3.4 Farmers’ Adoption of Innovations in Agriculture
3.4.1 Factors Influencing Adoption of Innovations
There are various studies across the world which shows factors affecting the adoption and use of the innovations packaged and disseminated to farmers. Erbaugh, Donnermeyer, Amujal (2007) assessed the impact of farmer field school (FFS) participation in the adoption of integrated pest management (IPM) in Uganda. They used probability sampling to select samples and applied structured interviews in data collection. The study found that farmers’
access to knowledge was the major factor which promoted adoption of innovations. Other factors which influenced adoption of IPM strategies included education, size of land and total income. The study observed that climate change and variability, market access and labour availability influenced farmers’ adoption. Contrary to many studies on the adoption of IPM, the Ugandan study postulates that higher total income farmers were less likely to adopt IPM strategies, due to farmers having other on-or-off-farm income-generating priorities other than cowpea farming. These other priorities meant that their interest, time and willingness to take on additional risks associated with the adoption of new practices was reduced. Age and gender did not influence adoption.
A study conducted in Ghana by Boahene, Snijders and Folmer (1999) found that the adoption was low and several factors contributed to the phenomenon. The research used simple random and purposive sampling and interviews to collect data from 103 farmers. The farmers had been involved in cultivating cocoa for a period of two years. Findings were that large- scale farmers had better access to bank loans thus increasing their chances of adoption, compared to small-scale farmers. The research found that small-scale-farmers’ adoption was
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highly influenced by information and communication through social networks. The study revealed that factors such as farmers’ access to information through extension officers, farmers’ education level and the availability of hired labour had positive effects on adoption.
Contrary to the findings from a number of studies, this study found that access to land, income, skills and family size had no significant influence on adoption. The study observed that an indirect supporting role was played by farmers’ social network supports and social status in the adoption of an innovation. The social network and status of farmers enhanced their adoption through access to bank loans, which they used in improving agricultural production.
Akudugu, Guo and Dadzie (2012) were other scholars in Ghana who explored factors influencing the adoption of modern agricultural production technologies by farmers. Their study used probability multistage and simple random sampling to identify respondents. They interviewed respondents using a household questionnaire. Key study findings were that households had a low adoption rate of modern agricultural production technologies. The study also deduced that economic factors which significantly influenced farmers’ decision to adopt were farm size, expected benefits from the technology, access to credit and off-farm income-generation activities. The social factors influencing adoption were age of farmers, level of education and gender while institutional factors were the extension services. Thus the study revealed that farmers’ decision to adopt agricultural technology can be greatly enhanced when socio-economic factors and institutional factors are favourable.
In Nigeria, Mattews-Njoku, Adesope and Iruba (2009) studied the acceptability of improved crop production practices among rural women in Anambra State. The study employed a structured questionnaire in data collection, where extension officers were used to administer the questionnaire. The study discovered that farmers were not receiving adequate technical information from extension officers, who were the key communication channels to enhance the spread of information. Inadequate receipt of technical information by farmers contributed to the low usage of improved crop production practices which, in turn, hindered agricultural production by women. These findings confirm those by Dimelu and Saingbe (2006), who reasoned that adoption and utilisation of appropriate agricultural technology by rural farmers is largely dependent on the relevance and effectiveness of information dissemination and the ability of the agents to convince the farmers. Mattews-Njoku, Adesope and Iruba (2009) observed that adoption of new agricultural technologies was affected by socio-economic
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variables, such as household size, number of farms owned and access to land, farming experience, extension contact with the farmers, level of income, access to appropriate farm input and access to land and credit facilities.
The findings of Mattews-Njoku, Adesope and Iruba (2009) in Nigeria, tend to confirm, and yet also differ from those of Mukhopadhyay (1994:99) in West Bengal, India. They confirm what Mukhopadhyay (1994:99) observed, that the adoption of innovation and technology was promoted by attributes such as farmers' knowledge of local conditions, experience and availability of extension services, quality of land owned and availability of irrigation. Yet the two studies differ, as that in India adduced the factors which appeared insignificant to adoption in the Nigerian study, that is the size of the land owned, the level of education and the value of the assets of the household, such as the house, machinery, cattle and so on, which were contrary to the findings of Mattews-Njoku, Adesope and Irubas (2009).
In Malawi, Masangano and Miles (2004) investigated the factors influencing the adoption by farmers of a new variety of bean known as Kalima. The study applied an interview schedule and focus group discussions in data collection. Key findings were that the decision to adopt was promoted by factors such as gender, literacy level and level of education. The study discovered that farmers had negative perceptions of the Kalima beans’ yield, pest susceptibility and tolerance, cooking time and colour. The study found that, despite other contributing factors such as gender, literacy level and education, favouring the adoption of the Kalima bean variety, information disseminated to farmers was poorly packaged and delivered. As a result, farmers accumulated negative perceptions, which restrained them from adopting the Kalima bean variety. The study stressed that to increase the rate of adoption, information concerning an innovation should be well structured and appropriately packaged to accommodate the low literacy level of farmers. Rogers (2003) stressed that for adoption of innovation to take place, farmers should have positive attitudes towards change, the information disseminated to the farmers should be less complex and compatible to their settings. The innovation should offer a relative advantage when compared to one they have been using.
In Kenya, Goldberger (2008) investigated the diffusion and adoption of organic agriculture in the semi-arid Makueni district. The study employed several data collection methods, such as structured and semi-structured interviews, observation and documentary analysis. The study
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showed that farmers were confused by the information about the application of organic and artificial fertilizers from information providers such as local and northern non-governmental organisations and government extension officers. The confusion arose from the observed poor co-ordination of organic agricultural programmes. The content packaged and disseminated by information providers differed between providers. As a result, farmers experienced difficulties in deciding which information to use and which to abandon. The study further found that farmers’ decisions to adopt an innovation was, to a greater extent, influenced by their personal preferences, knowledge levels, perceived needs and farm characteristics.
A study conducted by Rousan (2007) in Jordan, on factors influencing adoption of improved farming practices among women farmers, showed that, despite a number of interventions by the government of Jordan to improve agricultural production through women farmers’
participation, the country still was experiencing low food production. The research study used a simple random technique and a structured interview schedule in data collection found out that key determining factors influencing adoption of innovation by Jordanian women farmers was cost, relative advantage of an innovation and simplicity of an application. The study revealed that adopter characteristics, such as attitude to change, land tenure system, risk taking, income level, technical skill, educational level and labour availability were strong adopter characteristics in adoption. Rousan (2007) discovered that the characteristics of the information source such as credibility and competency and climate change variability promoted adoption. Furthermore, when a correlation test was conducted to show the association of features persuading women farmers to adopt the innovation and actual adoption rate, the findings were that credibility, cost, land tenure, capability to be shared, communication ability and the relative advantage had a positive and significant relationship with adoption of the innovation. Thus, to a great extent, these factors delineated in the study resonated well with the Diffusion of Innovations theoretical framework attributes such as compatibility, complexity, attitude towards change, attitude towards an innovation and relative advantages (Rogers 2003).
It was observed that for an innovation to be adopted by farmers, a situational analysis should be conducted prior to the introduction of the innovation, so as to take into account the farmers’ needs, beliefs, norms and taboos, rather than the researchers’ beliefs and scientific arguments about agricultural innovation (Sturdy, Jewitt and Lorentz 2008). Supporting this,
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Rousan (2007) found that cultural factors such as norms, beliefs and taboos greatly influence adoption. The social system norms which are a component of the Diffusion of Innovations theoretical framework accommodate the above factors which influence adoption.
In Honduras, Arellanes and Lee (2003) applied household interviews in collecting data to study farmers’ adoption of sustainable agricultural technologies. The study, which explored the determinants of the adoption of low-input sustainable agriculture technologies in hillside areas, found that farmers’ had adopted the use of minimum tillage agricultural practices. The minimum tillage practices included use of leguminous cover crops, commercial vegetation production and soil enrichment, including fertilizer usage. The study found that household income and farmers’ characteristics such as age, gender and level of education did not significantly influence adoption of minimum tillage. The study deduced that adoption of labranza minima, a minimum tillage innovation, was significantly influenced by the simplicity of the innovation, affordability, the availability of water through irrigation practices, land ownership, soil quality and farm land with steeper slopes.
In the USA, Scandizzo and Savastano (2010) studied perceptions on adoption of Genetically Modified (GM) crops. The study reviewed a dataset on the adoption and diffusion of GM crops over a period of eight years and found that, despite criticism, modern GM crops still positively contribute towards enhancing the farmers’ agricultural production, by minimising production risks. The benefit hinges on the ability of GM crops to resolve output and input uncertainties. Despite many scholastic studies, associating the slow pace of the adoption of GM crops with determinants such as lack of information, overstated risk perceptions and mistrust, findings showed that the economic return of GM crops through production and time investment through short duration profitability tend to supersede the risks. Apart from environmental concerns, the study underscores the finding that economic returns of GM crops in many developed countries such as China and Argentina can only be achieved if farmers’ uncertainty is conquered. Thus, notwithstanding the observed economic profitability of GM crops, the authors strongly emphasise the need to address institutional information obstacles through effective information dissemination to farmers and overcoming the institutional barriers such as administrative and government interventions.
A study in El Salvador and Honduras, in Central America, by Bravo-Ureta, Solis, Cocchi and Quiroga (2006) conducted a database analysis on the determinants of farm income and
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adoption among farmers participating in natural resource management interventions. The findings indicated that land use variables, such as soil conservation practices and structures, output diversification and adoption of forestry systems, had a statistically positive association with farm income. The research findings showed that land tenure was among the contributing factors to adoption, as farmers with larger farms and who owned land benefitted from higher farm incomes than those who did not. Thus, farmers’ adoption of conservation practices to a greater extent depended on the income farmers’ generated and the income generation was influenced by land ownership, farms size, access to credit and human capital.
In sub-Saharan Africa, Drechsel, Olaleye, Adeoti, Thiombiano, Barry and Vohland (2006) studied adoption drivers and constraints of resource conservation technologies. The study observed that adoption of resource conservation technologies is dependent on the farmers’
perceptions of the attributes of innovation, such as relative advantage, complexity, compatibility, trialability, observability/visibility, uniqueness of the technology introduced, farmers’ needs, the technology proposed and availability of land, knowledge, capital and credit, time, labour and skills, which are critical production factors. Other factors include information and knowledge sharing, farmers’ attitudes to trialability in experiments and risk tolerance, institutional support and the relevant policy being in place. The study further shows that cultural norms and taboos such as local traditional practices and indigenous knowledge (IK) should be handled sensitively when introducing an innovation for effective technological adoption. These attributes are also explained in the Rogers Diffusion of Innovations framework (Rogers 2003).
In Tanzania, where the current study was conducted, various authors revealed factors quite similar to those from other international studies. Kaliba, Verkuijl and Mwangi, (2000) used documentary analysis to collect data from farmers on the factors affecting the adoption of improved maize seed and the use of inorganic fertilizer for maize production in the intermediate and lowland zones. The study found that there was a low use of improved maize seeds and inorganic fertilizers for maize production by farmers. Issues that contributed to the low usage of inorganic fertilizer were inadequate extension services to supply information to farmers, poor implementation of on-farm field trials and poor rainfall distribution. The study revealed other factors which influenced adoption as risk, economic returns from farmers’
preferred maize varieties, which maximised profit with minimal loss, and the geographical characteristics of an area. The study found that collaboration between researchers and farmers
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was of profound importance in enhancing the adoption of an innovation, because participation between researchers and farmers enhanced the identification of farmers’ needs, information exchange and sharing for knowledge generation and usage. Similar findings were observed in South Africa by Sturdy, Jewitt and Lorentz (2008).
Other authors in Tanzania, namely Bengesi, Wambula and Ndunguru (2004), investigated farmers’ utilisation of agricultural innovations through examining their adoption of hybrid maize production technologies in Mwanga district. The study applied interviews and observation methods in data collection and found that adoption of hybrid maize by Mwanga maize farmers’ was, to a great extent, correlated with farm size, gender and annual income. In order to enhance farmers’ cultivation and use of hybrid maize in agricultural production, sensitization and education was of great value.
Sturdy, Jewitt and Lorentz (2008), in the Bergville district of South Africa, sought to understand the agricultural innovation adoption processes through farmer-driven experimentation. The study used various participatory learning and action research techniques, such as semi-structured interviews, group discussions, informal discussions, presentations, work sharing, process notes, direct observation, personal diaries, matrix scoring, key informants mentioned and technical instrumentation. It was observed from the study that farmers were being faced with multiple stressors, both biophysical and socio- economic, which impeded their decision to adapt to the agricultural innovations introduced to them. The authors noted that it has widely been observed that researchers and extension officers have accused farmers of not adopting disseminated innovations. Nonetheless, the authors learned that, in most cases, this was a false observation as, in reality, farmers needed innovations to improve their agricultural production and livelihoods. The study did not ignore other factors such as social and economic issues surrounding farmers, which needed to be dealt with if innovation was to take place successfully. The study observed that perceived need, participation, investment options and risks were the major factors which influenced the farmers’ adoption of agricultural innovations in this district.
For adoption to take place, scholars such as Salinger, Sivakumar and Motha (2005) demonstrated the need to engage farmers in participating in various agricultural projects, so that researchers can identify their needs and effectively disseminate information to farmers.
Sturdy, Jewitt and Lorentz (2008) found that collaboration between agricultural information
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disseminators was vital in enhancing effective information and knowledge transfer and sharing insights through farmers’ trial and error. Their study showed that farmers who were willing to learn and practise on garden farming developed skills and acquired new knowledge which assisted them to improve their agricultural production. These findings by Sturdy, Jewitt and Lorentz (2008) corroborate those by Diederen, Meijl, Wolters and Bijak (2003) in the Netherlands. These authors observed that farmers who were innovators were more engaged in improving agricultural innovations and used extension services more than early adopters. Thus as observed by Sturdy, Jewitt and Lorentz (2008), farmer-driven experimentation was an effective farmer/extension agent participatory tool, which enabled farmers to evaluate an innovation and allowed researchers to assess their innovations newly introduced to farmers. Researchers could scientifically identify reasons for their acceptance or rejections.
Contrary to many studies, a study by Feder and Savastano (2006) in Indonesia on the role of opinion leaders in the diffusion of new knowledge on IPM found that social economic factors and farming skills did not highly influence adoption, but rather opinion leaders’ superiority was a great determining factor to enhancing farmer’s adoption of an innovation. The study raised the need for opinion leaders not to be excessively superior, as they might unconsciously serve the interests of those individuals with the higher status as they associate with one another.
Thus, from the literature reviewed, findings have demonstrated that the most influential factors for adoption in many developing countries are access to information, knowledge, packaging and information dissemination, education level, capital/loan/grant, access to agricultural inputs, attitude and technology. Adoption of an innovation cannot be achieved if farmers lack access to information on best agricultural practices to improve agriculture and combat adverse impacts of climate change and variability. Adoption and diffusion of an innovation heavily depend on the format of the innovation. The more complex an innovation is, the less likely it will be utilised. As a result, in developing countries, the situation is likely to be worse, as most farmers have low levels of literacy and education (Gundu 2009).
A review study on determinants of agricultural best management practice adoption in the USA in the last 25 years, by Prokopy, Floress, Klotthor-Weinkauf and Baumgart-Getz (2008), showed a similarity in most of the factors found to influence adoption in many
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studies. However, a slight difference which farmers mentioned as influencing adoption is recognition of environmental issues, including climate change and variability. The study showed that highly cited factors promoting farmers’ adoption and diffusion of innovation in the USA were education levels, capital, income, farm size, access to information, positive environmental attitudes, environmental awareness and utilisation of social networks.