small-scale farming, forest based-activities and ......household activities, interaction between...
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Small-Scale Farming, Forest Based-Activities and Deforestation in the
Tridom Transboundary Sentinel Landscape - Congo Basin
(Work in progress)
Jonas Ngouhouo Poufoun (INRA/LEF, BETA), Sabine Chaupain-Guillot (BETA),
Eric Kere Nazindigouba (AfDB)
The International Society for Ecological Economics (ISEE2016)Transforming the Economy: Sustaining Food, Water, Energy and Justice
WASHINGTON DC, JUNE 26 - 29, 2016
1
OUTLINE
• Background & Research issue
• Literature review
• Objective and hypothesis
• Methodology
• Preliminary results
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Deforestation, Livelihood and GHGs emission
Tropical deforestation : 2,200 to 6,600 MtCO2e
Tackling tropical deforestation: core to any effort against climate change(Bellassen et al, 2008; Pachauri et al, 2008; Ray et al 2013)
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Deforestation, Livelihood and GHGs emission
World wide : Agriculture proximately drives 80% of deforestation : 10 to 12%of anthropogenic GHGs emmissions (Verchot, 2014).
In tropical Africa: Small-scale subsistence activities are among main drivers;subsistance agriculture drives 35% of deforestation (Angelsen, 1995;Hosonuma et al, 2012; Verchot et al, 2014 ).
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Deforestation, Livelihood and GHGs emission
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Small-scale farming
41%
Cash-cropping
19%
Hunting-Gathering
15%
Traditional mining
3%
Remaining22%
Fig: Rural full-time employemtIn the Congo Basin:78% full-time employment
Farming, Forest based employements and Deforestation
• During the last decade, 85,45% of household’s deforestation related to abovementioned livelihood activities during last two decades
• In the Congo basin, there is not a binding regime of land acquisition in the NonPermanent Forest Estate;
• Competition to land acquisition; No forest revival activities by households
• 70% of the households run slash-and-burn agriculture to keep the ancestralpractices, No optimal crop rotation
Farming, Forest based employements and Deforestation
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0.338 0.236
0,5
1.22
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Maximumyield of 75% of
thehouseholds
Tridom-TSLAverage yield
Averageperformance
limited- means
Potential yield
3,59 3,09
16,520
0
5
10
15
20
25
Maximumyield of 75%
of thehouseholds
Tridom-TSLAverage yield
Averageperformance
limited-means
Potential yield
COP 13, Cop 16 : Non-carbon benefits (NCBs) of REDD+
REDD+ "pro-poor" approach : sustainable livelihood and development
National Key NCBs of Central African countries : diversified and sustainableagriculture, sustainable livestock, sustainable fuelwood and improved stoves ;
Therefore, a good knowledge of farming and forest based livelihood strategies of local communities, that proximatelly cause local and indigenoues households’ deforestation and forest degradation, as well as its spatial distribution would be relevant to identify relevant action to enhance and strengthened the aforesaid NCBs.
Fig: Poor cashcrop and agriculture yields in the Tridom-TSL (t/ha)
Sustainable livelihood among the NCB of REDD+
Cocoa yield Banana yield
• Further, factor driving deforestation including the responsibility ofhousehold activities, interaction between them, as well as magnitudeof their effect on forest clearing is more complex and vary from placeto place depending on many settings (VanWey, Ostrom et Meretsky,2005; Babigumira et al, 2014; Rudel and Roper 1996).
• Filling the knowledge gap regarding households livelihood strategies :a pre-requirement to reducing households ecological footprint.
• An increasing need of understanding the variability of householdsdeforestation at various level as well as its spatial distribution toidentify crictical and priority areas where to start enhancing theaforesaid NCBs.
• What are the proximate and the underlying causes of householdsdeforestation in the Trodom-TSL?
• How much do local people livelihood strategies and other underlyingfactors contribute to small scale deforestation?
Research Questions
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• Increasing literature on drivers of tropical deforestation at national, regional, and global scales using macro-level data in developping countries (Geist and Lambin, 2002; Hosonuma and al, 2012 ; Wolfersberger et al, 2015) .
• Forest role in increasing livelihood, reducing poverty (Sunderland et al, 2005).
• Increasing spatially explicit econometric studies of drivers of deforestation as during the last decade (Ferretti, 2014):
– Spatial Spread Effect of deforestation in Brasilian Amazon (Mertens et al, 2002)
– Leakage, protected area may shift deforestation to neighboring municipalities (Amin et la, 2014)
– Spatial pillover effect of deforestation within tropical countries (Boubacar, 2012)
• Few research : linking livelihood production and deforestation at household’slevel in tropical Africa.
• Very poor micro-level of data and econometric studies in the Congo Basin. (Gbetkom, 2009; Hosonuma and al, 2012; Babigumira et al, 2014)
Litterature review and Contribution
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Contribution • This is a Sentinel Landscape pioneering study (Contribution to building long-
run reliable socioeconomic dataset related to landscape resilience.
• Our study uses a unique Dataset from a recent survey with 1035 households in the landscape Tridom.
• Address appropriately the drivers of small-scale deforestation in the Congo Basin condidering households activity portfolio as potential drivers.
– In the existing literature, households activities are often considered as single strategies. However, empirical evidence from a variety of different locations suggests that the more diverse the income portfolio the better-off is the rural household. indeed, a range of 50–80 per cent of rural households do indeed engage in multiple activities, diversifying their income souce to increase their standard of living income in sub-Saharan Africa
• Applying Spatial Durbing Econometrics model to the Tridom-TSL analysis.
– Proximity among individuals in the Tridom landscape yields interactions that probablyimpact their deforestation decision. For instance, in certain places, households closedeach other realize to some competition to land as land holding is an indicator of notoriety.
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Objectives:
• Assess Proximate and underlying drivers of small-scale deforestation
• Determine the impact of an increamental change of the drivers on households land holding
hypothesis:
• Household activity porfolios (Proximate) - Socioeconomic and demographic characteristics (underlying)
• Spatial Proximity or high levels of social interaction would yield positive direct spatial spillover effects on households deforestation.
Anselin (2002), farmer tend to determine the amount of farmland devoted to a crop accounting for the neighbouring farmers’ allocation
• Observable characteristics of neighbors
Objectives, Hypothesis
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Economic model (1/2)
Theoretical basis (Brueckner 2002),
Spatial Reaction or Spillover Model : Autocorrelation of the magnitude of a decision variable
Resource flow model : agent decision indirectly affected by other agents’ decision
Anselin (2002): spatial and or interaction model can rely on Utility (profit) maximization theory.
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Utility fonction of household i : is a fonction of • His land holding (𝑑𝑖), • His livelihoods strategies, socioeconomic characteristics (𝑥𝑖) • Neighbouring land holding (𝑑−𝑖) and • Neighbouring strategies and characteristics (𝑥−𝑖)• NPF : Non permanent Forest Estate Area
𝐴𝑟𝑔𝑚𝑎𝑥 𝑈 𝑑𝑖 , 𝑥𝑖 , 𝑓 𝑑−𝑖 , 𝑓(𝑥−𝑖)s.t. 𝑑𝑖 ≤ 𝑁𝑃𝐹 − 𝑑−𝑖
FOC𝜕𝑈 𝑑𝑖 , 𝑥𝑖 , 𝑓 𝑑−𝑖 , 𝑓 𝑥−𝑖
𝜕𝑑𝑖= 0
Reaction Fonction : 𝑑𝑖 = 𝑑 𝑋𝑖 ,𝑊𝑑−𝑖 ,𝑊𝑋−𝑖
Where: 𝑊𝑑−𝑖 is the spatial lag variable of deforestation, that capture the mean effect of the neighbouring deforestation𝑊𝑋−𝑖 is the spatial lag of independant variables, that capture the indirect spatial spillover effect on households deforestation
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Economic model 2/2
Testing for spatial spillover effects (Mansky, 1993; Klejian and Prucha, 1999; Lesage, 1999, 2008, 2009 and Elhorst , 2014)
Mansky Full interaction or General Nesting Spatial Model (GNSM) to test for the 3 spatial Spillover effects
𝑫𝒆𝒇𝒐𝒓𝒆𝒔𝒕 = 𝝆𝑾𝑫𝒆𝒇𝒐𝒓𝒆𝒔𝒕 + 𝜶𝑰𝑵 + 𝑿𝜷 +𝑾𝑿𝜽 + 𝝁
𝝁 = 𝝀𝑾𝝁 + 𝝃
𝑾𝑫𝒆𝒇𝒐𝒓𝒆𝒔𝒕 : Spatial Lag deforestation, Average depvar fromneighboring households Endogenous interaction or geographical shifts effect of deforestation, (𝝆 : strength of spatial dependance)
𝑾𝑿: spatial lag exogenous variable, Average indepvar from neighboringhouseholds for Exogenous interaction of indirect spatial spillover effect(𝜃)
𝑾𝝁 : spatial lag errors , Mean error of neighboring households
𝑾 : row-standardized nxn Spatial weight matrix
Tractability issues, identification and confusion among spatial spillovers
Spatial Econometric Procedures 1/3
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SAR: 𝑫𝒆𝒇𝒐𝒓𝒆𝒔𝒕 =𝝆𝑾𝑫𝒆𝒇𝒐𝒓𝒆𝒔𝒕 + 𝜶𝑰𝑵 + 𝑿 + 𝝁
SEM : 𝑫𝒆𝒇𝒐𝒓𝒆𝒔𝒕 = 𝜶𝑰𝑵 + 𝑿𝜷 + 𝝁
𝝁 = 𝝀𝑾𝝁+ 𝝃
SDM : 𝑫𝒆𝒇𝒐𝒓𝒆𝒔𝒕 = 𝝆𝑾𝑫𝒆𝒇𝒐𝒓𝒆𝒔𝒕 + 𝜶𝑰𝑵 + 𝑿𝜷 +𝑾𝑿𝜽 + 𝝁
OLS: 𝑫𝒆𝒇𝒐𝒓𝒆𝒔𝒕 = 𝜶𝑰𝑵 + 𝑿𝜷 + 𝝁
SLX: 𝑫𝒆𝒇𝒐𝒓𝒆𝒔𝒕 = 𝜶𝑰𝑵 + 𝑿 +𝑾𝑿𝜽 + 𝝁
Top-down approach : Lesage (2009), the one we used
Spatial Econometric Procedures 3/3
• STUDY AREA 191.541 km2, (7.5% CBF) 2/3 of 40,000km2 livable inter-zone One of The 12 CBFP priority landscapes 10 protected areas 3 objectives 1-7 inh./ km² , migration Economic stakes, 26 administrative units
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Study Area, Sampling and Survey
SURVEY Face-to-face questionnaires Random and Stratified Sample (1035 /
65140) December 2013 and July 2014 14 investigators in Cameroon and Gabon 6 training sessions and essay 8 GPS
10.92
48.50
16.23
75.7
22.71
1.64
- 50 100
Forest Based Activities
Agriculture/Cashcrop and forest
Agriculture and Cash crop and…
Portfolio of ativities
Single activities Share
none_activ2
10.92
35.27
13.24
16.23
2.42
13.82
3.00
3.48
1.64
- 10 20 30 40
Forest Based Activities
Agriculture and forest
Cashcrop and Forest
Agriculture, Cashcrop and Forest
Traditional Good Mining
Non Timber Forest Product
Small-scale Agriculture
Cashcrop
none_activ2
Po
rtfo
lio o
fat
ivit
ies
78
3Si
ngl
eac
tivi
ties
2
52
No
ne
Descriptive statistiques
Variable Mean Std. Dev. Min Max
Deforest 4,485 5,299 - 56,25
Gender 0,765 0,424 - 1,00
Ag 48,417 14,612 16,00 90,00
Ages_thr 213,324 247,142 0,22 1 719,56
Hsize 6,443 4,017 - 20,00
Total_Value1 6,774 13,284 - 258,05
Autocons_S~e 0,266 0,203 - 1,00
Stay_Vlge 26,877 20,749 - 90,00
Traditional Good Mining 0,025 0,156 - 1,00
Small-scale Agriculture 0,031 0,173 - 1,00
Non Timber Forest Product 0,130 0,337 - 1,00
Cashcrop 0,036 0,186 - 1,00
Forest Based Activities 0,110 0,312 - 1,00
Agriculture and forest 0,350 0,477 - 1,00
Cashcrop and Forest 0,135 0,342 - 1,00
Agriculture, Cashcrop and Forest 0,166 0,373 - 1,00
Households Strategies: Portfolio of Activities
Variables description
9.76
20.12
40.84
2.59
15.14
3.09
8.47
0
5
10
15
20
25
30
35
40
45
Main Activities Percent (%)Ngouhouo, chaupain-Guillot et al 2016 18
Results : Descriptive statistiques
Deforestation segmentation
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Average Deforestation : 4,48 ha
Descriptive statistiques : Multiple Correspondances analysis
Mvilla
Dja et Lobo
Boumba et Ngoko
Haut nyong
Mvoung
ZadieLope
Ivindo
Woleu
Haut-ntem
OkanoFemale
Male
rural
urban
No education
-Education
No group
Group
CE0 Elephantconflict
Cash crop
Fmu_foad
Gold mining
Hunt gath
Administrative activitiesOther activities
Smal farm
age < 30
30 ≤ age < 35
35 ≤ age < 45
45 ≤ age < 55
55 ≤ age < 65
65 ≤ age < 75
age ≥ 75
Household size < 3
3 ≤ Household
size < 5
5 ≤ householdsize < 7
7 ≤ householdsize < 9
9 ≤ householdsize < 11
Householdsize ≥ 11
Stay village < 5
10 ≤ stay
village < 20
10 ≤ stay
village < 20
20 ≤ stay village < 30
30 ≤ stay village < 40
40 ≤ stay village < 50
Stay village ≥ 50
Distance < 5
5 ≤ distance < 10
10 ≤ distance < 2020 ≤ distance < 30
30 ≤ distance < 40
40 ≤ distance < 60
Distance ≥ 60
No agricultural income
Agriculturalincome < 50 000
RA2
RA3
RA4
400 000 ≤ agriculturalincome < 800 000
RA6No
deforestationdeforest. < 1
1 ≤ deforest. < 2
2 ≤ deforest. < 4
4 ≤ deforest. < 6
6 ≤ deforest. < 10
deforest. > 10
segment 1
segment 2
segment 3
segment 4
segment 5
segment 6
segment 7
segment 8
segment 9segment 10
-2
-1
0
1
2
-1.5 -1 -0.5 0 0.5 1 1.5 2 2.5
axis 2 (3.7 %)
axis 1 (4.7 %)
Sum (1,2 ) = 8.4 %Treshold = 0.12
Ngouhouo, chaupain-Guillot et al 2016 20
Ordinary Least Square Spatial Durbin ModelVariable Coef. Std. Err. Coef. Std. Err.Constant 0,231 0,686 0,013 0,663 Age 0,803 *** 0,253 0,317 * 0,193 Ages_thr 0,015 * 0,008 0,017 *** 0,007 Household size - 0,001 ** 0,001 - 0,000 0,000 Total Value 0,222 *** 0,046 0,149 *** 0,023
Auto consumption share 0,042 * 0,024 0,042 *** 0,015 Stay in the Village - 1,604 *** 0,564 - 0,154 0,425
Gold_based_profile 0,035 *** 0,008 0,017 *** 0,005 Agriculture only - 3,039 *** 0,978 - 1,668 * 0,881 NTFP only 1,162 * 0,642 0,992 ** 0,466
Cash Cropping only - 0,692 * 0,371 - 1,038 *** 0,250 Forest based Porfolio 7,565 *** 1,631 4,762 *** 0,605
Cashcrop and Forest Based Activ - 1,570 *** 0,300 - 1,165 *** 0,259 Agriculture, Cashcrop and Forest 2,702 *** 0,544 2,316 *** 0,304 Age 3,593 *** 0,448 2,889 *** 0,290 W-Gender - - 0,481 ** 0,297 W-Ag - - - 0,005 0,010
W-Household size - - 0,013 0,035 W-Total Value - - 0,006 0,015 W-Cash Cropping only - - - 0,928 0,825 W-Forest based Porfolio - - - 0,303 * 0,209 W-Cashcrop and Forest Based Activ - - - 0,266 0,453
W-Agriculture, Cashc. and Forest - - 0,596 0,398
𝝆 - - 0,049 * 0,025
Results : Spatial Durbin Model
𝝆 significatif
• OLS is Biais end Inconsistentcompared to SDM
• The paramettersof the OLS covariate > paramettersof the SDM
• Indeed, OLS attributesvariations of dependentvariables to the covariateswhile,
• SDM attributethesevariation to Spatial Lag of deforestationand indirect spillovers
Ngouhouo, chaupain-Guillot et al 2016 21
Direct effect Indirect effect Total effect
Coef. Std. Err. Coef. Std. Err. Coef. Std. Err.
Gender 0,326* 0,192 0,513* 0,307 0,839 *** 0,353
Age 0,017*** 0,006 -0,004 0,011 0,013 0,012
Ages_thr -0,000 0,000 0,001 0,001 0,000 0,001
Household size 0,149*** 0,022 0,021 0,037 0,170 *** 0,042
Total Value 0,043*** 0,015 0,008 0,015 0,051 *** 0,020
Auto consumption share -0,158 0,425 - 0,268 0,740 - 0,426 0,859
Stay in the Village 0,017*** 0,005 0,001 0,007 0,018 ** 0,008
Gold_based_profile -1,697** 0,872 -1,634*** 1,013 -3,331 *** 0,975
Agriculture only 0,988** 0,467 -0,261 0,690 0,727 0,842
NTFP only -1,044*** 0,251 -0,317** 0,214 - 1,361 *** 0,494 Cash Cropping only 4,751*** 0,605 -0,717 0,861 4,034 *** 1,065
Forest based Porfolio -1,172*** 0,259 -0,372** 0,124 - 1,544 *** 0,505
Cashcrop and Forest Based Activ 2,313*** 0,303 -0,158 0,469 2,155 *** 0,515
Agriculture, Cashcrop and Forest 2,903*** 0,289 0,762* 0,418 3,666 *** 0,465
Spatial Durbin Model: Indirect, Direct and Total effects
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Key ResultsUnderlying Drivers: • An incremental change of the household’s size, of the head of the household’s
age and the seniority in the village increase households deforestation by 0,15 ha; 0,017 ha and 0,17ha respectivelly
• Gender: Households headed by women deforest in mean 0.33 ha less thanhouseholds headed by men
Proximate Drivers• An additional Households doing Cocoa/Rubber as single activity, « cashcrop-
forest » and « Agriculture cashcrop-forest » increase household deforestationby 4,7ha; 2,3ha and 2,9 ha respectivelly
• An additional Households running forest-based activities reduces householddeforestation by 1,7ha
Spatial Spillover Effects• Evidence of spatial dependance of a household’s deforestation on the
neighboring household deforestation decision (𝝆 significatif )• Evidence of Neighboring peer effect on households deforestation (Forest
Based activities, Traditional Gold Mining, Agricuture-Cashsrop association)
Ngouhouo, chaupain-Guillot et al 2016 23
06/07/2016 24
Thank you
Acknowdgement 2: CIFOR, NORAD, FMFA-SCAC
Acknowdgement 1 : "The travel grant to the ISEE2016 Conference was provided by the French National Research Agency (ANR) as part of the "Investissements d'Avenir" program
(ANR-11-LABEX-0002-01, Lab of Excellence ARBRE)"