Vol:.(1234567890)

Precision Agriculture (2024) 25:1958–1981
https://doi.org/10.1007/s11119-024-10148-7

1 3

Mapping varieties of farmers’ experience in the digital 
transformation: a new perspective on transformative 
dynamics

Valentin Knitsch1  · Lea Daniel2  · Juliane Welz1 

Accepted: 29 April 2024 / Published online: 4 June 2024 
© The Author(s) 2024

Abstract
The COVID-19 pandemic has highlighted the vulnerabilities of the global food system, 
underscoring the need for a sustainable transformation of the food system. With the 
advent of new digital technologies emerging as critical tools for achieving the agricultural 
shift, it is important to understand farmers’ adoption decisions better. This study aims to 
systematically uncover and delineate the varied forms of experiences farmers have with 
new digital technologies and investigate how these experiences impact the organizational 
adoption decisions on the farm. In this study, twenty interviews with apple growers, wine 
makers, and intermediaries from a German region encompassing Saxony, Thuringia, and 
Saxony–Anhalt were conducted and analyzed. Through the lens of the modified adaptive 
capacity wheel and alongside the interview data, five relevant types of experiences were 
identified. These types of experiences are closely related to farmers’ adaptation motivation 
(AM) and adaptation belief (AB), potentially influencing their future decisions about 
the adoption of digital technologies. This study highlights the importance of creating 
meaningful experiences with technologies to strengthen farmers’ AM and AB.

Keywords Digital transformation · New digital technologies · Agriculture · Experience · 
Smart farming · Adaptive capacity

Introduction

Today’s society depends heavily on a global interconnected and compound industrial 
agriculture network to meet its food demands (Carducci et al., 2021). While the agrifood 
system is considered robust enough to cope with smaller shocks, it has become increas-
ingly susceptible to major ones (Moersdorf et  al., 2024). The COVID-19 pandemic and 
the war in the Ukraine have exposed its unpreparedness against such events (Ben Hassen 

 * Valentin Knitsch 
 valentin.knitsch@imw.fraunhofer.de

1 Fraunhofer-Center for International Management and Knowledge Economy IMW, Leipzig, 
Germany

2 Institute for Human Factors Engineering and Technology Management, University of Stuttgart, 
Stuttgart, Germany

http://orcid.org/0009-0005-3115-5544
http://orcid.org/0000-0001-8651-5002
http://orcid.org/0000-0001-9049-3088
http://crossmark.crossref.org/dialog/?doi=10.1007/s11119-024-10148-7&domain=pdf


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& El Bilali, 2022; El Bilali et al., 2024; Moersdorf et al., 2024). Within the agricultural 
domain, small and medium-scale farmers are confronted by a myriad of challenges. These 
encompass the enduring scarcity of seasonal labour during critical planting and harvesting 
phases, the amplified costs and accessibility constraints concerning agricultural inputs such 
as seeds and fertilizers, and the volatile changes inherent in market dynamics (Meixner 
et al., 2022; Paudel et al., 2023; Stojcheska, 2021). These challenges not only affected indi-
vidual farming organizations but also disrupted the entirety of the agrifood system, result-
ing in 122 million more people suffering from hunger than before these events in 2019 
(FAO, 2023). Thus, a transformation towards a more resilient agrifood system is needed. 
New technologies are considered critical to this transformation (FAO, 2022). The term new 
digital technologies summarizes all recent innovations that potentially transform on farm 
routines and business procedures, e. g. Precision Agriculture Technologies and Farm Man-
agement Information Systems. Anticipated to bolster resilience against the aforementioned 
challenges, new digital technologies are expected to enhance the production output while 
offering possibilities to increase the agricultural inputs as well as labour efforts (Moysiadis 
et al., 2021; Sugandh et al., 2023) and enhance the market access for small and medium-
scale farmers by overcoming information barriers (Deichmann et al., 2016). Although new 
digital technologies continue to evolve and gain acceptance in the market (Osinga et al., 
2022), numerous unanswered questions concerning the digital transformation of the agri-
cultural sector remain.

Digital transformation scholars in agriculture have emphasized the need to enhance 
information access, establish financial support mechanisms, and improve technological 
services and education (Ammann et al., 2022; Kendall et al., 2022; Mizik, 2023). These 
suggestions are built upon various studies investigating factors that may either promote or 
hinder technology adoption among farmers. Typically, these studies employ technology 
acceptance models, such as the Unified Theory of Technology Acceptance (UTAUT), 
which have been utilized in numerous research (Li et  al., 2020; Otter & Deutsch, 2023; 
Pathak, 2019; Ronaghi & Forouharfar, 2020; Yatribi, 2020). However, these studies are 
criticized for their lack of explanatory value (Giua, 2022; Pathak, 2019; Tey & Brindal, 
2012; Yatribi, 2020). Additionally, the current literature neglects the organizational context 
in which such adoption decisions occur, tending to view farmers as isolated decision-
makers (Rondan-Cataluña, 2015).

A growing body of research (Engås et al., 2023; Kernecker et al., 2020; Schimmelpfennig 
& Ebel, 2016) suggests that prior experiences might influence the adoption decision of 
new digital technologies, however these studies only tangentially address the role of 
experience. Consequently, there is a noticeable gap in the literature regarding farmers’ 
prior technology experiences in the realm of implementing new digital technologies. 
Against this backdrop, this study aims to address this gap by exploring the varied forms of 
experiences farmers have before and with new digital technologies and investigating how 
these experiences impact the organizational adoption decisions on the farm. To do so, the 
conceptual framework of the Adaptive Capacity Wheel (ACW), developed by Gupta et al. 
(2010), in the modified version of Grothmann et al. (2013), is used in this study. The ACW 
suggests the individual factors adaptation motivation (AM) and adaptation belief (AB) to 
explain how an actor’s personal attitudes affect the organizational capacities to respond to 
change. Thus, the concept of adaptive capacity (AC) serves as a framework for exploring 
the relationship between farmer’s prior experiences with new digital technologies and 
the organizational capacity to respond to challenges of the digital transformation. This 
approach deviates from previous research by recognizing that the experiences made on the 
individual level affect the capabilities, motivation and beliefs of a farming organization to 



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make adoption decisions for new digital technologies. Thus, in this study it is assumed that 
prior experiences with a strong link to AM and AB are most promising to better understand 
the organizational decision making on the farm.

The primary aim of this research is to systematically uncover and delineate the 
various types of experiences farmers have had with new digital technologies. Once these 
experiences are identified, they are juxtaposed with adaptation motivation (AM) and 
adaptation belief (AB) to pinpoint the specific types of experiences that hold the most 
weight in influencing farmers’ decisions to adopt digital technologies. This comparative 
analysis is vital in determining which experiences are most likely to drive or hinder the 
adoption of digital transformations in agriculture.

Therefore, this study addresses two research questions:

RQ 1: What types of experiences do farmers have when engaging with new digital 
technologies?

RQ 2: How are these types of experiences relevant to farmers’ adaptation motivation and 
adaptation belief?

These questions are answered by conducting a qualitative study. Twenty German 
apple growers, wine makers and intermediaries have been interviewed. Pomiculture und 
viticulture were selected due to their shared technological requirements for cultivation and 
the challenges they face, such as the need for advanced automated steering systems and 
image recognition systems for monitoring fruit growth and facilitating robotic harvesting. 
Additionally, the business conditions for agriculture in Germany merit exploration due to 
their unique requirements. This study specifically focuses on the ‘Dreiländereck’ region of 
Germany, encompassing Saxony, Thuringia, and Saxony-Anhalt.

The paper is organized as follows: Sect.  “Theoretical background” provides an 
overview of existing literature on the adoption of smart farming technologies, farmers’ 
experiences, and introduces the ACW framework. Sect. “Material and methods” details the 
methodology employed for data collection and analysis. Findings and their implications are 
discussed in Sect. “Results and discussion”. Finally, the paper concludes by discussing the 
limitations of the study, and delineating its theoretical and practical contributions, as well 
as its relevance for future research endeavours.

Theoretical background

Experience in the agricultural digital transformation

Experience plays a pivotal role in technology acceptance, shaping the usage intention of 
new technologies (Eagly & Chaiken, 1993; Fishbein & Ajzen, 1975). Taylor and Todd 
(1995) distinguished between experienced and inexperienced technology users, laying 
the groundwork for future studies. However, Varma (2013) argued that researchers 
must differentiate between types of prior experience, as they can lead to varying levels 
of acceptance. In the agricultural digital transformation, various factors, including farm 
size, crop type, financial resources, education, attitude towards risks, age, and gender, 
have been identified as influential in technology adoption. Authors who tried to synthesize 
those findings in meta reviews state that many of the factors surveyed are likely to be 
interrelated, but relationships remain underexplored (Pathak, 2019; Yatribi, 2020). In 



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the Unified Theory of Acceptance and Use of Technology Model (UTAUT) (Venkatesh 
and Davies, 2000), experience is considered to impact the facilitating conditions before 
and after technology introduction. However, in the agricultural transformation research, 
only Li et  al. (2020) have included experience in their study on precision agriculture 
technology acceptance in the Chinese crop sector, replacing technology experience with 
general farming experience. Others have either discarded moderating variables (Ronaghi & 
Forouharfar, 2020) or substituted them with different variables (Giua et al., 2022; Otter & 
Deutsch, 2023). Whenever studied, experience is defined by the time a user spends with the 
studied technology.

Outside of the technology acceptance discourse, scholars’ interest in experiences with 
technology seems to increase. However, experience only plays a secondary role in all these 
studies, which is why there is still no common understanding of the term experience. A 
mismatch between farmers’ expectations and actual experiences with technologies often 
results in frustration (Kernecker et al., 2020). And, when the expectation in form of a goal 
for a yield is experienced to be met, this seems to positively influence the adoption decision 
(Schimmelpfennig & Ebel, 2016). But, non-adopters are found to be frustrated even before 
they decide to adapt a technology, due to frustrating experiences with the availability 
and accessibility of new technologies (Kernecker et al., 2020). Because past and current 
experiences have been found to shape individuals’ and groups’ technological frames and 
guide their actions during digital transformation (Engås et al., 2023), for this study, it is 
assumed that not only the expectations towards a technology have an influence on the 
adoption decision, but that this decision is also significantly influenced by all previous 
experiences made with (other) technologies.

In the context of this study, experience is defined as the dynamic interplay between an 
individual, their surroundings and a technology including both conscious and unconscious 
processing of stimuli, resulting in the acquisition of knowledge, skills, and memories that 
guide future actions and responses towards new digital technologies. Within this definition 
and research context, experience is not only a set of mental states of an individual person, 
experience can also be understood collectively or institutionalized, for example. To 
improve the informative value, studies on experience should explore their definition of 
the dynamic interplay depending on the research context (e.g. individual, company and 
or industry level) and present it more concretely. This is a major reason for exploring and 
mapping varieties of experience.

The adaptive capacity wheel framework

Adaptive capacities are the “inherent characteristics of institutions that empower social 
actors to respond to short and long-term impacts either through planned measures or 
through allowing and encouraging creative responses from society both ex ante and 
ex post” (Gupta et  al., 2010, p. 4). Thus, resilient institutions and organizations are 
those that allow their actors to navigate both anticipated and unforeseen circumstances 
with flexibility and creativity, all while preserving a sense of identity (Gupta et  al., 
2010). The ACW framework was developed by Gupta et  al. (2010) for assessing the 
described adaptation resilience in institutions and organizations. The authors state 
that institutions that promote adaptive capacity are those that (1) promote diverse 
perspectives, participants, and approaches; (2) empower actors to perpetually enhance 
their institutions; (3) empower actors to change their behaviour; (4) harness leadership; 
(5) mobilize resources to execute adaptation strategies, and (6) strengthen principles 



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of equitable governance. Built upon these six founding blocks, Gupta et  al. (2010) 
developed a framework with six dimensions to assess adaptive capacities. Grothmann 
et al. (2013) later incorporated two psychological dimensions to understand adaptation 
processes better.

Each of the eight dimensions is described by three to four criteria for measuring 
adaptive capacity (Gupta et  al., 2010). The first dimension, variety, refers to the 
(cognitive) room for a diverse framing of problem definitions, the engagement of 
different actors and stakeholders with different opinions, and a broad range of policy 
options to tackle a problem (Gupta et  al., 2010). The second dimension, learning 
capacity, addresses the presence of trust, the ability of organizations and institutions to 
learn from their experiences and adjust routines and patterns accordingly, opportunities 
to discuss doubts, and good monitoring practices to evaluate changes. Room for 
autonomous change, the third dimension, covers access to information, the ability to act 
according to plans as well as the capacity of organizations and institutions to improvise. 
The fourth dimension, leadership, addresses the importance of shared long-term 
visions and opportunities for leaders to stimulate change processes and collaboration. 
Dimension five tackles the availability of necessary resources. The sixth dimension, 
fair governance, highlights that organizations and institutions need to be embedded in 
a system that supports the process of change through legitimacy, equity, responsiveness 
and accountability. Dimensions seven and eight refer to the role of the individual and 
play the most pivotal role in this study. Adaptation motivation (AM) describes the 
motivation of an individual to realize, support and promote adaptation. Adaptation 
belief (AB) refers to an individual’s belief to be able perform transformative activities. 
AB is considered in two different ways: First, in the individual’s belief that the outcome 
of a process of change solves their initial problem—the outcome-efficacy belief—and 
second, the feeling of being capable to actually perform a response—the self-efficacy 
belief (Grothmann et al., 2013).

Both, climate adaptation and adaptation to challenges opposed by the digital trans-
formation aim to foster the resilience of organizations. Thus, challenges addressed by 
the ACW (Prutsch, 2014, Grothmann et  al., 2013), such as uncertainty, knowledge 
gaps, mainstreaming of adaptation, policy integration, equity, and adaptation barriers, 
are also prevalent in the digital transformation (Cook et al., 2022; Klerkx et al., 2019). 
Therefore, a horizontal borrowing (Fisher & Aguinis, 2017) of the ACW is performed. 
The framework has been modified for this study to adequately capture the context of 
the digital transformation in farming organizations. Those differ from the institutions 
described by Gupta et al. (2010) and Grothmann et al. (2013) in three ways: First, some 
of the farming organizations studied are rather small. Therefore, not all of them have the 
capacity to implement back-up systems (i.e. most of the pomiculture organizations only 
own one harvester) for creating redundancy. Because of that, the criteria ‘Redundancy’ 
has been removed from the Variety dimension. Second, the institutional or organiza-
tional knowledge in smaller farming organizations is often implicit and poorly docu-
mented or, if documented, sensible. This makes it difficult to compare the changes in the 
institutional knowledge body over time (institutional memory). Consequently, the crite-
ria ‘Institutional Memory’ has not been applied in this study. Third, ‘Accountability’ is 
described as one criteria of the ‘Fair Governance’ dimension (Gupta et al., 2010). This 
refers to the organizational responsibilities transferred to different parties. Although 
the studied organizations share and transfer accountability to different actors to some 
degree (i.e. by being a member of a cooperative), none of them had processes in place 
to evaluate their accountability procedures. For this reason, the criteria ‘Accountability’ 



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has not been studied. Figure 1 provides an overview of the ACW framework’s applica-
tion in this study.

Material and methods

Data collection

This research was primarily conducted to guide and inform various technology testing 
within the publicly funded digital experimentation field “EXPRESS” (funded by the Ger-
man Federal Ministry of Food and Agriculture). The objective of the project is to align the 
attitudes, factual circumstances, and interests of small and medium-sized wine-making and 
apple-growing businesses from Saxony, Thuringia, and Saxony-Anhalt with the agendas 
of applied research and testing. Numerous conversations in different settings took place to 

Fig. 1  Overview of the modified adaptive capacity wheel in this study based on Gupta et  al. (2010) and 
Grothmann et al. (2013)



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achieve this goal. For this study, only conversations that were facilitated using the inter-
view guidelines presented in 3.2.1 have been analyzed.

Thus, the data set consists of twenty interviews with apple growers, wine makers 
and different intermediaries from the viticulture and pomiculture sector. As depicted 
in Table  1, the size of the farms and the professions of the intermediaries varied 
significantly. This variety was intentional, as the goal was to capture a exploratively and 
qualitatively broad picture of the stakeholders within a particular region. The size of 
the farms surveyed is typical for the region, ranging from large successor enterprises of 
the former German Democratic Republic agricultural cooperatives to small-structured, 
often family-led, orchards and vineyards. Compared to the rest of Germany, this region 
is important but rather small. In 2020, the German Federal Statistical Office (Destatis) 
counted 360 of such farms (permanent crops) which means that the studies sample 
represents roughly 3.6% of all businesses in this region. Germany, in total, counts 
around 18,000 businesses of this kind with a standard output of 117,500 EUR per 
business and year (Destatis, 2021). The ‘Dreiländereck’ region represents 4.6% (9,946 
ha) of permanent crop land in Germany and has a 2.5 times higher standard output per 
business (292,600 EUR) compared to Germany in total, mainly due to few very large 
players (Destatis, 2021). Participants were not chosen at random and there was also no 
prior segmentation by company size; therefore, not all criteria of representativeness 
were met. However, this sample can be considered quite typical for a German permanent 
crop region. Additionally, one third of the total permanent cropland of this region (3058 
ha) is represented in this sample. It must be noted that permanent crops are in terms of 

Table 1  Overview of interviews

Title Type Profession Farm size (ha) Duration (h)

Intermediary_1 Face to face Agricultural consulting – 1:15
Intermediary_2 Phone Agricultural consulting – 1:22
Intermediary_3 Phone Agricultural trade – 1:15
Intermediary_4 Video call Machine manufacturer – 1:18
Intermediary_5 Phone Software provider – 1:05
Intermediary_6 Video call Machine manufacturer – 1:15
Intermediary_7 Face to Face Pomiculture interest group – 1:12
Pomiculture_1 Face to Face Pomiculture 30 0:47
Pomiculture_2 Phone Pomiculture 300 0:54
Pomiculture_3 Phone Pomiculture 102 1:15
Pomiculture_4 Phone Pomiculture 1500 1:13
Pomiculture_5 Video call Pomiculture 13 1:06
Pomiculture_6 Phone Pomiculture 500 1:23
Pomiculture_7 Phone Pomiculture 60 1:00
Pomiculture_8 Face to face Pomiculture 400 1:10
Viticulture_1 Face to face Vinery 90 1:15
Viticulture_2 Face to face Vinery 11 0:58
Viticulture_3 Face to face Vinery 25 1:27
Viticulture_4 Phone Vinery 22 1:24
Viticulture_5 Phone Vinery 5 1:05



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standard output comparable to annual crops in Germany (110,000 €). Cost structures, 
cultivation systems and machinery, however, vary heavily. Despite some interviewed 
farmers that seemed to be very skeptical towards digital transformation, a slight 
tendency towards a more technology-optimistic sample should be assumed with respect 
to participation bias. Participants were identified via web research and complementary 
official listings in agricultural organizations. They were recruited via email invitations 
that included a brief description of the “EXPRESS” project. Additionally, most of the 
farmers were contacted by phone. The intermediaries were chosen based on their area of 
expertise, following a principle of maximum diversity. A total of 65 stakeholders were 
formally contacted, most of them farmers. The response rate was 31%.

The interviews were initially scheduled for spring 2021. However, due to the travel 
restrictions imposed by the COVID-19 pandemic, some interviews were conducted 
online or via telephone, while others were postponed to spring 2022 to facilitate face-to-
face meetings. The average duration of the interviews is 70 min, and all interviews were 
conducted in German.

Method

According to the theoretical framework of the ACW, a qualitative in-depth interview 
study was employed, an approach that has been proven suitable for explorative studies in 
previous research (Gabriel & Gandorfer, 2023; Kendall et  al., 2017). In this study, long 
in-depth interviews proved effective in engaging meaningfully with farmers, providing 
them with a comfortable platform to share their opinions and past experiences openly. 
The conducted interviews were semi-structured. This interviewing technique supports 
the rigorous collection of open-ended data relevant to the topic explored. Moreover, it 
encourages participants to share their thoughts, feelings, and beliefs, and is thus suitable 
when addressing more sensitive issues (De Jonckheere & Vaughn, 2019).

Interview guidelines

The semi-structured format allows for flexibility and spontaneity. To ensure all dimensions 
of the ACW were adequately covered in each interview, a catalog of guiding questions 
was compiled. The guiding questions are presented with their link to the ACW dimensions 
below. Notably, in the AB category, a strategic decision was made to refrain from 
formulating specific inquiries pertaining to the two dimensions—self-efficacy belief and 
outcome-efficacy belief. This choice was motivated by the recognition that such questions 
could potentially introduce bias or suggestiveness.

During the interviews, the questions were organized thematically into four different 
blocks, each building upon the previous one. In adherence to the semi-structured interview 
approach, situational follow-up questions were utilized to delve deeper into topics of 
interest. The interviews were conducted and transcribed in German. Below, the  guiding 
questions are presented in their translated version (Table 2).

Coding and analysis

A comprehensive and systematic coding process was conducted using MaxQDA Plus 2022 
(Release 22.5.0). The interview transcripts were coded using the Adaptive Capacity Wheel 



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Table 2  Guiding questions for the interviews based on the ACW dimensions

Variety

 Diversity of problem frames What significant changes have you noticed in the last few years 
regarding the digitalization in special crop cultivation [fruit/wine 
growing]?

 Multiple actors, -sectors and 
-levels

With which other actors (networks) do you regularly cooperate in 
your daily work?

 Diversity of solutions In your perception, are there enough solutions and approaches to the 
challenges in the agricultural sector?

Learning capacity
 Trust Generally speaking, what is your opinion on new digital technologies 

in agriculture?
[Where do you find points of contact with this topic in your everyday 

work on the farm? To what extent would you describe your farm as 
‘digitized’?

Do you know of concrete examples of digital technologies that 
you would describe as aberrant for your everyday practice in 
[viticulture/fruit growing]?

 Single loop learning If you look in general at the machines and technologies used in your 
company: Can you spontaneously think of specific experiences or 
examples of what you have learned for yourself in dealing with the 
technology in everyday use?

 Double loop learning When you take some time away from your daily work, do you think 
about whether one or the other technology you bought was a good 
investment?

[Alternatively: To what extent do you notice that the use of new 
technology has changed your everyday work?]

 Discuss doubts Would you say that you like to experiment with digital technologies 
in your company?

Room for autonomous change
 Act according to plan What experience do you have in dealing with malfunctions and 

interruptions of digital devices (e.g., apps, networked machines, 
sensors in tractors or tanks, cellar technology, farm management 
system)?

How do you regularly cope with malfunctions of machines or digital 
technology?

[How do you prepare for this?]
 Capacity to improvise Have you recently or currently introduced new technologies?

How did you approach the topic? Please tell us how you got from 
idea to implementation

[What stumbling blocks did you encounter along the way? To what 
extent does your example additionally use digital technologies?]

 Continuous access to information How do you find out about new technologies in your company?
Do you feel like you have an overview of the most important current 

developments in your sector?
Leadership
 Visionary Have you already thought about how you want to develop your 

business in 5 years or where you want to be in 5 years?
Will digital technologies play a role in this?

 Entrepreneurial Do you have concrete plans to introduce (further) new digital 
technologies in the near future?

To what extent do you see opportunities and scope for actively 
shaping (digital) change in your agricultural business]?



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framework and a deductive approach, as illustrated in Fig. 2. The aim of this approach was 
not to evaluate the farmers’ adaptive capacity but rather to provide a clear structure to the 
material and to facilitate the generation of initial heuristics.

Post-coding, there was a deliberate period of reflection and distancing from both the 
coding process and the hypothesis generation. This distancing was important to facilitate 
a shift in analytical perspective from a broad consideration of adaptive capacity to a more 
focused examination of experience.

The analysis was then carried out in two stages. To answer the first research question 
(“What types of experiences do farmers have when engaging with new digital technolo-
gies?”) all codes were systematically reviewed to identify any promising references to 

Table 2  (continued)

Variety

 Collaborative Which networks and associations are you involved in, or which 
networks have proven to be important/valuable for you? Are there 
networks where you can inform yourself about new technologies in 
agriculture / exchange information on the topic?

How do you assess the necessity (in the future) to cooperate more 
with other [winegrowers/fruit growers] more closely?

Are there employees in your company with whom you can possibly 
exchange information on this topic?

Resources
 Human resources How does the skilled labor situation look in your context?
 Financial resources Do you have enough financial leeway for larger investments 

(including the purchase of new digital technologies)?
Are you familiar with the available subsidies?

 Authority Do you feel that you can make your general challenges or 
assessments of digitalization sufficiently heard vis-à-vis others 
(e.g., associations, farmers, administration)?

Fair governance
 Responsiveness How do you perceive the current agricultural policy?

Do you perceive that politics is directed at your needs?
 Equity How do you rate the legal situation and the legal framework for your 

agricultural production?
 Legitimacy When you think about consumers: How do you see the current 

relationship of consumers to agricultural production?
[What can be done here from your point of view?]

Adaptation motivation
From the current perspective, would you (continue to) invest in new 

technologies? [Under what circumstances would this be interesting 
for you?]

Asked in general terms: What advantages do you see in digitized 
agriculture?

[Do you believe that new technologies will contribute to better 
economic efficiency or more sustainable management?] [Why?]

[In your opinion, what are the obstacles to the digitalization of 
agriculture? What are the challenges that need to be overcome 
in order for the development to gain further momentum and for 
farmers to benefit in the best possible way?]

Adaptation belief
 Outcome-efficacy belief
 Self-efficacy belief

Do you feel that you can adapt to future challenges with the help of 
new technologies that will be available in the foreseeable future?



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experience. Once identified, a reflective step was taken to succinctly describe the connec-
tion to experience. The identified sections were then grouped into different types of experi-
ence, each consisting of one or two specific codes, descriptions, and key quotations.

Second, to address the second research question (“How are these types of experiences 
relevant to farmers’ adaptation motivation and adaptation belief?”), the MaxQDA code-
relations browser was used to investigate the relation of each experience types’ codes 
and the codes for AM and AB. The code-relations technique aids researchers to draw 
connections between different statements, so they can identify interrelations and patterns 
(Kuckartz, 2007; Saldaña, 2015). Thus, in this study it has been used to analyze how the 
identified types of experience relate to AM und AB. To further analyze the AB dimensions, 
the codes were divided into coding’s that suggest self-efficacy belief and those that suggest 
outcome-efficacy belief. Table  3 shows some examples of this allocation to improve 
comprehensibility.

Due to the explorative research design of this study, the code relations do not imply 
statistical relevance, nor do they provide an explanation for the observed correlations. 
However, the systematic analysis of linkages is well suited for theorizing and the 
development of further research agendas.

Results and discussion

Through the lens of the modified ACW and alongside quotes from the interview study, five 
types of experience show a close link to AB and AM. In the following section the relevant 
types of experience and their relation to the ACW factors are introduced (RQ1). Thus, for 
each type of experience, a table (Tables 4, 5, 6, 7 and 8) with the 2nd order dimension 
codes, the key quotes and a summary of the quotes is presented. Building up on this, the 
identified relation to AM and AB is discussed (RQ2).

Fig. 2  Example of the coding structure



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Tech competence and agricultural application

The factors “single loop learning” and “double loop learning” pertain to the general 
technical competencies required to learn from and reflect upon the usage of (digital) 
technologies. Also, they reflect on observable changes in farm operating procedures. 
This code was applied when farmers discussed their learning experiences related to the 
introduction and utilization of various technologies.

Within these narrations, farmers shared insights into their technical know-how, either 
by commenting on their technical competence or by reflecting on general limitations 
for introducing or using a solution. Consequently, throughout the agricultural digital 
transformation, farmers find themselves experiencing a lack of know-how on the one 
hand, and a sense of curiosity and willingness to learn about new technologies on the 
other. Thus, experiencing their tech competence influences farmers’ willingness to 
engage with new technologies.

Therefore, through the coding’s of single loop learning and double loop learning, the 
type (1) of experience: “Tech competence and agricultural application”, can be described.

When examining the relationship between this experience type and the codes for AM 
and AB, it is unsurprising to find that farmers with technical competence and know-how 
are likely to exhibit a strong motivation to adapt to new technologies. Furthermore, these 
farmers typically believe in their ability to manage the challenges associated with adopt-
ing new technology. In some instances, these farmers have even developed customized 

Table 3  Coding examples for self-efficacy belief and outcome-efficacy belief

1st order dimension 2nd order dimension Coding examples

Adaptation belief Self-efficacy belief ⋅ “I studied to be a farmer myself. I was already working with 
GPS, sensor-controlled fertilization and crop protection and 
so on in my apprenticeship. And then also with this digital 
recording, which was included, and I thought that it was a 
really great thing.” Viticulture_4

⋅ “In this respect, it really is the case that the drive comes 
more from curiosity and not so much that I say, “if we 
don’t do this, if we don’t use modern methods, if we don’t 
think about such things, then the company will go bust”.” 
Pomiculture_5

⋅ “That’s when it goes digital. That’s when we start taking our 
nitrogen measurements. That’s when we start measuring 
our leaf wetness values, our soil wetness values. That’s 
where it’s going to be very interesting.” Viticulture_1

Outcome-efficacy 
belief

⋅ “That is the future. We need this because every year we 
see how the workforce and availability are declining.” 
Pomiculture_1

⋅ “Well, I can tell relatively quickly whether or not it takes 
work off my hands. For example, we bought a digital 
measuring device for the cellar last year to measure the 
[grape] must weight during fermentation. And that paid off 
really well.” Viticulture_5

⋅ “These harvesting robots. That’s a huge topic for me. 
These harvesting robots also improve quality. The relief 
of physically hard work through harvesting robots. 
Monotonous work that can be taken away would certainly 
help, in my view.” Pomiculture_4



1970 Precision Agriculture (2024) 25:1958–1981

1 3

Ta
bl

e 
4 

 C
od

es
, q

uo
te

s a
nd

 su
m

m
ar

y 
of

 ty
pe

 1

2n
d 

or
de

r d
im

en
si

on
K

ey
 q

uo
te

Su
m

m
ar

y

Si
ng

le
 lo

op
 le

ar
ni

ng
“S

o 
th

e 
te

ch
no

lo
gy

 m
us

t b
e 

si
m

pl
e.

 S
o,

 w
he

n 
I s

ee
 th

at
 to

da
y.

 I 
en

te
r 

ou
r t

ra
ct

or
 a

nd
 I’

m
 n

ot
 th

at
 ig

no
ra

nt
. I

 a
lre

ad
y 

kn
ow

 m
y 

wa
y 

ar
ou

nd
 

th
e 

w
ho

le
 su

bj
ec

t. 
Bu

t I
 e

nt
er

 o
ur

 tr
ac

to
r a

nd
 I 

re
al

ly
 h

av
e 

to
 lo

ok
 a

t 
ev

er
yt

hi
ng

 fi
rs

t a
nd

 I 
ha

ve
 to

—
I c

an
’t 

ge
t i

t s
ta

rt
ed

 a
t a

ll.
 I 

ev
en

 c
an

’t 
ge

t t
he

 h
oi

st 
up

. T
he

n 
I h

av
e 

to
 c

al
l s

om
eo

ne
 a

nd
 a

sk
 "W

ha
t d

o 
I h

av
e 

to
 p

re
ss

 n
ow

?"
—

"T
hi

s a
nd

 th
is

 a
nd

 th
is

." 
B

ef
or

e 
I’v

e 
st

ar
te

d 
th

e 
w

ho
le

 
G

PS
 th

in
g 

th
er

e 
an

d 
an

d 
an

d.
 It

’s
 to

o 
co

m
pl

ic
at

ed
. A

nd
 th

at
’s

 w
hy

 it
 

ca
n’

t w
or

k.
 T

ha
t’s

 w
hy

 e
ve

ry
on

e 
cr

iti
ci

se
s i

t. 
W

he
th

er
 it

’s
 a

t F
en

dt
 o

r a
 

Jo
hn

 D
ee

re
. I

t i
s t

he
 sa

m
e.

 It
 is

 c
om

pl
ic

at
ed

” 
(P

om
ic

ul
tu

re
_1

)

⋅ T
ra

ct
or

s e
qu

ip
pe

d 
w

ith
 v

ar
io

us
 n

ew
 te

ch
no

lo
gi

es
 ra

is
e 

th
e 

co
m

pl
ex

ity
 o

f 
th

e 
m

ac
hi

ne
⋅ F

ar
m

er
 la

ck
s t

ec
hn

ic
al

 k
no

w
-h

ow
 to

 u
til

iz
e 

th
e 

ne
w

 fu
nc

tio
na

lit
ie

s
⋅ L

ac
k 

of
 k

no
w

le
dg

e 
re

su
lts

 in
 fr

us
tra

tio
n

D
ou

bl
e 

lo
op

 le
ar

ni
ng

“S
o 

w
e 

ha
d 

an
 a

pp
 th

at
 w

as
 su

pp
os

ed
 to

 re
co

rd
 [t

he
 w

or
ki

ng
 h

ou
rs

] G
PS

-
co

nt
ro

lle
d.

 […
], 

bu
t t

ha
t s

im
pl

y 
di

dn
’t 

wo
rk

 in
 p

ra
ct

ic
al

 a
pp

lic
at

io
n.

 
Th

er
e 

is
 n

ot
 a

lw
ay

s a
 G

PS
 si

gn
al

 a
va

ila
bl

e,
 o

r t
he

y 
ha

ve
 a

 fe
w

 m
or

e 
th

in
gs

 o
n 

in
 w

in
te

r a
nd

 w
or

ki
ng

 w
ith

 a
 sm

ar
tp

ho
ne

 is
 n

ot
 th

at
 m

uc
h 

of
 

a 
to

pi
c 

up
 h

er
e.

 […
] A

nd
 in

 th
e 

en
d,

 I 
di

sc
ar

de
d 

it 
be

ca
us

e,
 fr

om
 m

y 
po

in
t o

f v
ie

w
, t

he
 d

ist
rib

ut
or

 d
id

n’
t g

o 
in

 th
e 

di
re

ct
io

n 
I w

an
te

d 
an

d 
th

e 
re

su
lt 

w
as

n’
t a

s f
ea

si
bl

e 
or

 u
sa

bl
e 

fo
r m

e 
as

 I 
w

ou
ld

 h
av

e 
lik

ed
.” 

(P
om

ic
ul

tu
re

_6
)

⋅ F
ar

m
er

 h
as

 e
no

ug
h 

kn
ow

le
dg

e 
to

 a
pp

ly
 th

e 
ne

w
 te

ch
no

lo
gy

 in
 fa

rm
⋅ F

ar
m

er
 c

an
 c

rit
ic

al
ly

 e
va

lu
at

e 
th

e 
ut

ili
ty

 o
f t

he
 a

pp
 in

 u
se

⋅ F
ar

m
er

 d
ec

id
es

 to
 n

ot
 st

ic
k 

to
 a

 n
ew

 te
ch

no
lo

gy
 b

as
ed

 o
n 

te
ch

ni
ca

l k
no

w
-

ho
w

Si
ng

le
 lo

op
 le

ar
ni

ng
“I

: A
nd

 fo
r c

on
ce

rn
in

g 
th

at
 (t

em
pe

ra
tu

re
 lo

gg
in

g 
sy

ste
m

). 
W

he
re

 d
id

 y
ou

 
ge

t t
he

 k
no

w
le

dg
e 

to
 b

ui
ld

 it
 b

y 
yo

ur
se

lf?
 […

]
B

: Y
ou

Tu
be

.” 
(V

iti
cu

ltu
re

_5
)

⋅ B
as

ed
 o

n 
fo

rm
er

 e
xp

er
ie

nc
es

, f
ar

m
er

 is
 a

bl
e 

to
 fi

nd
 a

ll 
re

le
va

nt
 

in
fo

rm
at

io
n 

to
 e

ng
ag

e 
w

ith
 a

 n
ew

 te
ch

no
lo

gy
⋅ B

as
ed

 o
n 

th
e 

fa
rm

er
s k

no
w

le
dg

e,
 h

e 
ca

n 
ev

en
 c

us
to

m
iz

e 
ex

ist
in

g 
te

ch
no

lo
gi

es
 a

nd
 im

pl
em

en
t n

ew
 fu

nc
tio

na
lit

ie
s



1971Precision Agriculture (2024) 25:1958–1981 

1 3

Ta
bl

e 
5 

 C
od

es
, q

uo
te

s a
nd

 su
m

m
ar

y 
of

 ty
pe

 2

2n
d 

or
de

r d
im

en
si

on
K

ey
 q

uo
te

Su
m

m
ar

y

En
tre

pr
en

eu
ria

l
“A

nd
 th

er
e,

 a
t l

ea
st 

th
at

’s
 w

ha
t w

e 
w

er
e 

to
ld

 a
t t

he
 

un
iv

er
si

ty
, a

 u
su

al
 si

m
pl

e 
we

at
he

r s
ta

tio
n,

 n
ot

 
on

e 
fo

r a
 fi

ve
-fi

gu
re

 su
m

, w
ou

ld
 b

e 
su

ffi
ci

en
t f

or
 

th
is

, w
hi

ch
 c

an
 m

ea
su

re
 w

in
d,

 te
m

pe
ra

tu
re

 a
nd

 
pr

ec
ip

ita
tio

n,
 so

 to
 sp

ea
k.

 O
h,

 a
nd

 b
y 

th
e 

wa
y,

 th
at

’s
 

w
ha

t w
e’

re
 p

la
nn

in
g 

to
 d

o.
 I 

fo
rg

ot
 to

 te
ll 

yo
u 

th
at

. 
W

e 
ar

e 
pl

an
ni

ng
 to

 se
t i

t u
p 

in
 o

ur
 v

in
ey

ar
d 

as
 w

el
l.”

 
(V

iti
cu

ltu
re

_4
)

⋅ C
os

ts
 a

re
 o

ne
 im

po
rta

nt
 fa

ct
or

 in
 th

e 
di

gi
ta

l t
ra

ns
fo

rm
at

io
n 

of
 a

gr
ic

ul
tu

re
⋅ D

ec
is

io
n 

m
ak

in
g 

oc
cu

rs
 b

as
ed

 o
n 

co
st 

effi
ci

en
cy

 e
va

lu
at

io
ns

En
tre

pr
en

eu
ria

l
“I

n 
20

13
/2

01
4 

w
e 

bo
ug

ht
 a

 G
PS

 sy
ste

m
. [

…
] I

t c
am

e 
fro

m
 Jo

hn
 D

ee
re

 fo
r €

20
,0

00
. A

nd
 w

e 
in

te
gr

at
ed

 
it 

in
to

 th
e 

Fe
nd

t a
nd

 w
e 

go
t n

ot
hi

ng
 b

ut
 p

ro
bl

em
s. 

Th
ey

 a
ls

o 
to

ld
 u

s:
 T

hi
s i

s c
om

pa
tib

le
. [

…
] N

ot
hi

ng
 

is
 c

om
pa

tib
le

 to
ge

th
er

. [
…

] S
o 

we
 tr

ie
d 

th
at

 a
nd

 lo
st

 
m

on
ey

. I
 sh

ou
ld

n’
t h

av
e 

do
ne

 it
 a

t a
ll.

 A
s a

lw
ay

s, 
I 

sh
ou

ld
 h

av
e 

le
as

ed
 it

.”
 (P

om
ic

ul
tu

re
_1

)

⋅ C
os

ts
 a

re
 o

ne
 im

po
rta

nt
 fa

ct
or

 in
 th

e 
di

gi
ta

l t
ra

ns
fo

rm
at

io
n 

of
 a

gr
ic

ul
tu

re
⋅ E

rr
on

eo
us

 d
ec

is
io

n 
w

ith
 in

ve
sti

ng
 in

 n
ew

 te
ch

no
lo

gi
es

 in
flu

en
ce

s 
up

co
m

in
g 

in
ve

stm
en

t d
ec

is
io

ns



1972 Precision Agriculture (2024) 25:1958–1981

1 3

Ta
bl

e 
6 

 C
od

es
, q

uo
te

s a
nd

 su
m

m
ar

y 
of

 ty
pe

 3

2n
d 

or
de

r d
im

en
si

on
K

ey
 q

uo
te

Su
m

m
ar

y

D
is

cu
ss

 d
ou

bt
s

“O
ur

 o
rc

ha
rd

s a
re

 p
la

nt
ed

 si
gn

ifi
ca

nt
ly

 m
or

e 
de

ns
e 

co
m

pa
re

d 
to

 o
th

er
 

cu
lti

va
tio

n 
sy

ste
m

s i
n 

ot
he

r r
eg

io
ns

, a
nd

 th
at

’s
 w

hy
 w

e 
ve

ry
 m

uc
h 

lik
e 

to
 u

se
 th

es
e 

sa
dd

le
 te

ch
no

lo
gi

es
, w

hi
ch

 o
f c

ou
rs

e 
ar

e 
th

en
 a

ls
o 

m
uc

h 
m

or
e 

effi
ci

en
t. 

[…
] A

nd
 th

at
’s

 w
he

re
 a

n 
Ita

lia
n 

co
m

pa
ny

 c
am

e 
in

. W
e 

pu
rc

ha
se

d 
th

re
e 

su
ch

 [s
ad

dl
e 

sp
ra

ye
rs

] –
 th

ey
’re

 a
 d

is
as

te
r. 

It 
m

us
t b

e 
sa

id
 th

is
 w

ay
. T

he
y 

si
m

pl
y 

do
n’

t w
or

k.
 It

 w
as

 a
 ru

in
ou

s i
nv

es
tm

en
t. 

So
 

th
ey

 ru
n,

 b
ut

 it
 is

 ju
st

 n
ot

 w
ha

t w
e 

ha
d 

ho
pe

d 
fo

r.”
 (P

om
ic

ul
tu

re
_4

)

⋅ F
ar

m
-s

pe
ci

fic
 c

ul
tiv

at
io

n 
sy

ste
m

 g
ui

de
 fa

rm
er

’s
 d

ec
is

io
n 

m
ak

in
g

⋅ F
ar

m
er

s r
eq

ui
re

m
en

ts
 n

ot
 n

ec
es

sa
ril

y 
m

at
ch

 a
va

ila
bl

e 
te

ch
no

lo
gy

 o
n 

th
e 

m
ar

ke
t

⋅ E
xp

er
im

en
ta

tio
n 

an
d 

ris
k 

ta
ki

ng
 is

 n
ec

es
sa

ry
 to

 e
xp

lo
re

 th
e 

ad
de

d 
va

lu
e 

of
 

ne
w

 te
ch

no
lo

gy

D
is

cu
ss

 d
ou

bt
s

“W
el

l, 
I k

no
w

 th
at

 m
y 

sm
al

l v
in

ey
ar

d 
on

 th
e 

do
or

ste
p,

 w
hi

ch
 is

 n
ot

 e
ve

n 
a 

he
ct

ar
e 

in
 si

ze
, h

as
 a

 la
rg

er
 a

re
a 

th
at

 sl
op

es
 st

ee
pl

y.
 T

he
re

, t
he

 le
av

es
 

dr
y 

re
la

tiv
el

y 
ea

rly
 a

nd
 […

] t
ow

ar
ds

 th
e 

ca
st

le
 b

eh
in

d,
 w

he
re

 it
 g

oe
s 

do
w

n 
a 

bi
t, 

th
e 

le
av

es
 a

re
 m

uc
h 

m
or

e 
we

t a
t t

he
 sa

m
e 

tim
e.

 A
nd

 th
at

’s
 

no
t e

ve
n 

on
e 

he
ct

ar
e.

 I 
kn

ow
 th

at
 a

s a
 w

in
e 

m
ak

er
 b

ec
au

se
 I’

ve
 b

ee
n 

on
 

th
e 

vi
ne

ya
rd

 fo
r a

 lo
ng

 ti
m

e 
an

d 
I a

ls
o 

kn
ow

, l
et

’s
 sa

y,
 th

e 
tro

ub
le

so
m

e 
ar

ea
s o

f t
he

 y
ie

ld
. I

f I
 o

nl
y 

ha
d 

on
e 

se
ns

or
 so

m
ew

he
re

 in
 th

e 
m

id
dl

e 
of

 a
 fi

el
d,

 it
 w

ou
ld

 p
ro

vi
de

 m
e 

w
ith

 re
su

lts
 o

r t
hi

ng
s t

ha
t w

ou
ld

 n
ot

 b
e 

co
rr

ec
t. 

[…
] T

ha
t’s

 w
hy

 I’
m

 st
ill

 v
er

y 
sk

ep
tic

al
.” 

(V
iti

cu
ltu

re
_2

)

⋅ F
ar

m
-s

pe
ci

fic
 c

on
si

de
ra

tio
ns

 se
em

 to
 im

pa
ct

 fa
rm

er
’s

 d
ec

is
io

n 
m

ak
in

g
⋅ L

oc
al

 c
on

di
tio

ns
 d

ue
 to

 th
e 

sp
ec

ifi
c 

te
rr

ai
n 

ar
e 

de
ci

si
ve

 fo
r t

he
 

co
ns

id
er

at
io

n 
of

 b
en

efi
ts

⋅ E
xp

er
ie

nc
es

 w
ith

 fa
rm

 sp
ec

ifi
c 

cu
lti

va
tio

n 
co

nd
iti

on
s g

ui
de

 te
ch

no
lo

gy
 

de
lib

er
at

io
ns

D
is

cu
ss

 d
ou

bt
s

“[
…

] b
ut

 in
 th

e 
en

d,
 w

e 
ha

ve
 li

ttl
e,

 v
er

y 
lit

tle
 o

r n
o 

tim
e 

to
 d

ea
l w

ith
 

th
in

gs
 li

ke
 te

st
in

g.
 […

] B
ec

au
se

 w
e 

do
n’

t h
av

e 
th

e 
st

aff
 o

r t
he

 c
ap

ac
iti

es
 

to
 d

o 
so

m
et

hi
ng

 li
ke

 th
at

 o
n 

th
e 

si
de

 fo
r f

un
. S

o,
 e

ve
nt

ua
lly

 w
e 

try
 o

ut
 a

 
fe

w
 th

in
gs

 th
at

 m
ig

ht
 w

or
k.

 B
ut

 w
e 

ca
n’

t d
o 

pr
op

er
 te

sti
ng

 fo
r p

ra
ct

ic
e 

or
 d

ev
el

op
m

en
t.”

 (P
om

ic
ul

tu
re

_6
)

⋅ F
ar

m
-s

pe
ci

fic
 c

on
di

tio
ns

 (l
ac

k 
of

 re
so

ur
ce

s)
 im

pa
ct

 fa
rm

er
’s

 w
ill

in
gn

es
s 

to
 e

ng
ag

e 
in

 e
xp

er
im

en
tin

g 
w

ith
 n

ew
 te

ch
no

lo
gi

es
⋅ L

im
ite

d 
ro

om
 fo

r c
re

at
in

g 
fir

st
ha

nd
 e

xp
er

ie
nc

e



1973Precision Agriculture (2024) 25:1958–1981 

1 3

Ta
bl

e 
7 

 C
od

es
, q

uo
te

s a
nd

 su
m

m
ar

y 
of

 ty
pe

 4

2n
d 

or
de

r d
im

en
si

on
K

ey
 q

uo
te

Su
m

m
ar

y

H
um

an
 re

so
ur

ce
s

“I
’m

 3
4,

 I 
ha

ve
 a

t l
ea

st 
te

n 
pe

op
le

 le
av

in
g 

in
 th

e 
ne

xt
 fi

ve
 to

 si
x 

ye
ar

s 
w

ho
 a

re
 g

oi
ng

 in
to

 w
el

l-d
es

er
ve

d 
re

tir
em

en
t. 

Th
es

e 
ar

e 
al

l v
er

y 
go

od
 

pr
of

es
si

on
al

s. 
Th

ey
 a

re
 p

eo
pl

e 
w

ith
 a

 to
n 

of
 e

xp
er

ie
nc

e 
th

at
 y

ou
 si

m
pl

y 
lo

ok
 fo

r a
nd

 d
on

’t 
fin

d 
an

ym
or

e 
th

es
e 

da
ys

. A
nd

 o
f c

ou
rs

e,
 it

 is
 d

iffi
cu

lt 
to

 c
om

pe
ns

at
e 

fo
r t

he
m

.” 
(P

om
ic

ul
tu

re
_1

)

⋅ D
ra

in
 o

f k
no

w
le

dg
e 

an
d 

on
-fa

rm
 e

xp
er

ie
nc

e 
be

ca
us

e 
of

 w
or

ke
rs

 
re

tir
em

en
t

⋅ P
ot

en
tia

lly
 m

is
si

ng
 e

xp
er

tis
e 

aff
ec

ts
 fu

tu
re

 d
ec

is
io

n 
m

ak
in

g

H
um

an
 re

so
ur

ce
s

“W
e 

ha
ve

, f
or

 e
xa

m
pl

e,
 a

n 
em

pl
oy

ee
 w

ho
 is

 c
ur

re
nt

ly
 ta

ki
ng

 h
is

 
m

as
te

r’s
 c

ou
rs

e 
at

 th
e 

re
se

ar
ch

 in
sti

tu
te

 in
 [L

V
W

O
] W

ei
ns

be
rg

, a
nd

 
he

 is
 e

ss
en

tia
lly

 le
ar

ni
ng

 a
bo

ut
 d

ro
ne

 te
ch

no
lo

gy
 fo

r p
la

nt
 p

ro
te

ct
io

n 
di

re
ct

ly
 in

 c
la

ss
. S

o,
 w

e 
ex

pe
ri

en
ce

 m
or

e 
or

 le
ss

 li
ve

 h
ow

 th
is

 is
 b

ei
ng

 
ad

va
nc

in
g,

 a
nd

 e
sp

ec
ia

lly
 th

e 
re

su
lts

 o
f t

he
rm

al
 m

ea
su

re
m

en
ts

, N
D

R
E,

 
an

d 
N

D
V

I a
na

ly
se

s. 
I d

on
’t 

th
in

k 
th

er
e 

is
 a

ny
on

e 
in

 G
er

m
an

y 
w

ho
 

cu
rr

en
tly

 e
xt

en
si

ve
ly

 re
lie

s o
n 

th
em

 a
s m

uc
h 

as
 w

e 
do

.” 
(V

iti
cu

ltu
re

_1
)

⋅ S
pe

ci
al

ist
 k

no
w

le
dg

e 
an

d 
m

as
te

r c
ou

rs
e 

co
nc

er
ne

d 
w

ith
 U

AV
 te

ch
no

lo
gy

 
an

d 
op

tic
s m

ad
e 

im
pl

em
en

ta
tio

n 
of

 n
ew

 te
ch

no
lo

gi
es

 o
n 

fa
rm

 p
ro

ce
du

re
s 

po
ss

ib
le

⋅ A
cc

es
s t

o 
St

at
e-

of
-th

e-
A

rt 
re

se
ar

ch
 a

nd
 te

ch
no

lo
gy

 ra
is

es
 m

ot
iv

at
io

n 
fo

r 
te

sti
ng

H
um

an
 re

so
ur

ce
s

“I
 u

se
d 

to
 h

av
e 

an
 o

ld
 Jo

hn
 D

ee
re

. [
M

y 
em

pl
oy

ee
s]

 d
ro

ve
 it

 a
nd

 a
ny

on
e 

co
ul

d 
op

er
at

e 
it.

 A
ny

on
e,

 e
ve

n 
m

y 
ne

ar
-r

et
ire

es
, I

 w
ou

ld
 sa

y.
 T

he
y 

co
ul

d 
ha

nd
le

 it
. N

ow
 I’

ve
 b

ou
gh

t a
 n

ew
 F

en
dt

. [
…

] B
ut

 w
he

n 
yo

u 
ha

ve
 to

 
sp

en
d 

te
n 

m
in

ut
es

 a
dj

us
tin

g 
th

e 
m

ac
hi

ne
 so

ftw
ar

e 
[fo

r y
ou

r e
m

pl
oy

ee
] 

ju
st 

to
 m

ak
e 

su
re

 e
ve

ry
th

in
g 

w
or

ks
 c

or
re

ct
ly

, t
ha

t’s
 a

 p
ro

bl
em

. [
…

] 
Th

at
’s

 m
y 

ex
pe

ri
en

ce
. Y

es
. [

la
ug

hs
] I

 se
t i

t u
p 

fo
r m

y 
em

pl
oy

ee
s. 

[…
] 

Th
ey

 a
re

 si
m

pl
y 

to
o 

ol
d 

fo
r i

t, 
I w

ou
ld

 sa
y.

 O
r n

ot
 in

te
re

ste
d 

an
ym

or
e 

in
 

do
in

g 
it 

on
 th

ei
r o

w
n.

” 
(P

om
ic

ul
tu

re
_3

)

⋅ I
nt

ro
du

ct
io

n 
of

 n
ew

 te
ch

no
lo

gy
 a

cc
om

pa
ni

es
 th

ou
gh

ts
 a

bo
ut

 e
xp

er
ie

nc
es

 
of

 st
aff

 c
ap

ab
ili

ty
 u

nd
 e

xp
er

tis
e

⋅ N
ew

 m
ac

hi
ne

 fe
at

ur
es

 le
ad

 to
 lo

w
er

 p
ro

du
ct

iv
ity

 d
ue

 to
 st

aff
 

de
m

og
ra

ph
ic

s
⋅ F

ar
m

er
 se

es
 a

 p
ro

bl
em

 in
 m

at
ch

in
g 

ne
w

 te
ch

no
lo

gy
 w

ith
 st

aff
 c

ap
ab

ili
tie

s

H
um

an
 re

so
ur

ce
s

“E
xa

ct
ly

, s
o 

w
e 

ar
e 

th
re

e 
em

pl
oy

ee
s w

ho
 p

rim
ar

ily
 ta

ke
 c

ar
e 

of
 p

la
nt

 
pr

ot
ec

tio
n 

he
re

. T
he

re
’s

 a
n 

ol
de

r e
m

pl
oy

ee
 w

ho
 is

 a
pp

ro
ac

hi
ng

 h
is

 
si

xt
ie

s. 
Th

en
 th

er
e’

s m
e 

– 
I’

m
 in

 m
y 

m
id

-th
irt

ie
s, 

an
d 

an
ot

he
r y

ou
ng

er
 

em
pl

oy
ee

 w
ho

 is
 te

n 
ye

ar
s y

ou
ng

er
 th

an
 m

e.
 S

o,
 y

ou
 c

ou
ld

 sa
y 

th
er

e 
ar

e 
th

re
e 

ge
ne

ra
tio

ns
. W

e 
ar

e 
in

 c
on

st
an

t r
eg

ul
ar

 c
on

ta
ct

, a
tte

nd
in

g 
tra

in
in

gs
 

to
ge

th
er

 a
nd

 a
ll 

th
at

. [
…

] T
he

or
et

ic
al

 k
no

w
le

dg
e 

an
d 

kn
ow

le
dg

e 
fro

m
 o

th
er

 c
om

pa
ni

es
 m

ay
 b

e 
va

lu
ab

le
, b

ut
 it

’s
 a

ls
o 

in
va

lu
ab

le
 to

 
ha

ve
 so

m
eo

ne
 w

ho
 k

no
w

s t
he

 c
om

pa
ny

 w
ith

 a
ll 

its
 p

ec
ul

ia
rit

ie
s a

nd
 

di
ffi

cu
lt 

ar
ea

s [
…

] T
ha

t i
s s

til
l v

er
y 

im
po

rta
nt

. E
ve

n 
if 

we
 e

ve
nt

ua
lly

 
ha

ve
 m

or
e 

ac
cu

ra
te

 fo
re

ca
st

in
g 

m
od

el
s, 

we
 st

ill
 fa

ce
 th

e 
ch

al
le

ng
e 

of
 

im
pl

em
en

ta
tio

n.
” 

(V
iti

cu
ltu

re
_2

)

⋅ I
nt

er
ge

ne
ra

tio
na

l k
no

w
le

dg
e 

di
str

ib
ut

io
n 

is
 h

ig
hl

ig
ht

ed
 in

 th
e 

na
rr

at
io

n
⋅ M

at
ch

 b
et

w
ee

n 
ne

w
 te

ch
no

lo
gy

 a
nd

 st
aff

 e
xp

er
tis

e 
gu

id
es

 fa
rm

er
s’

 
de

ci
si

on
 m

ak
in

g



1974 Precision Agriculture (2024) 25:1958–1981

1 3

solutions for their farms, what empowers them to further research and explore avail-
able technologies. Thus, a high level of technical competence can positively influence 
a farmers’ capacity to adapt to new digital technologies by relating positively with AM 
and a self-efficacy belief. However, it is important to note that experiences with new 
digital technologies can also lead to a decreased AB in some cases.

Thus, farmers with a strong technical competence tend to (a) be motivated to experiment 
with new technologies, and (b) believe in their capability to do so. While it might be 
intuitively assumed that these farmers are also best equipped to critically assess the extent 
to which a new digital technology will meet expectations and whether its implementation 
will yield the expected benefits, the code relations reveal that these doubts are more 
prevalent among farmers with a low level of technical competence. This observation may 
be explained by the effect of frustration when the promises conveyed through professional 
consultation fail to align with the actual experience once a farmer begins implementing 
a new solution. This phenomenon of frustration has also been documented by Kernecker 
et al. (2020).

Reflections on strategic investment

The factor “entrepreneurial” captures the capabilities of leaders to initiate actions. It also 
represents signs of forward-looking, individual entrepreneurial behavior. This code was 
applied when farmers discussed the potential of their future technology investments, or 
the absence thereof. Remarkably, most farmers took this as an opportunity to delve into 

Table 8  Codes, quotes and summary of type 5

2nd order dimension Key quote Summary

Multi-actor, multi-level, 
multi-sector; collaborative

“B: No. It’s that easy. You go to your seller 
when you need a new tractor. Well, I 
needed one because one of my old ones 
had broken down—gearbox damage—it 
wasn’t worth repairing. So, I simply 
went to a salesman, whom I knew well. I 
asked him, made him an offer and then I 
bought it. That was…

I: Pragmatic
B: Quite pragmatic. I told him that it had 

to have so and so much horsepower and 
I didn’t really care about anything else.” 
(Pomiculture_3)

⋅ Consultants are seen as 
trustworthy to influence 
farmers’ investment decisions

⋅ Trust in the experience of 
others is common when 
making decisions for an 
investment in a farm

Multi-actor, multi-level, 
multi-sector; collaborative

“Yes, yes, so [a dialogue] is taking place. 
There are also pioneer farms that are 
very active […] in continuing […] to 
run the farm in a future-oriented way, 
and these are the pioneers with whom 
we like to cooperate or, of course, then 
also name them as a showcase farm, so 
that our customers can of course also 
go there […]. So, the farmers among 
themselves, it’s not that they’re hating 
each other’s guts, there is a bond by 
now.” (Intermediary_3)

⋅ Personal networks play 
a significant role when 
collaborating to explore new 
technologies

⋅ Farmers mutual trust 
for deliberating future 
investments is reported by 
industry representative



1975Precision Agriculture (2024) 25:1958–1981 

1 3

cost–benefit considerations, thereby justifying or explaining their previous experiences and 
business decisions.

Through the interpretation of the identified quotes, it becomes apparent that farmers’ 
reflections on their investment decisions are indicative of their attitudes towards strategies 
for implementing new technologies. Therefore, the ability of farmers to learn from 
their past business decisions, as well as from decisions made in their business network, 
encapsulates type (2) of experience: reflections on strategic investment.

It is likely that the juxtaposition of upcoming investment decisions with past successful 
or unsuccessful investments plays a role in shaping farmers’ motivation or belief. Farmers 
who perceive a cost-value benefit in new digital technologies and have had positive 
experiences with businesses integration tend to show a decent AM. Moreover, successful 
past strategic investment decisions in new digital technologies appear to contribute to a 
combination of self-efficacy belief and outcome-efficacy belief, both of which constitute 
the AB. Consequently, the experience of successful investing in new digital technologies 
seems to be an important driver overall.

Farm‑specific knowledge and capabilities for technology integration

The code “discuss doubts” encapsulates farmers’ openness to embrace risks and 
uncertainties as they navigate the learning and experimenting with new digital technologies. 
This code was applied when farmers shared their contemplations and learnings derived 
from weaving technology into their unique business environment.

When probed about their general attitude towards experimenting with new digital 
technologies, participants typically brought up their unique business prerequisites. Thus, 
when farmers evaluate a new technology, they engage in a multi-layered consideration 
process, often tethered to their company-specific circumstances. All narrations unveil that 
farmers reflect on their specific current and past business environment and preconditions to 
properly assess the feasibility of integrating a new digital technology. This results in type 
(3) of experience: farm-specific knowledge and capabilities for technology integration.

A match between the specific features of a technology and the on-farm prerequisites 
can fuel a farmer’s motivation to experiment with the technology in question. However, a 
farmer’s outcome-efficacy belief in a solution may deviate. Consequently, AB only aligns 
positively with this type of experience when previous attempts at incorporating “something 
new” have been fruitful. This experience is not exclusively limited to new digital 
technologies; also, the integration of other innovations, such as land-based technologies, 
equally serve as pertinent precedents for evaluating prospective new digital technology 
implementation.

On‑farm knowledge distribution

The code “human resources” encompasses the availability of expertise, knowledge and 
manpower as well as professional know-how of skilled workers. This code was utilized for 
interviewees’ general thoughts on the topic as well as for reflections on their farm specific 
situation. Farmers and stakeholders addressed not only the overall shortage of (skilled) 
workforce but also shared their thoughts on the distribution of knowledge within their 
organizations and how this affects their decision making.

How knowledge is distributed among the staff and how things worked out in the 
past guide farmers’ decision making. Naturally, farmers also think about substitutional 



1976 Precision Agriculture (2024) 25:1958–1981

1 3

technology due to shortage of workforce. In this context, various forms of experiences 
come together, whether it is the actual introduction of new technology or hypothetical 
considerations based on previous experiences and operational procedures. They address 
the distribution of know-how among skilled and seasonal workers, as well as the related 
experience of how technology can complement and replace this knowledge. This is 
illustrated in type (4) of experience: on-farm knowledge distribution.

Overall, there is a strong link between knowledge distribution and the digital 
transformation capabilities, as nearly all interviewed farmers address this topic in one or 
the other way. When past experiences indicate that new technologies have complicated 
farm operations, this results in a lower AM. Conversely, when skilled professionals were 
available to guide the implementation of new digital technologies, farmers considerations 
to adopt it become more serious, and the data shows a neutral or even positive AM and 
AB. Regarding technologies that are expected to support or replace seasonal labour, AM is 
higher, although scepticism about the actual implementation prevails and is reflected in a 
lower outcome-efficacy belief.

Agricultural knowledge networks

The codes “multi-Actor”, “multi-Level”, “multi-Sector” and “collaborative” represent the 
involvement of different employees and external stakeholders in designing operations, as 
well as the potential for management staff to initiate cross-company cooperation. These 
codes were applied when farmers discussed current, past and future business activities, 
as well as the different information sources, events, and stakeholders they rely on. The 
coding’s in this section highlight the distribution of knowledge throughout a farmer’s 
overall network. While the fourth type of experience was about describing individual-
bound competencies, these codes illustrate how farmers draw upon and rely on the 
experience of third parties to engage with new digital technologies.

Observing the reliance on the experience of others for decision making is challenging, 
yet it appears to be a pervasive theme in the data. This encompasses knowledge of the 
expertise level of actors within a farmer’s network. Additionally, farmers vary in their 
openness to specific technologies, depending on the local availability of professionals or 
experienced colleagues. Consequently, the intersection between farmers and their sources 
of consultation leads to type (5) of experience: Agricultural (knowledge) networks.

Past experiences of collaboration with agricultural trading companies, consultants or 
similar stakeholders play a crucial role in influencing the decisions of the farmers regarding 
the acquisition or implementation of new digital technologies.

When relationships of trust, built upon past positive experiences, are in place, some 
actors are inclined to continue these interactions. However, in the data analyzed, their AM 
and AB are not distinctly clear. On the other hand, when past experiences were negative, 
there is a negative impact on both AM and AB. Following Knierim et al. (2018) and Morris 
et al. (2017), it can be assumed that the absence of trustworthy relationships with advisory 
stakeholders negatively impacts AM and AB (see also Eastwood et al., 2019, Ayre et al., 
2019).



1977Precision Agriculture (2024) 25:1958–1981 

1 3

Conclusion

Five types of experience have been identified as highly relevant to the digital transformation 
in agriculture because it has been demonstrated that these types of experience are closely 
related to farmers’ AM and AB. A profound technical know-how and tech competence 
(type 1), reflections on strategic investment (type 2), farm-specific knowledge and 
capabilities for technology integration (type 3), on-farm knowledge distribution (type 4) 
and agricultural (knowledge) networks (type 5) show the variety of different modes of 
experience. These findings contribute to the existing literature by establishing the crucial 
link between the intention to adopt a new digital technology and its actual adoption by 
introducing the concept of adaptation motivation and adaptation belief. By providing 
a comprehensive picture on experience, this study sheds light on the paradox of farmers 
expressing interest in new technologies but often choosing to not adopt them. Consistent 
with findings from other domains (Giua, 2022; Leonardi, 2011; Selwyn, 2004) this study 
emphasizes the importance of creating meaningful experiences with technologies to 
strengthen farmers’ AM and AB. While this was demonstrated in an exploratory manner 
in this study, future research should consider to empirically examine and refine these 
plausible connections.

This work has several limitations, partly attributable to its exploratory nature. Firstly, 
the concept of participating "successfully" in the agricultural digital transformation is 
not systematically defined or measured, and there is no comparison with farmers’ own 
narratives of success, which could influence their AM and AB. Additionally, validating 
and measuring the outlined experiences needs further exploration due to farm-specific 
conditions and the diversity of established technologies. While this study has a regional 
focus and investigates two specific branches, viti- and pomiculture, also focusing on 
specific technologies might have enhanced the analysis.

Thus, future research should focus on specific technologies and their required on-site 
preconditions. Specifically, there is a lack of analytical tools and empirical studies 
addressing the context of technology matching in the agricultural sector, highlighting this 
as an important field of research. Beyond validating the five types, further studies must 
clarify the regional and sectoral transferability. The experience-oriented perspective of this 
study complements the debate about drivers and barriers for a technological transformation 
by highlighting the relevance of the subjective—the farmers and their experiences—as key 
factors to the transformation process. Therefore, future research should emphasize these 
experiences instead of primarily reflecting on economic conditions of farming businesses.

Practitioners, policy makers and industry representatives can glean from this study 
the importance of moderating farmers’ experiences with new digital technologies before, 
during and after the implementation. The value of this experience-oriented perspective 
lies in the connection of experiences and the AM and AB due to their associated potential 
for a digital transformation. Differentiating dimensions of experience stimulates other 
reflections from a more theoretical perspective than, for example, thinking about drivers 
and barriers of the digital transformation. Experiences are subliminal and effective in the 
long term. Therefore, the paper highlights a nuanced understanding of experiences. This 
not only makes an analysis of the variations possible, but it also provides starting points for 
practitioners. They read as follows:

• From Type 1 it can be deduced that a segmentation according to tech competence 
seems sensible, following the motto: freedom and risk support for 1st movers—support 



1978 Precision Agriculture (2024) 25:1958–1981

1 3

and step by step advice for late adopters. Differentiating offers or support instruments 
according to the farmer’s own perception of competence level might have a positive 
impact on AM and AB dynamics. Conversely, this could offer farmers the security to 
engage in either a simpler or more complex offer or support.

• From Type 2 it can be derived that investment experiences are very influential in 
change processes, more precisely—that both positive and negative investments seem to 
have a long-term effect. If practitioners can sensitize both sides more strongly, it may 
be possible to moderate the effects of negative spirals or positive snowball effects more 
strongly.

• Type 3 highlights the gap between technology and farm specifics. Matching instruments 
exist, but apparently, they don’t seem to play a role in everyday business. If farmers 
could match their experiences with a technology offer, experience values would be 
represented and investment decisions made easier. However, it might be necessary for 
such matching instruments to be offered independently of manufacturers.

• Type 4 teaches us that the business internal circulation of knowledge is very likely to 
increase AM and AB. Accordingly, encouraged time resources for farms in the form 
of specific training formats promise a positive effect on transformation processes in 
general.

• Type 5 touches on the importance of knowledge networks, whose possible positive 
effects have already been widely described. As farmers often build trust in technologies 
through the word-of-mouth within their networks, managing expectations realistically is 
crucial to protect farmers from poor investments. Specifically, this could mean financing 
long-term funded, independent, trustworthy, and highly competent consulting offices 
for individual advice, as farmers seem to be very open to such trustworthy sources. To 
the best of our knowledge, such dedicated independent technology consultations do not 
exist in the described region and beyond.

In general, practitioners should facilitate open discussions and share information about 
the possibilities of integrating new digital technologies into farm systems. From this study, 
it can be resumed that it is not only very important to empower farmers to experience 
the effectiveness and meaningful integration of new digital technologies through 
demonstrations and on-site testing but that there is also a need to address and manage prior 
bad or good experiences with new digital technologies to stimulate the discussion on the 
agricultural digital transformation. As explained in the methods section the implications 
of this study must be limited to permanent crop cultivation in Germany. However, we see 
great potential for transfer and generalization, not least because the identified varieties of 
experience are primarily of a general theoretical nature. Additionally, we trust that scholars 
will find in these results meaningful starting points for testing and elaborating varieties of 
experience within other regional and agricultural contexts.

Acknowledgements This research was funded in part by the German Federal Ministry of Food and Agricul-
ture under grant number 28DE102C18.

Funding Open Access funding enabled and organized by Projekt DEAL.

Data availability The participants of this study did not give written consent for their data to be shared pub-
licly, so due to the sensitive nature of the research supporting data is not available. However, anonymised 
components of the data may be available from the authors upon reasonable request.



1979Precision Agriculture (2024) 25:1958–1981 

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Declarations 

Competing interests The authors declare that they have no known competing financial interests or personal 
relationships that could have appeared to influence the work reported in this paper.

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	Mapping varieties of farmers’ experience in the digital transformation: a new perspective on transformative dynamics
	Abstract
	Introduction
	Theoretical background
	Experience in the agricultural digital transformation
	The adaptive capacity wheel framework

	Material and methods
	Data collection
	Method
	Interview guidelines

	Coding and analysis

	Results and discussion
	Tech competence and agricultural application
	Reflections on strategic investment
	Farm-specific knowledge and capabilities for technology integration
	On-farm knowledge distribution
	Agricultural knowledge networks

	Conclusion
	Acknowledgements 
	References