Participation in Community Forest Management in Kenya

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Participation in Community Forest Management in Kenya

Forest Management Decentralization in Kenya: Effects on Household Farm Forestry Decisions in Kakamega

Wilfred Nyangena School of Economics, University of Nairobi, Box 30197-00100, Nairobi, Kenya. Email: [email protected]

Maurice Juma Ogada Kenya Institute for Public Policy Research and Analysis, Bishops Garden Towers, Bishops Road, Box 56445-00200, Nairobi, Kenya. Email: [email protected] or [email protected]

Geophrey Sikei EfD-Kenya/Kenya Institute for Public Policy Research and Analysis, Bishops Garden Towers, Bishops Road, Box 56445-00200, Nairobi, Kenya. Email: [email protected] The New Forest Management Regime in Kenya: Effects on Household Farm Forestry in Kakamega

Abstract

This study investigates the effects of forest decentralization through the Kenya Forest Act of 2005, on farm forestry investment decisions. The study uses household-level data collected from Kakamega forest communities in March, 2010, and controls for selection bias arising from incidental truncation by means of Heckman two-step approach.

Our results reveal that participatory forest management, among other factors, significantly reduces the level of farm forestry investment among the poor rural households. This indicates that, although co-management is useful in protecting the existing government forests, a cocktail of other measures must accompany it for increased forest cover to be realized. These measures could include: increased farmer education, introduction of high-value fast-maturing farm trees and farm forestry incentive schemes.

Keywords: Participatory Forest Management, Selection Bias, Farm Forestry Development, Kenya JEL Classification: Q12, Q28, D52 Forest Management Decentralization in Kenya: Effects on Household Farm Forestry in Kakamega

1. Introduction Forest decentralization programs have rapidly spread in developing countries in the last twenty years (Agrawal, Chhatre and Hardin 2008). Support for the principle is derived from grounds of economic efficiency, public accountability, community and individual empowerment; as well as allocative efficiency (World Bank 2009). These reforms are designed recognize forest and land rights to a diversity of local people eking a living from these forests. These reforms are expected to reconcile both conservation and livelihood needs. In particular forest decentralization is aimed at enhancing peoples’ livelihoods, poverty alleviation and preservation of the forest condition.

Decentralization policies do not affect forest users’ behaviour directly- rather they change local incentive structures by altering security, access and the power structure of local governance which in turn lead to behavioural change. The expected outcomes, of regime change are mediated by forestry regulations that impose conditions for use of forest resources, and by the capacities of small holders and communities to adopt to those regulations. For instance the communities are required to implement workable systems of governance for their collective lands, exclude third parties and engage in competitive conditions with the forest markets. Indirectly, the outcomes of the reform are also influenced by access to financial and non-financial services. In the absence of these conditions, forest tenure reforms are unlikely to achieve their livelihood and conservation goals. Thus decentralization policies may change patterns of land use and local governance that produce a variety of outcomes, some desirable and some not. For example, many of the Community Forest Associations (CFAs) formed in Kenya were driven by expectations beyond what the legislation provided for (Ongugo, 2007; Ongugo et al., 2007). Indeed some CFAs anticipated converting forests into farmlands for production of cash and food crops (Ongugo et al., 2004). In some cases if property rights are devolved to diverse forest groups, the new property rights holders may hesitate to invest in forest resources if they fear that these changes are temporary.

These diverse outcomes may be explained by several reasons. First, for groups that lack experience and tradition of forestry operations such institutional arrangements are complex. Second, in some instances, these new organizations have had tensions with existing social organizations. The state assumes that these organizations have the capacity to govern on their own and provide the required support to local people. Lastly, with regard to market engagement, while some communities are capable to participate aggressively and adopt entrepreneurial activities, others do not have the capacity to do so (Monterroso 2008). Thus, to clearly understand the effects of forest decentralization, one should examine the plethora of relevant variables in different contexts that might be altered.

The links between national policy changes and their effects on the ground in terms of local level behavior are mediated by a host of complex processes that inhibit policy implementation (Sabatier 1986). Forests are usually located in remote and sometimes marginal areas with many poor people. It is no surprise that evaluations of forest decentralization have posted disappointing results (Agrawal and Ribot 1999). A solid understanding of how local users’ behaviour change in response to these policies is also critical and required.

Several studies have been conducted on community participation in forest management, effects of PFM on household poverty and opportunity cost of forest conservation (Borner et al., 2009; Colfer, 2005; Emerton, 1999; Guthiga et al., 2008; Mbuvi et al., 2007; Mogaka et al., 2001; and Ongugo, 2007).. Research on forest decentralization, like other forms of decentralization is plagued by analytical problems. First, decentralization is a general term that is applied to a diversity of policies that may include some combination of (a) increasing the decision making discretion of local level bureaucrats; (b) increasing the decision-making authority of local users; and (c) moving government officials from central locations to sites closer to forests (Cohen and Peterson, 1996). Second, decentralization policies interact with numerous context specific pressures and interactions to change governance institutions, forest user behavior and resulting forest conditions and livelihood outcomes (Andersson et al., 2008). Lastly, while there are several theoretical arguments relating benefits and costs of forest decentralization, but these fail to generate consistent predictions (Andersson, et al., 2008). These studies ignore behavioural changes resulting from decentralization among forest users in their empirical investigations

This study seeks to address this gap by analyzing household farm forestry investment decisions within the larger context of national decentralization policies. We seek to understand how decentralization policies filter down to local forest users. Economic theory does not provide clear predictions about the effects of decentralization policies on forest users’ behaviour. Instead we must derive from studies of how such policies interact with existing biophysical, socio-demographic variable such as age, gender and educational variables, wealth and other factors change incentives at the local level.

We test the effects of forest decentralization, arguing that the effects of decentralization need to be understood according to specific contexts. We investigate the effects of decentralization drawing on data collected from Kakamega forest in Western Kenya. In particular, we test the effects of decentralization on the farmers’ tree investment decisions on their own farms. Increased forestry cover is a key policy requirement in Kenya, where forest cover is only 3% far way below the recommended rate of 10%.

The rest of the paper is organized as follows: In the next section we review the history of decentralized forest reforms in Kenya. In section 3 we draw on existing literature to derive factors that influence household farm forest investment decisions. Section 4 examines methodological issues while Section 4 provides summary statistics of the variables used. In section 6, we report our empirical results and discuss these findings and in section 7 we conclude and draw policy recommendations. 2. Forest Decentralization trends in Kenya The colonial government of Kenya created a forest department in 1902, which alienate most prior existing community-managed forests. The Forest Department managed and controlled all forests in the country with policy focused on conservation. Following independence in 1963, a series of donor funded forestry programs focused on afforestation and reforestation on farms, with the goal of alleviating fuelwood shortages. The Forest department managed the forests without consultation outside the relevant government ministry. Conflicts increased in the late 1980s between communities who needed fuelwood from neighbouring forests, and the forest department (Ongugo and Njuguna 2004). The New Forest Act of 2005 saw the formation of the Kenya Forest Service (KFS), a semi autonomous government agency with representation from various government ministries. Under the Act, the KFS is expected to devolve powers to the private sector and to forest conservation committees and community forest associations (CFAs). Community participation is achieved primarily through CFAs, and integrated management of forests are central principles motivating the new policy (Ongugo, et al., 2007).

3. A review of farm forestry decisions by rural households

A clear understanding of tree planting decisions by households is required to estimate the likely impact of participatory forest management approaches on households’ decisions to establish farm forestry. To this end, this section reviews the link between participation in community forest management groups and households’ decisions to establish farm forestry. It also explores other factors that may motivate households to undertake on-farm tree growing.

The body of literature on farmers’ tree growing decisions highlights the complexity of factors involved in the behavioural function. The complexity arises from the diversity of circumstances under which smallholder farmers operate. It is generally recognized in the literature that a number of factors explain the differences in farm tree growing decisions by smallholder farmers. However, the specific socio-economic and institutional variables affecting the decisions differ across countries, regions, villages, and farms. Moreover, the direction and significance of influence of a given variable is not often consistent across studies.

Participation in forest management groups has been shown to influence decisions to plant more trees on-farm (Emtage and Suh, 2004). This, however, does not imply that households not participating in these groups are unlikely to plant. Participation in forest management groups greatly enhances people’s attached value to forest ecosystems and the need to protect them; which in turn results in their desire to increase forest cover on their farms. Participation in community-based conservation groups enhances farmers’ access to diversity, quality and quantity of tree species (Boffa et al, 2005). Groups increase farmers’ awareness on importance of trees and market access to the tree and other agricultural products. The argument for market access, however, only applies where markets work fairly well. In such cases, farm households would plant high value timber and fruit trees, not only to satisfy their monetary needs but also subsistence requirements (Emtage and Suh, 2004; Shively, 1999). This implies that closeness to market could also accelerate participation of households in establishing farm forests through two distinct channels—providing market for products of farm forests and creating alternative off-farm income generating avenues which compels households to set their own sources of fuel wood because of lack of time for collection from other sources far from households (Dewees, 1995; Dove, 1995; Gilmour, 1995).

Besides Participation in community forest management, households’ decisions to plant trees may be directly influenced by household-specific, plot-specific and institutional factors. For instance, farm forests have enormous environmental advantages beyond direct benefits to the farm households. To comprehend these indirect benefits, the decision-maker at household level requires some education, either formal or informal, obtained through schooling or extension services. Thus, better educated household heads or households with access to government or farmer-farmer extension services are better adopters of farm forestry (Muneer, 2008), either because they view tree planting as a means of improving the land (Dewees, 1995) or because they are able to appreciate other non-quantifiable benefits as ambiance, micro-climate modification or carbon sequestration. This also explains why households with good social networks may have a higher possibility of planting trees because they are able to get extension services through such networks (Gebreegziabher et al., 2010; Muneer, 2008).

Institutional factors have also been shown to influence the decision by households to plant trees. Secure land tenure arrangements, for example, have been found to influence tree planting decisions among farmer groups. Trees take a longer gestation period and only farmers who are confident of continued use of a given plot would be encouraged to plant them (Bannister and Nair, 2003; Deininger and Feder, 2001; Gebreegziabher et al., 2010; Warner, 1995). However, some studies do not agree with the idea that secure tenure may encourage tree planting and cite cases where communal ownership of land has been more conducive for development of farm forestry (German et al., 2009). Perhaps tree planting in areas with ambiguous land tenure system is a means used by households to place a claim of legitimacy of ownership and/or access.

4. Conceptual framework Kakamega forest region on which this study is based lies in the rural parts of Western Kenya. Like other parts of developing countries, agricultural households are expected to contend with either missing or highly imperfect markets characterized by high transaction costs and constraints on marketed quantities (Thorbecke, 1993; Ellis, 1993). Specifically, only a few households buy/sell forest products. Equally, very few households hire labour for farm activities or extraction of forest products from the neighbouring government forest. As a result, this study is premised on the non-separability of household production and consumption decisions first introduced by Singh et al (1986) and advanced by others like de Janvry et al (1991), Sadoulet et al (1996) and Taylor and Adelman (2003). This study adopts a theoretical framework of utility maximization by a household envisaged by Singh et al (1986). The household derives utility from consuming market goods, home-produced goods and household leisure time. Thus, the household’s optimization problem can be expressed as:

Max U =U(Ch, Cm, Th- Sh; Ηh) (1) s.t Q=f(K, L, A) (2)

m h h PmC = Ph(f(K, L, A)-C )-wL+ wS +I (3)

Th= Lh+ Sh (4)

L- Sh ≥0 (5)

Ch- Q ≥0 (6) The household maximizes the utility (Eq.1) subject to the production function (Eq.2), household’s income constraint (Eq.3), the household’s total time constraint (Eq.4) and market environment constraints (Eq.5 & Eq.6). Where U(.) is a well-behaved utility function, Ch is consumption of home-produced goods and Cm is consumption of market goods. Both Ch and Cm include agricultural crops and forest products. Lh is household leisure hours while Ηh is household characteristics. Q is the home output of both agricultural crops and forest products with f(.) being assumed to be increasing and concave in all its arguments, K is the capital input, L is labour demand for production while A refers to other factors that affect production. Pm is price of market goods, Ph is market price of home-produced goods, Th is total time available to the household, w is the wage rate, Sh is time spent on household production and off-farm wage earning (household labour supply) and I is the exogenous household income from non-wage and non-farm sources. For output of forest products, the household has two options—own farm forestry and government forest. However, in the area of study, households that are members of Community Forest association (CFA) presumably have more access to the government forest. The fact that a household is able to access government forest may not necessarily mean that it ceases to plant trees because the range of products from government forests is restricted.

The Lagrangian equation for this optimization problem becomes:

h m h h h h h m h h Z= U(C , C , T - S ; Η ) + λ[ Ph{f(K, L,A)-C }-wL+wS )+I-PmC ]+γ[C -Q]+θ[L-S ](7) From FONC; dU      h ph =λ[ p  ] (8a) dC h 

dU h  w   [w  / ] (8b) dS

df (8c) p  w  /  h dL

The FONC reveal that, so long as the market environment constraints are binding, market prices (Ph and w) cannot guide household decision-making. Instead the household is guided by virtual/shadow prices (shown in parentheses in Equations 8a and 8b). Equation 8c also shows that the value of the marginal product of labour is not equal to the market wage rate. Because the virtual prices reflect the true opportunity cost and benefits, households respond to them rather than market prices while making utility-maximizing choices (de Janvry et al., 1991; Singh et al., 1986). It is the sign of γ/λ and θ/λ that determine the size of virtual prices and the relevant wage which would vary by household depending on whether a household is self-sufficient in, net seller or net buyer of a produce or labour (Sadoulet et al., 1996). These variations in prices and wages are caused by transaction costs in buying and selling, household preferences, production technology and access to employment opportunities. Thus, it is important to include the causes of these variations in estimation of the production function. This is the approach adopted in this study despite the existence of ways of estimating virtual prices and wages (see Jacoby, 1993; Skoufias, 1994; Cooke, 1998; and Mekonnen, 1999). We are motivated by the fact that agricultural production is a process that takes longer gestation periods characterized by shocks, sequential decisions, irreversible choices, and complementarities and substitutability among inputs (de Janvry and Sadoulet, 2003). This leads to large inaccuracies in estimation of marginal productivities.

4.1 Analytical Model

The area of study has embraced community forest management introduced by the Forest Act (2005). Individuals are free to choose whether to join Community Forest Associations or not. However, only members of Community Forest Associations have the privilege to access a specified range of forest products. Members of Community Forest Associations, because of their access to forest resources from various parts of the larger Kakamega forest, may have no drive to establish own farm forests or they could simply reduce the number of trees planted on own farms. Alternatively, these people could expand forest cover on their own farms due to increased appreciation of forests and the need to protect them. This self-selection choice made by households which want to belong to Community Forest Associations (CFAs), if ignored in the analysis, would lead to inefficient parameter estimates and biased intercept (see Gronau, 1974; Lewis, 1974; Heckman, 1974). The argument, here, is that the unobservable household factors that drive participation in CFAs could be related to those that drive household tree planting behaviour. Thus, correcting for selection bias is a precursor to our analysis of level of farm forestry establishment by households in the area of study.

The study hypothesizes that besides conventional productive factors, individual, household and institutional factors contribute to farm forestry development at various levels of resource endowments. These factors express themselves through correlation of self-selection of households into CFAs or not and level of farm forestry development, and through inclusion of observable factors in explaining inter-household variation in levels of farm forestry development. The main econometric task is to determine how self- selection and other factors influence levels of farm forestry among farm households in Kakamega.

Following Heckman (1979) and Flood and Grasjo (1998; 2001), we specify our model, ordered into two regimes. The first regime is defined by whether a household is a member of a Community Forest Association or not. This is described by the selector equation;

' *   , μ~N (0, 1) (9a) d i  Z i  i

Where  1 if *  0, indicating that a household is a member of CFA, and zero d i d i otherwise; (9b)

' ' Prob(  1)  ( ) , Prob(  0)  1 ( ) , d i  Z i d i  Z i where Z i is a vector of explanatory variables, β is a vector of parameter estimates, μ i is the white noise, Prob(.) is the probability function and Ф(.) is the cumulative distribution function (CDF).

The level of farm forestry, measured by the number of trees planted by a household, is determined in the second step, defined by the equation;

'   (10), yi  X i ℓi applicable only if  1. d i

Where number of trees planted, α is a vector of parameter estimates, X is a vector of yi i exogenous variables and ℓi is the error term.

The two error terms are assumed follow a bivariate normal distribution. That is, (μi , ℓi )~bivariate normal{0, 0, 1, ℓ , ρ}; where ρ is the correlation coefficient. If μi and

ℓi are correlated (ρ≠0), there is enough justification to use Heckman Selection Model.

Model 9a is estimated by probit while model 10 is estimated by the standard OLS.

Transforming equation 10 into,

E[ /X , d =1]= ' + E[ /  1]  '   (11), yi i i  X i ℓi d i  X i  ℓ M i where M is the inverse mills ratio. Failure to incorporate M implies under fitting the model, leading to omitted variable bias in the estimation of α. Equations (9a) and (10) are simultaneously estimated by two stage approach for consistent and efficient estimates.

The Log-Likelihood function for our specification, following Aristei et al (2007), can be expressed as,

 '  '   (   '  '   i y i   Z i  X 1  yi  X i  )]  ln[    ] L= 0 ln[(1-Φ(  i  (12), Z  2       1       which can be simplified as,

 '  '    1  yi  X i  )]  ln[ '  ] L= 0 ln[(1-Φ(  i    (13), Z   Z i        if the error terms are not correlated (ρ=0)

The coefficients of the Heckman Model cannot be interpreted directly. Thus, we compute the marginal effects as;

dE(y / *  0, x) d     (Z)  i  i  ℓ (14), dxi where is the parameter estimate from the outcome equation, is the parameter  i  i estimate from the Selector equation, ρ is the correlation coefficient of the Selector and the outcome equations,  ℓ is the standard error of the residual in the outcome equation and  (Z) is the inverse Mills ratio. This applies for the variables that appear in both Selector and outcome equations (Sigelman and Zeng, 1999). For variables that appear exclusively in the Selector equation, the marginal effects, according to Maddala (1983) are computed as;

' ( i) Z  ( ' (15).  Z)Z k Z ik

One main limitation in an estimation of this nature is that if there is a strong multicollinearity between the independent variables and the inverse Mills ratio, the parameter estimates cease to be efficient. We overcome this in our analysis by introducing distance to the government forest and distance to the market for purchase of firewood which have the potential of influencing participation of a household in CFA but not the number of trees that the household may plant (see Sartori, 2003). Government forest is important as an alternative source of wood fuel and other forest products but only CFA members can access it. Also, those closer to markets for forest products may not have the incentive to join CFAs. Thus, the two variables could easily determine household participation in CFA but without directly influencing the number of trees planted by a household.

5. The Study Area and Data The study site for this survey was around Kakamega Forest, situated in Kakamega District in Western Province of Kenya (Figure 1). It lies north-east of the Lake Victoria between latitudes of 00°10’N and 00°21’N and longitudes of 34°47’E and 34°58’E at about 1600 m above sea level. The forest area is drained by two main river systems, the Isiukhu River to the north and the Yala River to the south. The forest is the only remaining rain forest in Kenya and is the furthest east remnant of the Guinea-Congolean rain forest. According to the 1994 welfare monitoring survey, 52% of the population in the district lives below the poverty line, meaning that they can hardly afford basic necessities like food, shelter, clothing, and education. As such there is a heavy reliance on the forest to supplement their daily necessities. This region has also been considered by the Kenya Woodfuel and Agro-forestry Programme (KWAP) as one of the areas that could benefit most from policies that target improvement of forestry projects due to its high population and high agricultural potential. Source: Biota Sub-project E13 data bank, 2006

Figure 1: Kakamega forest and its environs

The data for this study was collected from communities around Kakamega forest. A random sample of 318 households was interviewed using a detailed semi-structured questionnaire. The sampled households were randomly interspersed in the study area and across the three management regimes. Table 5.1 shows a summary statistics for the variables used in the paper. Table 5.1: Basic descriptive statistics

Variable Mean Std. dev. Individual attributes Age of head 48 14 Education of head (years of schooling) 9 5.8 Household size 5 1.9 Male headed households 0.73 .44

Farm characteristics Farm size in acres 2.2 2.2 Number of trees on farm 41 77 Households planting trees on farm 0.76 0.43 Value of total assets 22026 61038 Distance to nearest forest edge (km) 1.5 2.2 Market distance for selling forest products (km) 3.6 3.8 Access to extension services (1 if yes, 0 otherwise) 0.23 0.42 Access to credit facilities (1 if yes, 0 otherwise) 0.17 0.37

Institutional attributes Membership in social groups 0.73 .44 Membership in CFA (1 if yes, 0 otherwise) 0.52 0.50 Awareness on new forest laws (1 if yes, 0 otherwise 0.60 0.50 Tenure status Own land with title 38% Own land no title 59%

The household characteristics show that the average age of the household heads is 48 years, with the average number of schooling years being 9. The low education level of the household head is a contributor to their inability to secure more remunerative employment opportunities elsewhere, thereby resorting to farming activities. Education increases household’s off-farm employment opportunities. Furthermore, highly educated members of the household tend to look for greener pastures in off-farm activities. This is because of the traditional nature of farming activities within the region which many people view as not competitively rewarding compared to non-farming activities. The average household size measured in this survey is 5 members. Of the households interviewed 73 percent were male headed.

On the farm characteristics, the average farm size in Kakamega is 2.2 acres; 76 percent of households practice farm forestry while the mean value of household assets is KES. 22026. The average distance to the nearest forest edge is 1.5 km. On average, 23 percent and 17 percent of the households had access to extension services and credit facilities, respectively.

With regards to institutional attributes, 73 percent of households participate in social groups while 52 percent participate in community forest management through CFAs. In terms of tenure security, 38 percent of households owned land with title deeds while 59 percent owned non-titled land. 6. Results and Discussion

Table 5.1 presents a catalogue of results from the selector and outcome equations of the Heckman regression of household farm forestry development. Table 5.1 Heckman selection Results for Household Tree Planting Variable Coefficient p-value Outcome equation Individual attributes Education of household head -0.341 (0.021) 0.109 Household Size 0.097 (0.041) 0.017** Male-headed household 0.419 (0.167) 0.013** Farm characteristics Farm size in acres 0.092 (0.034) 0.008*** Log of value of total assets -0.010(0.052) 0.841 Distance to Market for selling forest 0.073 (0.042) 0.083 products (km) Market distance for selling forest -0.001 (0.001) 0.251 products (km) squared Institutional factors No. social groups 0.185 (0.070) 0.008*** Access to extension services (1 if yes, -0.199 (0.156) 0.204 0 otherwise) Access to credit facilities (1 if yes, 0 -0.027 (0.191) 0.889 otherwise) Secure land tenure (own titled) -0.504 (0.177) 0.004*** Constant 3.009 (0.532) 0.000*** Selection equation Individual attributes Education of household head 0.084 (0.034) 0.014** Household Size -0.051 (0.060) 0.392 Male-headed household -0.453 (0.266) 0.089 Age of head 0.004 (0.01) 0.699 Farm characteristics Farm size in acres 0.376 (0.116) 0.001*** Distance to nearest forest edge (km) -0.331 (0.168) 0.049** Distance to nearest forest edge (km) 0.033 (0.022) 0.129 squared Distance to buying market for forest -0.01 (0.069) 0.147 products Distance to selling Market (km) -0.240 (0.101) 0.018** Distance to selling Market (km) 0.005 (0.002) 0.034** squared Institutional factors Awareness on new forest laws (1 if 0.608 (0.239) 0.011** yes, 0 otherwise) Access to credit facilities (1 if yes, 0 -0.408 (0.284) 0.152 otherwise) Secure land tenure (own titled) 0.686 (0.265) 0.010*** Constant -0.004 (0.722) 0.996 Mills Ratio Lambda -0.936 (0.722) 0.004*** Rho -0.980 Sigma 0.955 Wald test of independent equations Chi 2= 69.25 Prob>Chi2= 0.000 ** Significant at 5%, *** Significant at 1% Standard errors in parentheses.

The results indicate that the error terms of the selection and outcome equations are correlated as shown by the highly significant chi-square p-value of 0.000 and the significance of the inverse Mills ratio. This justifies the use of Heckman procedure. The fact that lambda is significant and negative shows that a household’s participation in forest management through CFA reduces its level of investment in farm forestry. This means there is correlation between households which self-select themselves into CFAs and reduced investment in farm forestry due to unobservable effects. The most apparent explanation to this is that households that participate in CFA have access to forest products such as fuel wood, medicinal plant, wild fruits and vegetables, and certain kinds of fodder. For such households, land would rather be put to uses other than farm forestry. The rest of the results are discussed under respective equations:

6.1 The Selection Equation

In the selection equation, variables that significantly and positively influence selection include the household’s land size, the square of the selling market distance, education of the household head, household’s awareness of the new Forest Act and security of land tenure. Those that have negative influence include distance to the government forest, distance to the selling market, and male-headed households.

Land size, being a proxy of household’s wealth, could positively influence the choice to join CFAs because such households are able to pay the requisite fees for participation. Large landholders are also associated with large herds of cattle and this may increase demand for grazing fields, attainable from forests through membership to CFAs. The convex nature of the effect of distance to selling market on the selection could be embedded in the fact that such a distance influences access to information on formation and operation of CFAs, and for those who extract forest resources with the intention of selling, longer distance to the market makes joining CFAs less attractive. Reversal of the effect of market distance on selection after some limit could be explained by the fact that markets tend to be near urban centres. Thus, very far away from these markets are most likely typical rural settings with greater needs for primary livelihood products attainable from forests. This creates the impetus to join CFAs.

Better educated household heads are likely to be aware of formation of CFAs and the potential benefits. Consequently, where the head is better educated, the household has a higher chance of participating in CFA. It is also evident that households that enjoy secure land tenure are more likely to join CFAs. This is perhaps due to the fact that such households are made of native residents, who find it easier to work together with the other residents they have known for a longer time, some even being close relatives. Such natives may also have special attachments to the government forest, either for religious or cultural reasons. This creates the urge to join CFAs so as participate in protection of the forest and gain access to it as and when need arises.

Households that are aware of the new Forest Act, which actually introduces community participation in management of forests, are more likely to join CFAs. Such people definitely understand that it is only through participation in CFAs that they stand to gain from the government forest.

Distance to the government forest influences selection negatively because one of the main reasons for participating in CFAs is to gain access to the forest for extraction of specific forest products. As a result, those who live far from the forest have no motivation to join CFAs. Similar results are associated with male-headed households most probably because such households are able to raise own farm forests from which to obtain the basic products which would have otherwise made it necessary to access government forest through CFAs.

6.2 The Outcome Equation

In the outcome equation, distance to the selling market appears to have a concave relationship although only the coefficient of distance to the market is significant at 10 percent level. The reason for this could be that those living far from the market have to rely more on subsistence production of most of their domestic requirements. If they need forest products, they may be compelled to plant their own trees. The geography of Kakamega is such that the forest is closer to the town. The implication of this is that, if town is the main market, those living far from the market are also far from the government forest, the alternative source of forest products.

Of special interest in the outcome model are the coefficients of household heads, household size, social capital and farm size. All are significant and positive; indicating that, holding other factors constant, male-headed households, larger households, larger land size and more participation in social organizations are associated with planting of more trees. On the contrary, secure land tenure is associated with planting of fewer trees or under-development of farm forestry.

The fact that male household heads are associated with planting of more own farm trees is more cultural than economics. Planting of trees is viewed more as a man’s activity than a woman’s. Where labour markets do not function properly or where the household is less endowed with resources to hire labour, men could be better placed to plant trees because this activity is more strenuous. Other activities like fencing and construction of household’s dwelling units are also viewed as a man’s responsibility and these could compel a man into planting more trees in anticipation of future needs.

Larger households tend to plant more own farm trees. There are two main angles to this —larger households have larger requirements for forest products such as fruits, fuel wood and medicinal plants. This could make it more prudent and economical for such households to establish own farm forests. The second angle is that tree planting is labour- intensive and larger household are capable of using own labour to accomplish the tasks involved. Diversification of livelihood sources to meet the pressure of feeding a large family may provide an alternative explanation.

Social capital is critical for farmer-farmer extension and also for raising resources, physical, human and financial. Thus, households with wider interaction networks through participation in social organizations plant more trees, either because they have drawn lessons from others in their network loops or because they have benefited from rotational labour common among rural households. Through the social networks, relatively poor households can borrow the requisite farm tools or even obtain free tree seedlings from other group members.

Households with large pieces of land, all else equal, plant more trees. This is because trees compete with other crops for land. Those with smaller pieces of land may, therefore, devote the entire parcel for food crop production as opposed to large land holders who are able to either inter-crop food crops with trees or even set aside portions of land exclusively for trees. For households that use trees to mark plot boundaries, it follows that that the larger the piece of land, the more the trees planted.

The coefficient of security of land tenure looks perverse at first sight. One would expect households with secure land tenure to be more motivated to plant trees because trees take a longer time to manure. However, on second thought and taking cognizance of the Kenyan situation, trees are a means of entrenching land ownership, either on plots that are not regularly cultivated or on untitled land. Consequently, households with insecure land tenure are predisposed to plant more trees, not as an economic investment but as evidence of ownership should disputes arise. 6.3 Marginal Effects

Marginal effects computation combines effects of variables included in both the outcome and the selection equations, conditional on the dependent variable being observed or selected. Table 6.2 reports these results.

Table 6.2: Marginal effects of the Model

Variable dy/dx p-value Individual attributes Education of household head 0.019 (0.008) 0.016** Household Size -0.011 (0.013) 0.397 Male-headed household -0.090 (0.047) 0.053 Age of head 0.001 (0.002) 0.700

Farm characteristics Farm size in acres 0.084 (0.020) 0.000*** Distance to selling market (km) -0.053 (0.022) 0.014** Distance to selling market (km) 0.001 (0.0005) 0.030** squared Distance to nearest forest edge (km) -0.073 (0.038) 0.056 Distance to nearest forest edge (km) 0.007 (0.005) 0.134 squared Distance to buying market for forest 0.022 (0.015) 0.143 products

Institutional factors Awareness on new forest laws (1 if 0.149 (0.063) 0.018** yes, 0 otherwise Access to credit facilities (1 if yes, 0 -0.102 (0.078) 0.188 otherwise) Secure land tenure (own titled) 0.141 (0.052) 0.007*** ** Significant at 5%, *** Significant at 1% Standard errors in parentheses.

After accounting for selection, coefficient of distance to the selling market is negative while the coefficient of the square of the distance to the market is positive, showing a convex relationship. This implies that the number of trees planted by households falls as distance to market increases up to an inflection point and then starts to rise for households that lie very far from markets. Possible explanation to this is that households plant trees not only to meet their domestic needs but also to generate income. As such, the households near markets plant more trees because they are motivated by the possible large profits. But very far from the market could be a reflection of typical village life where demand for fuel wood, medicinal plants, fruits and fodder is very high yet each household has to rely on own production. What was noticed during the survey is that jaggery-making is a major economic activity in the remote villages of Kakamega—this could be fanning demand for fuel wood which translates into increased planting of trees.

Secure land tenure is found to positively influence the number of trees that a household plants. The underlying explanation to this is that trees take a longer time to mature and one would invest in them only if he/she is certain of using the land for a longer time. Similarly, size of landholding by the household turns out highly significant, increasing the number of trees planted. A household with a small piece of land may devote the land for production of subsistence crops and may use the land too intensively to allow planting of trees. Conversely, households with large pieces of land are capable of either undertaking agro-forestry or setting aside substantial portions of their land for growing trees. Even if trees are confined to farm boundaries, a larger plot would still accommodate a fairly large number of trees.

Other factors that increase the chance of planting more trees are the education level of the household head and household’s awareness of the new Forest Act. Better educated household heads are able to appreciate the role of forests—in soil and water conservation at the farm level and carbon sequestration at the global level. Such household heads may also be more inclined towards ensuring that the government forest remains intact, making them develop their own sources of forest products. The Forest Act (2005) which introduces community participation in forest management gives communities increased access to the forest but only for a restricted, well-defined range of products. It is also more punitive on the violators and the fact that it involves communities increases the chance of the violators being caught. Thus, for those who understand this Act, it would be more prudent to develop own forest if the interest goes beyond products allowable from government forests. For variables that appear only in the outcome equation, the parameter estimate is indeed interpreted as the marginal effect. Therefore, social capital can be said to be positively related with the number of trees planted by a household, other factors remaining unchanged. This is attributable to flow of information among members of social groups on the positives of planting trees. Other contributions of social capital could include inter- household rotational use of labour and farm implements which is well developed among rural households. Farm forestry is labour-intensive may require households to pool their labour resources especially in instances where households are not well endowed with financial resources to hire labour or where labour markets are not well developed. 7. Conclusion and Policy Recommendations

This study focuses on how participation in community forest management, introduced in Kenya by the Kenya Forest Act (2005), and other factors impacts on development of farm forestry which has been perceived as a vehicle through which to increase the country’s forest cover. Using household data collected from Kakamega forest communities and controlling for selection bias, the study find makes key findings with various policy implications.

First, households that are involved in community forest management through Community Forest Associations (CFAs) are associated with under-developed farm forestry possibly because they are able to meet their demands for forest products from government forests. The policy implication of this is that, while the new system of forest management may be important for protection of existing forests, it could be counter-productive to increasing the total forest cover in the country. An intensive campaign for farm forestry development should therefore accompany it.

Secondly, secure land tenure is important for development of farm forestry because trees take a longer time to mature. It is therefore important to guarantee security of access or ownership of land in the country. Properly functioning land market could be one of the options of ensuring that rights of land owners and land users are equally protected, and that longer tenancy periods are enforceable. Thus, the government should work towards developing land markets.

Third, small landholders are hesitant to plant trees possibly because the land cannot accommodate both crops and trees. One thing that peasants should be made to understand is that investing in trees could be more profitable than investing in food crops. Proper education and introduction of high value fast maturing tree species could overturn the current perception of the peasants. Fourth, education level of the household head and his/her awareness of the Forest Act (2005) favour development of farm forestry. This underscores the need to target household heads in dissemination of information on importance of forests at household, national and global level, and the content of the current Forest Act.

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