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LIST OF ACRONYMS AND ABBREVIATIONS
List of Tables
List of Appendices
List of Figures
1.1 Background of the study
1.2 Statement of the problem
1.3 The research questions
1.4 Objective of the study
1.5 Significance of the study
1.6 Scope and limitation of the Study
1.7. Organization of the paper
2. LITERATURE REVIEW
2.1. Definition of agricultural technology adoption
2.2. Theoretical Background of Agricultural technology adoption and its impacts.
2.2.1 Why agricultural technologies adoption is required?
2.2.2 Effects of Adoption of Modern Agricultural Technologies
2.2.3 Estimating the impact of the technology adoption by incomes of farm households
2.2.4 Adoption of Modern Agricultural Technology in Ethiopia
2.3 Empirical Studies on Determinants and impacts of Agricultural technology adoption
3.1 Description of the study area
3.1.1 Location and area
3.1.2 Climate and soils
3.1.3 Vegetation and wild life
3.1.4 Main crops grown
3.1.6 Financial institutions
3.2 Sampling procedure and sample size determination
3.2.1 Sampling procedure
3.2.2 Sample size determination
3.3 Method of Data Collection
3. 4. Econometric Model Specification and Estimation Procedure
3.4.1. Estimating the determinants of technology adoption
3.4.2 The impacts of modern agricultural technology adoption on welfare of households
4. RESULTS AND DISCUSSION
4.1 Descriptive Analysis
4.2 Results of Econometric Analysis
4.3. Estimating the impact of agricultural technology adoption decision
4.4 Sensitivity test for estimated average treatment effects (ATT)
5. SUMMARY, CONCLUSIONS AND RECOMMENDATIONS
Table 4.1 Table showing mean standard deviations and mean difference
Table 4.2 Table showing the mean difference of incomes, expenditures
Table 4.3 Table showing logistic regression results and their probabilities
Table 4.4 Table showing ATT estimation
Table 4.5 Table showing sensitivity test
Table7.1 Table showing format to record household members
Table7.2 Table showing technology adoption status
Table 7.3 Households off farm income recording format
Table 7.4 Yield recording format
Table 7.5 Land ownership
Table7.6 Expenditure recording format
Table 8.1 Table showing VIF
Table8.2 Table showing descriptive statistics
Table8.3 Table showing logistic regression
Table8.4 Table showing marginal effects
Table 8.5 Estimation of ATT on income with nearest neighbor matching method
Table 8.6 Estimation of ATT on expenditure with nearest neighbor matching
Table 8.7 Procedures of estimating ATT on Incomes and expenditures
Table 8.8 Tables showing steps of sensitivity test
Table 8.9 Sensitivity test
Table 8.10 Marginal effects
Table 9.1 Table showing the Production, productivity and amount farm
Table 9.2 Table showing trend 5 recent consective years of Gulliso woreda
Table 9.3 Sampling Kebeles
Appendix 1 Tables and figures of Questionnaires Format
Appendix 2 Tables and Figure of Data Analysis
Appendix 3 Table of secondary data
Fig 1 Gulliso Woreda Ma
Fig.2 Sample size calculation procedures
First, I would like to praise the Almighty God; and my Lord Jesus Christ Who provided me this opportunity and helped me in all my regards during my study at Wollega University
Next I would like to offer my deepest thanks to my major advisor Dr.Amsalu Bedemo for his vigorous and valuable advice and clear guidance just beginning from proposal development to the completion of the research work and his provision of reference materials and different econometric models, without which this thesis can be analyzed
I also grant my heartfelt thanks to my co-advisor, Mr. Gutu Gutema, for his genuine advise to me as well as his help with his valuable guidance and support throughout my research work. Both of my advisors had been working hard for the best possible result of this research thesis with full interest which resulted in the successful accomplishment of this material
I would also like to express my sincere appreciation to Gulliso Woreda Agricultural and Rural Development Office staff members and management as the whole for their sincere cooperation in arranging and helping in all necessary directions when I was collecting data which was the base for this thesis writing. Without their cooperation and tireless effort the quality data wouldn’t have been collected at the right time.
I also remain thankful to Wollega University department of Economics staff especially in their friendly follow-ups and advices beginning from writing the concept note to full thesis paper.
Finally I want to thank those who stood my side during my thesis writing such as Urgessa Tilahun.
illustration not visible in this excerpt
This study analyzed factors affecting modern agricultural technology adoption by farmers and the impact of technology adoption decision on the welfare of households in the study area. The data used for the study were obtained from 145 randomly selected sample households in the study area. Binary logit model was employed to analyze the determinants of farmers’ decisions to adopt modern technologies. Moreover, the average effect of adoption on household incomes and expenditure were estimated by using propensity score matching method. The result of the logistic regression showed that household heads’ education level, farm size, credit accessibility, perception of farmers about cost of the inputs and off-farm income positively and significantly affected the farm households’ adoption decision; while family size affected their decision negatively and significantly. The result of the propensity score matching estimation showed that the average income and consumption expenditure of adopters are greater than that of non-adopters. Based on these findings it is recommended that the zonal and the woreda leaders extension agents farm and education experts, policy makers and other development oriented organizations have to plan in such a way that the farm households in the study area will obtain sufficient education, credit accessibilities and also have to train farmers to make them understand the benefits obtained from adopting the new technologies. These bodies have also to arrange policy issues that improve farm labour participation of household members and also to arrange the ways in which farmers obtain means of income outside farming activities.
Key words: Agriculture, Farm household, Technology Adoption Logit Model, impact, propensity score match,
There has been much discussion on the need to increase productivity and sustainability in agriculture globally in the medium to long terms, but much less information is available on specific means to achieve this aim. Increasing agricultural productivity is critical to meet expected rising demand and, as such, it is instructive to examine recent performance in cases of modern agricultural technologies (FAO, 2012).
It is no longer possible to meet the needs of increasing numbers of world population and to achieve food security objectives by expanding areas under cultivation since the fertile land is not increasing over time. But this problem can only be solved more by increasing agricultural productivity of farm households. However, achieving agricultural productivity growth will not be possible without developing and disseminating yield-increasing technologies and application of these technologies by farm households. Agricultural research and technological improvements are therefore crucial to increase agricultural productivity and thereby reduce poverty and meet demands for food without irreversible degradation of the natural resource base. Agricultural research and technological improvements are also crucial in reducing poverty (Solomon (2010); Solomon et al, 2011).
Barriers to technology adoption, initial asset endowments, and constraints to market access may all inhibit the ability of the poorest to participate in the gains from agricultural productivity growth. This agricultural productivity growth can also be driven by improved farm technologies, including improved seeds, fertilizer, and water control (Johnston & Kilby, 1975).
Despite rapid yield growth in agricultural production all over the world, the realized yields are still well below their genetic potential. Deviations from potential yields appear to vary remarkably among countries and regions even after adjusting for different soil, moisture and temperature environments. Other conditioning factors, such as different farm sizes and management capacities, access to markets, and legislative/institutional factors, play heavily in determining yield performance (FAO, 2012).
The role of agriculture in economic development of Ethiopia has been well recognized for years. It accounts for roughly 43 % of GDP, and 90 % of exports and 85% to employment. Cereals dominate Ethiopian agriculture, accounting for about 70 per cent of agricultural GDP (MoARD, 2010). Agriculture is also the source of food and cash for those who are engaged in the sector and others. Most agricultural households earn the food they consume and the cash they need to cover other expenses only from farming activities so that improvement of agricultural productivity is very important to them (CSA, 2011).
An increase in agricultural productivity is a prevailing motive for farmers and a driving force in Ethiopia’s agricultural policy. Increasing productivity in smallholder agriculture is Government’s top priority, recognizing the importance of the smallholder sub-sector, the high prevalence of rural poverty and the large productivity gap (MoARD, 2010).
Agriculture in Ethiopia is subsistence-oriented, that is, households in the agricultural sector mostly produce on the basis of their demand on household level. In order to achieve the objectives of food security and nutrition for all and to reduce poverty through improving incomes of rural households, there is a need to progressively transform the agricultural sector away from subsistence oriented production towards an integrated economy. This transformation process can be fueled by agricultural productivity growth through the help of modern agricultural technologies (CSA, 2011).
In Gulliso woreda, most of the people are rural dwellers farm households. All of the problems which affect agricultural productivity of the country also equally affect the woreda. The farmers started the use of modern agricultural technology to overcome the problem of low production before ten years. All of the government efforts to using eradicate poverty and to ensure the food security by the people of the country are being practiced in Gulliso Woreda, in Wellega Zone. Government body administering the woreda is making its best performance to implement these policy issues. DA (Development Agents) s and other necessary farm experts are utilizing their best expertise knowledge to help the poor farm households to gain better output from their farm activities (GWARDO, 2012).
Considerable resources are being utilized by the Ethiopian government to realize agricultural productivity and alter the state of agriculture in the country. Human and material resources are rallied towards this end. Development agents, extension packages, and agricultural inputs are some of the resources that are made available to farmers to change their style of farming and augment productivity (CSA, 2011). Despite of all these efforts of the government, in Ethiopia, the crop yield of small farm households is very low. On CSA (2012) it was reported that the productivity of teff, barley, wheat, maize and sorghum are 12.81 qt/ha, 16.72 qt/ha, 20.29 qt/ha, 29.54 qt/ha and 20.54 qt/ha respectively. This low productivity is because of low utilization of improved technologies and traditionally accustomed ways of production.
As CSA (2011) report indicated, from total area cultivated 13,359,438Ha in Ethiopia during 2010/2011croping season, the total area covered by improved seeds in 2010/11 (2003 E.C) is745, 924Ha which is only 5.5%. This is very low when compared to some African countries like that of (Tanzania which is 27 %) ( Liberio, 2012).
Different development projects are being designed and implemented by the scarce resource of the country to improve the farm production and productivity at the woreda at desirable level. Improved seeds and chemical fertilizers are being distributed among the adopters of the modern technology. However, the households receive very low amount from their farm lands because of the dominant traditional way of farming.
Information received from the Gulliso Woreda Agricultural and Rural Development Office (GWARDO) in 2012 also shows that , the total number of farm households of the woreda is 11,935 and of this only 1,074(=0.09%)(at least applied fertilizer) are adopted the modern agricultural technologies. This shows that the level of technology adoption is very low leading the population to gain low production from their farming activities since the percentage of adopters is less than ten.
As data obtained from Gulliso woreda Agricultural and Rural Development Office (table9.1 in the appendix) indicated, in 2004/2005E.C cropping year 11,171 hectare of land is cultivated of which only 931.25Hectares which is 9.1% is cultivated by applying modern technologies. As an example, from the same table 9.1, it is clearly shown that the average productivity of maize for farmers applying both improved seed and chemical fertilizer is 87 Quintal/Hectare while those with only local seed without any improved seed and fertilizer obtained 18 quintal per hectare of maize. Though the difference 69 quintal per hectare is because the application of modern technologies (improved seed and fertilizer), 91.7% of cultivated land is not applied with those technologies. More than a decade is passed since the introduction of these technologies to the woreda but still very small proportion of farm land is applied with this precious technologies. What is the reason? Is it because the difference in output wholly transformed to cost of production without generating any positive outcome?
To answer the above key questions, studies are required to be conducted in the area but to my knowledge, no such adoption studies were conducted in West Wollega zone in general and Gulliso district in particular to identify the determinants of agricultural technology adoption and the impact of the adoption on the livelihood of adopters and to address the problem of very low adoption of the modern agricultural technologies for productivity and there by welfare improvements. To this end, this study was conducted on the study area to fill the gap by identifying those constraints of adoption decision and the impacts of the adoption on the lively hood of the adaptors.
The following main questions are generated from the above on the study area;
What factors determined the agricultural technology adoption decision of farm households?
What are the effects of the technology adoption decision on welfare of adopters as compared to non-adopters?
The main objective of this study is to analyze the determinants and welfare impacts of modern agricultural technology adoption by smallholder farmers in the study area. The specific objectives are to:
1. Identify the determinants of agricultural technology adoption by farm households.
2. Examine the impacts of modern agricultural technology decision on welfare of the adopter households.
This study is required to
1. Come up with findings that fill the gap of absence of adoption studies on the zone and the woreda to overcome the shortcomings of low agricultural technology adoption in the study area.
2. To resolve the problem of very low productivity in the study area by giving clear information for the woreda farmers for improvement of agricultural technology adoption by the farm households.
3. To give necessary guide for government bodies administering.
4. The findings of the study adds some inputs to the policy makers by filling the research gap of the country as a whole in case of agricultural technology adoption.
Studies are in short of the zone and the woreda on adoption of modern agricultural technologies. So this study was conducted for the purpose of identifying the results which will be used as source of reference for literature reviews of the next similar surveys to be conducted on adoption studies. It also helps as an input to policy makers to design effective policy to that makes the society beneficiaries from the well improved productivity of agricultural sector. NGOs and similar donor organization, who are interested to work on the growth of agricultural productivity, are also the beneficiaries of the result obtained by this in their concern of the poor farm households and agricultural productivity growth.
It is difficult to identify universally defined similar factors which determine households’ technology adoption decision and the impact of the decision on the adopters (Mulugeta, 2009).
This study was limited to
1. Identifying the constraints to adoption of improved modern agricultural technology focusing only on improved seeds and fertilizers use in the main crops being grown in the woreda: maize, sorghum and teff and millet.
2. Moreover, the study also assessed the impact of technology adoption only on the livelihood of adopters in the study area not considering the external impacts.
3. The study is also limited only to Gulliso woreda, expecting that the result obtained can be generalized to other woredas of the zone since they are socioeconomically, culturally, and climatic conditions are similar.
This paper is organized in to five chapters. The first chapter consists of Introduction part; the second chapter contains review of literatures in which theoretical backgrounds and empirical studies are discussed; chapter three consists of the research methodology in which every step of data analysis methods and estimation procedures were discussed; chapter four consist of the results and discussions in which the main findings of the study are analysed; chapter five is built of the summary, conclusions and recommendations and the last main topic being the reference part ,lists of appendices finalize the paper.
According to Doss (2007), defining technology adoption may be complicated by the complexity of defining the technology being adopted. For the adoption of improved seeds, the CIMMYT studies used several definitions ranging from farmers using a variety that was originally an “improved” hybrid but has been repeatedly, to farmers following extension service recommendations of using only new certified seed (Doss, 2007).
As Doss(2007) points out in his analysis, in defining agricultural technology adoption by the farmers, the first thing to consider is whether adoption is a discrete state with binary response variables or not. That means it depends on the fact that the farmer is an adopter of the technologies or non-adopter taking values o and 1 or the response is continuous variable. The appropriateness of each approach may depend on the particular context. So many CIMMYT studies were used a simple dichotomous variable approach (yes or no) which are binary measures of response variables in the farmers’ decisions of new technology adoption.
A farmer was defined as being an adopter if he or she was found to be growing any improved materials i.e. improved seed using chemical fertilizers. Thus, a farmer may be classified as an adopter and still grow some local materials. This approach is most appropriate when farmers typically grow either local varieties or improved varieties. If the interesting aspects of adoption are situations where farmers are increasingly planting more land to improved varieties while continuing to grow some local varieties, then a continuous measure of adoption is more appropriate (Doss, 2007). That means if the target of the adoption analysis focuses on the extent of the adoption of the new agricultural technologies, a continuous response measure is applied.
Agricultural technologies include all kinds of improved techniques and practices which affect the growth of agricultural output. Some of them are high-yielding varieties of seeds, chemical fertilizers, pesticides, weedicides, and use of machinery. By virtue of improved input/output relationships, new technology tends to raise output and reduces average cost of production, which in turn results in substantial gains in farm income. Adopters of improved technologies increase their productions, leading to productivity increase (Jain et al., 2009).
In a wider sense adoption studies are intended to analyze the process of farmer decision in adopting new agricultural technologies on their farm lands or not adopting. Such studies usually involve identification of factors which constrain or enhance adoption, spatial and temporal patterns of adoption, when various types of farmers adopt, the level at which a technology is applied by farmers (stepwise adoption), and which farmers don't adopt and why. As pointed out in the literatures, different factors determine the adoption of different agricultural innovations and technologies.
To reduce poverty and improve household food security throughout the developing world, particularly in areas at high risk of climatic shocks and with a high percentage of the population dependent on agriculture as in Ethiopia, Improving farm level resilience to agricultural production shocks is essential. The study by Dercon et al., (2005) & Doss et al., (2008) show that, one of the primary causes of household food insecurity in Ethiopia is the risk of agricultural production failure due to drought, resulting in reduced harvest and farm incomes. But it is well known that low productivity leads to low harvest.
So many studies conducted on adoption decision in different countries. But in Ethiopia improvement is required for this issue (CSA, 2011).
According to Norton (2004), agriculture has become a field which requires application of high-modern agricultural technology, with rapid advances in crop genetics such as variety of seeds and, chemical fertilizers, pest and livestock management, and machinery. The issue of access to these technologies and their application and diffusion are particularly important because, as in other high-technology fields, agricultural technology is now becoming very important internationally. Leading countries continually borrow and build on research from other countries. Many developing countries lag behind, in part because of self-imposed barriers to the introduction of private produced agricultural technologies.
Increasing agricultural productivity is all the more urgent LDC issue because the majority of the developing world’s poor are found in rural areas, and the sector’s average productivity is actually declining in many low-income countries (Norton, 2004). But agricultural productivity varies due to large differences in the level of adoption of selected agricultural technologies and the underlying determinants of adoption of these technologies. According to the study by Kimaro et al. (2010), in the developing countries, improvement of agricultural production, profitability and sustainability depends on the farmers to adopt changes and their innovative use of technologies, organizational approaches, management systems, institutions and availability of resources. Agricultural extension through advisory services and programs forges to strengthen the people’s capacity to innovate by providing access to knowledge and information. An increase in productivity and sustainability of agricultural production is the effect of accepting the new agricultural technologies. In Kenya, Salasya et al. (2007) showed that non-adoption of suitable maize varieties was identified as the second most important constraint responsible for low maize yields.
Throughout the early 1980s and 1990s, the Ethiopian government main initiative was to create an agricultural extension program that spotlighted increased yield technology for crop production. The extent to which household farmers can increase yields in a confined area is limited. In the past, Ethiopian farmers would fallow their land for long periods of time, sometimes up to 15 years. However, increases in population have significantly constrained the amount of time a field can be left uncultivated. This, coupled with rampant deforestation, has led to a decline in soil fertility. It is estimated that half of all of the useable land in Ethiopia is subject to soil degradation and erosion (Demeke, 1997).
As Bittinger (2010) showed in his analysis of “crop diversification and technology adoption: the role of market isolation in Ethiopia”, a small family farms contributed substantially to the economic growth of developing countries and so it is important to study their decision-making processes. Many projects are funded every year by international aid organizations to assess the economic standing of the least developed countries (LDCs). The situation is frequently the same around the world: populations in many LDCs are growing at such a rate that people cannot afford to satisfy their basic needs. Food is in limited supply, transportation infrastructure is either poorly maintained or does not exist, disease is rampant, and governments are often unreliable and self-serving. As a result, smallholders have to cope with many challenges in their day-to-day activities (Bittinger, 2010).This needs technology adoption of the farm households.
According to the analysis conducted by Mulugeta (2009), low production and productivity are mainly associated with poor adoption of improved technologies. Adoption of improved technologies is one of the most promising ways to increase productivity and production in Ethiopia. However, the adoption and dissemination of these technologies is constrained by various factors. A number of studies were conducted by different researchers to identify the factors that determine farm households’ decision to adopt new agricultural technologies and found different influential factors. There are many reasons for this disappointing outcome. For example, Croppenstedt (1997), estimated mean efficiencies at 40% for fertilizer compared with 76% for land and 55% and labor. According to his analysis, the time of application may also contribute to low fertilizer use efficiency.
The study by Akudugu et al. (2012) in Ghana indicated that low productivity of farmers in the country was due to low adoption of modern Agricultural technologies. There is extensive literature that examined how innovation characteristics, adoption constraints, and adopter characteristics influence adoption of modern agricultural technologies.
The socio-economic factors that influenced adoption of WH 502 were education of the farmer, distance to market, number of cattle owned, number of maize seasons in a year and locality characteristics in Kenya (Salasya et al., 2007). As Saleem et al (2011) pointed out the effect of occupation, farm type, farm status, farming experience, numbers of times credit attained availability of loan and providing sources and extension workers guidance are significant determinants of modern agricultural technology adoption by farm households. Some studies associate the low adoption of improved seed with the quality and price of seed.
Agricultural products are very crucial products which generate significant amount of foreign exchange and contributes to GDP of the country enhancing the development of the country. It is very important employer of manpower in the country. As such it is very important to produce it in a sufficient amount at a very desirable productivity level. Since the size of the farm land used for agricultural production does not increase in years but number of population in need of this activity is increasing from year to year, the households may lack the land to cultivate in future. So, to have new farmlands for agricultural production compared to increasing number of population is not promising. Based on this fact, it is not to be postponed to find factors that determine significant agricultural production at required efficiency level and to implement the information thus obtained to get high yield per hectare of existing farm lands to overcome the upcoming future limits of resource and the wastage of today’s resource.
Different methods have been developed and used in the literature to assess the impact of programs, policies and adoption of improved agricultural technologies on poverty reduction or welfare however, the results have been mixed. For instance Mendola (2007) adopted the Propensity Score Matching (PSM) methods to assess the impact of agricultural technology adoption on poverty in Bangladesh and observes that the adoption of high yielding improved varieties has a positive effect on household wellbeing in Bangladesh.
One method of analyzing the impact of modern agricultural technologies adoption is by considering the income differentials between adopters and non-adopters. Estimation of the impact of technology adoption on household welfare outcome variables (i.e., total income and consumption per ca pita, based on non experimental observations), is not trivial. What we cannot observe is the outcome variables for adopters, in case they did not adopt (their incomes had they not adopted the modern technology). That is, we do not observe the outcome variables of households that adopt had they not had adopted it (or the converse). In experimental studies, this problem is addressed by randomly assigning adoption to treatment and control status, which assures that the outcome variables observed on the control households without adoption are statistically representative of what would have occurred without adoption. However, adoption is not randomly distributed to the two groups of the households (as adopters and no adopters), but rather the household itself deciding to adopt given the information it has. Therefore, adopters and non adopters may be systematically not similar as desired. Following Angrist (2001), different econometric techniques such as propensity score match, were applied to correct for potential bias in estimating the impact of technology adoption on household welfare outcomes. Study by Mulubrhan et al. (2012) shows that the causal impact estimation from both the propensity score matching and switching regression suggests that maize/pigeon pea adoption has a positive and significant impact on income and consumption expenditure among sample households .
One main fact that both agricultural researchers and policy makers agree on is that, to realize a Green Revolution in Africa, an increase in the use of fertilizers and improved seed technologies is inevitable (Morris et al., 2007).Ethiopia being one of the African countries was developed strategic objectives to accomplish the objective of the Green Revolution and to increase agricultural productivity and production as a prerequisite for food security and agricultural-led industrialization. The following is one of the main ideas of these objectives:
Strategic objective1: “SO1 is expected to achieve a sustainable increase in agricultural productivity and production over the ten-year life of the PIF. This reflects the Government’s first priority for the agricultural sector is to increase agricultural productivity and production as a prerequisite for food security and agricultural-led industrialization. This productivity gains are expected to come from closing the large gap between leading farmers and the majority, whose productivity performance (as measured by yields per hectare, livestock unit etc) is far below potential. Proven and appropriate agricultural technologies will be up-scaled through a revitalized agricultural research and extension system, combined with improved supply channels for farm inputs, with a focus on high potential areas where the investment is likely to generate the best returns. The focus will be on simple and affordable agronomic packages including the use of improved seeds, fertilizers and fertility management, weed and pest control, and improved harvest and post-harvest management” (MoARD, 2010,2)
The above strategic objective of Ethiopia is clearly specified to achieve a sustainable in agricultural production and productivity. The means of achieving such objective is clearly by focusing on simple and affordable agronomic packages including the use of improved seeds, fertilizers and fertility management, weed and pest control, and improved harvest and post-harvest management. That means developing modern agricultural technologies and distributing among the farmers of the country which is the focus of the country to attain sustainable growth in agricultural production. So the need for technology adoption in the country is to increase agricultural productivity for the attainment of the issue of food securities and sustainable economic growth.
The low yields prevalent in Ethiopian agriculture are generally attributed to low usage and efficiency of modern inputs. The CSA national survey data show that, while about 40% of cereal production benefits from the use of fertilizer, only about 10% also gains from other inputs, such as improved seed or irrigation. The average yield gap in cereal production due purely to lack of fertilizer is actually quite small. Total cereal yields where fertilizer is used are about 1.4 metric tons per hectare, 20% higher than yields without the use of any modern inputs (CSA, 2011)
Many studies report similar findings regarding fertilizer use in Ethiopia. Croppenstedt and Demeke (1997) reported fertilizer elasticity’s in the range of 0.03 to 0.09 in the production function. This study also found that irrigation and stone terrace technology are associated with increased fertilizer and other modern input use, and their joint effect on land productivity as factors significant in Tigray. This outcome is consistent with the findings of Howard et al. (2003). Based on a maize plot survey in the Oromia region, average maize yields were 70% higher when improved seed and fertilizer were used compared with traditional seed and no fertilizer, and there is still a 40% potential for further improvement based on results from research stations. The econometric analysis conducted by the researchers also supported their findings. Though the fact is that of its importance, and profitability for farm households to use fertilizers by to be more productive in their farming activities, they are not practicing as required.
As the report of CSA (2011) indicates, the proportion of the households using farming technology (improved seedling) is only 5.6% at the whole country level and 5.8 and 1.9 at Oromia and West Wollega level respectively. Fertilizer usage is 50.4%,51%,15% in Ethiopia, Oromia and west Wollega respectively This shows that very insignificant number of farm households is using the modern method of its agricultural production. West Wollega is at very low position in adopting the modern technology (only 1.9% of total cultivated land of improved seed and 15% fertilizer use) when compared to the whole country, although the country as the whole itself is not at a significant position. This may indicate that there are some problems which hinder the zone to adoption the modern technology. However, the adoption of the technology is very important for high yield from the farm lands. Hence very significant number of farming households, more than 90%, is losing the benefit from the modern technology.
Numerous studies have also related household characteristics to adoption behavior with different findings. The following are some of the results of previous studies. In order to increase adoption of suitable varieties it is important to know the factors that influence the choice of variety and adoption.
A study from Kenya by Salasya et al. (2007) showed that the main attributes of WH(wheat hybrid) 502 that influenced its adoption were high yield, early maturity and non-lodging, whereas the important socio-economic factors were farm size, cattle ownership, education level of the farmer and locality specific characteristics.
Education: In almost all of other studies on technology of agriculture, education was taken as an important explanatory factor that positively affected the decision of households to adopt new agricultural technologies, see for example (Abay and Assefa, 2004; Salasya et al. 2007). Alene & Manyong (2007) studied the effect of education on agricultural productivity under traditional and improved technology in northern Nigeria using an endogenous switching regression analysis. They found that education had a positive and significant influence on agricultural technology adoption. The analysis by Beshir et al (2012) using double-hurdle method, showed the positive and significant result on the role of education on chemical fertilizer technology adoption in Northern Ethiopia.
Age: It is found significant and positive relationship in some studies especially when it is related to years of experience, as for example, (Adesina & Baidu, 1995) and significant and negatively related to adoption decision in other studies, While in some studies it is found that No significant difference is observable in the age and gender of the household head although the groups vary in terms of their marital status
Family size: Results of the study by Liberio(2012), using Descriptive and regression analyses and Statistical Package for Social Sciences, revealed that respondent's of education level, family size, farming experience, availability of sunflower market, and frequency of contacting extension officer significantly influenced the adoption of sunflower farming innovations in Tanzania. As Bamire et al., (2002) indicated, Family size has been recognized to play a vital role in the adoption of any particular technology or farm practice. On the one hand, family provides the human labor and management inputs. This can affect the level of use of technologies in terms of quality of management decision and the availability of labor required by the technology in question. Results of the study by Idrisa et al. (2012) also revealed a positive and significant relationship between household size and the extent of adoption of improved soybean seed as production technology in the study area. The value was significant at 5% level of probability.
Farm size: Idrisa et al (2012) analysed the determinants of the likelihood of adoption and extents of adoption in Nigeria using tobit and logit analysis and found that farm size and distance of respondents affected the adoption and extent of soya bean seed. Farm size was found as one the most important factors that significantly affected adoption decision (Akudugu et al., 2012; Salasya et al., 2007; Saleem et al., 2011). This means that farmers who have relatively large farm size will be more initiated to involve in adopting the new agricultural production technologies, and the reverse is true for small size farm land. Subsistence oriented small farmers are highly risk averse to apply innovation due to limited holding and uncertain outcome of technology (Mendola, 2007). Sharma et al (2011) conducted in UK Cereal Farmers indicated that total area farmed is positively related to the number of technologies adopted, whereas the number of years of experience of the farmer is negatively related
Asset: It is clear that adoption of new technologies with complementary inputs requires substantial amount of capital for purchase of modern agricultural inputs such as fertilizers an improved seeds, especially when farm land to be covered is significantly large. For this reason farmers who do not have their own asset on a required amount face a challenge to accept the new technology even if they possess enough knowledge about the technology.
Credit availability: Farmers who have access to formal credit are more probable to adopt improved technology than those who have no access to formal credit (Million & Getahun, 2001; Beshir et al, 2012). Saleem et al. (2011) also found the role of credit on adoption of modern agricultural technologies to be positive and significant. For this reason, the availability of farm credit especially from formal sources is vital components of the modernization of agriculture and to increase productivity. Those farmers who have access to agricultural credit are believed to adopt technology more than those who have no access to credit.
Off-farm: Off-farm income represents the amount of income the farmers earn in the year on other than on-farm activity. It is the amount of income (in Birr) generated from activities other than crop and livestock production. These include petty trading, charcoal selling, firewood selling and others. The study by Beshir et al (2012) showed the positive and significant effect of off-farm income chemical fertilizer technology adoption in North eastern highlands of Ethiopia. The households engaged in off-farm activities are better endowed with additional income to purchase initial seeds or other essential agricultural inputs for seed or seedling production. Therefore, it is expected that the availability of off-farm income is positively related with participation in seed production.
Farm experience: Experience of the farmers is likely to have a range of influences on adoption because with increase in working year the farmer gets more understanding about the system of farming. Experience expected to improve farmers’ involvement in seed production. A more experienced grower may have a lower level of uncertainty about the technology’s performance (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. Hence it was hypothesized to affect adoption positively. This being the case Sharma (2011) found negative relationship between farm size and technology adoption decision.
Climatic risk; Unexpected climatic factors may make farmers to hesitate to adopt the new technology. Results by Cavatassi et al. (2011) on Adoption rates of improved or modern varieties (MV) showed that risk-factors coupled with access to markets and social capital drive farmers’ decisions to adopt MVs or not to adopt. On the one hand, it appears that farmers use MVs to mitigate moderate risks. On the other hand, farmers who have been most vulnerable to extreme weather events are less likely to use MVs. This indicates that climatic risks negatively influence farmers’ adoption of modern technologies (Cavatassi et al., 2011).
Extension Services: More frequent DA visits, using different extension teaching methods like attending demonstrations and field day can help the farmers to adopt a new technology. If the farmers get better extension services, they are expected to adopt seed production technologies than others (Kaliba et al., 2000; Maiangwa et al., 2007). Extension service was shown to be an important source of knowledge for farmers that significantly influence the adoption of improved maize seeds and fertilizer. The positive and significant effect of extension contact found in many studies on adoption of inorganic fertilizer is indicative that extension systems must be strengthened to increase farmer knowledge and understanding of mineral fertilizer sources and other related technological options in a timely and accurate manner using the most appropriate communication and training methods and eliciting information about farmers’ concerns and problems with these technologies and conveying them to research and technology centers(Maiangwa et al,2007).
Availability of training: Farmers may obtain information from different source and may learn also from DA through extension program. However unless they can obtain required skill through training they may face some difficulty to understand and apply improved agricultural technology. So those farmers who got training on improved agricultural technology are more willing than those who didn’t get training. It is dummy variable measured as 1 if farmers got specific training on the technology (in this case improved seed of the main crops included in the study and fertilizer usage) and 0 otherwise.
Cost of input: Profitability of modern technology depends on the costs incurred on it. The more it increases; farmers hesitate to utilize the technology significantly.
Climatic risk; Crop failures as a result of unexpected climatic factors may make farmers hesitate to adopt the technology.1 if the farmer responds yes for fear of risk and zero otherwise.
Distance from the market center:- Idrisa et al ( 2012), in the study conducted in Nigeria found that farm size was determinant factor at 1% significant level in the soya bean adoption.It is a continuous variable measured in kilometer. It refers to the distance from farmer’s farmland to market centre. As farmers’ farm lands get closer to the main road or market centre, they can have access to transportation facilities and relatively better support from concerned bodies to their seed multiplication which might increase the use of technology. Therefore, in this study, it is hypothesized that this variable is negatively related to participation in technology adoption of farm households. As a farm household is nearer to market places, it is expected to be more likely participating in intensive farming activities that demands adoption of new agricultural technologies because it may decrease cost of transport both for input and outputs.
Gender: Access to new technology is crucial in maintaining and improving agricultural productivity. Gender gaps exist for a wide range of agricultural technologies, including machines and tools, improved plant varieties and animal breeds, fertilizers, pest control measures and management techniques. A number of constraints, including the gender gaps described above, lead to gender inequalities in access to and adoption of new technologies, as well as in the use of purchased inputs and existing technologies. The use of purchased inputs depends on the availability of complementary assets such as land, credit, education and labour, all of which tend to be more constrained for female-headed households than for male-headed households (FAO, 2011).
Incomes and expenditures: Solomon (2010) estimated the casual impact of technology adoption by utilizing endogenous switching regression and propensity score matching methods to assess results robustness, in the research conducted on Ethiopia and Tanzania,, and identified the welfare effect of technology adoption by controlling for the role of selection problem on production and adoption decisions. His analysis revealed that adoption of improved agricultural technologies has a significant positive impact on crop income although the impact on consumption expenditure is mixed. This result confirms the potential direct role of technology adoption on improving rural household welfare, as higher incomes from improved technology translate into lower income poverty.
The results of the analysis by Shiferaw et al (2008) conducted in Tanzania using an augmented double hurdle model indicated that the new pigeon pea varieties improved household incomes by up to 80% as disease-induced yield losses decrease from about 50% for local varieties to just about 5% for the new varieties. This also indicated the positive effect of agricultural technology adoption on the adopters.
Under this topic, location and area, climate and soils, vegetation and wild life, main crops grown, education and other issues of the study woreda are explained. Please see at page 21 in the appendix for the map of the woreda. The Woreda is also cash crops especially coffee dominated area by farmers so that the source of income of the farmers is not only from farm but also from non farm incomes such as coffee processing and trading. The woreda’s center town, Gulliso town is found on the main highway to Dembi Dolo. When seen from trade center, the surrounding woreda are using the Gulliso market as their center of interest. For example, Genji wereda and Lalo Asabi weredas, Figa Kobera farmers who are from Boji Dirmeji wereda are using Gulliso market similar to their own market. The farm households also grow very significantly, the main crops such as maize, sorghum and teff which are the focus of this study. The above mentioned and other facts make reasonable to choose the wereda as the representative of the other weredas of the zone in this study (GWARDO, 2012).
According to the report of GWARDO, Gulliso is one of the 21 woredas of West Wollega situated at the center of the zone. Currently, the district has 28 administrative Kebeles of which 26 are peasant associations and the reaming 2 are urban centers. Gulisso town is the center of the district located in the central eastern part of west Wollega Zone some 60 Km away from Zonal center (Gimbi town) and 500km to the West of Addis Ababa just on the main road to Dembi Dolo. The district is located between 9021- 90211 North latitude and 35061-350331 longitude. It is bounded by Boji Chokorsa in the North east, Gawo Dale district in the West and Lalo Asabi district in East. Generally the district has a total land area of 63,190 hectare of which 14378 hectare is covered by crops this agricultural season (GWRADO, 2012). The 26 peasant associations contain 11,935 farm households of which only 1,074(0.09%) farm households adopted the modern agricultural technology.
Abbildung in dieser Leseprobe nicht enthalten
As the information from the woreda Agricultural and Rural Development Office indicates, the major rainy seasons in the district include spring (May), summer (June-August) and autumn (September-October). From the type of natural vegetation and crops widely grown point of view, the district has 2 known climatic conditions, Woina Dega (61%), and Kolla (39%) agro climatic conditions. Average annual temperature of the woreda is about 25.6 0C while average annual rain fall of the woreda was 1500mm. There is no reliable data regarding soil types found in the woreda. However, rough information obtained from the office on the issue indicates that there are black soil, red soil and, gory brownish soil type found in the woreda. So the type of soil mentioned above is suitable for all kinds of agricultural production.
The information from the woreda Agricultural and Rural Development Office also indicated, about 3526 hectares, of the total area of the woreda is covered by natural vegetation, out of which wood land cover about 13000 hectares, while shrubs and bushes cover about 654 hectares. More over there is no man made forest protected by community. The major types of trees available in the district include Mila Azendrata, Jacaranda, Corda Africa, Acacia Species, and Gravila Robusta. As to wild life, there are different species of wild animals in the district. Some of the major types of wild animals in the district include pig, hyena, and tiger, Lions, apes, monkeys, fox, buffalo and so on. There are no reserved areas for wild life conservation in the district.
As the information from the GWARDO shows, the major crops produced in the district include maize sorghum, teff, barely, wheat and millet. Stock borer aphids .termite, cut warm, leaf warm, apes, monkeys, Buffalos are some of major crop pests present in the district. Similarly leaf blight, coffee Barry disease rust and smut are some of major crop diseases common in the district. According to information gained on irrigation about 420 hectares of lands were under traditional irrigation in 2005E.C in which the respective farmers engaged were 8731. Major crops grown on irrigated land were maize tomato potato, white onion sugar cane, coffee and the like.
There were 16 first cycle (1-4) primary school and 27 second cycle (5-8) primary schools and 2 senior secondary (9-10) school in the district. (1-4) primary schools and 19 second cycle (5-8) primary schools, 2 senior secondary (9-10) schools, and 1 preparatory school in the district and one vocational school.
As the information received from the Gulliso Woreda Finances and Economic Development Office indicated, there are five financial institutions in the woreda. One is the Commercial bank of Ethiopia Gulliso Branch which has long history in the woreda supporting both farmers and other traders by giving investment loans and serving saving services. Even though the established long ago its credit service in the field of agriculture is still today very limited and insignificant. The other institutions whose service directly focus on credit service for farmers are, CBO(Cooperative Bank of Oromia) which is very recent established one, Oromia Saving and Credit share company Gulliso branch, Busa Gonfa are the only institutions who give financial services. As some experts in the finance and economic development office argue, the rules and regulations through which these institutions give their service and their capital need improvement in order to serve farmers properly. That means the purpose of the credit service has to be more dominantly agricultural productivity growth than profit purpose which is different from the purpose of other banks.
The number of population in the woreda is 56712 male and 49637 female, which makes a total of 106349(GWARDO, 2012).
The data set used in this study consists of household sample survey data collected in rural area of Gulliso Woreda in West Wollega zone.
Gulliso woreda is selected purposively by the researcher because the researcher believed to obtain quality adoption data. This is because the information from the GWARDO collected in year 2012 by the researcher, shows that the farm households of the woreda started using the modern technology very early and there is big difference between the life of adopters and non-adopters observed even though no adoption analysis is performed on the woreda. The researcher is also familiar with the woreda as he was born there and was the education worker there nearly 10 years there.
The 145 households were selected from the 6 Kebeles of the 26 Kebeles of farm households by random sampling procedure. The six Kebeles were selected for sampling unit in this study as shown in table 9.3 in the appendix, nearly equal number of households were selected from each kebele without considering the ratio of the number of total farm household of each Kebele because the sampling is considered on the woreda basis. The reason for selecting the six kebeles is that it is very difficult to consider all kebeles the researcher faced time and finance limitations. Selection of the six kebeles is possible because the total distributions of the farm households of the woreda are socioeconomically, culturally and institutionally similar. The administration, technology diffusion procedures and plans of development by the woreda leaders are almost the same for all the 26 kebeles and so any household from any Kebele of the woreda can be representative of the woreda. The woreda farm households were categorized in to two categories:
A sample of adopters and non-adopters, Nearly 50% of the sample size (72), were selected randomly from the adopters of the technology and 50% of the sample size (73), were selected randomly from the non-adopters of the technology. This is done because as number of adopters, 1074 compared to non adopters, 10861 is very small and insignificant number of adopters may be included if the selection takes place randomly from the total farm households. So the procedure of categorizing is important since significant sample of adopters and non-adopters are required to analyze adoption decision and effect of adoption on the adopters.
As figure 4.1 indicates the Kothari (2004) formula which was used to calculate sample size. Hence depending on the information from Gulliso woreda (District) the sample size is found to be 145 households.
The sample size is calculated as the following The total number of farm households is 11,935 The formula for sample size determination for finite population is given by Kothari (2004) in such treatment and non-treatment analysis.
Given the precision, confidence level, population proportions p and q where q=1-p,
Fig. 4.1 Sample size determination procedure and formula
Abbildung in dieser Leseprobe nicht enthalten
Where, n stands for estimated sample size, e is the allowable error; N number of population under the study; z is confidence level.
Accordingly, for this study, N=11,935 the total number of population under the study in the district. e=4.5%=0.045 the maximum allowable error
p=0.09 the proportion of adopters of the agricultural technology in the district, q=1-0.09=0.91 the proportion of non adopters
Taking confidence level of 95.5% or probability of 0.955),
Then the sample size of farm households is found to be 145 applying the above formula.
Questionnaires depending on the variables of the analysis were prepared according to the number of sample farm households to be surveyed. Then at the study area, six data collectors were chosen depending on their expertise knowledge from extension workers. They are all models of the woreda because of their high performance in the woreda. The data collectors were trained according to the questionnaires for the successes of the objective of the study for two days. The trained Data collectors went to each sample household farmland (home) and collected the necessary data by interviewing the farm households by translating the questionnaires prepared in English language to the respondents’ native language. They were collected the data for 8 days. The researcher supervised the data collectors during the collecting activities guiding in some cases for best outcomes the researcher required. After the necessary data were collected, the researcher collected the response papers by careful checking up of the responses recorded specially missing data, by the data collectors.
The dependent variable to identify the determinants of adoption of the modern agricultural technology in this study is the response of the farm household whether to adopt new agricultural production technology or not if yes (for adoption) =1 and if not (not adopting) =0, thus binary response variable.
The logit, probit and Tobit models are used to identify such modern agricultural Technology adoption decision of the households. Both logit and probit models are equally applied for binary response dependent variables and they also provide almost equal results. But the logit model requires far fewer assumptions than the probit model mentioned above (Hosmer & Lemeshow, 1989). The logistic distribution (logit) is also more preferable than the others in the analysis of dichotomous outcome variable like this adoption decision, in that it is extremely flexible and easily used model from mathematical point of view and results in a meaningful interpretation, which also resolves the problem of heteroscedasticity. Logit model is chosen over probit model in this paper primarily because of its mathematical convenience and simplicity and its resolution of problem of heteroscedasticity (Greene, 2008).
The Model is formulated as follows.
Abbildung in dieser Leseprobe nicht enthalten
Where Abbildung in dieser Leseprobe nicht enthalten is a constant and Zi is equal to one (1) when a choice is made to adopt and zero (0) otherwise; this means: The equation represents a binary choice model involving the estimation of the probability of adoption of a given technology (Z) as a function of independent variables (X). Mathematically, this is represented as:
Abbildung in dieser Leseprobe nicht enthalten
Where, Zi is the observed response for the ith observation of the response variable, Z. This means that Zi =1 for an adopter (i.e. farmers who adopt modern agricultural production technologies) and Zi = 0 for anon-adopter (i.e. farmers who do not adopt modern agricultural production technologies). Xi is a set of independent variables such as farm size, family size, education of household head, among others, associated with the ith individual, which determine the probability of adoption, (P). The function, may take the form of a normal, logistic or probability function. The logit model uses a logistic cumulative distributive function to estimate, P given z by,
Abbildung in dieser Leseprobe nicht enthalten
Where, k represented number of independent variables to be analyzed in the study.
Since the model is non-linear, the parameters are not necessarily the marginal effects of the various independent variables. The maximum likelihood method was used to estimate the parameters.
The empirical model for the logit model estimation is specified as follows:
Abbildung in dieser Leseprobe nicht enthalten
Where the above formula is called log of odds ratio and Xi is the combined effects of X explanatory variables that promote or prevent farmers’ decision to adopt modern agricultural production technologies.
In other words the model lnAbbildung in dieser Leseprobe nicht enthalten in the formula represents log-odds in favor of farm households’ decision to adopt modern agricultural production technologies or not to adopt.
It is the logarithm of the ratio of probability of adopting the technologies (p) to probability of not adopting them (1-p).
The ratioAbbildung in dieser Leseprobe nicht enthalten, shows the odds ratio of probability of adopting the technology to not adopting it. That means it is the ratio of probability of adopting the technology (p) to not adopting the technologies (1-p) in the observational studies. The independent (explanatory) variables which are expected to determine the adoption decision of the farm households in this study are categorized into three. They are: The socio-cultural factors: such as age, education, family size, gender of farm household head which were hypothesized to influence agricultural technology adoption significantly.
Economic factors: such as farm size, farm income, non-farm income, cost of modern production inputs, distance to market center, and the
Institutional factors: such as access to credit, extension visits, and tenure
X1,..., Xi, are factors that promote or prevent farm households’ from adopting modern agricultural technologies. They are explanatory variables in the equation above described as follows:
1. Age of the household head: The age of the farmer specially related to farm experience is expected to affect the decision of adopting modern agricultural technology positively. It is measured by number of years of the farm household head and hence continuous variable (Adesina & Baidu, 1995).
2. Gender: Mostly cultural factors matter when it is seen from gender point of view. Most agricultural input decisions in Ethiopia are influenced by decision of the male household heads .Hence it is expected to affect the adoption decision of farm households. It is a dummy variable taking 1 for male and 0 for female (male=1 and female= 0, for this study)(FAO, 2011).
3. Education level of the house hold: It is well expected that farmers with more education are aware of more information, and be more efficient in evaluating and interpreting information about innovations than those with less education. Thus it is hypothesized that producers with more education are more likely to be adopters than farmers with less education. It is measured by number of years of schooling of the head of the households and hence a continuous variable (Abay and Assefa, 2004; Salasya et al. 2007, Alene & Manyong, 2007).
4. Family size: It is a continuous variable which indicate the number of person living in the house of the farmers. It is expected that as the size of the house hold increase the adoption of new technology increase provided that number of pendent family members in a household is less. This indicates the family with large number is more involved in adopting the new technology during their farm production effort (Liberio 2012, Idrisa 2012).
5. Off-farm income: Off-farm income represents the amount of income the farmers earn in the year on other than on-farm activity. It is the amount of income (in Birr) generated from activities other than crop and livestock production. These include petty trading, charcoal selling, firewood selling and others. It is expected that the availability of off-farm income is positively related with adoption decision since households engaged in off-farm activities are better endowed with additional income to purchase initial seeds or other essential agricultural inputs Beshir et al (2012).
6. Asset owned: It is continuous variable which is expected to affect the decision of the farm households positively. This is because as the asset becomes larger the household gets more money and materials and equipments to practice the new technology of production.
7. Farming experiences: Is measured in the number of years since a respondent started farming on his own. Experience of the farmers is likely to have a range of influences on adoption. Experience expected to improve farmers’ involvement in seed production. Farmers with higher experience appear to have often full information and better knowledge and were able to evaluate the advantage of the technology. Hence it was hypothesized to affect adoption positively (Chilot et al., 1996).
8. Access to credit facilities: It is a dummy variable, which takes a value of 1 if the farm household had access to credit and 0 otherwise. Adoption of new technology with complementary inputs require considerable amount of capital for purchase of modern agricultural inputs such as fertilizers and improved seeds, especially when farm land to be covered is. Farmers who have access to formal credit are more probable to adopt improved technology than those who have no access to formal credit (Million & Getahun, 2001; Beshir et al, 2012, Saleem et al., 2011, Akudugu et al. 2012).
9. Extension service: Extension service will help the farm households to understand the importance of the modern technology and enhance the accuracy of implementation of the technology packages. More frequent DA visits, using different extension teaching methods like attending demonstrations and field day can help the farmers to adopt a new technology. If the farmers get better extension services, they are expected to adopt seed production technologies than others. In this study this variable was treated as a dummy variable. That is if the farmers gets extension service it is coded as 1 and 0, otherwise (Akudugu et al, 2012, Kaliba et al., 2000; Maiangwa et al., 2007).
10. Availability of training: Farmers may obtain information from different source and may learn also from DA through extension programs. However unless they can obtain required skill through training they may face some difficulties to understand and apply improved agricultural technology. So those farmers who got training on improved agricultural technology are more willing than those who didn’t get training. It is dummy variable measured as 1 if farmers get specific training on the technology (in this case improved seed of the main crops included in the study and fertilizer usage) and 0 otherwise.
11. Perception of input prices: Profitability of modern technology depends on the costs incurred on it. The more it increases farmers hesitate to utilize the technology significantly.
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