Adoption of precision agriculture technologies in Ontario crop production

Mitchell, Sean; Weersink, Alfons; Erickson, Bruce

doi:10.1139/cjps-2017-0342

Cited by 46 publications

(24 citation statements)

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“…markets, policies), and producer perceptions (e.g. perceptions about profitability, environmental benefits) as the major determinants of PAT adoption in general (Pierpaoli et al 2013;Tey and Brindal 2012;Mitchell et al 2018;Paustian and Theuvsen 2017;Reichardt and Jürgens 2009;Robertson et al 2012;Tamirat et al 2018;Isgin et al 2008;Lambert et al 2015;Roberts et al 2004). However, studies on the adoption determinants of popular…”

Section: Variable Selectionmentioning

confidence: 99%

“…Studies from the United States, Canada, Australia, and the EU show that PAT adoption varies by technology type and region/countries (Griffin et al 2004;Erickson et al 2017;Miller et al 2019;Mitchell et al 2018;Robertson et al 2012;Tamirat et al 2018;Michels et al 2020;Paustian and Theuvsen 2017). Some studies show that adoption rates of information-intensive PATs are lower than those of embodied-knowledge PATs (Miller et al 2019;Erickson et al 2017;Mitchell et al 2018;Robertson et al 2012;Bramley and Ouzman 2019;Bullock and Lowenberg-DeBoer 2007). For example, auto guidance was used in 59% of crop area planted to corn in the United States in 2016, and by 77% of grain growers in Australia in 2012, whereas variable-rate technology (VRT) fertilizer was used in 29% of crop area planted to corn in the United States in 2016 and among 49% of grain growers in Australia in 2012 (Lowenberg-DeBoer and Erickson 2019).…”

Section: Introductionmentioning

confidence: 99%

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Determinants of adoption and adoption intensity of precision agriculture technologies: evidence from South Dakota

et al. 2020

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Precision agriculture technologies (PATs) are promoted as part of both economically efficient and environmentally sustainable agriculture practices. Available PATs are generally classified into two groups; namely, embodied-knowledge and information-intensive PATs. Adoption levels of embodied-knowledge PATs are high relative to information-intensive PATs. Previous studies on the adoption of PATs do not differentiate between embodiedknowledge and information-intensive PATs. Additionally, most studies focus on the adoption of one or two of the available PATs rather than on adoption intensity-defined as the total number of individual PATs adopted. This study fills this gap in the literature by focusing on PAT adoption in general, and adoption intensity of embodied-knowledge and information-intensive PATs in particular. The study uses data from 198 farm-operator respondents in eastern South Dakota from a 2017 survey and employs descriptive statistics and probit and Poisson regression models for the analyses. As per the study, GPS guidance, yield monitor, and automatic section control systems are the most popular PATs, each with adoption rates of over 50%. Overall, findings from the study show that the effect of cropland size, producers' perceptions of profitability, and operator off-farm income are similar for both adoption and adoption intensity of embodied-knowledge and information-intensive PATs. However, there are differences in the effect of land productivity and familiarity with computer use between adoption and adoption intensity and between embodied-knowledge and information-intensive PATs. The effect of producers' perception of the environmental benefits of PATs is inconclusive and needs to be investigated in future research. The results indicate that analyzing PATs as a group of technologies masks differences in determinants between embodied-knowledge and information-intensive PATs. The study provides insights for developing programs, policies, and outreach efforts that encourage the adoption and adoption intensity of both embodied-knowledge and information-intensive PATs. Findings from the study will also be of interest to precision agriculture researchers, extension personnel, agribusinesses, and policymakers who may consider PATs as tools for improving agricultural sustainability and food security.

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Section: Variable Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Determinants of adoption and adoption intensity of precision agriculture technologies: evidence from South Dakota

et al. 2020

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“…However, uptake has been unexpectedly slow, so the application provides an opportunity to assess the range of factors affecting adoption (Mitchell et al . ). The role of economic as well as other factors in the adoption process based on previous studies is then summarized.…”

Section: Introductionmentioning

confidence: 97%

“…The digital revolution, allowing the rate of fertilizer to vary spatially across the field in contrast to the traditional approach of a single uniform rate, has the potential to increase net returns while concurrently reducing the sector's environmental impact ). However, uptake has been unexpectedly slow, so the application provides an opportunity to assess the range of factors affecting adoption (Mitchell et al 2018). The role of economic as well as other factors in the adoption process based on previous studies is then summarized.…”

Section: Introductionmentioning

confidence: 99%

Limits to Profit Maximization as a Guide to Behavior Change

Weersink¹,

Fulton²

2020

Applied Eco Perspectives Pol

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Economists have long been interested in why farmers decide to adopt new technologies. Modeling decision making requires modeling behavior; the key behavioral assumption is profit maximization. In addition, most empirical studies assume a binary decision—adopt or not. This paper argues that adoption should be understood as a process with multiple stages in which the final decision to use the new technology only occurs if the previous stages are completed. While profit considerations are clearly important, particularly in the later stages of the process, they need to be supplemented with other social and cognitive considerations, particularly in the early stages. Understood this way, profit maximization assumptions can provide predictions for the upper or lower bounds of adoption. The adoption rate suggested by profit maximization will be an upper bound if noneconomic factors are expected to either slow down or deter adoption, while that rate will be a lower bound if noneconomic factors are expected to encourage adoption. To capture these elements, econometric models need to pay attention to the timing of decisions and use techniques that condition later‐stage decisions on previous stage outcomes. In addition, the paper suggests the use of expert systems that have economic, learning and social aspects explicitly built into them would be valuable.

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“…In a review of studies examining the factors affecting adoption all of which were conducted in the US and EU, Pathak et al (2019) found farm size and farmer education were the common determinants regardless of the type of precision agriculture considered. Recent Canadian studies include Mitchell et al (2018) and Steele (2018) while Turland and Slade (2020) examined the willingness of farmers to participate in Big Data platforms but not the adoption of the associated technologies. To assess adoption barriers, it is important to understand farmers' perceptions of the benefits, which can be multi-faceted and differ along the adoption process (Weersink and Fulton, 2020;Thompson et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Adoption barriers for precision agriculture technologies in Canadian crop production

2021

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Although precision agriculture (PA) is touted to enhance both farm returns and environmental quality, its perceived uptake is not as widespread as would be expected. The objective of this paper is to update and compare the adoption rates for precision agriculture technologies in Ontario. Geographic service technologies are the most adopted technology and showed an increase in adoption between 2017 and 2019. The survey was extended to include crop input suppliers across the country. Increasing adoption rates for PA technologies requires overcoming barriers, specifically reducing the cost and providing demonstrated value.

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Adoption of precision agriculture technologies in Ontario crop production

Cited by 46 publications

References 2 publications

Determinants of adoption and adoption intensity of precision agriculture technologies: evidence from South Dakota

Determinants of adoption and adoption intensity of precision agriculture technologies: evidence from South Dakota

Limits to Profit Maximization as a Guide to Behavior Change

Adoption barriers for precision agriculture technologies in Canadian crop production

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