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Climate change and future prospects of Norwegian farmers:
Do farmers see opportunities or risks?
Brita Aasprang
Student at Department of Sociology and Political Science, NTNU
[email protected]
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 Not for citation.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
1
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).
2
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
3
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).
4
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
Risks
-
Increased amounts of rain
-
Unpredictable weather
-
More frequent problems with insects, plant
-
diseases, fungus etc
Opportunities
Indirect effects of climate change
-
Longer growing seasons
-
Possibilities of introducing new species
-
Possibilities for increased biomass
production
5
Higher prices on imported input and
output factors (grain feed, fertilizers)
-
More regulations and restrictions
-
Higher demands for Norwegian
agricultural products
-
Increased food prices internationally
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
3200
Gross sample
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
Social status, race and religiosity are also often tested in connection with “the social bases of environmental
concern”.
1
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
How do you think climate change will affect your
farming during the next 10 years? (N 1608)
38.4
Percent
33.6
14.0
9.6
3.8
Very
negatively
0.6
Somewhat
No
Somewhat
negatively consequence positively
Very
I don't know
positively
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.
8
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)
Constant
Woman (ref. man)
Model 1
Model 2
Model 3
Model 4
Model 5
2,420***
2,402***
2,382***
2,433***
2,675***
-,138*
-,120*
-,113†
-,117*
-,119*
Age
,003
,003†
,003†
,003†
Household income
,011
,011
,007
,007
Education
,013
,003
,007
-,002
Agricultural education, upper secondary school
,057
,068
,061
Agricultural education, university/college
,088
,106
,100
1
1
Political parties (ref. Centre Party)
1
1
1
Progress Party (FrP)
,129
Conservative Party (H)
,145*
,128†
,129†
Christian Democratic Party (KrF)
,156†
-,170†
-,161†
Liberal Party (V)
,105
Norwegian Labor Party (Ap)
,009
Socialist Left Party (SV)
-,126
The Party Red (R)
-,239
Other party
,011
-,355*
-,374**
Not going to vote
-,332*
Not sure
,054
R-squared
,007
,009
,023
,019
,014
Adjusted r-squared
,004
,004
,011
,012
,012
2,409*
2,022†
1,958*
2,928**
5,016**
,048
,288
,040
,658
,282
F-ratio
Sig. F Change
† 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.
9
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
Constant
2,382
,136
Woman (ref. man)
-,117
,058
Age
,003
Household income
,007
Education
Agricultural education, upper
secondary school
Agricultural education,
university/college
Political parties (ref. Centre Party)
Progress Party (FrP)
t
Sig.
17,531
,000
-,055
-1,999
,046
,002
,051
1,850
,065
,008
,024
,844
,399
,007
,030
,007
,234
,815
,068
,043
,045
1,585
,113
,106
,081
,040
1,304
,192
1
1
1
1
1
,129
,109
,033
1,191
,234
Conservative Party (H)
,145
,071
,059
2,040
,041
Christian Democratic Party (KrF)
-,156
,092
-,047
-1,690
,091
Liberal party (V)
,105
,114
,025
,927
,354
Norwegian Labor Party (Ap)
,009
,073
,004
,127
,899
Socialist Left Party (SV)
-,126
,134
-,026
-,940
,347
The Party Red (R)
-,239
,247
-,026
-,969
,333
Other Party
,011
,261
,001
,042
,967
Not going to vote
-,332
,145
-,063
-2,296
,022
,055
,028
,969
,333
Not sure
,054
R-squared 0,023, adjusted r-squared 0,011, F 1,958, sig. 0,013.
Standardized Beta
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
If there were elections tomorrow, which party would you vote for?
Percent
45.0
18.6
10.1
8.8
3.4
5.3
2.2
0.6
3.1
0.8
2.1
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 Fchange. 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.
11
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
2,444***
2,608***
2,504***
2,499***
2,460***
Age
,003
,003†
,003†
,003†
,004†
Household income
,010
,015†
,015†
,015†
,015†
Constant
Province (ref. Trøndelag)
1
1
1
1
Østlandet
1
-,257***
-,254***
-,254***
-,256***
Agder and Rogaland
-,319***
-,339***
-,338***
-,321***
Vestlandet
-,375***
-,402***
-,399***
-,392***
,103
,072
,073
,073
1
1
1
1
Milk production
,145*
,138*
,127†
Animal farming
,082
,080
,071
,248***
,246***
,237**
,002
,001
Nord-Norge
Main production on the farm (ref. grain production)
1
Other production
Farm income
Areal owned by the farm
-,024
Areal operated by the farm
,018
Forest owned by the farm
,025
R-squared
,003
,048
,059
,059
,061
Adjusted r-squared
,001
,044
,053
,052
,052
F-ratio
1,734
11,367***
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
Constant
2,501
,132
Standardized Beta
t
Sig.
Age
,003
,002
,044
1,659
,097
Household income
,018
,008
,064
2,363
,018
Østlandet
-,256
,058
-,173
-4,379
,000
Agder and Rogaland
-,374
,075
-,166
-4,972
,000
Vestlandet
-,396
,065
-,227
-6,076
,000
Nord-Norge
Main production on the farm (ref. grain
production)
Milk production
,053
,078
,022
,672
,502
,152
,061
,093
2,485
,013
Animal farming
,085
,058
,057
1,462
,144
Other farm productions
,240
R-squared 0,058, adjusted r-squared 0,052, F 9,743, sig. 0,000.
,066
,118
3,633
,000
18,965 ,000
Province (ref. Trøndelag)
12
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 indepth 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
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
13
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
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.
14
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16
Appendix
Table A 1. Deskriptive statistics. The dependent variable and the individual and social independent variables.
Variable
Categories
N
1= Very negatively
How do you think climate change will affect 2= Somewhat negatively
3= No consequence
your farming during the next 10 years?
4= Somewhat positively
5= Very positively
0= Man
Woman
1= Woman
Age
1= Under 99.999 kroner
2= 100.999-199.999 kroner
3= 200.000-299.999 kroner
4= 300.000-399.999 kroner
5= 400.000-499.999 kroner
Household income
6= 500.000-599.999 kroner
7= 600.000-699.999 kroner
8= 700.000-799.999 kroner
9= 800.000-899.999 kroner
10= 900.000-999.999 kroner
11= Over 1.000.000 kroner
1=Primary education
2=Upper secondary school
Education
3=Higher education 1-4 years,
4=Higer education more that 4 years
Agricultural education, upper secondary
school
Agricultural education, higher education
Min Max
Mean
Std.
Deviation
1608
1
5
2,649
,730
1548
0
1
,140
,347
1585
22
86
52,444
10,988
1555
1
11
6,012
2,550
1635
1
4
2,196
,790
1616
0
1
,376
,485
1616
0
1
,080
,271
Progress party (FrP)
1588
0
1
,034
,181
Conservative Party (H)
1588
0
1
,101
,301
Christian Democratic Party (KrF)
1588
0
1
,053
,224
Liberal party (V)
1588
0
1
,031
,173
Centre Party (Sp) (ref.)
1588
0
1
,449
,498
Norwegian Labor Party (Ap)
1588
0
1
,088
,283
Socialist Left Party (SV)
1588
0
1
,022
,147
The Party Red (R)
1588
0
1
,006
,075
Other party
1588
0
1
,008
,087
Not going to vote
1588
0
1
,021
,145
Not sure
1588
0
1
,185
,389
Political parties
17
Table A 2 Descriptiv statistics. Independent variables. Farm characteristics.
Variable
Categories
N
Min Max
Mean
Std. Deviation
Province (dummies)
Østlandet
1638
0
1
,408
,492
Agder and Rogaland
1638
0
1
,126
,332
Vestlandet
1638
0
1
,225
,417
Trøndelag (ref.)
1638
0
1
,142
,349
Nord-Norge
1638
0
1
,098
,298
Milk production
1572
0
1
,270
,444
Animal farming
1572
0
1
,381
,486
Grain production
1572
0
1
,199
,399
Other farm production
1572
0
1
,149
,357
1555
1
9
4,320
2,288
1585
1
8
4,584
1,094
1575
1
8
4,979
1,236
1512
1
6
2,173
1,111
Main production on the farm (dummies)
Farm income
Areal owned by the farm
Areal operated by the farm
Forest areal owned by the farm
1= No income
2=1-49.999 kroner
3= 50.000-99.999 kroner
4= 100.000-149.999 kroner
5= 150.000-199.999 kroner
6= 200.000-299.999 kroner
7=300.000-399.999 kroner
8= 400.000-499.999 kroner
9= Over 500.000 kroner
1= 0-9 acre
2= 10-19 acre
3= 20-49 acre
4= 50-99 acre
5= 100-249 acre
6= 250-499 acre
7= 500-999 acre
8= Over 1000 acre
1= 0-9 acre
2= 10-19 acre
3= 20-49 acre
4= 50-99 acre
5= 100-249 acre
6= 250-499 acre
7= 500-999 acre
8= Over 1000 acre
1= 0-99 acre
2= 100-499 acre
3= 500-999 acre
4= 1000-4999 acre
5= 5000-9999 acre
6= Over 10.000 acre
18