































   Advancements in Agricultural Development 
  Volume 6, Issue 4, 2025 
  agdevresearch.org 

 

1. Niamh Dunphy, PhD student, School of Agriculture and Food Science, University College Dublin, Belfield, Dublin 4, Ireland, 

niamh.dunphy@ucdconnect.ie,  http://orcid.org/0009-0003-4603-170X 
2. Seamus Kearney, SignPost Climate Advisor, Teagasc Dungarvan, Shandon, Co. Waterford, Ireland, 

seamus.kearney@teagasc.ie,  http://orcid.org/0009-0000-2767-2390 
3. Sinéad Flannery, Assistant Professor in Behavioural Science in Agriculture and Rural Development, School of Agriculture 

and Food Science, University College Dublin, Belfield, Dublin 4, Ireland, sinead.flannery@ucd.ie,  

 http://orcid.org/0000-0002-9352-975X 
 

 
32 

 

Behavioural Drivers and Barriers for Adopting Climate 
Mitigation Actions on Dairy Farms 

 
N.Dunphy1, S. Kearney2, S. Flannery3 

 
 

Article History 
Received: February 13, 2025 
Accepted: October 21, 2025 
Published: November 15, 2025 
 
 
Keywords 
SDG 13: Climate Action; climate 
change; farmers; behaviour change   

Abstract 
The adoption of climate mitigation actions on farms has the potential to 
lessen the effects of climate change on agriculture. The purpose of this 
study is to identify the behavioural drivers and barriers for adopting 
climate mitigation actions on dairy farms. This study employed a 
qualitative methodological approach consisting of 24 semi–structured 
interviews with dairy farmers in the republic of Ireland. This study 
provides insights into the drivers and barriers for the adoption of climate 
mitigation actions at farm level. An increase in workload, being sceptical 
of the benefits of mitigation actions, and increases in costs incurred were 
the three biggest barriers for adopting climate mitigation actions at farm 
level. An increase in revenue and farm productivity, wanting to benefit 
the environment, and advisory support were the three biggest drivers 
when adopting climate mitigation actions on farm. From the findings of 
this study, policy and Agricultural Knowledge and Innovation Systems 
(AKIS) recommendations are offered. A call for policies that subsidise the 
cost of mitigation actions for farmers and promote actions that reduce 
workload levels on farms are proposed. AKIS recommendations on how 
agricultural advisors can support farmers when implementing mitigation 
actions are also offered.  

 

mailto:niamh.dunphy@ucdconnect.ie
http://orcid.org/0009-0003-4603-170X
mailto:seamus.kearney@teagasc.ie
http://orcid.org/0009-0000-2767-2390
mailto:sinead.flannery@ucd.ie
http://orcid.org/0000-0002-9352-975X


Dunphy et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i4.583   33 
 

Introduction and Problem Statement 
 
Agriculture is one of the biggest contributors to GHG (Greenhouse Gas) emissions, accounting 
for 11% of global emissions (Statista, 2024). The European Parliament declared a European and 
global climate emergency in 2019 (Erbach, 2021). Maintaining a sustainable food security relies 
on a reduction in GHG emissions from agriculture at farm level (Hatfield et al., 2011). 
Accordingly, the purpose of this paper is to identify the behavioural drivers and barriers for the 
adoption of climate mitigation actions at farm level. Farmer adoption of climate mitigation 
actions is a critical step in reducing agriculture’s significant contribution to GHG emissions.  
 
In 2015, the United Nations adopted 17 Sustainable Development Goals (SDG) (Opoku, 2016). 
These SDGs reflect a global shift to socio–economic development which is underpinned by 
environmental sustainability, social inclusion, and economic development (Sachs et al., 2016). 
SDG 13 is closely linked to this research as it relates to Climate Action. If SDGs are not achieved, 
there will be devastating implications: increased poverty and hunger, degradation of water 
quality, human health and well–being, and global warming will accelerate. Therefore, 
understanding how best to support farmers in the uptake of mitigation actions at farm level will 
contribute to SDG 13 (Lal, 2020). The UN Paris Agreement set out ambitious climate targets for 
the European Union (EU) to achieve by 2030 and 2050. The 2030 goal is to reduce GHG 
emissions by 55% compared to 1990 levels (Teevan et al., 2021), while the EU is legally 
committed to achieving net–zero emissions by 2050 (World Economic Forum, 2021). 
Knowledge on farmer decision–making is essential to reduce GHG emissions arising from 
agriculture (Farstad et al., 2022). This study explores the implementation of actions from 
Ireland’s Marginal Abatement Cost Curve (MACC) to identify drivers and barriers farmers 
experience when adopting these climate mitigation actions. Farmer engagement with 
discussion groups, having off–farm employment, age, education level, and size of family are all 
factors that influence farmer’s willingness to adopt mitigation actions (Crudeli et al., 2022; 
Wang et al., 2022). Therefore, identifying the behavioural drivers and barriers influencing Irish 
farmers' adoption of climate mitigation actions can help support a reduction in agricultural GHG 
emissions and contribute towards achieving the EU's 2030 and 2050 climate goals. 
 

Theoretical and Conceptual Framework 
 
The COM–B model 
Exploring farmers’ decision–making process is complex (Farstad et al., 2022). Darnhofer (2020) 
highlighted farmers’ decisions are not always based on science–informed actions and often 
they are made based on the farmer’s goals and values. However, it is important to also consider 
the behavioural and contextual conditions of farmers’ decision–making process (Brown et al., 
2021). Cofré–Bravo et al. (2019) highlighted the importance of farmer networks, and how their 
social environment significantly influences decision–making. Therefore, a theoretical model, 
which considered all of these factors, was required to achieve the purpose of this study. 
Accordingly, this study utilised the Capability–Opportunity–Motivation Behavioural model 
(COM–B) (Michie et al., 2011) as the theoretical and analytical framework.  

https://doi.org/10.37433/aad.v6i4.583


Dunphy et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i4.583   34 
 

 
The COM–B model was developed from an extensive literature review of previously existing 
frameworks (Michie et al., 2011). The COM–B model proposes human behaviour is a result of 
the interactions between capability, opportunity and motivation within an individual (Michie et 
al., 2011). If an individual is lacking in any three of the components (capability, opportunity or 
motivation), they will not achieve the desired behaviour. If sufficient levels of capability, 
opportunity and motivation are reached, the desired behaviour can be achieved. Capability is 
defined by an individual’s psychological and physical capacity to implement a behaviour, 
including having the necessary knowledge and skills (Michie et al., 2011). Opportunity is defined 
as all the factors that lie outside an individual or prompt the behaviour (Michie et al., 2011). 
Motivation is defined as all the brain processes that energise and direct behaviour. This 
includes habitual processes, emotional responding and analytical thinking (Michie et al., 2011). 
The COM–B model is used frequently in health care research (McDonagh et al., 2018; Murphy 
et al., 2023) and more recently has seen an increase in its use in agriculture (Burrell et al., 2024; 
Farrell et al., 2023). The use of the COM–B model in agriculture suggests farmers’ decision–
making process when opting to implement climate mitigation actions on their farm is 
influenced by a range of factors, both internal and external to the farmer. The COM–B model 
thus provides a theoretical lens to identify and classify the behavioural factors (i.e. drivers and 
barriers) influencing adoption of climate mitigation actions on farm.  
 

Purpose  
 
The purpose of this study was to identify the behavioural drivers and barriers for adopting 
climate mitigation actions on dairy farms using the COM–B model of behaviour change. This 
study used the COM–B model to shed light on the drivers and barriers influencing farmer 
adoption of climate mitigation actions at farm level. 
 

Methods 
 
A qualitative methodological approach was applied to this study to identify the behavioural 
drivers and barriers for adopting climate mitigation actions on dairy farms in Ireland. A 
qualitative research approach enables a researcher to develop new knowledge about human 
interaction, thoughts, experiences, behaviour and culture (Ohman, 2005). In–depth semi–
structured interviews with farmers (n = 24) were conducted. Questions in the semi–structured 
interview guide were informed by the COM–B model and centred around farmer 
engagement/disengagement with climate mitigation actions on farm and the reason for this. 
The study population consisted of dairy farmers who avail of public advisory services in the 
Republic of Ireland. Figure 1 shows the location and engagement levels of each semi–structured 
interviewee. All interviews were conducted between October and December 2023. Each 
interview took place on farm, lasting 50 minutes on average. All interviews were audio–
recorded and transcribed verbatim by the first author prior to analysis. 
 
 

https://doi.org/10.37433/aad.v6i4.583


Dunphy et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i4.583   35 
 

Figure 1 
 
Region, Location and Engagement Level of Semi–Structured Interviews in Ireland. 
 

 
 
Farmer selection for the semi–structured interviews was purposive. A criteria sheet was 
developed by the research team which ranked farmers into three categories; (a) lowly engaged 
(n = 6); (b) moderately engaged (n = 9); and (c) highly engaged (n = 9) with climate mitigation 
actions. The criteria sheet consisted of climate mitigation actions farmers could implement on 
their farm. The climate mitigation actions used in this study were derived from the Marginal 
Abatement Cost Curve (Lanigan et al., 2023). Advisors in each region were contacted by the first 
author to identify potential interviewees. Advisors completed the criteria sheets on behalf of 
the farmers as the information required was already known to them. Farmers were not aware 
of the criteria sheet or their engagement level. The advisor contacted farmers to seek 
permission to conduct the interview, once agreement was made, the first author made contact 
and a suitable time and date was arranged for the interview. If a farmer was engaged with five 
or less actions, they were deemed lowly engaged. If a farmer was engaged with less than 13 but 
more than five actions, they were deemed moderately engaged. Finally, if a farmer was 
engaged with 13 or more actions on the criteria sheet, they were deemed highly engaged. The 
research team created six regions across Ireland based on dairy farmer population to ensure 
similar spread of lowly, moderately, and highly engaged dairy farmers within each region. As 
the number of mitigation actions farmers are involved with influences their engagement with 
further mitigation actions (Tensi et al., 2022), this research methodology was applied to ensure 
drivers and barriers farmers experience come from a range of farmers, those who implement 
little to no actions and those who implement many actions. Although all semi–structured 
interviewees were male, an effort was made to include female farmers in this study. Female 
farmers were present in the sampling frame however, only one female was identified by 
advisors. This farmer declined to participate in an interview.  

https://doi.org/10.37433/aad.v6i4.583


Dunphy et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i4.583   36 
 

Table 1 
 
Profile of Semi–Structured Interview Participants 
 
Farmer Gender Region Engagement Level 
Farmer 1 Male 1 Moderate 
Farmer 2 Male 6 Moderate 
Farmer 3 Male 3 High 
Farmer 4 Male 5 Low 
Farmer 5 Male 2 Moderate 
Farmer 6 Male 2 Moderate 
Farmer 7 Male 1 High 
Farmer 8 Male 1 Low 
Farmer 9 Male 5 Moderate 
Farmer 10 Male 4 Moderate 
Farmer 11 Male 4 High 
Farmer 12 Male 3 Moderate 
Farmer 13 Male 6 High 
Farmer 14 Male 6 Low 
Farmer 15 Male 3 Low 
Farmer 16 Male 4 Moderate 
Farmer 17 Male 3 High 
Farmer 18 Male 4 High 
Farmer 19 Male 4 High 
Farmer 20 Male 5 High 
Farmer 21 Male 2 High 
Farmer 22 Male 2 Low 
Farmer 23 Male 4 Moderate 
Farmer 24 Male 2 Low 

 
Data Analysis 
The data collected was analysed using thematic analysis through the software package NVivo 
(version 12.7.0 (3873)). An inductive and then deductive approach was applied based on the 
known constructs of the COM–B model. To achieve data familiarisation, the semi–structured 
interview transcripts were read repeatedly. Inductive coding was utilised primarily, and themes 
were formed organically. After coding, the transcripts were reread to ensure no data had been 
omitted. A deductive approach was then taken to categorise the themes based on the known 
constructs of the COM–B model. The frequency of which themes emerged in the data was used 
as an indicator of their level of influence as drivers or barriers in implementation levels. 
Throughout this study the authors engaged with reflexivity by continuously self–reflecting on 
their positions and influence in data collection and analysis. Data coding was done 
independently by the lead author and confirmed by verifying findings with the other authors. 
By providing detailed descriptions of interviewees (Table 1) the authors have allowed readers 
to assess the applicability of the findings of this study to different cohorts of farmers. Finally, 

https://doi.org/10.37433/aad.v6i4.583


Dunphy et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i4.583   37 
 

credibility was achieved by conducting interviews with farmers in an environment they are 
comfortable in and over a prolonged engagement which ensured a nuanced understanding of 
their experiences with drivers and barriers when implementing climate mitigation actions on 
farms. Clear record keeping of semi–structured interview transcripts, coding schemes and 
author notes were kept to ensure transparency of the findings. 
 

Findings 
 
In line with the COM–B model, the factors that influenced the adoption of climate mitigation 
actions varied across the sample. Table 2 provides a summary of the findings from this study. In 
the sections that follow, each influencing factor is discussed in detail.  
 
Table 2 
 
COM–B Model for Climate Mitigation Actions at Farm Level 
 
Capability Opportunity Motivation 
Psychological 

• Knowledge & 
Educaion (–) 

 

Physical 
• Agricultural Advisor 

(+,–) 
• Policy (+) 

 

Automatic 
• Farmer Habit (+) 
• Wanting to benefit the 

environment (+) 

Physical 
 

Social 
• Publicity (+) 
• Other farmers (+,–) 

Reflective 
• Suits farm system (+,–) 
• Financial (+,–) 
• Workload (–) 
• Potential Success (–) 

Note. Barriers (–) and drivers (+) that influence farmer adoption of climate mitigation actions. 
 
Farmers’ Capability to Implement Climate Mitigation Actions 
Capability reflects a farmer’s psychological and physical ability to implement climate mitigation 
actions. Psychological capability relates to having the knowledge and cognitive ability to 
implement climate mitigation actions e.g., farmer knowledge and education. Physical capability 
refers to the ability required to implement climate mitigation actions e.g., physical strength and 
stamina. In relation to the implementation of climate mitigation actions, capability relates to 
engaging in the necessary thought processes and reasoning around implementation (Michie et 
al., 2011). The psychological capacity of farmers shaped their behaviour towards implementing 
climate mitigation actions.  
 
Farmer Knowledge and Education 
Farmer knowledge on how to implement climate mitigation actions on their farm acted as a 
barrier for adopting climate mitigation actions at farm level. Lack of knowledge and awareness 
around how to implement climate mitigation actions and not being fully aware of the 

https://doi.org/10.37433/aad.v6i4.583


Dunphy et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i4.583   38 
 

advantages of implementation restricted adoption levels. This aligns with previous research 
conducted with Scottish farmers where those with higher education levels tended to adopt 
mitigation actions quicker as education is likely to promote knowledge around environmental 
issues (Barnes & Toma, 2012; Prokopy et al., 2008). While some farmers in this study are 
interested in implementing a particular climate mitigation action on their farm, their lack of 
knowledge about them is preventing action. They feel they would “need to know more about 
them before [they would] put them in”. 
 
The findings of this study suggest having knowledge about mitigation actions has a positive 
influence on implementation levels of the actions. This aligns with the findings of Dunphy et al. 
(2025) and those of Moerkerken et al. (2020) who found that knowledge of GHGs is a robust 
determinant for reducing GHG emissions on farm. All farmers interviewed as part of this study 
have an awareness of climate mitigation actions, but they do not necessarily know how to 
implement these mitigation actions. For example, all farmers interviewed were aware of grass 
measuring, but a lack of procedural knowledge acted as a barrier when opting to implement 
grass measuring on their farm. Grass measuring relates to the climate mitigation action of 
measuring how much grass is available for grazing on farm which helps to optimise grass quality 
and quantity (Creighton et al., 2011). These findings align with those of Regan et al. (2021) who 
identified the drivers and barriers affecting dairy farmer behaviour to engaged with grass 
measuring in Ireland. They identified lack of farmer knowledge as having an influence on farmer 
decision–making when choosing to implement grass measuring which mirrors the findings of 
this study with a similar study population and location. As grass measuring requires a high level 
of knowledge to implement this is likely a reason for non–adoption on farms.  
 
Farmers’ Opportunity to Implement Climate Mitigation Actions 
Opportunity relates to factors external to a farmer (e.g., other farmers, advisors, and policies) 
that influences their decision–making when implementing climate mitigation actions on their 
farm. Policy measures, publicity, other farmers, and farm advisors were all factors that shaped 
farmers’ individual behaviour, positively and negatively, when implementing climate mitigation 
actions.  
 
Agricultural Advisor  
The role agricultural advisors play, both positively and negatively, in this study is evident 
throughout. Agricultural advisors play an important role as they have a responsibility to 
communicate expert advice and new research findings to farmers in a way farmers can adopt 
(Carlock, 2007). Most farmers reported their agricultural advisor as having a positive influence 
on their decision to implement climate mitigation actions on their farm. In this study, 
agricultural advisors and the role they play in facilitating discussion groups helped encourage 
farmers to implement climate mitigation actions: “I started grass measuring in 1998… It was the 
influence of Teagasc [agricultural advisor organisation] that got me started, I could not function 
without it [grass measuring] now” (Farmer 18). The pivotal role discussion groups play in driving 
behaviour change is apparent with encouraging farmers to incorporate biodiversity into their 
farm management systems (Leader et al., 2024) 
 

https://doi.org/10.37433/aad.v6i4.583


Dunphy et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i4.583   39 
 

Within this study, it was evident agricultural advisors have an influential role, the third biggest, 
in farmers’ decision to implement climate mitigation actions on their farm. Knowledge 
exchange and the supply of new knowledge is valued by farmers (Cofré–Bravo et al., 2019; 
Oreszczyn et al., 2010), and are essential for implementation of new practices because farmers 
rely on knowledge sources for farm innovation. This study also establishes knowledge exchange 
and the supply of new knowledge is valued by farmers (Moojen et al., 2024). However, 
agricultural advisors acted as both drivers and barriers for farmers when adopting climate 
mitigation actions in this study. For some farmers they acted as a barrier in relation to 
implementing climate mitigation actions on their farm: “I had my advisor out during the year 
and when I was asking her about it [sowing multi–species swards], she really did not convince 
me to go ahead with it, so I didn’t.” (Farmer 16) 
 
Policy 
Policy measures were found to influence farmers positively in relation to the uptake of 
mitigation actions on their farm. Although policy measures are forceful, for some farmers it 
meant that unless they were forced to change their farming habits, they would not: 
“Sometimes until you're forced to do certain things, you'll not do it.” (Farmer 1) 
Lane et al. (2018) found farmers view government regulations as a more critical issue than 
implementing climate actions affecting their decision–making process. Similar findings are seen 
in Liu et al. (2014) study that proved political ideology strongly influences farmers’ knowledge 
of climate change which enhances the opportunity for implementation. Similar findings were 
found this study: 

I will try it, because if it comes to the stage where we need to do it I'd like to have a 
head start on it, it would be nice to have your foot dipped into the water before you’re 
pushed in. (Farmer 1) 

 
Publicity 
In this study, publicity around climate mitigation actions acted as a driver for farmers to 
implement the a climate mitigation action. This was also seen in a study by Habib–Ur–Rahman 
et al. (2022) which illustrates how social media sites can be used to spread knowledge about 
climate change which encourages action in Asia. Some farmers in this study started using 
protected urea because it was a “buzz word.” Farmers also feel it is important to gain good 
publicity for implementing climate mitigation actions on their farm. Farmers are conscious of 
how the public view the farming sector: “Look, for a farmer to be seen to be doing something 
even just for the public to see it, so even as simple as getting the public on side.” (Farmer 11). 
This is also seen in Mills et al. (2017) study where farmers report they want to be seen “to be 
doing the right thing” and feel it is important to keep the public’s view of agriculture positive.  
 
Other Farmers 
It is unsurprising other farmers e.g., neighbours, farmers in discussion groups etc., are sources 
of information for farmers given the academic literature that supports it (Garforth et al., 2003). 
Peer–to–peer learning involves a two–way (or more) exchange of knowledge between farmers 
through explaining, listening, discussing and working together with another farmer who might 
be more knowledgeable on the topic (Cooreman et al., 2018). Other farmers had both a 

https://doi.org/10.37433/aad.v6i4.583


Dunphy et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i4.583   40 
 

positive and negative influence in implementing climate mitigation actions on farms. In this 
study, interviewees report they start doing an action because another farmer is implementing 
the action or encourages them to implement the action: “If you see your neighbour doing 
something it would get you thinking like we’re putting in solar panels now and the farmer next 
door is now as well because we have.” (Farmer 10).  
 
This is also seen in the findings from Farstad et al. (2022) where farmers have the confidence to 
implement mitigation measures as a result of positive learning experiences from their farming 
peers. Their findings also suggest farmers find their support for implementing climate 
mitigation actions in their local community with other farmers. The negative influence other 
farmers have on farmers decision–making process is largely related to farmers telling other 
farmers about negative experiences they had with implementing climate actions: “If I thought it 
would work of course I’d try it out but I just don’t think, from what I’ve seen from other farmer 
testimonials, I don’t think I’ll go ahead with it” (Farmer 10). In this study, other farmers mostly 
shaped farmer behaviour in a positive way i.e. increased adoption levels. The negative influence 
other farmers have on farmer behaviour can be seen as a leveraging factor but not as a barrier 
to behaviour.  
 
Farmers’ Motivation to Implement Climate Mitigation Actions 
Motivation is defined as the brain processes that direct and energise behaviour (Michie et al., 
2011). Automatic motivation relates to emotions and impulses while reflective motivation 
relates to evaluating and planning. Throughout this study, there are a number of factors which 
impact farmers’ motivation to implement climate mitigation actions on their farm.  
 
Farmer Habit 
Farmers’ habits acted as a driver when adopting mitigation actions on their farm. In relation to 
spreading protected urea instead of a type of fertiliser that emits more GHG emissions, farmers 
tend to stick with their habitual ways and continue to do what they have always done on their 
farm. While Le Dang et al. (2014) highlighted farmer habit as a barrier to farmer adoption as a 
general constraint, the findings from this study offer a different perspective by showing that 
habitual practices are particularly evident in fertiliser use where they prove especially resistant 
to change." 
 
However, for farmers who are in the habit of implementing climate mitigation actions on their 
farm, farmer habit has a positive effect on these farmers and reinforces the action. Some 
farmers have fenced off watercourses and stopped topping hedges as these were measures in 
an agri–environmental scheme in recent years and they got into the habit of implementing 
these mitigation actions. These farmers have continued to implement these actions since the 
scheme ended. There are similarities between the farmers in this study and those described by 
Mankad (2016) and Momenpour et al. (2024) where farmer habitual is a determining factor 
when adopting low–carbon agricultural technologies. Momenpour et al. (2024) explored 
farmers’ intentions to adopt low–carbon technologies in Iran. They concluded farmer habit had 
the second largest influence on farmer behaviour to adopt to climate change. This echoes the 

https://doi.org/10.37433/aad.v6i4.583


Dunphy et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i4.583   41 
 

findings presented in this study where other farmers milk record their herd annually, as “it’s 
something that has always been done on the farm.”  
 
Wanting to benefit the environment 
Farmers in this study reported they implement climate mitigation actions on their farm for the 
environmental benefits the action brings about. This was identified as the second biggest driver 
influencing the implementation of climate mitigation actions on their farm. This influence was 
especially evident when farmers speak about the hedgerows on their farm. There is a sense of 
pride amongst farmers, if they see other farmers doing something for environmental reasons 
they are proud of each other. Additionally, when farmers see the positive results of 
implementing a climate mitigation action, this encourages them to keep implementing the 
action each year on their farm. Some farmers reference how they “couldn’t go back to cutting 
the hedges.”  
 
Suits Farm System 
Farmers are influenced both positively and negatively to implement a mitigation action if it 
suits a farm system. For farmers who do not record milk production on their farm, they feel 
their current farm system works well and if they were to begin recording milk production, they 
would have to change what they do: “If I was milk recording, I’d feel like I’d have to be more 
ruthless when I am culling cows and then I’d have to buy in and I just don’t see the 
justification… What I have is working so far” (Farmer 15). 
 
Contrary to this, many farmers find implementing climate mitigation actions on their farm suits 
their farming system and this positively influences them to implement these actions each year. 
Farmers reported spreading slurry using Low Emission Slurry Spreading (LESS) techniques suited 
their farm systems because their contractor uses LESS techniques: “The main reason I use the 
low emission slurry spreader is because I can spread slurry in Spring and you can still graze it in 
a weeks’ time” (Farmer 23). 
 
Financial  
Financial risks and incurring costs to the farmer act as a barrier for farmers in the uptake of 
climate mitigation actions. In this study, increasing farm expenses was the third biggest barrier 
for farmers when implementing climate mitigation actions on their farm. This is in line with 
other studies examining barriers for implementation of climate mitigation actions which found 
farmers consider saving money their priority when adopting these actions (Moerkerken et al., 
2020; Roesch–McNally et al., 2018). In some cases, farmers had implemented climate 
mitigation actions on their farm and “stopped because of the cost.” However, the extra 
revenue created from engaging in climate mitigation actions was the biggest driver when 
implementing climate mitigation actions on farm. This finding was also observed in Moerkerken 
et al. (2020) study which showed the positive relationship between implementing climate 
mitigation actions and ‘cost saving’ as a motivation for taking action. Farmers in this study who 
record milk production on their farm do so because they feel they can increase production and 
income. Similarly, when farmers are asked why they have reduced their fertiliser input their 
responses alluded to the financial gain made to their farming business: “Well the biggest reason 

https://doi.org/10.37433/aad.v6i4.583


Dunphy et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i4.583   42 
 

[they reduced fertiliser inputs] was price and the second reason was I just wanted to reduce my 
input that was basically it, it was down to cost” (Farmer 12).  
 
Farmers in this study are not willing to spend money to reduce their GHG emissions. Farmers 
report “the big motivation is what you can save financially.” This is unsurprising as Farstad et al. 
(2022) also found that although farmers acknowledged the value climate mitigation actions 
have for the environment, their focus is on the economic performance of their farm. Climate 
mitigation outcomes often come as a by–product or side effect of practices implemented to 
improve profitability (reduce cost) of farming operations (Farstad et al., 2022).  
 
Workload 
This study found if a climate mitigation action increases a farmer’s workload, they are less likely 
to implement the action. Tasks that are time–consuming and laborious have a relatively low 
uptake on farm. Based on data frequency, a higher workload is the biggest barrier for farmers 
to implement climate mitigation actions on their farms. Similarities were found by Regan et al. 
(2021) as farmers perceived grass measuring as a time–expensive task. Farmers find recording 
milk production, grass measuring, incorporating multi–species swards, spreading protected 
urea, using sexed semen, and soil sampling all jobs that add to their workload: “I stopped milk 
recording and I never went back to it because it’s another job and I’m here on my own and it’s 
very slow in the morning and evening” (Farmer 14). These findings are similar to those of Mitter 
et al. (2019), who found climate change adopters cite workload on small–scale farms as a 
barrier to adoption. Farmers in the current study feel some climate mitigation actions are not 
always worth the time invested which compromises behaviour with these actions. These 
findings suggest adoption of mitigation actions that place considerable strain on time and 
resources will result in low implementation levels unless these barriers can be overcome.  
 
Potential Success of Mitigation Actions 
Being sceptical of the benefits of implementing climate mitigation actions on farm acted as the 
second biggest barrier to farmer adoption levels in this study. In relation to multi–species 
swards, farmers are sceptical of the persistence of the plant in swards: 

I don’t do multi–species because there is no retention… The animals don’t like it, it fades 
away after a few years and it’s hard to manage. (Farmer 13) 

 
These are rational reasons for not incorporating multi–species swards on farms (Vanclay & 
Lawrence, 1994). In relation to grass measuring, Regan et al. (2021) showed that all farmers are 
convinced of the benefits of grass measuring, but less than half of farmers were engaged with 
grass measuring on a regular basis. The findings presented in this study show some farmers do 
not see any benefits to engaging in this timely task: “I don’t see the benefit of it [grass 
measuring], the cows will tell you if you haven’t enough grass” (Farmer 15). A smaller 
percentage of farmers in this study are engaged with grass measuring on a regular basis 
highlighting the importance of perceiving grass measuring as beneficial.  
 
 

https://doi.org/10.37433/aad.v6i4.583


Dunphy et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i4.583   43 
 

Conclusions, Discussion, and Recommendations 
 
The findings from this study shed light on the influencing factors for adopting climate mitigation 
actions on farm. An increase in workload, being sceptical of the benefits of mitigation actions, 
and increases in costs incurred were the three biggest barriers when adopting climate 
mitigation actions on farms. An increase in revenue and farm productivity, wanting to benefit 
the environment, and advisory support were the three biggest drivers when adopting climate 
mitigation actions on farms. Considering these findings, recommendations to the AKIS and 
policies can be made to enhance implementation levels of climate mitigation actions on farms. 
Policy makers should develop policies that subsidise the cost of mitigation actions whilst also 
promoting actions that reduce workload levels for farmers. Participation in previous agri–
environmental schemes has led to the formation of habitual practices among farmers, resulting 
in the continued implementation of scheme–related actions even after financial incentives 
ceased. This suggests that such schemes can foster long–term behavioural change in the form 
of habit rather than financial motivation. Future policies should include a ‘carrot’ approach 
initially for farmers, which will hopefully result in sustained long–term change in the form of 
farmer habits. 
 
The AKIS should take into consideration the findings from this study; in particular, the influence 
agricultural advisors have on farmer behaviour. In this study, agricultural advisors played both 
positive and negative roles in the uptake of mitigation actions on farms. Some advisors 
influenced farmer decision–making based on their own unconscious biases as opposed to 
providing farmers with the knowledge around actions and letting farmers make their own 
decisions. The AKIS should provide agricultural advisors with training to help advisors become 
aware of their own biases when giving farmers advice on mitigation actions. The AKIS should 
consider expanding its advisory services to further support advisors in its crucial role of 
encouraging farmers to implement climate mitigation actions on their farm. Agricultural 
advisors should also promote peer–to–peer learning opportunities for farmers to facilitate 
knowledge exchange to address scepticism of mitigation actions experienced by farmers. Peer–
to–peer learning environments could be created through discussion groups, which have proved 
to be a successful educational space for farmers (O’Connor et al., 2021). Additionally, the AKIS 
should promote the use of advisory support tools that have been proven successful in raising 
farmer knowledge on mitigation actions available to them such as those of Dunphy et al. (2025) 
have developed. These changes to policy have the potential to increase adoption of climate 
mitigation actions on farm, which will result in a reduction of GHG emissions arising from 
agriculture and contribute to achieving SDGs, specifically SDG 13 (Climate Action).  
 
Several key areas warrant further research to verify and advance our understanding of the 
drivers and barriers influencing the uptake of climate mitigation actions on farms. Future 
research should consider the influence of farmer demographics in decision–making processes 
to further enhance the findings presented within this paper. This study focused on exploring 
factors that influence dairy farmer adoption of mitigation actions on farms. Extending the study 
to a larger subset of farmers associated with different enterprises (e.g., tillage, drystock, etc.) 

https://doi.org/10.37433/aad.v6i4.583


Dunphy et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i4.583   44 
 

would be useful to determine if similar drivers and barriers are experienced across enterprises. 
Furthermore, all farmers interviewed in this study were male. The population of female farmers 
in Ireland is low (13.4%) (CSO, 2020). Hence, it was difficult to recruit female farmers to 
participate in this study. Valuable future research should focus on drivers and barriers female 
farmers experience when implementing climate mitigation actions on their farms. Other drivers 
and barriers are likely to be uncovered if the circle of inquiry is expanded. Future research could 
also explore and compare typologies of farmer engagement. This may offer an enriched and 
further nuanced understanding of farmer behaviour towards climate mitigation actions and 
reveal additional valuable insights into farmer behaviour. The findings from this study fill a gap 
that was missing in the literature: information of the drivers and barriers dairy farmers in 
Ireland experience when implementing climate mitigation actions on their farms. Although this 
area requires further research, the findings of this study can be a foundational baseline of 
knowledge on this cohort and inform future research. 
 

Acknowledgments 
 
Funding Information: This work was supported by the Teagasc Walsh Scholarship Programme 
[grant number 2021309]. 
 
Conflict of interest: There are no identified conflicts of interest among the authors. 
 
Previous Dissemination: This paper was presented at the Agricultural Economics Society of 
Ireland Early Career Day Meeting in January 2025. https://aesi.ie/2025/01/31/early–career–
day–meeting–recap/ 
 
Artificial Intelligence: Artificial intelligence tools were not used in any stage of the study or the 
manuscript’s preparation. 
 
Author Contribution Statement: N. Dunphy – formal analysis, transcription, data coding, 
investigation, writing–original draft. S. Kearney – confirming data coding, writing–review and 
editing. S. Flannery –confirming data coding, writing–review and editing. 
 

References 
 
Barnes, A. P., & Toma, L. (2012). A typology of dairy farmer perceptions towards climate 

change. Climatic Change, 112, 507–522. https://doi.org/10.1007/s10584–011–0226–2  
 
Brown, C., Kovács, E., Herzon, I., Villamayor–Tomas, S., Albizua, A., Galanaki, A., 

Grammatikopoulou, I., Mccracken, D., Olsson, J. A., & Zinngrebe, Y. (2021). Simplistic 
understandings of farmer motivations could undermine the environmental potential of 
the common agricultural policy. Land Use Policy, 101, 105136. 
https://doi.org/10.1016/j.landusepol.2020.105136  

https://doi.org/10.37433/aad.v6i4.583
https://aesi.ie/2025/01/31/early-career-day-meeting-recap/
https://aesi.ie/2025/01/31/early-career-day-meeting-recap/
https://doi.org/10.1007/s10584-011-0226-2
https://doi.org/10.1016/j.landusepol.2020.105136


Dunphy et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i4.583   45 
 

Burrell, A. M., Balaine, L., Clifford, S., Mcgrath, M., Graham, D. A., Mccoy, F., Dillon, E., & Regan, 
Á. (2024). A multi–methods, multi–actor exploration of the benefits and barriers to milk 
recording on Irish farms using the com–b model. Preventive Veterinary Medicine, 227, 
106195. https://doi.org/10.1016/j.prevetmed.2024.106195  

 
Carlock, R. S. (2007). Leadership coaching in family businesses. Coach and couch: The 

psychology of making better leaders. Springer. https://doi.org/10.1007/978–1–137–
56161–9_11  

 
Cofré–Bravo, G., Klerkx, L., & Engler, A. (2019). Combinations of bonding, bridging, and linking 

social capital for farm innovation: How farmers configure different support networks. 
Journal of Rural Studies, 69, 53–64. https://doi.org/10.1016/j.jrurstud.2019.04.004  

 
Cooreman, H., Vandenabeele, J., Debruyne, L., Ingram, J., Chiswell, H., Koutsouris, A., Pappa, E., 

& Marchand, F. (2018). A conceptual framework to investigate the role of peer learning 
processes at on–farm demonstrations in the light of sustainable agriculture. 
International Journal of Agricultural Extension, 6, 91–103.  

 
Creighton, P., Kennedy, E., Shalloo, L., Boland, T., & O’donovan, M. (2011). A survey analysis of 

grassland dairy farming in ireland, investigating grassland management, technology 
adoption and sward renewal. Grass and Forage Science, 66(2), 251–264.  
https://doi.org/10.1111/j.1365–2494.2011.00784.x  

 
Crudeli, L., Mancinelli, S., Mazzanti, M., & Pitoro, R. (2022). Beyond individualistic behaviour: 

Social norms and innovation adoption in rural Mozambique. World Development, 157, 
105928.  https://doi.org/10.1016/j.worlddev.2022.10592  

 
CSO. (2020). Census of agriculture 2020 – preliminary results. Central Statistic Office. 

https://www.cso.ie/en/releasesandpublications/ep/p–coa/censusofagriculture2020–
preliminaryresults/demographicprofileoffarmholders/  

 
Darnhofer, I. (2020). Farming from a process-relational perspective: Making openings for 

change visible. Sociologia Ruralis, 60(2), 505–528. https://doi.org/10.1111/soru.12294 
 
Dunphy, N., Flannery, S., & Kearney, S. (2025). Development of an agricultural extension 

support tool to support farmer engagement in conversations about climate change. The 
Journal of Agricultural Education and Extension, 1–19. 
https://doi.org/10.1080/1389224X.2025.2477450   

 
Erbach, G. (2021). European climate law. Regulation (EU), 1119. 

https://www.univiu.org/images/aauniviu2017/GP/co–curr/European_climate_law.pdf  
 

https://doi.org/10.37433/aad.v6i4.583
https://doi.org/10.1016/j.prevetmed.2024.106195
https://doi.org/10.1007/978-1-137-56161-9_11
https://doi.org/10.1007/978-1-137-56161-9_11
https://doi.org/10.1016/j.jrurstud.2019.04.004
https://doi.org/10.1111/j.1365-2494.2011.00784.x
https://doi.org/10.1016/j.worlddev.2022.10592
https://www.cso.ie/en/releasesandpublications/ep/p-coa/censusofagriculture2020-preliminaryresults/demographicprofileoffarmholders/
https://www.cso.ie/en/releasesandpublications/ep/p-coa/censusofagriculture2020-preliminaryresults/demographicprofileoffarmholders/
https://doi.org/10.1111/soru.12294
https://doi.org/10.1080/1389224X.2025.2477450
https://www.univiu.org/images/aauniviu2017/GP/co-curr/European_climate_law.pdf


Dunphy et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i4.583   46 
 

Farrell, S., Benson, T., Mckernan, C., Regan, Á., Burrell, A. M., & Dean, M. (2023). Factors 
influencing dairy farmers' antibiotic use: An application of the com–b model. Journal of 
Dairy Science, 106(6), 4059–4071. https://doi.org/10.3168/jds.2022–22263  

 
Farstad, M., Melås, A. M., & Klerkx, L. (2022). Climate considerations aside: What really matters 

for farmers in their implementation of climate mitigation measures. Journal of Rural 
Studies, 96, 259–269. https://doi.org/10.1016/j.jrurstud.2022.11.003  

 
Garforth, C., Angell, B., Archer, J., & Green, K. (2003). Fragmentation or creative diversity? 

Options in the provision of land management advisory services. Land Use Policy, 20(4), 
323–333.  https://doi.org/10.1016/S0264–8377(03)00035–8  

 
Habib–Ur–Rahman, M., Ahmad, A., Raza, A., Hasnain, M. U., Alharby, H. F., Alzahrani, Y. M., 

Bamagoos, A. A., Hakeem, K. R., Ahmad, S., & Nasim, W. (2022). Impact of climate 
change on agricultural production: Issues, challenges, and opportunities in Asia. 
Frontiers in Plant Science, 13, 925548. https://doi.org/10.3389/fpls.2022.925548  

 
Hatfield, J. L., Boote, K. J., Kimball, B. A., Ziska, L., Izaurralde, R. C., Ort, D., Thomson, A. M., & 

Wolfe, D. (2011). Climate impacts on agriculture: Implications for crop production. 
Agronomy journal, 103(2), 351–370. https://doi.org/10.2134/agronj2010.0303 

 
Lal, R. (2020). Advancing climate change mitigation in agriculture while meeting global 

sustainable development goals. Soil and Water Conservation: A Celebration of 75 Years. 
https://www.swcs.org/static/media/cms/75th_Book__Chapter_2_FE50D48CBC96F.pdf 

 
Lane, D., Chatrchyan, A., Tobin, D., Thorn, K., Allred, S., & Radhakrishna, R. (2018). Climate 

change and agriculture in New York and Pennsylvania: Risk perceptions, vulnerability 
and adaptation among farmers. Renewable Agriculture and Food Systems, 33(3), 197–
205. https://doi.org/10.1017/S1742170517000710  

 
Lanigan, G., Black, K., Donnellan, T., Crosson, P., Beausang, C., Hanrahan, K., Buckley, C., Lahart, 

B., Herron, J., Redmond, J., & Shalloo, L. (2023). MACC 2023: An updated analysis of the 
greenhouse gas abatement potential of the Irish agriculture and land–use sectors 
between 2021 and 2030. Oak Park, Carlow, Ireland. 

 
Leader, A., Kinsella, J., & O’Brien, R. (2024). Making sense of farmland biodiversity 

management: An evaluation of a farmland biodiversity management communication 
strategy with farmers. Agriculture and Human Values, 41, 1–19.  
https://doi.org/10.1007/s10460–024–10573–4  

 
Le Dang, H., Li, E., Bruwer, J., & Nuberg, I. (2014). Farmers’ perceptions of climate variability 

and barriers to adaptation: Lessons learned from an exploratory study in Vietnam. 
Mitigation and adaptation strategies for global change, 19, 531–548. 
https://doi.org/10.1007/s11027–012–9447–6 

https://doi.org/10.37433/aad.v6i4.583
https://doi.org/10.3168/jds.2022-22263
https://doi.org/10.1016/j.jrurstud.2022.11.003
https://doi.org/10.1016/S0264-8377(03)00035-8
https://doi.org/10.3389/fpls.2022.925548
https://doi.org/10.2134/agronj2010.0303
https://www.swcs.org/static/media/cms/75th_Book__Chapter_2_FE50D48CBC96F.pdf
https://doi.org/10.1017/S1742170517000710
https://doi.org/10.1007/s10460-024-10573-4
https://doi.org/10.1007/s11027-012-9447-6


Dunphy et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i4.583   47 
 

Liu, Z., Smith, W. J., & Safi, A. S. (2014). Rancher and farmer perceptions of climate change in 
Nevada, USA. Climatic Change, 122, 313–327. https://doi.org/10.1007/s10584–013–
0979–x 

 
Mankad, A. (2016). Psychological influences on biosecurity control and farmer decision–making. 

A review. Agronomy for sustainable development, 36, 40. 
https://doi.org/10.1007/s13593–016–0375–9 

 
McDonagh, L. K., Saunders, J. M., Cassell, J., Curtis, T., Bastaki, H., Hartney, T., & Rait, G. (2018). 

Application of the Com–B model to barriers and facilitators to chlamydia testing in 
general practice for young people and primary care practitioners: A systematic review. 
Implementation Science, 13, 130.  https://doi.org/10.1186/s13012–018–0821–y 

 
Michie, S., Van Stralen, M. M., & West, R. (2011). The behaviour change wheel: A new method 

for characterising and designing behaviour change interventions. Implementation 
Science, 6, 42. https://doi.org/10.1186/1748–5908–6–42 

 
Mills, J., Gaskell, P., Ingram, J., Dwyer, J., Reed, M., & Short, C. (2017). Engaging farmers in 

environmental management through a better understanding of behaviour. Agriculture 
and Human Values, 34, 283–299. https://doi.org/10.1007/s10460–016–9705–4 

 
Mitter, H., Larcher, M., Schönhart, M., Stöttinger, M., & Schmid, E. (2019). Exploring farmers’ 

climate change perceptions and adaptation intentions: Empirical evidence from Austria. 
Environmental Management, 63, 804–821. https://doi.org/10.1007/s00267–019–
01158–7  

 
Moerkerken, A., Blasch, J., Van Beukering, P., & Van Well, E. (2020). A new approach to explain 

farmers’ adoption of climate change mitigation measures. Climatic Change, 159, 141–
161. https://doi.org/10.1007/s10584–019–02595–3 

 
Momenpour, Y., Choobchian, S., & Haji, L. (2024). Farmers' intention to adopt low–carbon 

agricultural technologies to mitigate climate change. Environmental and Sustainability 
Indicators, 23, 100432.  https://doi.org/10.1016/j.indic.2024.100432  

 
Moojen, F. G., Grillot, M., De Faccio Carvalho, P. C., & Ryschawy, J. (2024). Farm advisors play a 

key role in integrating crop–livestock at the farm level: Perceptions and experiences in 
Brazil and France. The Journal of Agricultural Education and Extension, 30(5), 683–707.  
https://doi.org/10.1080/1389224X.2023.2254308  

 
Murphy, K., Berk, J., Muhwava–Mbabala, L., Booley, S., Harbron, J., Ware, L., Norris, S., 

Zarowsky, C., Lambert, E. V., & Levitt, N. S. (2023). Using the com–b model and 
behaviour change wheel to develop a theory and evidence–based intervention for 
women with gestational diabetes (Indiago). BMC Public Health, 23, 894.  
https://doi.org/10.1186/s12889–023–15586–y 

https://doi.org/10.37433/aad.v6i4.583
https://doi.org/10.1007/s10584-013-0979-x
https://doi.org/10.1007/s10584-013-0979-x
https://doi.org/10.1007/s13593-016-0375-9
https://doi.org/10.1186/s13012-018-0821-y
https://doi.org/10.1186/1748-5908-6-42
https://doi.org/10.1007/s10460-016-9705-4
https://doi.org/10.1007/s00267-019-01158-7
https://doi.org/10.1007/s00267-019-01158-7
https://doi.org/10.1007/s10584-019-02595-3
https://doi.org/10.1016/j.indic.2024.100432
https://doi.org/10.1080/1389224X.2023.2254308
https://doi.org/10.1186/s12889-023-15586-y


Dunphy et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i4.583   48 
 

O’Connor, T., Meredith, D., McNamara, J., O’Hora, D., & Kinsella, J. (2021). Farmer discussion 
groups create space for peer learning about safety and health. Journal of agromedicine, 
26(2), 120–131. https://doi.org/10.1080/1059924X.2020.1720882 

 
Ohman, A. (2005). Qualitative methodology for rehabilitation research. Journal of Rehabilitation 

Medicine, 37(5), 273–280. https://doi.org/10.1080/16501970510040056  
 
Opoku, A. (2016). SDG2030: A sustainable built environment’s role in achieving the post–2015 

United Nations sustainable development goals.  Proceedings of the 32nd Annual ARCOM 
Conference, 2016. Association of Researchers in Construction Management 
Manchester, UK, 1149–1158. https://www.researchgate.net/profile/Alex–Opoku–
2/publication/307906714_SDG2030_A_SUSTAINABLE_BUILT_ENVIRONMENT'S_ROLE_I
N_ACHIEVING_THE_POST–
2015_UNITED_NATIONS_SUSTAINABLE_DEVELOPMENT_GOALS/links/57d14afb08ae639
9a38b401f/SDG2030–A–SUSTAINABLE–BUILT–ENVIRONMENTS–ROLE–IN–ACHIEVING–
THE–POST–2015–UNITED–NATIONS–SUSTAINABLE–DEVELOPMENT–GOALS.pdf 

 
Oreszczyn, S., Lane, A., & Carr, S. (2010). The role of networks of practice and webs of 

influencers on farmers' engagement with and learning about agricultural innovations. 
Journal of Rural Studies, 26(4), 404–417. https://doi.org/10.1016/j.jrurstud.2010.03.003 

 
Prokopy, L. S., Floress, K., Klotthor–Weinkauf, D., & Baumgart–Getz, A. (2008). Determinants of 

agricultural best management practice adoption: Evidence from the literature. Journal 
of Soil and Water Conservation, 63(5), 300–311.  https://doi.org/10.2489/jswc.63.5.300  

 
Regan, Á., Douglas, J., Maher, J., & O’Dwyer, T. (2021). Exploring farmers’ decisions to engage in 

grass measurement on dairy farms in Ireland. The Journal of Agricultural Education and 
Extension, 27(3), 355–380. https://doi.org/10.1080/1389224X.2020.1858892 

 
Roesch–McNally, G. E., Basche, A. D., Arbuckle, J. G., Tyndall, J. C., Miguez, F. E., Bowman, T., & 

Clay, R. (2018). The trouble with cover crops: Farmers’ experiences with overcoming 
barriers to adoption. Renewable Agriculture and Food Systems, 33(4), 322–333. 
https://doi.org/10.1017/S1742170517000096 

 
Sachs, J. D., Schmidt–Traub, G. & Durand–Delacre, D. (2016). Preliminary sustainable 

development goal (SDG) index and dashboard. SDSN Working Paper. 
https://planbleu.org/sites/default/files/upload/files/160215–Preliminary–SDG–Index–
and–SDG–Dashboard–working–paper–for–consultation.pdf  

 
Statista. (2024). Agriculture emissions worldwide – statistics & facts. Statista. 

https://www.statista.com/topics/10348/agriculture–emissions–
worldwide/#:~:text=In%202023%2C%20agricultural%20processes%20like,percent%20of
%20global%20GHG%20emissions 

 

https://doi.org/10.37433/aad.v6i4.583
https://doi.org/10.1080/1059924X.2020.1720882
https://doi.org/10.1080/16501970510040056
https://www.researchgate.net/profile/Alex-Opoku-2/publication/307906714_SDG2030_A_SUSTAINABLE_BUILT_ENVIRONMENT'S_ROLE_IN_ACHIEVING_THE_POST-2015_UNITED_NATIONS_SUSTAINABLE_DEVELOPMENT_GOALS/links/57d14afb08ae6399a38b401f/SDG2030-A-SUSTAINABLE-BUILT-ENVIRONMENTS-ROLE-IN-ACHIEVING-THE-POST-2015-UNITED-NATIONS-SUSTAINABLE-DEVELOPMENT-GOALS.pdf
https://www.researchgate.net/profile/Alex-Opoku-2/publication/307906714_SDG2030_A_SUSTAINABLE_BUILT_ENVIRONMENT'S_ROLE_IN_ACHIEVING_THE_POST-2015_UNITED_NATIONS_SUSTAINABLE_DEVELOPMENT_GOALS/links/57d14afb08ae6399a38b401f/SDG2030-A-SUSTAINABLE-BUILT-ENVIRONMENTS-ROLE-IN-ACHIEVING-THE-POST-2015-UNITED-NATIONS-SUSTAINABLE-DEVELOPMENT-GOALS.pdf
https://www.researchgate.net/profile/Alex-Opoku-2/publication/307906714_SDG2030_A_SUSTAINABLE_BUILT_ENVIRONMENT'S_ROLE_IN_ACHIEVING_THE_POST-2015_UNITED_NATIONS_SUSTAINABLE_DEVELOPMENT_GOALS/links/57d14afb08ae6399a38b401f/SDG2030-A-SUSTAINABLE-BUILT-ENVIRONMENTS-ROLE-IN-ACHIEVING-THE-POST-2015-UNITED-NATIONS-SUSTAINABLE-DEVELOPMENT-GOALS.pdf
https://www.researchgate.net/profile/Alex-Opoku-2/publication/307906714_SDG2030_A_SUSTAINABLE_BUILT_ENVIRONMENT'S_ROLE_IN_ACHIEVING_THE_POST-2015_UNITED_NATIONS_SUSTAINABLE_DEVELOPMENT_GOALS/links/57d14afb08ae6399a38b401f/SDG2030-A-SUSTAINABLE-BUILT-ENVIRONMENTS-ROLE-IN-ACHIEVING-THE-POST-2015-UNITED-NATIONS-SUSTAINABLE-DEVELOPMENT-GOALS.pdf
https://www.researchgate.net/profile/Alex-Opoku-2/publication/307906714_SDG2030_A_SUSTAINABLE_BUILT_ENVIRONMENT'S_ROLE_IN_ACHIEVING_THE_POST-2015_UNITED_NATIONS_SUSTAINABLE_DEVELOPMENT_GOALS/links/57d14afb08ae6399a38b401f/SDG2030-A-SUSTAINABLE-BUILT-ENVIRONMENTS-ROLE-IN-ACHIEVING-THE-POST-2015-UNITED-NATIONS-SUSTAINABLE-DEVELOPMENT-GOALS.pdf
https://www.researchgate.net/profile/Alex-Opoku-2/publication/307906714_SDG2030_A_SUSTAINABLE_BUILT_ENVIRONMENT'S_ROLE_IN_ACHIEVING_THE_POST-2015_UNITED_NATIONS_SUSTAINABLE_DEVELOPMENT_GOALS/links/57d14afb08ae6399a38b401f/SDG2030-A-SUSTAINABLE-BUILT-ENVIRONMENTS-ROLE-IN-ACHIEVING-THE-POST-2015-UNITED-NATIONS-SUSTAINABLE-DEVELOPMENT-GOALS.pdf
https://doi.org/10.1016/j.jrurstud.2010.03.003
https://doi.org/10.2489/jswc.63.5.300
https://doi.org/10.1080/1389224X.2020.1858892
https://doi.org/10.1017/S1742170517000096
https://planbleu.org/sites/default/files/upload/files/160215-Preliminary-SDG-Index-and-SDG-Dashboard-working-paper-for-consultation.pdf
https://planbleu.org/sites/default/files/upload/files/160215-Preliminary-SDG-Index-and-SDG-Dashboard-working-paper-for-consultation.pdf
https://www.statista.com/topics/10348/agriculture-emissions-worldwide/#:~:text=In%202023%2C%20agricultural%20processes%20like,percent%20of%20global%20GHG%20emissions
https://www.statista.com/topics/10348/agriculture-emissions-worldwide/#:~:text=In%202023%2C%20agricultural%20processes%20like,percent%20of%20global%20GHG%20emissions
https://www.statista.com/topics/10348/agriculture-emissions-worldwide/#:~:text=In%202023%2C%20agricultural%20processes%20like,percent%20of%20global%20GHG%20emissions


Dunphy et al.  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i4.583   49 
 

Teevan, C., Medinilla, A., & Sergejeff, K. (2021). The Green Deal in EU foreign and development 
policy. ECDPM Briefing Note, 131. https://euagenda.eu/upload/publications/green–
deal–eu–foreign–development–policy–ecdpm–briefing–note–131–2021.pdf  

 
Tensi, A. F., Ang, F., & Van der Fels–Klerx, H. J., 2022. Behavioural drivers and barriers for 

adopting microbial applications in arable farms: Evidence from the Netherlands and 
Germany. Technological Forecasting and Social Change, 182, 121825. 
https://doi.org/10.1016/j.techfore.2022.121825  

 
Vanclay, F., & Lawrence, G. (1994). Farmer rationality and the adoption of environmentally 

sound practices: A critique of the assumptions of traditional agricultural extension. 
European Journal of Agricultural Education and Extension, 1(1), 59–90.  
https://doi.org/10.1080/13892249485300061 

 
Wang, Q., Yu, L., Yang, Y., Zhao, H., Song, Y., Song, W., & Liu, J. (2022). Let the farmers embrace 

“carbon neutrality”: Taking the centralized biogas as an example. International Journal 
of Environmental Research and Public Health, 19(15), 9677.  
https://doi.org/10.3390/ijerph19159677  

 
World Economic Forum. (2021). What you need to know about the European Green Deal – and 

what comes next. World Economic Forum. 
https://www.weforum.org/agenda/2021/07/what–you–need–to–know–about–the–
european–green–deal–and– what–comes–next/  

 
 
© 2025 by authors. This article is an open access article distributed under the terms and conditions of 
the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/). 
 

https://doi.org/10.37433/aad.v6i4.583
https://euagenda.eu/upload/publications/green-deal-eu-foreign-development-policy-ecdpm-briefing-note-131-2021.pdf
https://euagenda.eu/upload/publications/green-deal-eu-foreign-development-policy-ecdpm-briefing-note-131-2021.pdf
https://doi.org/10.1016/j.techfore.2022.121825
https://doi.org/10.1080/13892249485300061
https://doi.org/10.3390/ijerph19159677

