Coffee Stumping in Dale Woreda
I confidently declare that this thesis has not been submitted to any other institutions anywhere for the award of any academic degree, diploma, or certificate. Brief quotations from this thesis are allowable without special permission, provided that accurate acknowledgement of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by Dean of the School of Graduate Studies when in his or her judgment the proposed use of the material is in the interests of scholarship.
In all other instances, however, permission must be obtained from the author. Name: Mulugeta Arega Signature: …………………… Place: Haramaya University Date of Submission: December, 2008 v BIOGRAPHY Mulugeta Arega Lidetie was born in Dessie town, in South Wollo zone of the Amhara region on March, in 1961. He attended his elementary and junior education in Dessie and Wogeltenna Junior schools. He also attended his high school education in Dessie at Woizero Sihen Comprehensive secondary school.
Coffee Stumping in Dale Woreda Essay Example
After completion of his high school education, he joined Debrezeit Junior Agricultural college (under Addis Abeba university) to attend a two years Diploma program in crop production and protection technology (CPPT) and graduated in July 1981. After graduation he was employed in ministry of Coffee and Tea Development, Coffee Improvement Project (CIP) and Ministry of Agriculture in different Administrative Regions and districts and served for more than 16 years. After this much time service he got an opportunity to join Debub University, now Hawassa University, and graduated in Plant Protection and Dry land Farming (PPDF) with B.
Sc. in 2004. Then after, he joined Haramaya University in July 2005 to pursue graduate studies for the M. sc. in Rural Development and Agricultural Extension. The author is married and has three children. vi ACKNOWLEDGEMENTS First and foremost, I would like to praise and extend my unshared thanks to Almighty God, Lord Jesus Christ for providing me the opportunity, smoothening of all aspects regarding my study and being with me in all aspects during my stay at Haramaya University and throughout my life. All things were made by him and without him was not any thing made that was made. » John 1? 3 I would like to extend my heartfelt thanks to my major advisor Dr. Getachew Kassaye for his valuable advice, insight and guidance starting from proposal development to the completion of the research work and his provision of reference material. I am again thankful to my coadvisor, Dr. Daniel Temesgen, for his willingness to advise me as well as his valuable guidance and support throughout my research work.
Both have worked hard to keep me on the right track and accomplishment of the study. I would also like to express my sincere appreciation to IPMS (ILRI) Project for funding my research and other study expenses and Dale woreda Agriculture and Rural Development for giving me chance to pursue my post graduate study and paying my salary during my study program. I would like also to thank all management and staff members of Dale woreda agriculture and rural development office for their hospitality and kind cooperation in all aspects.
Though not to exclude others, my special thanks go to Ato Belayenehi Baramo, head of Dale Woreda agricultural office for encouraging me morally and facilitating every arrangement required for my study. I also remain thankful to all the staff of IPMS project from wereda to head office that facilitated and assisted me in running any expenses of my study. I would like also to thank Ato Tesfa Mulugeta for his provision of personal Laptop computer during my thesis writing up with patience. vii I am grateful to all brothers and sisters in Jesus Christ who prayed and encouraged me morally for my study.
I would like also to thank Bati Full Gospel Belivers Church, Haramaya for providing me with all the necessary spiritual support. Last but not least my special thank go to my wife w/ro Woinshet Berhanu for being with me all the time providing me a moral support, encouragement and prayer while shouldering all my families burden with patience and endurance. Finally, I express my heartfelt gratitude to the whole family and my children for their prayer and all inconvenience they have during my absence for academic and research work. viii ABBREVIATIONS AI CADU CBD CIP Cms CSA df EPID GDP ha.
HHs IATP IPMS Km LIMDEP LPM Masl MEDAC Mm MOA Adoption Index Chilalo Agricultural Development Unit Coffee Berry Disease Coffee Improvement Project Centimeters Central Statistical Authority Degree of freedom Extension and Project Implementation Development Gross Domestic Product hectare House Holds International Agricultural Training Program Improving Productivity and Market Successes Kilometers Limited Dependent Variable Linear Probability Model Meter above sea level Ministry of Economic Development and Cooperation Millimeter Ministry Of Agriculture ix
New technologies were later extended to areas out of the CADU mandate area by the Extension and Project Implementation Department (EPID) of the Ministry of Agriculture (MOA), through a minimum package program in 1970/71(MOA,1974). Since then considerable efforts have been made to extend the new technologies like fertilizers, improved varieties, herbicide, insecticide and other improved agronomic practices. Like other agricultural technologies the dissemination of stumping technology started in the 1960, by Coffee Boards and Jimma Coffee Research Center and ater continued by Coffee Improvement Project (CIP) of the Ministry of Coffee and Tea Development. Despite the continued efforts, however, coffee stumping technology has not been widely adopted by smallholder farmers. 1. 2. Statement of Problem It is repeatedly stated that coffee is the most important export commodity crop for Ethiopia. It accounts 60% of the total foreign exchange earnings of the country. Ethiopia’s different agroecological zones with different elevations and climates provide great potential for the development of coffee plantation. The total area covered by coffee plantation is estimated to be around 400,000 hectares (ha).
Annual average production per ha has not so for exceeded 5 quintals in major coffee growing areas. The over all annual natural production is estimated to be not more than 200,000 tons. About 95% of this coffee production comes from smallholder coffee farmers. Coffee occupies 0. 4% of the land size of the country and 4% of the total cultivated areas. In SNNPRS, total area of coffee is 234,250 ha. Out of which 65% is garden coffee, 23% is semi forest, 10% is forest and 2% is plantation. The coffee potential woredas are about 50 and among them 7 are high coffee growing or potential area, 11 are medium and 32 are low.
From the total coffee area about 70% are old coffee (RAO, 2007). Dale woreda is one of the highest coffee potential woreda from the region and as well as to the whole Ethiopia. It holds 16,641 ha coffee, among these 9661 ha previously planting local coffee, 6029 ha new cultivar planted coffee which is released from coffee research center. At 2 the same time 951 ha is newly stumped coffee which is done from 2003 to 2007, which is low to compare with the existing old coffee area, estimated about 70% from the total area.
Also, the average coffee production of the woreda sent to central market is 4984 tons clean coffee annually (WAO, 2007). But the farmers and the country have not got the expected benefit as of having such amount of coffee area and potential, because of market related and different production problems. Some of the problems related to the production are: inefficient extension services; traditional cultural practices; old age of coffee trees; lack of credit facilities; pest and disease; lack of skilled man power; transportation and financial problems (I ATP, 1995).
Therefore, to improve the production and productivity of coffee and as the result to increase foreign exchange earnings and benefit farmers from the revenue generated by coffee production, the Agricultural Extension Offices has introduced many new technologies. Among the introduced technologies, coffee stumping technology is the one to alleviate or solve problem of decline of production due to age of coffee trees. And because of this, at this time stumping is a very great issue of government, and agricultural and rural development offices.
But the factors that influence adoption of technologies are not studied and well under stood. Also the rate and intensity of the technology adoption is not well known at this time. This is especially true for Dale woreda where adoption studies have not been done on old coffee stumping technologies up to present. The adoption of agricultural technologies in developing countries attracts considerable attention because it can provide the basis for increasing production and income.
Small-scale farmers’ decisions to adapt or reject agricultural technologies depend on their objectives and constraints as well as cost and benefit accruing to it (Million and Belay, 2004). The problem is, therefore, to determine factors that positively or negatively affect the adoption and intensity of adoption of new technologies in the study area. 3 1. 3. Objectives of the Study The specific objectives of the study were: 1) To assess the intensity of adoption of old coffee stumping technology among adopters. ) To identify factors determining adoption and intensity of adoption of old coffee stumping technology. 1. 4. Research Questions 1) What is the intensity of adoption of old coffee stumping technology among adopters? 2) What are the factors determining adoption and intensity of adoption of old coffee stumping technology in the study area? 1. 5. Significance of the Study Agriculture, which is the single most important economic sector of the Ethiopian economy, is dominated by small-scale farmers whose production and productivity are among the lowest in the world (Spencer, 1993).
Information on technology adoption and its impacts on farmers income are important for focusing future research, extension and other development programs aimed at benefiting the majority of Ethiopian farmers. The determination of factors influencing old coffee stumping technology adoption is essential in taking measures to remove or at least alleviate the constraints affecting adoption. Identification of factors that accelerate the adoption of technology can enhance the formulation and implementation of technology dissemination programs.
Researchers and extension specialists can utilize the results of this study in fine-tuning research and extension activities. Hence, this study will attempt to find out factors affecting adoption of old coffee stumping technology and its rate and intensity of adoption by smallholder formers’ in the study area. Also development policy makers can benefit from the result of this study since they require micro level information to formulate suitable policies. 4 1. 6. Scope and Limitation This study was undertaken in Dale woreda which is one of the major coffee growing areas in SNNPR state.
As any other technology adoption, old coffee stumping technology is influenced by many factors. A factor which is found to enhance adoption of a particular technology in one locality at one time might be found to hinder it or to be irrelevant for adoption of the same technology in another locality at the same or different time for the same or different technology or the other way round. From these conflict results that it is difficult to identify universally defined factors either impeding or enhancing adoption of technology. Therefore, this study which was undertaken in this woreda is important due to the above reasons.
On the other hand, since this study was limited by time, financial constraints and human resources it is restricted to the above mentioned woreda and therefore, we couldn’t conclude from the result gained to the whole coffee growing area of Ethiopia, except having practical validity mainly to areas having similar feature with the selected woreda. 5 2. REVIEW OF LITERATURE The literature review encompasses rehabilitation of old coffee by stumping, definition of concepts, theories of behavioral change and selected behavioral change models, conceptual framework, and empirical adoption studies. 2. 1.
Rehabilitation of Old Coffee Plants After the establishment of a coffee farm, the trees generally remain productive for eight crops before they reach the stage of exhaustion. The rehabilitation of coffee trees at this age is very important as the yield starts to become uneconomic. To make the trees economically productive again, they need to be pruned to cut out old, unproductive wood and stimulate the growth of new wood that will bear fruit. There are various methods of old coffee tree rehabilitation. The major rehabilitation methods are stumping, side pruning and in some cases topping (Ren’e Coste, 1992). . 1. 1. Rehabilitation by stumping Stumping is the systematic renewal of old coffee plants and can take a number of forms, but the most known and largely applied in our country is clean stumping. Clean stumping should be carried out as soon as after the harvesting of the previous crop has been completed. This will counter the temptation to leave the old stem which might have flowered or budded and shown some crop potential. The cutting of the coffee tree should be done with a saw at an angel of 450 and a height of about 40 cms. Any rough edge n the cut should be pared off with a sharp knife (Ren’e Coste, 1992). 2. 1. 1. 1. De-suckering After stumping, the coffee stump will start producing a mass of new suckers at about 10cm long; there should be a first selection of the most vigorous growth of four suckers being 6 chosen from the ones on the out side of the stump system. These four suckers should be reduced further to the two strongest and most vigorous, preferably on opposite sides of the stump, when they attain a length of about 20 cms, and also there should be subsequent sucker control (Ren’e Coste, 1992). 2. 1. 1. 2.
Inter planting It is customary in some areas, like south and west regions of Ethiopia to inter plant food crops between rows of coffee. In these cases only dwarf varieties of beans and peas should be planted. In no circumstances should tall crops such as maize, Horse Beans, or runner Beans be used, as these restrict the light requirements of the coffee plant or sucker, making the growth weak and spindly. The inter planted crop should be planted in rows down the center of the strip of land between the coffee tree rows, so that there is a space of at least 50 cms between the coffee and food crops. 2. 2.
Theoretical Perspective of Adoption 2. 2. 1. Definition of Concepts Adoption of technological innovations in agriculture has attracted considerable attention among development economists because the majority of the population of less developed countries derives their livelihood from agricultural production and a new technology, which apparently offers opportunities to increase production and productivity (Feder et al. , 1985). It is also believed that the use of new technologies in farming is a crucial means to lift up production and productivity of the resources used in the subsistence agriculture.
New technologies enable the farmer to produce more by using available farm resources. More effectively, innovations in agriculture and their adoption are important in improving food security at the family, village and national levels (Ashri, 1996). The adoption of an innovation within a social system takes place through its adoption by individuals or groups. According to Feder et al. (1985), adoption may be defined as the 7 integration of an innovation into farmers’ normal farming activities over an extended period of time.
Dasgupta (1989) also noted that adoption, however, is not a permanent behavior. This implies that an individual may decide to discontinue the use of an innovation for a variety of personal, institutional, and social reasons one of which might be the availability of another practice that is better in satisfying farmers’ needs. Rogers (1983) defines the adoption process as the mental process through which an individual passes from first hearing about an innovation or technology to final adoption.
This indicates that adoption is not a sudden event but a process. Farmers may not accept innovations immediately; they need time to think over things before reaching a decision. Colman and Young (1989) define adoption as it relates to the use or non-use of a particular innovation by individuals (Say farmers) at a point in time or during an extended period of time. Adoption, therefore, presupposes that the innovation (technological change) exists and studies of the adoption process analyze the reasons or determinants of whether and when adoption takes place.
In the words of Yapa and Mayfield (1978) the adoption of an entrepreneurial innovation by an individual requires the satisfaction of at least three conditions. These are (i) the availability of sufficient information (ii) the existence of a favorable attitude towards the innovation, and (iii) the physical availability of the innovation. In the context of aggregate adoption as opposed to the final adoption at the individual farmer level, diffusion is defined as the process of spread of a new technology within a region (Rogers, 1983).
In other words, diffusion is a cumulative process of adoption measured in successive time periods (Colman and Young, 1989). The introduction of agricultural innovation into a given geographical area in a given period of time may be through both private and public initiatives and the rate of diffusion depends on, among other things, extension communication, the extent to which farmers discuss agricultural issues among them selves on a day to day basis and consistency of performance with the message (Fliegel, 1984).
Following a lucid and extended description of an innovation Presser (1969) concluded that an innovation is something new and novel in human knowledge and experience. Van den Ban 8 and Hawkins (1988) define innovation as an idea, method, or object which is regarded as new by an individual, but which is not necessarily the result of recent research. An innovation has a point of origin in place and time. At its point of origin, it must be an innovation, but it is more commonly called an innovation, a research result, or a new development of some older idea (s).
In time, as knowledge and use of the innovation diffuse to other people in the surrounding area, the idea ceases to be an innovation in that area. The rate of adoption is defined as the percentage of farmers who have adopted a given technology. The intensity of adoption is defined as the level of adoption of a given technology. The number of hectares planted with improved seed (also tested as the percentage of each farm planted to improved seed) or the amount of input applied per hectare will be referred to as the intensity of adoption of the respective technologies (Nkonya et al. 1997). The importance of agricultural innovations in the transformation process of economies of developing countries has become, with out doubt, the major concern of governments, citizens and development agencies alike. Agricultural economists in the development field have made a particular study of the adoption and diffusion of technical innovation because of the opportunities for increased output and higher levels of income which technological change can offer (Colman and Young, 1989). 2. 2. 2.
Adoption pattern and factors affecting adoption of technologies Leathers and Smale (1991) have identified the following adoption patterns from the large body of empirical evidence: for the most part, farmers choose to adopt inputs sequentially, adopting initially only one component of the package and subsequently adding components overtime, one at a time; in some instances, farmers adopt a component and subsequently revert to traditional practices; adoption patterns vary by agro ecological zones, between farmers facing different markets and institutions.
Adoption is not the final event of change but rather a decision-making process. Individuals pass through various learning and experimenting stages from becoming aware of a problem 9 and its potential solutions to finally adopting or rejecting the innovations under considerations (Enters, 1996). A number of studies on adoption behavior pointed out that a host of explanatory factors influence adoption behavior of farmers. For instance, Hansel (1974) identified factors such as individual characteristics (like education, access to change agents, size of holding, etc. ; regional characteristics (system and organization of rural change agencies, population densities, etc. ); and innovation characteristics (like accordance with local norms, economic advantage, etc. ) as influencing the adoption of technologies. Giger et al. (1999) stated that if the technology promoted is not profitable from the farmers’ point of view, it is highly doubtful that the use of direct incentives will lead to sustained adoption of a technology in the long term. The technology will almost be abandoned as soon as the project is phased out, and no replication beyond the boundaries and the lifetime of project can be expected.
They further explained that rapid economic benefits are very important conditions for success and it is most probably much more important than the use of incentives in terms of achieving genuine, durable adoption. According to Cary et al. (1997) there is an obvious need to understand the relative importance of factors, which may influence individual adoption of conservation practices, which ameliorate land degradation. The economic costs to landholder of many conservation practices may exceed the on-farm benefits on a short-term and possibly long-term basis.
The lack of immediate financial incentive in a dynamic economy may result in many landholders not to adopt conservation practices. 2. 3. Empirical Studies on the Adoption and Diffusion of Agricultural Technologies An empirical study of adoption and diffusion of innovations through interviews with potential uses of the innovation, according to Van den Ban and Hawkins (1988) is an important approach to investigate and find answers to the following set of questions; (i) what decision making path ways do individuals follow when considering whether or not to adopt an 0 innovation? Which sources of information are important? (ii) What are the differences among people who adopt innovations quickly or slowly? (iii) How do the characteristics of innovations affect the rate of adoption? (iv) How do potential users communicate among themselves about these innovations? Who plays the important role of opinion leader in this communication process? And (v) how does an innovation diffuse through a society over time? Because of these a number of empirical studies have been conducted by different peoples.
Until 1980 more than 3000 publications have appeared, of which over 2000 represent results of empirical research on adoption of innovations and detailed analyses of differences between adopter categories with respect to a host of personal, social and cultural characteristics (Rogers, 1983). Views and findings are not, however, consistent with respect to the role of these factors on adoption behavior of farmers and the subject is of considerable controversy around the globe.
No single conclusion has been drawn with respect to the key factors which favor or impede adoption decision at a given time and place becomes lest impotent or even induce an impediment on the adoption behaviors of farmers at another time and /or place. Hence review of empirical works is important for various reasons. First, it helps to assess the present state of knowledge of the adoption process. Second, it helps to enhance the interpretation of empirical models and their results and its implications as against the conceptual or theoretical models (Feder et al. , 1985).
However, the study are mainly conducted around major cereals and due to this study conducted in the area of coffee, perennial crop is scanty. As a result of this, the review mainly included the studies conducted mainly on cereals, particularly maize and wheat with very few related horticultural crops. For ease of grouping, the variables so far identified as having relationship with adoption are categorized as household personal and demographic variables, socio-economic factors, technology related factors, intervening (psychological) variables and institutional factors. 1 2. 3. 1. Household’s personal and demographic variables Household’s personal and demographic variables are among the most common household characteristics which are mostly associated with farmers’ adoption behavior. From this category of variables age, sex, education, family size and farming experience were reviewed in this study. Age of the household is usually considered with the assumption that older farmers will have more knowledge and skill with farming which enables them to easily understand the benefits of the technology better than others.
However, with regard to age different studies report different results. For example a study conducted by Gockowski and Ndoumbe (2004) on the adoption of intensive mono-crop, horticulture in southern Cameroon indicated that younger farmers were more likely to adopt and the effect of age on the probability of adoption was elastic. Similarly, Mulugeta (1994), on his study on smallholder wheat technology adoption in South Eastern highlands of Ethiopia reported that age had a negative effect on the adoption of wheat technologies.
In addition, Kidane (2001) on the study he conducted on factors influencing adoption of improved wheat and maize varieties in Hawzien wereda of Tigray found that age is negatively related with farmers’ adoption of improved wheat variety. How ever, there are also others who reported positive relation ship of age with adoption. For instance, AsanteMensah and Seepersad (1992), on the study they conducted on factors affecting adoption of recommended practices by cocoa farmers in Ghana reported positive relationship of age with adoption.
Gender differentials are one of the most important factors influencing adoption of improved agricultural technologies. Due to long lasted cultural and social grounds in many societies of developing countries, women have less access to household resources and also have less access to institutional services. Regarding the relationship of household’s sex with adoption of agricultural technologies, many previous studies reported that household’s gender has positive effect on adoption in favor of males. For example, Techane (2002), in his study on determinants of fertilizer adoption in Ethiopia found that male headed households are more 12 ikely to adopt fertilizer than female headed households. Similarly, Mulugeta et al. (2001), reported that gender differentials among the farm households was positively influenced adoption and intensity of adoption of fertilizer use. With regard to education, there is a general agreement that education is associated with adoption because education is believed to increase farmers’ ability to obtain, and analyze information that helps him to make appropriate decision. studies conducted by Itana (1985); Chilot et al. (1996); Kansana et al. (1996); Asfaw et al. (1997); Mwanga et al. (1998) and Tesfaye et al. 2001) have reported that education had positive relationship with adoption. Similarly, Nkonya et al. (1997) reported positive relationship of education with adoption and intensity of adoption improved maize seed. On the other hand, study conducted by Tesfaye (2003), on soil and water conservation practices in Wello, Wolaita and Konso areas of Ethiopia revealed that there is no variation between literacy and illiteracy rates in terms of soil and water conservation practices. Family size is one of the other important household demographic variables which have influence on farmers’ adoption behavior.
Large family size usually implies availability of labor provided that majority or all of the family members are within the age range of active labor force (15-64 years). In most studies family size had positive relationship with adoption of improved agricultural technologies. For instance, Kidane (2001) on the study he conducted on factors influencing adoption of new wheat and maize varieties in Tigray reported positive and significant relationship of family size with adoption. Similarly, Haji (2003), reported positive effect of family size on adoption of cross-bred dairy cows.
Others, for instance, Asante-Mensah and Seepersad (1992); Degnet et al. (2001) have also reported similar results. Contrary to this, Million and Belay (2004) reported that family size negatively affected adoption of physical soil conservation measures. Farming experience is another important household related variable that has relationship with adoption. Longer farming experience implies accumulated farming knowledge and skill which has contribution for adoption. Many studies supported this argument. Endrias (2003) reported positive relationship of farming experience in sweet potato production with adoption of sweet 3 potato varieties. Similarly, result of study in Ghana on factors influencing adoption of recommended cocoa production practices by Asante-Mensah and Seepersad (1992) indicated positive relationship of experience in cocoa farming with adoption of recommended cocoa production practices. On the same line, Legesse (1992); Kidane (2001); Melaku (2005) and Yishak (2005) have reported similar result. Contrary to this, Gockowski and Ndoumbe (2004) reported negative relationship of farming experience with adoption of intensive monocrop, horticulture in southern Cameroon. 2. 3. 2. Farm characteristics
Farm related variables such as farm size and other farm characteristics influence farmers’ adoption behavior as farm is an important unit where agricultural activities take place. Concerning farm size, different studies reported its effect differently. For example, a study by Itana (1985); Mulugeta (2000); Million and Belay (2004) and Yishak (2005) indicated positive relationship between farm size and adoption. Contrary to this, a study conducted by Gockowski and Ndoumbe (2004) on the adoption of intensive mono-crop horticulture in southern Cameroon reported the negative relation of farm size with adoption.
Similarly, Legesse (1992) and Degnet et al. (2001) reported negative relationship between farm size and adoption. 2. 3. 3. Household’s economic variables Economic factors influence household’s adoption decision of agricultural technologies. According to Semgalawe (1998), economic factors such as household’s resource ownership and economic objectives play a great role in determining the willingness and ability to invest in the adoption of agricultural technologies. In rural context, livestock holding is an important indicator of household’s wealth position.
Livestock are also an important income sources which enables farmers to invest on the adoption of improved agricultural technologies. No doubt that in most cases, livestock 14 holding has positive contribution to household’s adoption of agricultural technologies. This is evident from many of the past adoption studies which have reported positive effect of livestock holding on adoption. To mention some of them, for instance, Chilot (1994); Degnet et al. (2001); Kidane (2001); Birhanu (2002); Techane (2002) and Endrias (2003) have found that livestock holding has positive influence on adoption of improved agricultural technologies.
Households’ income position is one of the important factors determining adoption of improved technologies. In the context of rural households, annual farm income obtained from sale of crop and/or livestock, off-farm and non-farm income are important income sources. Regarding annual farm income, almost all empirical studies reviewed shows the effect of farm income on household’s adoption decision is positive (Degnet et al. , 2001; Kidane, 2001; Getahun, 2004 and Gockowski and Ndoumbe, 2004). Off-farm and non-farm activities are the other important activities through which rural households get additional income.
The income obtained from such activities helps farmers to purchase farm outputs. Review of some of the past empirical studies shows that the findings regarding the influence of off-farm/ non-farm income on adoption vary from one study to the other. However, majority of the studies reported positive contribution of off-farm and nonfarm income to household’s adoption of improved agricultural technologies. For instance, a study conducted by Kidane (2001); Mulugeta et al. (2001); Birhanu (2002) and Mesfin (2005) indicated positive relationship between off-farm / non-farm income and adoption.
Contrary to this, Techane (2002) in his study on determinants of fertilizer adoption in Ethiopia reported the negative influence of participation in off-farm income on farmers’ adoption of chemical fertilizer. Availability of household labor is the other important variable which in most cases has an effect on household’s decision to adopt new technologies. Several studies reported the positive effect of household labor availability on adoption of improved agricultural technologies. For instance, Million and Belay (2004) in their study on factors influencing 15 doption of soil conservation measures in southern Ethiopia found positive effect of household’s labor availability on adoption of soil conservation measures. 2. 3. 4. Institutional factors Farmers make decisions within a broader environment or context. Institutional factors are part of such broader environment which affects farmers’ adoption decision of agricultural technologies. Institutional factors in the context of this study include support provided by various institutions and organizations to enhance the use of improved technologies such as extension and credit services and other inputs.
Extension provides farmers with information related to agricultural technologies. In collaboration with other organizations or alone, it can also channel credits and other incentives to the farming community to enable them improve production and productivity. The relationship between farmers’ access to extension services and adoption has been repeatedly reported as positive by many authors. For example, study conducted by Kansana et al. (1996) indicated that participation in training, access to communication sources and number of information sources had positive association with level of knowledge and adoption of improved wheat varieties.
Similarly, Nkonya et al. (1997) reported that visit by extension agents had positive influence on improved maize and fertilizer in Northern Tanzania. Many other authors such as Aregay (1980); Chilot et al. (1996); Degnet (1999); Kidane (2001); Tesfaye et al. (2001); Birhanu (2002); Techane (2002); Endrias (2003) and Haji (2003) also reported positive relationship of access to extension and adoption of agricultural technologies. Other sources of information such as mass media and neighboring farmers in the area are also important in diffusion of agricultural innovations.
Particularly, interpersonal communication networks among farmers are important and reported in many studies to have positive influence on farmers’ adoption decision. Mass media also plays the greatest role in provision of information in the shortest possible time over large area of coverage. Many studies reported positive relationship of mass media with adoption of agricultural technologies (Yishak, 2005). 16 The other institutional support that farmers need to get to improve production and productivity is, credit service and other inputs. Capital and isk constraints are key factors that limit the adoption of high value crops by small scale farmers because these crops generally are much more costly to produce per hectare than traditional crops and most growers require credit to finance their production. In line with this, study conducted by Gockowski and Ndoumbe (2004) on the adoption of intensive mono-crop horticulture in Southern Cameroon indicated that cash requirements for intensive horticulture production combined with the failure of formal rural credit institutions significantly affected adoption of especially resource poor households.
Similarly, other authors who conducted studies on adoption of cereals (wheat and maize) such as Legesse (1992); Mulugeta (1994); Chilot et al. (1996); Kansana et al. (1996); Asfaw et al. (1997); Bekele et al. (1998); Mwannga et al. (1998); Wolday (1999) and Tesfaye et al. (2001) have also reported positive relationship of credit with adoption of improved technologies by farmers. Timely availability of inputs, input and output prices are also another important issues which is expected to have influence on household’s adoption behavior.
Particularly the production of horticultural crops is highly linked with market conditions (price & market access) for inputs and outputs. Study conducted by Wolday (1999) indicated that price is significantly related to use of improved seeds. Similarly, Itana (1985) reported positive relationship of output price with adoption of improved varieties. 2. 3. 5. Psychological related variables Behavioral change process involves decision-making, which implies cognitive engagement in deciding whether to adopt or reject a given innovation (Koch, 1986).
According to Duvel (1991), psychological related factors which he distinguished as needs, perception and knowledge are the most important determinants of farmers’ adoption behavior. Many of the studies which have considered these variables reported their significant relationship with adoption behavior. To mention some, a study conducted in Sera-Leone by Adesina and Zinnah (1993) showed that farmers’ perception of specific characteristics of technology 17 significantly condition adoption decision.
They further indicated that the omission of such variables in adoption model might bias the results of factors determining adoption decision of farmers by ignoring their possible and important influence on adoption behavior. Similarly, Chilot et al. (1996) found that perceived relative profitability of improved wheat variety over the traditional one has significantly affected adoption. Different studies have been conducted in South Africa to see the effect of intervening variables particularly need and perception on adoption behavior.
For example, studies conducted by Botha (1986); Louw & Duvel (1993) and Duvel & Botha (1999) confirm the positive and significant relationship of perception with adoption behavior. Similarly, Botha (1986) indicated that farmers’ technical know-how of the innovation is important in adoption. On the other hand, a study conducted by Abd-Ella et al. (1981) on adoption behavior in family farm systems in Iowa indicated that knowledge about the recommended farming practices is positively related with adoption.
Mulugeta (1994) in his analysis of smallholder wheat production and technology adoption in south eastern highlands of Ethiopia also indicated that farmers’ knowledge of recommended fertilizer application rates was the critical variable influencing the decision to use higher rates of fertilizer per hectare. A study by Degnet (1999) also reported that adopters were found to have better knowledge on fertilizer application than non-adopters did. 2. 4. Conceptual Framework of the Study Adoption decisions of different technologies across space and time are influenced by different factors and their associations.
Factors such as personal, socioeconomic, institutional and psychological factors determine the probability of adoption and use intensity of technologies such as old coffee stumping technology. It is obvious that different studies have been conducted to look into the direction and magnitude of the influence of different factors on farmers’ adoption decision of agricultural technologies. A factor, which is found to enhance adoption of a particular technology in one locality at one time, was found to hinder it or to be irrelevant to adoption of the same technology in another locality.
Although some known determinants tend to have general applicability; it is difficult to develop a universal 18 model of the process of technology adoption with defined determinants and hypotheses that hold to everywhere. The dynamic nature of the determinants and the distinctive nature of the areas make it difficult to generalize what factors influence which technology adoption. The framework emphasized mainly on the relationship of the explanatory variables with the dependent variable. The relationship between explanatory variables was not shown in the diagram.
This does not mean that there is no relationship between explanatory variables, but simply to concentrate on their relationship with the dependent variable rather than relationship among themselves. Hence, the following conceptual framework depicted the most important variables expected to influence the adoption of old coffee stumping technology considering the study area specifically. 19 Figure 1. Conceptual frame-work of factors affecting intensity & adoption of old coffee stumping technology Asset endowment and other income source Institution al variables
Psychological related variable Adoption and intensity of adoption of old coffee stumping technology Household, personal & demographic factors Farm characteristics Source: Based on Duvel; cited in Habtemariam (2004), modified. 20 3. RESEARCH METHODOLOGY 3. 1. Description of the Study Area 2 Dale Woreda is one of the10 Woredas in Sidama Zone covering a total area of 1,411 km , at about 320 km south of Addis Ababa. The Woreda is subdivided into 76 PAs. According to CSA (2003), the population of the Woreda is estimated at 369,548 of which female account for 57. % of the population. The altitude of the Woreda ranges from 1170 masl around Lake Abaya to the west, reaching about 3200 masl in the eastern part of the Woreda. The altitude at Yirgalem, which is the Woreda headquarter, is 1765 masl. The mean annual rainfall recorded at Awada Research sub-centre in Yirgalem is 1314 mm. Rainfall declines as one move from the highlands in the east to the lowlands in the west. There are two cropping seasons in the area, Belg (short rainy season) from March to April and Meher (main rainy season) from June to September.
Belg rains are mainly used for land preparation and planting long cycle crops such as maize and seedbed preparation for Meher crops. The Meher rains are used for planting of cereal crops like barley, teff, wheat and vegetable crops. Meher rains are also responsible for the growth and development of perennial crops such as enset, coffee and chat. Livestock also play a major role in crop production in areas of the mid highlands and lowlands for cereal production (draught power) in addition to meat and milk; it also denotes prestige and asset to the households.
Farming systems According to IPMS (2005), two main farming systems can be found in Dale Woreda. The garden coffee, enset, and livestock (hereafter referred to as coffee/livestock system) system is found east of the main road transecting Dale from north to south. The terrain is hilly and soils are red (Nitosols). Rainfall is higher and more reliable than in the dry midlands haricot bean/livestock system. The farming system is composed of garden coffee, enset, and cattle, which are tethered and kept for manure and production of dairy products.
Other crops in the system are haricot beans (as an intercrop), yam, cereals, fruits, mainly avocado and bananas. 21 Because of the perennial nature of the crop and the small holding size (between 0. 25-0. 5 ha per family), hand hoeing is the predominant method of cultivation. The Cereals, enset, haricot beans, garden coffee, and livestock (here after referred to as haricot bean/livestock system) system is the other main farming system in Dale Woreda. This system is found west of the road transecting Dale from North to South.
The terrain varies from relatively flat to hilly. Black soils (Pellic Vertisols) are commonly found on the flat areas and red soils on the slopes (IPMS, 2005). Rainfall is lower and more erratic than in the coffee system. This system is dominated by cereals (maize, teff) rotated with haricot beans. Enset is cultivated near the homesteads. Garden coffee is grown in small patches, on the red soils. Extensive grazing areas are found, which are used for herding the oxen, cattle and goats. Average farm size is estimated at 1. 5 ha. The farmers use oxen for their cultivation.
Besides these two major systems, two smaller systems can be found, one in the extreme east at the high altitude where farmers grow horticultural crops (shallots) and the other one in the extreme west, near Lake Abaya where a pastoralist system is found (IPMS, 2005). Crop Production The government is clear in its strategy for a market led development in that it has chosen two crops for this woreda. That are, Coffee and Haricot bean (white variety-Awash 1). According to the available statistics, the area under coffee is 16,641 ha and a total of 9. million kg of red cherry was sold in 2002/03 and 5. 7 million in 2003/04. Garden coffee improvement is being promoted predominantly in the coffee/livestock system. A total of 42 PAs have been targeted for this specialization, while, there are 59 PAs where coffee is grown. The commercialization of the haricot beans is targeted for the haricot bean/livestock farming system. The area under beans at the moment is still small i. e, 2,300 ha and the estimated production is 670 tons. A total of 22 PAs are targeted for specialization.
The government intends to commercialize haricot bean for export purposes, using the Awash 1 variety (small white seeds). This is a new introduction to the area which can either be added to and/or replace the area already sown with the local red Wollayta variety (IPMS, 2005). 22 Livestock The main livestock species in the Woreda are cattle, goats and sheep. The livestock resources are cattle 225,698 (82,666 local cows and 1584 crossbred dairy animals, 80% are in urban and peri-urban areas); sheep 30,152; Goats 31,443; Poultry 218,923; Horses 2,498; Mules 431; Donkeys 16,321; and Beehives 10,949.
Production systems range from extensive system in the lowlands (haricot bean/livestock system) to intensive tethered system in the major coffee/livestock system. Sheep production is important in the Dega (highlands) areas. Cattle, sheep and goat production is major in the mid-altitudes and goat, cattle, and sheep production are important in the lowland or Kolla areas. Land preparation is mainly done by oxen power in the coffee/livestock system or human power using hoe in the coffee/livestock, depending on land size and availability of oxen.
Oxen ownership is very low and farmers share their oxen for ploughing. In the Woreda, only 16% of the farmers have a pair of oxen, 26% have one ox and 58% have no oxen. There is a large resource of production of skins and hides in the Woreda. However, only 37% of the marketable skins and hides were officially marketed in 2004. There is a plan to increase the proportion of marketable skins and hides to 70% in three years. Production of fattened cattle, goat and sheep has great potential and there is a plan to enhance meat production in the Woreda. The oultry production system is traditional using local birds. The market-led priority livestock commodities incorporated in the Woreda development plan are: (1) Dairy Production; (2) Meat production from fattened ruminants (mainly cattle and goats); (3) Skins and Hides; (4) Poultry production, and (5) Apiculture is identified as a potential commodity for development (IPMS, 2005). 23 Figure 2. Location of the study area 24 3. 2 Survey Design and Data Collection Method 3. 2. 1. Data collection methods Both primary and secondary data were used for this study.
Primary data related to personal, socioeconomic, institutional variables and other relevant data were collected. Secondary information from published and unpublished documents and reports from relevant organizations were gathered to supplement primary data. Primary data were collected using quantitative approach by means of household survey. The household survey was carried out from November to February, 2008. The qualitative method of data collection was also employed. It consisted of in-depth open-ended interviews, direct observations and written documents. The interview method was mainly emphasized.
Group discussion and individual interviews were held to have reactions of the farmers concerning their detail experiences and their perceptions of the technology and their priority problem. Discussions with woreda experts of the agricultural office and key informants were also conducted. Before the administration of the structured and semi-structured interview schedules, exploratory farm surveys were conducted and the respondents were informed about the objectives of the survey. The interview schedules were pre-tested before actual data collection and amendments were made to modify some of the questions to make them fit to the context.
Eight enumerators and one supervisor were recruited. They were trained on the objective and contents of the interview schedule. The eight enumerators conducted the interview in the local language, Sidamgna with the supervisor and researcher follow-up. 3. 2. 2. Sample size and sampling techniques This study was conducted in Dale woreda, because of it is one of the sponsor, IPMS project woreda as well as the working place of the researcher and also the highest coffee growing woreda in SNNPR and Ethiopia.
In this study sample size was determined by taking different factors such as research cost, time, human resource, accessibility and availability of transport facility. By taking these factors into account, it was fixed to cover four Peasant Associations 25 out of 59 PAs and 160 household head respondents from the total 3752 household heads of four sampled PAs. The four stage sampling techniques were applied in sample selection processes. In the first stage from the total 76 PAs of the woreda 59 coffee grower Peasant Associations (PAs) were selected based on woreda category purposively.
In the second stage the 59 PAs were stratified into 42 specialized coffee grower PAs and 17 diversified coffee grower PAs according to Agricultural office of the woreda categorization. In the third stage 3 PAs from specialized coffee grower PAs 1 PA from diversified coffee grower PAs were selected according to proportional to size (PPS) using simple random selection. In the fourth stage 160 HHs were selected based on proportional to size and sex from adopters and nonadopters accordingly from each four PAs of total households who have coffee farms (Table 1&2).
Table 1. Distribution of sampled peasant association’s households by adoption category and sex PAs Type of coffee potential Ajewa Dagia Birachale Bokasso Total specialized specialized specialized diversified HHs number by level of adoption and sex in each PAs. Adopters M 248 321 286 113 968 F 6 7 9 5 27 Total 254 328 295 118 995 M 624 820 475 637 2556 Non-adopters F 104 43 33 21 201 Total 728 863 508 658 2757 M 872 1141 761 750 3524 Total F 110 50 42 26 228 Total 982 1191 803 776 3752 (Source: Woreda Agricultural Office. 2008) 26 Table 2.
Distribution of sampled households by adoption categories and sex Peasant association Adopters M Ajewa Dagia Birachale Bokasso Total 11 14 13 5 43 F 0 0 0 0 0 House holds Non-adopters Total M 11 14 13 5 43 26 35 19 27 107 F 5 2 2 1 10 Total 31 37 21 28 117 Total M 37 49 32 32 150 F 5 2 2 1 10 Total 42 51 34 33 160 (Source: Computed from own survey data. 2008) 27 Dale woreda 76 PAs 1st Stage 59 Coffee-grower PAs Purposively 2nd Stage 17PAs (Diversified) 42PAs (Specialized) Stratified 3rd Stage 1 PAs 3 PAs PPS 4th stage PA 4 -Adp. 5 -NAdp. 28 PA 3 -Adp. 13 -NAdp. 21 PA 2 -Adp. 14 -NAdp. 37 PA 1 -Adp. 1 -NAdp. 31 PPS 160 sampled respondents 117 non-adopters (73%) 43 adopters (27%) Key: Adp= Adopters; NAdp=Non-adopters; PPS=Proportional to size Figure 3. Sketch of sampling procedure 28 3. 3. Analytical Techniques The data were analyzed using software SPSS version 15. 0 and software Limdep version 7. 0. Appropriate techniques and procedures were used in the analysis to identify the influence of personal, socioeconomic, technical and institutional variables on the adoption decision process of the technology. Descriptive statistics were used to provide a summary statistics related to variables of interest.
Chi-square test and an independent sample t-test were used to identify variables that vary significantly between adopters and non-adopter. The chi-square test was conducted to compare some qualitative characteristics of the adopters and nonadopters. The t-test was run to see if there is any statistically significant difference between the mean of the respective adopter and non adopter categories with respect to continuous variables. The Tobit model was employed to identify the determinants of the technology package adoption and analyze farmers’ probability of technology adoption and the intensity of adoption.
VIF (Variance inflation factor) for association among the metric explanatory variables and contingency coefficients for categorical variables were used as tests of multi-co linearity. 3. 3. 1. Determination of intensity For multiple practices (package), there are two options of measuring adoption; (i) adoption index: measures the extent of adoption at the time of the survey or (ii) adoption quotient: measures the degree or extent of use with reference to the optimum possible without taking time into consideration. In this study, the first option was employed.
Accordingly, adoption index which shows to what extent the respondent farmer has adopted the whole set of package was calculated using the following weights. In order to know the intensity of adoption of old coffee stumping technology, first we had listed the main component of the technology or package with experts and model farmers and because of the package components have not equal weights in intensity of adoption measurement they gave weights for each components as follow(Table. 3). And based on this weight all the adopter respondents’ intensity of adoption is calculated. 29 Table 3.
Types of components and its’ share weight in total intensity of adoption of the technology and rating methods Sr. no Types of components 1 Total area of stumped coffee Weights 0. 4 Methods of rating Ratio of stumped coffee to total area of old coffee 2 Use of sucker control 0. 2 Round of sucker control in stumping year, 1 times = 0. 025; 2 = 0. 05; 3 = 0. 075; 4 = 0. 1; >4 = 0. 2 3 Final remaining stem 0. 1 Final no. of sucker/stem remains,1stem=0. 1; 2 = 0. 1; 3 = 0. 05; ? 4 = 0; 4 Used & type of intercropping 0. 1 crops 5 Used & amount of fertilizers (compost) 6 Height of stumping 0. 05 40cm = 0. 25; at 40 cm = 0. 05 7 Direction and shape of stumping 0. 05 Slant & opposite to sun rise & set = 0. 05; slant to any direction = 0. 025; flat = 0 Total intensity of adoption (Source: Leguminous = 0. 1; cereals = 0. 025; root crops = 0. 025; mixed = 0. 05; no = 0 0. 1 Recommended = 0. 1; as I got = 0. 05; No = 0 1. 0 Computed with woreda experts and model farmers. 2008) 3. 3. 2. The Tobit Model and specification The Tobit Model Adoption studies based upon dichotomous regression models have attempted to explain only the probability of adoption versus non-adoption rather than the extent and intensity of adoption.
Knowledge that a farmer is using high yielding variety may not provide much information about farmer behavior because he/she may be using 1 percent or 100 percent of his/her farm for the new technology. Similarly, with respect to adoption of fertilizers, a farmer 30 may be using a small amount or a large amount per unit area. Hence, a strictly dichotomous variable often is not sufficient for examining the extent and intensity of adoption for some problems such as fertilizers (Feder et al. , 1985). There is also a broad class of models that have both discrete and continuous arts. One important model in this category is the Tobit. It is an extension of the Probit model and it is really one approach to dealing with the problem of censored data (Johnston and Dinardo, 1997). Some authors call such models limited dependent variable models, because of the restriction put on the values taken by the regressed (Gujarati, 1995). Examining the empirical studies in the literature, many researchers have employed the Tobit model to identify factors influencing the adoption and intensity of technology use. For example, Nkonya et al. 1997); Lelissa (1998) ; Bezabih (2000) and Croppenstedt et al. (1999) used the Tobit model to estimate the probability and the intensity of fertilizer use. According to Adesina and Zinnah (1993), as cited by Shiyani et al. (2000), the advantage of the Tobit model is that, it does not only measure the probability of adoption of technology but also takes care of the intensity of its adoption. Specification of the Tobit Model The Tobit model applied for analyzing factors influencing adoption and intensity of old coffee stumping technology is the Tobit model shown in equation (1).
Yi* = ? Xi+ u i Yi = Yi* if Yi* > 0 = 0 if Yi * ? 0 Where, Yi = the observed dependent variable, in our case index of adoption of old coffee stumping Technology Yi* = the latent variable which is not observable. Xi = vector of factors affecting adoption and intensity of old coffee stumping technology i = 1, 2 …. n ————————————————– (1) ? i = vector of unknown parameters 31 u i = residuals that are independently and normally distributed with mean zero and a common variance ( ? 2 ). Note that the threshold value in the above model is zero.
This is not a very restrictive assumption, because the threshold value can be set to zero or assumed to be any known or unknown value (Amemiya, 1985). The Tobit model shown above is also called a censored regression model because it is possible to view the problem as one where observations of Y* at or below zero are censored (Johnston and Dinardo, 1997). The model parameters are estimated by maximizing the Tobit likelihood function of the following form (Maddala, 1997; Amemiya, 1985). ?Y ? ? X ?? i i i ? ? ? ? 1 L= yi*>0 ? ? ? ? ? Yi *? 0 ? ? ? ?i X i ? F ? ? ? ? ? —————————– (2)
Where ? and F are respectively, the density function and cumulative distribution function of Yi*. ? means the product over those i for which Yi* ? 0, and Yi* ? 0 Yi* > 0 ? means the product over those i for which Yi*>0. An econometric software known as “Limdep” was employed to run the Tobit model. It may not be sensible to interpret the coefficients of a Tobit in the same way as one interprets coefficients in an uncensored linear model (Johnston and Dinardo, 1997). Hence, one has to compute the derivatives of the estimated Tobit model to predict the effects of changes in the exogenous variables.
As cited in Maddala (1997), Johnston and Dinardo (1997) and Nkonya et al. , (1997), McDonald and Moffit proposed the following techniques to decompose the effects of explanatory variables into adoption and intensity effects. Thus, a change in Xi (explanatory variables) has two effects. It affects the conditional mean of Yi* in the positive part of the distribution, and it affects the probability that the observation will fall in that part of the distribution. Similar approach is used in this study. 32 The marginal effect of an explanatory variable on the expected value of the dependent variable is: ?? Yi ) = F ( z ) ? i ? X i ————————————————————— (3) Where, ?i X i is denoted by z, following Maddala, (1997) ? The Change in the probability of adopting a technology as independent variable Xi changes is: ? ? F ( Z ) = ? (z) i ? ?X i ——————————————————————– (4) The change in intensity of adoption with respect to a change in an explanatory variable among adopters is: 2 ? ?E (Yi / Yi * > 0) f ( z) ? f ( z) ? ? ? ? ?? = ? i ? 1 ? Z F ( z) ? F ( z) ? ? ? X i ? ? ? ? ? ————————————– (5)
Where, F(z) is the cumulative normal distribution of Z, ? (z) is the value of the derivative of the normal curve at a given point (i. e. , unit normal density), Z is the z-score for the area under normal curve, ? is a vector of Tobit maximum likelihood estimates and ? is the standard error of the error term. Before running the Tobit model all the hypothesized explanatory variables were checked for the existence of multi-collinearity problem. Two measures namely, variance inflation factor (VIF) and contingency coefficients were used to test multicollinearity problem for continuous and dummy variables, respectively.
According to Maddala (1992), VIF can be defined as: VIF (Xi ) = 1 ,Where Ri2 is the 2 1 ? Ri squared multiple correlation coefficient between Xi and the other explanatory variables. The larger the value of VIF is the more troublesome. As a rule of thumb, if the VIF of a variable exceeds 10 (this will happen if Ri2 exceeds 0. 95), that variable is said to be highly collinear (Gujarati, 1995). 33 Similarly, contingency coefficients were computed for dummy variables using the following formula. C= ?2 n+ ? 2 Where, C is contingency coefficient, ? 2 is chi-square value and n = total sample size.
For dummy variables if the value of contingency coefficient is greater than 0. 75, the variable is said to be collinear (Healy, 1984 as cited in Mesfin, 2005). 3. 3. 3. Definition of Variables and Working Hypothesis 3. 3. 3. 1. Dependent variable The dependent variable in this study is adoption index (AI) which indicates respondent farmers’ adoption and intensity of adoption of old coffee stumping technology. Adoption index is one of the technique that is used in the case of adoption study of multiple practices (package) and measures adoption and intensity of adoption of old coffee stumping technology.
Adoption index in this case is a continuous dependent variable. 3. 3. 3. 2. Independent or explanatory variables The explanatory variables of importance in this study are those variables, which are thought to have influence on adoption and intensity of adoption of old coffee stumping technology. These include household’s personal and demographic variables, farm characteristics, household economic variables, institutional variables and psychological factors. Household’s personal and demographic variables Age (AGE): This refers to the age of the household in years.
Old coffee stumping technology is a knowledge demanding activities; which requires knowledge of pruning and sucker control. Moreover, it entails risks, but older people are usually risk averters. Because of this, they tend to be reluctant in adoption of old coffee stumping technology. Therefore, age was 34 hypothesized to negatively influence adoption and intensity of adoption of old coffee stumping technology. Sex (SEXHH): It is a dummy variable which takes a value of 1 if the respondent is male and 0, otherwise.
In most cases male headed households have better access to information on improved technologies and are more likely to adopt new technologies than female. Sex is therefore expected to positively influence adoption and intensity of adoption of old coffee stumping technology. Family size (TFAMS): Total family size in this study refers to the number of members who are currently living within the family. Large family size is an indicator for availability of labor provided that the majority of the family members are within the age range of active labor force.
Availability of labor in the household is again one of the important resources in coffee production in general and stumping activities in particular. Based on this assumption, this variable was hypothesized to have positive relationship with adoption and intensity of adoption of old coffee stumping technology. Education level of the household (EDUHH): It represents the level of formal schooling completed by the household head at the time of the survey. Education enhances farmers’ ability to perceive, interpret and respond to the new events.
Therefore, in this study education was expected to positively influence adoption and intensity of adoption of old coffee stumping technology. Experience in coffee farming (EXPCOF): is to be measured in number of years since a respondent started coffee farming on his own. Experience of the farmer is likely to have a range of influences on adoption. Experience will improve the farmer’s skill on the production of coffee. Higher skill increases the opportunity cost of not growing the traditional enterprise. A more experienced grower may have a lower level of uncertainty about the innovation’s performance (Abadi et al, 1999; Chilot et al, 1996).
Farmers with higher experience appear to have often full information and better knowledge and were able to evaluate the advantage of the technology in question. Hence, experience of the head of the household in farming was hypothesized to affect adoption positively. 35 Farm characteristics Land holding (LANDHOLD): Refers to the amount of land the household owned measured in timad (4 timad is one ha). Land is perhaps the single most important resource as it is a base for any economic activity especially in agrarian society. Farm size influences households’ decision to adopt or reject new technologies.
It also influences scale of technology use. Hence, landholding was hypothesized to have positive relationship with adoption and intensity of adoption of old coffee stumping technology. Size of coffee land (SIZCLAND): It refers to the size of the coffee land measured in timad. In the study area, coffee production is mainly carried out. A farmer who has relatively larger plot of coffee land can stump his old coffee, because he can have remaining coffee plants which are not stumped and can get production until the stumped coffee reach to production stage.
Therefore, the size of the coffee land was expected to positively affect adoption and intensity of adoption of old coffee stumping technology. Having of old aged coffee (HAVOLCF): the farmers who have aged coffee trees and gained low production tend to rejuvenate the coffee by stumping but farmers who have relatively younger coffee and gained better production tend not to stump their coffee and therefore, it is hypothesized that a farmer having aged coffee have more probability of adopting old coffee stumping technologies.
It was measured in having of old coffee or not and the number of coffee trees they have. Using of improved new coffee cultivar (USINCC): In the area there was a project, Coffee Improvement Project (CIP) under the earlier Ministry of Coffee and Tea Development now under Ministry of Agriculture and Rural Development. It had worked a lot of extension activity especially in improving coffee production by producing improved CBD (coffee berry disease) resistant coffee seedlings and provided to the farmers and teaching modern planting and management system through extension system.
Through this project most of the farmers had planted this improved CBD resistant cultivar and the plantation of this coffee was raw planting and the number coffee trees per hectare is up to 3500, where as the local coffee population is not more than 1800, due to this reason the modern plantation yields per hectare 36 is high when it is compared to the local one for limited consecutive years, up to 15 years and above these years the production decline and needs re-cycling of the production system.
Therefore, those who had more of this coffee face a decline of production and inclined to stump their coffee when it became compacted and exhausts as compared to those having old local cultivar. Due to this reason this variable was hypothesized to have positive relationship to old coffee stumping technology and measured by using of this new variety or not and the number of coffee trees they have. Getting coffee plants from distribution of common holdings (GCPFCH): In the regime of “Derg” i. e. hen Ethiopia was under the military government, there was farmer producer cooperative and peasant association common holding coffee plantation in the area which were planted and managed by the community commonly. Because of the productivity of these common holdings decline time to time the state changed the command economy policy to mixed economy policy; also these common holdings were distributed to individual farmers by setting some criteria. As these coffee plants were planted in row and compacted, at certain years the production declined and need re-cycling comparing to the local coffee plantation.
Hence, because of this reason these coffee plantations need rejuvenation than the local one. Due to this fact those who had got this common holding plantation tends to stump their coffee than others. Therefore, this factor was hypothesized that to had positive relation to adoption of old coffee stumping technology and measured by getting or not and the number of coffee trees they got. Producing or preparing coffee seedlings (PROCOFSD): In the area also one of the extension innovation introduced by the CIP and extended by the agricultural and rural development was modern coffee seedling production.
This helps many farmers to involve in producing coffee seedlings for their own planting and commercial purpose and benefited a lot. In other way producing coffee seedlings used as a source of finance and getting intensive extension support, that is the farmers would have opportunity to hear about other technology and also could have chance to participate in field days and training. Therefore, this variable was hypothesized that to had positive relationship to old coffee stumping technology and has measured whether they produce or not and the number of produced seedlings. 7 Transmission conditions of coffee wilt disease (TRCCWD): The prevalence of coffee wilt disease is now becomes a series problem in the area and can transmit with stumping tools through wounds. Due to this reason protectionists advice the farmer to take care when stumping is done and hence the farmer may tend not to stump the coffee. Therefore, this variable was hypothesized that if there is high prevalence of a disease it can affect adoption of old coffee stumping technology negatively and measured by severity of the incidence.
Household economic variables Labor availability (LABAVAIL): Old coffee stumping technology is a labor-intensive activity. Moreover, large working labor force in a family means, the household may have more additional labor to all old coffee stumping technology activities. Therefore, it was hypothesized to have positive relationship with adoption and intensity of adoption of old coffee stumping technology. It is measured in terms of man equivalent and it refers to the active labor force the household owns. Livestock ownership (LIVOWN): In rural context, livestock holding is an important indicator of household’s wealth position.
Livestock serves as an important source of cash. In the study area, farmers in addition to other farming practices they rear livestock. Old coffee stumping activity has risks that for more than 2 years it couldn’t give yield and the farmer also may fear that after stumping new suckers may not grow. There fore livestock could be used as insurance for such kind of fearing. Based on this assumption this variable was hypothesized to have positive relation with adoption and intensity of adoption of old coffee stumping technology.
Participation in Off-farm activities (POFFARM): Participation in off-farm activities is believed to have a bearing on the income of households. Additional income earned through participation in these activities improves farmers’ financial capacity and increases the ability to adopt new technology. Hence, participation in off-farm activities was hypothesized to positively influence adoption and intensity of adoption of old coffee stumping technology. It is a dummy variable that takes a value of 1 if the farm household members participate in offfarm activities and 0 otherwise and the amount of money gained. 8 Total farm income (FARMINC): Households’ income position is one of the important factors determining adoption of improved technologies. The amount of household total income obtained from different types of farm income as it is cash crop area can be used for different requirements including household consumption. Therefore, a household with relatively higher farm income was expected to better adopt old coffee stumping technology and farm income expected to positively influence adoption and intensity of adoption of old coffee stumping technology. Institutional Factors Getting extension service