Predicting farmer uptake of new agricultural practices: A tool for research, extension and policy

Kuehne, Geoff; Llewellyn, Rick; Pannell, David J.; Wilkinson, Roger; Dolling, P. J.; Ouzman, Jackie; Ewing, Michael A.

doi:10.1016/j.agsy.2017.06.007

Cited by 268 publications

(211 citation statements)

References 50 publications

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“…We found relatively few studies pairing adopters' preferences, attitudes, intentions and beliefs with corresponding aspects of relative advantage of the innovation (profit advantage, environmental advantage, risk reduction) as suggested by the ADOPT conceptual model (Kuehne et al . ). If they include both aspects (i.e.…”

Section: Discussionmentioning

confidence: 97%

“…In either case, it is often assumed that an adopter or population of adopters displaying certain preferences would be predisposed to adopt any technology that would deliver to those preferences. We found relatively few studies pairing adopters' preferences, attitudes, intentions and beliefs with corresponding aspects of relative advantage of the innovation (profit advantage, environmental advantage, risk reduction) as suggested by the ADOPT conceptual model (Kuehne et al 2017). If they include both aspects (i.e.…”

Section: Discussionmentioning

confidence: 99%

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The Adopters versus the Technology: Which Matters More when Predicting or Explaining Adoption?

Munguia¹,

Llewellyn²

2020

Applied Eco Perspectives Pol

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Research on the determinants of adoption of agricultural innovations (technologies and practices) has failed to converge towards a consistent explanation for why farmers choose to adopt new technologies and practices. This absence of convergence matters because it indicates that agricultural extension and policy are influenced by a body of literature that is often not able to offer clear recommendations on the variables that can be used to design policy interventions. Our analysis suggests that, in general, researchers have been too focused on understanding the adopter and the general farming context, and too little concerned with understanding the performance of the technology itself.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

The Adopters versus the Technology: Which Matters More when Predicting or Explaining Adoption?

Munguia¹,

Llewellyn²

2020

Applied Eco Perspectives Pol

Self Cite

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“…The Adoption and Diffusion Outcome Prediction Tool (ADOPT) (Kuehne et al 2017) has become widely used to engage those involved with planning and delivering agricultural research, development, and extension (RD&E) projects and programs in the evaluation of factors influencing adoption of specified innovations for a specified population. Initial efforts to develop a Smallholder ADOPT version (Brown et al 2016) have so far only included relatively minor modifications within the original framework.…”

Section: The Adopt Frameworkmentioning

confidence: 99%

“…Responses are then used in functions that describe quantitatively how the variables relate to each other, and how they influence time to peak adoption and peak adoption level. A full description of the ADOPT model for developed countries, including a detailed version of the conceptual framework shown in figure 1, is presented in Kuehne et al (2017).…”

Section: Adopt Variablesmentioning

confidence: 99%

Predicting Adoption of Innovations by Farmers: What is Different in Smallholder Agriculture?

Llewellyn¹,

Brown²

2020

Applied Eco Perspectives Pol

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Predictions of the speed and extent of adoption of new agricultural practices and technologies are needed to inform decisions and plans in agricultural policy, research and extension. Using an existing tool for predicting the adoption of agricultural innovations in developed countries as the starting point, we identify a number of distinctive features of smallholder agriculture in developing countries that affect agricultural adoption. Additional factors that need to be considered when making predictions of adoption by smallholder populations include: They may be relatively more heterogeneous in their constraints, capabilities, resources, attitudes and priorities; they may be more influenced by particular cultural norms; they may prioritize subsistence over profits; they may be less reliant on agriculture as their primary source of income; they may have relatively high future discount rates; nonlandowner farmers may be less able to capture the benefits resulting from an innovation; and there may be lower and slower diffusion of information across the farming population, with more variable extension quantity and quality.

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“…Instead, adoption of technology involves active consideration of values, beliefs, identities, goals and social networks (Kuehne et al 2017;Allen et al 2017;Pannell et al 2006;Stone and Hochman 2004;Vanclay 2004;Leeuwis 2004). Knowledge of this social context also requires knowledge of the institutions interacting with farmers and farm communities including technology designers and producers, industry bodies, brokers and marketers, supply chain partners and supermarkets and consumers (Carberry et al 2002;Stone and Hochman 2004;Jakku and Thorburn 2010;van Meensel et al 2012;Allen et al 2017).…”

Section: Conceptual Framework: Discourse Analysis As a Tool To Increamentioning

confidence: 99%

Is big data for big farming or for everyone? Perceptions in the Australian grains industry

Fleming

Jakku

Lim-Camacho

et al. 2018

Agron. Sustain. Dev.

114

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Continued population growth and land intensification put increasing pressure on agricultural production and point to a need for a 'step change' in agriculture to meet the demand. Advances in digital technology-often encapsulated in the term 'big data'-are increasingly assumed to be the way this challenge will be met. For this to be achieved, it is necessary to understand the ways that farmers and other industry stakeholders perceive big data and how big data might change the industry. It is also necessary to address emerging moral and ethical questions about access, cost, scale and support, which will determine whether farms will be able to be 'big data enabled'. We conducted a discourse analysis of 26 interviews with stakeholders in the grains industry in Australia. Two main discourses were identified: (1) big data as a technology that will significantly benefit a few larger farms or businesses-Big Data is for Big Farming-and conversely (2) big data as a way for every farmer to benefit-Big Data is for Everyone. We relate these findings and the literature on adoption of technology and social studies in agriculture to the potential of farmers to embrace big data, from basic concerns about network infrastructure through to more complex issues of data collection and storage. The study highlights that there are key questions and issues that need to be addressed in further development of digital technology and big data in agriculture, specifically around trust, equity, distribution of benefits and access. This is the first study of big data in agriculture that takes a discourse analysis approach and thus interrogates the status quo and the prevailing norms and values driving decisions with impacts on both farmers and wider society.

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Predicting farmer uptake of new agricultural practices: A tool for research, extension and policy

Cited by 268 publications

References 50 publications

The Adopters versus the Technology: Which Matters More when Predicting or Explaining Adoption?

The Adopters versus the Technology: Which Matters More when Predicting or Explaining Adoption?

Predicting Adoption of Innovations by Farmers: What is Different in Smallholder Agriculture?

Is big data for big farming or for everyone? Perceptions in the Australian grains industry

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