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Farmers' Adoption of Rotational Woodlot Technology In CORE Metadata, citation and similar papers at core.ac.uk Provided by ScholarSpace at University of Hawai'i at Manoa Farmers’ Adoption of Rotational Woodlot Technology in Kigorobya Sub-County of Hoima District, Western Uganda Research M. Buyinza, A.Y. Banana, G. Nabanoga and A. Ntakimanye Abstract This paper evaluates, using logistic and multiple regres- A rotational woodlot is a method involving growing trees with sion analyses, the socio-economic factors that influence crops for two to three years until the trees start competing farmers’ decisions to adopt rotational woodlot technology with the crops. Thereafter, the woodlot is left as a source of in the farming systems of Uganda, based on a household fuelwood, building poles or fodder while restoring soil fertil- survey carried out between May and December 2004, in- ity. Farmers then start cutting down the trees and growing volving 120 farmers in Kigorobya sub-county, Hoima dis- crops between the stumps four to five years later (Nyadziet trict. The analyses demonstrate that farmers make deci- al. 2003). Rotational woodlot technology involves growing sions about woodlot technology based on the household trees and crops on farms in three inter-related phases: (i) and field characteristics. The factors that significantly in- An initial tree establishment phase in which trees are inter- fluenced the decision to adopt rotational woodlot technol- cropped with crops; (ii) a tree fallow phase; and (iii) a crop- ping phase after harvesting the trees. Each of these phases ogy included: gender, tree tenure security, seed supply, can be managed specifically to provide products and ser- contact with extension and research agencies, soil ero- vices of economic, social and environmental value. sion index, size of landholding, fuelwood scarcity, and main source of family income. To promote greater adop- Some studies in developing countries have stressed a scar- tion of rotational woodlot technology, particular attention city of fuelwood as one of the key factors to motivate farm- should be placed on the use of appropriate socioeconom- ers in adopting rotational woodlot technology (Jacovelli & ic characterization, to better target technologies to areas Caevalho 1999, Nyadzi et al. 2003). As long as fuel wood with greater adoption potential. could be collected without paying for it, farmers had little in- centive to plant fuelwood producing trees (FAO 2003). Ny- Introduction adzi et al. (2003), however, reported that high fuel wood demand stimulates tree production, and that this is only the Many regions in Africa are presently facing severe short- case when there is a fuel wood crisis. Thus, the high cost ages of fuel wood, fodder and food primarily due to in- creasing human and livestock populations and crop pro- duction using little or no external inputs (FAO 2003). Farmers resort to using marginal and erosion-prone soils Correspondence and to encroaching upon forests (Nyirenda et al. 2001). M. Buyinza, Faculty of Forestry and Nature Conservation, Mak- In most parts of sub-Saharan Africa, the traditional long erere University, P.O. Box 7062 Kampala, UGANDA duration fallows and shifting cultivation, which helps to [email protected] replenish soil fertility to some extent, are no longer pos- A.Y. Banana, G. Nabanoga, A. Ntakimanye, Faculty of Forestry sible. Over the last two decades, woodlots have become and Nature Conservation, Makerere University, P.O. Box 7062 popular among the development agencies in Africa as a Kampala, UGANDA means of improving fuelwood supply to rural communities and generating income for households (Jacovelli & Cae- valho 1999). Ethnobotany Research & Applications 6:107-115 (2008) www.ethnobotanyjournal.org/vol6/i1547-3465-06-107.pdf 108 Ethnobotany Research & Applications of fuel wood may motivate farmers to establish woodlots Data Collection (Yin & Hyde 2000). Data for the study were collected through a cross section- Past studies (Masangano 1996, Omuregbee 1998) have al farm-level survey carried out between May and Decem- identified some of the farmers’ characteristics that may in- ber 2004, by means of a structured questionnaire. The questionnaire was administered to 120 farmers to deter- fluence adoption of agroforestry technologies. These in- mine the profile of farmers’ socio-economic variables and clude the age, gender, and education level of the house- farm characteristics. The survey was done in two stages hold head, wealth, family size, group membership and (Arkin & Cotton 1963). In the first stage, focused group farm resources (such as farm size, land tenure, credit, or discussions were used to obtain background information other inputs, and availability of labor). Farmers’ adoption on the adoption of rotational woodlot technology. This in- behavior, especially in low income countries is influenced formation was used to design a structured questionnaire by a complex set of socioeconomic, demographic, tech- administered to respondents during the second stage of nical, institutional and bio-physical factors (Masangano the survey. Selection of the survey villages was accom- 1996). This paper, therefore, evaluates the socioeconom- plished through a stratified random sampling procedure. A ic factors that influence farmers’ decisions to adopt rota- complete list of villages where rotational woodlot technol- tional woodlot technology in the farming systems in Kig- ogy has been previously introduced was available (FAO orobya sub-county, Hoima district, western Uganda. 2003). Sample villages were selected based on the number of Materials and Methods years of farmer exposure to woodlot technology, num- ber of farmers exposed to woodlot technology, and an in- Description of Study Area formed assessment by key informants on the extent of the adoption of rotational woodlot technology in each vil- The study was conducted in Kigorobya sub-county, Hoima lage. From each selected village, lists were developed of district between July and September 2005. The district is (a) all farmers who had been exposed to woodlot tech- nology, and (b) those without such knowledge. A random located in mid-western Uganda (1o00’ - 2o00’ N, 30o30’ sample was taken from each of the two groups of farm- - 31o45’ E). It is bordered by Lake Albert on the west, ers. Descriptive statistics were used to describe the farm- Kibaale on the south, Masindi to the northeast and Kiboga ers’ socio-economic characteristics, while simultaneous to the east). It has a population of about 341,700 people equation logistic models were employed to estimate the and a total area of 5,775 square kilometers. It lies at an intensity of adoption (Maddala 1983). A series of explana- altitude of 600 – 1000 m above sea level with undulating tory variables included: gender of farmer, education level hills and predominantly savannah grasslands. The climate of farmer, family size, memberships to farmers’ organiza- is characterized by small variations in temperature and tions, contact with extension agency, tree tenure securi- humidity throughout the year. Mean annual rainfall ranges ty, fuel wood scarcity index, supply of planting materials, from 700 – 1000 mm, most of which is received between household source of income, and size of landholding (Ta- October and April. Mean annual temperature is approxi- ble 1). mately 28oC with a range of 15 - 32oC. Vegetation varies Analytical Model widely ranging from medium altitude moist forests through forest/savannah mosaic, swamp to post cultivation com- A two-stage regression approach was adopted. In the first munities (Oluka-Akileng et al. 2000). The soils are main- stage, a discrete variable logit assessment of the choice ly yellowish-red clay loams on sedimentary beds that oc- to adopt or not was carried out. In this case, a 100 percent cupy parts of Bugahya and Buhaguzi counties (Siriri & sample was included in the model because it is a reason- Bekunda 2001). able first choice for any farmer. The choice of the farmer to adopt woodlot technology or not was framed as a binary- Subsistence agriculture is the major economic activity, choice model which assumed that individuals were faced employing about 84% of the population (UBOS 2002). with a choice between two alternatives and the choice de- The bulk of the agricultural production is from manually pended on identifiable characteristics. The probability of adopting choice, Pr(T = 1), was cumulative density func- cultivated rain-fed crops such as tobacco, cotton, sugar- i tion F evaluated at χβ , where χ is a vector of explanatory cane, and food crops. Typical food crops are maize, cas- i i variables and β an unknown parameter (Maddala 1983). sava and sweet potatoes. Kagorobya sub-county. was se- This kind of cumulative density function can be modeled lected as a study site because it is economically backward using logistic probability function, which has the following with a subsistence economy. In each village there exists form: χβ well documented secondary data on farmers that have adopted the woodlot technology (MWLE 2002). Choice to adopt exp(χiβ) ................(1) =PR(Ti = 1) = rotational woodlot 1+exp(χiβ) www.ethnobotanyjournal.org/vol6/i1547-3465-06-107.pdf Buyinza et al. - Farmers’ Adoption of Rotational Woodlot Technology in 109 Kigorobya Sub-County of Hoima District, Western Uganda The Statistical Package for Social Scientists (SPSS VER. to establish woodlot of the farm field. Source of income 11) program for Windows was used for the analysis. The (FINC) measures the farmer’s main source of income and estimated model was: it takes the value 1, if the main source is agriculture, and 0 if otherwise. Studies have shown that agriculture as WOOD = b0+ b1(GND) + b2(HSIZ) + b3(EDUC) + b4(EXT) + the main source of income has a negative impact on the b5(EROS) + b6(FINC) + b7(FWD) + b8(TENUR) +b9(ORG) adoption of new agroforestry technologies (Adesina et al. + b10(LND) + b11(SEED).................................................(2) 2000, Nyirenda et al. 2001). Fuelwood scarcity (FWD) in- dexes the extent of wood scarcity in the village where the The qualitative dependent variable is woodlot technolo- farmer is located.
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