Dependent variables
Actual and potential adaptation adoption
Average potential adoption across all six practices was 2.46 (very unlikely = 1, very likely = 5, Table 1). Among non-adopters, building water storage facilities was the most likely adaptation strategy (48 % likely or very likely). Other strategies were less popular with water monitoring technologies (38 %), drilling more wells or seeking alternative water sources (31 %) and pumping more groundwater (28 %) being the most likely strategies among non-adopters. Among already adopting farmers, water monitoring technologies were the most frequent (35 % implementation). Other practices were less common with participation in a community irrigation scheme (14 %) and water storage facilities (10 %) being the most adopted practices (Fig. 1).
Actual and potential mitigation adoption
On average, likely adoption of mitigation behaviors was 2.75 (Table 1). However, in some instances a majority of non-adopting farmers indicated they would be likely or very likely to implement mitigation behaviors especially installing solar panels and wind turbines (52 %) and investing in more fuel efficient farm equipment (50 %). One-third of farmers have already planted trees on low performing areas and 29 % have reduced their nitrogen fertilizer rate. On the contrary, only 2 % are using biomass or biofuels on farm and only 8 % have installed solar panels or wind turbines (Fig. 1).
Independent variables
Climate change concern
Farmers trended slightly towards belief that humans contributed to climate change (mean = 3.21, 1 = strongly disagree, 5 = strongly agree) with 44 % agreeing or strongly agreeing that humans play a role in climate change (Table 1).
Biophysical concerns
Farmers were on average somewhat concerned about potential climate-related risks (mean 2.46, 1 = not concerned, 4 = very concerned). Overall, farmers were most concerned about pests and diseases (mean 3.11) and weeds/invasive species (mean 2.74). Water supply changes and increases in severe droughts were both concerning (mean 2.68) followed by changes in rainfall (mean 2.60) and increase in floods (mean 2.41). On the contrary, temperature impacts were less concerning with heat waves being most concerning (mean 2.32), frosts (mean 2.13), increases in warmer temperatures (mean 2.11) and finally decreases in winter chill hours (mean 1.91) (Table 1).
Environmental policy concerns
Overall farmers tended to disagree that New Zealand should have an emissions trading scheme, that agriculture should be part of it and that environmental regulations are effective (mean 2.44, 1 strongly disagree, 5 strongly agree). Farmers were in most disagreement about an ETS for New Zealand (mean 2.03) followed by agriculture’s inclusion within an ETS (mean 2.15) and were on average neutral about whether environmental regulations were effective (mean 3.06) (Table 1).
Local and global perceived capacity
Farmers overall (1 not confident, 5 very confident) have confidence in their ability to adapt to climate change (Global Perceived Capacity, mean = 3.92). Nearly 73 % of farmers indicated that they were confident or very confident. On average farmers were less confident in their perceived capacity to reduce their GHGs (Local Perceived Capacity, mean = 3.24) (Table 1). Only 50 % of farmers agreed or strongly agreed that they could reduce their GHGs.
Profit and community adoption
On average, profit and cost related factors were of higher concern to farmers than community and other related factors (mean 4.16 and 3.86 respectively on a scale from 1, not important to 5, very important). Average concerns were cost (mean 4.49), farm productivity (mean 4.33), community impact (mean 3.89), environmental impact (mean 3.86), ethical concerns (mean 3.82) and time (mean 3.66) (Table 1).
Contact scale
Farmers had a mean of 2.33 on a scale from 1 (never) to 5 (weekly) of contact about agricultural information. Other farmers were the most likely point of contact for agricultural information (mean 4.23) followed by supply representatives (mean 3.72), agricultural industry representatives (mean 2.43), the Regional Council (mean 2.40), farm consultants (mean 1.90), research organizations (mean 1.78) and Federated Farmers (a New Zealand farmer group, mean 1.73) (Table 1).
Farmer characteristics
Eighty percent of respondents were older than 45 and 50 % of farmers were over the age of 55. The majority of respondents were male (82 %). Farmers had a diversity of formal education- 19 % had a high school diploma while an additional 13 % had some university or apprenticeship training. Thirty-one percent of farmers had a degree from university, technical school or an apprenticeship. The majority (74 %) of farmers were full-time.
Farm characteristics
Sixty-eight percent of respondents had acreage in sheep, beef or deer. Twenty nine percent of farmers had acreage in viticulture. Horticulture were 11 % of respondents while cropping/arable was 8 % of farmers, dairy was 7 % and forestry was 3 % of farm respondents. Since farmers were able to indicate if they had mixed systems, these numbers are not exclusive land types. Average farm size was 427 ha. The majority (52 %) of farmers had some form of a farm succession plan. Seven percent of farms were certified organic or biodynamic.
Model results
Table 2 shows all of the results for our four separate models, which we highlight in additional detail below.
Table 2 Factors affecting likely and actual adoption of climate practices
Likely adoption: adaptation
Attitudes including belief in human-induced climate change (p ≤ 0.05) and concern for biophysical impacts in the future (p ≤ 0.01) were significantly correlated with likelihood to adopt adaptation practices (H1). We found no effect of norms on likely adaptation adoption (H2). Local perceived capacity (H3) to reduce emissions was positively associated with increased adoption (p ≤ 0.05). Likely adaptation adoption was influenced by farmers who were also likely to adopt mitigation practices (H4) (p ≤ 0.01). Higher contact with organizations (p ≤ 0.10) and older farmers were less likely to indicate they would adopt adaptation behaviors (p ≤ 0.05). Model variables explained 32 % of the variance in the dependent variable (R2 = 0.32).
Likely adoption: mitigation
Attitudes were associated with likely mitigation adoption with belief in human-caused climate change (p ≤ 0.05) and concern for biophysical impacts (p ≤ 0.10) positively associated (H1). We found no effect of norms on likely mitigation adoption (H2). Local perceived capacity to reduce emissions (p ≤ 0.01) was significantly associated with likely mitigation adoption (H3). Farmers who indicated likely adaptation practice adoption were more likely to indicate their willingness to adopt mitigation practices in the future (p ≤ 0.01). However, farmers who had actually adopted mitigation practices were associated with less likelihood to adopt other mitigation practices in the future (p ≤ 0.01) (H4). In addition, Community Adopt was also significant (p ≤ 0.01), suggesting that farmers who valued community, ethical or environmental issues when considering new practices were more likely to indicate mitigation adoptions. Variables in the model explained 37 % of variance (R2 = 0.37).
Actual adoption: adaptation
We found no effect of belief/attitudes for climate change and its risks on adoption (H1). Farmers who had a favorable perception of environmental regulations was positively associated with adaptation behaviors (p ≤ 0.05) (H2). Global perceived capacity was positively associated with actual adaptation adoption (p ≤ 0.05) (H3). Actual adoption of adaptation behaviors was not influenced by likely adopters (in other words stated intention to adopt adaptation practices was not associated with actual adoption of adaptation practices). However, actual adopters of mitigation behaviors was significant (p ≤ 0.01), suggesting that farmers who have already changed behavior in one realm (adaptation) are more likely to also do so in other realms (mitigation) (H4). Level of contact was positively associated with actual adaptation adoption (p ≤ 0.05), Profit Adopt was slightly significant (p ≤ 0.10) and full-time farmers were less likely to have adopted adaptation behaviors (p ≤ 0.05). Model variables explained 12 % of the variance in the dependent variable (R2 = 0.12).
Actual adoption: mitigation
We found that belief/attitudes as well as environmental policy perceptions/norms were not significantly associated with actual mitigation behavior adoption (H1, H2). Both Global Perceived Capacity and Local Perceived Capacity were significantly associated with mitigation adoption (p ≤ 0.05) (H3). Farmers who had indicated that they would likely adopt mitigation practices was negatively associated with actual adopters of mitigation practices (p ≤ 0.01). On the contrary, those who had already adopted adaptation practices were also more likely to have adopted mitigation practices (p ≤ 0.01) (H4). Older farmers, women farmers (p ≤ 0.10), education and organic farmers (p ≤ 0.05) were positively associated with adoption. Overall, model variables explained 18 % of the variance (R2 = 0.18).