Beti- MSc Project

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Trainee Farmer Knowledge and Adoption of
Climate Smart Agriculture practices in Ireland
By Eileen Elizabeth O’Connor
Student number: 11514803
The thesis is submitted to NUI Galway in part fulfilment of the
requirements for the degree of Masters in Climate Change,
Agriculture and Food Security, which is a taught postgraduate
program within the School of Natural Sciences.
Supervisors: Dr. Charles Spillane, Dr. Peter McKeown & Dr. Kevin Kilcline
Date of submission: 15/08/2016

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Table of Contents
Declaration..................................................................................................................................3
Acknowledgements ...................................................................................................................... 4
Abstract.......................................................................................................................................5
Literature Review ......................................................................................................................... 6
Introduction............................................................................................................................. 6
IrishAgriculture........................................................................................................................ 6
Climate Change....................................................................................................................... 11
Climate-Smart Agriculture and the Marginal Abatement Cost Curve .......................................... 12
Carbon Navigator.................................................................................................................... 14
How the Carbon Navigator works......................................................................................... 17
The Beef Data and Genomics Programme ................................................................................ 24
Common Agricultural Policy..................................................................................................... 24
Farmers’ awareness of and attitude towards climate change..................................................... 25
Technology adoptionin agriculture.......................................................................................... 27
Research Goal & Objectives......................................................................................................... 28
Research Objectives................................................................................................................ 28
Methodology.............................................................................................................................. 31
Topics covered in the survey.................................................................................................... 31
Questionnaire/Survey Design .................................................................................................. 31
Statistical Analysis................................................................................................................... 32
Results....................................................................................................................................... 33
Section 1: Frequencies ............................................................................................................ 33
Section 2- Research Questions................................................................................................. 61
Further results analysed.......................................................................................................... 66
Result 1: ............................................................................................................................. 66
Discussion.................................................................................................................................. 72
Age, Gender and Off-farm occupation...................................................................................... 72
Knowledge, attitude’s and opinions......................................................................................... 72
Conclusion ................................................................................................................................. 75
Recommendations...................................................................................................................... 75
References/ Bibliography............................................................................................................ 76
Appendices ................................................................................................................................ 79
Appendix 1: Trainee Farmer Survey.......................................................................................... 79
Appendix 2: An account of MAC farm......................................................................................103
Appendix 3 – Statistics explanations........................................................................................106

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Declaration
I hereby declare that this thesis is all my own work, and that I have not obtained a degree in
this University, or elsewhere, on the basis of this work.

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Acknowledgements
Specific thanks must be given to Colm Duffy for all his help and guidance.

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Abstract
Anthropogenic climate change has become a public concern in recent years. Ireland
depends on its agriculture industry heavily, as Gross Agricultural Output was valued at €7.12
billion (Department of Agriculture 2014). Importance is being placed on increasing agri-
sector productivity, yet at the same time reducing greenhouse gas emissions from Irish
agriculture.
Therefore, it is valuable to consider the knowledge, opinions and attitudes of farmers and
trainee farmers in Ireland, to get an understanding of their concerns.
The major findings from this study are;
The younger farmers of today are more concerned about climate change impact in the
coming years, older farmers do not seem to be as concerned. 95% of the trainee farmers
observed hold a part-time job, yet this does not seem to have an influence on their
willingness to engage in on-farm climate change mitigation operations. Climate change
awareness also does not seem to have an influence on trainee farmers’ willingness to
engage in on-farm climate change mitigation operations.

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Literature Review
Introduction
The issue of climate change is one of the most significant challenges being faced in the
world today. This is evident by the coming together of different country governments from
around the world, in a conference of the parties (COP), to help solidify a reasonable
approach to tackling climate change and sustainability issues (Tzemi, Breen et al. 2016).
Climate change is occurring at a rapid rate. These changing will inflict serious consequences
on Ireland and the world if not dealt with appropriately. Impacts to food security and
sustainability would be evident (Lobell, Burke et al. 2008). This is why Ireland needs to
figure out ways in which to combat climate change, while remaining at the forefront of
agriculture production.
Irish Agriculture
Ireland’s agriculture is centred on a rain-fed, fertile land production system (Department of
Agriculture 2014). The total land area of Ireland is 6.9 million hectares. Of this, 4.5 million
hectares is used for agriculture, and an additional 730,000 hectares is allocated to forestry
(Department of Agriculture 2016).
Agriculture has always been important in Ireland. Throughout history, the occupation of
‘Farmer’ has always been one of the most common among Irish workers. Currently, there is
estimated to be around 139,600 family farms in Ireland (see Figure 1). However, as people
move away from rural life, the number of people either taking up farming or people
inheriting the farm is decreasing. This is representative by the age categories of farmers in
2013 (see Figure 2 below). The national average size of a farm is 32.7 hectares (CSO 2010).
At a national level, farmers are getting older. Of the estimated 139,600 farms in Ireland, a
quarter of farmers are over the age of 65 years old. 6% are under the age of 35 years old.
Young people of Ireland are not as likely to get into farming as their parents or grandparents
were. This could be due to lack of interest, emigration, low profit outcomes, or many other
variables.

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At a European level, the number of Irish farmers over the age of 65 (26.5%) is less than the
European Union countries’ average (31.1%) (see Figure 3). When comparing the number of
farmers under the age of 35, Ireland had a slightly higher number (6.3%) than the European
Union average (6.0%) (see Figure 4).
Figure 1: The number of farms in Ireland and the average farm size, as of 2013.
Figure 2: Responses by participants of the Central Statistics Offices’ Farm Structures
Survey 2013, to the question ‘Farmer’s age?’

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Figure 3: Comparing the number of Irish farmers over the age of 65 to the European
number of farmers over the age of 65, 2013.
Figure 4: Comparing the number of Irish farmers under the age of 35 to the European
number of farmers over the age of 65, 2013.

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The Department of Agriculture, Food and the Marine, state that of the 111,134 farmers in
Ireland, only 13% are women (Department of Agriculture 2016). Women have always been
involved in Irish farming. However, in recent times they have not been represented as well
as they maybe had been in the past. This could be due to the present economic situation, in
which one or more occupant in a home may need to seek off-farm employment as well as
the family farm (see Figure 5).
Findings from the Teagasc National Farm Survey 2014 (see Figure 6), found that 29.8% of
farmers and 36.2% of their spouses had an off-farm occupation (Department of Agriculture
2016). According to the National Farm Survey by Teagasc in 2012, around 60% of farmers
are considered part-time farmers (Revenue 2015).
Figure 5: The number of men and women in agriculture, and their age, 2014.

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Figure 6: Farmer or spouse with off-farm employment.
Ireland is over 600% self-sufficient in beef production. As of 2016, the population of Ireland
is over 4.7 million people (CSO 2016). According to the June 2015 livestock survey Herd
sizes are continuing to rise, with total cattle numbers increasing by almost 3% in 2015
(BordBia 2015). With the abolishment of the Milk Quota in April 2015, dairy cattle numbers
are expected to continue to increase. Suckler numbers increased in 2015 for the first time
since 2012 (BordBia 2015). Bovine numbers in the EU had a small increase in 2015 (see
Figure 7). Ireland is a net exporter of beef, exporting 90% of the beef produced. Of the 90%
exported, the United Kingdom receives 41% of all our agri-food and drink exports (BordBia
2016). Of the remaining 59%, other EU member states received just over half, and
international markets received the rest (see Figure 8).
Ireland’s agri-food sector is very important for economic sustainability. The 500,000 tonnes
of beef exported in 2015 was worth about €2.41 billion and the 178,000 cattle exported was
worth about €135 million (BordBia 2016). Total dairy and ingredients exports accumulated
to €3.24 billion in 2015.

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Figure 7: Global Bovine numbers from the Beef and Livestock Report 2015
Figure 8: Destination of Ireland’s exports, source Bord Bia, 2016.
Climate Change
In the coming years, the world faces a great challenge- how to produce more food to feed
the growing population, while at the same time reduce greenhouse gas emissions from
Destinations of Ireland's agri-food
exports
UK
Other EU
International Markets

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agriculture production. The population of the world is expected to rise to 9 billion people
by 2050 (Cohen 2003). Food production will have to increase by about 60% (Campbell,
Thornton et al. 2014). Increased food production can be achieved through the increasing of
yields by farmers (Van Wart, Kersebaum et al. 2013). Also noteworthy is the fact that food
consumption patterns are seen to be shifting- people are consuming more meats now than
in the past (Campbell, Thornton et al. 2014).
Climate change is posing a real problem for agricultural production around the world
(Duguma, Wambugu et al. 2014). Severe flooding and drought has been at the forefront of
agricultural problems faced by farmers worldwide for many years now (Steenwerth, Hodson
et al. 2014). Ireland has not escaped these problems. (Sweeney, Albanito et al. 2008) state
that climate change will impact agricultural production in Ireland in the coming years, as
winters become wetter and summers become drier, which will not complement Irelands
dependency on outdoor productions. This will contribute to biotic and abiotic stresses in
plants, resulting in profit losses for Irish farmers (Kumar 2013).
But Ireland is not just suffering the effects of climate change; it is also contributing to
climate change (Scherr, Shames et al. 2012). Irish agriculture is responsible for around 30%
of our national greenhouse gas emissions. The European average is 9% and the global
average is 13.5% (Pachauri and Reisinger 2007). Although Ireland has been reducing its
emissions in relation to agriculture production in recent years, the sheer amount of
agricultural productivity in this country means our emissions are still high in comparison to
other sectors, such as industry. This is why it is so important for Ireland to adopt mitigation
practices to reduce GHG emissions (McCarthy 2009). Agricultural policies play in integral
role in achieving mitigation goals in Ireland.
Climate-Smart Agriculture and the Marginal Abatement Cost Curve
Climate-smart agriculture (CSA) is defined as agricultural practices that sustainably increase
production and resilience, while at the same time reducing greenhouse gas emissions
(Lipper, Thornton et al. 2014). The Food and Agriculture Organisation (FAO) coined the
term ‘Climate-Smart Agriculture’, and defines it as an approach to identifying productivity

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methods that can respond best to climate change, by adjusting the local environmental
needs (Steenwerth, Hodson et al. 2014). This approach can help develop agricultural
practices in order to support sustainable development and food security (Branca, McCarthy
et al. 2011)
There are 3 main goals of climate-smart agriculture are as follows;
1. Increase agricultural productivity in a sustainable manor
2. Adapt to and build resilience to climate change impacts and its results on food
security
3. Reduce greenhouse gas emissions produced from the agricultural sector
Teagasc’s research on greenhouse gas emissions has been influential in determining
measures to deal with climate change. This research has been widely acclaimed in the
European Union (TeagascWorkingGroup 2015). Ireland is also a founding member country
of the International Research Alliance on Agricultural Greenhouse Gas Research
(Department of Agriculture 2014). This organisation gathers research and knowledge on
emissions reduction and concentrates on developing mitigation measures that can be taken
up at farm level.
Teagasc have undertaken research on mitigation options for Ireland, which if managed well,
could see a reduction in greenhouse gas emissions of about 1.1 MtCO2 equiv. per annum in
the agricultural sector (Department of Agriculture 2010). Mitigation measures identified by
Teagasc to help meet our Food Harvest 2020 targets have been outlined in a Marginal
Abatement Cost Curve. Several of the measures outlined here support the measures in the
draft Rural Development Programme, as well as being outlined to farmers through the
Carbon Navigator Decision Support tool (Schulte and Donnellan 2012).
Abatement cost is the cost of reducing environmental negatives such as pollution. Marginal
cost is an economic concept that measures the cost of an additional unit. The marginal
abatement cost (MAC), in general, measures the cost of reducing one more unit of pollution
(Bockel, Sutter et al. 2012). When addressing measures of mitigation for Irish farmers,
Teagasc announced ten methods in their Marginal Abatement Cost Curve (MACC). These
measures are based upon both Life Cycle Assessments and Inventory Methodologies.

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Focus is given to the five measures that are considered win-win options, meaning as well as
reducing the carbon footprint, they also help reduce costs. The five cost efficient measures
are outlined by (Department of Agriculture 2014) as being:
1. Improving genetic gain
2. Increasing daily animal weight gain
3. Extending grazing season for dairy
4. Extending grazing season for beef
5. Improving nitrogen efficiency
In order to implement these measures, Teagasc has teamed up with Bord Bia to help
knowledge transfer between researchers developing these strategies, and farmers who will
implement them. This can be done through the Carbon Navigator Decision Support tool.
Carbon Navigator
The carbon navigator was established to assist in the task of reducing carbon emissions in
the dairy and beef sectors of agriculture in Ireland. The carbon navigator is a knowledge
transfer (KT) tool, intended to be of help at farm level, in determining possible mitigation
options and communicating well with the farmer (Murphy, Crosson et al. 2013). It is
designed to inform farmers of practices in which they can adopt and ways in which to
improve performance, in order to both reduce emissions, and also increase profitability.
The Carbon Navigator was specifically designed to act as a advice and feedback tool for the
Origin Green Scheme. Origin Green defines Irelands determination to become a world
leader in food and drink product sustainability (Department of Agriculture 2014).
It is important to note that, the Carbon Navigator does not account for all GHG emissions
associated with a farm (Murphy, Crosson et al. 2013). The Carbon Navigator is a learning

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tool for farmers, and therefore intended to keep the interest of farmers, and to inform them
on how to reduce their emissions outputs.
So why do we need a carbon navigator? Food Harvest 2020, the sectoral development plan
for Irish agriculture, recommends growth in output over the coming years (Murphy, Crosson
et al. 2013)and (Department of Agriculture Fisheries and Food, 2010). Of this, there is to be
a 50% increase in dairy output (the milk quotas were abolished in 2015), and a 20% increase
in the value of beef (Murphy, Crosson et al. 2013). However, at the same time in which we
are to be increasing our beef and dairy outputs, we are also meant to be reducing our
greenhouse gas emissions, as outlined by Food Harvest 2020 (Department of Agriculture
Fisheries and Food, 2010). Ireland has pledged to reduce its non-emissions-traded sectors
GHG emissions by 20% by 2020. If we want to achieve both these objectives, then we need
a way in which to achieve greenhouse gas reductions at farm level. This is why the Carbon
Navigator is so important. It is a decision support tool that can help farmers improve
productivity, while at the same time improve carbon efficiency, and thus, increase income
as well (Murphy, Crosson et al. 2013).
Ireland’s anthropogenic greenhouse gas emissions from agriculture are high, at about 29-
30%, compared to the global average, which is around 13.5% (Pachauri and Reisinger 2007).
Researchers are constantly trying to understand where these emissions come from, as once
they can locate the source, they can create new technologies and practices for mitigation
potential. The carbon navigator is an online decision support system that can aid farmers in
evaluating their present greenhouse gas mitigation practices, and advise methods in order
to improve (Murphy, Crosson et al. 2013).
The Carbon Navigator has been designed to encourage the uptake of carbon-efficient
farming ((Murphy, Crosson et al. 2013). It is a tool that has been designed through the
collaboration of Bord Bia (The Irish Food Board) and Teagasc (The Agriculture and Food
Development Authority). Teagasc and Bord Bia have worked together in the past, using old
models of GHG emissions from Beef (Foley, Crosson et al. 2011) and dairy (O’Brien, Shalloo
et al. 2011), to develop whole-farm system carbon audit programmes (Murphy, Crosson et
al. 2013).

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(Crosson, Shalloo et al. 2011) tells of how the methods in which the Intergovernmental
Panel on Climate Change (IPCC) use to collect its data on greenhouse gas emissions on farms
is not without its limitations. Therefore, a whole-farm modelling approach is widely
adopted (Murphy et al, 2013). Again, (Crosson, Shalloo et al. 2011) states that although the
findings by the IPCC will continue to be main source for reporting national emissions, this
whole-farm modelling system is still widely accepted (Murphy, Crosson et al. 2013).
It is important to note, when discussing ways in which Ireland can reduce its GHG emissions
from agriculture, simply cutting down on production is not a solution. This is due to the fact
that the Earth’s population is rising, and therefore, so is the demand for food. Hence, if
Ireland was to reduce its production, then food production would increase elsewhere,
meaning the global GHG emissions would remain unchanged over all (Schulte, Lanigan et al.
2011). Also, if Europe was to reduce GHG emissions by restricting the amount of permanent
grasslands converted to arable land, we would see a reduction of around 1.6% (Van Zeijts,
Overmars et al. 2011), yet also a loss in production of goods, which would then be
supplemented with imported goods, concluding in no overall, worldwide reduction in GHG
emissions (Westhoek, Van Zeijts et al. 2014).
(Schulte and Donnellan 2012) focuses on three classifications of mitigation measure;
o Measures based on efficiency improvements
o Measures based on land-use change
o Measures based on technology involvement
Research has been able to identify areas of possible mitigation options for Irish agriculture.
Next, we need to focus on how we can implement these options (Murphy, Crosson et al.
2013). In order to control GHG emissions from livestock production enterprises, the farmer
will need to either (a) be given financial incentive by the government in return for support
and implementation of necessary changes; or (b) be shown how there is a chance for the
farmer to increase profitability by implementing these changes (Lovett, Shalloo et al. 2006)
and (EU-Commission 2005). This is a very important fact to note- if we want our new
emissions reducing technologies to be implemented well, we need the support of the
farmers, and therefore, they need support as well, in forms of grant schemes and other
incentives. With these incentives, farmers will then want to know the scientific basis behind

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emissions reduction and how they can implement change on their farm. This is where the
Carbon Navigator becomes necessary.
How the Carbon Navigator works
Bord Bia has established a comprehensive data collection process, acquired access to
national databases like the Irish Cattle Breeding Federation (ICBF) and the Animal
Identification and Movement System (AIM), and created an IT infrastructure that can
support these schemes (Murphy, Crosson et al. 2013). These scheme require auditors to
visit each farm on a regular basis (no more than 18 month intervals), to gather information
concerning these schemes. Here, they also now collect information for the Carbon
Navigator. The information collected is then processed through the online databases
acquired by Bord Bia, to determine the performance of each farmer.
The farmer can then access the Carbon Navigator system online, with help from their
adviser. Here, they will see a detailed outline of their current performance and also where
they can potentially improve their performance, and what the outcomes will be for these
improvements. These figures are illustrated through graphical and numerical forms (see
Figure 9). Ultimately, the Carbon Navigator shows the farmer emission mitigation potential
and also financial profitability potential.

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Figure 9: An example of a Carbon Navigator output screen. The information seen is what a
farmer will use to implement change. The metrics used are kg CO2e/kg beef live weight and
kg CO2e/kg milk solids (fats and proteins). The information here was gathered by attending
a Carbon Navigator Training Day at Teagasc, in June 2016.

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The choice of measures to be implemented in the Carbon Navigator is established through a
string of criteria (Murphy, Crosson et al. 2013). These include:
o Measures lead to scientifically verifiable decreases in greenhouse gas emission
intensities
o Measures that reduce emissions nationally and are capable of being incorporated in
to current IPCC-based national inventory accounting were given high value
o Measures that are capable of being implemented by farmers without any difficulty
o Measures that are capable of enhancing profitability for the farmers
o Measures that are similar with current enterprise knowledge transfer priorities
o Measures need to be quantifiable in regards to the level of practice adoption and the
effect of the adoption on greenhouse gas emissions
At this stage, the Carbon Navigator has two designs, the Dairy (D) Carbon Navigator and the
Beef (B) Carbon Navigator. The navigator highlights certain technologies to mitigate GHG
emissions at farm level. These include:
-Increased Economic Breeding Index (EBI) (D)
-Longer grazing season (B & D)
-Improved nitrogen use efficiency (B & D)
-Improved slurry management (B & D)
-Energy efficiency (D)
-Calving rate (B)
-Improved Live-weight performance (B)
-Age at first calving (B)

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Economic Breeding Index
If you increase genetic merit by means of EBI, you have a chance of reducing GHG emission
intensities through four practices (O’Brien, Shalloo et al. 2011). These include:
-Increasing milk yield – by improving milk yield and composition, production becomes more
efficient, and thus, emissions per unit of product are reduced
-Improving survival and health- by reducing the number of deaths and rates of disease,
production levels rise and replacement rates lower
-Improving fertility- this can lead to shorter calving intervals and lower replacement rates,
which would then reduce enteric methane (CH4) emissions per unit of product
-Earlier calving- can add more grazed grass to a diet, which is of better quality to a diet than
silage fed, and therefore can lower culling and replacement rates
How can you increase EBI on your farm? It is important to identify the key characteristics
needed to improve milk production and fertility. Fertility can be the main weakness in a
herd, and improving this will help production efficiency. Choosing a high EBI bull is vital.
Breed heifers with these high EBI bulls in order to improve the herd, and increase
profitability.
Grazing Season Length
There are many benefits to extending the grazing season. Firstly, the grass based diet is
more digestible to the animal than that of a silage based diet. This results in increased
productivity, in addition to a reduction in methane (CH4) emissions in the form of dietary
energy. Another way in which to reduce CH4 emissions is by minimising the spread of
slurry. When cattle can be grazed on grass for longer, then this reduces the housing season,
and thus, reduces the amount of slurry build up. This reduces methane and nitrous oxide
(N20) levels. Finally, through a longer grazing season, reductions in fuel emissions are
possible. This is due to the fact that there would then be a lower requirement for food
harvesting, as well as a lower requirement for inorganic fertilizers.

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Nitrogen Use Efficiency and Slurry Management
Nitrous oxide (N20) is 300 times more lethal than CO2 in terms of its global warming
potential. Therefore, it is vitally important to try and reduce the amount of N2O that enters
the atmosphere. However, N20 enters the atmosphere after it fails to be taken up by
plants. When a plant does not utilize its fertilizer (organic and chemical), then the left over
nitrogen is released to the atmosphere. Only about 24% of Nitrogen put on land is actually
used (Lalor and Lanigan 2010). Therefore, it is important to use methods of fertilizer
application in which will generate the most N uptake by plants.
One of the key methods here is slurry management. Slurry should not be applied on hot
sunny summer days. This would result in quick uptake of ammonia from the atmosphere.
Therefore, it is important to know the correct time to apply fertilizer in order to ensure
optimal uptake by plants. Spring application is advised. When applied in the spring, the
weather conditions are more damp and misty, which reduces ammonia emissions. Farmers
are advised to apply slurry in the evenings if they think the day is too sunny. Finally, when
considering slurry application, the spreader itself is an important factor. It is proposed that
a slurry spreader should release the slurry to the soil from a low height. This means keeping
the appliance low to the ground, in order to ensure accurate spread, which would therefore
minimize run-off and atmospheric uptake. An example of this would be a trailing shoe
(Figure 9). It is also important to note that due to earlier spreading, storage losses of
methane are reduced.

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Figure 10: A trailing shoe. Slurry that is applied through this technique is more cost
effective than slurry applied through the splash-plate technique, due to the fact that less
slurry is wasted. Photo is by Robert Jones, the Irish Independent. Published 17/06/2015
Energy Efficiency
The use of energy on a farm is not the major source of emissions on a farm, however it has
an incredible opportunity for reductions. Teagasc discovered that electricity consumption
on a farm can vary from 53-108 W/I produced and cost from as low as 0.23 to as high as
0.76 cent per litre produced (Murphy, Crosson et al. 2013). These variables are quite
significant. There are three main areas in which to reduce energy costs and emissions. The
first is using a Plate Heat Exchanger for effective pre-cooling. The next is Variable Speed
Drive (VSD) Vacuum Pumps, and the final one is Energy efficient water heating systems.
Calving Rate
Cows are expected to produce offspring once a year. The average calving rate on an Irish
farm is 0.84 calves per cow, per annum (Murphy, Crosson et al. 2013). Suckler farming is
seen as having a high environmental overhead, with 70-80 kg methane per year (Murphy,

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Crosson et al. 2013). Therefore, having an increase in calving rates will reduce emissions.
For example, consider the GHG emissions produced per 100 cows per year, and that this
results in only 84 calves per year. Now consider the same amount of GHG emissions
produced by 100 cows per year, yet the calving rate has increased to more than 84 calves
per year. It is clear that increasing calving rate is beneficial to the farms efficiency.
Improved live-weight performance
(Casey and Holden 2006) discussed how improving live-weight performance in beef cattle is
significant in the reduction of emissions per kilogram of beef produced. If you can improve
the live-weight performance of an animal, then there will be a shorter lifetime to slaughter,
which would then result in lower emissions.
Age at first calving
The current average age of a replacement heifer for first-calving is 30 months. However,
the top 10% of heifers in Ireland can produce an offspring after 26 months (Murphy,
Crosson et al. 2013). Again, if we can reduce the time period from birth to first-calving, then
GHG emissions would be reduced as well, as the extra months result in extra GHG
emissions, at 0.01% kg beef carcass for every day that first-calving is more than 24 months
where the baseline replacement rate is 20% (Foley, Crosson et al. 2011). The financial
outcome is roughly €1.65 per day per suckler cow (ICBF, 2012).
Conclusion
Irish agriculture is facing a tough objective; to produce more products, while at the same
time, reduce GHG emissions. International requirements will see Ireland need to become
more carbon efficient, which will be difficult as the Irish agricultural sector is led by
ruminant agriculture, whose emissions are high (Murphy, Crosson et al. 2013). It is vital that
the uptake of mitigation techniques at farm level be taken seriously, through policies that
encourage knowledge transfer (KT), through advancements of successful knowledge

24
transfer decision support systems, and finally through making sure adequate resources are
available to farmers (Murphy, Crosson et al. 2013).
Currently, researches are investigating whether the use of sexed semen could increase the
profitability of dairy farms. This technique has the ability to drastically reduce the carbon
footprint of both dairy and beef farming, by means of reducing the amount of dairy bull
calves born, while increasing the number of beef progeny that are more carbon efficient
thanks to faster growth rates and higher output. This new technology could aid in solving
Irelands emissions v production dilemma.
The Beef Data and Genomics Programme
The Beef Data and Genomics Programme is a follow on from the Beef Data and Genomics
Scheme (ICBF). This scheme is set to run until 2020, and is focused on improving the genetic
merit of suckler herds, while at the same time reducing greenhouse gas emissions. The Beef
Data and Genomics Programme is supported by the European Commission as part of
Ireland’s 2014-2020 Rural Development Programme (Department of Agriculture 2013). The
programme was launched in May 2015. In 2015, around 29,000 farmers applied for the
scheme, and 560,000 animals were involved (Department of Agriculture 2013). The main
priority of this programme is to encourage the uptake of animals of higher genetic merit
into the Irish beef herd, as a means to combatting greenhouse gas emissions in agriculture.
Common Agricultural Policy
The agri-food sector in Ireland is incredibly important for the economic sustainability of
Ireland, resulting in 7.1% of Gross Value Added and providing employment for 170,000
people in Ireland (Department of Agriculture 2015). As farmers face more challenges with
regards to climate change, rural decline, rising cost, and other variables, they are considered
very vulnerable (EU-Commission 2005). Luckily, as a member state of the European Union,

25
Ireland can rely on other EU countries for support, through a policy known as the Common
Agricultural Policy (CAP).
The Common Agricultural Policy was established in 1962, after the Treaty of Rome in 1957.
The European Commission collaborates with a whole range of stakeholders when drawing
up proposals. Since 2005, the CAP has been integral in providing a clear framework for
sustainable agricultural management (Department of Agriculture 2014). Farmers rely on
CAP payments for financial security. Under the CAP, farmers must fulfil three obligatory
‘greening provisions’ if they wish to receive full payment, otherwise a 30% penalty will apply
(Department of Agriculture 2014). These measures include; maintenance of permanent
grasslands, crop diversification and maintenance of area’s if ecological focus (Westhoek,
Van Zeijts et al. 2014). These recent initiatives are in line with other long-standing EU
requirements already in place.
The Common Agriculture Policy sets out to assist farmers to meet their needs. The main
objectives of the CAP are to guarantee a decent standard of living for farmers, while also
providing a stable and safe food supply at reasonable prices for consumers
(EuropeanCommission 2013). The CAP uses many incentives that encourage farmers to
improve their farms and income, while at the same time encourages them to seek new
development opportunities, for example, renewable ‘green’ energy sources, to help combat
climate change.
Farmers’ awareness of and attitude towards climate change
There haven’t been many studies done in developed countries on farmers’ awareness and
attitudes to climate change. Some studies have been carried out in developing countries,
such as (Deressa, Hassan et al. 2011) who studied the perceptions of and adaptions to
climate change by farmers in Ethiopia. However, these kind of studies in developing
countries are somewhat expected as their economies and environmental conditions are

26
more fragile (Tzemi, Breen et al. 2016). Some studies have been conducted in developed
countries, such as (Harrington and Lu 2002), who surveyed cattle farmers in Kansas, USA in
order to have an understanding of their knowledge and opinions on climate change and
industry. They concluded that over half of respondents to the survey did not believe that
global warming caused by fossil fuel burning is a proven theory and they also believed that
climate change was not going to be an issue in the future. (Harrington and Lu 2002) found
some indication however, that farmers would be willing to pay various amounts of money in
order to reduce global warming, yet one third indicated that they would not be willing to
contribute to the cause at all.
A study in North Carolina, USA, by (Rejesus 2012) found that older farmers were shown to
be less sceptical than younger farmers with regards to climate change being scientifically
proven (Tzemi, Breen et al. 2016). The study also showed that a mere 18.3% of respondents
thought that climate change would impact negatively on farming yields in the coming 25
years. A study in Iowa by (Arbuckle Jr, Prokopy et al. 2013) was concerned with farmers
beliefs as to whether or not climate change is real. Their results showed that 68% of
respondents considered climate change to be happening, yet when asked to clarify how,
only 10% believed climate change to be the result of human activity. 23% believed climate
change to be a natural occurrence.
Moving closer to home, a study conducted in Scotland with dairy farmers showed that the
respondents were unsure and split in their opinions, as to how to answer questions in
relation to climate change (Barnes and Toma 2012). They concluded that over half of
farmers’ surveyed believed that man-made greenhouse gas emissions play a factor in global
climate change. However, their results indicated that farmer knowledge became unsure
when asked more in depth questions surrounding climate change. Of these respondents,
27.7% of them felt that climate change would impact productivity in a negative manor in 20
years’ time. However, 28.9% of the same sample group considered there would be no
impact to productivity in 20 years due to climate change. 19.7% of the sample group listed
their answer as unsure. This indicates that farmer knowledge in relation to climate change
impacts varies.
(Tzemi, Breen et al. 2016) posed the question whether or not they had received any agri-
environmental advice or training’ to their survey group. Of the respondents, 32.2% said

27
they had received training, 28.5% stated they had not received training or advice, but would
be interested in doing so, while 39.3% stated they had not received training or advice and
would not be interested in doing so. When asked if they would be willing to incur an
additional cost to see a reduction of greenhouse gas emissions of 5%, the majority (77.6%)
of farmers surveyed said they would not be willing to incur any financial costs. A mere 18%
said they would be willing to incur an increase of between 0-5%.
Technology adoption in agriculture
A hypothesis referring to the adoption of mitigation measures is that larger farmers, rather
than smaller farmers, tend to be more willing to engage in these measures. Similarly,
mitigation measures were best adopted in younger farmers as opposed to older farmers
(Prokopy, Floress et al. 2008). (Gasson and Errington 1993) found that, in the US, older
farmers were less likely to adopt these measures, especially if they their farms were not
going to be inherited by their children.
Decision-making and adoption of new technologies were examined by (Saltiel, Bauder et al.
1994). They found that profitability of the farm influenced the decision-making of farmers.
Whether or not farmers had off-farm jobs is typically positively related to the uptake of
mitigation techniques (Davey and Furtan 2008). (Keelan, Thorne et al. 2010) on the other
hand, reported a negative relationship between off-farm income and the adoption of
genetically modified technologies. The results from (Tzemi, Breen et al. 2016) showed that
farmers who were more climate aware were also more likely to be willing to adopt an
advisory tool with regards to reducing greenhouse gas emissions. (Tzemi, Breen et al. 2016)
also found that farmers’ willingness to adopt an advisory tool with regards to reducing
greenhouse gas emissions was negatively influenced by holding an off-farm job. They
suggest that time constraints could play a factor in this. They also found that environmental
subsidies positively influenced farmers’ willingness to adopt an advisory tool.

28
Research Goal & Objectives
Research Goal
To investigate the perceptions, priorities and potentials of trainee farmers to contribute to
agri-sector climate change mitigation and adaptation in Ireland.
Research Objectives
Objectives
To determine if;
1. Knowledge of climate change has a significant relationship with age of a trainee
farmer
2. Having an off-farm job influences a trainee farmers willingness to engage in on-farm
climate change mitigation operations
3. Being climate aware influences a trainee farmers willingness to engage in on-farm
climate change mitigation operations
Research rationale
Agriculture accounts for 33% of total Irish GHG emissions. The EU average is 9% (Pachauri
and Reisinger 2007). This is due to the fact that Ireland relies heavily on its Agricultural
sector compared to other EU countries. It is significant to note, Ireland has the lowest
carbon footprint of milk in the EU, and the fifth lowest carbon footprint of beef in the EU
(Department of Agriculture 2010). However, despite this, Ireland can be expected to be
assigned increasingly strict targets to cut its emissions. Therefore, it is important to get the
view points of the Irish farmers, in order to understand their concerns, and to uncover ways
in which Ireland can contribute to mitigation of Greenhouse Gas emissions.

29
According to (Department of Agriculture 2016), there are 139,000 farmers in Ireland, of
which 60% are part-time farmers (Revenue 2015).
Why Mountbellew Agricultural College?
Mountbellew Agricultural College is located North-East of Galway city, Co. Galway, Ireland.
It was founded in 1904 and is a training college for the farming and agricultural industry. It
is a private college, but runs in conjunction with the Irish governments Agricultural and Food
Development Authority (Teagasc) and the nearby Galway-Mayo Institute of Technology
(GMIT).
The alliance between the Plant and AgriBiosciences Research Centre (PABC) in NUI Galway
and Teagasc supports that Mountbellew is a suitable location to carry out the necessary
research. Also, Mountbellew is located only about one hours drive away from the
University.
The survey was implemented over the course of two days, with students from the Distance
Learning Programme. This would include trainee farmers from across Galway and nearby
counties, and from a wide variety of age groups.
Distance Learning Programme
This distance education programme incorporates a Level 5 Certificate in Agriculture and a
Level 6 ‘Green Cert’ course. As the course involves a substantial amount of skill and practical
instruction, farm planning and discussion group activities; actual attendance is required for
a significant number of contact days. The course normally takes 15 to 18 months to fully
complete. Again your Education Officer can advise you on course requirements. (See the
“Know Your Education Officer” section)

30
Why the Distance Learning Programme?
The questionnaire was assembled with different farming backgrounds in mind. The trainee
farmers from the Distance Learning Programme would represent this in a meaningful way.
The Distance Learning Programme is designed for people who need to acquire the Green
Certificate, in order to run their own farms. Participants include varieties such as full-time
farmers, part-time farmers, new entrants to farming, people who have been farming for
years, as well as a mix of men and women, and age categories. It is important that the
survey conveys the knowledge and opinions of more than just one sub-group of farmers.

31
Methodology
The survey was implemented over the course of two days at Mountbellew Agricultural
College, to students in the Distance Learning Programme in agriculture. This included
trainee farmers from across Galway and nearby counties, and from a wide variety of ages.
The Distance Learning Programme participants are divided into two classes, each held on a
separate day, by the teachers at Mountbellew Agricultural College, as only half the
participants are asked to attend at a time, due to classroom sizes. A hard copy of the
questionnaire was distributed to each trainee farmer personally (see Appendix 1 for the
complete Questionnaire Survey). Participants were asked to complete it to the best of their
knowledge, and to input their opinions where necessary. The suggested time allocation for
the completion of the questionnaire was fifteen minutes. Supervision of the data collection
was observed personally.
Topics covered in the survey
Data was collected with guidance from the Department of Agriculture, Food and the Marine,
the Eurobarometer series by the European Commission, and the National Farm Surveys by
Teagasc.
The following topics were included in the survey;
Gender, age and region, Education, Farming background, Relationship status and occupation
of significant other, Reasons behind completing the course, Livestock owned, Whether or
not a Teagasc client, and availing of schemes, Climate Change and Agriculture, Climate
Smart Agriculture, Sources of information, Mitigation and Carbon Navigator, and
Greenhouse Gas Emissions
Questionnaire/Survey Design
Data was collected with guidance from the Department of Agriculture, Food and the Marine,
the Eurobarometer series by the European Commission, and the National Farm Surveys by

32
Teagasc. Eurobarometer is a series of public opinion questionnaires which are conducted
regularly, since 1973, in conjunction with the European Commission. These surveys are
applied throughout the EU member states.
Statistical Analysis
Data from the trainee farmer survey was recorded using the SPSS Statistics version 22
software package, used for statistical analysis. Questions and answers were coded on the
programme, which allowed for graphics, figures and tables to be constructed from the
statistical analysis.

33
Results
Section 1: Frequencies
In this section, the answered given by the trainee farmers from Mountbellew Agricultural
College (MAC) to the survey on climate impacts on agriculture are summarised.
1a. Demographic profile of the trainee farmer group surveyed
Initially, the demographics of the participant group were determined from the responses of
the 103 participants.
Of these, 87.4% were male, while 12.6% of them were female (Table 1). A third were
between 25-30 years of age, while the biggest group (almost 40%) belonged to the 30-35
year old category (Table 2). Older people were less well represented, as expected for a
cohort of trainee farmers. 101 participants answered the question concerning which region
of Ireland they were from. It is clear that the majority (94%) of them are quite local to
Mountbellew Agricultural College as they come from the Galway, Mayo and Roscommon
region, with just a few participants (six) from the other regions. With regards to education,
59.4% of participants who responded report holding a Bachelor’s degree, while 13.9% of
them just completed the Leaving Certificate (Table 4). Of the sample, 3% hold a Master’s
degree, while 2% have gained a PhD. This means just over one fifth of participants make up
the other qualifications specified.
Gender Number of Participants Percentage
Male 90 87.4
Female 13 12.6
Table 1: Summary of responses to the question, ‘Are you male or Female?’

34
Age Number of Participants Percentage
20-25 8 7.8
25-30 34 33
30-35 41 39.8
35-40 16 15.5
40-45 3 2.9
45-50 1 1
Table 2: Summary of responses to the question, ‘What age group are you?’
Region Number of Participants Percentage
Louth, Leitrim, Sligo,
Donegal, Monaghan
2 2.0
Kildare, Meath, Wicklow 1 1.0
Clare, Limerick, Tipperary
N.R.
3 3.0
Galway, Mayo, Roscommon 95 94.1
Table 3: Summary of responses to the question, ‘What region are you in?’
Education Number of Participants Percentage
Some secondary school
(Junior Cert)
2 2.0
Completed secondary school
(Leaving Cert)
14 13.9
Bachelor’s degree 60 59.4

35
Master’s degree 3 3.0
PhD 2 2.0
Apprentiship 5 5.0
Higher Cert 1 1
National Craft Cert 4 4.0
Trade 2 2.0
Post Leaving Cert 1 1.0
FETAC 3 3.0
Quality and Qualifications
Ireland Level 6
1 1.0
FAS 2 2.0
Certificate in Business 1 1.0
Table 4: Summary of responses to the question ‘What is your highest level of education?’
1b. Agricultural experience of the survey participants
In order to determine the agricultural experience of the survey participants, the following is
a summary of the responses of the 103 participants.
Of these, the majority of the participants reported that they have not received any prior
agricultural training (only 4% having done so; Tables 5-6), which presumably explains why
they are participating in the course. 57% are the owner or operator of their farms, while
43% are not (Table 7). The majority are also new entrants to farming (Table 8), which is also
likely to be a factor in determining why they are taking the course at MAC. Curiously,
despite owning or operating their farms, almost two thirds of them do not report being the
main decision maker with regard to farm management practices (Table 9). Most of those
who do (78.4%) have been the decision maker for less than five years (Figure 10), in
agreement with their generally young age profile. More experienced farm managers are not
excluded however- at least one respondent reported having been the decision maker for
over 20 years. Of those whom do not yet own their own, 63% said they expect to own the

36
farm within 5 years, while 20.5% expect to own the farm in 10 years, and 16.4% expect it to
be 10 + years (Figure 11).
Strikingly, the majority (95%) farm on a part-time basis (Tables 9-10). Of the 95 participants
who stated their reasons for taking the Distance Learning Course, 22 (23.3%) said they were
doing it to enhance their farming knowledge, 22 (23.2%) said it was to obtain a Green
Certificate, 19 (20%) said it was to avail of grants and 11 (11.6%) said it was to avoid
inheritance tax and stamp duty.
Training Number of Participants Percentage
Yes 4 4
No 96 96
Table 5: Summary of responses to the question, ‘Have you any specialised agricultural
training?’
Number of participants Percentage
Certificate in farming 1 33.3%
Course less than 60hours 2 66.7%
Table 6: Summary of responses to the question ‘What is the specialised agricultural
training?’

37
Owner/Operator Number of Participants Percentage
Yes 43 42.6
No 58 57.4
Table 7: Summary of responses to the question, ‘Are you the owner / operator of the
farm?’
New Entrant Number of Participants Percentage
Yes 60 58.3
No 43 41.7
Table 8: Summary of responses to the question, ‘Are you a new entrant to farming?’
Decision Maker Number of Participants Percentage
Yes 37 35.9
No 66 64.1
Table 9: Summary of responses to the question ‘Are you the main decision maker with
regards to farm management practices?’

38
Figure 10: Summary of responses to the question, ‘How many years have you been the
main decision maker on the farm?’
Figure 11: Summary of responses to the question, ‘When do you expect to own your own
farm?’
How many years have you been the main
decision maker on the farm?
0-5 yrs
5-10 yrs
10-15 yrs
15-20 yrs
20+ yrs

39
Full-time 5 4.9
Part-time 97 95.1
Table 10: Summary of responses to the question, ‘Are you engaged in farming on a full-
time or part-time basis?’
Number of Participants Percentage
Enhance farming knowledge 22 23.2
Avail of grants 19 20.0
No other choice 2 2.1
To run own farm 7 7.4
Advised to, for getting into
farming
2 2.1
To obtain Green Certificate 22 23.2
To avoid inheritance tax and
stamp duty
11 11.6
Tax Benefits 3 3.2
Convenient 3 3.2
To obtain a Herd Number 3 3.2
Young Farmers Scheme 1 1.1
Table 11: Summary of responses to the question, ‘What is your main reason for doing the
Distance Learning Course?’

40
1c. Current Positions of the survey participants
The responses of the 103 participants were used to determine their current positions in
areas of their lives.
Of these, 95% have off-farm jobs (Table 12). Types of jobs held by the participants varied;
17 (17.9%) are Engineers, 9 (9.4%) are Carpenters, 9 (9.4%) are also Plumbers, and 6 (6.3%)
are Teachers (Table 13). When asked if they are in a relationship, two thirds (67.6%)
revealed they are currently in a relationship (Table 14). Of these, one third (66.7%) are
employed outside the farm (Table 15) in jobs related to Business (18.8%), Teaching (18.8%),
Nursing (10.4%), and Hairdressing/ Beautician (6.3%), (Table 16).
When asked whether or not they plan to undertake another agricultural course, 15%
admitted they would be interested in undertaking another course (Figure 3), yet one third of
these are not sure yet wat course they would like to do, 25% said they would like to do
AgriBusiness, and 16.7% said they intend to do Farm Spraying (Table 17). When asked if
they had received any training in relation to how climate change could impact them as
farmers, only 2% said they had received any training (Table 12).
To determine the main enterprise of the farm, participants were asked to list their primary
and secondary enterprises. 60% of the participants in this survey were primarily dry stock
enterprises, while 23.2% of participants consider sheep farming their main enterprise
(Figure 14). Similarly, dry stock was listed as the secondary enterprise amongst the
participants with 38.6% and almost 30% said sheep farming was their secondary enterprise
(Figure 15). Considering 60% of primary enterprises were dry stock and almost 40% (38.6%)
of secondary enterprises was also dry stock, the results indicate that very few of the
participants didn’t manage some quantity of dry stock. 78% of participants are sole traders,
20% are in a partnership and 2% are in a limited company (Figure 16).
Just under half of the participants (44.1%) are Teagasc clients (Table 18). Of these, over a
third (37.3%) are registered with the GLAS scheme (Table 19). The percentage of land
dedicated to the GLAS scheme varies, ranging from 23.1% dedicating 0-5% of their land,
28.2% dedicating 5-10% and 18.7% dedicating over 10% to the scheme (Figure 17).
However, when asked whether they would be involved in any GLAS activities should they

41
not be incentivised, almost 60% said they would not partake (Table 20). Finally, when asked
if the participants have ever received any training in relation to how climate change could
impact them as farmers, 98% said they had not received any training (Table 21).
Yes 98 95.1
No 5 4.9
Table 12: Summary of responses to the question, ‘Do you currently have an off-farm job?’
Occupation Number of Participants Percentage
Production Administrator 2 2.1
Engineer 17 17.9
Mechanic 3 3.2
Business Owner 2 2.1
Gym Instructor 1 1.1
Fitter 2 2.1
Social Care Manager 1 1.1
Dentist 1 1.1
Garda 2 2.1
Teacher 6 6.3
Plumber 8 8.4
Nurse 2 2.1
Medic 1 1.1
Fabricator 1 1.1

42
Bank Official 1 1.1
Special Needs Assistant 2 2.1
Quantity Surveyor 2 2.1
Company Director 1 1.1
Quality Control Inspector 2 2.1
HSEQ Officer 1 1.1
Electrician 4 4.2
Technician (Environmental
Science)
1 1.1
Carpenter 9 9.5
Building Contractor 1 1.1
Easyfix Rubber Products
Employee
1 1.1
Accountant 4 4.2
Unit Leader Abbott
Diagnostics
1 1.1
Laboratory Technician 1 1.1
Cabinet Maker 1 1.1
Construction 4 4.2
Leisure Club Attendant 1 1.1
Tool Maker 1 1.1
Driver 1 1.1
Retail Manager 1 1.1
Training Centre Instructor 1 1.1
Pharmaceutical Warehouse
Manager
1 1.1
Optometrist 1 1.1
Regulatory Affairs 1 1.1
Retailer 1 1.1
IT 1 1.1
Table 13: Summary of responses to the question, ‘Please specify other occupation’’

43
Single 33 32.4
In a relationship
(married/partner)
69 67.6
Table 14: Summary of responses to the question, ‘What is your relationship status?’
Yes 48 66.7
No 24 33.3
Table 15: Summary of responses to the question ‘Is your spouse/partner engaged in off-
farm work?’
Fashion Designer 1 2.1
Hairdresser/Beautician 3 6.3
Childcare Worker 4 8.3
Teacher 9 18.8
Nurse 5 10.4
Medic 1 2.1
Business Related 9 18.8
Farmer 1 2.1
Factory Supervisor 1 2.1
Civil Servant 1 2.1
Mental Health Service 1 2.1

44
Civil Engineer 1 2.1
Health Care 1 2.1
Accountant 3 6.3
Legal Secretary 1 2.1
Waitress 2 4.2
Lorry Driver 1 2.1
Electrician 1 2.1
Medical Devices 1 2.1
Council Employee 1 2.1
Table 16: Summary of responses to the question ‘What is your spouse/partners
occupation?’
Figure 12: Summary of responses to the question, ‘Do you plan to do any other
agricultural courses?’
Do you plan to do any other
agricultural courses?
Yes
No

45
Course Number of Participants Percentage
Farm Spraying 2 16.7
Agri Business 3 25.0
Not Sure Yet 4 33.3
Agricultural Science 1 8.3
Bachelor’s degree 1 8.3
Dairy Management 1 8.3
Table 17: Summary of responses to the question, ‘Please specify what type of agricultural
course do you intend on undertaking?’
Figure 13: Summary responses to the question, ‘Have you received any training in relation
to how climate change could impact you as a farmer?’
Have you received any training in relation
to how climate change could impact you
as a farmer?
Yes
No

46
Figure 14: Summary of responses to the question, ‘What is the main enterprise on the
farm?’
Figure 15: Summary of responses to the question, ‘What is the secondary enterprise on
the farm?’
2 1
60.6
23.2
3
9.1
1
0
10
20
30
40
50
60
70
Cremery
Milk
Liquid Milk Dry stock Sheep Dry Stock &
Tillage
Suckler
Beef
Horses
Percentage
Enterprise
Main enterprise on the farm
38.6
29.8
7 7
3.5 1.8
8.8
3.5
0
5
10
15
20
25
30
35
40
45
Dry Stock Sheep Tillage Dry Stock
& Tillage
Forestry Horses Suckler Beef
Percentage[%]
Enterprise
Secondary enterprise on farm

47
Figure 16: Summary of response’s to the question, ‘What is your farm trading status?’
Teagasc Client 45 44.1
Not a Teagasc Client 57 55.9
Table 18: Summary of responses to the question, ‘Are you a Teagasc Client?’
78
20
2
0 10 20 30 40 50 60 70 80 90
Sole Trader
Partnership
Limited Company
Percentage [%]
Status Farm trading status

48
Registered with the GLAS
scheme
38 37.3
Not registered with the
GLAS scheme
64 62.7
Table 19: Summary of responses to the question, ‘Are you registered with the GLAS
scheme?’
Figure 17: Summary of responses to the question, ‘What percentage of farm land is
dedicated to the GLAS scheme’
Yes 35 39.3
No 53 59.6
Table 20: Summary of responses to the question ‘Would you be willing to engage in any
GLAS activities if they were not incentivised?’
Percentage of land dedicated to the GLAS
scheme
0-5%
5-10%
Over 10%

49
Yes 2 2
No 98 98
Table 21: Summary of responses to the question, ‘Have you received any training in
relation to how climate change could impact you as a farmer?’
1d. Future intensions of survey participants
In order to determine the future intensions of the participants, several areas were assessed.
After their training at Mountbellew Agricultural College, almost three quarters of
participants said they will be the main decision maker with regards to farm management
practices on their own farm (Table 22). This is an increase from 35.9% of participants whom
claim to be currently the main decision maker with regards to farm management practices
(see Table 9). 25% of participants indicate that they will be the main decision maker with
regards to farm management practices, on the farm of others once they have completed the
Distance Learning Programme (Table 23).
When asked what their future farming intensions were, participants had varied responses
(Table 24). However, over half (54.9%) said they would like to increase herd size. 11%
would like to increase farm efficiency. Although over half would like to increase in size, 11%
of participants indicated that they intend to maintain current farming status.
Yes 74 74
No 26 26
Table 22: Summary of responses to the question, ‘After your training in MAC, will you be
the main decision maker with regards to farm management practices on your farm?’

50
Yes 24 25.0
No 72 75.0
Table 23: Summary of responses to the question, ‘After your training in MAC, will you be
the main decision maker with regards to farm management practices on the farm of
others?’
Increase herd size 45 54.9
Increase farm efficiency 9 11.0
Beef production 4 4.9
Increase farm size 3 3.7
Increase farm activity 2 2.4
Maintain current status 9 11.0
Increase farm size and
diversity
3 3.7
Unsure 2 2.4
Explore other options of
farming
1 1.2
Set up a calf to beef
enterprise
1 1.2
Learn how to grow crops 1 1.2
Farm Buildings 2 2.4
Table 24: Summary of responses to the question, ‘What are your future farming
intensions?’

51
1e. Problems facing farmers and possible solutions
In order to assess current and future problems for farmers, participants were asked to
answer a series of questions. These questions could also be used to assess possible
solutions.
When asked what they considered to be the most serious problem facing the world as a
whole, participants ranked international terrorism as the most serious problem, with 37.4%
of them agreeing on this (Figure 18). One quarter of participants felt that climate change is
the most serious problem facing the world as a whole. 17.2% of participants considered
poverty, hunger and lack of drinking water to be the most serious issue.
Next, when asked what is the most serious problem they face with regards to future
profitability of their farming livelihood, problems such as price inflation (22.7%) lack of
profits to be made (17%) and weather and climate (12.5%) were determined to be the most
serious for the participants (Table 25). Also considered as issues related to future
profitability, market forces, cost of inputs and grant availability (26.3%, 25% and 13.8%
respectively), were taken into consideration by participants (Table 26). Although not seen
as the most important issues, weather and climate was still ranked as the third most serious
issue relating to future profitability for 18.5% of participants and 13.8% considered the cost
of inputs to be of some importance as well (Table 27).
To determine participants opinion on what climate smart agriculture (CSA) stands for,
participants were asked to select a term they felt best described it, to the best of their
knowledge. 48% of participants considered increased resilience and sustainability of
agriculture production to be the most accurate, while 30% felt increased agricultural
efficiency to fit best (Figure 19). 12% and 10% respectively considered increased market
profitability of agriculture and intensification of agricultural production to best describe
climate smart agriculture (Figure 19).
90.1% of participants have not heard of the Carbon Navigator Decision Support tool (Table
28). However, of the 15 participants who ranked the usefulness of the tool, only 13.3% of
participants found it to be very useful, while 20% found it to be of some use and 20% found
it to be of no use at all (Figure 20).

52
40% of participants said they would be willing to set aside 1-2% of their weekly wage, if it
meant reducing greenhouse gas emissions from their farms (Figure 21). 5.1% of participants
said they would be willing to set aside 9-10% of their weekly income, however, almost
quarter said they would not be willing to set aside any of their weekly income in order to
reduce greenhouse gas emissions from their farms (Figure 21). Of the sample participants,
less than 10% indicated that their on farm decision making was influenced by climate
change mitigation techniques (Table 29). However, of those who claim to take mitigation
techniques into consideration, practices undertaken were reduction in granulated (PHO)
fertilizer, spray and fertilizer, and Beef Data and Genomic practices (Table 30). When asked
if they would be interested in being involved in mitigation operations, only 10% expressed
that they would be very interested (Figure 22). A quarter of participants expressed no
desire to be involved. Finally, when asked whether they would be willing to pay 10% more
for agricultural products if it meant they were produced in a way that does not increase
climate change, 5.1% of participants said they would totally agree to pay that amount, while
28.3% expressed that they would totally disagree with paying that amount extra for climate
friendly agricultural products (Figure 23).
Figure 18: Summary of responses to the question, ‘What do you consider to be the most
serious problem facing the world as a whole?’
25.3
37.4
17.2
7.1 8.1
2 3
0
5
10
15
20
25
30
35
40
Climate
Change
International
Terrorism
Poverty,
hunger, lack
of drinking
water
Increasing
global
population
Armed
conflict
Spread of
infectious
disease
proliferation
of nuclear
weapons
Percentage[%]
Problems
Most serious problemsfacing the world as a
whole

53
Issue Number of participants Percentage
Brexit 2 2.3
Price of Inflation 20 22.7
Zoonotic diseases 1 1.1
Irish Farmers Association 1 1.1
Knowledge 1 1.1
Grant Availability 6 6.8
Health and Safety 3 3.4
Weather and Climate 11 12.5
Government 1 1.1
Poor Profits 15 17.0
The need to build housing
units
1 1.1
Stocking Rate 2 2.3
The push towards larger
farms
1 1.1
Farm efficiency 1 1.1
Lack of workers 1 1.1
Regulations on farming
practices
2 2.3
Cost of production 3 3.4
Land shortages 1 1.1
CAP 1 1.1
Animal health 2 2.3
Live exports 1 1.1
Fertilizer use 1 1.1
Availability of silage/hay 1 1.1
Market forces 8 9.1
Continuation of BPS 1 1.1

54
Table 25: Summary response to the number 1 rank from the question, ‘Please list, in order
of importance, the top three issues that affect the future profitability of your farming
business/livelihood’.
Issue Number of Participants Percentage
CAP 1 1.3
Market forces 21 26.3
Soil fertility 1 1.3
Brexit 2 2.5
Grant availability 11 13.8
Quality of production 4 5.0
General farming 1 1.3
Government 1 1.3
The need for new machinery 2 2.5
Costs of Inputs 20 25.0
The cheap supply of food
from other countries
2 2.5
Price inflation 1 1.3
Adequate amounts of
fodder
1 1.3
Land management 1 1.3
Live trade 1 1.3
New farming enterprise 1 1.3
business/livelihood’.

55
Issue Number of participants Percentage
Population increase 1 1.5
Poor organisation 2 3.1
Immigration 1 1.5
Uncertainty 2 3.1
Market Forces 7 10.8
Animal health 2 3.1
Grant availability 8 12.3
Brexit 3 4.6
Quality of production 1 1.5
Environmentalists 1 1.5
Profitability 2 3.1
Lack of young farmers 2 3.1
Costs of running a farm 9 13.8
Reduction in farm supports,
e.g. fall in income with GLAS
compared to REPS
1 1.5
Tax 1 1.5
The availability to work on
the farm
2 3.1
Global markets 2 3.1
Good grassland 1 1.5
Soil carbon 1 1.5
The cost of Land 1 1.5
EU regulations 3 4.6
business/livelihood.’

56
Figure 19: Summary response to the question, ‘Which of the following best describes
Climate-Smart Agriculture?’
Yes 10 9.9
No 91 90.1
Table 28: Summary response to the question, ‘Have you heard of the Carbon Navigator
Decision Support tool?’
Which best describes Climate-Smart
Agriculture
Intensification of Agricultural
Production
Increased market profitability of
Agriculture
Increased resilience and sustainability
of Agricultural production
Increased Agricultural efficiency

57
Figure 20: Summary response to the question, ‘Please rank the usefulness of the Carbon
Navigator Decision Support tool’.
Figure 21: Summary response to the question, ‘What percentage of your weekly income
would you be willing to set aside to reduce greenhouse gas emissions from your farm?’
Usefulnessof the Carbon Navigator
Support tool
Very useful
Kind of useful
Neither useful nor unuseful
Not very useful
Not useful at all
Percentage of weekly income that you would
be willing to set aside to reduce GHG
emissionsfrom your farm
0%
1-2%
3-4%
5-6%
7-8%
9-10%

58
Yes 9 9
No 91 91
Table 29: Summary response to the question, ‘Is your on farm decision-making influenced
by climate change mitigation techniques?’
Practice Number of participants Percentage
Reduction in Granulated
(PHO) Fertilizer
1 33.3
Spray and Fertilizer 1 33.3
Beef Data and Genomics
Practices
1 33.3
Table 30: Summary response to the question, ‘Please specify what practices have been
influenced by mitigation operations’.

59
Figure 22: Summary response to the question, ‘How interested are you in being involved
in on-farm mitigation operations?’
Figure 23: Summary response to the question, ‘Would you pay 10% more for agricultural
products if they were produced in a way that does not increase climate change?’
10
12
39
14
25
0 5 10 15 20 25 30 35 40 45
Very interested
Some interest
neither interested nor uninterested
little interest
Not interested
Percentage [%]
Levelofinterest Interest in being involved in on-farm climate
change mitigation operations
5.1
16.2
31.3
19.2
28.3
0 5 10 15 20 25 30 35
Totally agree
Tend to agree
Neither agree nor disagree
Tend to disagree
Totally disagree
Percentage [%]
Opinions
You are willing topay 10% more for agri-products if they
are producedin a way in which does not increase climate
change

60
1f. Respondents knowledge, attitudes and opinions
Of my sample group, 43.7% were undecided as to whether or not climate change will impact
Irish farmers in the next 10 years. Less than 10% felt that climate change will have little
impact to Irish farmers in the next 10 years, while 13.6% think climate change will have a
serious impact on Irish farmers in the next 10 years (Figure 24). When asked whether they
thought climate change was currently impacting their farming livelihood, 14.7% said they
did not think climate change was impacting their farming livelihood at all, while 21.6% felt it
had somewhat of an impact (Figure 25)
Figure 24: Summary response to the question, ‘How much do you think climate change
will impact Irish farmers within the next 10 years?’
13.6
32
43.7
9.7
1
0 10 20 30 40 50
Serious Impact
Somewhat Impact
Neutral
Little Impact
No Impact
Percentage [%]
ScaleofImpact
Climate change will impact Irish farmers
in the next 10 years

61
Figure 25: Summary of responses to the question, ‘Do you think climate change is
currently impacting your farming livelihood?’
Section 2- Research Questions
Research Question 1
Is there an association between opinion on how much climate change will impact Irish
farmers within the next 10 years and variables such as gender, being a full time or part time
farmer, being a Teagasc client, and age?
T-Test
An independent samples t-test is used when you want to compare the mean score, on some
continuous variables, for two different groups of participants.
8.8
21.6
26.5
28.4
14.7
0 5 10 15 20 25 30
Serious Impact
Somewhat Impact
Neutral
Little Impact
Serious Impact
Percentage [%]
Scaleofseriousness Climate change is currently impacting
your livelihood

62
Impact & Gender
farmers within the next 10 years and the participant’s gender?
An independent t-test was conducted to determine the association between the two topics.
The assumption of homogeneity of variance was assessed by looking at Levene’s test for
equality of variances. Levene’s test was found not to be significant (p= .958); therefore the
assumption of variance was not violated.
The results of the t-test for equality of means showed significance (p = .008). Males had a
mean of 2.61 and a standard deviation of .844. Females had a mean of 1.92 and a standard
deviation of .954. The results conclude that females were significantly more likely than
males to think climate change will impact Irish farmers within the next 10 years.
Impact and Teagasc Client
farmers within the next 10 years and whether the participant is a Teagasc client?
equality of variances. Levene’s test was found to be significant (p= .036); therefore the
assumption of variance was violated.
The results of the t-test for equality of means showed no significance (p= .547). Teagasc
clients had a mean of 2.58 and a standard deviation of .753. Non-Teagasc clients had a
mean of 2.47 and a standard deviation of .984. the results conclude that clients and non-
clients of Teagasc showed no significance in their opinions that climate change will impact
Irish farmers within the next 10 years.

63
Impact and Full-time or Part-time Farmer
farmers within the next 10 years and whether the participant is a full-time or part-time
farmer?
equality of variances. Levene’s test was found to not be significant (p= .586); therefore the
assumption of variance was not violated.
The results of the t-test for equality of means showed no significance (p= .487). Full-time
farmers had a mean of 2.80 and a standard deviation of .837. Part-time farmers had a mean
of 2.52 and a standard deviation of .891. The results conclude that Full-time and Part-time
farmers showed no significance in their opinions that climate change will impact Irish
farmers within the next 10 years.
Nonparametric Correlations
Spearman’s correlation coefficient is a statistical measure of the strength of a monotonic
relationship between paired data. A monotonic function is one that either never increases
or never decreases as its independent variable increases. Note, unlike Pearson’s
correlation, there is no requirement of normality, and hence it is a nonparametric statistic.
Impact and Age
I used a Spearman’s correlation to determine the relationship between participants’ age and
their opinion on how much climate change will impact Irish farmers within the next 10
years.
Spearman’s correlation works by calculating Pearson’s correlation on the ranked values of
this data. Ranking (from low to high) is obtained by assessing a rank of 1 to the lowest
value, 2 to the next lowest, and so on.

64
Significance (p) occurs when p < 0.05
What age
group are you
How much do
you think that
climate change
will impact Irish
farmers within
the next 10
years
Spearman’s rho What age group
are you
Correlation
Coefficient
Sig. (2-tailed)
N
1.000
103
.272
.005
103
How much do
you think that
climate change
will impact Irish
farmers within
the next 10
years
Correlation
Coefficient
Sig. (2-tailed)
N
.272
.005
103
1.000
103
Table 31: the relationship between age and knowledge of impact on Irish farmers within
10 years
As p = .005, there is a significant relationship between age and thoughts that climate change
will impact Irish farmers within the next 10 years. As r= 0.272, r squared indicates that 27%
of the variance in thoughts on climate change can be explained by age. The information
projected indicates that, the younger the participant, the more likely they think climate
change will impact farmers in the next 10 years.
Research Question 2:

65
Is being willing to be involved in on-farm climate change mitigation operations more likely
when the farmer holds an off-farm job?
As p=.333, there is no significant relationship between a farmers willingness to being
involved in on-farm climate change mitigation operations and whether or not they hold an
off-farm job. As r=.098, r squared indicates that 9% of the variance in thoughts on
willingness to be involved in mitigation operations can be explained by whether or not the
farm holds an off-farm job. The information projected indicates that, holding an off-farm
job does not necessarily mean farmers are more willing to be involved in on-farm mitigation
operations.
p= significance of <0.05
Table 32: A spearman’s correlation to determine the relationship between willingness of a
trainee farmer to being involved in on-farm climate change mitigation operations and
whether or not the farmer holds an off-farm job
Research Question 3
Is there a significant relationship between knowledge of climate change and farmers
willingness to engage in on-farm climate change mitigation operations?

66
As p= 0.459, there is no significant relationship between knowledge of climate change and
farmers willingness to engage in on-farm climate change mitigation operations. As r=.075, r
squared indicates that 7% of the variance in willingness to being engaged in on-farm
mitigation operations can be explained by knowledge on climate change.
p=significance <0.05
Table 33: A spearman’s correlation to determine the relationship between willingness of a
trainee farmer to being involved in on-farm climate change mitigation operations and
knowledge of climate change.
Further results analysed
Result 1:
Is there an association between knowledge of climate change and variables such as age and
being the main decision maker with regards to farm management?
Knowledge and Age

67
I used a Spearman’s correlation to determine the relationship between participant’s
knowledge of climate change and their age.
What age are
you
Rate your
current
knowledge on
climate change
Spearman’s rho What age group
are you
Correlation
Coefficient
Sig (2-tailed)
N
1.000
-
103
-.024
.808
103
Rate your
current
knowledge on
climate change
Correlation
Coefficient
Sig (2-tailed)
N
-.024
.808
103
1.000
-
103
Explanations: Sig= significance, N=number of participants.
Table 34: the relationship between knowledge of climate change and age
As p = .808, there is shown to be no significance between the relationship of age and
current knowledge on climate change. As r= -.024, r squared indicates that 24% of the
variance in current knowledge can be explained by age.
Knowledge and Main Decision Maker
Is there an association between knowledge of climate change and whether or not the farmer
is the main decision maker on the farm?
First, an independent t-test was conducted to determine the association between the two
topics. The assumption of homogeneity of variance was assessed by looking at Levene’s test
for equality of variances. Levene’s test was found to be significant (p= .038); therefore the
assumption of variance was violated.

68
The results of the t-test for equality of means showed significance (p = .305). Decision
makers had a mean of 3.14 and a standard deviation of .673. Non-decision makers had a
mean of 3.29 and a standard deviation of .799. The results conclude that decision makers
and non-decision makers showed no difference in their knowledge of climate change.
Cross-tabulation
Next, a cross-tabulation was used to summarize the relationship between the two
categorical variables. A cross-tabulation is a table that depicts the number of times each of
the possible category combinations occurred in the sample data.
Table 35: A cross-tabulation was used to analyse the relationship between current
knowledge on climate change and whether or not the participant is the main decision maker
with regards to farm management practices.
Are you the main decision maker with regards to farm management practices
Rate your
current
knowledge
on climate
change
Yes No Total
Excellent
knowledge
Count
% within rate
your current
knowledge
on climate
change
% of total
1
50%
1.0%
1
50.0%
1.0%
2
100.0%
1.9%
Good
knowledge
Count
% within rate
your current
knowledge
on climate
2
22.2%
1
77.8%
9
100.0%

69
change
% of total 1.9% 6.8% 8.7%
Standard Count
% within rate
your current
knowledge
on climate
change
% of total
26
43.3%
25.2%
34
56.7%
33.0%
60
100.0%
58.3%
A little
knowledge
Count
% within rate
your current
knowledge
on climate
change
% of total
7
25.9%
6.8%
20
74.1%
19.4%
27
100.0%
26.2%
No
knowledge
at all
Count
% within rate
your current
knowledge
on climate
change
% of total
1
20%
1.0%
4
80.0%
3.9%
5
100.0%
4.9%
Total Count 37 66 103

70
% within rate
your current
knowledge
on climate
change
% of total
35.9%
35.9%
64.1%
64.1%
100.0%
100.0%
Table 35: the relationship between climate change and being the main decision maker
A cross-tabulation to measure the relationship between decision making and climate change
knowledge showed that, of the 60 participants who claim to have standard knowledge on
climate change, 43.3% said they are the main decision maker with regards to farm
management practices, while 56.7% said they were not the main decision maker. Of the
two participants who said their knowledge on climate change was excellent, 50% said they
were the main decision maker and 50% said they were not the main decision maker. Of the
5 participants who said they have no knowledge at all on climate change, 20% said they
were the main decision maker, while 80% said they were not the decision maker with
regards to farm management practices.
Chi Square test
Next, a Chi-Square test was conducted to test the relationship between main decision
maker and climate change knowledge. Chi-square is a statistical test commonly used to
compare observed data with data we would expect to obtain according to a specific
hypothesis.
Value Df Asymp. Sig. (2-sided)
Pearson Chi-Square 4.061 4 .398

71
Likelihood Ratio 4.190 4 .381
Linear-by-Linear
Association
.966 1 .326
N of Valid Cases 103
Asymp.Sig = Asymptotic Significance, Df= Degrees of freedom.
Table 36: the relationship between knowledge on climate change and being the main
decision maker
The main value of interest from the above test is the Pearson Chi-Square. The Pearson Chi-
Square value is 4.061, with an associated significance level of .398. The value of .398 is not
significant (p <0.05). This means that there is no difference between participants who are
the main decision maker and those who are not the main decision maker, with regards to
their knowledge on climate change.

72
Discussion
Age, Gender and Off-farm occupation
My results showed that 12.6% of my respondents were female and 87.4 % were male (Table
1). This corresponds to the findings by the Department of Agriculture, Food and the Marine,
which showed that 13% of farmers are female and 87% are male (Figure 5). This concludes
that my results accurately depicted National average for gender of farmers.
My results showed that almost 40% of my respondents were between the ages of 30-35
(Table 2). One third of my respondents reported to being between the ages of 25-30 years.
Older farmers were not well represented in my survey sample. The results found by the
Department of Agriculture, Food and the Marine showed that 26.5% of farmers are over the
age of 65, while 6.3% of farmers are under 25 (Figure 2). While my results do not replicate
those found by the Department of Agriculture, Food and the Marine, this is understandable
considering my focus group. My focus group were students of the Distance Learning
Programme at Mountbellew Agricultural College in Galway. Considering they are mostly
made up of new entrants to farming, some of whom are undertaking the course in order to
receive their Green Certificate (to qualify them for grants), it is understandable that their
average age is lower than that of National averages.
My results concluded that 95% of the respondents hold a part-time job (Tables 10 and 12).
The findings from the Department of Agriculture, Food and the Marine conclude that only
29.8% of the sample group hold a part-time job (Figure 6). Again, this does not correspond
to my findings, however this can be attributed to the fact my sample group are in the
Distance Learning Programme and 95% of them are only part-time farmers.
Knowledge, attitude’s and opinions

73
My results concluded that 13.6% of respondents believe climate change will have a serious
impact on Irish farmers within the next 10 years. 32% believe climate change will have
somewhat of an impact on Irish farmers within the next 10 years. When (Harrington and Lu
2002) posed the same question to their sample group of cattle farmers in Kansas, USA, they
found that 50% of farmers did not believe climate change would have a serious impact on
them within the next 50-100 years. The results gathered by (Barnes and Toma 2012)
showed that 27% of farmers think that climate change will have a negative impact on
productivity within the next 20 years. Comparing these results, I conclude that opinions
amongst farmers vary in relation to future climate change impacts. When asked whether
they would be willing to set aside some of their weekly income to reduce greenhouse gas
emissions, my results showed 40% of participants are willing to set aside 1-2%, 5.1% are
willing to set aside 9-10% and almost 50% are not willing to set aside any of their weekly
incomes to reduce greenhouse gas emissions (Figure 21).
The sample group from (Harrington and Lu 2002) were asked a similar question- whether
they would be willing to set aside various amounts of money to reduce global warming. Of
the sample group, 25% said they would be willing to pay $250, 21% said they would pay
$500 and 25% said they would pay $1,000. However, one third said they would not be
willing to undergo any additional costs. The findings from both suggest there is somewhat
of a stigma around paying any costs to reduce greenhouse gas emissions. Interestingly, of
my sample group, 5% said they would be willing to pay 9-10% of their weekly income to
reduce greenhouse gas emissions. This could be interpreted as quite a large amount, and
the findings were interesting to see.
My results found that 28.4% of respondents believe climate change is currently posing little
threat to the impact of their farming livelihoods (Figure 25). 8.8% believe climate change is
currently having a large impact on their farming livelihoods. (Arbuckle Jr, Prokopy et al.
2013) posed the question of whether or not farmers thought climate change was
happening. 68% thought climate change was happening, but only 10% felt it was as a result
to human activity.
My results showed that only 2% of respondents have received any training in relation to
how climate change could impact them as farmers (Figure 13). Responses from the study
conducted by (Tzemi, Breen et al. 2016) indicated that 32.2% had received some agri-

74
environmental training or advice. As my sample group are doing the Distance Learning
Programme, it was not expected that many would have any training in relation to how
climate change could impact them as farmers.
My results showed that the younger the farmer, the more likely they were to think climate
change will impact farmers within the next 10 years (Table 31). The results from (Rejesus
2012) explained that the older the farmer, the less likely they were to think climate change
would be a serious problem. These findings indicate similar opinions by the different focus
groups.
My results showed that there was no significant relationship between a trainee farmer in
the Distance Learning Programmes’ willingness to engage in on-farm mitigation operations
and whether or not they held an off-farm job (Table 32). The results obtained by (Davey
and Furtan 2008) indicated that there was a significance in the relationship between
farmers willingness to engage in on-farm climate change mitigation operations and whether
or not they held an off-farm job. They concluded that having an off-farm job meant they
were more willing to be involved in on-farm mitigation operations. (Keelan, Thorne et al.
2010) had opposite results. Their findings determined that having an off-farm job meant
the farmer was less likely to be willing to engage in on-farm mitigation operations.
My results showed that there was no significant relationship between a trainee farmer in
the Distance Learning Programmes’ willingness to engage in on-farm mitigation operations
and their knowledge on climate change (Table 33). The results obtained by (Tzemi, Breen et
al. 2016) indicated that climate aware farmers were more likely to be willing to engage in
on-farm climate change mitigation operations.

75
Conclusion
A questionnaire survey was produced using such references as The Department of
Agriculture, Food and the Marine (DAFM), Teagasc’s National Farm Survey and the
Eurobarometer series of surveys by the European Commission as guidelines. The questions
asked in the survey ranged from demographic, to questions relating to climate change
knowledge and questions relating to climate change opinions and attitudes.
The questionnaire survey was administered to students of the Distance Learning
Programme at Mountbellew Agricultural College in Co. Galway. The main findings from the
questionnaire survey suggest that trainee farmers are generally of a younger age than the
national average of farmers. This is expected as they are generally new entrants to farming.
The results suggest age plays a factor in opinions of climate change. Older farmers are less
likely to think climate change will impact the farming community negatively, while younger
farmers are more climate aware. Trainee farmers did not express much willingness to be
engaged in on-farm mitigation operations, and there seemed to be no variable in which
influenced this opinion.
Recommendations
Based on the research conducted, it is recommended that these results not be considered
definite, rather declared as exploratory due to the hasted manor in which the survey was
produced and distributed. It is recommended that the results obtained from this MScCCAFS
thesis be used as a hypothesis for further projects.

76
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