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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

Irish Agriculture.................................................................................................................................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 adoption in 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.................................................................................................................................................34

Section 1: Frequencies.....................................................................................................................34

Section 2- Research Questions........................................................................................................62

Further results analysed..................................................................................................................67

Result 1:.......................................................................................................................................67

Discussion............................................................................................................................................73

Age, Gender and Off-farm occupation.............................................................................................73

Knowledge, attitude’s and opinions................................................................................................73

Conclusion...........................................................................................................................................76

Recommendations...............................................................................................................................76

References/ Bibliography....................................................................................................................77

Appendices..........................................................................................................................................80

Appendix 1: Trainee Farmer Survey.................................................................................................80

Appendix 2: An account of MAC farm...........................................................................................104

Appendix 3 – Statistics explanations..............................................................................................107

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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

Destinations of Ireland's agri-food exports

UKOther EUInternational Markets

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

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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 de-

signed 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 as-

sociated with a farm (Murphy, Crosson et al. 2013). The Carbon Navigator is a learning tool

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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 pro-

ductivity, 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 farm-

ing ((Murphy, Crosson et al. 2013). It is a tool that has been designed through the collabora-

tion 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 adop-

ted (Murphy et al, 2013). Again, (Crosson, Shalloo et al. 2011) states that although the find-

ings 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 Ire-

land 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 grass-

lands converted to arable land, we would see a reduction of around 1.6% (Van Zeijts, Over-

mars 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 emis-

sions 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 emissions

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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 na-

tional 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 informa-

tion collected is then processed through the online databases acquired by Bord Bia, to de-

termine the performance of each farmer.

The farmer can then access the Carbon Navigator system online, with help from their ad-

viser. 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 Fig-

ure 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 in-

tensities

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

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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,

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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

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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

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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.

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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 sec-

tor 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 (Depart-

ment 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.

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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 re-

search. 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 com-

plete. Again your Education Officer can advise you on course requirements. (See the “Know

Your Education Officer” section)

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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 sur -

vey conveys the knowledge and opinions of more than just one sub-group of farmers.

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Methodology

The survey was implemented over the course of two days at Mountbellew Agricultural Col -

lege, 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 parti-

cipants are asked to attend at a time, due to classroom sizes. A hard copy of the question-

naire 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 know-

ledge, 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 Green-

house 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

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Teagasc. Eurobarometer is a series of public opinion questionnaires which are conducted

regularly, since 1973, in conjunction with the European Commission. These surveys are ap-

plied throughout the EU member states.

Statistical Analysis

Data from the trainee farmer survey was recorded using the SPSS Statistics version 22 soft-

ware package, used for statistical analysis. Questions and answers were coded on the pro-

gramme, which allowed for graphics, figures and tables to be constructed from the statist -

ical analysis.

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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 co-

hort 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 Mount -

bellew 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?’

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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 (Ju-

nior Cert)

2 2.0

Completed secondary school

(Leaving Cert)

14 13.9

Bachelor’s degree 60 59.4

Master’s degree 3 3.0

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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 ag-

ricultural 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 own-

ing or operating their farms, almost two thirds of them do not report being the main de-

cision 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 how-

ever- 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 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).

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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 Certi-

ficate, 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 train-

ing?’

Owner/Operator Number of Participants Percentage

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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 re-

gards to farm management practices?’

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How many years have you been the main decision maker on the farm?

0-5 yrs5-10 yrs10-15 yrs15-20 yrs20+ yrs

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?’

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Number of participants Percentage

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?’

1c. Current Positions of the survey participants

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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%) re-

vealed they are currently in a relationship (Table 14). Of these, one third (66.7%) are em -

ployed 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% admit-

ted 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 Ag -

riBusiness, 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 (Fig-

ure 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 second-

ary 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 dedic-

ated 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 not be in-

centivised, almost 60% said they would not partake (Table 20). Finally, when asked if the

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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).

Number of Participants Percentage

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

Bank Official 1 1.1

Special Needs Assistant 2 2.1

Quantity Surveyor 2 2.1

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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 Em-

ployee

1 1.1

Accountant 4 4.2

Unit Leader Abbott Dia-

gnostics

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’’

Number of participants Percentage

Single 33 32.4

In a relationship (married/ 69 67.6

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partner)

Table 14: Summary of responses to the question, ‘What is your relationship status?’

Number of Participants Percentage

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?’

Number of Participants Percentage

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

Civil Engineer 1 2.1

Health Care 1 2.1

Accountant 3 6.3

Legal Secretary 1 2.1

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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 occupa-

tion?’

Figure 12: Summary of responses to the question, ‘Do you plan to do any other agricul-

tural courses?’

Course Number of Participants Percentage

Farm Spraying 2 16.7

Agri Business 3 25.0

44

Do you plan to do any other agri-cultural courses?

YesNo

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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?’

Have you received any training in relation to how climate change could impact you

as a farmer?

YesNo

Figure 13: Summary responses to the question, ‘Have you received any training in relation

to how climate change could impact you as a farmer?’

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Figure

14:

Sum-

mary

of re-

sponses 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?’

46

Cremery Milk

Liquid Milk Dry stock Sheep Dry Stock & Tillage

Suckler Beef

Horses0

10

20

30

40

50

60

70

2 1

60.6

23.2

39.1

1

Main enterprise on the farm

Enterprise

Perc

enta

ge

Dry Stock Sheep Tillage Dry Stock & Tillage

Forestry Horses Suckler Beef05

1015202530354045

38.6

29.8

7 73.5 1.8

8.83.5

Secondary enterprise on farm

Enterprise

Perc

enta

ge [%

]

Page 47: Beti- MSc Project

Sole Trader

Partnership

Limited Company

0 10 20 30 40 50 60 70 80 90

78

20

2

Farm trading status

Percentage [%]

Stat

us

Figure 16: Summary of response’s to the question, ‘What is your farm trading status?’

Number of participants Percentage

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?’

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Number of participants Percentage

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?’

Percentage of land dedicated to the GLAS scheme

0-5%5-10%Over 10%

Figure 17: Summary of responses to the question, ‘What percentage of farm land is dedic-

ated to the GLAS scheme’

Number of Participants Percentage

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?’

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Number of participants Percentage

Yes 2 2

No 98 98

Table 21: Summary of responses to the question, ‘Have you received any training in rela-

tion 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 parti-

cipants said they will be the main decision maker with regards to farm management prac-

tices 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.

Number of participants Percentage

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?’

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Number of participants Percentage

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 oth-

ers?’

Number of participants Percentage

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 di-

versity

3 3.7

Unsure 2 2.4

Explore other options of

farming

1 1.2

Set up a calf to beef enter-

prise

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 inten-

sions?’

1e. Problems facing farmers and possible solutions

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In order to assess current and future problems for farmers, participants were asked to an-

swer 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 profit-

ability 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, mar-

ket 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 import-

ant 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, parti-

cipants were asked to select a term they felt best described it, to the best of their know-

ledge. 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 ag-

riculture and intensification of agricultural production to best describe climate smart agricul-

ture (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).

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

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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 re-

duce 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) fer-

tilizer, 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 de-

sire 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).

Climate

Change

Internati

onal Te

rroris

m

Poverty,

hunger, l

ack of d

rinkin

g wate

r

Increasi

ng global

population

Armed

conflict

Sprea

d of infec

tious dise

ase

prolife

ration of n

uclear

weapons

010203040

25.337.4

17.27.1 8.1

2 3

Most serious problems facing the world as a whole

Problems

Perc

enta

ge [%

]

Figure 18: Summary of responses to the question, ‘What do you consider to be the most

serious problem facing the world as a whole?’

Issue Number of participants Percentage

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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

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’.

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Issue Number of Participants Percentage

CAP 1 1.3

Market forces 21 26.3

Soil fertility 1 1.3

Brexit 2 2.5

Weather and Climate 9 11.3

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 fod-

der

1 1.3

Land management 1 1.3

Live trade 1 1.3

New farming enterprise 1 1.3

Table 26: Summary response to the number 2 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

Population increase 1 1.5

Weather and Climate 12 18.5

Poor organisation 2 3.1

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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

Table 27: Summary response to the number 3 rank from the question, ‘Please list, in order

of importance, the top three issues that affect the future profitability of your farming

business/livelihood.’

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Which best describes Climate-Smart Agriculture

Intensification of Agricultural Production

Increased market profitability of Agricul-ture

Increased resilience and sustainability of Agricultural production

Increased Agricultural efficiency

Figure 19: Summary response to the question, ‘Which of the following best describes Cli-

mate-Smart Agriculture?’

Number of participants Percentage

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?’

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Usefulness of the Carbon Navigator Support tool

Very usefulKind of usefulNeither useful nor unusefulNot very usefulNot useful at all

Figure 20: Summary response to the question, ‘Please rank the usefulness of the Carbon

Navigator Decision Support tool’.

Percentage of weekly income that you would be willing to set aside to reduce GHG emis-

sions from your farm

0%1-2%3-4%5-6%7-8%9-10%

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?’

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Number of Participants Percentage

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 in-

fluenced by mitigation operations’.

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Very interested

Some interest

neither interested nor uninterested

little interest

Not interested

0 5 10 15 20 25 30 35 40 45

10

12

39

14

25

Interest in being involved in on-farm climate change mitigation operations

Percentage [%]

Leve

l of i

nter

est

Figure 22: Summary response to the question, ‘How interested are you in being involved

in on-farm mitigation operations?’

Totally agree

Tend to agree

Neither agree nor disagree

Tend to disagree

Totally disagree

0 5 10 15 20 25 30 35

5.1

16.2

31.3

19.2

28.3

You are willing to pay 10% more for agri-products if they are produced in a way in which does not increase climate

change

Percentage [%]

Opi

nion

s

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?’

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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 im-

pact to Irish farmers in the next 10 years, while 13.6% think climate change will have a seri -

ous 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)

Serious Impact

Somewhat Impact

Neutral

Little Impact

No Impact

0 5 10 15 20 25 30 35 40 45 50

13.6

32

43.7

9.7

1

Climate change will impact Irish farmers in the next 10 years

Percentage [%]

Scal

e of

Impa

ct

Figure 24: Summary response to the question, ‘How much do you think climate change

will impact Irish farmers within the next 10 years?’

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Serious Impact

Somewhat Impact

Neutral

Little Impact

Serious Impact

0 5 10 15 20 25 30

8.8

21.6

26.5

28.4

14.7

Climate change is currently impacting your livelihood

Percentage [%]

Scal

e of

serio

usne

ss

Figure 25: Summary of responses to the question, ‘Do you think climate change is cur-

rently 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 farm-

ers 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.

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Impact & Gender

Is there an association between opinion on how much climate change will impact Irish farm-

ers 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

Is there an association between opinion on how much climate change will impact Irish farm-

ers within the next 10 years and whether the participant is a Teagasc client?

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= .036); therefore the as-

sumption of variance was violated.

The results of the t-test for equality of means showed no significance (p= .547). Teagasc cli-

ents 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 farm-

ers within the next 10 years.

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Impact and Full-time or Part-time Farmer

Is there an association between opinion on how much climate change will impact Irish farm-

ers within the next 10 years and whether the participant is a full-time or part-time farmer?

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 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 farm-

ers within the next 10 years.

Nonparametric Correlations

Spearman’s correlation coefficient is a statistical measure of the strength of a monotonic re-

lationship 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.

Significance (p) occurs when p < 0.05

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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:

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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 3Is there a significant relationship between knowledge of climate change and farmers

willingness to engage in on-farm climate change mitigation operations?

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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

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I used a Spearman’s correlation to determine the relationship between participant’s know-

ledge of climate change and their age.

What age are

you

Rate your cur-

rent 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 cur-

rent knowledge

on climate

change

Correlation

Coefficient

Sig (2-tailed)

N

-.024

.808

103

1.000

-

103Explanations: 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 cur-

rent 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.

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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 categor-

ical variables. A cross-tabulation is a table that depicts the number of times each of the pos-

sible category combinations occurred in the sample data.

Table 35: A cross-tabulation was used to analyse the relationship between current know-

ledge 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 know-

ledge

Count

% within rate

your current

knowledge

on climate

2

22.2%

1

77.8%

9

100.0%

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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 know-

ledge 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

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% 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 manage-

ment practices, while 56.7% said they were not the main decision maker. Of the two parti-

cipants 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 parti -

cipants 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 hypo-

thesis.

Value Df Asymp. Sig. (2-sided)

Pearson Chi-Square 4.061 4 .398

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Likelihood Ratio 4.190 4 .381

Linear-by-Linear As-

sociation

.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 de-

cision 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.

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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

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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-

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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.

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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.

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References/ Bibliography

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Barnes, A. P. and L. Toma (2012). "A typology of dairy farmer perceptions towards climate change." Climatic Change 112(2): 507-522.

Bockel, L., et al. (2012). "Using marginal abatement cost curves to realize the economic appraisal of climate smart agriculture policy options." Methodology 3(1).

BordBia (2015). Meat and Livestock Review and Outlook 2015/2016, The Thinking House.

BordBia (2016). Factsheet on the Irish Agriculture and Food & Drink Sector.

Branca, G., et al. (2011). "Climate-smart agriculture: a synthesis of empirical evidence of food secu-rity and mitigation benefits from improved cropland management." Mitigation of climate change in agriculture series 3: 1-42.

Campbell, B. M., et al. (2014). "Sustainable intensification: What is its role in climate smart agricul-ture?" Current Opinion in Environmental Sustainability 8: 39-43.

Casey, J. and N. Holden (2006). "Quantification of GHG emissions from sucker-beef production in Ire-land." Agricultural Systems 90(1): 79-98.

Cohen, J. E. (2003). "Human population: the next half century." Science 302(5648): 1172-1175.

Crosson, P., et al. (2011). "A review of whole farm systems models of greenhouse gas emissions from beef and dairy cattle production systems." Animal Feed Science and Technology 166: 29-45.

CSO (2010). Census of Agriculture 2010- Final Results. Dubln, Ireland, Stationery Office.

CSO (2016). Census of Population - Preliminary Results. C. S. Office. Cork.

Davey, K. A. and W. H. Furtan (2008). "Factors that affect the adoption decision of conservation tillage in the prairie region of Canada." Canadian Journal of Agricultural Economics/Revue canadi-enne d'agroeconomie 56(3): 257-275.

Department of Agriculture, F. a. t. M. (2013). Terms and Conditions of the Beef Data and Genomics Programme (BDGP) 2015-2020.

Department of Agriculture, F. a. t. M. (2014). Food Harvest 2020: Milestones for Success. F. a. t. M. Department of Agriculture: 1-69.

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Department of Agriculture, F. a. t. M. (2016). Annual Review and Outlook for Agriculture, Food and the Marine 2015-2016.

Department of Agriculture, F. F. (2010). Food Harvest 2020: A Vision for Irish Agri-food and Fisheries. F. F. Department of Agriculture, Department of Agriculture, Fisheries & Food.

Department of Agriculture, F. F. (2015). A discussion document on the potential for Greenhouse Gas (GHG) mitigation within the Agriculture and Forestry sector. F. F. Department of Agriculture.

Deressa, T., et al. (2011). "Perception of and adaptation to climate change by farmers in the Nile basin of Ethiopia." The Journal of Agricultural Science 149(1): 23.

Duguma, L. A., et al. (2014). "A systematic analysis of enabling conditions for synergy between cli-mate change mitigation and adaptation measures in developing countries." Environmental Science & Policy 42: 138-148.

EU-Commission (2005). "Agri-environment Measures-Overview on General Principles." Types of Measures, and Application (EAFRD).

EuropeanCommission (2013). The common agricultural policy (CAP) and agriculture in Europe – Fre-quently asked questions E. Commission. Brussels.

Foley, P., et al. (2011). "Whole-farm systems modelling of greenhouse gas emissions from pastoral suckler beef cow production systems." Agriculture, ecosystems & environment 142(3): 222-230.

Gasson, R. and A. J. Errington (1993). The farm family business, Cab International.

Harrington, L. M. and M. Lu (2002). "Beef feedlots in southwestern Kansas: local change, percep-tions, and the global change context." Global Environmental Change 12(4): 273-282.

Keelan, C., et al. (2010). "Predicted willingness of Irish farmers to adopt GM technology."

Kumar, M. (2013). "Crop Plants and Abiotic Stresses." Biomedical Research & Therapeutics 3(1).

Lalor, S. and G. Lanigan (2010). "The effect of application timing on ammonia emissions from cattle slurry in Ireland." Advances in Animal Biosciences 1(01): 72-72.

Lipper, L., et al. (2014). "Climate-smart agriculture for food security." Nature Climate Change 4(12): 1068-1072.

Lobell, D. B., et al. (2008). "Prioritizing climate change adaptation needs for food security in 2030." Science 319(5863): 607-610.

Lovett, D., et al. (2006). "A systems approach to quantify greenhouse gas fluxes from pastoral dairy production as affected by management regime." Agricultural Systems 88(2): 156-179.

McCarthy, J. J. (2009). "Reflections on: our planet and its life, origins, and futures." Science 326(5960): 1646-1655.

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O’Brien, D., et al. (2011). "The effect of methodology on estimates of greenhouse gas emissions from grass-based dairy systems." Agriculture, ecosystems & environment 141(1): 39-48.

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Westhoek, H., et al. (2014). "Greening the CAP." An analysis of the effects of the European Commis-sion’s proposals for the Common Agricultural Policy 2020.

Appendices

Appendix 1: Trainee Farmer Survey

Trainee Farmer Survey: Mitigation of Greenhouse Gas Emissions

from Irish Farms

This is a questionnaire designed by Beti O'Connor, MSc student at NUI Galway & Colm Duffy PhD student at

NUI Galway.

Thank you for taking the time to complete this survey.

The goal of this survey is to investigate the knowledge, attitudes, opinions and priorities of Mountbellew Agri-

cultural College trainees in relation to mitigation (reduction) of Greenhouse Gas Emissions from Irish

Farms.

We would greatly appreciate your voluntary participation in this survey. Please respond to the best of your

knowledge.

The questionnaire is being implemented by Beti O’Connor, a Masters student in Climate Change, Agriculture

and Food Security and Colm Duffy, a PhD student at the National University of Ireland, Galway and it should

take no longer than 15 minutes to complete. All information collected will be treated as confidential, and your

details will not be saved.

As thanks for your participation, you will also be entered into a draw for one of three All4One vouchers

worth between €50 and €25.

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Lastly, here are some definitions to help you with the terminology.

Climate change mitigation = reducing emissions from agriculture (e.g. Financial incentives and regulations for

improved land management; maintaining soil carbon content; efficient use of fertilisers and irrigation).

Climate change adaptation = adapting farms to future/changing climates (e.g. Adjustment of planting dates and

crop variety; crop relocation; improved land management- erosion control and soil protection through tree plant-

ing).

DEMOGRAPHIC DATA

(1) Are you male or female?

Male

Female

(2) What age group are you?

Under 20

20 – 25

25 – 30

30 – 35

35 – 40

40 – 45

45- 50

50 – 55

55 – 60

60 Plus

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(3) What region are you in? (tick one)

Louth, Leitrim, Sligo, Done-

gal, Monaghan

1

Dublin, excluding from NFS

analysis due to small sample

forms

2

Kildare, Meath, Wicklow 3

Laois, Longford, Offaly, West-

meath

4

Clare, Limerick, Tipperary

N.R.

5

Carlow, Kilkenny, Wexford,

Tipperary S.R., Waterford

6

Cork, Kerry 7

Galway, Mayo, Roscommon 8

(4) What is your highest level of education

Primary School

Some secondary School (eg. Junior cert)

Completed secondary school (leaving cert)

Bachelor’s degree

Masters degree

PhD

Other __________________________

(5) Have you any specialised agricultural training?

Yes

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No

(6) If yes Q5, what is the specialist agricultural training ?

Certificate in farming

Third level degree in agriculture

Masters degree in agriculture

Farm apprenticeship scheme/trainee farmer scheme

1 year at an agricultural college

Course greater that 60 hours

Course less than 60 hours

Other (specify) _____________________________

(7) Are you the owner/operator of the farm?

Yes

No

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(8) Are you a new entrant to farming?

Yes

No

(9) Are you the main decision maker with regards to farm management practices?

Yes

No

(10) If yes to Q9, how many years have you been the decision maker on the farm?

0-5

5-10

10-15

15-20

20 +

(11) If no Q9, do you expect to own your own farm in:

5 yrs

10 yrs

10 yrs +

(12) Are you engaged in farming on a fulltime or part-time basis?

Fulltime

Part-time

(13) Do you currently have an off-farm job?

Yes

No

(14) If yes to Q13, please specify other occupation

_______________________________________________________________________________

(15) Are you :

Single

In a relationship (married/partner)

Other (eg. Widowed/seperated)

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(16) if in a relationship, is your spouse/partner engaged in off-farm work?

Yes

No

(17) If yes to Q16, please specify

(18) What is your main reason for doing a distance learning course at MAC?

(19) Do you plan to do any other agricultural courses?

Yes

No

(20) If yes, please specify course type

_________________________________________________________

Farm Profile

(21) What is the main (principal) enterprise on the farm?

Creamery Milk

Liquid Milk

Dry Stock

Sheep

Tillage

Dry Stock and Tillage

Other ( Specify)

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(22) What is the secondary enterprise on the farm?

Creamery Milk

Liquid Milk

Dry Stock

Sheep

Tillage

Dry Stock and Tillage

Other ( Specify)

(23) What size is your farm

HECTARES ACRES

A What is the total agricultural area of your farm

in hectares or acres*?

B How many hectares or acres is utilizable agri-

cultural area?

C How many hectares or acres do you own?

D How many hectares or acres are rented – in?

E How many hectares or acres are let-out?

*1 hectare=2.471 acres

(24) How many separate parcels of land make up the total farmed area

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(25)Please specify the quantities of animals owned

Pig: ______________________

Cattle (dairy): ____________________

Cattle (suckler):___________________

Cattle (finishing):___________________

Sheep:___________________________

Poultry:_________________

Horses:_________________

Other:___________________

(26) Which of the following best describes the farms trading status ?

Sole trader

Partnership

Limited Company

Other (specify) _____________________

(27) Are you a Teagasc client ?

Yes

No

(28) Are you involved in / registered with the GLAS scheme?

Yes

No

(29) If yes to Q28, please indicate which activities you are engaged in?

Arable Margins

Bat Boxes

Bird Boxes

Catch Crops

Conservation of Solitary Bees

Coppicing Hedgerows

Environmental Management of Fallow Lands

Laying Hegerows

Low-emissions slurry spreading

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Low-input permanent pasture

Minimum Tillage

Planting native trees

Planting new hedge rows

Protection of Archaeological sites

Protection of water courses

Riparian Margins

Traditional Hay Meadow

Traditional Orchards

Stone wall maintenance

Wild Bird Cover

(30) What percentage of farm land is dedicated to GLAS Scheme?

0 – 5%

5-10%

Over 10%

(31) Would you be willing to engage in any GLAS activities if they were not incentivised?

Yes

No

(32) Have you received any training in relation to how climate change could impact you as a farmer?

Yes

No

(33) If you have received training in relation to climate change impacts at the farm level, please indicate,

in order of importance, where you received that training (1= most important; 5=least important).

Teagasc

Agricultural College

University Degree/Diploma Programme

Distance Learning

Other (specify)

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(34) After your training in MAC, will you be the main decision maker with regards to farm management

practices on your own farm?

Yes

No

(35) After your training in MAC, will you be the main decision maker with regards to farm management

practices on the farm of others?

Yes

No

(36) What are your future farming intentions ? (eg. Expand dairy herd within 3 years)

___________________________________________________________________________

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Climate Change & Agriculture

(37) What do you consider to be the most serious problems facing the world as a whole?

(select one)

Climate Change

International Terrorism

Poverty, hunger and lack of drinking water

Economic situation

The increasing global population

Armed conflict

The spread of infectious disease

The proliferation of nuclear weapons

(38) How would you rate your current knowledge on climate change ? ( 1= very knowledgeable; 5= no

knowledge at all)

Excellent knowledge

1

2

3

4

5

No knowledge at all

(39) How much do you think that climate change will impact Irish farmers in the within next 10 years?

(1=Serious impact on Irish farmers; 5=No impact at all)

Serious Impact

1

2

3

4

5

No impact at all

(40) Do you think climate change will be a serious problem for the Irish agri-food sector within the next

10 years? (1=Serious impact on Irish farmers; 5=No impact at all)

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Serious Impact

1

2

3

4

5

No impact at all

(41) Which of the following statements best describes Climate Smart Agriculture? (tick one)

Intensification of Agricultural production

Increased market profitability of Agriculture

Increased resilience and sustainability of Agricultural production

Increased Agricultural efficiency

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(42) Please rank (1-5) the major sources of information regarding how climate change will impact you as

a farmer

(1= Most important; 5= Least important)

Social Media (Facebook; Twitter; Instagram; youtube)

Farmers Journal

Online Newspapers

Training Colleges

Local Radio

National Radio

Teagasc

Print Newspapers

Television

Other (specify)

(43) Please rank (1-9) the following institutions in order of importance with regards to helping Irish farm-

ers reduce their greenhouse gas emissions;

(1= Most important; 9 = Least important)

Department of Agriculture

Teagasc

Bord Bia

Environmental Protection Agency

Irish Farmers Association

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Co-operatives

Environmental NGOs

Input suppliers

Other (specify )

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MITIGATION SECTION

(44) Please list, in order of importance, the top three issues that effect the future profitability of your

farming business/livelihood (1 = most important, etc).

1

2

3

(45) How much does the Green House Gas emissions of a practice impact your decision to use the prac-

tice?

(1=A lot; 5= Not at all )

A Lot

1

2

3

4

5

Not at all

(46) How much do you think your choice of farming practice impacts on the farms over all greeenhouse

gas emissions?

1= Very significantly; 5= Not at all )

Very significantly

1

2

3

4

5

Not at all

(47) On a scale of 1-5, do you consider that climate change is CURRENTLY impacting your farming live-

lihood? (1= Large impact; 5=No impact at all)

Large impact

1

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2

3

4

5

No impact at all

(48) Explain your answer to the previous question

__________________________________________________________________________________

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(49) On a scale of 1-5, please indicate how much you think climate change will impact your farming liveli-

hood in the next 10 years

(1= Large impact; 5=No impact at all)

Large impact

1

2

3

4

5

No impact at all

(50) To the best of your knowledge, rank in order from MOST to LEAST, the farm systems - according to

which they generate the most greenhouse gas emissions (per hectare)

(1=generates the most; 8=generates the least)

Dairy

cattle rearing

Tillage

Sheep

Pig

Poultry

Horses

mixed livestock

(51) Below is a list of six agricultural policy aims, please rank them from 1-6 (most important- least im-

portant), in terms of priority of the National Government in terms of agriculture and rural development

policy (in your opinion);

Ensuring that agricultural products are of good quality,

healthy and safe

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Ensuring reasonable food prices for consumers

Ensuring a fair standard of living for farmers

Protecting the environment and dealing with climate

change

Developing rural area’s while preserving the countryside

Securing food supply in the country

(52) What % of your weekly income would you be willing to set aside to reduce greenhouse gas emissions

from your farm?

0 %

1-2%

3-4%

5-6%

7-8%

9-10 %

more than 10%

(53) Who should pay for the costs of reducing greenhouse gas emissions from Irish farms? Please rank 1-

6, from MOST to LEAST;

Farmers

Processors

Coops

Retailers

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Consumers

Government (from taxation)

Other (specify):

(54) Is your on-farm decision making influenced by climate change mitigation techniques?

Yes

No

(55) If yes to Q54, please specify which practices have been influenced by mitigation techniques?

_________________________________________________________________________

(56) How interested are you in being involved in on-farm climate change mitigation operations?

(1=very interested; 5=not interested at all)

Very interested

1

2

3

4

5

Not interested at all

(57) Have you heard of the Carbon Navigator Decision Support tool?

Yes

No

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(58) If yes to Q57, please rate its usefulness (1=very usefull; 5=not useful at all)

Very useful

1

2

3

4

5

Not useful at all

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Greenhouse Gas Emissions on Irish Farms

Please inidcate whether you agree or disagree with each of the following statements with regards to agri-

culture and climate change

(1)totally agree(2) tend to agree(3) don't know, (4) tend to disagree, (5)totally disagree.

(59) Agriculture is one of the major causes of climate change

Totally agree

1

2

3

4

5

Totally disagree

(60) In the coming years, agriculture will suffer strongly from the impacts of climate change

Totally agree

1

2

3

4

5

Totally disagree

(61) The government need to help farmers to change the way they work in order to fight climate change

Totally agree

1

2

3

4

5

Totally disagree

(62) Irish farmers need to change the way they work in order to fight climate change, even if it means that

Irish agriculture will be less competitive

Totally agree

1

2

3

4

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5

Totally disagree

(63) Irish Agriculture has already made a major contribution in fighting climate change

Totally agree

1

2

3

4

5

Totally disagree

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(64) You are ready to pay 10% more for agricultural products if they are produced in a way that does not

increase climate change

Totally agree

1

2

3

4

5

Totally disagree

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Greenhouse Gas Emissions on Irish Farms

Please indicate your level of agreement with each of the following statements regarding Irish farmers.

The Irish government should encourage its farmers...

(1)totally agree(2) tend to agree(3) don't know, (4) tend to disagree, (5)totally disagree.

(65) To produce more organic products

Totally agree

1

2

3

4

5

Totally disagree

(66) To contribute to the production of renewable energy (e.g. biogas, biomass, and biofuels)

Totally agree

1

2

3

4

5

Totally disagree

(67) To take advantage of progress in biotechnology

Totally agree

1

2

3

4

5

Totally disagree

Greenhouse Gas Emissions on Irish Farms

The questionnaire is now complete,

Thank you for taking the time to fill out this questionnaire.

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Don't forget, on completion of this questionnaire, you will be automatically entered into a draw to be in with a

chance of winning one of three All4One vouchers worth 1x 50 euro and 2x 25 euro.

Thank you

Beti O'Connor, MSc student in Climate Change, Agriculture and Food Security (2015-2016)

MScCCAFS Program, Plant & AgriBiosciences Research Centre (PABC), NUI Galway

Colm Duffy, PhD student Climate Smart Agricyltyre. Plant & AgriBiosciences Research Centre (PABC), NUI

Galway

Appendix 2: An account of MAC farm

The farm itself is a short distance from the college.

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The windmill is not yet up and running.

They have trace elements fertilizer, in order to keep the levels of trace elements sufficent in

the soil.

Nitrogen is the main fertilizer they use.

They recycle the water in the milking parlour as best they can, by using the water to wash

the parlour.

The milk tanks need water in order to be cooled.

The cattle are fed ration while in the milking parlour.

The bull is kept in a separate paddock to the cows. All the cattle are in paddocks that are

reachable by walking from the farm yard, therefore there is no need to use a vechicle to

reach the cattle. The calves are kept in a paddock at the front of the farm yard. All the

cattle were currently out on grass (end of May). The farm has a total of 80 cattle.

The dairy farm technician is proposing a new layout to the farm, to make the farm more

user friendly. These methods include dividing the paddocks into smaller paddocks with

more road ways.

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Appendix 3 – Statistics explanationsCorrelations, T-Tests, Chi Square, Cross-Tabulations

In this section, we will focus on the results obtained from performing correlations, t-tests,

chi-squares and cross-tabulations.

Table: Explanation for summary statistics

For each explanatory variable (X), a number of statistics that summarize their influence on

the independent variable (Y) on the error and significance are calculated within the model.

These are explained below.

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