1 The social and economic determinants of farm succession in 1 Ireland 2 Mika Wakamatsu Shin1 *, Jason Loughrey2 , Emma Dillon2 and Anne Kinsella2 3 4 Affiliation: 5 1 Currently freelance. Former affiliate: Agricultural Economics and Farm Surveys 6 Department, Teagasc Rural Economy and Development Programme, Teagasc, Athenry, 7 Co. Galway, Ireland 8 *Corresponding Author Email: wkmtmk0127@gmail.com ORCID: 0000-0001-7259-9 4862 10 11 2 Agricultural Economics and Farm Surveys Department, Teagasc Rural Economy and 12 Development Programme, Teagasc, Athenry, Co. Galway, Ireland 13 Email: jason.loughrey@teagasc.ie ORCID: 0000-0001-9658-0801, 14 Email: emma.dillon@teagasc.ie ORCID: 0000-0002-3712-9575, 15 Email: anne.kinsella@teagasc.ie ORCID: https://orcid.org/0000-0001-5670-9025 16 17 18 This article has been accepted for publication and undergone full peer review but has 19 not been through the copyediting, typesetting, pagination and proofreading process, 20 which may lead to differences between this version and the Version of Record. 21 Please cite this article as: 22 Shin MW, Loughrey J, Dillon E (2025). The social and economic determinants of farm 23 succession in Ireland, Bio-Based and Applied Economics, Just Accepted. DOI: 24 10.36253/bae-16403 25 26 Abstract 27 28 This study explores the multifaceted factors influencing farm succession in Ireland, 29 emphasising the interaction among economic, social, and environmental aspects. With 30 an ageing farm population, the need for effective succession strategies is critical to 31 ensuring sustainable agricultural practices. We analyse the impact of drivers and 32 barriers to succession, highlighting the importance of considering social factors along 33 with economic factors using a probit model to examine these relationships. Our findings 34 reveal that while farm size and dairy farming status show complex relationships with 35 the likelihood of presence of a successor, social factors such as excessive workload 36 impact decision-making. Our findings confirm expected relationships while offering 37 mailto:anne.kinsella@teagasc.ie 2 new insights into farm succession and the farmer’s life cycle. Beyond profitability, 38 social factors—such as workload and its perception by the next generation—play a 39 crucial role in successor identification. Highlighting these dynamics, our study 40 underscores the importance of social sustainability in securing farming’s future. 41 JEL code J10, Q12, Q18 42 Keywords Farm Succession, Social factors, Farmer Ageing, Probit Model, 43 Endogenous succession cycle 44 Acknowledgement 45 Funding provided by the Department of Agriculture, Food and Marine under the project 46 entitled RENEW2050 - Rural Generational Renewal 2050 - 2019R414. 47 48 1. Introduction 49 Improving the sustainability of agriculture, across economic, environmental, and 50 social dimensions, is recognised as central to delivering the key objectives of the 51 Common Agricultural Policy (CAP). One such objective is to support generational 52 renewal. An ageing of the farm population is evident in Ireland (Meredith and Crowley, 53 2018) and across Europe (Bertolozzi-Caredio et al., 2020, May et al., 2019). The share 54 of farm holders aged 65 or over is now almost one-third in Ireland compared to one-55 fifth in 1991 (CSO, 2021), underlining the extent of the challenge in the Irish context. 56 Farm succession, the transfer of managerial control of the farm, is critical to continued 57 farm sustainability (Leonard et al., 2017a,b, Russell, et al., 2020) with previous 58 literature highlighting its importance from both the farm household and rural 59 community perspectives. Farm succession is significant for the uptake of innovation, 60 efficient and effective farm management (Nuthall and Old, 2017). Most farms in Ireland 61 are family-owned, and entry into farming sector is primarily through inheritance 62 (Hennessy and Rehman 2006; Deming et al 2019). 63 3 The complex nature of farmer decision making around succession and inheritance is 64 multifaceted, with a broad range of economic, personal and social factors at play 65 (Conway et al., 2016, 2021, 2022; Leonard, et al., 2017a, b; Góngora et al., 2019). These 66 factors can be classified as drivers and barriers to succession. Previous research has 67 highlighted the significance of individual farm circumstances (Conway et al., 2016; 68 Eistrup et al., 2019; Schlesser 2021; Rech et al., 2021). Conway et al. (2021, 2022) also 69 highlight the limited capacity of financial incentives alone in facilitating generational 70 renewal and land mobility and contend that policy also needs to address the emotional 71 and social wellbeing of older farmers, as well as their sense of purpose in later life. An 72 improved understanding of these influential factors is vital for the design of effective 73 policy to support generational renewal in the context of the new CAP. 74 To secure the farm for the next generation, it is also important to consider the 75 environmental performance, which is increasingly recognised as being integral to the 76 sustainability of agriculture (Barral and Detang-Dessendre 2023). Much recent research 77 points that younger farmers in Europe are more aware of environmental issues, quicker 78 to adopt eco-friendly technologies, and more adaptable to policy changes (Perez et al., 79 2020; Läpple and Kelley, 2015). Despite the significant role of younger farmers in 80 sustainability, studies indicate that there remain barriers to new entrants taking over a 81 farm (Hartarska et al., 2021; Schlesser et al., 2021; Zagata et al., 2017). 82 Regarding the relationship between succession and sustainability, Potter and Lobley 83 (1996) coined the term ‘succession, successor and retirement effects’ to describe the 84 processes whereby an identified successor or lack thereof, can significantly influence 85 the farm holder’s level of interest and investment in the farm when approaching what 86 should be their own retirement from farming. Thus, the sustainability of the farm 87 business is influenced by the presence of successors. Furthermore, Leonard et al. (2017) 88 4 emphasises economic concerns, whether a farm can generate enough income to support 89 both the farmer and their successor, as well as the farmer's residual income if they 90 transfer the farm prior to death. Transfer decisions and procedures are often difficult 91 and stressful and may relate to the valuation of the farm as retiring farmers seek to sell 92 farmland at the highest possible price (Jeanneaux et al 2022). 93 Despite widespread recognition of the connection between socio-economic factors and 94 farm succession, there is considerable space in the academic literature for further 95 exploration of the potential association between social aspects and farm succession. 96 Previous research acknowledges the limited capacity of financial incentives alone in 97 facilitating generational renewal. Through this study, we expect to make 98 recommendations to policy makers on how social aspects should be considered in the 99 design of future generational renewal policies. This paper identifies the endogenous 100 nature of farm succession and examines the relationship between farm-level economic 101 and social factors and the identification of a successor. The factors are intricately 102 intertwined, making it challenging to consider these intertwined factors and the 103 presence of a successor. This paper is innovative in that it conceptualises farm 104 succession within the framework of the farm life cycle, elucidating the endogenous 105 factors. The significance lies not in listing barriers and drivers, but in analysing the 106 relationship between these factors within the farm life cycle and the presence or absence 107 of a successor. The objective of our study is to explore some of the drivers and barriers 108 to farm succession in the Irish context, with a particular emphasis on relevant social 109 and economic factors. 110 The next section discusses potential drivers and barriers to farm succession based on 111 the literature. Section 3 provides an overview of the data and methods used in this paper. 112 5 Section 4 contains the results of the data analyis, section 5 provides some discussion 113 and conclusions. 114 2. Literature review 115 The general process of farm inter-generational transfer is well described in the existing 116 literature, which distinguishes between three distinct but inter-related concepts. 117 Inheritance, the legal transfer of ownership of farm business assets including land; 118 retirement, the withdrawal of the existing manager from active managerial control 119 and/or involvement in manual work on the farm and: succession, the transfer of 120 managerial control over the use of these assets (Errington, 2002). 121 There exists particular policy instruments and financial incentives to encourage early-122 stage inter-generational farm transfer, however, delayed succession remains a concern 123 throughout the world (Lobley et al 2010). The literature identifies barriers to land 124 transfer to non-family members relating to the value that farmers place on identity, 125 occupation, control, and status in the community (Duesberg et al. 2017). Schlesser et 126 al. (2021) investigate social and economic factors affecting farm succession using focus 127 groups, highlighting the complex challenges for both the older generation and 128 successors. Understanding these obstacles is crucial for our study to improve insights 129 into the issue. 130 2.1 Economic Factors 131 Farm succession may be anticipated by current farmers’ behaviour in making additional 132 investment to expand their farm business (Calus et al., 2008). Farmers with identified 133 successors are more likely to invest or expand in anticipation of a takeover of the farm 134 by a son or daughter than those who do not have a successor, which is known as the 135 succession effect. The prospect of a successor often leads incumbent farmers to make 136 management decisions they might not otherwise make, resulting in substantial 137 6 improvements to the farm's productivity and sustainability (Leonard et al., 2017; Lobley 138 et al., 2010; Uchiyama et al., 2008). On the other hand, they describe the ‘retirement 139 effect’, which generally has a negative impact on farms such that the farmer who has 140 not identified a successor attempts no additional investment and scales down the farm 141 business. Similarly, Carreira and Teixeira (2011) investigate that firms that exit the 142 market appear to exhibit a noticeable productivity disadvantage compared to those that 143 survive, not only in the year preceding exit but also for a significant number of years 144 leading up to the exit. This means that farmers who do not secure a successor will not 145 engage in investment activities for several years before the actual retirement. Much 146 economic research has dealt with the question of farm succession and non-succession 147 by highlighting important explanatory factors at the farm level such as farm holder’s 148 age, off-farm employment, farm size, economic viability, farmer’s education and 149 household composition (Kimhi and López, 1999; Stiglbauer and Weiss, 2000; Glauben 150 et al., 2006). 151 The economic viability of the farm is well documented as playing a central role in the 152 succession process (Glauben et al., 2009; Hennessy and Rehman 2007; Zagata and 153 Sutherland, 2015). Farm size is found to have a positive stable effect on farm succession 154 in the case of farming in Italy (Bertoni and Cavicchioli, 2016; Cavicchioli et al., 2018). 155 Successors on larger family farm (both in physical (area) and economic size) are more 156 likely to enter the farm as full-time. 157 Farmland ownership is also discussed as influencing the farm succession process. 158 Ireland has low land mobility as older farmers accumulate capital to secure their future 159 financial situation and are unwilling to transfer their farm assets (Leonard et al. 2017). 160 The major farm transfers occur with non-market arrangements, usually inheritance. 161 7 Irish farmers influence agricultural land markets as most farm transfers occur within 162 the family and this is attributed to the strong emotional attachment to land in Ireland 163 (Bradfield et al., 2023). Rented land (both conacre, the short-term, 11-month land 164 leasing and long-term leasing) accounts for only 18% of Utilised Agricultural Area 165 (UAA) in Ireland, that is the second lowest in EU (European Commission 2021). Policy 166 measures to facilitate land transfer to younger generations have had limited success 167 (Bika, 2007; Geoghegan and O’Donoghue, 2018; Geoghegan et al., 2021). 168 2.2 Social Factors 169 In addition to economic factors, the absence of successors is associated with older 170 farmers’ unwillingness to retire, which stems from emotional attachment to the farm 171 (Conway et al. 2022). Farming is a way of life for many older farmers throughout the 172 world and retirement from their daily routines and social circles in the farming 173 community may affect their emotion negatively. Studies show that existing retirement 174 incentives bring limited success in encouraging farmers to retire at or before pension 175 age and seem not to change the traditional family farm transfer (Gilmore 1999; Contzen 176 et al., 2017). Bika (2007) writes that in addition to financial reasons, Irish farmers’ 177 reticence in using the early retirement scheme1 could have been due to ‘sentimental 178 bonds with the land’. Indeed, policy around generational renewal can often involve 179 strategies to encourage older farmers to ‘step aside’ to facilitate new entrants while 180 farmers often wish to remain ‘rooted in place’ on the farm (Conway et al., 2022) and 181 work for as long as possible (Uchiyama et al. 2008). This farmers’ ‘never retire’ attitude 182 is associated with reduced likelihood of a successor being identified in several countries 183 1 The Early Farm Retirement Scheme (EFRS) was introduced to encourage older farmers to retire and to attract younger farmers into the Industry. In Ireland, there have been three rounds to the EFRS scheme, in 1993, 2000 and 2007 but it ceased in 2009. These schemes enabled farmers to retire early (from age 55) and provided them with a pension (up to €15,000 a year for a maximum of 10 years) provided they retired from farming completely by transferring, selling or leasing their farm to a young trained farmer (Hayden et al 2021). 8 (Lobley et al., 2010). Thus, retirement in farming is not only influenced by economic 184 factors, but also emotional and social status. 185 One such consideration is the desire for social inclusion and the avoidance of isolation. 186 Social inclusion, driven by the concept of social capital, plays a crucial role in farmers' 187 decision-making processes (Shortall 2008). This social capital is composed of resources 188 from networks of relationships based on social structure and acknowledged as 189 important component in farmers’ decision-making (Arnott et al.2021; Cofré-Bravo et 190 al. 2019). This facilitates discussions and the designation of a successor on the farm 191 (Abdala et al. 2022)2. By maintaining these positive relationships, older farmers can 192 navigate the complexities of succession planning more effectively, ensuring the 193 continuity of their agricultural operations. 194 Social factors such as stress or anxiety, and excessive workload are also among the 195 factors that can be considered as barriers to farm succession. Due to the rapid expansion 196 of the dairy sector following the removal of EU milk quotas in 2015, dairy farmers 197 experience an increase in labour intensity in Ireland. Brennan et al. (2021) state that 198 workload related stress was the second highest source of stress among farmers in 199 Ireland, next to poor weather. This stress not only affects the mental well-being of older 200 farmers but also influences the perspectives of potential successors, deterring them 201 from considering a future on the family farm (Brennan et al. 2022). Conversely, 202 strategies such as increasing human capital through workload and information sharing 203 among family members and neighbours can help alleviate this problem, incentivizing 204 potential successors to remain in the agricultural sector (Bertoni and Cavicchioli, 2016). 205 2 Abdala et al (2022) refer to social capital as the willingness of individuals and groups to obtain information, influence, and nurture solidarity with other social actors through the structure and content of existing relationships (Adler and Kwon, 2002) 9 Human capital here is considered according to the OECD definition as the productive 206 wealth embodied in labour, skills and knowledge (Tan, 2014) or any stock of 207 knowledge or the innate/acquired characteristics a person has that contributes to his or 208 her economic productivity (Garibaldi, 2006). Given the increasing focus on quality of 209 life issues (Contzen and Häberli, 2021), there is a strong case for more emphasis on 210 collaborations that enable a more sustainable farm workload. 211 Although a wider range of social factors have emerged in the discussion of generational 212 renewal (Brennan et al., 2021; 2022), there are very few recent studies explicitly 213 accounting for social capital and farm succession, except for Abdala et al. (2022), which 214 identifies some relationships between farm succession and social factors in the case of 215 Brazil. Abdala et al. (2022) found that farmers with social capital, such as access to 216 information or networks with customers and suppliers, are more likely to identify a 217 successor. Such social capital includes agricultural education, which can enhance 218 farmers' ability to manage a farm more effectively (Läpple et al., 2015). 219 Engaging with farm advisory services through formal advisory contracts is another 220 form of social capital, as it involves leveraging networks to acquire knowledge and 221 information (Cofré-Bravo et al., 2019). However, despite recognition of the importance 222 of social aspects, studies addressing these factors remain insufficient. 223 The pursuit of better agricultural knowledge can be an indicator of a higher possibility 224 of farm succession as it may indicate willingness to improve and continue the farm 225 business. 226 Agri-environmental scheme (AES) participation is recognised as one of the strategies 227 for pursuing sustainable agriculture and these types of strategies may be translated into 228 a more sustainable agricultural business for future generations. Farmers with a 229 10 sustainable strategy tend to participate in AES. Various factors contribute to the 230 decision to participate in AES, including economic concerns, the educational level of 231 farmers, and the environmental features on farms. According to Cullen et al. (2020), 232 farmers who are having a "forward-looking" self-identity are more likely to participate 233 in AES. This type of identity influences AES participation, aligning with research that 234 AES participation is favoured as one possible survival strategy when foreseeing the 235 sustainability of agriculture (Ingram et al., 2013; Cullen et al., 2021). In this sense, 236 motivations to join AES are shaped by the desire to continue the farm, which may be 237 associated with the presence of a successor. In other words, the lack of a successor can 238 be a reason not to enter AES due to a winding down and poor availability of labour with 239 an inability to meet the AES management requirements (Riley, 2006). 240 2.3 Life cycle 241 The above mentioned drivers and barriers to succession are intertwined and emerge 242 under different conditions at the farm level. Understanding this complexity can be 243 facilitated by conceptualizing farm succession within a farm life cycle approach. 244 Succession processes in family farming must be appreciated for their long duration 245 (Fischer and Burton, 2014). In Ireland, where most new entrants inherit farms, the 246 identity of successors fostered during the life cycle shapes the structure of farming. 247 Potential successors develop their farm business skills by gradually getting involved in 248 farming, influencing the current farm manager's behavior towards succession, such as 249 making additional investments. This life-cycle approach shows that succession is an 250 endogenous process, where practical involvement on the farm increases successor 251 identification and vice versa (Fischer and Burton, 2014). Negative experiences can 252 deter potential successors even if the farm is viable. 253 11 Socio-economic studies suggest that factors such as the presence of a male heir and the 254 number of children influence farm transfer decisions (O’Gráda, 1980; Banovic et al., 255 2015; Kennedy, 1991). In Northern Ireland, Jack et al. (2019) found that succession 256 decisions are shaped by the gender of children, with a preference for male farm 257 employees. Econometric studies from various countries further indicate that a larger 258 number of children increases the likelihood of farm transfer while also delaying farm 259 closure (Väre, 2006; Banovic et al., 2015). Additionally, having more family members 260 present on the farm enhances the probability of family succession (Stiglbauer and 261 Weiss, 2000). However, regional differences exist, as Cavicchioli et al. (2015; 2018) 262 found both weak positive and negative associations between the number of children and 263 farm succession. 264 From these studies, we hypothesize that farmers with large land ownership and capital 265 assets are more likely to identify successors; those with less social interaction are less 266 likely; AES participation is associated with a higher likelihood of identifying 267 successors; and farmers with identified successors are more likely to invest in their farm 268 in anticipation of succession. 269 3. Data and Method 270 3.1 Data 271 The main dataset for this research is the Teagasc National Farm Survey (NFS) data 272 from 20183. This dataset contains detailed information about farms in terms of their 273 economic, social and environmental performance in Ireland. The choice of time period 274 is based on data availability as the 2018 data includes information about farm 275 3 The Teagasc NFS is part of the EU Farm Accountancy Data Network (FADN), this data was collected in addition to the core FADN dataset. 12 succession and whether or not farm holders have identified a successor. The agricultural 276 sector in 2018 was affected by unusual weather with abnormal rainfall levels in spring 277 and drought in the summer. The drought had a more significant impact on the south and 278 east NUTS34 region relative to the Border, Midlands and West region (Falzoi et al., 279 2019). Some of the survey responses may reflect the difficult conditions of this year 280 (particularly regarding incidence of farm related stress). 281 In the Teagasc NFS, a farm accounts book is recorded for each year for a nationally 282 representative survey of farms throughout Ireland, selected on the basis of a stratified 283 random sample using information provided by the Central Statistics Office. The sample 284 size is approximately 900 farms in recent years. This sample of farms is randomly 285 sampled each year to represent approximately 90,000 farms in Ireland. Each farm is 286 assigned a weighting factor based on the farm system and farm size so that the results 287 of the survey are representative of the national population of farms. The analysis 288 focuses on farms with holders over 50 years old, a critical age for succession decisions. 289 Farmers are actively shaping the future trajectory of their farms. This approach allowed 290 us to examine the factors influencing farm succession decisions while accounting for 291 the gradual and nuanced nature of the succession process. For instance, the decision-292 making process for farm succession can begin several years before the actual succession 293 event takes place (Calus et al. 2008). Moreover, even at an earlier stage, forward-294 looking farmers may undertake new investments to keep a fragile business afloat if they 295 pursue farm transfer to a known successor, while others may prefer to sell the business. 296 Such farmer’s behaviour is observed and influenced by various factors even before 297 retirement age (Paroissien et al. 2021). Examining this age group allows for a larger 298 4 The Nomenclature of Territorial Units for Statistics (NUTS) 3 corresponds to Border, West, Mid-west, South East, South-west, Dublin, Mid-east, and Midlands of Ireland. 13 and more representative sample for robust econometric analysis. This provides a sample 299 of 538 farmers representing approximately 58,000 farms nationally. 300 Succession decisions are made with factors influencing over a long-time frame (Lobley 301 et al, 2010). Teagasc NFS data from 2013 is used in our model to provide a lagged value 302 for the presence of young people in farm households. This variable is included to 303 examine the importance of family networks, which affect the number of farmers who 304 choose successors as of 2018. Young adults (aged 20 to 44 years) in the farm household 305 may move out of the farm household to enter third-level education or employment 306 elsewhere, but they may still be part of a family network and interested in contributing 307 to the farm. Thus, the presence of young adults in 2013 might be related to the status of 308 successor identification in 2018. Attrition is present in the Teagasc NFS data but most 309 farms participating in the 2018 survey provided data as part of the 2013 survey. The 310 year 2013 is chosen as important changes in household composition can take place in a 311 five-year period and the choice of this year limits the amount of attrition. In the 2018 312 dataset, 538 farms were available for analysis. To construct the lagged variable for the 313 presence of a young adult in the household, we used data from 2013. Among these, 371 314 farms could be matched across both years and had relevant information on young adults. 315 In addition, due to missing values—particularly arising from the creation of interaction 316 variables—the final sample size for Model 2 was reduced to 349. For Model 1, which 317 does not include the lagged variable but includes interaction terms, the sample size was 318 reduced to 501. This reduction is likely due to listwise deletion in cases with missing 319 values introduced by the interaction variables. 320 3.2 Methods 321 14 In order to analyse the drivers and barriers of farm succession, we use a probit model 322 with the dependent variable being the presence of a chosen successor as a binary 323 variable where successor =1, otherwise=0. 324 i) Econometric model 325 The probit model is used to test the potential relationship between a number of farm, 326 farmer and farm household characteristics (economic and social) with the presence of 327 a chosen successor. Note that due to the absence of adequate long-term survey data to 328 conclusively verify whether the farmer in NFS has indeed undergone farm succession, 329 the identification a successor is employed as a dependent variable for the analysis. The 330 choice of explanatory variables is based on the need to account for particular farm, 331 family and social factors. Farmer age, land quality, and an interaction between the 332 presence of a dairy enterprise and the size of land ownership are included because much 333 research indicates a positive relationship between farm size and farm succession 334 (Uchiyama et al., 2008; Glauben et al., 2009; Wheeler et al., 2012). Average dairy farm 335 income is higher than the income of other systems in Ireland (Donnellan et al., 2020). 336 We therefore expect that higher farm income attracts more successors and thus we use 337 the presence of a specialist dairy farm as an independent variable in this model. 338 A variable representing investment is included among the independent variables since 339 investment indicates a willingness of the farm holder to further develop the farm. At 340 the same time, investment is another factor that increases a potential successor’s 341 willingness to take over the farm business (Calus et al., 2008). The investment variable 342 is based on Net New Investment, which is defined as all capital expenditure during the 343 year less capital sales and grants. The cost of major repairs to farm buildings, plant and 344 machinery as well as land improvements is also included. It does not include 345 investments in land purchases (Dillon et al., 2021, p.89). 346 15 Many of the independent variables tend not to vary much in value over time and this 347 reduces the likelihood of reverse causality. We attempt to account for the influence of 348 past household composition by the inclusion of a lagged independent variable using 349 data from the 2013 Teagasc NFS survey. We create a lagged variable indicating the 350 presence of young adults aged 20 to 44 years old in the household in 2013. The NFS 351 2018 data provides information about social factors including isolation (defined as 352 those farmers living alone) and the presence of workload stress/pressure 5 . These 353 variables were ascertained as part of an additional special survey of Teagasc NFS farms 354 in autumn 2018. These social variables are likely to be endogenous but are an important 355 consideration given that they are likely to be closely linked to the construction of 356 successor availability and identification. However, to avoid unnecessary sample loss 357 and preserve statistical power, we presented Model 1, which focuses on the full 2018 358 dataset. 359 Farm income and farm viability (defined as the farm receiving at least the minimum 360 wage for family labour and a 5% return on non-land assets) are likely to be associated 361 with farm succession (Leonard et al., 2017). Both income and viability variables are 362 likely to be highly endogenous in terms of their relationship with farm succession. 363 Potential successors may play a very important role in influencing the current farm 364 income and viability due to the labour provided on the farm. At the same time, higher 365 farm income and farm viability may incentivise potential successors to seek to take over 366 the farm business at some point in the future. Farm income has a number of components 367 that are exogenous or weakly endogenous including soil quality, land ownership and 368 5 Farmers are asked if they have experienced stress/anxiety from any aspect of their farm business in the last 5 years. Ten possible sources of stress/anxiety are listed in the questionnaire and farmers are requested to list the top three sources. Those who chose “Workload e.g. work life balance, seasonal demands (calving, lambing)” as one of the three sources are included in this variable. 16 agricultural training. In the econometric analysis, we therefore include these variables 369 rather than the farm income or farm viability variables individually. 370 A binary variable representing whether farmers have formal advisory (extension) 371 service contract is included since it could be an indicator of the willingness to improve 372 farming practices, thus, continuation of the farm business. 373 Participation in an Agri-Environment Scheme (AES) is included since the motivations 374 for participating in AES could be tied to the presence of a potential successor. 375 Conversely, the absence of a successor can be associated with less likelihood of AES 376 participation, as it may indicate a winding-down of the farm business operations, 377 limited availability of labour, and an inability to meet the management requirements set 378 by AES. 379 Figure 1 shows the average and median age of the farm holder by farm type in Ireland. 380 It indicates that certain farm system faces more severe aging problem than others. 381 Fig. 1 Average and median age of the farm holder by farm type in 2020 382 Source: CSO Census of Agriculture 2020 383 384 17 3.3 Summary Statistics 385 We describe the summary statistics for the variables used in the econometric analysis. 386 Using the Teagasc NFS 2018 sample of farmers of at least 50 years old, the statistics 387 show that 57 per cent of farmers have identified a successor at that point in time (Table 388 1). Among them, 34% were dairy farmers. The proportion with a successor varies 389 between farming systems with 53.8 per cent in the case of sheep farms and 58.6 per 390 cent for dairy farms. For the purposes of the econometric modelling and due to the 391 limited sample size, we concentrate the succession analysis on farm holders aged over 392 50 years. 393 Table 1 394 Farmers who identified successor (over 50 years old) 395 Farm System Share of successor farms in each system (%) Dairy 58.6 Cattle rearing 55.7 Cattle other 57.2 Sheep 53.8 Tillage 54.3 Total 56.7 Source: Teagasc National Farm Survey 2018 396 The descriptive statistics are provided in Table 2. For this particular sample, the average 397 age is 63 years old. The average age of farm holders with a chosen successor is 65 years 398 old and without a successor is 60.4 years old. Dairy farms are similarly represented in 399 both succession and non-succession groups. Dairy farms account for 16 per cent of the 400 farms with a successor and 17 per cent of the farms without a successor. As expected, 401 the proportion of farms in the best soil quality category is higher for those with a 402 successor (32 per cent) relative to those without a successor (26 per cent). 403 The average Net New Investment is higher on farms with a chosen successor relative 404 to those farms without a successor (€8,085 per annum versus €5,777 per annum). This 405 18 suggests that investment is associated with the probability of farm succession although 406 the direction of causality may not be clear. The presence of an excessive workload or 407 evidence of farm related stress (as reported by the farm holder) is more common on 408 farms without a successor (35 per cent) relative to farms with a successor (24 per cent). 409 There is a notable difference between the two groups in terms of the share of farm 410 holders living alone. Table 2 shows that 32 per cent of farm holdings without a chosen 411 successor are living alone. This is much lower at 15 per cent for holdings where a 412 successor is chosen. 413 Table 2 414 Descriptive statistics of variables used in the analysis 415 All Farm Holdings (N=538) Farm Holders with a chosen successor (N= 305) Farm Holders without chosen successor (N=233) Expec ted result6 Variable Description Unit Mean SD Mean SD Mean SD Chosen Successor Whether or not the current farm holder has chosen a successor (0 = No,1 = Yes) 0.56 0.50 1.00 0.00 N/A N/A Independe nt Variables Dairy Farm Farm is Specialist Dairy farm (0 = No,1 = Yes) 0.16 0.37 0.16 0.37 0.17 0.37 + Land Ownership Amount of farmland owned Hectares 37.15 28.92 37.30 30.25 36.95 27.16 + Interaction of Dairy and Land Ownership Interaction between Dairy farm and Land Ownership variables Hectares 6.66 17.63 8.65 22.39 7.78 20.46 Farmer Age Age of the Farm Holder Years 63.01 7.79 65.00 7.37 60.44 7.57 + Best Soil Category Farm is located on soils with wide range of use (0 = No,1 = Yes) 0.30 0.46 0.32 0.47 0.26 0.44 + Number of Household Members Age 20 to 44 in 2013* Household members in this age category in 2013 Number of people 0.53 0.82 0.54 0.83 0.17 0.44 + 6 This indicates the expected direction of influence each variable has on the probability of having a chosen successor, based on prior literatures mentioned earlier section. 19 All Farm Holdings (N=538) Farm Holders with a chosen successor (N= 305) Farm Holders without chosen successor (N=233) Expec ted result6 Variable Description Unit Mean SD Mean SD Mean SD Isolation of Living Alone Farm Holder is living alone (0 = No,1 = Yes) 0.23 0.42 0.15 0.36 0.32 0.47 - Excessive workload Self-reported presence of excessive workload (0 = No,1 = Yes) 0.29 0.45 0.24 0.43 0.35 0.48 - Net New Investment Capital expenditure less capital sales and grants Euro 7,080 21,683 8,085 24,783 5,777 16,797 + Formal Advisory Contact Farm Holder has a formal contract for advisory (0 = No,1 = Yes) 0.44 0.50 0.43 0.50 0.46 0.50 + Participatio n in AES Farm Participates in an Agri- Environment Scheme (See ADAS 2020) (0 = No,1 = Yes) 0.37 0.48 0.36 0.48 0.37 0.48 + Source: Teagasc National Farm Survey 2018 416 SD – Standard Deviation 417 *Available for 371 observations 418 419 4. Result 420 In this section, we show the results of the probit model in Table 3. The lagged variable 421 representing the presence of a young adult within the household is available only for 422 371 observations. This is because the creation of the lagged variable relied on 2013 423 data, and the number of farms that existed in both 2013 and 2018 was smaller than those 424 present only in 2018, leading to a reduced sample size in Model 2 compared to the full 425 sample. However, this remains an important variable for examining the potential role 426 of household composition in influencing succession. We therefore present the results 427 of the probit model both excluding and including the lagged variable (shown as Model 428 1 and Model 2, respectively). This way allows us for greater statistical power in 429 20 estimating the core relationships. Marginal effects are calculated to show the change in 430 the probability of succession due to a change in the value of the independent variables. 431 Farm income has components that are exogenous or weakly endogenous including soil 432 quality, land ownership and agricultural training. In addition, land ownership is 433 included since Irish farm succession mostly occurs via inheritance. The area of owned 434 land could be associated positively with identification of a successor. In this model, we 435 use these variables rather than the reported farm income. 436 We analyse whether the presence of a dairy farm or the farm size influences the 437 likelihood of succession. We expected that the presence of a dairy farm would increase 438 the likelihood of succession due to the relatively higher farm income on dairy farms 439 relative to non-dairy farms (Donnellan et al., 2020). However, our model finds land 440 ownership (as opposed to rent) is not statistically significant as shown in Table 4 in 441 Appendix, even though these factors are considered positive indicators for identifying 442 a successor. This is not consistent with previous studies showing a positive relationship 443 between farm size and the probability of farm succession (Kimhi and Bollman, 1999; 444 Breustedt and Glauben, 2007; Morais et al., 2018). 445 The absence of a significant relationship between land ownership size and succession 446 and between dairy farming and succession motivated us to explore the possible 447 interaction of dairy farming and (owned) farm size. We therefore add an interaction 448 variable for the presence of a dairy farm with land ownership (hectares). As a result of 449 this interaction, farm size is not significant for non-dairy but is significant for dairy 450 farms. This indicates that only larger dairy farms are associated with a higher 451 probability of farm succession. These results are presented in Table 3, which we regard 452 as our main results because the model better captures system-specific succession 453 dynamics and reveals patterns that were not evident in the initial specification shown 454 21 in Table 4. The results from the 2020 CSO Census of Agriculture point to an increase 455 in the average size of dairy farms with a reduction in the number of farms classified as 456 specialist dairy. This indicates a rising concentration within dairy farming. The 457 inclusion of the interaction term in Table 3 provides greater explanatory power and 458 offers a more nuanced understanding of the conditions under which farm size matters. 459 For this reason, we present Table 3 as our main model. 460 The age of the farm holder is positive and statistically significant. This is expected given 461 that older farmers are more likely to have reached the point of identifying a successor 462 (May et al., 2019). The marginal effect indicates that the probability of having a 463 successor increases by approximately 2 per cent per year. Stiglbauer and Weiss (2000) 464 also found a positive relation between the age of the farm holder and farm succession. 465 The soil quality variable is positive and statistically significant in the farm succession 466 decision. It indicates that better soil quality brings higher productivity, that provide 467 sufficient income to support two generations during succession process. 468 Table 3 469 Probit Model Results: Determinants of Farm Succession in 2018 470 471 Model 1 Model 2 VARIABLES Coef. sig Marginal effect Coef. sig Marginal effect Interaction of Dairy Farm and Land Ownership 0.0095 ** 0.0092 * Farmer Age 0.0535 *** 0.02 0.0585 *** 0.02 Best Soil Category 0.2139 * 0.08 0.198 0.07 Excessive Workload -0.262 ** -0.09 -0.160 -0.06 Isolation of Living Alone -0.13 - 0.05 -0.109 -0.04 Net New Investment (€) 0.004 0.001 0.002 0.0009 Formal Advisory Contract 0.0170 0.006 0.025 0.009 Participation in AES 0.317 ** 0.11 0.226 0.08 Number of Household Members Age 20 to 44 in 2013** - - 0.0003 0.0001 Constant -3.318 *** -3.645 *** Observations 501 349 *** p<0.01, ** p<0.05, * p<0.1 472 473 22 The results in Table 3 point to the importance of social factors, including excessive 474 workload. Farmers who report excessive workload and related stress are less likely to 475 have identified a successor. While this may seem intuitive, its importance lies in the 476 broader context of succession as a long-term, endogenous process. May et al. (2019) 477 found that farmers experiencing hardship may be reluctant to encourage their children 478 to pursue farming, reinforcing the idea that such stress has cumulative intergenerational 479 effects. Succession is a gradual process shaped by accumulated experiences, evolving 480 identities, and intergenerational interactions (Fischer and Burton, 2014). Continuous 481 exposure to workload stress as observed by the next generation, can alter perceptions 482 of farming as a viable or desirable career. Such social aspects therefore warrant 483 consideration alongside economic drivers to inform more holistic policy and advisory 484 strategies. 485 Isolation is not statistically significant in both model (with/without lagged variable) and 486 has a negative coefficient as expected. It is noteworthy that a substantial proportion of 487 the sampled farmers, approximately 23%, live alone, and these farmers tend to be less 488 likely to identify successors. The marginal effect of isolation is relatively substantial at 489 -5%, compared to other binary variables. While this finding does not reach statistical 490 significance, it aligns with previous research by Dudek (2016), which emphasises the 491 importance of household composition in farm succession based on panel data from 492 Poland. One possible reason for the lack of significance in our case could be sample 493 size limitations. Nonetheless, the results suggest that social factors, particularly 494 household structure, may play an important role in farm succession decisions. 495 Therefore, incorporating social dynamics into succession models remains a relevant 496 avenue for further research. 497 23 Investment was expected to have a strong association with the probability of succession 498 as reported in a previous study in Belgium (Calus et al., 2008). Here it is found to be 499 positive and not significant. Investment could be potentially endogenous given that the 500 presence of a successor could motivate the current holder to invest more. However, we 501 include investment in our model considering its importance, and the fact that farm 502 succession itself has been verified as an endogenous cycle (Fischer and Burton, 2014). 503 Although the investment was not statistically significant in our results, previous 504 research has established it as a key indicator of farm succession. For instance, Calus et 505 al. (2008) found that succession intentions begin influencing farm investment decisions 506 up to ten years before the actual transfer. This suggests that even if this variable does 507 not appear significant in our model, it remains a crucial factor in succession planning, 508 reinforcing the need for early successor identification to ensure farm continuity. The 509 lack of statistical significance in our findings may be partly attributed to sample size 510 limitations. Further research with larger datasets would be beneficial to fully capture 511 the role of investment in farm succession dynamics. 512 We expected that formal advisory (extension) contract could be an indicator of a higher 513 possibility of farm succession since it may indicate openness or willingness to improve 514 farming activities. While it is not statistically significant, it showed a positive 515 coefficient. Including this variable is valuable, as research on farm succession has often 516 placed less emphasis on social factors. Access to advisory services can be seen as a 517 form of social capital, potentially influencing farm succession decisions by providing 518 guidance and strategic planning support as Abdala et al., (2022) investigated. The lack 519 of statistical significance in our model could be due to heterogeneity in how advisory 520 services are utilised, such as the timing of advisory service use may vary, with some 521 24 farmers engaging with advisors too early or too late in the succession process for 522 measurable effects to be captured within our dataset. 523 Farmer participation in an AES is found to be positive and statistically significant in 524 model 1. This supports the conclusion of Cullen et al. (2020) that participation in such 525 schemes has a positive association with the willingness to continue farming. 526 Participation in environmental schemes can also provide economic incentives to 527 younger farmers. 528 Finally, the lagged independent variable for the presence of young adults in the farm 529 household is positive and not significant, which is only included in model 2. Although 530 this lagged variable is not statistically significant, it is retained in Model 2 due to its 531 conceptual importance in succession literature and to test robustness. While this result 532 does not provide strong evidence in our model, the presence of young adults is often 533 considered a key factor in succession decisions. The use of this lagged variable allows 534 us to capture long-term household dynamics that may influence successor availability. 535 The lack of significance in our results could be due to sample size, timing differences 536 in succession planning, or unobserved factors influencing young adults’ career choices 537 around the age categories. Nevertheless, as previous research of Fischer and Burton 538 (2014) suggests, early exposure to farm life and involvement in decision-making 539 processes play a crucial role in the eventual transfer of farm management. 540 5. Discussion and Conclusion 541 In this study, we use econometric methods to explore the drivers and barriers to farm 542 succession, incorporating both social and economic factors. While profitability and land 543 ownership are often key drivers of succession decisions (Pitson et al., 2020), our 544 findings indicate that social factors, such as workload stress and participation in agri-545 25 environmental schemes (AES), also play a significant role in shaping succession 546 outcomes. These insights suggest the importance of considering social sustainability 547 alongside economic factors in the farm succession process. 548 Our results reveal that economic characteristics, particularly land ownership size and 549 soil quality, are key determinants in identifying successors associated with greater 550 successor potential. However, social factors also influence succession decisions by 551 shaping both the current farmer’s management practices and the potential successors’ 552 perceptions of the farm’s long-term viability. For example, workload stress can affect 553 how farmers operate day-to-day, while participation in AES may reflect a more future-554 oriented outlook, which may appeal to younger generations. 555 Succession should not be viewed as a one-time decision but as a continuous process, 556 influenced by ongoing actions and experiences. The decisions of the current farm 557 manager, such as improving soil quality or expanding land, directly affect how potential 558 successors view the farm’s future and their potential role. These accumulated 559 experiences contribute to the successor’s identity formation and long-term engagement 560 with the farm. 561 In line with Calus (2009), our findings challenge the traditional view that succession is 562 driven solely by economic incentives. Instead, we show that succession unfolds 563 gradually, shaped by evolving social and familial dynamics. Social and economic 564 elements interact over time, making succession a dynamic process rather than a fixed 565 event. Supporting this view, findings from a cluster analysis of Irish farm types further 566 confirm that succession decisions are nuanced and shaped by farm-specific economic 567 viability and household or workload-related social conditions (Loughrey et al., 2025). 568 Notably, our study contributes to the growing body of literature by quantitatively 569 examining social factors (Abdala et al., 2022; Špička and Berg, 2022) and providing 570 unique insights into their role in farm succession dynamics. High work intensity, 571 26 especially in demanding systems like dairy farming, can contribute to farm exits 572 (Ferjani et al., 2015), further influencing succession dynamics. In addition, succession 573 decisions evolve gradually, shaped by long-term experiences rather than isolated events 574 (Lobley, 2010). The long-term impact of witnessing parental stress from farming can 575 influence the decisions of potential successors, underlining the importance of resilience 576 across both social and material dimensions (Darnhofer, 2020). 577 Further supporting this perspective, Bertolozzi-Caredio (2024) confirms that the 578 presence of a successor influences the behaviour of incumbent farmers, reinforcing the 579 idea that farm succession is a dynamic and mutual process. To facilitate smoother 580 transitions, targeted strategies are needed to support both successors and incumbents. 581 One such approach includes fostering farmer partnership agreements or hiring 582 additional labour that have proven beneficial but remain underutilized (Conway et al., 583 2017; Garcia et al., 2023; Shin et al., 2023). These measures not only alleviate excessive 584 workloads but also enhance the attractiveness of farming as a viable career path for the 585 next generation. 586 Farms with lower incomes can seek to improve farm viability through participation in 587 AES schemes for example as an economic incentive, particularly on less intensive 588 farms, to stabilise farms. Cullen et al. (2020) notes that a forward-looking self-identity 589 is linked to greater AES participation, ensuring farm continuity for future generations. 590 Furthermore, innovative environmental practices, such as organic farming, may attract 591 younger farmers with new financial incentives through the CAP (Farrell et al., 2022). 592 Policy should broaden its focus to enable succession not only within families but also 593 through hired labour or non-relatives. Our findings imply that policies should address 594 the pathways for both new entrants and incumbents, potentially facilitating partnerships 595 27 to share workload, knowledge, and profits, thereby ensuring the sustainable 596 continuation of farming enterprises. 597 A limitation of this study is that that it relies on farmers’ self-reports about whether 598 they have identified a successor, rather than tracking actual succession events. In 599 addition, capturing farm succession over a longer period would be beneficial. The 600 endogenous cycle of farm succession highlights the link between farmers' identity 601 development and the farm business ladder, which could be better understood using a 602 panel data framework, as in Dudek and Pawłowska (2022). This would help establish 603 the cause-and-effect relationship over time and provide more evidence on the impact of 604 succession on farm continuity, productivity, and sustainability. 605 This study provides valuable insights into farm succession by utilizing a nationally 606 representative dataset. While the dataset used is from 2018, making it one of the most 607 recent sources available for analyzing long-term succession trends, it does not capture 608 the potential impacts of recent economic and social changes, such as the COVID-19 609 pandemic or the ongoing inflationary crisis. These events may have influenced 610 succession decisions by altering economic stability, labour availability, and 611 generational attitudes toward farming. Future research should incorporate more recent 612 data to assess these effects. 613 The statistical significance of some results is limited, partly due to the sample size 614 constraints. Expanding the dataset or complementing quantitative findings with 615 qualitative approaches, such as interviews with farmers and advisors, could provide 616 deeper insights into the role of social capital in succession planning. 617 Despite these limitations, this study highlights the importance of understanding both 618 economic and social factors in farm succession. Future research should continue 619 28 exploring these aspects, particularly in the context of evolving economic conditions and 620 policy changes that shape generational renewal in agriculture. 621 622 Acknowledgement 623 Funding provided by the Department of Agriculture, Food and the Marine under the 624 project entitled RENEW2050 - Rural Generational Renewal 2050 - 2019R414. 625 References 626 Abdala, R. G., Binotto, E., and Borges, J. A. R. (2022). Family farm succession: 627 evidence from absorptive capacity, social capital, and socioeconomic aspects. Revista 628 de Economia e Sociologia Rural, 60. DOI: 10.1590/1806-9479.2021.235777 629 ADAS (2020). Evaluation of the Green Low-Carbon Agri-Environment Scheme 630 (GLAS) – Synthesis of Evidence. 631 Adler, P. S., and Kwon, S. W. (2002). Social capital: Prospects for a new concept. 632 Academy of management review, 27(1), 17-40. 633 Banovic, M., Duesberg, S., Renwick, A., Keane, M. T., and Bogue, P.(2015). The Field: 634 Land mobility measures as seen through the eyes of Irish farmers. The Agricultural 635 Economics Society's 89th Annual Conference, University of Warwick, United 636 Kingdom, 13-15 April 2015. DOI: 10.1111/1477-9552.12125 637 Barral, S., and Detang-Dessendre, C. (2023). Reforming the Common Agricultural 638 Policy (2023–2027): multidisciplinary views. Review of Agricultural, Food and 639 Environmental Studies, 1-4. 640 Bertolozzi-Caredio, D., Bardaji, I., Coopmans, I., Soriano, B., and Garrido, A. (2020). 641 Key steps and dynamics of family farm succession in marginal extensive livestock 642 farming. Journal of Rural Studies, 76, 131-141. DOI: 643 https://doi.org/10.1016/j.jrurstud.2020.04.030 644 29 Bertolozzi-Caredio D. (2024). The farm succession effect on farmers’ management 645 choices. Land Use Policy, Volume 137. DOI: 646 https://doi.org/10.1016/j.landusepol.2023.107014 647 Bertoni, D., and Cavicchioli, D. (2016). Process description, qualitative analysis and 648 causal relationships in farm succession. CAB Reviews, 11(043), 1-11. DOI: 649 https://doi.org/10.1016/j.jrurstud.2020.04.030 650 Bika, Z. (2007). The territorial impact of the farmers' early retirement scheme. 651 Sociologia Ruralis, 47(3), 246-272. DOI: https://doi.org/10.1111/j.1467-652 9523.2007.00436.x 653 Bradfield, T., Butler, R., Dillon, E. J., Hennessy, T., and Loughrey, J. (2023). 654 Attachment to land and its downfalls: Can policy encourage land mobility? Journal of 655 Rural Studies, 97, 192-201.DOI: https://doi.org/10.1016/j.jrurstud.2022.12.014 656 Brennan, M., Hennessy, T., Meredith, D., and Dillon, E. (2021). Weather, Workload 657 and Money: Determining and Evaluating Sources of Stress for Farmers in Ireland. 658 Journal of Agromedicine, 1-11. DOI: https://doi.org/10.1080/1059924X.2021.1988020 659 Brennan, M., Hennessy, T., Dillon, E., and Meredith, D. (2022). Putting social into 660 agricultural sustainability: Integrating assessments of quality of life and wellbeing into 661 farm sustainability indicators. Sociologia Ruralis. DOI: 662 https://doi.org/10.1111/soru.12417 663 Breustedt, G., and Glauben, T. (2007). Driving forces behind exiting from farming in 664 Western Europe. Journal of Agricultural Economics, 58(1), 115-127. DOI: 665 https://doi.org/10.1111/j.1477-9552.2007.00082.x 666 Calus, M. (2009). Factors explaining farm succession and transfer in Flanders. PhD 667 Thesis, Ghent University. 668 Calus, M., Van Huylenbroeck, G., and Van Lierde, D. (2008). The relationship between 669 farm succession and farm assets on Belgian farms. Sociologia ruralis, 48(1), 38-56. 670 DOI: https://doi.org/10.1111/j.1467-9523.2008.00448.x 671 30 Carreira, C., and Teixeira, P. (2011). The shadow of death: analysing the pre-exit 672 productivity of Portuguese manufacturing firms. Small Business Economics, 36, 337-673 351.con 674 Cavicchioli, D., Bertoni, D., and Pretolani, R. (2018). Farm succession at a crossroads: 675 The interaction among farm characteristics, labour market conditions, and gender and 676 birth order effects. Journal of Rural Studies, 61, 73-83. DOI: 677 https://doi.org/10.1016/j.jrurstud.2018.06.002 678 Cavicchioli, D., Bertoni, D., Tesser, F., and Frisio, D. G. (2015). What factors 679 encourage intrafamily farm succession in mountain areas? Mountain Research and 680 Development, 35(2), 152-160. DOI: https://doi.org/10.1659/MRD-JOURNAL-D-14-681 00107.1 682 Central Statistics Office (2022). Census of Agriculture 2020 - Preliminary Results. 683 Retrieved 1st February 2022 from https://www.cso.ie/en/releasesandpublications/ep/p-684 coa/censusofagriculture2020-preliminaryresults/kf/ 685 Cofré-Bravo, G., Klerkx, L., and Engler, A. (2019). Combinations of bonding, bridging, 686 and linking social capital for farm innovation: How farmers configure different support 687 networks. Journal of Rural Studies, 69, 53-64. DOI: 688 https://doi.org/10.1016/j.jrurstud.2019.04.004 689 Contzen, S., and Häberli, I. (2021). Exploring dairy farmers’ quality of life perceptions–690 A Swiss case study. Journal of Rural Studies, 88, 227-238. DOI: 691 https://doi.org/10.1016/j.jrurstud.2021.11.007 692 Contzen, S., Zbinden, K., Neuenschwander, C., and Métrailler, M. (2017). Retirement 693 as a Discrete Life‐Stage of Farming Men and Women's Biography? Sociologia 694 Ruralis, 57, 730-751.DOI: https://doi.org/10.1111/soru.12154 695 Conway, S.F., McDonagh, J., Farrell, M., and Kinsella, A.(2016). Cease agricultural 696 activity forever? Underestimating the importance of symbolic capital, Journal of Rural 697 Studies, 44. DOI: https://doi.org/10.1016/j.jrurstud.2016.01.016. 698 31 Conway, S. F., Farrell, M., McDonagh, J., and Kinsella, A. (2022). ‘Farmers Don’t 699 Retire’: Re-Evaluating How We Engage with and Understand the ‘Older’Farmer’s 700 Perspective. Sustainability, 14(5), 2533. DOI: 701 https://doi.org/10.1016/j.jrurstud.2016.01.016 702 Conway, S. F., McDonagh, J., Farrell, M., and Kinsella, A. (2021). Going against the 703 grain: Unravelling the habitus of older farmers to help facilitate generational renewal 704 in agriculture. Sociologia Ruralis, 61(3), 602-622. DOI: 705 https://doi.org/10.1111/soru.12355 706 Cullen, P., Hynes, S., Ryan, M., and O'Donoghue, C. (2021). More than two decades 707 of Agri-Environment schemes: Has the profile of participating farms changed? Journal 708 of Environmental Management, 292, 112826. DOI: 709 https://doi.org/10.1016/j.jenvman.2021.112826 710 Cullen, P., Ryan, M., O’Donoghue, C., Hynes, S., and Sheridan, H. (2020). Impact of 711 farmer self-identity and attitudes on participation in agri-environment schemes. Land 712 Use Policy, 95, 104660. DOI: https://doi.org/10.1016/j.landusepol.2020.104660 713 Darnhofer, I. (2020). Farm resilience in the face of the unexpected: Lessons from the 714 COVID-19 pandemic. Agriculture and Human Values, 37(3), 605-606. 715 Deming, J., Macken-Walsh, Á., O'Brien, B., and Kinsella, J. (2019). Entering the 716 occupational category of ‘Farmer’: new pathways through professional agricultural 717 education in Ireland. The Journal of Agricultural Education and Extension, 25(1), 63-718 78. DOI: https://doi.org/10.1080/1389224X.2018.1529605 719 Dudek, M. (2016). A matter of family? An analysis of determinants of farm succession 720 in Polish agriculture. Studies in Agricultural Economics, 118(2), 61-67. DOI: 721 10.22004/ag.econ.246256 722 Dudek, M., and Pawłowska, A. (2022). Can succession improve the economic situation 723 of family farms in the short term? Evidence from Poland based on panel data. Land Use 724 Policy, 112, 105852. DOI: https://doi.org/10.1016/j.landusepol.2021.105852 725 32 Duesberg, S., Bogue, P., and Renwick, A. (2017). Retirement farming or sustainable 726 growth–land transfer choices for farmers without a successor. Land Use Policy, 61, 727 526-535.DOI: https://doi.org/10.1016/j.landusepol.2016.12.007 728 Dillon, E., Donnellan, T., Moran, B., and Lennon, J. (2021). Agricultural Economics 729 and Farm Surveys Department, Rural Economy Development Programme, Teagasc. 730 https://www.teagasc.ie/media/website/publications/2021/Teagasc-National-Farm-731 Survey-2020.pdf 732 Donnellan, T., Moran, B., Lennon, J., and Dillon, E. (2020). Teagasc national farm 733 survey 2019 preliminary results, Teagasc. 734 https://www.teagasc.ie/media/website/publications/2020/Teagasc-National-Farm-735 Survey-2019.pdf 736 Eistrup, M., Sanches, A. R., Muñoz-Rojas, J., and Pinto-Correia, T. (2019). A “young 737 farmer problem”? Opportunities and constraints for generational renewal in farm 738 management: an example from Southern Europe. Land, 8(4), 70. DOI: 739 https://doi.org/10.3390/land8040070 740 Errington, A. (2002). Handing Over the Reins: A Comparative Study of 741 Intergenerational Farm Transfers in England, France and Canada. (No. 723-2016-742 48847). Conference paper, European Association of Agricultural Economists (EAAE) 743 2002 International Congress, August 28-31, 2002, Zaragoza, Spain. DOI: 744 10.22004/ag.econ.24905 745 Falzoi, S., Gleeson, E., Lambkin, K., Zimmermann, J., Marwaha, R., O'Hara, R. and 746 Fratianni, S. (2019). Analysis of the severe drought in Ireland in 2018. Weather, 74(11), 747 368-373. DOI:10.1002/wea.3587 748 Farrell, M., Murtagh, A., Weir, L., Conway, S. F., McDonagh, J., and Mahon, M. 749 (2022). Irish Organics, Innovation and Farm Collaboration: A Pathway to Farm 750 Viability and Generational Renewal. Sustainability, 14(1), 93. DOI: 751 https://doi.org/10.3390/su14010093 752 33 Ferjani, A., Zimmermann, A., and Roesch, A. (2015). Determining factors of farm exit 753 in agriculture in Switzerland. Agricultural Economics Review, 16(389-2016-23517), 754 59-72. DOI:10.22004/ag.econ.253691 755 Fischer, H., and Burton, R. J. (2014). Understanding farm succession as socially 756 constructed endogenous cycles. Sociologia ruralis, 54(4), 417-438. DOI: 757 https://doi.org/10.1111/soru.12055 758 Garibaldi, P. (2006). Personnel economics in imperfect labor markets. Oxford, 759 England: Oxford Press. 760 Geoghegan, C., and O'Donoghue, C. (2018). Socioeconomic drivers of land mobility in 761 Irish agriculture. International Journal of Agricultural Management, 7(1029-2020-362), 762 26-34. https://doi.org/10.22004/ag.econ.301162 763 Geoghegan, C., Kinsella, A., and O'Donoghue, C. (2021). The effect of farmer attitudes 764 on openness to land transactions: evidence for Ireland. Bio-based and Applied 765 Economics, 10(2), 153-168. 10.22004/ag.econ.317030 DOI: 766 http://digital.casalini.it/5069765 767 Gilmore, D. A., (1999). The Scheme of Early Retirement from Farming in the Republic 768 of Ireland. Irish Geography, 32(2), 78-86. DOI: 769 https://doi.org/10.1080/00750779909478602 770 Glauben, T., Tietje, H., and Weiss, C. (2006). Agriculture on the move: Exploring 771 regional differences in farm exit rates in Western Germany. Jahrbuch für 772 regionalwissenschaft, 26(1), 103-118. DOI: https://doi.org/10.1007/s10037-004-0062-773 1 774 Glauben, T., Petrick, M., Tietje, H., and Weiss, C. (2009). Probability and timing of 775 succession or closure in family firms: a switching regression analysis of farm 776 households in Germany. Applied Economics, 41(1), 45-54. DOI: 777 https://doi.org/10.1080/00036840601131722 778 34 Góngora, R., Milán, M.J., and López-i-Gelats, F. (2019). Pathways of incorporation of 779 young farmers into livestock farming. Land Use Policy, 85. DOI: 780 https://doi.org/10.1016/j.landusepol.2019.03.052. 781 Garcia-Covarrubias, L., Läpple, D., Dillon, E., and Thorne, F. (2023). Automation and 782 efficiency: a latent class analysis of Irish dairy farms. Q Open, qoad015. 783 Gráda, C. Ó., 1980. Primogeniture and ultimogeniture in rural Ireland. The Journal of 784 Interdisciplinary History, 10(3), 491-497. DOI: https://doi.org/10.2307/203190 785 Hartarska, V., Nadolnyak, D., and Sehrawat, N. (2022). Beginning farmers' entry and 786 exit: evidence from county level data. Agricultural Finance Review, 82(3), 577-596. 787 Hayden, M. T., McNally, B., and Kinsella, A. (2021). Exploring state pension provision 788 policy for the farming community. Journal of Rural Studies, 86, 262-269. 789 Hennessy, T. C., and Rehman, T.(2007). An investigation into factors affecting the 790 occupational choices of nominated farm heirs in Ireland. Journal of Agricultural 791 Economics, 58(1), 61-75. DOI: https://doi.org/10.1111/j.1477-9552.2007.00078.x. 792 DOI https://doi.org/10.1007/s10460-015-9608-9. 793 Ingram, J., Gaskell, P., Mills, J., and Short, C. (2013). Incorporating agri-environment 794 schemes into farm development pathways: A temporal analysis of farmer motivations. 795 Land use policy, 31, 267-279. 796 https://doi.org/https://doi.org/10.1016/j.landusepol.2012.07.007 797 Jack, C., Miller, A. C., Ashfield, A., and Anderson, D. (2019). New entrants and 798 succession into farming: A Northern Ireland perspective. International Journal of 799 Agricultural Management, 8(2), 56-64. https://doi.org/10.5836/ijam/2019-08-56 800 Jeanneaux, P., Desjeux, Y., Enjolras, G., and Latruffe, L. (2022). Farm valuation: A 801 comparison of methods for French farms. Agribusiness, 38(4), 786-809. 802 Kennedy, L., (1991). Farm succession in modern Ireland: elements of a theory of 803 inheritance 1. The Economic History Review, 44(3), 477-499. DOI: 804 https://doi.org/10.1111/j.1468-0289.1991.tb01275.x 805 35 Kimhi, A., and Bollman, R. (1999). Family farm dynamics in Canada and Israel: the 806 case of farm exits. Agricultural Economics, 21(1), 69-79. DOI: 807 https://doi.org/10.1111/j.1574-0862.1999.tb00584.x 808 Kimhi, A., and Lopez, R. (1999). A note on farmers' retirement and succession 809 considerations: Evidence from a household survey. Journal of Agricultural Economics, 810 50(1), 154-162. DOI: https://doi.org/10.1111/j.1477-9552.1999.tb00802.x 811 Loughrey, J., Shin, M., Dillon, E., and Kinsella, A. (2025). Generational Renewal and 812 Farm Succession: Insights from Ireland. EuroChoices, 24(1), 27-35. DOI: 813 10.1111/1746-692X.12456 814 Läpple, D., Hennessy, T., and Newman, C. (2013). Quantifying the economic return to 815 participatory extension programmes in Ireland: an endogenous switching regression 816 analysis. Journal of Agricultural Economics, 64(2), 467-482. DOI: 817 https://doi.org/10.1111/1477-9552.12000 818 Läpple, D., and Kelley, H. (2015). Spatial dependence in the adoption of organic 819 drystock farming in Ireland. European Review of Agricultural Economics, 42(2), 315-820 337. DOI: https://doi.org/10.1093/erae/jbu024 821 Leonard, B., Farrell, M., Mahon, M., Kinsella, A., and O'Donoghue, C. (2020). Risky 822 (farm) business: Perceptions of economic risk in farm succession and inheritance. 823 Journal of Rural Studies. 75, 57-69. DOI: 824 https://doi.org/10.1016/j.jrurstud.2019.12.007Leonard, B., Kinsella, A., O’Donoghue, 825 C., Farrell, M., Mahon, M., 2017a. Policy drivers of farm succession and inheritance, 826 Land Use Policy, 61. DOI: https://doi.org/10.1016/j.landusepol.2016.09.006. 827 Leonard, B., Mahon, M., Kinsella, A., O’Donoghue, C., Farrell, M., Curran, T., and 828 Hennessy, T.(2017b). The potential of farm partnerships to facilitate farm succession 829 and inheritance. International Journal of Agricultural Management, Volume 6 Issue 1. 830 DOI: 10.5836/ijam/2017-06-04 DOI: 10.22004/ag.econ.287266 831 Lobley, M., Baker, J. R., and Whitehead, I.(2010). Farm succession and retirement: 832 some international comparisons. Journal of Agriculture, Food Systems, and 833 https://doi.org/10.1111/j.1477-9552.1999.tb00802.x 36 Community Development, 1(1), 49-64. DOI: 834 https://doi.org/10.5304/jafscd.2010.011.009 835 Mann, S. (2007). Understanding farm succession by the objective hermeneutics 836 method. Sociologia Ruralis, 47(4), 369-383.DOI: https://doi.org/10.1111/j.1467-837 9523.2007.00442.x 838 May, D., Arancibia, S., Behrendt, K., and Adams, J.(2019). Preventing young farmers 839 from leaving the farm: Investigating the effectiveness of the young farmer payment 840 using a behavioural approach. Land Use Policy, 82, 317-327. DOI: 841 https://doi.org/10.1016/j.landusepol.2018.12.019 842 Meredith, D., and Crowley, C. (2018). Continuity and Change: The geo-demographic 843 structure of Ireland’s population of farmers. Irish Geography, 50(2), 111-136. DOI: 844 10.2014/igj.v50i2.1318 845 Morais, M., Borges, J. A. R., and Binotto, E. (2018). Using the reasoned action 846 approach to understand Brazilian successors’ intention to take over the farm. Land Use 847 Policy, 71, 445-452. DOI: https://doi.org/10.1016/j.landusepol.2017.11.002 848 Nuthall, P.L. and Old, K.M. (2017). Farm owners’ reluctance to embrace family 849 succession and the implications for extension: the case of family farms in New Zealand, 850 The Journal of Agricultural Education and Extension, 23:1, 39-60, DOI: 851 10.1080/1389224X.2016.1200992 852 Paroissien,E., Latruffe, L., Piet, L. (2021). Early exit from business, performance and 853 neighbours’ influence: a study of farmers in France, European Review of Agricultural 854 Economics, 48 (5) 1132–1161. DOI: https://doi.org/10.1093/erae/jbab008 855 Pitson, C., Bijttebier, J., Appel, F. and Balmann, A. (2020). How much farm succession 856 is needed to ensure resilience of farming systems? EuroChoices, 19(2): 37–44. 857 Potter, C., and Lobley, M.(1996). The farm family life cycle, succession paths and 858 environmental change in Britain's countryside. Journal of Agricultural Economics, 859 47(1‐4), 172-190. DOI: https://doi.org/10.1111/j.1477-9552.1996.tb00683.x 860 37 Rech, L.R., Binotto, E., Cremon, T. and Bunsit, T.(2021). What are the options for 861 farm succession? Models for farm business continuity, Journal of Rural Studies, 88. 862 DOI: https://doi.org/10.1016/j.jrurstud.2021.09.022. 863 Riley, M. (2006). Reconsidering conceptualisations of farm conservation activity: the 864 case of conserving hay meadows. Journal of Rural Studies, 22(3), 337-353. DOI: 865 https://doi.org/10.1016/j.jrurstud.2005.10.005 866 Russell, T., Breen, J., Gorman, M., and Heanue, K. (2020). Advisors perceptions of 867 their role in supporting farm succession and inheritance, The Journal of Agricultural 868 Education and Extension, 26:5, 485-496, DOI: 869 https://doi.org/10.1080/1389224X.2020.1773284 870 Schlesser, H., Stuttgen, S., Binversie, L., and Kirkpatrick, J. (2021). Insights into 871 Barriers and Educational Needs for Farm Succession Programming. Journal of 872 Extension, 59(4), 4. DOI: 4. https://doi.org/10.34068/joe.59.04.04 873 Shin, M. W., Kinsella, A., Hayden, M. T. and McNally, B. (2023). The role of 874 collaborative farming in generational renewal and farm succession. International Food 875 and Agribusiness Management Review, 27(1): 94–116. DOI: 876 10.22434/IFAMR2023.0070 877 Shortall, S. (2008). Are rural development programmes socially inclusive? Social 878 inclusion, civic engagement, participation, and social capital: Exploring the differences. 879 Journal of Rural Studies, 24(4), 450-457. DOI: 880 https://doi.org/10.1016/j.jrurstud.2008.01.001 881 Špička J. and Berg S. (2022). The impact of human values on the chance of farming 882 continuity. International Journal of Agricultural sustainability, DOI: 883 10.1080/14735903.2022.2047469 884 Stiglbauer, A. M., and Weiss, C. R. (2000). Family and non-family succession in the 885 Upper-Austrian farm sector. Cahiers d'Economie et de Sociologie Rurales, 54, 5-26. 886 DOI: 10.22004/ag.econ.206182 887 https://doi.org/10.34068/joe.59.04.04 38 Tan, E., (2014). Human capital theory: A holistic criticism. Review of educational 888 research, 84(3), 411-445. DOI: https://doi.org/10.3102/0034654314532696 889 Uchiyama, T., Lobley, M., Errington, A., and Yanagimura, S. (2008). Dimensions of 890 intergenerational farm business transfers in Canada, England, the USA and Japan. The 891 Japanese Journal of Rural Economics, 10, 33-48. DOI: 892 https://doi.org/10.18480/jjre.10.33 893 Väre, M. (2006). Spousal effect and timing of retirement. Journal of Agricultural 894 Economics, 57(1), 65-80. DOI: https://doi.org/10.1111/j.1477-9552.2006.00032.x 895 Wheeler, S., Bjornlund, H., Zuo, A., and Edwards, J. (2012). Handing down the farm? 896 The increasing uncertainty of irrigated farm succession in Australia. Journal of Rural 897 Studies, 28(3), 266-275. DOI: https://doi.org/10.1016/j.jrurstud.2012.04.001 898 Zagata, L., Hrabák, J., Lošťák, M., Bavorová, M., Ratinger, T., Sutherland, L.-A., and 899 McKee, A. (2017). Research for AGRI Committee-Young farmers-Policy 900 implementation after the 2013 CAP reform. Retrieved from 901 https://www.europarl.europa.eu/thinktank/en/document/IPOL_STU(2017)602006 on 902 09 May 2023. 903 Zagata, L., and Sutherland, L.-A. (2015). Deconstructing the ‘young farmer problem in 904 Europe’: Towards a research agenda. Journal of Rural Studies, 38, 39-51. DOI: 905 https://doi.org/10.1016/j.jrurstud.2015.01.003 906 907 https://doi.org/10.1016/j.jrurstud.2015.01.003 39 Appendix 908 Table 4 909 Probit Model Results: Determinants of Farm Succession in 2018 910 Model 1 Model 2 Coef. Sig Marginal effect Coef. Sig Marginal effect Dairy Farm 0.3019 0.12 0.3504 * 0.12 Land Ownership -0.0008203 0.00 -0.000959 0.00 Farmer Age 0.05858 *** 0.02 0.05407 *** 0.02 Best Soil Category 0.1991 0.07 0.2050 0.07 Isolation of Living Alone -0.1683 -0.05 -0.1717 -0.06 Excessive Workload -0.1421 -0.05 -0.2589 * -0.09 Net New Investment 0.000000949 8 0.00 0.0000008113 0.00 Formal Advisory Contract 0.01844 0.01 0.008074 0.00 Participation in AES 0.1900 0.07 0.2910 * 0.1 Number of Household Members Age 25 to 44 in 2013** -0.005908 -0.00 Constant -3.736 *** -3.443 *** Number of obs 349 501 Pseudo r-squared 0.161 0.124 *** p<.01, ** p<.05, * p<.10 911 912