3. Basic Concepts of Agricultural Technologies Adoption
The theory of 'diffusion of innovation,' established by Rogers in 1995, provides a framework for studying the acceptance and adoption of innovations. Rogers synthesized over 508 diffusion studies to develop this theory, which focuses on how individuals and organizations adopt new innovations
[8] | PC Lia. 2017. the literature review on technology adoption models and theories for the novelty technology. Journal of Information Systems and Technology Management. Vol. 14, No. 1, Jan/Apr., 2017 pp. 21-38. |
[8]
. According to Rogers, adoption refers to the decision to fully utilize an innovation as the best available course of action, while rejection is the decision not to adopt an innovation. Diffusion, on the other hand, is the process through which an innovation is communicated through specific channels over time among members of a social system. The core idea of the theory showed four elements that influence the spread of technologies: (1) innovation, (2) communication channels, (3) time, and (4) the social system.
3.1. Four Main Elements in the Diffusion of Innovations
3.1.1. Innovation
As
[9] | Rogers, E. 1983. Diffusion of innovations. New York: Free Press. |
[9]
defines, "innovation" refers to an idea, practice, or project that is perceived as new by an individual or adoption unit, regardless of when it was originally invented. The new characteristic of an innovation is more closely tied to the three stages of the innovation-decision process - knowledge, persuasion, and decision-making - rather than the actual invention timeline. In essence, something can be considered an innovation as long as it is novel from the perspective of the individual or group contemplating its adoption, even if it has existed for a long time objectively.
3.1.2. Communication Channels
A communication channel is the medium through which messages are transmitted from one individual to another. It includes mass media and interpersonal. Mass media channels, such as radio, TV, and newspapers, enable a source to reach a large audience. In contrast, interpersonal channels involving face-to-face exchange between similar individuals are more effective at persuading someone to adopt a new idea. While mass media can create awareness, interpersonal channels are more powerful for the persuasion stage of the innovation-decision process
[9] | Rogers, E. 1983. Diffusion of innovations. New York: Free Press. |
[9]
.
3.1.3. Time
The time aspect is ignored in most research. He argues that including the time dimension in diffusion research illustrates one of its strengths. The innovation-diffusion process, adopter categorization, and rate of adoptions all include a time dimension
[9] | Rogers, E. 1983. Diffusion of innovations. New York: Free Press. |
[9]
.
3.1.4. Social System
The social system is defined as a set of interrelated units working together towards a common goal. Since innovation diffusion occurs within the social system, it is influenced by the system's social structure - the patterned arrangements of its units. The nature of the social structure affects individuals' innovativeness, which is the primary basis for categorizing adopters of an innovation. In other words, the structural characteristics of the social system shape the innovativeness and adoption behaviors of its members
[9] | Rogers, E. 1983. Diffusion of innovations. New York: Free Press. |
[9]
.
3.2. Categories of Adopters
The process of diffusion results in five categories of adopters in a social system. The categories included innovators, early adopters, early majority, late majority, and laggards
[9] | Rogers, E. 1983. Diffusion of innovations. New York: Free Press. |
[10] | Mosher, T. A. 1979. An introduction to agricultural extension. Singapore University Press for the Agricultural Development Council. |
[9, 10]
. It is indicated that the majority of early adopters are expected to be younger, more educated, venturesome, and willing to take risks. Contrary to this group, the late adopters are expected to be older, less educated, conservative, and not willing to take risks
[9] | Rogers, E. 1983. Diffusion of innovations. New York: Free Press. |
[9]
.
3.3. Mode and Sequence of Agricultural Technology Adoption
In the adoption literature, two approaches are common: mode (approach) and sequence of adoption of agricultural technology. The first approach emphasizes the adoption of the whole package, while the second stresses the step-wise or sequential adoption of components of a package. Technical scientists often recommend the former approach, while field practitioners, specifically farming systems, and participatory research groups advance the latter. However, there is a great tendency in agricultural extension programs in developing countries to promote technologies as a package, and farmers are expected to adopt the whole package.
Some argue against the "whole package" approach to technology adoption, as farmers do not tend to adopt technologies as a complete package. Instead, farmers often choose to adopt individual components or a few suitable technologies sequentially, rather than the full technology package. Different studies by
[11] | Byerlee, D. and E. H. De Polanco. 1986. Farmers' stepwise adoption of technological packages: evidence from the Mexican Altiplano. American Journal of Agricultural Economics, 68: 520-527. |
[12] | Nagy, J. G and H. Sanders. 1990. Agricultural technology development and dissemination within a farming systems perspective. Agricultural system, 32: 305-320. |
[13] | Leathers, H. D. and M. Smale. 1991. A Bayesian approach to explaining sequential adoption of components of technological package. American Journal of Agricultural Economics, 68: 519-527. |
[11-13]
indicated that farmers typically select to adopt inputs and innovations in a step-wise manner, rather than all at once as a comprehensive package. Initially, adopting only one component of the package and subsequently adding components over time, one at a time, The major reasons often given for the sequential adoption of a package of technologies are profitability, riskiness, uncertainty, lumpiness of investment, and institutional constraints
[11] | Byerlee, D. and E. H. De Polanco. 1986. Farmers' stepwise adoption of technological packages: evidence from the Mexican Altiplano. American Journal of Agricultural Economics, 68: 520-527. |
[13] | Leathers, H. D. and M. Smale. 1991. A Bayesian approach to explaining sequential adoption of components of technological package. American Journal of Agricultural Economics, 68: 519-527. |
[11, 13]
. The studies by
[14] | Ryan, J. G. and Subrahamanyam, K. V. 1975. Package of practice approach in adoption of high - yielding varieties: An appraisal, Economic and Political Weekly Dec. 1975, P. A101. |
[14]
indicated that, rather than adopting full technology packages, farmers often choose to sequentially adopt individual components. This step-wise approach is a rational choice for risk-averse farmers with limited cash, as each component is seen as less risky than the complete package.
In general, the nature and effectiveness of the social system, modes and sequences of technology adoption, and even categories of adoption are influenced by farmers or farm social, economic, and institutional factors.
3.4. Factors Influencing Adoption of Agricultural Technology
The explanatory indicators vary from study to study based on their contextual applicability. Accordingly, the factors include: 1) farm size, 2) risk exposure and capacity to bear risk, 3) human capital, 4) labor availability, 5) credit constraints, 6) tenure, and 7) access to commodity markets. In delineating these particular factors, scholars point out that the categories are not discrete or exclusive and that boundaries may overlap due to the interdependent relationship between indicators.
3.4.1. Farm Size
The relationship between farm size and technology adoption is nuanced. Farm size does not always have a consistent effect - the impact varies based on the specific technology and the local institutional context. A primary driver is fixed costs, where larger farms can spread these costs over more land, enabling adoption. However, farm size may also serve as a proxy for other socioeconomic factors like access to credit, rather than directly causing adoption. Overall, the literature indicates the farm size-adoption link is complex and contextual, not a simple linear relationship.
3.4.2. Risk and Uncertainty
Technology adoption decisions involve a mix of subjective and objective risks. Subjective risks stem from farmer uncertainty about unfamiliar techniques. Objective risks arise from external factors like weather, pests, and input access. The observed adoption patterns are shaped by individual farmer risk preferences and their capacity to bear the risks of a new endeavor. Farmers with greater risk tolerance and risk-bearing ability are more inclined to adopt novel technologies.
3.4.3. Human Capital
These technology adoption variables encompass individual and community characteristics such as education levels, human health indicators, age demographics, and gender composition. The relationship between these variables and technology adoption is one of potential influence, rather than guaranteed causation. The conceptualization of human capital distinguishes between worker ability and allocative ability, with the latter defined as the capacity to adapt to change
[15] | Welch, F. 1970. Education in Production,” in The Journal of Political Economy, (Vol. 78, No. 1, Jan-Feb 1970), pp 35-5. |
[15]
. It is proposed that farmers with higher educational attainment possess greater allocative abilities, enabling them to adjust more swiftly to evolving farm and market conditions.
3.4.4. Labor Availability
The labor market context significantly shapes technology adoption patterns. Areas with net labor shortages versus surpluses will see divergent effects. Seasonal labor availability adds another dimension. The nature of the technology itself also matters - whether it is labor-saving or labor-intensive.
3.4.5. Credit Constraints
Access to credit is an underlying factor that manifests through other variables influencing technology adoption. For instance, farm size is related to credit access, as larger farms can leverage more collateral to borrow against compared to smaller operations, all else being equal. Additionally, human capital, in the form of higher farmer education levels, enables better understanding of credit practices and the ability to shop for competitive interest rates. Finally, land tenure status is linked to credit access - farmers who own their land can borrow against its value, whereas sharecroppers lack this collateral. In essence, credit availability is a fundamental element that shapes adoption indirectly through its relationships with farm size, human capital, and land tenure circumstances.
3.4.6. Tenure
Tenure incorporates issues addressed in the sections on credit constraints, risk, and uncertainty. As mentioned above, the uncertainty associated with a change of course is an impediment to technology adoption. It is the most vulnerable communities, those that are least able to afford a decrease in output, that are the most risk-averse. The most vulnerable communities are also more likely to have insecure tenure rights. The self-reinforcing nature of vulnerability creates a cycle where those least able to bear risk become trapped in poverty due to their risk-averse behaviors. Farmers with limited resources and high exposure to potential losses are the most hesitant to adopt new, uncertain technologies. Poverty status is also related to land insecurity, further reducing these communities’ incentives to adopt risky technology and further promoting the risk-poverty-tenure cycle.
3.4.7. Commodity Market Access
New technologies often require the repeated and consistent use of new inputs such as fertilizers and pesticides. Even low external-input sustainable agriculture activities usually demand significant amounts of construction materials for land preparation activities. Insecure access to critical resources and markets makes farmers reluctant to adopt input-dependent technologies, as it would leave them vulnerable to disruptions in those supply chains. Poorer farmers, least able to bear risk, require the greatest assurances that adopting new technologies will not leave them without the essential inputs needed to sustain their families and earn income. Their vulnerability to risk acts as a barrier to technological adoption. But access to markets is also needed as an outlet for production and not just as a means of securing inputs. Farmers need something to do with their increased output. If there are no markets that can bear the extra supply without creating a reactionary price decline, their investment in new agricultural technologies will be for naught.
It is confirmed that the literature on agricultural technology adoption is enormous and somewhat difficult to summarize closely. Though it is difficult, the conventional analysis of agricultural technology adoption focused on imperfect information, risk, uncertainty, institutional constraints, human capital, input availability, and infrastructure as potential explanations for adoption decisions. They also pointed out that the recent literature focuses on social networks and learning to explain factors determining the adoption behavior of agricultural technology
[16] | Obayelu A, Ajayi O, Oluwalana E, Ogunmola O. What Does Literature Say About the Determinants of Adoption of Agricultural Technologies by Smallholders Farmers?. Agri Res & Tech: Open Access J. 2017. |
[16]
.
Different scholars considered different factors that influenced adoption. Based on the study by
[17] | Akudugu MA, Guo E, Dadzie SK. (2012). Adoption of modern agricultural production technologies by farm households in Ghana: What factors influence their decisions? Journal of Biology, Agriculture and Healthcare 2(3): 1-13. |
[17]
, determinants of adoption are classified as; economic, social and institutional factors; Based on the study by
[18] | Kebede Y, Gunjal K, Coffin G. 1990. Adoption of new technologies in Ethiopian agriculture: the case of Teguelet-Bulga District, Shoa Province. Agric Econ 4: 27-43. |
[18]
influencing factors of adoption are categorized into social, economic and physical factors. Furthermore,
[19] | McNamara KT, Wetzstein ME, Douce GK. 1991. Factors affecting peanut producer adoption of Integrated Pest Management. Review of Agricultural Economics 13(1): 129-139. |
[19]
categorized the factors into farmer characteristics, farm structure, institutional characteristics and managerial structure. Here we can see the focusing points of the authors mentioned above in one or other way are farmer characteristics and related institutional factors that determine technology adoption. Hence, this fact tells us considering farmers characteristics diversity during development interventions will be the decisive factor in enhancing adoption or dis adoption of agricultural technologies.
3.5. Empirical Evidence on Rice Technology Adoption
Over 90% of the world’s total rice crop is produced in South and East Asia
[20] | Tamirat Belayneh and Jember Tekle. 2017. Review on adoption, trend, potential and constraints on rice production to livelihood in Ethiopia. International Journal of research Granthaalayah. Vol. 5(Iss. 6): June, 2017. |
[20]
. The study done in one of the leading rice-producing countries of the Asia-Pacific, the Philippines, showed that the Philippines was one of the earliest adopters of “green revolution” seeds and fertilizer technologies, and in 2003, the area of the country planted to modern varieties was almost 100% in both irrigated and rainfed areas
[21] | Launio, C. C, Guadalupe O. R. and Beltran. J. C. 2010. Recent Adoption and Spatial Diversity of Modern Rice Varieties in the Philippines. |
[21]
. Based on the study done by
[22] | Diagne, A., Glover, S., Groom, B., and Phillips J. 2012. “Africa’s Green Revolution? The determinants of the adoption of NERICAs in West Africa” SOAS Department of Economics Working Paper Series, No. 174, SOAS, University of London. |
[22]
, the determinants of the adoption of NERICAs in West Africa are that the adoption rates are sufficiently high to suggest that widespread adoption could stimulate and support a Green Revolution. The study results showed that adoption rates are 88% in Gambia, 55% in Guinea, and 39% in Cote d’Ivoire. It also emphasized that the success of the Green Revolution in these areas was coming from utilization of NERICAs together with other technologies such as fertilizer application and farm management
[22] | Diagne, A., Glover, S., Groom, B., and Phillips J. 2012. “Africa’s Green Revolution? The determinants of the adoption of NERICAs in West Africa” SOAS Department of Economics Working Paper Series, No. 174, SOAS, University of London. |
[22]
.
The study done by IFPRI in 2013 titled Patterns of Adoption of Improved Rice Technologies in Ghana, with the objectives of determining current technology adoption levels and better understanding the constraints and incentives for adoption, showed that (1) adoption of modern varieties accounted for 58 percent of the rice area. Traditional varieties are still popular, especially in northern Ghana. (2) fertilizer use in rice plots is quite high (66 percent of rice area); (3) the adoption of soil fertility management practices is limited; (4) due to the cheap price of pesticides, pesticide use has become very popular, with 84 percent of rice area treated with herbicides. According to the study by
[23] | Kijima, Y., Otsuka, K., and Sserunkuuma D. 2008. Determinants of Changing Behaviors of NERICA Adoption: An Analysis of Panel Data from Uganda. |
[23]
, a major determinant of dropout, which accounts for 37% of the sample households, is the large variation in rainfall, indicating that some farmers adopted NERICA in areas unsuitable for its production. The study found that the availability of seed distribution programs was a critical determinant of NERICA adoption in the early stages (2004) but not in 2006, most likely because the use of farmer-produced seed was widespread in 2006. The shorter distance to rice millers significantly increased NERICA adoption.
Another survey conducted in Uganda in 2005 on 900 farmers result showed that the adoption rate of NERICA is disappointingly low, ranging between 1% and 2%
[23] | Kijima, Y., Otsuka, K., and Sserunkuuma D. 2008. Determinants of Changing Behaviors of NERICA Adoption: An Analysis of Panel Data from Uganda. |
[23]
. This study pointed that the failure of widespread NERICA diffusion was partly due to inappropriate extension activities to promote NERICA in unsuitable areas, such as those predisposed to excessive variations in rainfall. Failure to disseminate appropriate methods for producing high-quality farmer-produced seed is another important factor, which will likely reduce adoption of NERICA as well
[23] | Kijima, Y., Otsuka, K., and Sserunkuuma D. 2008. Determinants of Changing Behaviors of NERICA Adoption: An Analysis of Panel Data from Uganda. |
[23]
.
According to the study done by
[24] | Afework Hagos and Lemma Zemedu. 2015. Determinants of adoption of rice improved varieties in Fogera district of Ethiopia. Sci. Technol. Arts Res. J., Jan-March 2015, 4(1): 221-228. |
[24]
, the factors that influence farmers to use improved varieties were found to be households labor availability, education level of the household head, land holding, distance to the nearest village market, proximity to the main market, distance to access agricultural extension, access to the source of rice seed, access to new varieties and off farm income. Studies in the same area identified nine variables were found to significantly affect adoption: sex of the household head, agricultural organization membership, household heads' participation in field days related to improved upland rice, household head contact with extension agents, participation in social organizations, achievement motivation, attitude towards improved upland rice variety, distance to the input and output markets from the residence of the household, and active labor force of the household. In general, the determining factors mentioned in the above study could be utilized as input for characterizing farms and/or farmers so as to implement farm-specific development intervention to have better technology adoption.
3.6. Farm Typologies and Its Definition
The term “classification” is often misused as synonym for typology, but as argued by
[25] | Marradi, A. 1990. Classification, typology, taxonomy. Quality and Quantity XXIV (2): 129–157. |
[25]
, classification should be understood as the operation itself, whereas classificatory schemes and typologies are products of the operation
[26] | Matus, S. L. S. 2016. Literature Review Report and Proposal for an International Framework for Farm Typologies. Technical Report Series GO-16-2016. |
[26]
. Typology’ is defined in Oxford Dictionary as; ‘the study and interpretation of types. A ‘Type’ is defined as; ‘a class of things or persons having common characteristics’. Central to a typology, therefore, is the design and application of a classification scheme. It is indicated that indicated that the role and utility of any typology is relative to the theoretical or practical perspective within which it is situated
[7] | Emtage N. and Suh J. 2005. Variations in socioeconomic characteristics, farming assets and livelihood systems of Leyte rural households. Ann Trop Res 27: 35–54. |
[7]
.
3.7. Theoretical Background for Creating Farm Typologies
There is a variety of theoretical perspectives that have been used to construct and develop typologies of farmers and rural households
[7] | Emtage N. and Suh J. 2005. Variations in socioeconomic characteristics, farming assets and livelihood systems of Leyte rural households. Ann Trop Res 27: 35–54. |
[7]
. These include Farming styles, Sustainable livelihood, Farming context and Market structure theory. All of these theories strive to account for the behavior of individuals or households and each designates the behavior as a consequence of the interaction between factors such as social, cultural, economic, institutional, biophysical and personal factors. The four theories used to construct typologies of farmers are disused below.
3.7.1. Farming Styles Theory
It relates to a distinct set of styles which farmers are acutely aware of and from which they make decisions. Studies that have used farming styles as a theoretical background emphasize the importance of the farmer as an individual in terms of decision-making, and tend to place more emphasis on qualitative rather than quantitative methods to identify different types
[7] | Emtage N. and Suh J. 2005. Variations in socioeconomic characteristics, farming assets and livelihood systems of Leyte rural households. Ann Trop Res 27: 35–54. |
[7]
.
3.7.2. Farming Context Theory
This theory suggests that behavior in farming is influenced by personal, social, biophysical, and economic factors. This theory focuses on understanding variations in farming practices within similar agricultural enterprises and considers how the enterprise evolves based on available resources, objectives, and practices
[7] | Emtage N. and Suh J. 2005. Variations in socioeconomic characteristics, farming assets and livelihood systems of Leyte rural households. Ann Trop Res 27: 35–54. |
[7]
.
3.7.3. Market Structure Theory
The market structure theory has been used to create typologies of farmers and uses methodologies from marketing studies to guide the typology development. This seek to use typologies to analyze the diversity of consumers for a particular product
[7] | Emtage N. and Suh J. 2005. Variations in socioeconomic characteristics, farming assets and livelihood systems of Leyte rural households. Ann Trop Res 27: 35–54. |
[7]
.
3.7.4. Sustainable Livelihood Theory
The Sustainable Livelihood (SL) approach used to typology development and has profoundly shaped rural development thinking and practice. It is multidisciplinary in the sense that it incorporates insights from a wide range of disciplines including, political, sociological, agricultural, and/or environmental perspectives
[7] | Emtage N. and Suh J. 2005. Variations in socioeconomic characteristics, farming assets and livelihood systems of Leyte rural households. Ann Trop Res 27: 35–54. |
[7]
. Thus, it includes complex interactions of how rural livelihoods intersect with political, economic and environmental processes.
SL approach has been adopted in order to identify, design and assess new initiatives, to review existing activities, to inform strategic decision-making and for further research
[27] | Righia, E., Dogliotti, S., Stefaninic, F. & Pacinia, G., 2011. Capturing farm diversity at regional level to up-scale farm Level impact assessment of sustainable development options. Agriculture, Ecosystems and Environment, 142, pp. 63-74. |
[27]
. The SL approach incorporates three key elements. First, it is a set of principles that specify developmental activity which should be people-centered, locally differentiated according to relevant criteria and multi-level for the purpose of understanding livelihoods. Second, SL uses conventional analytical frameworks (economic, social, institutional etc.) that enable the identification of poor people’s options and constraints. Third, the developmental objective of SL should be clear i.e. to enhance the overall level of sustainability of livelihoods. In its application to agriculture, the SL approach has routinely been applied to the development of farming household typologies
[27] | Righia, E., Dogliotti, S., Stefaninic, F. & Pacinia, G., 2011. Capturing farm diversity at regional level to up-scale farm Level impact assessment of sustainable development options. Agriculture, Ecosystems and Environment, 142, pp. 63-74. |
[27]
. While following SL approach the analysis is focused on households rather than individual farms thereby recognizing the importance of the household as the primary decision-makers in livelihood choices. Thus, the household is seen as the decision- making hub and the outcome of the SL research is directed to improve the livelihoods of poor households. This is done by improving food security, cash income and the environment
[7] | Emtage N. and Suh J. 2005. Variations in socioeconomic characteristics, farming assets and livelihood systems of Leyte rural households. Ann Trop Res 27: 35–54. |
[7]
. Though all of them have its own advantages to use based on the objectives of the study, SL theories is very comprehensive to do farmers typology study.
3.8. Approaches to Constructing Typologies
There are three fundamental approaches used to construct typologies in the rural or farming context
[27] | Righia, E., Dogliotti, S., Stefaninic, F. & Pacinia, G., 2011. Capturing farm diversity at regional level to up-scale farm Level impact assessment of sustainable development options. Agriculture, Ecosystems and Environment, 142, pp. 63-74. |
[27]
. These include; (1) taxonomic, a positivist approach that identifies typologies using empirical data, (2) relational, a realist approach which identifies groupings based on theoretical assumptions on structural relations; and (3) experiential, a hermeneutic approach using human reasoning to identify groups
[7] | Emtage N. and Suh J. 2005. Variations in socioeconomic characteristics, farming assets and livelihood systems of Leyte rural households. Ann Trop Res 27: 35–54. |
[7]
.
The taxonomic approach is used most frequently in developing rural typologies. The ‘relational’ approach identifies groups by their coherent patterns of socio-economic relations by the object of study and its structural context in terms of theoretical considerations while the ‘experiential’ approach identifies groups by the interpretation of the human actors that inhabit the land to give meaning to certain ‘folk’ or ‘experiential’ groups
[7] | Emtage N. and Suh J. 2005. Variations in socioeconomic characteristics, farming assets and livelihood systems of Leyte rural households. Ann Trop Res 27: 35–54. |
[7]
.
Based on the perspective by
[16] | Obayelu A, Ajayi O, Oluwalana E, Ogunmola O. What Does Literature Say About the Determinants of Adoption of Agricultural Technologies by Smallholders Farmers?. Agri Res & Tech: Open Access J. 2017. |
[16]
, there is distinguishing between a ‘structural’ and ‘functional’ typology. The ‘structural’ typology examines the factors of production and how these are structured, while the ‘functional’ typology relates to the decision making of farmers within their biophysical and social environment.
3.8.1. Qualitative Approach
Qualitative typologies are often based on a priori classification and depend on expert knowledge. These classification schemes, also referred to as deductive systems, rely on the knowledge and judgment of the researcher in order to define the specific segmentation of different groups according to their characteristics
[27] | Righia, E., Dogliotti, S., Stefaninic, F. & Pacinia, G., 2011. Capturing farm diversity at regional level to up-scale farm Level impact assessment of sustainable development options. Agriculture, Ecosystems and Environment, 142, pp. 63-74. |
[27]
. The focus of this approach is on identifying and describing what is typical for the different types of farmers instead of defining the boundaries that cause differentiation between groups
[27] | Righia, E., Dogliotti, S., Stefaninic, F. & Pacinia, G., 2011. Capturing farm diversity at regional level to up-scale farm Level impact assessment of sustainable development options. Agriculture, Ecosystems and Environment, 142, pp. 63-74. |
[27]
. Studies that have applied the qualitative approach in the development of typologies include wealth rankings, farming styles and studies that created constructed types
[7] | Emtage N. and Suh J. 2005. Variations in socioeconomic characteristics, farming assets and livelihood systems of Leyte rural households. Ann Trop Res 27: 35–54. |
[7]
.
Within the qualitative approach, typologies can be built on formal discussions (interviews) between researchers and those being researched in a participatory fashion. Those interviewed will then identify the important differences within the population to be used as criteria in the typology development
[7] | Emtage N. and Suh J. 2005. Variations in socioeconomic characteristics, farming assets and livelihood systems of Leyte rural households. Ann Trop Res 27: 35–54. |
[7]
. Alternatively, in the qualitative approach, typologies can be developed by means of the researcher’s expert opinion to define types. These typologies are developed based on a priori knowledge by experts, followed by detailed on- farm questionnaires, to develop a typology on the analysis of the patterns of responses in the quantitative data
[7] | Emtage N. and Suh J. 2005. Variations in socioeconomic characteristics, farming assets and livelihood systems of Leyte rural households. Ann Trop Res 27: 35–54. |
[7]
. Both of these are said to be structural typologies according
[26] | Matus, S. L. S. 2016. Literature Review Report and Proposal for an International Framework for Farm Typologies. Technical Report Series GO-16-2016. |
[26]
.
Classification the former corresponds to the ‘relational’ approach and the latter to the ‘experiential’ approach. Qualitative typologies have therefore most often been used in the farming styles literature
[28] | Bidogeza J. C., Berentsen P. B. M., De Graaff J. and Oude Lansink A. G. J. M. 2007. Multivariate Typology of Farm Households Based on Socio-Economic Characteristics Explaining Adoption of New Technology in Rwanda. AAAE Conference Proceedings (2007) 275-281. |
[28]
. The qualitative approach starts off with the establishment of the theoretical framework. After the theoretical framework has been identified, the next step in the typology development would be to select the criteria that will be used to measure differentiation between farm types. This is done by choosing the specific indicator variables that will be used in the analysis. The specific choice of variables will ultimately have the greatest influence on the results of the classification and is in itself a form of classification. The selected variables should be relevant and be investigated before being used in the classification scheme.
Once the theoretical framework and criteria have been selected, the researcher would then seek to formulate a provisional typology based on
a priori classification that relies formally on the knowledge, understanding and judgment of the researcher to define the characteristics of the segmentation. These methods use mostly arbitrary and ad hoc considerations
[28] | Bidogeza J. C., Berentsen P. B. M., De Graaff J. and Oude Lansink A. G. J. M. 2007. Multivariate Typology of Farm Households Based on Socio-Economic Characteristics Explaining Adoption of New Technology in Rwanda. AAAE Conference Proceedings (2007) 275-281. |
[28]
. Following the provisional typology by the researcher, interviews and surveys will follow on a number of the farms in the specific study area in order to verify each farm type and to establish whether or not the provisional typology is valid. Next, revisions of the provisional typology will be based on the results of the interviews until the researcher is satisfied with the results and will then produce a complete typology of the different types of farms.
One of the main advantages of using the qualitative approach lies in the actor-orientation towards the classification which makes sure that the farmers themselves can identify with the groups
[28] | Bidogeza J. C., Berentsen P. B. M., De Graaff J. and Oude Lansink A. G. J. M. 2007. Multivariate Typology of Farm Households Based on Socio-Economic Characteristics Explaining Adoption of New Technology in Rwanda. AAAE Conference Proceedings (2007) 275-281. |
[28]
. Some of the disadvantages of this approach include a high dependence on the researcher; the inability to make full use of the available data; the lack of statistical foundation and the difficulty in reproducing these typologies
[28] | Bidogeza J. C., Berentsen P. B. M., De Graaff J. and Oude Lansink A. G. J. M. 2007. Multivariate Typology of Farm Households Based on Socio-Economic Characteristics Explaining Adoption of New Technology in Rwanda. AAAE Conference Proceedings (2007) 275-281. |
[28]
.
3.8.2. Quantitative Approach
This approach utilizes multivariate analysis and study diversity by using a finite number of variables to categorize farms, which is more precise and closer to reality
[28] | Bidogeza J. C., Berentsen P. B. M., De Graaff J. and Oude Lansink A. G. J. M. 2007. Multivariate Typology of Farm Households Based on Socio-Economic Characteristics Explaining Adoption of New Technology in Rwanda. AAAE Conference Proceedings (2007) 275-281. |
[28]
. In recent years many studies have utilized the quantitative approach in order to create farm typologies
[28] | Bidogeza J. C., Berentsen P. B. M., De Graaff J. and Oude Lansink A. G. J. M. 2007. Multivariate Typology of Farm Households Based on Socio-Economic Characteristics Explaining Adoption of New Technology in Rwanda. AAAE Conference Proceedings (2007) 275-281. |
[28]
.
Steps followed in using quantitative approach.
Figure 1. General framework of the typology adopted from CGIAR typology Guideline developed in 2014.
Step 1 and 2 involves the establishment of the theoretical framework and the variable selection. Step 3 involves the data collection process. After the data is ready for analysis, the specific method to create the specific groups within the data is determined and applied in step 4. Consequently, the researcher can either move directly to Cluster Analysis (CA) or choose to use one of several data reduction tools or techniques. When CA is used directly after
step 3, the data needs to be standardized by calculating the z-scores
[7] | Emtage N. and Suh J. 2005. Variations in socioeconomic characteristics, farming assets and livelihood systems of Leyte rural households. Ann Trop Res 27: 35–54. |
[7]
. When CA is not directly applied to the data, several statistical methods have been used in
step 4. The most notable and frequently applied methodologies include Principle Component Analysis (PCA), Multi-dimensional Scaling (MDS), Multiple-correspondence Analysis (MCA) and Categorical Principle Component Analysis (CatPCA)
[29] | Dossa, L., Abdulkadir, A., Amadou, H., Sangare, S. & Schlecht, E., 2011. Exploring the diversity of urban and peri-urban agricultural systems in Sudano-Sahelian West Africa: An attempt towards a regional typology. Landscape and Urban Planning, 102, pp. 197-206. |
[29]
. These techniques are all used for data reduction purposes. In Step 5 CA is applied to either the original standardized data or the new data factors created in
Step 4. Cluster Analysis refers to a set of multivariate techniques that seek to classify objects (individuals, households, products etc.) according to their characteristics into groups
[29] | Dossa, L., Abdulkadir, A., Amadou, H., Sangare, S. & Schlecht, E., 2011. Exploring the diversity of urban and peri-urban agricultural systems in Sudano-Sahelian West Africa: An attempt towards a regional typology. Landscape and Urban Planning, 102, pp. 197-206. |
[29]
. Step 6 (the final step) in the quantitative typification comes in the form of a validation of the results from the CA. It is important that these groups are stable and not merely imposed on the data by the classification process
[30] | Kobrich, C., Rehman, T. & Khan, M., 2003. Typification of Farming Systems for Constructing Representative Farm Models: Two Illustrations of the Application of Multivariate Analyses in Chile and Pakistan. Agricultural Systems, 76, pp. 141-57. |
[30]
. The general framework for typology process is shown in
Figure 1 below. On contrary, advantages of this approach are, it does not have a high dependence on the researcher during typology construction like that of qualitative approach; has ability to make full use of the available data and the major one is having statistical foundation that solve difficulty in reproducing these typologies.
3.9. Evidences on the Contribution of Typology in Improving Technology Adoption
Banerjee and his friends in 2014 explained that site-specific nutrient management (SSNM) helps to achieve agronomic and economic benefits while maintaining socially and environmentally sustainable crop production systems. However, to provide appropriate recommendations, a SSNM-based nutrient recommendation needs to be integrated with the classification of farmers as per their resource endowment. Grouping farmers within a domain in different resource endowment classes is an essential step in the realistic evaluation of the constraints and opportunities that exists within farm households for appropriate interventions. In doing so, the study done by Banerjee and his friends in titled Farm Typology-based Phosphorus Management for Maize in West Bengal identified six farm types that were characterized by a host of socio-economic, crop management, and related variables and then used for site-specific nutrient recommendations
[31] | Banerjee, H., R. Goswami, S. Chakraborty, S. Dutta, K. Majumdar, T. Saty-anarayana, M. L. Jat, and S. Zingore. 2014. Understanding biophysical and socio-economic determinants of maize (Zea mays L.) yield variability in eastern India NJAS-Wageningen Journal of Life Sci. 70: 79-93. |
[31]
. In their study, they concluded that farm typology-based nutrient recommendations, in terms of phosphate fertilization, demonstrated a significant increase in agronomic and economic benefit over current farmer fertilizer practices.
The study by Bidogeza and his friends with title Multivariate Typology of Farm Households Based on Socio-Economic Characteristics Explaining Adoption of New Technology in Rwanda stated that for the past two decades, Rwandan research has focused, on the development and promotion of low cost technology such as agroforestry, fast-growing nitrogen-fixing legumes for improved fallows, inter-or relay-cropping, green manure, farmyard manure, composting, mulching systems and combining green manure and others fertilizers
[32] | Drechsel, P. and B. Reck. 1998. "Composted shrubprunings and other organic manures for smallholder farming systems in southern Rwanda." Agroforestry Systems 39(1): 1-12. |
[32]
. However, despite the positive effects of these new technologies on nutrient cycling, reduction of soil loss, crop yields, fodder and firewood production, owning to homogeneity in farming population, particularly with respect to socioeconomic variables, promoted new technology has not matched with socio-economic circumstances, their adoption has remained low
[32] | Drechsel, P. and B. Reck. 1998. "Composted shrubprunings and other organic manures for smallholder farming systems in southern Rwanda." Agroforestry Systems 39(1): 1-12. |
[32]
.
To solve the above-mentioned challenges, the study applies clustering farm households using multivariate analysis and identified five typical farm households with respect to new technology adoption. The first group is characterized by female headed farm households with a relatively high use of compost, green manure, and improved seeds. The second group represents tenants with the smallest farm size. These farmers intensify farming with a high use of green manure. The third group embodies male headed farm households, younger and literate. These farmers intensify farming by using many chemical fertilizers and improved seeds. The fourth group includes illiterate and full-time farm households. The technologies they use most are fallow and manure. The fifth group embodies large farm households with the lowest returns per hectare. The only technology being adopted by them is improved livestock. Hence this typology results clearly pointed out that which type of technologies will be appropriate for all the five groups so as to have better adoption.
The studies conducted with the objective of identifying the predominant farm types in coastal agro-ecosystem of India and to characterize farm by some important socio- economic indicators using 144 sample farm households
[33] | Banerjee, H., Goswami, R., Dutta, S. K., Chakraborty, S. and Majumdar, K. 2015. Farm Typology based Phosphorus Management for Maize in West Bengal. |
[33]
. The study identified four main farm types with different income sources that may be used as a decision support tool by extension agencies. For instance, their study summarized and put forward intervention suggestion based on cluster I as follow: Cluster I is comprised of households having large land holding, large family size, relatively higher family education, and relatively higher crop diversification. The households both lease out and lease in land, land. In terms of economic performance indicators, this cluster is characterized by relatively high system gross return, higher cost of cultivation and system net return and relatively higher cost-benefit ratio. These farms may be supported for technically sound intensification of agriculture with assured input and advisory services. Since these groups pursue a capital intensive diversified farming, access to credit is important for them. It shows relevance of farm typology study when farming characters are heterogeneous and in need of appropriate technology for agricultural sustainability.
A 2015 typology study indicated, of 70 smallholder farm households in Ghana's Northern Region stratified farm households into six distinct types based on factors like household labor, land use, livestock, and income
[34] | Kuivanena K. S., Alvareza S., Michalschecka M., Adjei-Nsiahb S., Descheemaekerc K., Mellon- Bedib S. and Groota J.C. 2016. Characterizing the diversity of smallholder farming systems and their constraints and opportunities for innovation: A case study from the Northern Region, Ghana. NJAS - Wageningen Journal of Life Sciences. |
[34]
. This study clearly demonstrates that using a farm household typology provides a practical framework for identifying type-specific opportunities and constraints. This allows more targeted agricultural interventions and innovations to be developed and implemented, rather than a one-size-fits-all approach. The heterogeneity within the smallholder sector, as revealed by this typology analysis, underscores the importance of tailoring development efforts to the diverse circumstances of different farm household types.
Now a day, a number of studies have focused on defining farm typologies in various countries., especially in sub-Saharan Africa where smallholder farming households’ production takes place in diverse socio-economic and biophysical environments
[5] | Alvarez, S., Paas, W., Descheemaeker, K., Tittonell, P., Groot, J. C. J., 2014. Constructing typologies, a way to deal with farm diversity: general guidelines for the Humid tropics. Report for the CGIAR Research Program on Integrated Systems for the Humid Tropics. Plant Sciences Group, Wageningen University, the Netherlands. |
[5]
. In this context rural farming households develop different livelihood strategies according to their different opportunities and constraints. Governments in many countries are focusing on promoting sustainable development
[7] | Emtage N. and Suh J. 2005. Variations in socioeconomic characteristics, farming assets and livelihood systems of Leyte rural households. Ann Trop Res 27: 35–54. |
[7]
. In this regard, typologies are widely used in the literature in order to understand structural changes in farming with regards to output, employment, arming intensity and impacts of policy reforms
[34] | Kuivanena K. S., Alvareza S., Michalschecka M., Adjei-Nsiahb S., Descheemaekerc K., Mellon- Bedib S. and Groota J.C. 2016. Characterizing the diversity of smallholder farming systems and their constraints and opportunities for innovation: A case study from the Northern Region, Ghana. NJAS - Wageningen Journal of Life Sciences. |
[34]
.
In general, literature indicated that intents of farm typology can be summarized to (1) address specific issues (improvement in productivity, food security, income generation etc.) (2) create specific development policies (3) prioritize investments/scarce resource use (4) propose recommendation domains (5) promoting research and development interventions and (6) create and measure impact. Alvarez and his friends in their general guidelines called Constructing typologies, a way to deal with farm diversity summarized reasons to develop a typology in to four areas. They are:
1. Targeting: the distinction between farming systems is aimed at identifying appropriate interventions per farming system type;
2. Scaling-out: typologies contribute to understanding how appropriate interventions can be disseminated at a large scale;
3. Selection: typologies support the selection of representative farms or the formulation of (average) prototype farms for detailed analyses.
4. Scaling-up: typologies support the extrapolation of ex-ante impact assessments to larger spatial or organizational scales.