ANALYSIS OF TECHNOLOGY ADOPTION AMONG GROUNDNUT FARMERS IN KPETORKOPE COMMUNITY OF THE VOLTA REGION OF GHANA
ADZAHO OLU KWADZO AGBETSI IGNATIUS (PG7062916)
ABSTRACTThere is much growing concerns over the world on ensuring food security in the coming years. Technology adoption in agriculture has therefore, been given much attention over the years as one sure way of meeting this food security objective. The impact of technology adoption in agriculture has been to a large extent, the focus of many studies. However, there is little literature available on the Volta part of Ghana. Using primary data on 92 respondents who were on groundnut farmers in the Kpetorkope community of the Volta region of Ghana, the study examined the availability, constraints and determinants of technology adoption. The study found basic agricultural technologies available for most groundnut farmers in Kpetorkope were tractors, weedicides, pesticides and fertilizers. An improved groundnut variety such as the New Prototype Stripper (NPS) was barely available to farmers in the community. The estimated logistic probably regression model on factors influencing the decision to adopt modern technology found the variable age and gender as insignificant factors. Education was found to have a significant positive influence on adoption choices of groundnut farmers. Also economic factors were crucial in adoption choices as the study found farm income and farm size as a significant positive determinant. However, institutional factors of contact with extension service personnel was found to be insignificant.Concerning constraint factors on adoption, the study found lack of access to credit, high cost of technology adoption, biophysical factors, limited knowledge on technology usage as well as complexity of some technologies as the top-most five (5) constraints facing groundnut farmers in adopting technology and in their farming activities in general. This was determined using the Kendall’s coefficient of concordance. The statistics also showed a 59.2% agreement among respondents with respect to the constraints.
INTRODUCTIONBACKGROUND TO THE STUDYFor most African developing economies, agriculture is the main source of livelihood employment,as it served as main income source for about 50% to 90% of theirworking populations (Schiff ; Valdés, 1992).Despite the fact that agriculture contributed significantly to the enriched growth performance in most African developing economies, it is however evident that current agricultural contribution is far less than its potential, and is currently experiencing growth rates that are muchlower than other sectors(Al-Hassan ;Jatoe, 2002; Diao et al., 2010).
Ghana’s development after independency was predominantly dependent on agriculture. For instance, in 1960, agriculture contributed about 45.22% of Ghana’s GDP and this increased to 60% in 1980 but has gradually reduced over the years and as at in 2016, it now contributes 19.59% of the nations’ GDP. However, Agriculture happens to be the largest employer of Ghana’s workforce. For instance, available statistics reveals that about 42% of Ghana’s workforce are engaged in agriculture (Ghana statistical service, 2010). This implies that the per capita income as well as productivity of farmers in Ghana are much lower when compared to than those in the other sectors. Thus, livelihood empowerment of the extremely poor against poverty in Ghana can be reached via improvement in the agricultural sector, as it employs the mass of its work force (Irz et al., 2001; Cervantes-Godoy &Dewbre, 2010).
Ghana’s agriculture is dominant in regions that covers its forest vegetation zones namely; Ashanti, Eastern, Western, Brong-Ahafo and Volta Region. Among these forest regional zones noted for agricultural activities, the Volta region happens to be relatively least developed. According to the Ghana Poverty Mapping Report, Volta Region had apoverty incidence rate record of 33.8% making it the fourth in national ranking statistics (Ghana Statistical Service, 2015). Thus, excluding the regions in the North of Ghana, Volta region has the highest record of poverty rate among the remaining seven (7) regions. The 2010 population censusfuture indicated that about 74% of Voltaianswho are economically active are engaged in agriculture (Ghana Statistical Service, 2010).
According to (Appiah, 1990),researchpoints out great potential for enhancedproductivity in agrarian activities in Ghana via agricultural transformation, governmental interventions, adoption of innovations and technologies (Ampofo, 1990). History of such agricultural interventionpolicies in Ghana showed improved crop yields, lowered poverty levels as farmers benefited increased incomes (ISSER, 2006; 2007 and 2008) in addition to the adoption of technologies that complemented existing practices (Al-Hassan, 2004; Faltermeier, 2007).
During the 1970’s Volta region was Ghana’s main production source of cocoa. Now, the Volta region is known for the cultivation of coffee, maize, yam, rice, cocoa, plantain and groundnut in the country (Duncan, 2004).However, the Kpetorkope Community of the Volta Region where the researcher dwells, is known to be predominant in groundnut production. According to Ivanic et al (2011), groundnut serves as a staple food as well as prized cash crop for over 90% of thousands of households in developing economies. Economies such as Senegal, Nigeria and Ghana are among the main ten worldwide groundnuts producers constituting about 12% of its global market share (Pazderka; Emmott, 2010).
Groundnut (Arachishypogaea) which is also referred to as peanuts, is avital leguminous crop in the global economy via its important benefits. It offers a variety of advantages to farmers especially in developing economies as it resolves atmospheric nitrogen in the earths, makes the soil more fertile and reduces cost of buying certain fertilizers especially in eras of increasing prices of chemical fertilizers. (CGIAR, 2004-2005). It serves as a vital protein energy and vitamin source for consumers. Groundnut can also be used as livestock feeds since its seed cake isprotein content rich.
However, Huang et al (2002) argues that although there has been significant advancement in global technologies regarding farming and hence agriculture, there are certain productivity constraint limitingAfrican farmers. Hence, the concern is making African agricultural activities adopting such technological advancement as well as innovations that would help increase their productivity.Simtowe et al (2010) regards the adoption of better suited innovations from research and development in the growing of groundnut to achieve high crop yield.
Evenson;Westphal (1995) and Evenson (2003) regards the decade periods of the 80s and 90s as eras where crop yields were high, making agriculture to be more productive. This was due to the use of high yield grains/seeds as a result of crop genetic research into new seed varieties. Via the aid of government and non-governmental organizations, varying technological innovations in agriculture has been extended to farmers over the decades with the aim of raising agricultural productivity, crop/harvest yield and hence, their incomes (Asare, 2004; Diao et al., 2014). With regards to the fundamental assumption concerning innovations and technology adoption as substitute for intensity production, Lohr and Park (2002) noted that, using one new technologies does not completely prevent the adoption of the other. Isgin et al. (2008) also noted that, adopting one type of technology does not limit the farmers’ adoption of the other, so far as the adoption if profitable.Isgin et al. (2008) in their study on Ohio State farmers, found that 12% of sampled farmers adopted more than 50% of the available new agricultural technologies. Thus, implying the possibility of diffused or partial use of available technology as evidential. Nonetheless, there has been very limited research into the analysis of technology adoption among groundnut farmers.
PROBLEM STATEMENTAgriculture remains the backbone of the Ghanaian economy for so many years. It has employed over 56% of the country’s population and contributes 28.3% of the GDP of the economy (ISSER, 2012; CIA World Fact Book 2012). Ghana’s successful economic performance over the past two decades can be attributed to agricultural sector. In the Volta region of Ghana, agriculture is the dominant economic activity. Agriculture serves as the major source of livelihood for the people. Apart from providing food for the people, it also serves as source of income to the households. Most farmers in this part of the country cultivate groundnuts either on large scale or as a minor crop (SADA, 2010).Over the years, many types of technology have been discovered in the area of agriculture in general and groundnut production in particular. This ranges from fertilizers to the introduction of improved varieties. The reasons behind their introduction are to bring about increased productivity and improved yields. In spite of the numerous attempts by the government and other concerned agencies to improve technology in groundnut production, there exist diverse views among farmers on the reasons for their introduction as well as the impacts it had had on their productivity. Some of these farmers are also addicted to the use of rudimentary technology in their farming activities.
Farmers in rural areas are generally noted for adamancy towards the adoption of innovation. This is due to their cultural beliefs and the fear of failure of the innovation. Kpetorkope in the Ho Municipality of the Volta Region of Ghana is no exception.Although there have been countless studies carried out on the subject matter, their focus has often been on the impacts of adoption of technology on the productivity. In addition, enough literature does not exist on the Volta region of Ghana in this regard. Questions on the availability, factors and the depth of adoption of these technologies as well as the constraints facing groundnut farmers are what this study seeks to address.
OBJECTIVES OF THE STUDYThe main objective of this study is to examine the technological adoption among groundnut farmers in Kpetorkope community of the Volta region of Ghana. The study specific objectives are as follows;
To identify technologies available for groundnut farmers
To estimate factors influencing technology adoption among groundnut farmers
To analyse the constraints facing groundnut farmers.
RESEARCH QUESTIONSWhat are the technologies available for groundnut farmers?
What factors influence technology adoption among groundnut farmers?
What are the pressing constraints among groundnut farmers in Kpetorkope Community?
METHODOLOGYA Cross-sectional data was collected using self-administered structured questionnaires to obtain primary data from literate farmers whiles face-to-face interviews was usedin conducting interviews for those illiterate section of the targeted population. The gathered data was captured and analysed in accordance to the study objectives. The study first employed a summary statist via tables, then proceeds with a probit analysis for decision to adopt farm technology and finally uses the Kendall’s coefficient of concordance to analyse technology adoption constraint.
SIGNIFICANCE OF THE STUDY.Despite numerous studies on farm technology adoption, its application in a small community such as Kpetorkope was not found in literature. The challenges faced in farm technology adoption in such a small community could be said as a distinctendeavour. Hence, this study renders a valuable contribution in analysing the adoption of modern reform (technology).
The relevance of the study is to bridge the knowledge gap behind the adoption of technology among groundnut farmers in Kpetorkope. This research is solely for academic purpose. This research attempts to analyse technology adoption among groundnut farmers and it will also present constraints facing agriculture (groundnut farmers).In a nutshell, the study will bring to bear the kind of technology suitable for the community so as to enable government, NGOs and other concern agencies to support groundnut farmers in the agriculture sector.
ORGANISATION OF THE STUDY.This study is structured into five chapters. Chapter One introduces the study. It is comprised of background to the study, statement of problem, research objectives, methodology and relevance of the study. Chapter Two reviews related literature, both theoretical and empirical relating to the theme of the thesis. Chapter Three, discuss the methodology employed for the study analysis, with the study results and its discussions presented in Chapter Four. Finally, Chapter Five concludes the study by presenting the summary, conclusions and recommendations arising from the study findings.
LITERATURE REVIEWINTRODUCTIONThis chapter reviews existing literatures on technology adoption in relation to agriculture. It is organised into two main sections. The first section explores theories surrounding the topic as well as definitions of relevant concepts. The second section presentsthe empirical findings on factors affecting technological adoption and adoption constraints.
THEORETICAL REVIEWThis section discusses on theories related too technology adoption. It proceeds by first discussing definition of study terms and concepts. It then presents some related theories that seeks to explain technology adoption choices and its determinants.
Definition of terms and concepts
TechnologyWhereas several authors defined “technology” in diverse manners, Rogers (1995) on the other hand used ‘technology’ and ‘innovation’ synonymously. This study defines technology following Enos and Park (1988) as “general knowledge that allows the accomplishment of tasks, service rendering, or the production/manufacturing of products”. According to Abara and Singh (1993), the application of such knowledge is what is termed as ‘technology’. Thus, agricultural technologies render less effort as it makes work easier, faster and improves outcome with regards to the entity to which it is applied.
Technology adoptionAdoption as used in this study, follows the definition of Roger (1962) as “a psychological process from the period an individual obtains first time information about an innovation to the time the person finally utilizes the information”. Thus, technology adoption are new/improved practices that adds to the farmers’ effort in realising relatively improved outcome. The adoption of technology involves the stages from ‘awareness of a technology’, to other stages such as the building of interest, conscious rendering of evaluations, acceptance of the technology, making trials and then finally adopting it (Lionberger, 1960; Enos and Park, 1988).
Theories of technology adoption
There are numerous theories that seeks to explain the decision of adopting new technologies. This section presents some of this theories namely the rational expectation theory of Muth’s (1961), Signalling theory and the theory of adoption clustering.
Rational expectation theory of Muth’s (1961)
Muth (1961) in his theory, proposes that individuals are capable to study quickly so as to adjust to deviations in economic settings, and to antedate future economic performance via an examination of economic activity (demand/consumption) patterns.Muth (1961) theory therefore assumes all economic agents as predictors and analyst of economic outcomes/events. This assumptions postulates that, individuals therefore use all available information effectively. In the context of agricultural practices, the theory helps explain why future agricultural crop yield is determined by current seeds planted and technologies used, and the future harvest is based on predicted trend of demand patterns. Thus, if the agronomist expects economic trends to increase in future, which would lead to more demand of his produce, then the farmer would adopt related technologies that would aid in current production to meet the future demand, since planting and farming takes a long while to harvest for market demands.
Signaling or information transmission theory
Kreps (1990) refers to the information transmission theory as signaling. Signaling occurs where one party (such as a firm) possessing information of a knowledge transmits that information to another party (another firm). Although signaling also applies in the labour market for hiring and employment decisions, it also applies in the context of information adoption (Zhu and Weyant, 2003). According to Zhu and Weyant (2003), this is analyzed in the context of research and development, where one firm (the research institute) contains more information than another firm ( for instance, the ministry of agriculture). This, implies the existence of information asymmetry since where one has more technological knowledge than the other. The ministry may then transmit this obtained knowledge to field extensions who would teach it to farmers. Signaling therefore enables other economic agents to learn the value of new knowledge or technology so as to adjust their own expectation and possible adopt it (Zhu, 2004).
Theory of adoption clustering
Au et al (2005) explains that, the technology adoption clustering theory seeks to explain why multiple individuals or firms can be observed to all adopt a given technology around the same time frame. Assuming there are K number of economic agents deciding to adopt or to not adopt a supposed new technology. The theory also assumes that, the decision to adopt now, is weighed against waiting for a possible better future technology against current new technology. The new technology is assumed to have stronger network externalities. Thus, for one economic agent to benefit from expectations of the network externalities, this agent would only adopt if the remaining other K-1 agents are all willing and ready to adopt it. The firm does so to avoid getting locked in investment and becoming stranded with that technology.
Determinants of technology AdoptionNumerous scholars theorised factors influencing the adoption process of technological know-hows and as such provided extensive literature on theories establishing technology adoption in their field of interest (see Griliches, 1957; Feder et al., 1985; Kebede et al., 1990; Roger, 1995;Alston et al., 1995; Cameron, 1999). Empirical investigation on agricultural adoption in emerging economies was pioneered by Feder et al (1985).
The study realises that, most literature on possible factors influencing the adoption of technology were grouped into categories due to field of academia and policy reasons. Literature on likely factors affecting the adoption of technology are reviewed in the following sections. For easy discussions and analytical purpose, these factors are categorised in this study into economic, social and institutional factors.
This section discusses economic factors that can affect the adoption of technology. some of these factors are farm size, cost of the technology, expected benefit returns and off-farm hours.
The size of the agricultural land to which the technology being adopted is to be applied to is regarded as a crucial factor. Most scholars regard the total farm land size and not the total crop acre in the analysis. Whereas farmland size is regarded as a common factor, using the crop acreage is regarded by Lowenberg-DeBoer(2000) as a superior measure for rate and extent of technological adoption, and not just the choice to adopt.
Hence, farm size is a commonly used variable by studies on technology adoption. Here, the section of total farmland suitable for the new technology is employed as the measurement for farm size.The influence of farm size on the adoption of new technology is regarded to be positive.The size of the farm affects the production intensity of workers and hence the possibility of using new technologies where applicable. Those with small farm size may find adoption less feasible owing to the fact that, the adoption cost would be too huge to be compensated by returns on the small farm land. Thus, agricultural farm practices that can adopt such technologies would find it more feasible when their farm size is large, making room for try-and-error testing and large scale commercial practices that goes in tandem with new innovations and practices. The relationship between farm size and adoption of technology is found positive in the works of McNamara et al (1991) and Fernandez-Cornejo (1996). Others also found a negative relationship (seeYaron et al., 1992). This is due to the fact that; some technologies are ‘scale-dependent’ whiles others are contrary. Thus, depending on the technology adoption being studied, the relationship could be positive of negative (Shakya and Flinn, 1985).
Technology adoption cost
Deciding to employ the use of new technology in agricultural practices is an investment decision. Thus, this is a shift in production practices choices by farmers facing such investment options (Caswell et al., 2001). As a result, the decision to adopt such new technology depends on how much it will cost the farmer to adopt it and also depend on other needed resources such as training skills and knowledge application of the technology. As argued by Elosta and Morehart (1999), capital intensive technologies are affordable to farmers with large resources who are usually wealthy. Since the cost of technology adoption is the price of the technology including expenditures incurred in applying it, Khanna (2001) contends that technologies that have lower adoption cost are easily and quickly preferred. From economic demand theories, lower prices of a commodity results to more quantity demanded.
Agricultural farmers engage in their respective farming activities so as to gain economic returns, generally income. Thus, the expected margin or difference of gain from current agricultural technology in use and that of new technology adoption affects the decision to adopt. Higher expected return is expected to positively influence the decision to adopt. Here, higher expected return could be reduced time and effort on field work, reduced yield losses or increased output from the use of improved or new technology. According to McNamara et al (1991), agreaterproportion of overall household farm revenue/returnsas a result of increased yield is positively correlated with new technology use. Thus, difference in returns enjoyed by farmer’s prior the adoption and after technology adoption of a peculiar agricultural technology is the measure of technological gains (Fernandez-Cornejo, 1996). Thus, agronomists who have higher expect utility from adoption would quickly adopting that particular technology. However, those with insignificant difference in outcomes, especially small-scale agriculturalists are less likely to adopt new alternatives to conventional practices (Abara and Singh, 1993).
As argued by Mugisa-Mutetikka et al (2000), agricultural technologies that are more likely to take away leisure hours of farmers due to as a result of its adoption are less desirable to technologies that gives room for labour effort replenishment. Farmers can therefore use the gained time into other income accumulation economic activities. Farmers who also have more available time can use such hours to learn or try the new technology after off-working hours. Thus, availability of time can therefore influence new agricultural technology adoption decisions.
The variable age is regarded as a primary dominant socio-biological factor influencing the decision process of adopting new agricultural technology. According to McNamara et al (1991) and Tjornhom (1995), the relationship between farmers age and decision to adopt agricultural technology is debatable. In one context is an agriculturalist that have practiced existing farming techniques and knows the maximum attainment output and as well as significant short comings such as susceptible of crop yield to disease infestations among others. This individual over time would come to have a firm understanding of the various existing practices, their benefits and shortfalls. Such an individual in the old-methods would gladly welcome new technologies that rectifies identified weaknesses and makes room for improvement.
On the other hand, is an agriculturalist who have invested several years of his farm work using a particular farming knowledge or practice and have come to fully accept that peculiar knowledge. The presence of a new technology may be ignored since this individual will be less willing to attempt employing the new knowledge with arguments that, he cannot jeopardize his long-life work practices. Such elderly farmers may regard new technologies as ideas of the youth, and may have less regard for the youth as the elderly is glued to the old ways.Tjornhom (1995) further argued that, such elderly farmers who embrace new technology do so over time, at a slow pace.
Adesiina et al (1995) found a positive effect of age on the adoption choices of sorghum farmers in Burkina Faso. McNamara et al (1991) also found a positive correlation between age and new groundnut technology adoption choices of Georgia farmers.Adesiina and Baidu-Forson, (1995) on the other hand, found a negative relationship between age and technology adoption choices of rice farmers in Guinea. Boahene et al (1999) in the context of Ghanaian cocoa farmers found an inverse correlation between age and hybrid cocoa technology adoption.
Education nurtures individual to become literates who can read and write in addition to knowledge gain, as they fit into society. Educated agricultural farmers are able to read and understand instructions and other important aspects of technological packages such as the appropriate measurement and application of fertilizers as well as the control of certain innovative farming equipment (Caswell et al., 2001). Less educated farmers may found complex instructions for new technology as cumbersome and thereby serving as a hindrance to adoption (Ehler and Bottrell, 2000). Available literature on technology, adoption highlights that technologies that are more complicated inversely affect adoption due to its complexities (Rogers 1983).
Waller et al (1998) contends that, education creates an enabling mental environment for the training, learning and acceptance of new innovative knowledge. Thus, the more educated a farmer is, the more the likelihood to adopt improved technological options.Scholars such as Tjornhom (1995) and Feder; Slade (1984) established a significant positive relationship between education and technological adoption.
Technology adoption choice is an issue of expected utility, adoption effort and degree of adoption usage. Male farmers may have relative physical strength compared to female farmers. However,Overfield and Fleming (2001) regards the technical efficiency of male and females as indifferent. The roles of female farmers relative to male farmers in adoption choice and effort/intensity can be similar, rendering their effect indifferent. Nonetheless, the degree of expectations may vary across gender concerns. In the works of Overfield and Fleming (2001) on coffee farming found gender as an insignificant participation variable. Doss ; Morris (2001) in examining technology adoption determinants on maize farmers in Ghana, found gender as insignificant factor affecting the choice to utilize improved maize farming technologies.
Increased information about a particular technology induces adoption so far as it is beneficial and affordable (Feder and Slade, 1984). The availability of relevant information about the technology can foster sound adoption decision making by farmers, since their decisions are based on well informed knowledge. On the contrary, in a setting where farmers have limited knowledge about a particular technology, the onset of available information may trigger more questions about that technology which may result into a higher vacuum of uncertainty and associated risks.Caswell et al (2001) explains that, information about a given new technology from reliable sources such as extension officers, farm meetings and organisations as well as recognised media outlets reduces uncertainty constraints, thereby fostering positive acceptance and hence adoption. This means that, having an accurate information mix of a new technology is necessary for need evaluation and effective adoption.
Thus, the right mix of information properties for a particular technology is needed for effectiveness in its impact on adoption. Information in this context is the relative information available about the technology in question that is to be adopted (McGuirk et al., 1992; Klotz et al., 1995).
Contacts with agricultural extension personnel
According to the International Food Policy Research Institute, a new technological innovation or knowledge is as good as its dissemination mechanism (IFPRI, 1995). Programmes and contacts with agricultural extension personnelare a key channels of informing farmers about new research findings, applications and farming practices. Yaron et al (1992) found extension service contacts as a strong positive determinant that influence adoption choices of farmers. Their study found that, contacts with extension officers, paves way for those less literates to have a level understanding of new knowledge, thereby counter balancing the negative influence of no or little education on adoption choices.
Existence of complementarity relationship
This is a fourth aspect of adoption decision factors of agricultural technology. Feder et al (1985)and Rogers (1995) opines that, the choice to adopt a given new technology is more welcomed where complementarity relationship exist between current practices and new technology to be adopted. Complementarity relationship is said to exist as two distinct levels; one is at the factor level and the other is technology level.
According to Lionberger (1960), factor complementarity has to do with the manner in which inputs of agricultural output production are used together. As such, where there is limited availability of one input, the use of the remaining productions factors and hence use of the new technology is significantly challenged. For instance, a situation where groundnut farmers depend on rain for watering their legumes, and now use watering can to fetch water from nearby streams to water the plant. Water supply therefore becomes a crucial factor for any irrigation innovations adoption be it via water can, manual pump pipes or water holes/pipes connecting the farm site to the river/stream. Thus, unavailability of nearby water supply may hinder the adoption of modern irrigation innovations. Intrinsically, where inputs that are critical for adoption are in short supply (for instance, water supply that is critical for irrigation technology adoption), the unavailability may hinder adoption. Thus, crucial inputs must be readily available in order to encourage adoption (Lionberger, 1960; Nkonya et al.,1997).
Technological complementarity exists where the adoption of one new technology positively affects the adoption of another new technology (Green and Ng’ong’ola, 1993). For instance, the adoption of a new high yielding leguminous seeds may also need the adoption of a certain new fertilizer application to help realize the effectiveness of the new seeds’ yield capacity.Nkonya et al (1997) found farmers in Tanzania that adopted a new variety of maize seeds also adopted it in combination with new fertilizers, thus a technology complementarity was found. Other studies such as Boahene et al (1999) and Kato (2000) also found the existence of complementarity relationship in farming technology adoption.
In addition, Yaron et al (1992) asserts that, several dissimilarities in farm resource endowments and farmer characteristics exist from area to another. Hence. Adoption factors in one farming community may not necessarily apply to another community. As such, farmers in a geographical area may have differing adoption pattern of a particular technology across communities. It is for this reason that this study focuses strictly on Kpetorkope community in the Volta region of Ghana where the researcher resides.
EMPIRICAL REVIEWThis section reviews empirical literature on influential factors of technology adoption and constraints. This section therefore presents related findings of studies on constraints facing groundnut farmers in the cultivation, harvesting and processing of the product as well as tools and methods used in their findings.
Owusu-Baah (1996) examined technology adoption among small-scale farmers in Ghana using a tabular analytical tool. Employing timely relevant information from the ministry of food and agriculture, the study found lack of improved crop varieties, lack of favourable pricing policy for groundnut and its inputs as well as poor transportation and credit facilities are the constraints facing groundnut farmers. In addition, lack of knowledge or skills, cost of technology, non-availability of technology, labour requirements and lack of material accounted for the non-adoption of technology. The study concluded that the reverse of these were the pro-technological adoption influencing factors.
Banabana-Wabbi, (2002) assessed factors affecting adoption of agricultural technologies with regards to integrated pest management in Kumi District. stated farmers’ participation in on-farm trials demonstrations, accessing agricultural knowledge through researchers and a prior participation in pest training for groundnut farmers as some reasons for the increased adoption of most Integrated Pest Management (IPM) practices in Kumi district of Eastern Uganda. With the use of logistical regression model, her findings further revealed that low level of adoption (;25%) was attributed to five of the eight technologies to be adopted while three of the available technologies had high adoption levels (;75%).
Donkor and Awuni (2006), in their study into farmers’ perception and adoption of improved farming techniques in low-land rice production in northern Ghana. Using a sample of 300 farmers and focus group discussion, the study employed the logistical regression technique for the analysis. The study found increase in soil fertility and for that matter crop yield, cost of technology, availability of the technology easy usage of the technology and finally, level of side effects of the technology are factors that influence the adoption of technology.
Kipkoech et al (2007) studied the Production Efficiency and Economic Potential of Different Soil Fertility Management Among Groundnut Farmers of Kenya. With the support of questionnaires, the study collected cross-sectional data on 332 farmers from three districts of western Kenya. In the analysis, a logistical regression model was employed. Their study employed the Cobb-Douglas Production Function to determine whether the adoption of technology would increase household incomes and production efficiency. They found out that there is a high potential for farmers to increase their groundnut yields and incomes. For the adoption of fertilizer (technology), increase yields and income are the major determinants.
Technology adoption also has a lot to do with the production efficiency and increase in income of the rural farmer. Kassie et al (2010) researched into Adoption of Improved Groundnut Varieties and its impact on Rural Poverty in rural areas of Uganda. They used a propensity score matching, stochastic dominance, switching regression and Resenbaum bounds procedure to check the sensitivity of estimated adoption and came out that among other influential factors of technology adoption estimates; adoption of improved groundnut varieties had significant impact on income and reduced poverty levels.
Simtowe et al. (2010) used 594 groundnut farmers in examining the Determinants of Agricultural Technology Adoption: The Case of Improved Groundnut Varieties in Malawi. This study used the simple random sampling, a probability sampling technique and interview to collect a cross-sectional data while probability (probit or tobit) models were used as the analytical tool. The estimated probability model came out that, adoption is largely constrained by economic factors. Furthermore, the study found out that, the single most important factor of adopting improved groundnut varieties in Malawi is information access. They further stated that the finding is indicative of relatively large unmet demand for improved groundnut varieties and lack of access to credit.
Carlberg et al (2012) used 150 groundnut farmers as a sample size in examining the effects of Integrated Pest Management (IPM) Techniques Farmers Field Schools on Groundnut Productivity: Evidence from Ghana. The study used the probability sampling (stratified sampling and systematic sampling) with the help of interview to collect a cross-sectional data. In this analysis, Full-Information Maximum Likelihood and a Two-Step Estimator of the treatment model were employed. They realized that in view of attending Farmers’ Field School to enable farmers adopt technology, the following factors come into play; level of education (positive relationship), regular visits by extension officers (positive), the distance to road (negative). Thus, the farther away farmers are from a paved road, the less access the farmer has to information and programs. Age and experience were also adduced as factors. The study also discovered that Farmers’ Field Schools are effective tools to spread information and technologies that increase groundnut productivity but it is constrained by geographical areas or location.
Ronner&Giller (2013) researched into needs assessment among groundnut farmers in Malawi and Tanzania. By the use of field surveys, they found out that small farmers in developing countries have access to only rudimentary tools (like hoe) for harvesting and processing their groundnut. However, the group made up of a group of international engineers who considered Africa as their co-designers are currently collaborating with African farmers to receive feedback in order to adopt the drudgery reduction, time saving and income raising designs. Below are some technologies they identified.
Asenkenye et al (2016) analysed productivity gaps among smallholder groundnut farmers in Uganda and Kenya. Using a sample of 321 groundnut farmers from both countries were analysed using data from the 2009 growing seasons employing the stochastic model (Single-Step Maximum Likelihood Approach) analytical technique. The study found biophysical factors, cultural practices, socio-economic conditions, institutional and policy constraints as well as inadequate efforts to transfer technologies and poor market linkages contributed to the 78% yield gap in the case of groundnut production in Uganda.
The impact of technology adoption in agriculture has been to a large extent, the focus of many studies in especially developing countries. Again, empirically and methodologically, several methods and models were used by several researchers and with divers findings depending on the topic in question, the tool been used and the statistical goal it is aimed at. It is therefore not surprising that an undertaking such as this will shape the outcome of this research. With regards to factors determining the decision of a farmer to adopt technology in groundnut farming in Ghana, these theories and empirical oft several possible factors may help to explain the pattern of technology adoption among groundnut farmers especially in theKpetorkope Community of the Volta Region of Ghana.
RESEARCH METHODOLOGYINTRODUCTIONThis chapter deals with the method and approaches employed in the study to meet its objectives. It discusses the design of the research, sampling issues data sources as well as empirical model specifications and estimation methods that were used in this work.
RESEARCH DESIGNThis study is an academic research which would basically contribute to knowledge. This study attempts to quantity factors influencing their decisions to adopt agricultural technologies in their groundnut farming experiences. It therefore employs surveyof groundnut farmers’ response to analysis technology adoption, using both quantitative variables and qualitative information of farmers. Hence, this study is both a qualitative and a quantitative research, thus a mixed research.
DESCRIPTION OF TARGET AREAKpetorkope is one of the communities in South Dayi District of Volta Region. The district covers a land area of about thousand kilometer square with twenty (20%) of it being of the Volta lake. The climate of the area is described as tropical as it is mostly influenced by winds from the Atlantic Ocean at the south of Ghana and sometimes by the dry harmattan winds from the Sahara Desert. The citizens in the district are generally ruled by lineage chiefs at their various settlement communities. According to the 2010 Census, the literacy rate is about 49.1% with majority of the children no in school enrolment due to rural poor nature of the households. The onset of the Free Senior High School Education is expected to reduce the problem, however, the positive impact is not well felt since education in the district is constrained by a fixed number of classrooms in public institutions. Aside trading/commerce and formal employment in few civil job sectors, most of the populace are into agriculture as the whole Volta Region itself is basically into agriculture, hunting, forestry industry. The community being in Volta is dominantly of Ewe ethnics with few others being Akans and Guans.
SAMPLE SIZE AND SAMPLING PROCEDUREThe target population of the study will consist of individual households that engage in groundnut farming in Kpetorkope community. An estimated number of 120 households was obtained from interaction with community heads, thus 120 households is targeted as the sample frame. Based on this statistic, the sample size of the study would be determined using the sample size formulaof Yamane (1967) and Israel (1992) as;
Sample size(n)= N1+N(e)2where N= total households and
e = margin of error
Plugin the 120 households into groundnut farming into the above formula yields a sample of 92 needed to be a fair representation of groundnuts farmers in the Kpetorkope community. To overcome challenges in meeting the estimated sample number target due to problems of incomplete or non-participatory response possibilities of some farmers, especially where the estimated population to be sampled is small, the study therefore administers interview questionnaire to all groundnut farmers, on condition that the farmer does not reject the interview session request by the researcher.
Since, the researcher does not have a list of households into groundnut farming, the use of simple random sampling techniques would be impossible. Thus, the study first finds any farmer who is into groundnut farming, and from then, finds the next available respondent based on referrals of the farmer interviewed. Thus, participants were obtained using the chain-referral sampling. The study therefore employed the snowball sampling technique in administering the questionnaires.
The non–probability sampling technique that would be employed for this study would be the purposive sampling. This technique would be used to obtain information on available technologies from MoFA and the targeted farmers.
RESEARCH INSTRUMENTSelf-administered questionnaires were used to obtain all the primary data from literate farmers whiles face-to-face interviews were conducted for the illiterate section of the targeted population. This was structured to retrieve responses from the students. Sekaran (2003) asserted an efficient instrument necessary and sufficient for the gathering of data is the questionnaire (refer to appendix), provided the study points out clearly what actually is required and how variables of interest are to be measured. The questionnaire consists of dominated closed-ended questions with other areas having open-ended questions. The questionnaires were designed to encompass the five main sections namely the profile of the farmers, the technology available for the farmers, the factors influencing the adoption of technology by farmers, the drivers for intensity of adoption and constraints facing groundnut farmers.
TYPES AND SOURCE OF DATAThe study gathered Cross-sectional data on groundnut farmers. Since the data was gathered via the use of administered questionnaires, the study therefore used primary sourced data. The primary sourced data weregathered mainly from the field through field survey (Observations, interviews, focus group discussion and questionnaires). In addition to the primary sourced data, secondary information was obtained from the Ministry of Food and Agriculture (MoFA) and literature on the types of technologies available for groundnut farmers and the constraints of technology adoption.
DATA ANALYSISThis section discusses on the model specification, estimation technique and the variable description used for the data analysis.
Factors influencing technology adoption
An individual farmer will either adopt technology or not. The logistic regression which is a choice model would be used to identify the determinant of technology adoption. The study estimates the adoption decision model for groundnut famers with reference to the logit model specification of Donkor and Awuni (2006) as;
Yi=fsoc, econ (3.1)Where Yi = Adoption of technology in groundnut farming that is not primitive (such as animal mounted implements), Soc = social factors and econ= economic factors affecting decision to adopt.
The outcome variable ‘Yi’ is a binary decision variable, where;
Yi = 1, if adoption is Yes and,
Yi = 0, if non-adoption of technology.
The study therefore employs a binary probability model to examine the determinants that results to the likelihood of an outcome or Y=1, given the dependent variables as;
Yi=b0+b1x1+b2x2+b3x3+…+bnxn+u 3.2The study employs the logistic estimation method, which is the natural logs of odds outcome as;
Odds=LnP1-P (3.3)WhereOddsi is the probability ratio of occurrences to non-occurrences of an event. Thus, the probability that, a farmer adopts technology is given by equation (3.3).
LnP1-P=b0+b1x1+b2x2+b3x3+…+bnxn+u (3.3)Y = adoption and non-adoption of technology (dummy variable, adoption = 1 and zero to non-adoption)
P = probability of adoption of technology
1 – P = probability of non-adoption
Ln = natural logarithm function
bo, b1- bn=logistic regression coefficient
xi= adoption decision factors such as age, education, gender, farm size, income, cost of technology and contact with extension service personnel.
u = stochastic error term.
The relative effect of each explanatory variable on the likelihood that a farmer will adopt technology is given by the marginal effect as:
?(dependent)?(explanatoryi)=bijP1-PWhere bij=coefficient of the jth explanatory variables and
P = mean of dependent variable.
Variable Description and Expected priori
Below is a tabular summary of all variables, their definitions, units of measurement as well as their hypothesised relationships.
Table 3.1:Summary of variables and expected priori XE “Table 3.1: Summary of variables and expected priori”
Variable Definition Sign Source
Dependent Variable Y Adoption of technology
=1, if adopted
=0, if not adopted Independent Variables Age Age of farmer in years +/- McNamara et al (1991)
Adesiina et al (1995),Boahene et al (1999)
Education Level of education in years + Waller et al. (1998)
Caswell et al. (2001)
Gender Sex Dummy
(Male = 0,
Female = 1) +/- Overfield and Fleming (2001)
Doss ; Morris (2001)
Income Income (GH?) from farm yield + MeNarnara et al., 1991; Fernandez-Cornejo, 1996
Extension Contact Has contact with extension officers
No=1) + Yaron et al. (1992
Cost of Technology Percieved cost of adopting technology
Not Expensive = 1) – Caswell et al. (2001
Farm Size Farm size Dummy
less than an acre=0
1-3 acres =1
above 3 acres=2 + Harper et al. (1990) and Yaron et al. (1992)
Constraints of technology adoption
Constraints of technology adoption will be identified and respondents will be required to rank them by order of importance. Legendre (2005) indicates that Kendall’s coefficient of concordance (W) is a measure of the agreement among several (p) judges who are assessing a given set of n objects. The Kendall’s coefficient of concordance test will be used to test the hypothesis that there exists no agreement among respondents on the constraints of technology adoption. This is specified as
w=12?T2-?T2nm2(n2-1)Where w= Kendall’s coefficient
T= total score for each constraint
n=number of constraints
m= total of number of respondents
Ho: there is no agreement among respondents on the constraints that confronts groundnut farmers.
H1: there is agreement among respondents on the constraints facing groundnut farmers.
The Kendall’s coefficient (w) falls between 0 and 1; where 1 represents perfect agreement and 0 signifies perfect disagreement. The F-test would be used to test the significance of the Kendall’s coefficient. This is specified as
Where DFnumerator= Degrees of freedom for the numerator
DF(denominator)= Degrees of freedom for the denominator
The decision is to reject the null hypothesis if F-calculated is greater than F-critical value.
DATA ANALYSIS AND DISCUSSION OF EMPIRICAL RESULTSINTRODUCTIONThis chapter presents the empirical estimation result. It begins with a descriptive statistics of the study variables. It then proceeds by presenting the estimated model alongside interpretation of findings.
SUMMARY STATISTICSThis section offers a brief statistics of the regression variables. Dummy variables are in percentages whiles means and standard deviations statistics are on continues variables
Table 4.1:Descriptive statistics of regression variables XE “Table 4.1: Descriptive statistics of regression variables”
Variable n Percent(%) Mean Std. Dev. Min Max
Gender Male 33 35.87% Female 59 64.13% Total 92 100% 0 1
Extension Personnel Contact Yes 58 63.04% No 34 36.96% Total 92 100% 0.370 0.485 0 1
Percieved Cost of Adopting Expensive 33 35.87% Not Expensive 59 64.13% Total 92 100% 0 1
Level of Education No education 49 53.26% Primary 17 18.48% JHS/Middle 10 10.87% Secondary 11 11.96% Tertiary 5 5.43% Total 92 100% 0 4
Age 92 100% 41.978 14.225 18 84
Farm size 92 100% 2.739 1.425 0.5 6
Source: Field Survey, 2017.
From the summary statistics shown on Table 4.1, out of the total 92 respondents that were interviewed during the study, there59 males representing 64% whiles female number was 33 representing about 36%. This shows that, there were more male groundnut farmers than females in the Kpetorkope community. With regards to age, the mean age of respondent was 41.9 years while the lowest recorded age of a groundnut farmer was 18 years and the oldest farmer surprisingly being of 84 years. However, it is generally accepted that, rural old folks who are into especially farming are more agile at old age than those in same age range in urban areas.In addition, what is more informing was that, a total of 32 respondents (35%) had no formal education, 23 respondents (25%) had primary education and 13 respondents (14%) had JHS/Middle education, 16 respondents (17%) had secondary education and 8 respondents (9%) had tertiary education. This implies that, about 74% of the groundnut farmers who were respondents did not attain senior high school education.
TECHNOLOGIES AVAILABLE FOR GROUNDNUT FARMERSThe availability of technology as identified by respondents is shown in Table 4.2. Animal mounted implements although available, was considered as obsolete and as a primitive method. It was discovered that tractor mounted implements, weedicides, fertilizers and pesticides were the main technologies available for groundnut farmers (see Table 4.2).
Table 4.2: Technologies Available and their Percentage of Awareness XE “Table 4.2: Technologies Available and their Percentage of Awareness”
Tractor mounted implements 98%
Animal mounted implements 34%
Improved groundnut varieties 9%
Total technology availability 57%
Source: Field Survey, 2017.
Few farmers however identified improved groundnut varieties as available to them. Among these identified technologies, tractor mounted implements happens to be widely known and widely adopted. Almost all adopters responded positively to the adoption of tractor mounted implements. This is closely followed by weedicides, fertilizers and pesticides. Only a handful of respondents claimed they adopt improved groundnut varieties.
This finding on the available technologies in the Kpetorkope community is consistent with the a priori expectations of this study where it was asserted that rural farmers because of their general low level of education are likely to adopt the most basic of all agricultural technologies. Perhaps with more effort by Compactible Technologies International (CTI), more improved and efficient groundnut production technologies such as the New Prototype Stripper (NPS), the New Prototype Sheller (NPS2) and the New Prototype Harvester (NPH) could be made available to more rural areas including Kpetorkope community.
FACTORS INFLUENCING DECISION TO ADOPT TECHNOLOGYThe factors influencing a farmer’s decision to adopt modern technology in groundnut farming were grouped into three main categories namely economic, social and institutional factors. The economic factors werefarm revenue/income, farm size and cost of adoption The social factors included variables such as the age of the groundnut farmer, gender and the level of education. The institutional factor employed was contact with extension services personnel. The probit estimation for technology adoption determinants in groundnut farming in the Kpetorkope community of Volta region is shown in Table 4.3.
The overall estimated logistic regression model had a Wald chi2 statistic of 21.50 which was significant at 1% indicating that overall estimated logistic probability model was statistically significant.
Table 4.3: Logistic Regression Result XE “Table 4.3: Logistic Regression Result”
Dependent = Adopted Technology Logit Coefficient Marginal Effect (dy/dx)
Social Factors Age 0.00013 0.00001
Level of Education
No education (base)
(1.85967) JHS/Middle 0.44242
(1.09189) Secondary 0.87532 *
(1.39202) Tertiary (empty)
Economic Factors Farm Income 0.01612 *** 0.00118 ***
Farm size 4.54323 *** 0.331634 ***
Percieved Cost of Adopting
Not Expensive 0.64103
(0.89356) Institutional factors Extension Contact No (base) Yes 2.67752 (1.66726)
(1.94536) Number of obs 87 Wald chi2(9) 21.50 Prob; chi2 0.0106 Pseudo R2 0.6025 Note: *, **, *** represents statistical significant at 10%, 5% and 1% respectively
Standard errors are in parenthesis ‘( )’.
Discussions on the determinants factors affecting the decision to adopt modern technology is discussed under the categories of social, economic and institutional factors as already explained.
The logic probability result that a groundnut farmer in the Kpetorkope community would adopt technology as shown in Table 4.3 reveals that, female groundnut farmers were more likely to adopt modern technology compared to their male counterparts. Also, the variable age was found to have a positive estimated coefficient. However, the gender of the farmer as well as the farmers’ age was not statistically significant in influencing the decision to adopt modern technology.
The variable education was seen to be the only social factor that was significant. Groundnut farmers with secondary education were more likely to adopt technology with reference to those with no education. This was however weakly significant, as it was only statistically significant at 10% error level. Caswell et al (2001) highlighted that education nurtures individual to become literates who can read and write in addition to knowledge gain, as they fit into society. Educated agricultural farmers are able to read and understand instructions and other important aspects of technological packages such as the appropriate measurement and application of fertilizers as well as the control of certain innovative farming equipment. Waller et al (1998) also a positive impact from education on technology adoption decision.
With regards to economic factors, the variable farm income was found to have a positive impact on the decision to adopt modern technology. This was highly significant at 1% error. Agricultural farmers engage in their respective farming activities so as to gain economic returns, generally income. McNamara et al (1991) found that a greater percentage of overall household with higher farm revenue have the purchasing ability to command and new technology. Farmers with higher farm revenues therefore have high revenue expectations to absorb current cost incurred in the adoption of technology (Fernandez-Cornejo, 1996). Abara and Singh (1993) found that, farmers with small output yield and hence lower farm revenues less likely to adopt modern technology alternatives to conventional practices (Abara and Singh, 1993).
Farm size was also found to be positively related to the decision to adopt modern technology and was statistically significant at 1% error. This was in line to the a priori expectation. This means that, the larger the farm size, the more likely it is for the farmer to adopt modern technology in groundnut farming.Shakya and Flinn (1985) argues that, depending on the technology adoption being studied, the relationship could be positive of negative as some technologies are ‘scale-dependent’ whiles others are contrary. The use of modern farming technologies such as tractor mounted machines, fertilizers, weedicides, improved seed varieties and others are more geared towards large scale famers for large commercial production as they have lager farm size. This was found to be problematic inthe promotion of technological adoption inKpetorkope since quite a number of these farmers were small holder farmers. The positive impact of farm size on technology adoption decision was in line with the conclusions of McNamara et al (1991) and Fernandez-Cornejo (1996
Furthermore, cost of technology, although not statistically significant, was found to be positively related to the probability of adoption. The implication of this finding is that, although theoretically, cost is a crucial factor, groundnut farmers in the Kpetorkope community do not regard Percieved expensiveness of adoption cost but rather the income. Empirical evidence also revealed that expected benefit from the farming activity (income generated from farming or on-farm income) is positively related to the probability of adopting technology.
This variable is significant at 1% level of significance. Higher percentage of a farmer’s income coming from farming activities as a result of increased yield tend to be positively correlated with adoption of new technologies (MeNarnara et al., 1991; Fernandez-Cornejo, 1996). The result shows that for every GFI expected or generated from farm as a result of improved yield, there is 0.01% chance of adopting technology.
In analysing the adoption of innovation in agriculture, the role of extension service personnel cannot be downplayed. Access to extension services as asserted by Yaron et al. (1992) can counter balance the negative effect of lack of years of formal education in the overall decision to adopt some technologies. Evidence from the estimated logistic result showed that access to extension services although positively, was not significant in the probability of adopting technology.This means that, the signalling or information transmission theory role played by extension service personnel could be ineffective. Based on the result, a farmer who has access to extension service does not affect his chance of adopting technology than a farmer who has no access to extension service. This is also possibly due to other factors such as farm size and income in the adoption decision process.
CONSTRAINTS FACING GROUNDNUT FARMERSSeveral constraints were listed and respondents were asked to rank from the most pressing constraints to the least pressing constraint, where a value of 1 represents most pressing constraint and 10 being the least pressing constraint. The Kendall’s Coefficient of Concordance was then used to determine the agreement among respondent with respect to the most pressing constraints.The Kendall’s W – value was 0.592 indicating a 59.2% agreement among respondents with respect to the constraint facing them.
In view of predicting the constraints that are likely to affect groundnut farmers pertaining to technology adoption and groundnut farming in general, a wide range of constraints were listed namely lack of knowledge on technology usage, complexity of technology, high cost of technology adoption, unavailability of some technologies, lack of access to credit, biophysical factors, cultural practices, transportation, policy constraints and poor marketing. The ranking of constraints by respondents is shown in the Table 4.4.
Table 4.4: Rank of Constraints and Kendall’s Test Statistics XE “Table 4.4: Rank of Constraints and Kendall’s Test Statistics”
Constraint Mean Rank Score Position
Lack of Access to Credit 1.91 176 1st
High Cost of Adoption 2.62 241 2nd
Biophysical Factors 3.66 388 3rd
Limited Knowledge on Technology adoption 4.18 385 4th
Complexity of Technology Usage 5.45 501 5th
Unavailability of Technology 6.07 599 6th
Poor Markets 6.77 6247th
Transportation Problems 7.48 722 8th
Cultural Practices 799 735 9th
Policy Constraints 8.49 781 10th
N = 92 df=9
Kendall’s W = 0.592Asymptotic Significance = .000
Chi-Square = 490.533
Source: Field Survey, 2017.
As enumerated above, the top constraints of farmers of Kpetorkope community with regards to technology adoption and groundnut farming in general according to the order of merit are lack of access to credit, high cost of adoption of technology, unavailability of some technologies, and lack of knowledge on technology usage and complexity of technology.
The variable lack of access to credit, was referred to as where farmers lack the requisite capital that is adequate to engage into farming that would exhaust their potentials and reap the maximum benefit from their farms. It had the highest rank of 1.91.
High cost of adoption in the context of this study refers to how high farmers consider the cost incurred in trying to adopt new innovation. This constraint had a rank of 2.62 and considered the second most pressing constraint in the Kpetorkope community.
Also, biophysical factors in the context of this study refers to the fact that soil characteristics such as fertility, climatic conditions such as rainfall patterns might have restricted the use of technology and consequently farming as a whole. For instance, some soil type may not support some fertilizer types or some varieties of groundnut. This constraint had a rank of 3.66 and it is the third most important constraint.
In addition, limited knowledge on technology usage had a rank of 4.18 and is the fourth important constraint. Limited knowledge in this study refers to the fact that farmers have incomplete or inadequate understanding on how to use the technology to yield the maximum expected return.
Finally, complexity of technology refers to how difficult or complicated a technology is to use. New innovations in general are considered to be intricate. This constraint is ranked the fifth among the ten constraints and has a rank of 5.45.
It must be noted however, that these findings did not totally confirm a particular study or reviewed literature but rather, picked from a variety of them in reconfirmation. For example, the first most pressing constraint (lack of access to credit) in part, confirmed the findings of Simtowe et al. (2010) in their attempt to document the Actual and Potential Adoption Rate of Improved Groundnut Varieties and Their Determinants Conditioned on Farmers’ Awareness of Technology. Thus, among other constraints, lack of access to credit was among their first five constraints. Again, this has not been any different from the study of Owusu-Baah (1996) on ‘Technology Adoption among Small-Scale Farmers in Ghana’ where lack of access to credit facilities was their fourth most pressing constraint.
In conclusion, if these constraints are resolved (namely access to credit. high cost of adoption, unavailability of some technologies, limited knowledge on technology usage and complexity of technologies), one can predictively say that technology adoption among groundnut farmers in Kpetorkope community may be improved.
SUMMARY, CONCLUSION AND RECOMMENDATIONSINTRODUCTIONThis chapter concludes the study. It first summarizes the major findings from the study and draws conclusions from these findings and suggest possible recommendations thereof.
SUMMARY OF FINDINGSEmpirical evidence from the field revealed that only basic agricultural technologies were available for most groundnut farmers in Kpetorkope. These include tractors, weedicides, pesticides and fertilizers. An improved groundnut variety is barely available to farmers in the community. Groundnut harvesting and shelling technologies such as the New Prototype Stripper (NPS), the New Prototype Sheller (NPS2) and the New Prototype Harvester (NPH) were not available to the people.
Factors expected to affect the decision of farmers to adopt technology were social (sex, age and education), economical (farm size, cost and expected benefit or return otherwise referred to in the context of this study as on-farm income or simply income) and institutional factors (access to extension).
The estimated logistic probably regression model on factors influencing the decision to adopt modern technology found the variable age and gender as insignificant factors. Education was found to have a significant positive influence on adoption choices of groundnut farmers. Also economic factors were crucial in adoption choices as the study found farm income and farm size as a significant positive determinant. However, institutional factors of contact with extension service personnel was found to be insignificant.
Concerning constraint factors on adoption, the study found lack of access to credit, high cost of technology adoption, biophysical factors, limited knowledge on technology usage as well as complexity of some technologies as the top-most five (5) constraints facing groundnut farmers in adopting technology and in their farming activities in general. This was determined using the Kendall’s coefficient of concordance. The statistics also showed a 59.2% agreement among respondents with respect to the constraints.
CONCLUSIONThere is much growing concerns over the world on ensuring food security in the coming years. Technology adoption in agriculture has therefore, been given much attention over the years as one sure way of meeting this food security objective. The impact of technology adoption in agriculture has been to a large extent, the focus of many studies in Ghana to the neglect of factors determining the decision of a farmer to adopt technology in groundnut farming. This delinquency, coupled with the fact that there is no enough literature available on the Volta part of the country formed the basis of this research. Using primary data 92 respondents who were on groundnut farmers in the community, the study examined the availability, constraints and determinants of technology adoption.
The study foundthree (3) key factors as playing a vital role in the decision to adopt technology. Among them are education, income, farm size and extension contact.The result from the study •further suggests that lack of access to credit, high cost of adoption, biophysical factors, limited knowledge on technology, and biophysical factors are the main constraints facing groundnut farmers in adopting technology.
RECOMMENDATIONSBased on the findings of this study, the following course of actions is suggested for policy makers and implementers in their attempt to promote modem technology adoption in Kpetorkope community. These recommendations among others include:
The study found lack of access to credit as part of the most pressing constraint towards modern farming technology by the groundnut farmers. The study therefore suggests that, microfinance institutions and the ministry of agriculture, should introduce favourable micro-credit schemes to these farmers in order to enable them invest in their groundnut farming activities via adoption of technology that would boost yield and income. This could be done if farmers are given individual credits or advised to form Farmers’ Groups to be able to access group loans. The access to credit can also help these farmers to expand their farm size via land tenure acquisitions, which would not only provide more yield and room for the use of mechanised farm proactive, but would boost the standard of living. Larger farm size was found as a crucial positive determinant in technology adoption.
The study found contact with extension service personnel to have a positive influence, but it was insignificant. The study recommends that, agricultural extension service personnel activities, approaches and schemes should be revised so as to make their contributions to farmers especially via educating them on the need to employ modern technological innovations in the farming activities. Perhaps, extension contact with the farmers should be intensified to provide much information to the farmers about the usage of these modern technologies and farming practices. This would go a long way to solve complex nature of some technologies as identified as a one of the most pressing constraints facing groundnut farmers in adopting technology.
Income was found to be a strong positive determinant. However, since most of these farmers are small-scale farmers, their income is constrained with regards to affording modern technologies needed. The study therefore suggests that, available technologies that seem unaffordable but very advantageous to both the farmers and the community can be subsidized under a set up scheme. The scheme would oversee the sale of the farm yield/produce at the end (harvesting) so as to deduct cost payments in installments. This can help make it more possible for willing adopters to have access to as far as the cost of the technology is concerned. This is in line with the findings on the adverse relationship between cost of the technology and the probability of technology adoption.
Partnership with such organization with interest in agricultural technologies as Compactible Technologies International (CTI) to make available more technologies for groundnut farming such as the New Prototype Sheller, Stripper and Harvesters in the Kpetorkope community at an affordable cost to enhance its easy adoption. This is to solve the problem of unavailability of some technologies as identified by the farmers in the community.
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