Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction

Shang, Linmei; Heckelei, Thomas; Gerullis, Maria K.; Börner, Jan; Rasch, Sebastian

doi:10.1016/j.agsy.2021.103074

Cited by 143 publications

(84 citation statements)

References 94 publications

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“…smart farming & agriculture 4.0) has potential to transform food animal farming systems to become more sustainable by reducing the use of antimicrobials (thus preventing the emergence of antimicrobial resistance, AMR), promoting animal health and welfare, and creating conditions for achieving sufficient supplies of good quality and safe food. Having in mind that global agri-food systems are already facing challenges regarding the increasing demand for animal protein, the process of digitalization of livestock farming can have a beneficial impact by optimizing and increasing overall productivity in a sustainable manner without further pollution of soil, water and other agro-ecological systems, as well as improving resilience and adaptation of the food system to climate change (Cole et al, 2018;Shang et al, 2021).…”

Section: Digitalization On Farmmentioning

confidence: 99%

“…The adoption behavior is multidimensional and depends not only on farmers but also on the technical, structural, political and economic conditions of the agri-food chain; the adoption of digital farming can also evolve over time, based on the behavior of farmers and their interactions with the legislative, commercial and political environment (Alexander et al, 2013;Shang et al, 2021). The most important factors influencing the adoption of digital farming technologies are defined in 6 groups (Shang et al, 2021): (I) farm characteristics (farm size, biophysical conditions, livestock ownership, land use, labor availability, farm succession); (II) operator characteristics (education, age, farming as a main occupation, income, computer use, farming experience, innovativeness, knowledge & capacity, risk preference); (III) interactions (consultants, extensions, farmers' associations, technology providers, trade shows, workshops, information sources); (IV) institutions (subsidy/credit, legislation); (V) attributes of technology (relative advantage -perceived usefulness, complexity -ease of use, compatibility, data safety, trialability, observability); (VI) psychological factors (attitude, subjective norm, perceived behavioral control). The most significant factors influencing the adoption of digital farming include: farm size and biophysical condition, level of education and age of farmers, quality of interaction with consultants and extension services, availability of subsidies and credits, usefulness and ease of use of the technology, and farmers' attitude (Shang et al, 2021).…”

Section: Digitalization On Farmmentioning

confidence: 99%

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Digitalization in the meat chain

Nastasijević¹,

Vesković-Moračanin²

2021

Acta agriculturae Serbica

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The meat supply chain should be regularly monitored to ensure the safety of products. Recent developments in Artificial Intelligence, Internet of Things and blockchain technologies will have a tremendous and disruptive effect on the meat supply chain system in terms of improving the monitoring and surveillance of hazards at multiple points along the meat chain (pre-harvest, harvest, post-harvest), in the farm-to-retail continuum, as well as providing better quality of information to consumers for informed decisions regarding food purchase. The farm-to-fork food chain continuum should be managed to provide an appropriate level of consumer protection (ALOP). This can be achieved by science-based risk assessment, which includes information about the prevalence and concentration of major public health hazards (epidemiological indicators) of zoonotic origin (Salmonella, Campylobacter, Listeria monocytogenes, Yersinia, Shiga toxin-producing Escherichia coli/STEC) in all modules along the food (meat) chain: farm - transport - slaughterhouse - meat processing - distribution - retail - consumers. The integration of digital technologies to the Food (Meat) Safety Management System or Meat Safety Assurance System will enhance the integration of Food Chain Information and Harmonized Epidemiological Indicators along the meat chain, from farm to retail (bottom-up) and vice versa from retail to farm (top-down). Meat chain digitalization will also increase the transparency and visibility of all actors involved in meat production, processing, distribution and retail (farmers, meat business operators, competent authorities, retailers), and provide the basis for benchmarking producers and retail chains, as well as pro-active consumer participation in defining future food policies at the national and global level.

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Section: Digitalization On Farmmentioning

confidence: 99%

Section: Digitalization On Farmmentioning

confidence: 99%

Digitalization in the meat chain

Nastasijević¹,

Vesković-Moračanin²

2021

Acta agriculturae Serbica

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“…The theory depicts a five-stage innovation-decision process through which decisions makers pass as they overcome uncertainty and gain knowledge of an (Rogers, 2010). In this regard, Shang et al, (2021) synthesised how perceptions towards the relative advantage, ease of use, compatibility and trialability are often identified as critical factors which impact the adoption of digital technologies within farming systems. These factors are likely to play a key role in helping policy makers to understand actor how/why different actors are more or less motivated to undertake particular actions, and their perceptions towards potential project opportunities and threats (Fig.…”

Section: Including An Explicit 'Further Insight Analysis' (Additional Detailed Analyses For Step 5)mentioning

confidence: 99%

A review of the applicability of the Motivations and Abilities (MOTA) framework for assessing the implementation success of water resources management plans and policies

Conallin

Ning

Bond

et al. 2021

Preprint

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Abstract. Implementation failure is widely acknowledged as a major impediment to the success of water resource plans and policies, yet there are very few proactive approaches available for analysing potential implementation issues during the planning stage. The Motivations and Abilities (MOTA) framework was established to address this planning stage gap, by offering a multi-stakeholder, multi-level approach to evaluate the implementation feasibility of plans and policies. MOTA is a stepwise process focusing on the relationship between trigger, motivation, and ability. Here we outline the base model of the MOTA framework and review existing MOTA applications in assorted water resource management contexts. From our review we identify the strengths and limitations of the MOTA framework in various institutional implementation and social adoptability contexts. Our findings indicate that the existing MOTA base model framework has been successful in identifying the motivations and abilities of the stakeholders involved in a range of bottom-up water resource planning contexts, and in subsequently providing insight into the types of capacity- or consent-building strategies needed for effective implementation. We propose several complementary add-in applications to complement the base model, which specific applications may benefit from. Specifically, the incorporation of formal context and stakeholder analyses during the problem definition stage (Step 1), could provide a more considered basis for designing the latter steps within the MOTA analyses. In addition, the resolution of the MOTA analyses could be enhanced by developing more nuanced scoring approaches, or by adopting empirically proven ones from well-established published models. Through setting the base model application, additional add-in applications can easily be added to enhance different aspects of the analysis while still maintaining comparability with other MOTA applications. With a robust base model and a suite of add-in applications, there is great potential for the MOTA framework to become a staple tool for optimising implementation success in any water planning and policy-making context.

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“…The declining availability of agricultural land, ecological losses and environmental degradation are leading to the adoption and diffusion of digital agricultural technologies that contribute to the transformation of current agricultural systems towards sustainability [2], [3], [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Advanced method of managing soil conservation works in Smart Farms

2021

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The paper presents the results obtained in the SMART farm by using an advanced method of managing soil conservation works. These works involve, first of all, the collection and storage of data on the spot regarding the following parameters in which the plants will grow: disease climate (air temperature, dew point), growth climate (air temperature, solar radiation, deficiency vapours pressure, relative air humidity, precipitation, wind speed, maximum wind speed, daily evaporation) and soil monitoring (soil moisture, precipitation, soil salinity, soil temperature). Data collection is done through intelligent sensors from a wireless weather station and the reception of this information in real-time on a computer/smartphone by the farmer. Therefore, the farmer will be able to make instant decisions on soil conservation work, thusly saving time and workforce for additional on farm inspections.

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Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction

Cited by 143 publications

References 94 publications

Digitalization in the meat chain

Digitalization in the meat chain

A review of the applicability of the Motivations and Abilities (MOTA) framework for assessing the implementation success of water resources management plans and policies

Advanced method of managing soil conservation works in Smart Farms

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