small-scale farming, forest based-activities and ......household activities, interaction between...

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

Ngouhouo, chaupain-Guillot et al 2016 2


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)



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 ).




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



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


• 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


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.


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


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.


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


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



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


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


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



Householdsize ≥ 11

Stay village < 5

10 ≤ stay

village < 20

10 ≤ stay

village < 20

20 ≤ stay village < 30



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


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


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


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)


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)"

small-scale farming, forest based-activities and ......household activities, interaction between...

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