0.100 R-square=0.010 R-square=0.105 Path Analysis in 2groups Group: High community trust (N=277)
Job dummy (Farmer = 1) Demand for community management Living location (downstream = 1) (middle stream = 2) (upstream = 3) Community efficacy to sustainable forest management Perceived seriousness of deforestation 0.368*** 0.086 -0.063 Significance probability p<0.01:***, p<0.05:**, p<0.10:* 0.022 0.103 0.172* 0.192** 0.548*** -0.033 CFI=0.971 TLI=0.738 RMSEA=0.089 R-square=0.146 R-square=0.295 R-square=0.029 R-square=0.058 Path Analysis in 2groups Group: Low community trust (N=176)
Summary of Contextual Effect of Social Trust (1)
Regardless the level of social trust, community efficacy constantly increases demand for community management right
There observed are several contextual effects:
Living Location both directly and indirectly affects the demand for community management right only when either trust to community/government is high.
⇒ at first glance people in up stream area demands strongly own management right, but when social trust is low they are not ready to accept delegation of right
Summary of Contextual Effect of Social Trust (2)
When seriousness of deforestation is very severe, community needs help from the higher level of government. But their demand to the government is revealed only when they trust government.
Trust to the government and community is closely correlated.
⇒ This suggests that community trust is the bases of government trust. The community trust would be the basis of condition for government intervention work successfully. Low community trust village would not be ready to accept government help.
Land Cover/Land Use Changes Assessment
Spatial analysis is based on the collation of data (remotely sensing data, GIS and statistical data) from various sources (Ministry of Environment, UNEP, Ministry of Forestry, BAKOSURTANAL/National Survey and Mapping Agency, others) and additional limited data acquisition and ground survey to complete the analysis
LANDSAT Remote Sensing Data and Land Cover Map (1990,1995, 2000)
Forest Concession Boundary from RePPProT (Ministry of Transmigration/Ministry of Forestry)
Additional remote sensing data of year 2005 and 2007 with additional ground truth survey in accordance with household surveys
Limited statistical based spatial data from BPS/National Bureau of Statistics, such as Village Potency Data/PODES and Statistics at district level.
Adjustments made in the area where the cloud cover is high
As very little efforts have been made to systematically collect and adjust data on land cover/land use at appropriate scale, this analysis is intended to introduce an approach for further data incorporation that cover spatial as well as socio-economic data that may useful to understand the driving force of deforestation and more precise policy assessment, especially at micro level.
MOE/National Aerospace Agency Technical Guideline on Land Cover Change Analysis of LANDSAT Data is used as a basis for further land cover/use analysis. However, further precise data analysis are encourage to use and develop are needed as more precise data resolution (QUICK Bird, etc.) are available in the market.
Assessment Methodology: An Overview
Supervised classification is employed to derive land use/cover maps
Various scale of topographic maps are used based on national standard (1:250,000, 1:50,000)
Administrative boundary is based on National Bureau of Statistics/BPS)
Multispectral LANDSAT Data (1990, 1995,2000,2005,2007) Other GIS Maps and Statistical Data (forest Status, socio-economic, etc.) Data Correction and Mosaics for Study Area
Tentative Land Cover/Use Maps SPATIAL ANALYSIS (1) Land Use and Land Cover Maps by administrative boundary(district, sub-district, villages) (2) Input-Output Analysis of Land Use Changes Pattern (3) Overlay Analysis with other layers Final Land Cover/Use Maps Ground-truth Verification Remotely Sensed Data Analysis
1990 1995 2000 2005 2007 The Screenshots of Land Cover/Use Changes in Banyumas Region (1990-2007)
Finding (1): Deforestation and Land Use Changes (1990-2007) (1). In 1990-2007, deforestation rate is about 9,300 Ha./year . From the total of 170,000ha forest area , More than 90% of the area has been converted to various land uses. The remaining forest area is about 1.2% (12,000 Ha.) (2). Predominant types of land use changes area are paddy field , settlement , and mix garden . (3). Land use conversion/changes are unevenly distributed in the region . In some of the north part (Tegal and Brebes districts) and some part of the South ( Purbalingga and Cilacap districts ) of is higher than that of in the south. This may indicates that in some part the north and some part of the extensive land conversion was increasingly occurred as a result of the growing manufacturing industries and supporting infrastructure in the region and surroundings. However, in some part of the areas ( Purbalingga, Banjarnegara and Brebes ) were kept unchanged (both west and east areas) as agriculture production areas (mix garden, plantation and paddy field). This may indicates that the south areas were depended on agriculture based industry . (4). The slightly slower changes is observed in some areas, especially in steep sloping land that may difficult to reach and only suitable for certain crops production. Land Cover Class 1990 1995 *) 2000 2005 2007*) Area (Ha.) % Area (Ha.) % Area (Ha.) % Area (Ha.) % Area (Ha.) % Nature Forest 49,406.889 5.14% 39,117.067 4.07% 18,913.105 1.97% 2,627.852 0.27% 11,950.632 1.24% Dryland Forest 120,767.558 12.56% NA NA 5,376.267 0.56% 9,333.231 0.97% NA NA Forest 170,174.447 17.70% 39,117.067 4.07% 24,289 2.53% 11,961.083 1.24% 11,950.632 1.24% Mix Garden 163,617.994 17.01% 88,449.999 9.20% 59,595.198 6.20% 107,034.247 11.13% 204,390.092 21.25% Plantation 107,374.778 11.17% 284,588.738 29.59% 253,092.282 26.32% 132,629.797 13.79% 7,544.543 0.78% Open Field 58,435.748 6.08% 5,647.748 0.59% 25,149.890 2.62% 21,903.911 2.28% 5,644.524 0.59% Mangrove 10,145.354 1.05% 0.000 0.00% 771.948 0.08% 217.433 0.02% 198.671 0.02% Settlement 6,014.719 0.63% 133,629.000 13.90% 168,793.504 17.55% 194,592.612 20.23% 226,424.492 23.54% Swamp 11,512.354 1.20% 11,512.354 1.20% 6,544.894 0.68% 6,598.743 0.69% 4,835.511 0.50% Paddy Field 71,370.598 7.42% 223,755.920 23.27% 285,018.267 29.64% 414,682.359 43.12% 461,568.968 48.00% Brushwood 97,197.730 10.11% 43,285.625 4.50% 19,851.498 2.06% 5,847.352 0.61% 2,686.644 0.28% Fishpond 20,981.837 2.18% 11,631.078 1.21% 33,161.142 3.45% 19,154.993 1.99% 11,473.253 1.19% Dry. Agriculture 231,696.754 24.09% 107,060.596 11.13% 70,389.573 7.32% 33,025.328 3.43% 9,624.018 1.00% Water Body 13,163.417 1.37% 13,007.605 1.35% 15,028.162 1.56% 14,037.872 1.46% 15,344.382 1.60% non-Forest 791,511.283 82.30% 922,568.663 95.93% 937,396.358 97.47% 949,724.647 98.76% 949,735.098 98.76% Total 961,685.730 100.00% 961,685.730 100.00% 961,685.730 100.00% 961,685.730 100.00% 961,685.730 100.00%
Finding (2) Trend of Land Use Change Pattern (1). In the period of 1990-1995 deforestation is hugely occurred, and has converted more than 80% of forest area and is subsequently followed by land use exchange/shifting among different classes, mainly increasing of mix garden and plantation, settlement and paddy field areas. But, it is decreasing in dry land agriculture area. (2). From 1995 onward, the trend of mix garden and plantation and dry-land agriculture changes are steadily declining. However, settlement and paddy field are still continues to increase, reaching at 23.54% and 48% respectively in 2007 . (3). The rate of change of mix garden and plantation is lower than that of dry land agriculture where closed to the paddy field and settlement areas. This may indicates that land use changes in flat areas most likely occurred as a result of settlement and paddy field expansion. They may strong correlation between land uses, physical constraints as well as other factors/driving force (economy, policy, market access, ownerships, etc.). (4). It seems that since 2005 deforestation is likely to stop and reach stability .
Finding (3) Trend of Land Use Changes in Protection and Conservation Areas (1). Since 1990, more than 40% of the total of forest in protection and conservation areas (18,000 Ha) has been encroached and converted into mix garden, plantation, paddy field, and settlement(30%, 7,024 Ha). (2). The predominant land uses are paddy field and mix garden, that steadily increasing since 1995 .
Finding (3) Trend of Land Use Changes in Forest Concession Areas
Since 1995, forest encroachment in protection and conservation areas have been occurring and converting into plantation, paddy field, dry land agriculture and settlement areas. However, the figure shows that changes in these areas are steadily unchanged in the observed period (1995-2007). It may indicates limited activities/subsistence living by local communities occurred.
Almost 30% (5,000 ha.), forest in protection areas is encroached and converted into plantation, paddy field, dry land agriculture and settlement areas.
Almost whole of conservation areas in the forest areas have been converted plantation, paddy field, dry land agriculture and settlement areas.
Proposed Policy Direction
POLICY ISSUES – DEFORESTATION AND LAND USE CHANGES PATTERN
The deforestation and forest degradation has been proceeding since 1990 and steadily unchanged from period 2000-2007. In the period of 1990-1995 deforestation is hugely occurred, and has converted more than 80% of forest area and is subsequently followed by land use exchange/shifting among different classes, mainly increasing of mix garden and plantation, settlement and paddy field areas. It seems that since 2005 deforestation is likely to stop and reach stability . (Finding 1)
From 1995 onward, the trend of mix garden and plantation and dry-land agriculture changes are steadily declining. However, settlement and paddy field are still continues to increase, reaching at 23.54% and 48% respectively in 2007 . The rate of change of mix garden and plantation is lower than that of dry land agriculture where closed to the paddy field and settlement areas. This may indicates that land use changes in flat areas most likely occurred as a result of settlement and paddy field expansion. There is possibility to stop the ongoing deforestation by keeping the remaining forest, and develop efficient and sustainable production system in the area that have been converted into production areas, such as plantation, mix garden and other temporary seasonal land uses (open field, brushwood). (Finding 2).
In the area where forest designated as protected and conservation areas has been experiencing forest encroachment and changes for other uses. Limited activities from local communities inside protected and conservation areas (plantation, settlement ) have been observed, is steadily unchanged from 1995-2005. It may also indicates that the monitoring and enforcement are needed in those areas. (Finding 3)
Tentative Policy Direction (con’t)
POLICY FRAMEWORK AND DIRECTIONS
Three levels of new land use regulation and control into the region of Banyumas: (1). Setting goal of total land use change in the region (2). Land use regulation and controlling in the area that need to be protected and/or no more development/land use changes (3). Introducing a new land use regulation in in the village areas.
Provincial Government should take lead in regulating land use across districts in the region.
A consensus building should be established to introduce a new land use change regulation and controlling in conjunction with creation of incentives for production systems to be developed that may include economic incentives(credit access, technology, etc.) as well as inviting innovative ideas from village communities.
(1). Keep remaining forest ( 1.24%, around 12,000 ha.) from any land use changes.
(2). Protect the area of slope hilly land ( 22%, around 210 ha ) for any land use changes and keep the area as production areas and introduce efficient and sustainable production systems.
(3). The protection and conservation areas (2% of the total region, 18,000 ha.) should be restorted into the original function to maintain hydro-ecological cycle for the region of Banyumas. In case of the local community can not be relocated, only strictly limited activities for subsistence living are allowed. Other eco-friendly activities, such as eco-tourism may be introduced to incorporate local community in protecting the areas as well as to support sustainable livelihood.
Limitation of the Research – Data Reliability Issues
Longer span of satellite data (1970-2007) is required for better understanding land use changes drivers in the region as a result of different institutional intervention changes during the period of time.
Longer span of socio-economic data in the above same period are also needed to understand the socio-economic changes in some areas, especially to understand why in some areas deforestation/degradation are happening, while in other part are not.
Most of the household data survey are based on limited areas, especially in the areas closed to forest village ( desa hutan ).
Further institutional analysis at administrative level (district, sub-district, village as well as community) should be done to understand the influence/impact of any institutional setups in any region.
As very little efforts have been made to systematically collect and adjust data in the study area on land cover/land use at appropriate scale, therefore more precise data acquisition and analysis are required.
Proposed Outline of Further Research Themes and Decision Support Systems (DSS) Land Use Changes Ecosystem Technology Economy Social Capital Institutional Change Wealth Production System Spatial Dynamics On-going research activities
Brief Outline of the Research
For components that may influence in understanding forestry governance:
Social capital, socio, economic and institutional factors rooted in the community that may influence individual or community behavior and drive deforestation and land use change processes.
Spatial dynamics, interactions among land use and ecosystem that influence spatial changes.
Wealth production systems, production systems employed in the area as a result of intensive interactions of technology and economic factors. It may operate as internal interaction or may be as a result of external influence.
Institutional changes , institutional factors that drive land use change, such as government policy on industry, tax and subsidy scheme for agriculture development, etc.
A systematic, comprehensive and precise data collection and analysis at appropriate scale are required for precisely understanding main driver of deforestation and developing a comprehensive policy and strategy package to overcome the declining of forest area as well as the environmental deterioration as a result of land use changes.
This framework is developed to complete, calibrate existing available data and to model interrelationship among various factors above that influence as a basis to develop a model to understand the pattern in other region, especially in the outer islands of Java (Sumatra and Kalimantan). Also, to facilitate scientific policy exercises to develop sustainable scenario options for resource management at macro and micro levels.
Research Network among agencies with various background is extremely required to share knowledge and bridge the gap of the findings .