Climate change and future prospects of Norwegian farmers: Do farmers see opportunities or risks?

Brita AasprangStudent at Department of Sociology and Political Science, NTNUbritaa@stud.ntnu.no

Paper for the 26th Conference of the Nordic Sociological Association 15-18 August 2012, University of Iceland, Reykjavik. Working group: Environment, risk and expertise .

Draft only. Not for citation.

Abstract

The role that agriculture plays concerning climate change is an important topic in Norwegian agricultural politics. It’s often claimed that Norwegian agriculture will be positively affected by climate change in most parts of the country due to increased temperatures, but there’s also predicted a series of challenges that Norwegian farmers might have to deal with. There might be increased occurrences of diseases on plants and animals, increased amount of rain, and more frequent extreme weather events. Farmers are located in a vulnerable situation since they both depend on nature and on agricultural and environmental politics. Heavy rain might destroy their crops, but they might also be affected by political regulations and policies that are implemented in the agricultural politics as a response to climate change. In this paper I look at variables that can affect how Norwegian farmers see their own situation when it comes to how climate change might affect their farming practices. One of the questions I ask is whether farmers are optimistic about the future concerning climate change or whether they are more concerned about the risks that that are related to it. In my research I use data collected by the Centre for Rural Research in Norway. Analyses are carried out on a cross-sectional survey called “Trender i norsk landbruk” (Trends in Norwegian Agriculture) that is collected every other year. The survey consists of postal interviews of a random sample of Norwegian farmers. In this paper I look at how the farmers answered a question about how they think their own farms will be affected by climate change during the next ten years.

Key-words: agriculture, climate change, risk

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Introduction

Climate change is a topic that has engaged people all over the world (Kvaløy, Finseraas, & Listhaug,

2012). Climate change has been on the agenda in Norwegian politics for a while, and there is largely

consensus about the statement that climate change is a problem that needs to be solved (Meld.St.21,

2011-2012). In 2009 the Norwegian agriculture got its own White paper on how agriculture can be “a

part of the solution” to the climate challenges (St.meld.nr. 39., 2008-2009).

Agriculture is especially vulnerable to changes to the climate. Changes in weather is nothing new to

farmers, but during the past years there has been a number of prediction on what challenges

agriculture can expect due to global climate changes (Faures, Bernardi, & Gommes, 2010). When it

comes to Norwegian agriculture, climate change expected to have different consequences in different

parts of the country and in different farm productions (O'Brien, Eriksen, Sygna, & Naess, 2006).

Some research have been carried out on how the Norwegian agriculture might be affected by climate

change (Hanssen-Bauer, Hygen, & Skaugen, 2010; Rønning, 2011; Øygarden, 2009), but there is still

a lack of social science studies on how Norwegian farmers relates to the issue. In this paper I’m

looking at Norwegian farmers and their viewpoints on climate change. My main focus is on how they

think their own farming will be affected by climate change. My research questions are: Do Norwegian

farmers believe their own farms will be affected by climate change during the next 10 years? What

characterizes the farmers who believe they will be negatively affected compared to the farmers who

believe climate change will affect them in a positive way?

The main focus will be how the farmers answered the question: How do you think climate change will

affect your farming during the next 10 years? I’ll use OLS-regression to look at what kind of farmers

see climate change as a risk and who sees it as an opportunity to their farm production.

Agriculture in Norway

There was about 45.500 farms in Norway in 2011, and in 2007 2,5 percent of the Norwegian

population was employed in farm businesses (Produsentregisteret, 2012a; SSB, 2010, 2012). The

production of meat, milk, grain and potatoes are the most central productions (Ladstein & Skoglund,

2008). In 2011 3,2 percent of the Norwegian land was cultured land (Knutsen, 2011:2).

The Norwegian agriculture is strongly subsidized and regulated, and has strong tariff protection

(Kvalvik et al., 2011:32). The agricultural politics is focused on ensuring food security, the

maintenance of agriculture all over the country, enhanced values and a sustainable agriculture

(Landbruks- og matdepartementet, 2011:14-15).

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Norwegian agriculture is varied. There are big differences across regions, production types and farm

size. There has been a development towards fewer, but larger farms, but Norwegian agriculture in

general can still be considered small scale in an international perspective (Almås & Gjerdåker, 2004;

Ladstein & Skoglund, 2008; SSB, 2010).

There are limitations considering in what scale there is possible to do agriculture in Norway. The light

conditions and the low temperatures causes relatively short growing seasons and provides limitations

to what kind of plant species that are possible to grow. On the other side, the low temperatures results

in relatively few problems considering infestations of crops, and most seasons there are more than

enough rain (Knutsen, 2011).

Climate change and risk

Climate change is a “threat” that we mainly hear about in the media (Lowe et al., 2006:436). We are

constantly warned that climate change can have serious consequences, but it still seems abstract and

elusive to us, since we don’t experience climate change in our everyday life (Giddens, 2009:2).

There have always been risks, and dealing with the risk of changing weather is certainly not something

new for farmers (Faures et al., 2010). What is new with the risk, in the sense Beck and Giddens

describes are the global forms of risk (Beck, 1992[1986]:21). In Becks portrayal of the risk society,

risk isn’t bound to one location. The “new risks”, such as the global climate changes, puts all life on

Earth in danger (Beck, 1992[1986]:22).

Giddens sees climate changes as a problem that is unlike any other problems, both because of the

scale, but also because it’s about the future (Giddens, 2009:2). It can be hard for people to imagine

what they see as a distant future (O'Neill & Nicholson-Cole, 2009:361). Problems like global

warming, extinct species, and nuclear radiation can seem as impossible to reverse and it takes more

than a lifetime to repair (Newton, 2007:46). There can also be a tendency to think that climate change

is something that will affect other people, somewhere else in the world, and not yourself (O'Neill &

Nicholson-Cole, 2009:362).

Climate change and agriculture – prospects and scenarios

Agriculture is one of the industries that will be the most affected by an varying climate (Faures et al.,

2010:533). We can divide between direct and indirect effects when talking about how climate change

can affect farmers (Kvalvik et al., 2011; O'Brien et al., 2006). With direct effects we mean how

changes in climate can affect farm production in terms of changes in terms of growing conditions like

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higher temperatures and depositing, changing cycles of freeze-thaw-stability, new species of plants,

fungus and insects and more frequent occurrence of extreme weather (Kvalvik et al., 2011:27). Direct

effects of climate change and annual variations can be difficult to distinguish (Kvalvik et al., 2011:32).

Some research indicates that there have already been climatic changes in Norway (O'Brien et al.,

2006). In measurements of temperatures, both in the air and in different depths underground, they

found that the growing season have become 4 weeks longer during the 20 years they have been doing

the measures (Höglind, Thorsen, Østrem, & Jørgensen, 2009; Rafoss, 2009). Longer growing seasons

gives possibilities for increasing the amount of crops that are cultivated per season (Kvalvik et al.,

2011). The advantages of longer growing seasons, on the other hand, can be limited by the light

conditions (Hanslin, 2009). Low temperatures and limited light conditions can reduce the advantages

of higher temperatures (Hanslin, 2009:78; Rognli & Skrøppa, 2009:123).

There has been many attempts to make scenarios of how climate change will affect Norwegian

agriculture (Hanssen-Bauer et al., 2010; Höglind et al., 2009). One study predicts that the growing

season might become up to three months longer some places in Norway in year 2071 to 2100,

compared to what it was between 1961 and 1990 (Höglind et al., 2009). The average temperature is

predicted to become higher all over the country and for all months, and the decrease in temperatures

will be greatest in the fall according to this study. There will also rain more frequently, especially in

the fall (Höglind et al., 2009:72). Increased amounts of rain can lead to increased erosion and a loss of

nutrition in the soil (Deelstra, Øygarden, Blankenberg, & Eggestad, 2012).

The effects of climate change is expected to have different impacts in different places around the

country (O'Brien et al., 2006:51). In Trøndelag and Østlandet there are predicted increased amounts of

rain that can lead to difficulties considering sowing in the fall, working the soil and harvesting the

crops (Deelstra et al., 2012:52). In the coastal areas in the vest of Norway there are also predicted

increased amounts of precipitation, that can lead to deposition and sedimentation (O'Brien et al.,

2006:53). Some places there is predicted more rainfall in winter and spring and some places there will

be less rain (Höglind et al., 2009:72). Places where there will be less rain early in the year might be

exposed to drought on the grain crops in the spring. There weather might also become more

unpredictable (Tørresen, Netland, & Rafoss, 2009:76). Higher temperatures could give new

possibilities for growing more grain in the fall, but it can also lead to an increase in weeds and plant

diseases (Brodal, Abrahamsen, Elen, Hofgaard, & Netland, 2012; Tørresen et al., 2009).

Plant production might be positively affected by increased temperatures. There might be increased

possibilities for biomass production, increased amount of crops per season and longer grazing seasons,

especially in the northern parts of Norway (Rognli & Skrøppa, 2009). There might also be

opportunities for introducing new species that can’t be grown in Norway today because of the cold

climate (Rognli & Skrøppa, 2009:122).

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In a study of farmers in northern Norway they found that the farmers generally saw opportunities

considering the predicted effects of climate change (O'Brien et al., 2006; Rønning, 2011). The farmers

in this study was concerned with the possibilities that higher temperatures and longer growing seasons

could bring, like introducing new cultivars and species. The farmers did express some concerns

considering more frequent freeze-thaw cycles and wet soil in the fall that could cause winter damages

(O'Brien et al., 2006:33).

Examples of indirect effects of climate change are consequences on the agriculture in other countries

that in the next turn affects Norwegian farmers, in terms of increased food prizes or prizes on input

and output factors, like fertilizers. Crises in the agricultural sector in other countries could lead to an

increase in demand for Norwegian agricultural products. Other examples of indirect effects are

policies and regulations that is introduced to the agricultural sector as a response to the climate change

threat (Kvalvik et al., 2011; O'Brien et al., 2006). Kvalvik et al. (2011) points out that farmers are

more vulnerable to changes in agricultural politics than direct effects of climate change.

From what I’ve presented so far we can divide possible effects that climate change might have on

agriculture in direct and indirect, and positive (risks) and negative (opportunities) consequences, as

I’ve shown in Table 1:

Table 1. Risks and opportunities considering predictions of how climate change might affect Norwegian agriculture.

Direct effects of climate change Indirect effects of climate change

Risks

- Increased amounts of rain

- Unpredictable weather

- More frequent problems with insects, plant

diseases, fungus etc

- Higher prices on imported input

and output factors (grain feed,

fertilizers)

- More regulations and restrictions

Opportunities

- Longer growing seasons

- Possibilities of introducing new species

- Possibilities for increased biomass

production

- Higher demands for Norwegian

agricultural products

- Increased food prices

internationally

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Data and methods

The data I’m using in this analysis is the survey Trends in Norwegian agriculture (Trender i norsk

landbruk). The sample is drawn from the Norwegian producer register (Produsentregisteret), with

consists of all registered agricultural enterprises in Norway that has applied for production subsidies or

which is registered in other related registers (Produsentregisteret, 2012b). It is assumed that as good as

all relevant farms in practice is registered in the producer register (Logstein, 2010).

Table 2. Trends in Norwegian agriculture 2012.

Population (number of people in the producer register when the sample was drawn) 43953

Gross sample 3200

Final gross sample (after dismantled farms, deceased persons, etc. were excluded) 3142

Net sample 1641

Response rate 52 %

The dependent variable is the question “How do you think climate change will affect your farming

during the next 10 years?” First I’ll simply look at what the farmers answered to this question. Then

I’m using OLS-regressions to test what characterizes farmers who have different perspectives on how

their farms will be affected by climate change. I’m doing two different analyses, the first using

individual and social variables, and the second using characteristics of the respondents’ farm practices

to look at possible characteristics of who sees climate change as an opportunity to their farm practices

and who is more focused on the risks. The variable is coded so that it consists of a scale from “very

negatively” to “very positively”, with the categories “no consequence” and “I don’t know” as

“neutral” categories.

In the first regression analysis the independent variables are gender, age, education, income and

political orientation. These variables are some of the variables that are termed “the social bases of

environmental concern” (Hamilton, Colocousis, & Duncan, 2010; Jones & Dunlap, 1992; Sharp &

Adua, 2009; Van Liere & Dunlap, 1980)1. These are variables that are often considered to affect

people’s attitudes towards environmental questions. The reason I’m using these in this case is because

we can assume that people who is concerned about the environment is also concerned about climate

change.

In the second analysis I look at how the variables province, main production on the farm, farm income

and the size of the farm affects the dependent variable. These variables are included in the analysis

1 Social status, race and religiosity are also often tested in connection with “the social bases of environmental concern”.

6

because, as we’ve seen over, different farming conditions are predicted to meet different challenges

and opportunities when it comes to climate change.

For an overview of the variables used in this analysis, and how they are coded in the regression

analysis, see the descriptive statistics in the appendix.

Analysis/results

First I’m looking at how the farmers think that their farm production will be affected by climate

change during the next 10 years. From Figure 1 we see that most of the respondents think that they’ll

either be somewhat negatively affected by climate change, or that climate change will have no

consequences for their farming. 9,6 of the farmers think that they’ll be somewhat positively affected

by climate change. Few of the farmers think that climate change will affect that very positively or

negatively. 61 out of 1608 farmers think that they’ll be very negatively affected, while only 10 of the

farmers answered that they think they’ll be very positively affected. 14 percent answered that they

don’t know, and the variable has 33 missing values.

52,4 percent responds that they think climate change will affect their farming negatively or positively

to some degrees. From the scenarios presented over we can assume that the farmers who answered that

they think climate change will affect them negatively sees the risks that are connected to climate

change, such as direct effects like increased rain, plant diseases and vermin or indirect effects like

growth in prizes on feed concentrates and fertilizers. When it comes to the farmers who thinks climate

change will affect their farms positively they might see opportunities for direct effects like longer

growing seasons or the possibilities to grow new species that has not been possible to grow earlier in

Norway because of the low temperatures. When it comes to indirect effect the farmers might also

think that higher food crisis in other countries will lead to an increased demand for Norwegian

agricultural products.

7

3.8

38.433.6

9.6

0.6

14.0

How do you think climate change will affect your farm-ing during the next 10 years? (N 1608)

Perc

ent

Figure 1. The dependent variable.

Next I’ll take a look at how individual and social variables affects whether the farmers tend to see

climate change as a risk or an opportunity to their farm production. In Table 3 we see the model

development of the individual and social variables on the dependent variable. In Model 1 I start off

with the variables gender, age, income and education. Next, in model 2, I include variables that asks

whether the farmers have agricultural education from upper secondary school or university/college. In

Model 3 I include dummy variables for which political party the farmers would vote for if there were

an election. In Model 4 the political party-dummies that are not statistically significant on a 10 percent

level or less, and in Model 5 I exclude all variables that are not statistically significant.

If we look at the variable Age we see that it’s not statistically significant in the first model. After

putting the variables for agricultural education in the model, however, age becomes statistically

significant in a 10 percent level. Age has a positive correlation with the dependent variable, which tells

us that the older the respondents in this sample is, the more inclined they are to believe that climate

change will affect their farming in a positive way during the next 10 years.

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Table 3. OLS regression. Model development. Individual and social variables. Dependent variable: How do you think climate change will affect your farming during the next 10 years? (N 1376)

Model 1 Model 2 Model 3 Model 4 Model 5Constant 2,420*** 2,402*** 2,382*** 2,433*** 2,675***Woman (ref. man) -,120* -,113† -,117* -,119* -,138*Age ,003 ,003† ,003† ,003†Household income ,011 ,011 ,007 ,007Education ,013 ,003 ,007 -,002Agricultural education, upper secondary school

,057 ,068 ,061

Agricultural education, university/college ,088 ,106 ,100Political parties (ref. Centre Party) 1 1 1 1 1Progress Party (FrP) ,129Conservative Party (H) ,145* ,128† ,129†Christian Democratic Party (KrF) ,156† -,170† -,161†Liberal Party (V) ,105Norwegian Labor Party (Ap) ,009Socialist Left Party (SV) -,126The Party Red (R) -,239Other party ,011Not going to vote -,332* -,355* -,374**Not sure ,054R-squared ,007 ,009 ,023 ,019 ,014Adjusted r-squared ,004 ,004 ,011 ,012 ,012F-ratio 2,409* 2,022† 1,958* 2,928** 5,016**Sig. F Change ,048 ,288 ,040 ,658 ,282† significant coefficient value <0,1 * significant coefficient value < 0,05 ** significant coefficient value < 0,01 *** significant coefficient value <0,001

Since the adjusted r-squared doesn’t seem to get much higher even if we eliminate the variables that

are not statistically significant on a 10 percent lever or lower I’m keeping the model with all the

individual and social independent variables further in the analysis. In Table 3 we see a more

descriptive version of model 3 from the model development above.

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Table 4. OLS-regression. Model 3. Dependent variable: How do you think climate change will affect your farming during the next 10 years? (N 1376)

  Unstandardized B Std. Error Standardized Beta t Sig.

Constant 2,382 ,136 17,531 ,000Woman (ref. man) -,117 ,058 -,055 -1,999 ,046Age ,003 ,002 ,051 1,850 ,065Household income ,007 ,008 ,024 ,844 ,399Education ,007 ,030 ,007 ,234 ,815Agricultural education, upper secondary school ,068 ,043 ,045 1,585 ,113

Agricultural education, university/college ,106 ,081 ,040 1,304 ,192

Political parties (ref. Centre Party) 1 1 1 1 1

Progress Party (FrP) ,129 ,109 ,033 1,191 ,234Conservative Party (H) ,145 ,071 ,059 2,040 ,041Christian Democratic Party (KrF) -,156 ,092 -,047 -1,690 ,091Liberal party (V) ,105 ,114 ,025 ,927 ,354Norwegian Labor Party (Ap) ,009 ,073 ,004 ,127 ,899Socialist Left Party (SV) -,126 ,134 -,026 -,940 ,347The Party Red (R) -,239 ,247 -,026 -,969 ,333Other Party ,011 ,261 ,001 ,042 ,967Not going to vote -,332 ,145 -,063 -2,296 ,022Not sure ,054 ,055 ,028 ,969 ,333R-squared 0,023, adjusted r-squared 0,011, F 1,958, sig. 0,013.

In Table 4 we see that the women are more negative than the men when it comes to how they think

climate change will affect their farm. This variable is statistically significant on a 5 percent level.

When it comes to age we saw in the model development over that age was not statistically significant

in the first model, but became statistically significant on an 10 percent level when the variables for

agricultural education was introduced in the model.

Either the education variables or the income variable is statistically significant in this model, and the

coefficients are very low. Even if they are not statistically significant, we can observe that both the

education variables and the income variable are positively correlated to the dependent variables. This

could mean that higher education and higher income might lead to a higher optimism or a belief in the

opportunities that climate change can bring for some farmers.

When it comes to the political parties The Conservative Party and the dummy-variable “not going to

vote” are statistically significant on a 5 percent level. The farmers who vote for The Conservative

Party are more positive to how they will be affected by climate change that the farmers who vote for

The Centre Party (the reference category).

10

Norweg

ian Lab

or Pa

rty (A

p)

Progres

s Part

y (FrP)

Conser

vativ

e Part

y (H)

Christi

an Dem

ocrat

ic Pa

rty (K

rF)

The Part

y Red

(R)

The Cen

ter Part

y (Sp

)

Socia

list L

eft Pa

rty (S

V)

Liberal

party

(V)

Other P

arty

Not go

ing to

vote

Not sur

e

8.83.4

10.15.3

0.6

45.0

2.2 3.1 0.8 2.1

18.6

If there were elections tomorrow, which party would you vote for?

Perc

ent

Figure 2. What political parties the respondents would vote for in an election.

A problem with the variable for political orientation is that most farmers vote for The Center Party,

which is considered the farmers-party in Norway. It we look at Figure 2 below we see that 45 percent

of the farmers would vote for The Center Party, while 10 percent would vote for The Conservative

Party.

In the second regression analysis I’m looking at how the types of farms affect the farmers’ perceptions

on how they will be affected by climate change during the next 10 years. In Table 5 we see the model

development for these variables. In model 6 we have two control variables, age and household income.

In the next model province is included, then main production, farm income and at last three variables

that measure the size of the farm. We see that including province and main production improved the

model significantly, while farm income and farm size is not improving the model according to F-

change. Farm income and farm size is not improving the model according to adjusted r-squared and

none of these four variables are statistically significant on a 10 percent level or lower. Therefore I’m

keeping model 8, with province and main production variables for further analysis.

In Table 6 we have the same model as model 8 in Table 5, except that there are less missing values in

this model, since its run independently of the other models concerning farm characteristics. This

changes the coefficients a little in table 6 compared to Table 5. 86 missing values that are missing

from model 8 in Table 5, because of the high number of missing values in the farm income variable

and the farm size variables, are included in the model in Table 6.

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Table 5. OLS-regression. Model development. Farm characteristics. Dependent variable: How do you think climate change will affect your farming during the next 10 years? (N 1352)

Model 6 Model 7 Model 8 Model 9 Model 10

Constant 2,444*** 2,608*** 2,504***2,499**

*2,460***

Age ,003 ,003† ,003† ,003† ,004†Household income ,010 ,015† ,015† ,015† ,015†Province (ref. Trøndelag) 1 1 1 1 1Østlandet -,257*** -,254*** -,254*** -,256***Agder and Rogaland -,319*** -,339*** -,338*** -,321***Vestlandet -,375*** -,402*** -,399*** -,392***Nord-Norge ,103 ,072 ,073 ,073Main production on the farm (ref. grain production)

1 1 1 1 1

Milk production ,145* ,138* ,127†Animal farming ,082 ,080 ,071Other production ,248*** ,246*** ,237**Farm income ,002 ,001Areal owned by the farm -,024Areal operated by the farm ,018Forest owned by the farm ,025R-squared ,003 ,048 ,059 ,059 ,061Adjusted r-squared ,001 ,044 ,053 ,052 ,052

F-ratio 1,73411,367**

*9,351***

8,417***

6,692***

Sig. F Change ,177 ,000 ,002 ,082 ,418† significant coefficient value <0,1 * significant coefficient value < 0,05 ** significant coefficient value < 0,01 *** significant coefficient value <0,001

Table 6. Regression. Model 8. Dependent variable: How do you think climate change will affect your farming during the next 10 years? (N 1438)

  Unstandardized B Std. Error Standardized Beta t Sig.Constant 2,501 ,132 18,965 ,000Age ,003 ,002 ,044 1,659 ,097Household income ,018 ,008 ,064 2,363 ,018Province (ref. Trøndelag)Østlandet -,256 ,058 -,173 -4,379 ,000Agder and Rogaland -,374 ,075 -,166 -4,972 ,000Vestlandet -,396 ,065 -,227 -6,076 ,000Nord-Norge ,053 ,078 ,022 ,672 ,502Main production on the farm (ref. grain production)Milk production ,152 ,061 ,093 2,485 ,013

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Animal farming ,085 ,058 ,057 1,462 ,144Other farm productions ,240 ,066 ,118 3,633 ,000R-squared 0,058, adjusted r-squared 0,052, F 9,743, sig. 0,000.

In Table 6 we see that the household income variable, which is included as a control variable, has gone

from being significant on a 10 percent level in Table 5 to become significant on a 5 percent level in

Table 6. This implies that the missing variables in the model development that is included in the model

in Table 6 makes a difference concerning the income variable.

We see that farmers from Østlandet, Agder and Rogaland and Vestlandet are more negative

considering how they think climate change will affect their farm production the next 10 years than

farmers from Trøndelag. This variable is however positively correlated to the dependent variable,

something that can suggest that farmers from the northern parts of Norway are more positive that

farmers from southern countries. The variable for Northern Norway, however, is not statistically

significant and the coefficient is considerably lower than for the other provinces.

When it comes to main production on the farm, milk production, animal production and other

production are all more positive to how climate change will affect their farms that grain production.

Milk production is statistically significant on a 5 percent level and other productions are statistically

significant on a 0,1 percent level. Animal farming is not statistically significant in this case.

Discussion

As we have seen in the analysis over, it looks like Norwegian farmers more often see the risks

connected to climate change rather than the opportunities. We also saw that the variables concerning

the farms location and the main production affects whether the respondents think climate change will

affect them positively or negatively. Farmers from southern parts of Norway seems more negative

than farmers from the north and farmers who produce grain seems more negative than animal farmers,

something that corresponds to the climate change scenarios presented earlier in the paper.

One question we can ask ourselves is what the farmers in this survey think when they hear the term

climate change. In his article “Sociological ambivalence and climate change” Michael Carolan (2010)

looks at how people answer questions about global climate change in surveys. Carolan did some in-

depth personal interviews where he looked at the questions from the US Gallup poll, and had his

informants explain what they would have answered to the question, and then have them talk about

their answer. An example is when respondents in a survey is asked if they believe that the seriousness

of climate change is exaggerated by the news. Carolan found that the informants who thought the

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media was exaggerating the dangers of climate change did believe that there are changes in the climate

going on and they did believe that global climate change is anthropogenic. The reason they answered

the way they did on the question of the media’s portrayal of climate change was that they disagreed

with the rhetoric they consider the media to use, with the portrayal of a bleak future and the end of the

world scenarios (Carolan, 2010-315). Carolan states that believing that the media exaggerated the

seriousness of climate change is not the same as saying these people isn’t worried about climate

change themselves (Carolan, 2010:314). Another aspect that Carolan points out is the importance of

noticing how a question is asked. To continue on the example presented over, the respondents were

asked if they thought the media generally exaggerates the seriousness of climate change, not if the

media totally exaggerates climate change (Carolan, 2010:314).

Transferring Caronaln’s findings to my own case, we can say that we know many farmers think that

their farming practices won’t be affected by climate change during the next 10 years, but we don’t

know why they think that way. When we ask them if they think climate change will affect their

production on the farm during the next 10 years we really don’t know if they see climate change as

something global and far away or something that will affect their production directly on a local plan

by changes in the weather. Also, we don’t know if the time span of 10 years is too short. They might

think that climate change is something that will affect Norwegian agriculture, but in a far away future.

Looking at the farmers responses we do, however, see that climate change is something that is

meaningful for them to give a response to. 42,2 percent of the farmers responded that they think

climate change will affect them negatively during the next 10 years. 10,2 percent responded that they

think they will be positively affected by climate change. We don’t know what they mean by giving

these responses, but if the question did not make any sense to them, they could have answered “I don’t

know” or they could have chosen not to answer the question at all. However, only 14 percent of those

who answered this question responded that they don’t know, and the question has only 33 missing

values out of a total of 1641 respondents.

Based on these findings we could divide the dependent variable into four different variables where we

look at the differences between a local and a global aspect, and between short term and long term

perspective. We should, however, do interviews with farmers to look for more problems with the data

that we might have missed.

Carolan’s point is that there are a lot of quantitative literature on climate change, compared to

qualitative literature, even though qualitative research is important for finding out why we’re thinking

the way we are about climate change (Carolan, 2010:310). And this is, in my opinion the problem we

have with the analysis that I’ve been presenting here. We know in what way farmers thinks that their

farms will be affected by climate change, but we don’t know what the farmers defines as climate

change. Here it would be interesting to follow Carolan’s example and interview farmers, first having

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them choose between the categories presented in the questionnaire, and then have them explain to us

what exactly it is that they have been answering. Carolan points out that qualitative research is needed

in terms of understanding the context of how people think like they do (Carolan, 2010:310). Asking

what the respondents thought when answering the survey questions would help us understand the

quantitative data better, and give us a chance to improve the questionnaire for the next survey round.

References

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Appendix

Table A 1. Deskriptive statistics. The dependent variable and the individual and social independent variables.

Variable Categories N Min Max Mean Std. Deviation

How do you think climate change will affect your farming during the next 10 years?

1= Very negatively2= Somewhat negatively3= No consequence4= Somewhat positively5= Very positively

1608 1 5 2,649 ,730

Woman 0= Man1= Woman 1548 0 1 ,140 ,347

Age 1585 22 86 52,444 10,988

Household income

1= Under 99.999 kroner2= 100.999-199.999 kroner3= 200.000-299.999 kroner4= 300.000-399.999 kroner5= 400.000-499.999 kroner6= 500.000-599.999 kroner7= 600.000-699.999 kroner8= 700.000-799.999 kroner9= 800.000-899.999 kroner10= 900.000-999.999 kroner11= Over 1.000.000 kroner

1555 1 11 6,012 2,550

Education

1=Primary education2=Upper secondary school3=Higher education 1-4 years, 4=Higer education more that 4 years

1635 1 4 2,196 ,790

Agricultural education, upper secondary school 1616 0 1 ,376 ,485

Agricultural education, higher education 1616 0 1 ,080 ,271Political partiesProgress party (FrP) 1588 0 1 ,034 ,181Conservative Party (H) 1588 0 1 ,101 ,301Christian Democratic Party (KrF) 1588 0 1 ,053 ,224Liberal party (V) 1588 0 1 ,031 ,173Centre Party (Sp) (ref.) 1588 0 1 ,449 ,498Norwegian Labor Party (Ap) 1588 0 1 ,088 ,283Socialist Left Party (SV) 1588 0 1 ,022 ,147The Party Red (R) 1588 0 1 ,006 ,075Other party 1588 0 1 ,008 ,087Not going to vote 1588 0 1 ,021 ,145Not sure 1588 0 1 ,185 ,389

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Table A 2 Descriptiv statistics. Independent variables. Farm characteristics.

Variable Categories N Min Max Mean Std. DeviationProvince (dummies)Østlandet 1638 0 1 ,408 ,492Agder and Rogaland 1638 0 1 ,126 ,332Vestlandet 1638 0 1 ,225 ,417Trøndelag (ref.) 1638 0 1 ,142 ,349Nord-Norge 1638 0 1 ,098 ,298Main production on the farm (dummies)Milk production 1572 0 1 ,270 ,444Animal farming 1572 0 1 ,381 ,486Grain production 1572 0 1 ,199 ,399Other farm production 1572 0 1 ,149 ,357

Farm income

1= No income2=1-49.999 kroner3= 50.000-99.999 kroner4= 100.000-149.999 kroner5= 150.000-199.999 kroner6= 200.000-299.999 kroner7=300.000-399.999 kroner8= 400.000-499.999 kroner9= Over 500.000 kroner

1555 1 9 4,320 2,288

Areal owned by the farm

1= 0-9 acre2= 10-19 acre3= 20-49 acre4= 50-99 acre5= 100-249 acre6= 250-499 acre7= 500-999 acre8= Over 1000 acre

1585 1 8 4,584 1,094

Areal operated by the farm

1= 0-9 acre2= 10-19 acre3= 20-49 acre4= 50-99 acre5= 100-249 acre6= 250-499 acre7= 500-999 acre8= Over 1000 acre

1575 1 8 4,979 1,236

Forest areal owned by the farm

1= 0-99 acre2= 100-499 acre3= 500-999 acre4= 1000-4999 acre5= 5000-9999 acre6= Over 10.000 acre

1512 1 6 2,173 1,111

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