AGRICULTURAL AND FOOD SCIENCE Agricultural and Food Science (2025) 34: 12–37 12 https://doi.org/10.23986/afsci.146997 Factors explaining the differences in the adoption of circular economy measures among farms in Southwest Finland Antti Hynni1, Jukka Käyhkö1 and Tuomas Kuhmonen2 1Department of Geography and Geology, Faculty of Mathematics and Natural Sciences, University of Turku, FI-20014 Turun yliopisto 2Finland Futures Research Centre, Turku School of Economics, University of Turku, FI-20014 Turun yliopisto e-mail: anhehy@utu.fi Transitioning to a circular economy is essential for addressing the inefficiencies and environmental impacts of tra- ditional agricultural practices that rely on synthetic fertilizers and fossil resources. These conventional methods de- grade soil, increase greenhouse gas emissions, and pollute waterways through nutrient runoff. A circular economy enhances sustainability by minimizing waste, optimizing resource use, and recycling nutrients. This study analyzes the adoption of circular economy practices among farms in Southwest Finland and identifies influencing factors. A survey distributed to farmers measured the extent of circular practices and the challenges faced. Cross-tabulations and logistic regression analyses evaluated how farm characteristics and farmer attitudes impact implementation. Results show a slow and variable transition among farms. About 50% of respondents have reduced mineral ferti- lizer use, although trading in byproducts and especially on-farm energy production from byproducts remain un- common. Many farmers highlight insufficient policy support. The study concludes that policy interventions, train- ing, and education are essential to accelerate adoption and stresses the importance of tailored, regional decision- making for effective policy development. Key words: agriculture, byproducts, fertilization, nutrient management, statistical analysis Introduction: agriculture and circular economy Modern food production faces significant sustainability challenges. Agriculture relies heavily on non-renewable resources, such as fossil fuels and mineral fertilizers, which have altered the global nutrient cycles of nitrogen and phosphorus. This has led to nutrient buildup on land, runoff into waterways, and greenhouse gas emis- sions (Vitousek et al. 1997, Rockström et al. 2009, Elser and Bennett 2011). Additionally, food systems are often inefficient, assuming that natural resources are infinite and that the environment can indefinitely absorb waste (Marín-Beltrán et al. 2022). Soil quality degradation is another global issue, exacerbated by industrial farming practices and climate change (Sofo et al. 2022). Heavy use of synthetic fertilizers reduces soil organic matter and carbon content, while mono- culture farming decreases biodiversity and microbial life. Water management practices, such as excessive irrigation and poor drainage, often result in either water scarcity or surplus, both of which can harm crops and soil health (Magdoff and Van Es 2021). Climate change further complicates agriculture, making weather patterns unpredict- able and increasing the frequency of extreme weather events (Masson-Delmotte et al. 2021). In Finland, despite high food self-sufficiency – where only 20% of food relies on imports (Huan-Niemi et al. 2021) – agriculture is still heavily dependent on imported fertilizers, feed, and energy. Recent geopolitical conflicts and the COVID-19 pandemic have highlighted the vulnerability of this dependency (OSF 2022b, OSF 2023b, Rimhanen et al. 2023). Moreover, the reliance on inorganic fertilizers and fossil fuels has led to agriculture contributing about 12% of Finland’s total greenhouse gas emissions (OSF 2022a). Thus, Finnish agriculture is both influenced by external factors and a significant contributor to the climate crisis. A proposed solution to these sustainability challenges is transitioning to a circular economy (CE). Traditional lin- ear economic models – characterized by a take-make-dispose approach – are prevalent in food production. This model disrupts the natural cycles of critical nutrients like nitrogen and phosphorus, primarily due to the use of in- organic fertilizers (Rockström et al. 2009, Richardson et al. 2023). CE in agriculture offers opportunities to conserve resources and reduce emissions (Rodias et al. 2021). For instance, farms can use processed animal and human waste as fertilizers, facilitating the transfer of nutrients from surplus to deficit areas (Spiegal et al. 2020). Effective Received 17 July 2024 / Accepted 14 January 2025 The Scientific Agricultural Society of Finland ©This is an open access article under the CC BY 4.0 A. Hynni et al. 13 nutrient management can also be achieved through diverse crop rotations, maintaining continuous vegetation cover, enhancing soil health, and managing water efficiently (McDaniel et al. 2014, Magdoff and Van Es 2021). Addition- ally, farm machinery can be powered by biogas, produced from food production byproducts (Metson et al. 2022). To address the pressing sustainability challenges in agriculture, our research on implementing CE practices provides essential insights that bridge theoretical frameworks with practical applications. By focusing on how CE measures can be effectively adopted at the farm level, particularly in Southwest Finland, this study offers an evidence-based approach to transforming the circular economy from concept to practice in the agricultural sector. The findings contribute data for policymakers, farmers, and stakeholders to scale CE practices regionally, reinforcing the path- way toward a more resilient, low-waste agricultural system aligned with global sustainability goals. Our research focuses on the practical application of CE measures within agriculture. It critically examines these measures, acknowledging that CE is not a panacea for all sustainability challenges (Åkerman et al. 2020). Further- more, the study considers how agricultural policies influence the adoption of CE practices and the development of closed-loop systems at various scales. The study investigates the adoption of CE practices among farms in Southwest Finland. It aims to analyze the dif- ferences in conditions that affect CE practice adoption among these farms. Initially, the study evaluates the cur- rent level of CE adoption in the region (RQ1). Subsequently, it explores the factors related to farmers and farm characteristics that predict CE adoption (RQ2). To address these questions, we present results from a survey conducted among farmers in Southwest Finland. By focusing on a specific region, we can analyze CE and agriculture on a scale often overlooked in research (Kop- pelmäki et al. 2021). This includes presenting the frequencies of responses to survey questions about barriers to CE adoption. The study further uses cross-tabulations and logistic regressions to understand why some farms are more equipped to implement CE practices than others. This statistical analysis helps identify the reasons behind the differences in farming conditions and readiness to adopt CE operations. Materials and methods Survey We conducted an online survey targeting primary agricultural entrepreneurs in Southwest Finland, who formed the population of the study. Southwest Finland, the country’s third most populous province, is a crucial area for food production, contributing significantly to the national grain harvest and housing many pig and poultry farms (OSF 2023a, OSF 2023c). The region benefits from an extended growing season, consistent rainfall, and nutrient- rich soils, favourable for diverse crop cultivation (Kersalo and Pirinen 2009, Peltonen-Sainio et al. 2019). Located along the Baltic Sea, the region’s rural landscape includes a long coastline and the extensive Turku Archi- pelago, with approximately 20 000 islands (Fig. 1). Agricultural runoff can quickly reach the marine environment, impacting the Baltic Sea, a shallow inland sea particularly susceptible to such disturbances. The Archipelago Sea, a pollution hotspot mainly due to agricultural runoff, is a focus for Finland’s commitment to remove it from HEL- COM’s (2020) list by 2027 (Laurila et al. 2022). Before the actual collection of survey responses, we piloted the questionnaire with five test respondents who at that time were agricultural entrepreneurs in Southwest Finland. We received their contact information from the Central Union of Agricultural Producers and Forest Owners. Based on their comments, we further improved the questionnaire. The contact information of the actual survey respondents was provided to us by the Finnish Food Authority. We limited the sample to those farmers in Southwest Finland who had applied for agricultural subsi- dies in 2022. We conducted the survey using the Webropol survey system and sent a link to the survey by email. The sur- vey was available from November 30 to December 14, 2022, which we estimated – based on our previous ex- perience – would be long enough for participants to respond and short enough so that time would not become a determining factor in response. We sent a reminder email two days before the questionnaire closed to improve the survey response rate. Agricultural and Food Science (2025) 34: 12–37 14 We aimed the survey to identify barriers and factors affecting the adoption of CE practices at the farm level. CE practices here include replacing mineral fertilizers with organic ones, utilizing production side streams, and im- proving nutrient management. We also asked respondents to define CE and assess how current agricultural poli- cies support CE transitions. We formulated the questions to be accessible and non-leading. This study focuses on the quantitative results, while qualitative aspects are explored in another study. The survey initially began with ten background questions providing insights into farmers’ engagement with CE practices and allowing a comparison between the farms. We categorized respondents by age and the farms by the total arable area. Farms were also grouped into locational categories based on their postal code and by using the urban-rural classification (Fig. 1) from the Finnish Environment Institute (2020). All closed-ended questions in the survey were mandatory. Conducted during a time of heightened geopolitical tensions and fluctuating energy markets due to the conflict in Ukraine, the survey addressed changes in the use of mineral fertilizers over five-year periods. We also acknowl- edged that specific examples in the nutrient management section might have influenced responses. See Appendix 7 for the complete survey questionnaire. Statistical analyses Cross-tabulation We analyzed the survey data using IBM® SPSS® software, starting with cross-tabulation to explore pairwise asso- ciations between variables. Cross-tabulation provides chi-square (χ2) values which tell us if there is a statistically significant association between the two variables and how strong it is. To interpret the chi-square value, it is essential to examine the p-value, which indicates whether the observed re- lationship is likely attributable to chance or is statistically significant. A smaller p-value (typically less than 0.05) suggests that the relationship between the variables is unlikely to result from random variation, providing greater confidence in the existence of a genuine association. In other words, a low p-value supports the conclusion that a meaningful link exists between the variables under investigation. We constructed a total of 74 cross-tabulations based on seven response variables and eleven explanatory vari- ables (Appendix 2). Investment in enhancing nutrient management and view on the current state of agricultural Fig. 1. The province of Southwest Finland divided by the 2018 urban-rural classification and showing the total number of farms by municipality in 2022 (circles). Index map source: Fenn-O-Manic/ Wikimedia Commons. A. Hynni et al. 15 policy serve as both response variables and explanatory variables. Based on the cross-tabulations, we can claim whether the characteristics of farmers and their farms have a statistically significant association with whether they implement circular economy practices or not and whether they see current agricultural policy as supporting the circular economy transition or not. Following the examination of these associations individually, their combined effects can be analyzed through logistic regression. Logistic regression Following cross-tabulations, we applied logistic regression analysis. This technique extends linear regression to explain the occurrence of events through variations in multiple explanatory variables (Domínguez-Almendros et al. 2011, James et al. 2021). In our analysis, response variables represented CE practices, while explanatory vari- ables described farm or respondent characteristics. This method helps identify whether individual background factors influence the adoption of CE practices. Logistic regression is suitable for categorical variables, estimating the probability of a specific outcome relative to all other outcomes. This probability is described by the regres- sion coefficient B, which tells us how much a one-unit change in the value of the explanatory variable changes the value of the response variable. More interesting than coefficients in terms of interpretation is the odds ratio (OR), which is obtained by raising the regression coefficient B to the power of e (Napier’s constant; Burgess 2013). An OR compares the odds of an out- come happening with and without a specific factor. If an OR is greater than 1, it suggests that the factor increases the likelihood of the outcome, and, conversely, an OR below 1 suggests the factor decreases the likelihood of the outcome. An OR of exactly 1 means the factor has no effect on the likelihood of the outcome. We conducted logistic regression for seven response variables (Appendix 2). Binary logistic regression was used for variables with two possible outcomes (Domínguez-Almendros et al. 2011, James et al. 2021). For the analyses, a No response was coded as 0 and a Yes response as 1. In this way, we were able to model the probability that a survey respondent would not implement a circular economy practice based on their own or their farm’s characteristics. Multinomial logistic regression was applied to variables with three or more outcomes (Domínguez-Almendros et al. 2011, James et al. 2021). For analyses, outcomes were coded as numerals. Using multinomial logistic regres- sion, we modelled the probability that a respondent would not implement a circular economy practice relative to all forms of practice implementation based on the background characteristics. Ordinal logistic regression was used for Likert scale variables (O’Connell 2006). It is possible to analyze the vari- ation of a Likert-scale variable using logistic regression analysis if the response options are coded as numerals in numerical order (1–5). Thus, the probability of a respondent choosing a certain answer option is modelled based on background variables. In the case of our research, we structured the Likert-scale questions so that they meas- ured the direction and degree of the respondent’s action: is the farm moving closer to or further away from cir- cular economy operations, and if so, to what extent? Each analysis included all nine explanatory variables (entry method; Appendix 2). Models with high p-values were not automatically rejected; individual variable p-values were also considered. Generally, high p-values weaken the model’s explanatory power. Key indicators in logistic regression include the p-value, ORs, and the Pseudo R-squared value (R²). R2 is a coef- ficient of determination that estimates how much of the observed variation in a variable a logistic regression model is able to explain (Hemmert et al. 2018). It is very commonly used in empirical studies to assess the overall acceptability of the model. In simplified terms, higher R² values indicate a better fit to the data. We reported Cox & Snell R2, Nagelkerke R2, and McFadden R2 values to enhance model credibility, except for binary models where McFadden R2 is not output in SPSS®. We also report the regression coefficients and 95% confidence intervals for each model. The confidence inter- vals better capture the degree of uncertainty related to the regression models and their interpretation than the p-values alone. Again, in our analysis, the response variables represent CE practices and the explanatory variables represent farmer and farm characteristics, but unlike cross-tabulations, we entered all explanatory variables into each model simul- taneously. This allowed us to identify situations where two or more explanatory variables together contributed to the values obtained by the response variable. Agricultural and Food Science (2025) 34: 12–37 16 Use of artificial intelligence To refine this article, we used OpenAI’s ChatGPT model to enhance clarity and conciseness, particularly in summa- rizing the main findings and core content. The output was reviewed and edited to ensure accuracy and alignment with the research objectives. As authors, we take full responsibility for the content of this article. Results Representativeness of the sample We distributed the survey to 4 167 agricultural entrepreneurs in Southwest Finland, receiving 389 valid respons- es – a 9.3% response rate. Despite the moderate response rate, the sample represents the population excellently across all key variables, as shown by the Tables 1–2. Respondents included a diverse range of agricultural profes- sionals and activities spanning all 27 municipalities in the province (Fig. 2; see also Appendix 1 for the municipal- ity-specific comparisons). This distribution ensures that the sample captures the regional variation and structur- al diversity of farming in Southwest Finland, meeting the criteria for reliable analysis even with lower response rates (Fosnacht et al. 2017). Table 1. The group of survey respondents (sample) compared to all farmers in Southwest Finland (population). Sources for all farms: OSF 2020b, OSF 2022c, OSF 2022d. 1 Information for all farms from 2022. 2 Including oilseeds 3 E.g., potato, sugar beet. 4 Sheep and goat farms only. 5 Information for all farms from 2020. Sample (n=389) % Population (4 554 in total) % Share of agricultural and horticultural enterprises1 Cereal farming 64.02 54.9 Horticulture 4.9 7.3 Other plant farming3 13.6 20.0 Dairy cattle farming 2.6 2.6 Other cattle farming 3.6 3.1 Pig farming 3.8 2.0 Poultry farming 3.6 3.1 Other livestock farming 3.8 1.44 Mixed farming 14.7 6.2 Organic farming 14.7 10.6 Share of agricultural and horticultural enterprises by farm size category1 0–50 ha 43.4 56.0 50–100 ha 27.8 23.8 Over 100 ha 28.8 20.2 Share of farmers on privately owned farms by age group1 under 40 12.1 13.3 40–55 38.0 35.3 Over 55 49.9 51.4 The average age of farmers 53.6 54 Share of multi-sector agricultural and horticultural enterprises5 36.2 31.6 Farms with land in cultivation1 100.0 97.0 A. Hynni et al. 17 Table 2. Descriptive statistics for survey variables. SD = Standard Deviation Variable Mean SD Frequency Percentage Age 53.64 11.974 Gender Male 330 84.8 Female 57 14.7 Other 2 0.5 Farm location Sparsely populated rural areas 5 1.3 Rural heartland areas 198 50.9 Rural areas close to urban areas 115 29.6 Peri-urban area 65 16.7 Outer urban area 4 1.0 Inner urban area 1 0.3 Education Primary school 28 7.2 Vocational school 116 29.8 Upper secondary school 38 9.8 University of applied sciences 129 33.2 University 78 20.1 Share of agricultural income in total income (1=0–24%,..., 4=76–100%) 2.54 1.263 Arable area (in hectares) 81.41 90.699 The main form of production Cereal and oilseed farming 249 64.0 Horticulture 19 4.9 Other plant production 53 13.6 Cattle farming 24 6.2 Pig and poultry farming 29 7.5 Other livestock production 15 3.9 Mixed farm Yes 332 85.3 No 57 14.7 Method of production Conventional farming 332 85.3 Organic farming 51 13.1 Combination of organic and conventional farming 6 1.5 Change in the amount of mineral fertilizers used 2018–2022 (1=Significantly increased,..., 5=Significantly decreased) 3.63 0.809 Intention to change the amount of mineral fertilizers used 2023–2027 (1=Significantly increase,..., 5=Significantly decrease) 3.60 0.796 Use of manure/recycled fertilizers Yes, both 38 9.8 Manure 134 34.4 Recycled fertilizers 20 5.1 Neither 197 50.6 Agricultural and Food Science (2025) 34: 12–37 18 On-farm energy production from side streams Yes 11 2.8 No 378 97.2 Off-farm utilization of side streams Yes 138 35.5 No 251 64.5 Investment in enhancing nutrient management (1=Very little or none,..., 5=Very much) 3.20 1.017 View on the state of agricultural policy (1=Very poorly,..., 5=Very well) 2.39 0.973 Adoption of circular economy practices in Southwest Finland agriculture The survey revealed diverse practices and trends in production forms and methods among the respondents. 15% of the farms managed mixed operations involving both plant and animal production, while 13% engaged in organic farming, and an additional 1.5% were in the process of transitioning to organic practices. Mineral fertilizer usage showed notable changes between 2018 and 2022, with 50% of respondents reporting a decrease and only 3.1% indicating an increase in usage. These trends are expected to persist, as half of the re- spondents planned to further reduce mineral fertilizer use between 2023 and 2027, and 47% intended to main- tain current usage levels. At the time of the survey, 49% of respondents’ farms used organic fertilizers, with manure being the predominant choice (44%), followed by processed recycled fertilizers (15%). The application of organic fertilizers varied signifi- cantly by farm type, with 96% of animal farms using them compared to 40% of plant farms. Only a small proportion (2.8%) of farms reported producing energy from byproducts generated in their operations. However, 35% of respondents indicated that they distributed, exchanged, or sold their byproducts externally, primarily for use as bedding, fertilizer, or feed. Additional uses included soil conditioning, growing mediums, and ground cover. Note: Mean and SD are not applicable for categorical variables. Mean and SD are reported for Likert scale as an approximation of central tendency. Fig. 2. Number of farms that responded to the survey (389 in total) per postal code area in the province of Southwest Finland. Index map source: Fenn-O-Manic/Wikimedia Commons. A. Hynni et al. 19 In terms of nutrient management, 38% of respondents reported making significant or substantial investments in this area, while 21% indicated little to no investment. Finally, perspectives on agricultural policy support for tran- sitioning to a CE varied. Just over half of the respondents (51%) believed that current policies poorly support the CE transition, while 27% viewed them as neutral. Only 1.0% felt that agricultural policies support the transition very well. Factors influencing the adoption of circular economy practices Results of cross-tabulations We used cross-tabulation to examine how background factors were associated with adoption of CE practices (Table 3). Only the statistically significant analysis results are covered in the following text. Table 3. Pairwise associations between background factors and circular economy practices: chi-square (χ²) values and p-values for cross-tabulations Dependent variable Independent variable χ² p-value Change in the amount of mineral fertilizers used 2018– 2022 Age 6.562 0.585 Gender 2.839 0.585 Farm location 1.809 0.986 Education 21.844 0.148 Share of agricultural income in total income 10.376 0.583 Arable area 15.676 0.476 The main form of production 13.352 0.053 Mixed farm 2.689 0.611 Method of production 29.098 <0.001 Investment in enhancing nutrient management 16.875 0.394 View on the state of agricultural policy 32.519 0.038 Intention to change the amount of mineral fertilizers used 2023–2027 Age 16.611 0.034 Gender 4.314 0.365 Farm location 16.867 0.661 Education 19.733 0.232 Share of agricultural income in total income 11.883 0.455 Arable area 19.727 0.233 The main form of production 24.301 0.229 Mixed farm 2.847 0.584 Method of production 29.962 <0.001 Investment in enhancing nutrient management 26.860 0.043 View on the state of agricultural policy 39.683 0.005 Use of manure/recycled fertilizers Age 24.468 <0.001 Gender 9.087 0.028 Farm location 17.238 0.305 Education 29.344 0.004 Share of agricultural income in total income 28.654 <0.001 Arable area 56.525 <0.001 The main form of production 100.302 <0.001 Mixed farm 70.443 <0.001 Method of production 25.434 <0.001 Investment in enhancing nutrient management 53.275 <0.001 View on the state of agricultural policy 10.126 0.812 Agricultural and Food Science (2025) 34: 12–37 20 The type of farm production significantly affected the change in mineral fertilizer use from 2018 to 2022. Organic farms and those combining organic and conventional methods were more likely to reduce usage. Farmers satis- fied with current policies also tended to reduce mineral fertilizer use. Future plans to adjust the amount of mineral fertilizers used from 2023 to 2027 were influenced by the farmer’s age and the farm’s production method. Younger farmers and those on organic farms were more open to adjust- ing the amounts. Farmers who invested heavily in nutrient management were less likely to plan adjustments but were more satisfied with current policies. Note: statistically significant (<0.05) p-values are in bold. On-farm energy production from side streams Age 1.308 0.520 Gender 0.287 0.592 Farm location 4.627 0.463 Education 5.293 0.259 Share of agricultural income in total income 2.211 0.530 Arable area 6.458 0.167 The main form of production 2.386 0.794 Mixed farm 0.280 0.597 Method of production 1.442 0.230 Investment in enhancing nutrient management 2.066 0.724 View on the state of agricultural policy 9.189 0.146 Off-farm utilization of side streams Age 2.665 0.264 Gender 0.085 0.771 Farm location 4.916 0.426 Education 6.935 0.139 Share of agricultural income in total income 4.974 0.174 Arable area 6.792 0.147 The main form of production 15.822 0.007 Mixed farm 4.127 0.042 Method of production 1.282 0.257 Investment in enhancing nutrient management 10.271 0.036 View on the state of agricultural policy 2.462 0.782 Investment in enhancing nutrient management Age 13.680 0.090 Gender 0.548 0.969 Farm location 22.741 0.302 Education 28.626 0.027 Share of agricultural income in total income 30.552 0.002 Arable area 34.732 <0.001 The main form of production 29.556 0.077 Mixed farm 7.107 0.130 Method of production 7.254 0.123 Investment in enhancing nutrient management 28.124 0.107 View on the state of agricultural policy Age 20.438 0.025 Gender 4.706 0.453 Farm location 14.615 0.950 Education 13.703 0.845 Share of agricultural income in total income 5.270 0.990 Arable area 22.554 0.311 The main form of production 20.229 0.735 Mixed farm 1.572 0.905 Method of production 1.593 0.902 A. Hynni et al. 21 The use of manure and recycled fertilizers was more prevalent among younger farmers, female farmers, and those with higher education or larger farms. Organic and mixed farms were more likely to use both manure and recy- cled fertilizers, and farms with significant agricultural income invested more in nutrient management and used these fertilizers more frequently. No significant background factors explained energy production from byproducts. However, external utilization of byproducts was more common on animal and mixed farms compared to plant farms. Farms investing in nutrient management were more likely to utilize byproducts externally. Investment in nutrient management varied by education level, farm income structure, and size. Farmers with low- er education and smaller farms invested less, while those with a higher agricultural income percentage invested more. Older farmers were generally more critical of current agricultural policies than younger ones. Results of logistic regressions Logistic regression was used to analyze changes in mineral fertilizer use and other CE practices (Tables 4–7), in terms of the most important indicators for each seven regression models. Below we present the results of statis- tically significant models and the statistically significant results of individual variables from statistically non-signif- icant models. The full results of the logistic regression models can be found in Appendices 3–6. Farms in sparsely populated rural areas were more likely to reduce fertilizer usage from 2018 to 2022 compared to those in rural heartlands. For future fertilizer use plans (2023–2027), the model suggested that farms in sparse- ly populated areas and those with mixed production methods were inclined to plan reductions. Livestock farms were less likely to plan decreases compared to cereal and oilseed farms. The regression model for the use of manure and recycled fertilizers was significant. It indicated that animal farms, younger farmers, and those with higher education were more likely to use manure. Farms in rural heartlands and involved in organic farming preferred recycled fertilizers. Larger farms and those with substantial agricultural in- come were more likely to use both manure and recycled fertilizers. Although statistically non-significant (p= 0.425), farms that combine organic and conventional production meth- ods might be more likely to produce energy from byproducts compared to farms that are entirely in conventional production. In another statistically non-significant model (p= 0.123) related to external utilization of byproducts, farms focused on other plant production were more likely to utilize byproducts externally compared to cereal and oilseed farms. Model related to investment in nutrient management was statistically significant. Older farmers invested less, while those with a higher percentage of income from agriculture invested more. On the other hand, no specific background factors significantly explained respondents’ views on agricultural policy from a CE perspective. Agricultural and Food Science (2025) 34: 12–37 22 Table 4. Which variables predict adherence to reduce the amount of mineral fertilizer used: statistically significant results of the first two logistic regression models Response variable: Change in the amount of mineral fertilizers used 2018-2022 (Significantly decreased/ Somewhat decreased/Remained the same/Somewhat increased/Significantly increased) Response variable: Intention to change the amount of mineral fertilizers used 2023– 2027 (Significantly decrease/Somewhat decrease/Keep the same/Somewhat increase/ Significantly increase) Method Reference category p-value McFadden R2 Nagelkerke R2 Cox & Snell R2 Method Reference category p-value McFadden R2 Nagelkerke R2 Cox & Snell R2 Ordinal Significantly increased 0.040 0.041 0.100 0.089 Ordinal Significantly increase 0.072 0.039 0.093 0.083 Independent variables OR (B) [95% CI] Independent variables OR (B) [95% CI] Farm location (reference category: Rural heartland areas) Farm location (reference category: Rural heartland areas) Sparsely populated rural areas 0.061 (-2.803)* -0.006, 0.618 Sparsely populated rural areas 15.271 (2.726)** 1.976, 118.002 Rural areas close to urban areas 0.012 (4.413)* 0.000, 1.749 The main form of production (reference category: Cereal and oilseed farming) Other livestock production 0.280 (-1.274)* 0.088, 0.894 Method of production (reference category: Conventional farming) Combination of conventional and organic farming 6.922 (1.935)** 1.422, 33.706 R2 = R-Squared, OR = Odds ratio, B = regression coefficient, CI = Confidence interval for odds ratio. *** p< 0.001 ** p< 0.01 * p< 0.05 A. Hynni et al. 23 Table 5. Which variables predict adherence to use manure and recycled fertilizers: statistically significant results of the third logistic regression model Response variable: Use of manure/recycled fertilizers (Yes, both/Manure/Recycled fertilizers/Neither) Method Reference category p-value McFadden R2 Nagelkerke R2 Cox & Snell R2 Multinomial Neither <0.001 0.275 0.508 0.450 Independent variables Manure Recycled fertilizers Yes, both OR (B) [95% CI] OR (B) [95% CI] OR (B) [95% CI] Age 0.953 (-0.048)*** 0.929, 0.979 Farm location (reference category: Rural heartland areas) Peri-urban area 0.107 (-2.232)* 0.012, 0.947 0.124 (-2.084)* 0.024, 0.632 Education (reference category (Primary school) Higher education degree (applied sciences) 7.230 (1.978)* 1.381, 37.855 % of agricultural income in total income (reference category: 0–25) 25–50 4.349 (1.470) 1.213, 15.587 The main form of production (reference category: Cereal and oilseed cultivation) Cattle farming 24.867 (3.214)** 2.842, 217.579 10.880 (2.387)* 1.055, 112.256 Pig and poultry farming 17.718 (2.875)* 1.803, 174.105 Other livestock production 40.936 (3.712)** 4.190, 399.903 Mixed farm (reference category: No) Yes 10.245 (2.327)* 2.769, 37.905 Method of production (reference category: Conventional farming) 3.177 (1.156)* 1.281, 7.878 Organic farming 7.376 (1.998)** 2.002, 27.172 8.105 (2.092)*** 2.591, 25.349 R2 = R-Squared, OR = Odds ratio, B = regression coefficient, CI = Confidence interval for odds ratio. *** p< 0.001 ** p< 0.01 * p< 0.05 Agricultural and Food Science (2025) 34: 12–37 24 Table 6. Which variables predict adherence to utilize byproducts: statistically significant results of the fourth and fifth logistic regression model Response variable: On-farm energy production from side streams (Yes/No) Response variable: Off-farm utilization of side streams (Yes/No) Method Reference category p-value McFadden R2 Nagelkerke R2 Cox & Snell R2 Method Reference category p-value McFadden R2 Nagelkerke R2 Cox & Snell R2 Binary No 0.425 N/A 0.260 0.059 Binary No 0.123 N/A 0.106 0.077 Independent variables OR (B) [95% CI] Independent variables OR (B) [95% CI] Method of production (reference category: Conventional farming) The main form of production (reference category: Cereal and oilseed farming) Combination of conventional and organic farming 29.429 (3.382)* 1.224, 707.501 Other plant production 2.080 (0.732)* 1.089, 3.975 R2 = R-Squared, OR = Odds ratio, B = regression coefficient, CI = Confidence interval for odds ratio. *** p< 0.001 ** p< 0.01 * p< 0.05 Table 7. Which variables predict adherence to invest in nutrient management and attitude towards prevailing agricultural policy: statistically significant results of the sixth and seventh logistic regression model Response variable: Investment in enhancing nutrient management (Very much/Quite much/Somewhat/ Rather little/Very little or none) Response variable: View on the state of agricultural policy (Very well/Quite well/Neither well nor poorly/Rather poorly/Very poorly) Method Reference category p-value McFadden R2 Nagelkerke R2 Cox & Snell R2 Method Reference category p-value McFadden R2 Nagelkerke R2 Cox & Snell R2 Ordinal Very little or none <0.001 0.048 0.133 0.125 Ordinal Very poorly 0.859 0.017 0.047 0.044 Independent variables OR (B) [95% CI] Independent variables OR (B) [95% CI] Age 1.018 (0.018)* 1.001, 1.036 (No statistically significant results in this model) % of agricultural income in total income (reference category: 0–25) 75–100 0.478 (-0.737)** 0.273, 0.839 R2 = R-Squared, OR = Odds ratio, B = regression coefficient, CI = Confidence interval for odds ratio. *** p< 0.001 ** p< 0.01 * p< 0.05 A. Hynni et al. 25 Discussion The results of this study can be summarized in three main arguments: 1) Farms are moving towards organic fer- tilizers at a markedly different pace, 2) The production method and the main form of production may explain the degree of byproduct utilization, and 3) Young and full-time farmers are the most willing to invest in nutrient man- agement – despite few political incentives. We have structured the Discussion chapter according to these three arguments. At the end of the chapter, we provide recommendations on which topics it would be important for decision-makers and researchers to focus on to promote the transition to a circular economy in agriculture. Farms are moving towards organic fertilizers at a markedly different pace The survey reveals a gradual decrease in mineral fertilizer use among farmers in Southwest Finland. While many farms maintained their usage levels several years before reducing them, younger farmers show a greater propen- sity to adjust fertilizer use over five years. This aligns with similar trends observed in Swedish (Lima et al. 2024) and Danish (Case et al. 2017) agriculture. Unexpectedly, reductions are more prevalent in sparsely populated rural areas. This may be due to larger live- stock farms in these areas utilizing manure from their animals, providing a natural alternative to mineral fertilizers. 50% of the farms in Southwest Finland use organic fertilizers, predominantly manure. This is lower compared to Sweden and Denmark, where the usage is 80% and 72%, respectively (Case et al. 2017, Lima et al. 2024). The con- tinued preference for mineral fertilizers on plant farms in Southwest Finland could be due to logistical constraints. For comparison, in Denmark, 65% of farmers engaged in plant production in 2014 (Case et al. 2017), compared to 82% in Southwest Finland in 2022. “Around 50%” of Danish farms had a manure exchange agreement in 2011 (Asai et al. 2014), while in Southwest Finland, 6.6% of farms exported manure and 14.7% imported manure in 2020 (OSF 2020a). This suggests that in Denmark, the separation of animal and plant production from each other has not been as great as in Finland, and that Danish plant farms have more manure-producing animal farms near them than plant farms in Southwest Finland do. Manure and recycled fertilizers are more commonly used on larger farms and those engaged in organic or mixed farming. This mirrors trends in Denmark but is limited by the separation of plant and animal production regions. Organic farms, bound by EU certification standards, favour entirely organic fertilizers (EC 2018). Gender influences fertilizer use, with women managing farms that utilize manure and men more likely to use re- cycled fertilizers. This partly contrasts with Central European trends where male farmers predominantly adopt all kinds of sustainable practices (Herrera et al. 2023). Older farmers tend to avoid both manure and recycled fertiliz- ers, differing from findings that link longer tenure with higher adoption of emission reduction practices. Higher education and deriving significant income from agriculture correlate with a greater likelihood of using organic fertilizers, reinforcing the connection between education, primary agricultural reliance, and sustainable farming (Nordin et al. 2022, Herrera et al. 2023). The production method and the main form of production may explain the degree of byproduct utilization On-farm energy production from byproducts remains rare. The analysis of survey responses concerning energy production yielded a single statistically significant result: organic farms in Southwest Finland may be more involved in energy production from byproducts. According to Rizzo et al.’s (2024) systematic literature review, environmen- tal awareness is prioritized over immediate economic benefits more frequently on organic farms than on conven- tional ones. This suggests that the observed connection might indeed be valid. Conversely, the sale, exchange, or transfer of byproducts for off-farm use is moderately common, with one-third of farms in Southwest Finland participating. According to Kämäräinen et al. (2014), the restructuring of Finnish agriculture has decreased the proportion of farms involved in these practices due to the decline in farm numbers, increased farm sizes, greater distances between farms, and specialization. Agricultural and Food Science (2025) 34: 12–37 26 The type of production significantly affects whether farms utilize byproducts externally, although the specific na- ture of this relationship varies, making it difficult to draw definitive conclusions. Young and full-time farmers are the most willing to invest in nutrient management – despite few political incentives Investment in nutrient management is modest across most farms, likely influenced by the requirement to meet minimum environmental subsidy standards (Finnish Food Authority 2023). Younger farmers, however, are more inclined to invest in nutrient management compared to older farmers. This contrasts with findings from Central Europe, where older farmers are more likely to adopt emission-reduction practices (Herrera et al. 2023), but it aligns with Serebrennikov et al.’s (2020) literature review, which indicates that younger farmers are more willing to switch to organic production than those nearing retirement. Farmers with elementary education tend to invest less in nutrient management, while those with a higher pro- portion of income from agriculture and larger farms show a greater willingness to invest. This trend supports the link between education, income dependence on agriculture, and sustainable practices (Serebrennikov et al. 2020, Nordin et al. 2022). There is a general sentiment among farmers in Southwest Finland that current agricultural policies do not adequate- ly support the transition to sustainable food production. This criticism is more pronounced among older farmers. Limitations of the study While the survey sample and population are largely consistent, certain discrepancies should be noted. Cereal farms were overrepresented in the survey relative to the population. However, this discrepancy narrows to 2.7 percent- age points when cereal farming is combined with other crop production, reflecting differences in how these cat- egories are classified in the Official Statistics of Finland (OSF 2022d). The survey respondents were geographically more concentrated than farmers in Southwest Finland in general. 52.6% of respondents came from the four largest respondent municipalities (see Appendix 1). Therefore, some degree of bias can be assumed in the analyses based on farm locations. Another bias arises from the definition of mixed farms. In this study, mixed farms were defined as those with at least one plant production form and one livestock production form, based on farmers’ self-reports. In contrast, the Official Statistics of Finland define mixed farms more stringently, as those where at least two-thirds of total out- put comes from a single product. This definitional difference likely accounts for the observed disparity between the sample and the population in this category. Furthermore, livestock and organic farmers were slightly overrepresented in the sample. This may be attributed to livestock farmers’ heightened interest in nutrient recycling due to their reliance on animal manure, and organ- ic farmers’ greater awareness of environmental impacts, which may make them more inclined to participate in studies of this nature. These biases should be taken into account when interpreting the results and generalizing findings to the wider farming population. Overall, analyzing the quantitative results of a single survey is only scratching the surface of the realities of farm- ers ahead of the sustainability transition in agriculture. Further research is warranted for a deeper understanding of the impact of both the relative and absolute locations of farms, as well as the influence of national policies and regulations, on farmers’ readiness to adopt circular economy practices. Recommendations and conclusions The transition to a circular economy in Southwest Finland’s agriculture is progressing slowly, driven mainly by a few adopted practices. To accelerate this shift, policy interventions are crucial, particularly in promoting energy production. Decision-making, tailored to the diverse needs of different farm types, is essential. Policies should focus on paying attention to small farms managed by part-time farmers, integrating agricultural viability with A. Hynni et al. 27 broader livelihood options. Additionally, there seems to be a need for targeted training on circular economy prin- ciples, especially for farmers lacking formal agricultural education. Our research indicates that farms in Southwest Finland differ significantly in their extent of adopting circular econ- omy measures. This diversity can be attributed to various factors, including the type of circular economy measures being considered, the farmer’s age, educational background, the balance of income derived from agriculture ver- sus other sources, the farm’s production method, and geographical location. Notably, the combined influence of these factors is most apparent in the willingness to use organic fertilizers, with farm size and the farmer’s gender also playing critical roles. These insights underscore the need to recognize the unique profiles of farms, ensur- ing that the transition to a circular economy is inclusive, feasible, and impactful across all sectors of agriculture. Acknowledgements We want to thank all the respondents who put their time and effort into answering our survey, and the Finnish Food Authority for providing the farmers’ contact information for our research use. We also thank the volunteers who commented on the preliminary version of our questionnaire, and the Central Union of Agricultural Produc- ers and Forest Owners for their cooperation in reaching the volunteers. Special thanks to the University of Turku Language Centre for their help in improving the language of this article. References Asai, M., Langer, V. & Frederiksen, P. 2014. Responding to environmental regulations through collaborative arrangements: So- cial aspects of manure partnerships in Denmark. Livestock Science 167: 370–380. https://doi.org/10.1016/j.livsci.2014.07.002 Burgess, S. 2013. Identifying the odds ratio estimated by a two-stage instrumental variable analysis with a logistic regression model. 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The response variables and explanatory variables used in cross-tabulations and logistic regression analyses Response variable Response variable description Explanatory variable Explanatory variable description Change in the amount of mineral fertilizers used 2018–2022 The respondent’s own assessment of how much and in which direction the amount of mineral fertilizer used on their farm has changed between 2018 and 2022 Age Respondent’s age in years Intention to change the amount of mineral fertilizers used 2023– 2027 The respondent’s own assessment of how much and in which direction the amount of mineral fertilizer used on their farm will change between 2023 and 2027 Gender Respondent’s gender Use of manure/recycled fertilizers Does the respondent’s farm utilize manure, processed recycled fertili- zers, both, or neither? Farm location The location of the respondent’s farm according to the Urban-rural classification On-farm energy production from side streams Are side streams generated on the respondent’s farm utilized for energy within the farm? Education Respondent’s highest completed degree Off-farm utilization of side streams Does the respondent’s farm sell, exchange, or transfer side streams generated in its production for off- farm use? Share of agricultural income in total income What percentage of the respondent’s total income comes directly from agriculture? Investment in enhancing nutrient management Respondent’s own assessment of how much effort is being invested in improving nutrient management on their farm? Arable area Total area of the respondent’s own and leased arable land on the farm View on the state of agri- cultural policy Respondent’s assessment of how well current agricultural policy supports farms’ transition to a circular economy The main form of production From which form of agricultural production does the respondent’s enterprise receive the most sales revenue? Mixed farm Does the respondent’s farm involve both plant production and animal husbandry? Method of production Is the respondent’s farm in conventional production, organic production, or in transition to organic production? A. Hynni et al. 31 Appendix 3. Full results of the first two logistic regression models Response variable: Change in the amount of mineral fertilizers used 2018–2022 (Significantly decreased/Somewhat decreased/Remained the same/Somewhat increased/Significantly increased) Response variable: Intention to change the amount of mineral fertilizers used 2023–2027 (Significantly decrease/Somewhat decrease/Keep the same/Somewhat increase/Significantly increase) Method Reference category p-value McFadden R2 Nagelkerke R2 Cox & Snell R2 Method Reference category p-value McFadden R2 Nagelkerke R2 Cox & Snell R2 Ordinal Significantly increased 0.040 0.041 0.100 0.089 Ordinal Significantly increase 0.072 0.039 0.093 0.083 Independent variables OR (B) [95% CI] Independent variables OR (B) [95% CI] Age 1.014 (0.014) 0.996, 1.033 Age 1.000 (0.000) 0.982, 1.018 Gender (reference category: Male) Gender (reference category: Male) Female 0.944 (-0.058) 0.521, 1.710 Female 0.676 (-0.391) 0.374, 1.224 Farm location (reference category: Rural heartland areas) Farm location (reference category: Rural heartland areas) Sparsely populated rural areas 0.061 (-2.803)* 0.006, 0.618 Sparsely populated rural areas 15.271 (2.726)** 1.976, 118.002 Rural areas close to urban areas 0.012 (-4.413)* 0.000, 1.749 Rural areas close to urban areas 0.988 (-0.012) 0.623, 1.566 Peri-urban area 0.100 (-2.298) 0.005, 1.879 Peri-urban area 1.083 (0.079) 0.625, 1.876 Outer urban area 0.082 (-2.503) 0.008, 0.853 Outer urban area 1.479 (0.391) 0.205, 10.644 Inner urban area 0.060 (-2.813) 0.006, 0.609 Inner urban area 0.237 (-1.440) 0.003, 18.644 Education (reference category: Primary school) Education (reference category: Primary school) Vocational school 1.021 (0.021) 0.586, 1.779 Vocational school 0.443 (-0.815) 0.194, 1.008 Upper secondary school 0.462 (-0.771) 0.208, 1.028 Upper secondary school 0.633 (-0.458) 0.240, 1.665 Higher education degree (applied sciences) 0.660 (-0.416) 0.360, 1.207 Higher education degree (applied sciences) 0.548 (-0.602) 0.242, 1.241 Higher education degree (university) 1.219 (0.198) 0.505, 2.939 Higher education degree (university) 0.445 (-0.810) 0.183, 1.084 % of agricultural income in total income (reference category: 0–25) % of agricultural income in total income (reference category: 0-25) 25–50 0.736 (-0.307) 0.412, 1.315 25–50 1.138 (-0.129) 0.497, 1.557 50–75 0.658 (-0.419) 0.360, 1.200 50–75 0.836 (-0.179) 0.427, 1.637 75–100 1.327 (-0.307) 0.697, 2.526 75–100 0.773 (-0.258) 0.432, 1.381 Arable area 0.998 (-0.002) 0.995, 1.000 Arable area 0.999 (-0.001) 0.997, 1.002 The main form of production (reference category: Cereal and oilseed farming) The main form of production (reference category: Cereal and oilseed farming) Horticulture 0.758 (-0.277) 0.190, 3.025 Horticulture 1.242 (0.217) 0.485, 3.180 Other plant production 1.389 (0.329) 0.365, 5.289 Other plant production 0.848 (-0.165) 0.470, 1.530 Cattle farming 0.632 (-0.459) 0.194, 2.064 Cattle farming 0.366 (-1.005) 0.134, 0.998 Pig and poultry farming 0.697 (-0.360) 0.180, 2.701 Pig and poultry farming 0.984 (-0.016) 0.361, 2.687 Other livestock production 0.617 (-0.483) 0.209, 1.825 Other livestock production 0.280 (-1.274)* 0.088, 0.894 Mixed farm (reference category: No) Mixed farm (reference category: No) Yes 0.874 (-0.135) 0.412, 1.854 Yes 0.792 (-0.233) 0.368, 1.706 Method of production (reference category: Conventional farming) Method of production (reference category: Conventional farming) Organic farming 0.218 (-1.524) 0.043, 1.092 Organic farming 0.872 (-0.137) 0.476, 1.599 Combination of conventional and organic farming 0.286 (-1.252) 0.062, 1.328 Combination of conventional and organic farming 6.922 (1.935)** 1.422, 33.706 R2 = R-Squared, OR = Odds ratio, B = regression coefficient, CI = Confidence interval for odds ratio. *** p< 0.001 ** p< 0.01 * p< 0.05 Agricultural and Food Science (2025) 34: 12–37 32 Appendix 4. Full results of the third logistic regression model Response variable: Use of manure/recycled fertilizers (Yes, both/Manure/Recycled fertilizers/Neither) Method Reference category p-value McFadden R2 Nagelkerke R2 Cox & Snell R2 Multinomial Neither <0.001 0.275 0.508 0.450 Independent variables Manure Recycled fertilizers Yes, both OR (B) [95% CI] OR (B) [95% CI] OR (B) [95% CI] Age 0.953 (-0.048)*** 0.929, 0.979 0.980 (-0.020) 0.935, 1.027 0.984 (-0.016) 0.947, 1.022 Gender (reference category: Male) Female 0.654 (-0.424) 0.250, 1.714 0.250 (-1.387) 0.025, 2.474 0.384 (-0.957) 0.085, 1.730 Farm location (reference category: Rural heartland areas) Sparsely populated rural areas 4.168 (1.427) 0.347, 50.031 0.519 (-14.095) 0.000, # 0.468 (-14.103) 0.179, 1.222 Rural areas close to urban areas 1.000 (0.000) 0.518, 1.928 7.561x10-7 (-0.657) 0.164, 1.644 7.503x10-7 (-0.760) 0.000, # Peri-urban area 0.544 (-0.609) 0.241, 1.228 0.107 (-2.232)* 0.012, 0.947 0.124 (-2.084)* 0.024, 0.632 Outer urban area 2.356 (0.857) 0.065, 86.042 1.504x10-8 (-13.407) 0.000, # 22.192 (3.100) 0.784, 627.971 Inner urban area 6.912x10-8 (-16.487) 0.000, # 1.213x10-8 (-18.227) 1.213x10-8, 1.213x10-8 1.169x10-8 (-18.264) 0.000, # Education (reference category: Primary school) Vocational school 3.973 (1.380) 0.749, 21.067 1.201 (0.183) 0.336, 47.061 3.162 (1.151) 0.298, 33.522 Upper secondary school 4.461 (1.495) 0.708, 28.107 1.073x10-6 (-13.745) 0.276, 33.752 1.332 (0.287) 0.087, 20.516 Higher education degree (applied sciences) 7.230 (1.978)* 1.381, 37.855 3.055 (1.117) 0.000, # 5.185 (1.646) 0.491, 54.714 Higher education degree (university) 2.647 (0.973) 0.456, 15.368 3.974 (1.380) 0.100, 14.367 6.049 (1.800) 0.544, 67.245 % of agricultural income in total income (reference category: 0-25) 25–50 1.017 (0.017) 0.458, 2.259 2.376 (0.865) 0.517, 10.917 4.349 (1.470)* 1.213, 15.587 50–75 1.756 (0.563) 0.687, 4.488 3.389 (1.220) 0.664, 17.307 3.928 (1.368) 0.951, 16.228 75–100 0.831 (-0.185) 0.341, 2.023 0.995 (-0.005) 0.210, 4.723 1.334 (0.288) 0.312, 5.704 Arable area 1.002 (0.002) 0.998, 1.006 1.000 (0.000) 0.991, 1.010 1.005 (0.004) 1.000, 1.009 The main form of production (reference category: Cereal and oilseed cultivation) Horticulture 1.842 (0.611) 0.512, 6.628 0.826 (-0.191) 0.079, 8.649 1.128 (0.120) 0.195, 6.513 Other plant production 1.640 (0.494) 0.735, 3.655 0.988 (-0.012) 0.256, 3.817 0.691 (-0.369) 0.209, 2.281 Cattle farming 24.867 (3.214)** 2.842, 217.579 1.712x10-6 (-13.278) 0.000, # 10.880 (2.387)* 1.055, 112.256 Pig and poultry farming 17.718 (2.875)* 1.803, 174.105 1.064x10-5 (-11.451) 0.000, # 4.098 (1.410) 0.285, 59.006 Other livestock production 40.936 (3.712)** 4.190, 399.903 7.812x10-6 (-11.760) 0.000, # 12.153 (2.498) 0.454, 325.432 Mixed farm (reference category: No) Yes 10.245 (2.327)* 2.769, 37.905 3.637x10-6 (-12.524) 0.000, # 4.705 (1.549) 0.868, 25.494 Method of production (reference category: Conventional farming) Organic farming 3.177 (1.156)* 1.281, 7.878 7.376 (1.998)** 2.002, 27.172 8.105 (2.092)*** 2.591, 25.349 Combination of conventional and organic farming 3.160 (1.151) 0.204, 48.908 8.468 (2.136) 0.346, 206.947 14.333 (2.663) 0.825, 249.101 R2 = R-Squared, OR = Odds ratio, B = Regression coefficient, CI = Confidence interval for coefficient, # = number is too large to display. *** p< 0.001 ** p< 0.01 * p< 0.05 A. Hynni et al. 33 Appendix 5. Full results of the fourth and fifth logistic regression model Response variable: On-farm energy production from side streams (Yes/No) Response variable: Off-farm utilization of side streams (Yes/No) Method Reference category p-value McFadden R2 Nagelkerke R2 Cox & Snell R2 Method Reference category p-value McFadden R2 Nagelkerke R2 Cox & Snell R2 Binary No 0.425 N/A 0.260 0.059 Binary No 0.123 N/A 0.106 0.077 Independent variables OR (B) [95% CI] Independent variables OR (B) [95% CI] Age 0.972 (-0.028) 0.913, 1.036 Age 0.992 (-0.008) 0.972, 1.012 Gender (reference category: Male) Gender (reference category: Male) Female 0.448 (-0.802) 0.041, 4.864 Female 0.911 (-0.094) 0.456, 1.820 Farm location (reference category: Rural heartland areas) Farm location (reference category: Rural heartland areas) Sparsely populated rural areas 0.000 (-17.972) 0.000, # Sparsely populated rural areas 9.957 (-0.044) 0.124, 7.387 Rural areas close to urban areas 2.157 (0.769) 0.540, 8.611 Rural areas close to urban areas 0.713 (-0.338) 0.420, 1.210 Peri-urban area 0.000 (-18.040) 0.000, # Peri-urban area 0.601 (-0.509) 0.313,1.157 Outer urban area 0.000 (-18.154) 0.000, # Outer urban area 1.698 (0.530) 0.203, 14.221 Inner urban area 0.000 (-18.325) 0.000, # Inner urban area 0.000 (-20.550) 0.000, # Education (reference category: Primary school) Education (reference category: Primary school) Vocational school 0.170 (-1.770) 0.008, 3.568 Vocational school 1.195 (0.178) 0.436, 3.277 Upper secondary school 4.239 (1.444) 0.319, 56.320 Upper secondary school 1.945 (0.665) 0.616, 6.135 Higher education degree (applied sciences) 0.636 (-0.452) 0.054, 7.519 Higher education degree (applied sciences) 1.890 (0.637) 0.697, 5.128 Higher education degree (university) 0.354 (-1.038) 0.017, 7.358 Higher education degree (university) 0.980 (-0.020) 0.330, 2.912 % of agricultural income in total income (reference category: 0-25) % of agricultural income in total income (reference category: 0-25) 25–50 0.312 (-1.165) 0.025, 3.855 25–50 1.487 (0.397) 0.766, 2.886 50–75 1.453 (0.374) 0.216, 9.757 50–75 1.599 (0.470) 0.751, 3.407 75–100 0.687 (-0.375) 0.077, 6.123 75–100 1.460 (0.378) 0.749, 2.843 Arable area 1.002 (0.002) 0.993, 1.011 Arable area 1.000 (0.000) 0.998, 1.003 The main form of production (reference category: Cereal and oilseed farming) The main form of production (reference category: Cereal and oilseed farming) Horticulture 3.823 (1.341) 0.298, 48.966 Horticulture 0.999 (-0.001) 0.341, 2.925 Other plant production 0.340 (-1.080) 0.029, 3.916 Other plant production 2.080 (0.732)* 1.089, 3.975 Cattle farming 1.897 (0.640) 0.092, 39.090 Cattle farming 0.880 (-0.128) 0.305, 2.539 Pig and poultry farming 0.000 (-17.773) 0.000, # Pig and poultry farming 2.225 (0.800) 0.743, 6.665 Other livestock production 2.991 (1.096) 0.231, 38.730 Other livestock production 2.704 (0.995) 0.826, 8.852 Mixed farm (reference category: No) Mixed farm (reference category: No) Yes 0.340 (-1.078) 0.017, 6.994 Yes 1.019 (0.019) 0.439, 2.367 Method of production (reference category: Conventional farming) Method of production (reference category: Conventional farming) Organic farming 1.441 (0.365) 0.224, 9.271 Organic farming 1.523 (0.420) 0.778, 2.980 Combination of conventional and organic farming 29.429 (3.382)* 1.224, 707.501 Combination of conventional and organic farming 1.805 (0.590) 0.332, 9.812 R2 = R-Squared, OR = Odds ratio, B = Regression coefficient, CI = Confidence interval for coefficient, # = number is too large to display. *** p< 0.001 ** p< 0.01 * p< 0.05 Agricultural and Food Science (2025) 34: 12–37 34 Appendix 6. Full results of the sixth and seventh logistic regression model Response variable: Investment in enhancing nutrient management (Very much/Quite much/Somewhat/Rather little/ Very little or none) Response variable: View on the state of agricultural policy (Very well/Quite well/Neither well nor poorly/Rather poorly/Very poorly) Method Reference category p-value McFadden R2 Nagelkerke R2 Cox & Snell R2 Method Reference category p-value McFadden R2 Nagelkerke R2 Cox & Snell R2 Ordinal Very little or none <0.001 0.048 0.133 0.125 Ordinal Very poorly 0.859 0.017 0.047 0.044 Independent variables OR (B) [95% CI] Independent variables OR (B) [95% CI] Age 1.018 (0.018)* 1.001, 1.036 Age 1.016 (0.016) 0.998, 1.034 Gender (reference category: Male) Gender (reference category: Male) Female 1.312 (0.272) 0.738, 2.333 Female 0.823 (-0.195) 0.451, 1.501 Farm location (reference category: Rural heartland areas) Farm location (reference category: Rural heartland areas) Sparsely populated rural areas 1.336 (0.289) 0.239, 7.467 Sparsely populated rural areas 0.413 (-0.885) 0.060, 2.831 Rural areas close to urban areas 1.117 (0.111) 0.716, 1.742 Rural areas close to urban areas 0.799 (-0.225) 0.502, 1.271 Peri-urban area 1.140 (0.131) 0.669, 1.943 Peri-urban area 1.271 (0.240) 0.730, 2.213 Outer urban area 4.194 (1.434) 0.619, 28.433 Outer urban area 0.561 (-0.578) 0.084, 3.741 Inner urban area 6.367 (1.851) 0.156, 259.914 Inner urban area 0.299 (-1.206) 0.008, 11.711 Education (reference category: Primary school) Education (reference category: Primary school) Vocational school 0.774 (-0.257) 0.349, 1.715 Vocational school 1.070 (0.068) 0.463, 2.478 Upper secondary school 0.827 (-0.190) 0.323, 2.116 Upper secondary school 0.465 (-0.766) 0.174, 1.241 Higher education degree (applied sciences) 0.714 (-0.337) 0.323, 1.579 Higher education degree (applied sciences) 0.812 (-0.208) 0.352, 1.877 Higher education degree (university) 0.421 (-0.865) 0.178, 0.999 Higher education degree (university) 0.924 (-0.079) 0.373, 2.289 % of agricultural income in tot. income (reference category: 0-25) % of agricultural income in tot. income (reference category: 0–25) 25–50 1.058 (0.056) 0.609, 1. 836 25–50 1.067 (0.065) 0.603, 1.887 50–75 0.659 (-0.416) 0.345, 1.259 50–75 1.362 (0.309) 0.691, 2.688 75–100 0.478 (-0.737)** 0.273, 0.839 75–100 0.908 (-0.096) 0.507, 1.629 Arable area 0.998 (-0.002) 0.995, 1.000 Arable area 1.000 (0.000) 0.997, 1.002 The main form of production (reference category: Cereal and oilseed farming) The main form of production (reference category: Cereal and oilseed farming) Horticulture 0.743 (-0.297) 0.300, 1.840 Horticulture 0.566 (-0.569) 0.209, 1.532 Other plant production 0.834 (-0.181) 0.472, 1.473 Other plant production 1.027 (0.027) 0.566, 1.864 Cattle farming 0.911 (-0.093) 0.369, 2.250 Cattle farming 1.923 (0.654) 0.748, 4.948 Pig and poultry farming 1.581 (0.458) 0.600, 4.167 Pig and poultry farming 1.171 (0.158) 0.437, 3.142 Other livestock production 1.199 (0.182) 0.423, 3.403 Other livestock production 0.975 (-0.025) 0.342, 2.780 Mixed farm (reference category: No) Mixed farm (reference category: No) Yes 0.680 (-0.386) 0.327, 1.413 Yes 0.947 (-0.054) 0.453, 1.984 Method of production (reference category: Conventional farming) Method of production (reference category: Conventional farming) Organic farming 0.822 (-0.196) 0.461, 1.467 Organic farming 1.092 (0.088) 0.598, 1.992 Combination of conventional and organic farming 2.510 (0.920) 0.554, 11.362 Combination of conventional and organic farming 1.022 (0.022) 0.228, 4.577 R2 = R-Squared, OR = Odds ratio, B = Regression coefficient, CI = Confidence interval for coefficient. *** p< 0.001 ** p< 0.01 * p< 0.05 A. Hynni et al. 35 Appendix 7. Original questionnaire Barriers to the transition to a Circular Economy in agriculture in Southwest Finland Mandatory questions are marked with a star (*) Dear Respondent, Our methods of producing and consuming food are experiencing unprecedented changes. We should feed our population in a way that ensures farmers earn a fair living, the environment’s carrying capacity is maintained, and consumers become more integrated into the food systems. One proposed method to manage this food revolution is the transition to a circular economy. Ideally, activities based on the circular economy model enhance the well-being of both people and the environment. This survey seeks to understand the perspectives of the agricultural entrepreneurs in Southwest Finland on transitioning to a circular economy and the challenges involved in this transition. Unless specified otherwise, please answer the questions from the perspective of your own business. You can save your answers using the “Save and continue later” button if you need more time and return to complete the sur- vey later. The information you provide will be used solely for this research and will be deleted once the research is complete. You may request the deletion of your data at any time by contacting us, such as by replying to this email. Thank you for deciding to respond to the survey. Your response is vital to us and contributes significantly to our research, which aims to generate actionable insights for the sustainable development of the Finnish food system. The survey will take approximately 10–15 minutes to complete. Antti Hynni PhD researcher Department of Geography and Geology University of Turku Jukka Käyhkö Professor Department of Geography and Geology University of Turku Tuomas Kuhmonen Research director Finland Futures Research Centre University of Turku 1. I have read the privacy notice of the survey (attached to the email) and I accept its terms * Yes / No PART 1: Respondent’s background information 2. Age in years * 3. Gender * Female / Male / Other / Prefer not to answer 4. Postal code of your farm * 5. Highest completed education * Agricultural and Food Science (2025) 34: 12–37 36 Primary school / Vocational school / Upper secondary school / University of applied sciences / University 6. Share of income from agriculture in your total personal income (%) Farmer * Spouse under 25 / 25–50 / 51–75 / over 75 7. Cultivated arable area in your farm (ha) * Total area of which leased 8. Main production form of your farm which generates you the most sales income * Dairy farming / Other cattle farming / Pig farming / Poultry farming / Other animal farming / Cereal or oilseed cultivation / Specialized cultivation (potato, sugar beet, other seeds, etc.) / Horticulture / Other plant farming 9. Possible other production forms, select all that apply * Dairy farming / Other cattle farming / Pig farming / Poultry farming / Other animal farming / Cereal or oilseed cultivation / Spe- cialized cultivation (potato, sugar beet, other seeds, etc.) / Horticulture / Other plant farming / No other forms 10. Is there any other business activity on your farm? * Yes, what? / No 11. Production method of your farm * Conventional / Organic / Combination of conventional and organic PART 2: Circular economy and agriculture 12. Questions 13–26 deal with circular economy measures in agriculture. Before answering those questions, we ask you to de- fine the concept of circular economy in your own words. What do you think the circular economy means? * 13. Has the amount of mineral fertilizers used on your farm changed between 2018 and 2022? * Significantly decreased / Somewhat decreased / Remained the same / Somewhat increased / Significantly increased 14. Do you intend to change the amount of mineral fertilizers used between 2023 and 2027? * Significantly decrease / Somewhat decrease / Keep the same / Somewhat increase / Significantly increase 15. What barriers do you see to the realization of your intention? 16. Does your farm use manure or processed recycled fertilizers? * Yes, both / Manure / Recycled fertilizers / Neither 17. What barriers do you see to the introduction/use of manure and/or recycled fertilizers? 18. Do you process side streams or residues (manure, straw, peeling waste, etc.) generated on your farm into energy, for ex- ample, biogas? * A. Hynni et al. 37 Yes / No 19. What barriers do you see for processing energy from side streams, residues, etc.? 20. Are side streams or residues generated on your farm (manure, straw, peeling waste, etc.) used outside your farm? Select all that apply * Energy production / Fertilizer / Fodder / Bedding / Other, what? / No 21. What obstacles do you see to the sale, exchange, or transfer of side streams, residues, etc. for off-farm utilization? 22. How much is invested in improving the nutrient management on your farm? * Very little or none / Rather little / Somewhat / Quite much / Very much 23. Describe what means of enhancing nutrient management you use (e.g. crop rotation, winter vegetation cover of fields, gypsum treatment) 24. What barriers do you see to improving nutrient management? 25. How well do you think the prevailing agricultural policy supports the transition of agricultural enterprises to the circular economy? * Very poorly / Rather poorly / Neither well nor poorly / Quite well / Very well / Do not know 26. How would you develop agricultural policy so that it would better support the transition to a circular economy as part of sustainable food production? PART 3: Contact information 27. Leave your contact information here if you want to register to be interviewed. We will interview some of those who left their contact information later. Leaving contact information is not binding. Contact information is only used to communicate with the interviewees and is not combined with survey responses. We handle your contact information confidentially as stat- ed in the privacy notice (attached to the e-mail). First Name Last Name Cell phone E-mail Factors explaining the differences in the adoption of circulareconomy measures among farms in Southwest Finland Introduction: agriculture and circular economy Materials and methods Survey Statistical analyses Cross-tabulation Logistic regression Use of artificial intelligence Results Representativeness of the sample Adoption of circular economy practices in Southwest Finland agriculture Factors influencing the adoption of circular economy practices Results of cross-tabulations Results of logistic regressions Discussion Farms are moving towards organic fertilizers at a markedly different pace The production method and the main form of production may explain the degree ofbyproduct utilization Young and full-time farmers are the most willing to invest in nutrient management –despite few political incentives Limitations of the study Recommendations and conclusions Acknowledgements References Appendices