Description of the study area
The study area, Gamo highlands, lies in a remote part of Ethiopia, within the former Gamo-Gofa province of Southern Ethiopia, some 500 kilometer to the south of Addis Ababa, the capital of Ethiopia. Gamo-Gofa is one of the ten administrative zones with total population of 1,595,570 in 2007 of which more than 90% lives in rural areas and the majority are in the highlands (CSA, 2008). The zone has a total area of 12,581.4 square kilometer and consists of 15 districts (namely, Arbaminch Zuria, Kucha, Kemba, Boreda, Chencha, Daramalo, Dita, Zala, Melakoza, Bonke, Ubadebretsehay, Mirab-Abaya, Geze-Gofa, Demba-Gofa, and Oyda) and two town administrations (Arbaminch and Sawula).
Rising up from the west of twin lakes Abaya and Chamo, Gamo highlands reach an altitude of 4200 meters above sea level at mount Gughe, the highest peak in SNNPR region of Ethiopia. Gamo highlanders, who reside along the undulating chains of this mountain, form the larger proportion of the 1,595,570 population of the Gamo-Gofa province in 2007 (see also CSA 2008). The economy of people in Gamo highlands, like many other rural areas in Ethiopia, depends on agriculture, which feed and economize almost all the people in the area. More than 90% of the populations depend on the products from this sector. Cultivation of ‘Enset’, potato and cereals (such as Barley and Wheat) form the basis of subsistence in the higher altitudes while Maize and Sorghum are important food sources on the lower slopes.
In Gamo highlands, natural resources can be considered as opportunities and threats to the lives, if unmanaged. For example, the monsoonal rains and deep aquifers streaming from top highlands continuously feed the Sago, Zage, Maze, Domba, Deme, Kulano, Gogora, Saware, Wajifo, Baso, Harre, Kullufo, Sile and Elgo rivers. These in turn wear down and provide water and eroded loam soil for the people and the fertile fields in the lowlands (Desalegn 2007). Factually, the land of Gamo highlands were believed rich and referred as land of loam soil called ‘ModhdhoBiita’ (Malebo 2005). It was used to serve as a drought season home for the nearby lowlanders. But now days, things have been reversing back. As a result, the livelihoods of people in more recent times in Gamo highlands would tend to depend on subsistent agriculture and some off-farm economic activities such as labor-intensive petty trade, traditional weaving, wage work, and collection and sale of firewood are the outshining alternatives.
Associated with environmental degradation, the unsustainable use of natural resources, poor performance of the agricultural sector and few opportunitiesfor generating income to people in the area, the traditional labor-intensive weaving,which was believed innovatedby one of the Gamo tribes called ‘Dorze’ in Chencha district, as an off-farm income generating activity, has been pushing significant proportion of the highlanders (including school age children) to migrate to the urban areas in other parts of Ethiopia. The problem is aggravated by the fact that population pressure and its density are high;most farmers run small-scale agricultural production that characterizes fragmented land holding and subsistent farming and farm productivity is poor to meet an ever-increasing food and material need ofthe people.However,sufficient rainfall, deforestation,poor land management practices, environmental degradation, soil erosion, soil infertility, population pressure, poor agricultural productivity, crop failure, and absolute povertyare the primary challenges hindering development in the Gamo highlands (Malebo 2005).
Materials
The data used in this study sourced from five districts located in Gamo highlands (Chencha, Dita, Deramalo, Arbaminch Zuria, and Bonkie). We used detailed structured questionnaire survey for face-to-face personnel interviews with smallholder household heads who reside in eleven peasant associations or ‘kebele’ (the smallest administrative unit in Ethiopia) that were selected from the aforementioned five districts.The survey was conducted in 2011/2012.The questionnaire was developed using a lesson acquired through informal pre-survey interviews with key informants of officials and professionals. The identification strategy followed sequential stages. First, as the role of heterogeneity across agents has been recognized in the adoption literature (Feder et al., 1985), we purposively selected five districts that characterize varying agro-ecologies and tree coverage. Second; we randomly selected representative peasant associations from each district. Lastly, 335 proportionately sampled households were selected.
Agricultural extension workers working and living in each peasant associations carried out the survey. The interviewers got training about survey questioner and data collection techniques before the survey kicked off. The focal person for the interview was the head of the household. Household heads as a manager of the household has relatively a wider availability of farm information and knowledge than anyone else in the household (Malebo 2005).Thus, they are the first line people who make almost all economic decisions refer to the household.We also conducted focused group discussions with government officials and professionalsand direct personnel observation. These all enabled us to collect unfailing data that covered a broad range of socio-economic, demographic, institutional, behavioral, and farm specific characteristics.
Methods
Determinants of technology adoption and the hypotheses on conservational tree growing adoption
Traditionally, the literature on the adoption of agricultural technologies (or failure thereof) has focused on broad range of factors counting socio-economic, demographic, institutional, behavioral, and farm specific characteristics. Among others, perfection of information, availability of inputs, infrastructure, institutional constraints, and human capital found potential factors that explain variation in the adoption decisions (example, Foster and Rosenzweig 1995). Table 1 describes the determinants of conservational tree growing (OFATR) with their corresponding expected hypothesized relationships, and the significance of these factors in the adoption of conservational tree growing is exhaustively explored in latter stage of the paper.
A. Demographic Factors
Most empirical studies measure demographic factors either by labor availability, sex as a proxy for gender, age and family size. Planting, nursing, and growing trees are labor-intensive endeavors in the study area. We expect that labor constraints can limit the adoptions. Arguably, the larger family size (FAMS) can associate with larger number of labor force and hence with larger number and category of trees grown. Likewise, younger people have a greater chance of acquiring and applying new knowledge and skill than older people (Rogers 1995; Sidibe 2005). In contrary, across age, people can develop experience and skill of doing things. These can make the age effect on tree growing behavior dilemmatic. Thus, we were unclear to hypothesize the role of household head’s age (AGE) on the adoption of OFATR butexpect a positive influence of family size on OFATR adoption behavior of smallholders (Table 1).
Gender (GEND) is another demographic factor that can influence the extent of adoptions. Being female or male-headedness proxy our gender variable. Females in the study areas play a role of household heads if and only if they were divorced or unmarried at all; if their husbands were dead; or when their son is too child to lead the household (Malebo 2005). This suggests the possibility that labor supply would tend to be short of with female-headedness. Owing larger labor force demand for OFATR adoption, we hypothesized a negative influence of female-headedness (GEND) or a positive influence of male-headedness and a positive association of active labor force (ACLF) availability to OFATR adoption (Table 1).
B. Socio-economic Factors
Socio-economic factors are also assumed to influence the adoption decisions. We use education, occupation, and wealth of the respondents as proxy. Technology adoption literature explains an easier and well familiar adoption of agricultural technologies with higher levels of education and training than those with lower levels (Tassew 2004; Sidibe 2005). Availability of skilled labor can ease such opportunities and likely to influence current technology adoption behavior of households. Thus, we hypothesized a positive influence of education (EDUC-CAT), tree growing experience (TGEXP), and training (TRAIN) on the adoption of OFATR. However, as labor demand in agriculture varies across seasons (peak during planting and harvesting and off-peak otherwise) the occupation variable (OCCUP) might dually influence on OFATR adoption (Table 1).
On the one hand, sufficient availability of off-farm economic occupation can minimize work avoidance during off-peak periods. This might help to generate more income for those who supply more labor hours away from the household. Accordingly, one can expect positive income effect of off-farm occupation for the adoption of farm technologies as it can build farmers ability to invest in productive technologies and other high pay-off inputs and avert risks associate with the adoption. Yet, there are evidences that report negative role of off-farm economic occupation for the adoption of on-farm soil conservation measures. Abera’s (2003) study in Ethiopia estimates that off-farm economic occupations constrain household labor supply to on-farm economic activities. However, we expect that a household’s sole dependence on on-farm incomes and generation of cash income from tree (YTR) can force them to plant more trees. Consequently, we hypothesized positive link between YTR and OFATR and remained unclear to hypothesize the association between off-farm occupations (OCCUP) and OFATR adoption (Table 1).
Table 1
Determinants of OFATR with expected hypothesized relationships
Acronym
|
Description of variables
|
Measurement
|
Expected sign
|
Dependent variable
|
OFATR
|
Whether a household adopted conservational tree growing or not
|
Dummy (1 if grows ecological trees including a mix of indigenous species, 0 otherwise)
|
|
Independent variables
|
1. Demographic and socio-economicfactors
|
FAMS
|
Family size of the household
|
Number of people in the household
|
+
|
AGE
|
Age of the household head
|
Formal age in number of years
|
?
|
GEND
|
Sex of the household head
|
Dummy (1 if female, 0 if male)
|
|
EDUC
|
Formal educational background of the household head
|
Categorical variable (Cannot read and write or no schooling = 1, grades 1–4 = 2, grades 5–8 = 3, grades 9–12 = 4, and grades above 12 = 5)
|
+
|
OCCUP
|
Occupation background of the household head
|
Dummy (1 if farming only, 0 if both farming and others)
|
?
|
YTR
|
Source of household’s income
|
Dummy (1 if collects cash income from trees, 0 otherwise)
|
+
|
ACLF
|
Activelabor force in the household
|
Number of active labor members
|
+
|
TRAIN
|
Training received about the role of tree growing in environmental conservation
|
Dummy (1 if yes, 0 if no)
|
+
|
TGEXP
|
Tree growing experience in the past
|
Dummy (1 if yes, 0 if no)
|
+
|
2. Agro-ecological and farm specific factors
|
SULNTR
|
Availability of suitable land area for tree growing
|
Hectares of land area owned by the household and located on sloppy mountainous areas
|
+
|
FARMS
|
Farm size of the household
|
Total hectares of land owned by the households (in hectares)
|
+
|
AVATR
|
Sufficient availability of seedlings
|
Dummy (1 if yes, 0 if no)
|
+
|
HPP
|
High potential perennial agro-ecological zone
|
Dummy (1 if the respondent belongs to HPP zone, 0 otherwise)
|
|
HPC
|
High potential cereal agro-ecological zone
|
Dummy (1 if the respondent belongs to HPC zone, 0 otherwise)
|
|
LPC
|
Low potential cereal agro-ecological zone
|
Dummy (1 if the respondent belongs to LPP zone, 0 otherwise)
|
|
Source: Authors’ compilation |
Agro-ecological And Farm Specific Factors
Farm size, land suitability, and proximity of farms to the nearest input and output markets are among the agro-ecological and farm specific factors assumed to determine technology adoption behavior. Agro-ecological factors were proxies by agro-ecological zones of the respondents. The agro-ecological zones of Ethiopian highlands were classified into three broad major categories: (i) the high potential perennial (HPP) zone, (ii) the high potential cereal (HPC) zone, and (iii) the low potential cereal (LPC) zone (Table 2). These often were defined in terms of temperature, stored soil moisture and number of days in a year that plants grow without irrigation (Bishaw 2009).
We included these agro-ecological variables to control for all the unobserved agro-ecology specific factors associate with the adoption OFATR of in the smallholder farmers. We expect larger adoption of conservational trees in LPC zone than the other two as it characterizes high variability of climate and occasional occurrence of droughts (Bishaw 2009).
Table 2
Major Agro-Ecological Zones of the Ethiopian Highlands
Agro-ecological zones
|
Climate
|
Growing period in number of days
|
HPP zone
|
Warm and more humid
|
Mainly > 240
|
HPC zone
|
Intermediate rainfall
|
Usually > 180
|
LPC zone
|
High variability and occasional drought
|
Mainly 90–150
|
Source: Bishaw (2009) |
As far as farm specific factors are concerned, we use farm size, land suitability for tree growing and sufficient availability of seedling sources. A basic possible hypothesis on-farm size is that the adoption of an innovation will tend to take place earlier on larger farms than on smaller farms. This can be largely due to cost issues. For instance, Feder and O’Mara (1981) demonstrate that fixed transaction costs associated with the adoption innovations prevents small farms from adopting technologies. Land ownership can also influence land related investments by many other ways. The larger the farm size (FARMS) a farm household owns the more land area can be accessed for planting and growing trees. Land suitability for tree growing (SULNTR) is another farm specific factor that might influence farmers’ technology adoptions; the availability of more mountains land area, the greater the likelihood of adopting conservational investments. Sufficient availability of seedling sources (AVATR) is also basic as investors use factor inputs for production. We thus expect that OFATR is positively associated with SULNTR, AVATR, and FARMS (Table 1).
Model Specifications
The analysis and presentation of the study was quantitative. To this end, the data were quantified to scores as shown in Table 1 and the analysis employed a mix of both descriptive statistics and econometric tools. Initially, the inter-relation between the potential predicators was analyzed by spearman correlation and then regression analysis was utilized principally. Since our observations take limited categories with zero values on the dependent variable the orthodox Ordinary Least Squares (OLS) regression models cannot properly accommodate the data. This failure directed us to utilize estimators built on the principle of maximum likelihood (MLE) estimators. The most common of these models used in the adoption literature are the logit and the probit. As Anemiya (1985), Wooldridge (2000) and Verbeek (2004) conclude that the choice of which model to use cannot be justified theoretically.They estimate almost similar results. However, there are empirical suggestions that force us to prioritize between them. Arguably, logistic regression analysis provides response probability estimates that are asymptotically consistent and computationally easier to use than probit (Pindyck and Rubinfeld 1981).
Following this framework, logistic modeling approach founds customary in empirical studies that examine factors determining technology adoption, particularly in agriculture (Green and Ng’ong’ola 1993; Chaves and Riley 2001; Tadesse and Belay 2004; Asfawa and Admassie 2004; Mercer, et. al., 2005; Iqbal et al., 2006; Zeleke and Bliss, 2010). Evidently, the assessment of factors influencing the adoption of integrated pest management for coffee in Colombia (Chaves and Riley, 2001), the adoption of fertilizer use in Africa (Green and Ng’ong’ola, 1993) and the assessment of factors determining rubber–tea intercropping by the smallholder farmers in Sri Lanka (Iqbal, et., 2006) are worth mentioning. Consistently, we apply the logit model to estimate factors influencing household’s decision to grow conservational trees.
A common starting point for logit model is a ‘random utility framework’, in which the utility of each alternative is a linear function of observed characteristics and the error term of the model (Verbeek 2004, see also Asfawa and Admassie 2004). Following the context we assumed individuals choose to adopt or not to adopt OFATR depending on their utility maximizing behavior. That is, an individual household can decide to adopt or not to adopt OFATR if he/she expects the adoption will generate him/her the highest possible benefit or utility than else.
Therefore, relying onthe frameworks developed by the past studies and following the notation of Wooldridge (2000), the general specification of the logit identifying the probability, Pi, of the ith household’s behavior towards the adoption of ecological tree growing technologyis given by:
1.1
Where Zi is an indirect utility derived from the adoption decision or random variable that predicts the probability of the ith farmer adopting OFATR. Zi is also referred to as the log of the odds ratio in favor of the adoption. The odds ratio is defined as the ratio of the probability that a farm household adopts OFATR(Pi) to the probability of non-adoptionof OFATR(1 - Pi).
For an individual farm household, we construct Zi as a linear function of explanatory variables (Xis), where βi is an unknown parameter; Xi is set parameters which influence the ith farm household’s adoption decision to grow ecological trees (OFATR).The unknown parameter βi associated with each Xi is determined by an iterative process that makes use of a maximum likelihood estimate (Wooldridge 2002).
1.2
Taking natural log in both sides of (1.2), the final standard form of the logistic model estimating the likelihood of adopting OFATR by smallholder households becomes Eq. (1.3). In the Eq. 1.3,i refers to the ith observation in the sample, k refers to number of explanatory variables, β0 refers to the intercept term, and β1, β2… βk refer coefficients associated with explanatory variables X1, X2, …, Xk respectively.
1.3
Since the parameters βis are unbiased and normally distributed, we used an analogue of student’s t-test to test the significance of the regression. Throughout the estimation, we use t statistics based on standard errors that are robust to heteroskedasticity. The significances of the coefficients of the variables presented in the logistic model were tested using a log-likelihood ratio assuming a chi-square (χ2) data distribution (see also Pindyck and Rubinfeld 1981). All the analyses were run using a mix of STATA and Microsoft offices excel program software packages.