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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 Not for citation.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 References Almås, R., & Gjerdåker, B. (2004). Norwegian agricultural history. Trondheim: Tapir Academic Press. Beck, U. (1992[1986]). Risk society: towards a new modernity. London: Sage. Brodal, G., Abrahamsen, U., Elen, O., Hofgaard, I., & Netland, J. (2012). Redusert bruk av plantevernmidler - fortsatt mulig? In E. Fløistad & M. Günther (Eds.), FOKUS. Bioforskkonferansen 2012. Vol. 7, Nr. 2. (pp. 62-64). Ås: Bioforsk. Carolan, M. S. (2010). Sociological ambivalence and climate change. Local Environment, 15(4), 309321. Deelstra, J., Øygarden, L., Blankenberg, A.-G. B., & Eggestad, H.-O. (2012). Ekstremvær - avrenning fra jordbruksareal - behov for nye tiltak. In E. Fløistad & M. Günther (Eds.), FOKUS. Bioforsk-konferansen 2012. Vol. 7, Nr. 2. (pp. 52-54). Ås: Bioforsk. Faures, J. M., Bernardi, M., & Gommes, R. (2010). There Is No Such Thing as an Average: How Farmers Manage Uncertainty Related to Climate and Other Factors. International Journal of Water Resources Development, 26(4), 523-542. Giddens, A. (2009). The politics of climate change. Cambridge: Polity. Hamilton, L. C., Colocousis, C. R., & Duncan, C. M. (2010). Place effects on environmental views. Rural Sociology, 75(2), 326-347. Hanslin, H. M. (2009). Vinterbiologi i endret klima - begrenset lystilgang. In E. Fløistad & K. Munthe (Eds.), FOKUS. Bioforsk-konferansen 2009. Mat, vann og klima. Vol. 4, Nr. 2. (pp. 78-79). Ås: Bioforsk. Hanssen-Bauer, I., Hygen, H. O., & Skaugen, T. E. (2010). Climatic basis for vulnerability studies of the agricultural sector in selected municipalities in northern Norway. Rapport nr. 19/2010. Oslo: Norwegian Meteorological Institute (met.no). Höglind, M., Thorsen, S. M., Østrem, L., & Jørgensen, M. (2009). Hvordan vil endret klima påvirke overvintring og grasvekst i Norge? In E. Fløistad & K. Munthe (Eds.), FOKUS. Bioforskkonferansen 2009. Mat, vann og klima. Vol. 4, No. 2. (pp. 72-73). Ås: Bioforsk. Jones, R. E., & Dunlap, R. E. (1992). The Social Bases of Environmental Concern - Have They Changed Over Time? Rural Sociology, 57(1), 28-47. Knutsen, H. (Ed.). (2011). Utsyn over norsk landbruk. Tilstand og utviklingstrekk 2011. Oslo: Norsk institutt for landbruksøkonomisk forskning (NILF). Kvalvik, I., Dalmannsdottir, S., Dannevig, H., Hovelsrud, G., Rønning, L., & Uleberg, E. (2011). Climate change vulnerability and adaptive capacity in the agricultural sector in Northern Norway. Acta Agriculturae Scandinavica, Section B - Soil & Plant Science, 61(sup1), 27-37. Kvaløy, B., Finseraas, H., & Listhaug, O. (2012). The publics' concern for global warming: A crossnational study of 47 countries. Journal of Peace Research, 49(1), 11-22. Ladstein, T., & Skoglund, T. (2008). Utviklingen i norsk jordbruk 1950-2005. In A. L. Brathaug & T. Skoglund (Eds.), Norsk økonomi. Artikler med utgangspunkt i nasjonalregnskapet (pp. 41-46). Oslo-Kongsvinger: Statistisk sentralbyrå. Landbruks- og matdepartementet. (2011). Meld. St. 9 (2011-2012) Landbruks- og matpolitikken: velkommen til bords. Oslo: Departementenes servicesenter, Informasjonsforvaltning. Logstein, B. (2010). Trender i norsk landbruk 2010: frekvensrapport Rapport (Vol. 4/10, pp. XII, 55, 16 s.). Trondheim: Bygdeforskning. Lowe, T., Brown, K., Dessai, S., De França Doria, M., Haynes, K., & Vincent, K. (2006). Does tomorrow ever come? Disaster narrative and public perceptions of climate change. Public Understanding of Science, 15(4), 435-457. Meld.St.21. (2011-2012). Norsk klimapolitikk. Oslo: Miljøverndepartementet. Newton, T. (2007). Nature and sociology. London: Routledge. O'Brien, K., Eriksen, S., Sygna, L., & Naess, L. O. (2006). Questioning complacency: Climate change impacts, vulnerability, and adaptation in Norway. Ambio, 35(2), 50-56. O'Neill, S., & Nicholson-Cole, S. (2009). “Fear Won’t Do It”: Promoting Positive Engagement With Climate Change Through Visual and Iconic Representations. Science Communication. Produsentregisteret. (2012a) Retrieved 07.08.2012, from https://www.prodreg.no/default.cfm?obj=menu&act=displayMenu&men=40&sub=40. 15 Produsentregisteret. (2012b). Home page. Retrieved 07.08.2012, from https://www.prodreg.no/. Rafoss, T. (2009). Økning i vekstsesongen de siste 20 åra basert på jordtemperatur. In E. Fløistad & K. munthe (Eds.), FOKUS. Bioforsk-konferansen 2009. Mat, vann og klima. Vol.4, Nr. 2. (pp. 7475). Ås: Bioforsk. Rognli, O. A., & Skrøppa, T. (2009). Genetiske ressurser under endret klima - hvordan klarer plantene seg? In E. Fløistad & K. Munthe (Eds.), FOKUS. Bioforsk-konferansen 2009. Mat, vann og klima. Vol. 4, Nr. 2. (pp. 122-123). Ås: Bioforsk. Rønning, L. (2011). Klimatisering av landbrukspolitikken. Notat nr. 1009/2011. Bodø: Norlandsforskning. Sharp, J., & Adua, L. (2009). The Social Basis of Agro-Environmental Concern: Physical versus Social Proximity. Rural Sociology, 74(1), 56-85. SSB. (2010). Dette er Norge 2009. Hva tallene forteller. Revidert utgave 2009. Kongsvinger: Statistisk sentralbyrå. SSB. (2012). 1 100 færre jordbruksbedrifter Retrieved 27.02.2012, from http://www.ssb.no/10/04/10/stjord/. St.meld.nr. 39. (2008-2009). Klimautfordringene - landbruket en del av løsningen. Oslo: Landbruksog matdepartementet. Tørresen, K. S., Netland, J., & Rafoss, T. (2009). Ugrassituasjonen ved endret klima og mer høstkorndyrking. In E. Fløistad & K. Munthe (Eds.), FOKUS. Bioforsk-konferansen 2009. Mat, vann og klima. Vol 4, Nr. 2. (pp. 76-77). Ås: Bioforsk. Van Liere, K. D., & Dunlap, R. E. (1980). The social bases of environmental concern: A review of hypotheses, explanations and empirical evidence. Public Opinion Quarterly, 44(2), 181-197. Øygarden, L. (2009). Landbrukets klimautfordringer - hva kan forskningen bidra med? In E. Fløistad & K. Munthe (Eds.), FOKUS. Bioforskkonferansen 2009. Mat, vann og klima. Vol 4, Nr. 2. (pp. 70-71). Ås: Bioforsk. 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