A multi-dimensional adoption approach for improved sorghum varieties in eastern Kenya: a climate change adaptation perspective

Mwangi, Backson; Macharia, Ibrahim; Bett, Eric K.

doi:10.1080/17565529.2020.1763237

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…The step-hazard approach has been successfully used to represent Rogers' adoption process in several studies [32], and there are some examples in the marketing literature using a step-hazard approach at a national or international level [26,32,33]. However, there are very few similar studies in agriculture (e.g., [34,35]). Figure 2 shows how the adoption process proposed by Pannell et al [30] can be represented as a step-hazard model.…”

Section: Staged Adoption Modelling Approachmentioning

confidence: 99%

Understanding the Adoption of Innovations in Agriculture: A Review of Selected Conceptual Models

2021

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Models can provide a structured way to think about adoption and provide a method to investigate the impacts of different factors in the adoption process. With at least 70 years of research in the adoption of agricultural innovations, there has been a proliferation of adoption models, both conceptual and numerical. This diversity has resulted in a lack of convergence in the way adoption is defined, explained, and measured, causing agricultural extension and policy to rely on a body of literature that is often not able to offer clear recommendations on the variables or mechanisms that can be used to design interventions. We conducted a review of conceptual models to clarify the concepts and approaches used in the practice of modeling adoption in agriculture. We described general adoption conceptual models originating from sociology, psychology, economics, and marketing and reviewed examples of models specifically defined for the study of adoption in agriculture. We also broadly assessed the ability of conceptual models to support building numerical models. Our review covered a range of modeling approaches for diffusion and individual adoption, illustrating different perspectives used in the literature. We found that key elements that should be used in adoption models for agriculture include: a way to assess the performance of the proposed new technology (e.g., relative advantage, both economic and non-economic) in relation to the existing technology or practice in place, the process of learning about this advantage, the interaction between individual decision-making and external influences, and characteristics of potential adopters affecting their attitudes towards the technology. We also detected inconsistencies in how different elements are treated in different conceptual models, particularly behavioral elements such as attitudes, motivations, intentions, and external influences. In terms of modeling, the main implication of these inconsistencies is the difficulty to generate quantitative evidence to support these models since multiple interpretations make it difficult to achieve consistency in the definition of observable, measurable variables that can be used to quantify cause-effect relationships. Suggestions for further research in the field include: questioning whether the adoption of all technologies and practices can be represented by the same adoption or learning process, exploring the dynamics in the relationship between adopters and technology before and after adoption, and questioning the basic assumptions behind the process of individual decision-making models and the role of collective decision-making. Findings from this review can be considered by adoption researchers and modelers in their work to assist policy and extension efforts to improve the uptake of future beneficial agricultural innovations.

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Section: Staged Adoption Modelling Approachmentioning

confidence: 99%

Understanding the Adoption of Innovations in Agriculture: A Review of Selected Conceptual Models

2021

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“…Generally, few systematic adoption studies at the national level exist for the three countries. 6 In eastern Kenya, Mwangi et al (2021) find that 57% of the sampled households adopted an improved sorghum variety. Chimoita et al (2017) find a 58% adoption rate of improved sorghum in Embu County in Kenya.…”

Section: Resultsmentioning

confidence: 99%

Contextualizing private sector-based seed system development: The case of sorghum in Eastern Africa

2021

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Private sector-based seed system development remains a key development intervention in Sub-Saharan Africa. Seed system interventions promoting the adoption of improved varieties through the private sector generally follow a linear, market-oriented technological adoption logic. A qualitative case study of the sorghum seed system in Kenya, Uganda, and Tanzania demonstrates that this model may not be able to drive the broad-scale adoption of improved sorghum varieties and to generate significant benefits for small sorghum-farming households. The findings suggest that the agro-ecological, social, and political-economic contexts critically determine the role improved varieties and the private sector can play in rural development. Improved sorghum varieties promoted by both the public and private sectors may not suit the needs, preferences and contexts of farming households. Seed companies hold sorghum as an add-on in their portfolio, investing less resources and research into sorghum compared to more profitable crops such as vegetable and maize seeds. Significant political-economic obstacles exist that favor the support of cash crops such as maize and rice, limiting the growth and development of the private sector in the sorghum seed system. We conclude that future interventions should build on approaches that aim to develop more diverse channels of seed delivery in both the formal and informal seed systems, adopt a livelihoods perspective to evaluate the costs, benefits, and risks associated with the adoption of new technologies, and acknowledge that seed system interventions are only one out of a portfolio of interventions to generate rural development.

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“…is study was closely related to identifying restrictions on farm family production and productivity using better dairy technologies. For the sake of simplification, in this research, the number of cattle owned by the respondent families was translated into tropical livestock units [63,64]. Both sample respondents had an average of 3.3 tropical livestock units in the research area.…”

Section: Tropical Livestock Unit (Tlu)mentioning

confidence: 99%

Determinants of Adoption of Improved Dairy Technologies: The Case of Offa Woreda, Wolaita Zone, Southern Ethiopia

Zemarku¹,

Senapathy

Bojago

2022

Advances in Agriculture

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One of the agricultural activities of rural farmers is dairy farming. Furthermore, by providing income, it has the potential to make farm households rich. The objective of this study was to analyze the general factors that influence adoption decisions, as well as the status of adoption of dairy technology in the Offa district, Wolaita zone, southern Ethiopia. The study area has fundamental factors influencing the adoption of dairy technologies which were unknown. This research was started to fill a knowledge gap for responsible bodies. Four kebeles were selected randomly for the study. 150 sample households (63 adopters and 87 nonadopters) were chosen using the SRS (systematic random sampling) method. The interview schedule was used to collect primary data from these households. Secondary data were gathered from a variety of sources, including reports, research findings, documents, and publications. Both quantitative and qualitative data were analyzed by using descriptive and econometric analyzes by SPSS version 21. Focus group discussions and key informant interviews were also used to collect qualitative data. A binary logistic regression model was used to examine the factors that influence the adoption of dairy technologies. The results of the binary logistic regression model revealed that both dairy technology participants and nonparticipant households experienced a combination of personal, demographic, economic, and institutional factors that hindered adoption. Respondent age, access to credit services, size of household landholding, frequency of the extension contact, involvement of outside income, livestock ownership, and distance from the nearest market were all significant negative contributing factors. On the other hand, farm income and dairy farming experience were positively significant variables in the model. Technical assistance to improve farm productivity and income, experience sharing and field visit programs to the fields of early experienced dairy farmers, improvement of farmland usage, improvement of road infrastructure in the study area, provision of appropriate and modernized training and extension services, and so on are among the recommendations.

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A multi-dimensional adoption approach for improved sorghum varieties in eastern Kenya: a climate change adaptation perspective

Cited by 8 publications

References 20 publications

Understanding the Adoption of Innovations in Agriculture: A Review of Selected Conceptual Models

Understanding the Adoption of Innovations in Agriculture: A Review of Selected Conceptual Models

Contextualizing private sector-based seed system development: The case of sorghum in Eastern Africa

Determinants of Adoption of Improved Dairy Technologies: The Case of Offa Woreda, Wolaita Zone, Southern Ethiopia

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