1 Introduction

The term and type of land leasing play an important role in the adoption of sustainable land management practices and the uptake of agri-environmental schemes (Adenuga et al., 2021; Myyra et al., 2005; Ranjan et al., 2019). Sustainable techniques of production, including the conservation of land as an important agricultural resource, require long-term investments in land (Carolan et al., 2004). For example, a study by Ayamga et al. (2016) that analysed farm households’ investment decisions under varying land tenure arrangements found that the duration and security of land tenure positively influenced their decision to invest in conservation measures and soil improvement. Similarly, a study by Kousar and Abdulai (2016) has also shown that land tenure security increases the intensity of investment in long-term soil-improving measures which in turn results in significant and positive effects on farm productivity. Another study by Geoghegan and O’Donoghue (2018) also showed that land rented over short rental period (conacre system) in Ireland tends to be of poorer quality compared to farms under long-term lease or owner-occupied. Long-term land leasing offers greater security to the farmer to invest in soil and fertility management. When farmers do not have the assurance that they will be able to recoup their investment in land improvement, they are more likely to adopt management strategies that maximize short-term benefits even if this gradually diminishes the fertility of the soil (Adenuga et al., 2023). This might contribute to negative externalities and possibly lead to market failure (Adenuga et al., 2019, 2020; Ranjan et al., 2019).

The objective of this study is to analyse the behavioural drivers of farmers’ intention to adopt long-term land leasing in Northern Ireland. Land mobility is a significant issue in the region. Most farms are family-owned with the transfer of land through open-market sales being very limited and land prices are high. In addition, access to land through leasing is constrained by the short-term conacre land rental system common in the region (Adenuga et al., 2021; Milne et al., 2022). The Conacre land rental system which is unique to the island of Ireland involves the renting of land nominally for 11 months or 364 days and permits land to be let to other farmers without the need for either party to enter into a long-term commitment. Currently around one-third (about 300,000 hectares) of agricultural land in Northern Ireland is farmed under conacre agreements (Adenuga et al., 2021). However, the short-term nature of the conacre system does not provide security of tenure over a longer time frame to encourage investment in land. This may have a consequential effect on the overall competitiveness, environmental sustainability and productivity of the Northern Ireland agri-food sector (Milne et al., 2022). By making use of a behavioural economics approach and understanding the values and psychological constructs underlying farmers’ decisions to undertake long-term land leasing, this study makes an important contribution to the land economics literature. To the best of our knowledge, this study offers the first attempt to analyse the behavioural factors influencing farmers’ intention to adopt long-term land leasing using an extended theory of planned behaviour (TPB) methodology. While it has been acknowledged in the literature that farmers land use decisions are influenced by socioeconomic and farm structural characteristics (Adenuga et al., 2023), there has not been any empirical study on the influence of social and psychological characteristics on farmers intention to adopt long-term land leasing. The study provides reliable information on the factors that are likely to drive the adoption of long-term land leasing among farmers to improve the sustainability and environmental management of the land.

In this study, we not only used the TPB model to analyse our data but also extended it to include two additional constructs of perceived risk and environmental constructs. These constructs are hypothesised to influence the adoption of long-term land leasing. By extending the TPB model, we are able to increase the predictive power of the model. It also provides a better and broader understanding of the reasons behind farmers’ continued use of the conacre land rental system not minding other alternatives. The study will provide an evidence base to design policies and programmes aimed at stimulating the adoption of long-term land leasing.

The remaining part of this paper is organised as follows: In Sect. 2 we explained the theory of planned behaviour concept and the research hypothesis. Section 3 describes the study methodology. The study results are presented in Sect. 4, while the results are discussed in Sect. 5. Finally, we conclude in Sect. 6 by presenting an overview of the study outcomes alongside relevant policy recommendations.

2 Literature review

2.1 The theory of planned behaviour

Farmers in making their decisions generally aim to reach a higher level of utility by considering both material and socioeconomic benefits. This often depends on social relations and perceived transaction costs. The incorporation of behavioural factors in a study aimed at encouraging farmers to change from the status quo and take up long-term land leasing would be essential in designing evidence-based policies. It is widely acknowledged in the literature that farmers’ behaviour and decision-making takes place in a dynamic and complex environment, being potentially influenced by a number of factors including social, economic, political, and ecological factors (Hayden et al., 2021; Rose et al., 2018).

The use of the theory of planned behaviour (TPB) to explain human behaviour has been established in the behavioural economics literature (Ajzen, 1991; Faisal et al., 2020; Mingolla et al., 2021). The model encompasses multidimensional constructs and can be extended with the inclusion of additional constructs (Borges et al., 2019; Faisal et al., 2020). It is a social-psychological model that aims to explain the variance in volitional behaviour (Ajzen, 1991). Although it originates from social psychology, it has been successfully applied to different fields of research to predict and understand the factors underlying human behaviour (Baeuml et al., 2021; Hansson et al., 2013; Mingolla et al., 2021). It was developed on the premise that people’s actual behaviour is driven by their intention to perform that particular behaviour. Intention, is assumed to capture the motivational factors that influence a behaviour is an indication of how hard people are willing to try, or how much an effort they are planning to exert, in order to perform the behaviour. As a general rule, the stronger the intention to engage in a behaviour, the more likely should be its performance (Ajzen, 1991). The TPB stipulates that intention is jointly determined by three psychological constructs: attitude, subjective norm, and perceived behavioural control toward the behaviour (Faisal et al., 2020; Yazdanpanah et al., 2014). In a nutshell, the TPB implies that in order to generate an intention to undertake a particular behaviour, just focusing on positive attitudes towards the behaviour is not enough. Individuals also need to perceive that such behaviour would be supported by others within his or her social network and that he or she has the ability to perform that behaviour (Ajzen, 1991). Attitude by definition refers to the evaluation of the behaviour of interest in terms of how it is being viewed as either favourable or unfavourable. Subjective norms on the other hand refer to perceived social pressure towards the behaviour while perceived behavioural control is the personal assessment of the feasibility of executing the behaviour in a given context (Ajzen, 1991). The intention to engage in an activity would be greater when the attitude and the subjective norm constructs are more favourable, and the perceived behavioural control is high. The constructs are usually non-observable, latent constructs determined by observable measurable indicators (Hansson et al., 2012). It is however important to ensure that in measuring the latent constructs, causality exists between them and the observable measurement indicators (Hansson et al., 2012).

The TPB is extended in this study with the inclusion of two additional constructs of perceived risk and environmental attitude. Previous studies have shown that extending the TPB increases its predictive power (Arunrat et al., 2017; Bagheri et al., 2019; da Silva et al., 2020; Daxini et al., 2019; Faisal et al., 2020). For example, Faisal et al. (2020) extended the TPB by including moral norms, risk perception, and social attributes as additional constructs in their analysis of the factors influencing livestock herders’ intention to adopt climate-smart practices. Similarly, Yazdanpanah et al. (2014) in their study of factors influencing farmers’ intention to conserve water also included perceived risk as an additional construct.

The specific action to be analysed in relation to the TPB is the intention to adopt long-term land leasing. Intention (INT), in this case, is defined as the anticipation of letting out land for a farmland owner or letting in land for a farmer on a long-term contract of at least five years with and without tax incentives. It is hypothesized that intention as defined, is influenced by farmers’ attitude (ATT) towards long-term land leasing, perceived pressure from significant others (SN, neighbours, land agents, accountants, family members etc.), farmers’ perception of their ability to execute and undertake long-term land leasing contracts (PBC), the risk attitude of the farmers and landowners (PR) and attitude towards land and environmental management (EV). Mediation is also allowed between the variables, for example, ATT mediates between EV and INT. The risk attitude of farmers is included as an additional construct it is believed that risk-averse farmers are less likely to adopt long-term land leasing. Variability exists in the extent to which farmers take risks and this can have an effect on their land use decisions (Andersson, 2012; Howley et al., 2015). Previous studies by O’Connor et al. (1999) and Arunrat et al. (2017) have demonstrated that attitude to risk can influence the willingness to change behaviour. The risk attitude of farmers may therefore be a significant contributor to their intention to adopt long-term land leasing. We also include environmental orientation because it is believed that farmers’ underlying environmental values could influence their adoption decisions about long-term land leasing. For example, a previous study by Howley et al. (2015) has shown that farmers’ attitude towards the environment is influenced by their decision to plant trees. The following hypotheses were tested in this study.

H1: Farmers’ attitude (ATT) has a positive influence on their intention to take up long-term land leasing (INT).

H2: Farmers perceived behavioural control (PBC) has a positive influence on INT.

H3: Farmers’ subjective norm (SN) has a positive influence on INT.

H4: Farmers’ PBC has a positive influence on their ATT.

H5: Farmers’ SN has a positive influence on their ATT.

H6 Farmers’ SN has a positive influence on their PBC.

H7: Farmers’ attitude towards the environment (EV) has a positive influence on INT.

H8: Famers’ perception of risk (PR) has a negative influence on INT.

H9: Farmers EV has a positive influence on ATT.

H10: Farmers’ PR has a positive influence on ATT.

The first five hypotheses H1, H2, H3, H4 and H5 mainly belong to the focal constructs of TPB (ATT, SN, PBC) while the sixth to ninth hypotheses are related to the extended TPB.

3 Methodology

To achieve the study objective, we employed a mixed methods approach in which we combined qualitative and quantitative analytical techniques. The qualitative aspect of the methodology involved undertaking key informant interviews and focus group discussions (FGDs) with key stakeholders in the Northern Ireland agricultural sector. The FGDs were geographically dispersed, with the first group containing nine farmers from the Southwest region of Northern Ireland, while the second group contained seven farmers from the Northeast region. The interviewees largely reflect Northern Ireland’s farming sector, where the majority (twelve participants) belonged to dairy, beef and sheep backgrounds, while the rest belonged to arable, poultry or mixed farms sector. The quantitative aspect of the analysis combines structural equation modelling (SEM) with the TPB framework. Previous studies for example Borges, Oude Lansink, Marques Ribeiro, and Lutke (2014), and Martínez-García et al. (2013) have combined the TPB with correlations. The limitation of such an approach is that it is only able to assess the relationship between just one construct and intention at a time, with no means of assessing the relative importance of each construct. The use of SEM overcomes this shortcoming by simultaneously estimating all relationships between the constructs and intention in the TPB models (Borges et al., 2016; De Leeuw et al., 2015). The flow chart for the study methodology is presented in Fig. 1.

Fig. 1
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Flow chart of study’s research components and methods Study sample and data collection

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The sampling frame is the Northern Ireland agricultural census data which consists of 25,000 farms. For the year 2020, 12,747 farmers completed the census survey. To select our sample, we employed a stratified random sampling technique. The farmers were grouped into six strata, and they include (1) farmers that farm on owned land only, (2) farmers that farm on owned and rented land, (3) farmers that farm on owned and rented land but also let out land, (4) farmers that farm on owned land only but also let out land, (5) farmers that farm only on rented land and (6) farmland owners that have let out all their land. Given the large number of farmers in the group of farmers farm on owned land only and farmers that farm on owned and rented land, we randomly selected 20% of farmers from the total number of farmers in these two groups. We included 100% of all the remaining four strata in our sample. In total, 4029 farmers were selected and administered with the questionnaire in a hybrid format that allows it to be completed either online or on paper. More details on the data collection method can be found in related publications (Adenuga et al., 2023; Adenuga, Jack, McCarry, & Caskie, Adenuga et al., 2023a, b). Out of the 4029 questionnaires administered, 1228 paper questionnaires were returned in the pre-paid envelopes sent alongside the questionnaires before the deadline date while 499 questionnaires were completed online. In total, we received 1727 responses for analyses. Questionnaires that do not contain information relating to renting-in or renting-out land were not considered eligible for inclusion in the analyses. The data for the paper questionnaires were entered into a Microsoft Office Excel document by one of the researchers and the others were contracted to an external company to input the data. Variables were checked for erroneous data. All blank entries from each questionnaire were cross-referenced to identify invalid responses versus no responses were possible. In the process of cleaning the data, 31 observations were dropped. As a result, we were left with 1696 observations for analyses. Although with some missing data as some of the farmers did not fill out the questionnaires.

3.1 Structural equation modelling (SEM)

Structural equation modelling (SEM) was employed in this study to test the causal relationships between the intention to undertake long-term land leasing and the constructs of the extended TPB. The use of SEM is appropriate given that the adoption of long-term land leasing is hypothesised to be influenced by multiple mediators (Legg et al., 2023; McEachan et al., 2010). Unlike the traditional regression equations, the use of SEM allows for the simultaneous determination of the extent to which the extended TPB constructs mediate the adoption of long-term land leasing (McEachan et al., 2010). In the context of multiple independent variables, multiple mediators and multiple dependent variables as hypothesised in this study, SEM is an appropriate approach, enabling estimation of the direct and indirect effects of the variables alongside their standard errors (McEachan et al., 2010). Structural equation modelling as a modelling framework consists of two parts, namely the measurement model and the structural model (Kaplan, 2008). The measurement model is the part of the model which uses observed variables (indicators) to measure latent variables, via a restricted (confirmatory) factor model. On the other hand, the structural model is the part of the model that states the causal relationship between the latent variables via systems of simultaneous equations (Kaplan, 2008). In SEM, some variables can be dependent in some equations and independent inothers (Toma et al., 2013). The path diagram in SEM which specifies the model structure provides an excellent line to measure the relationships between the latent and observational variables. This provides the opportunity for a rigorous test of the behavioural models using multiple indices. SEM is generally aimed at confirmatory rather than exploratory analysis, hence it applies more to theory testing than theory development (Toma et al., 2013).

The general SEM model is presented in Eq. (1)

$$Y=\alpha +BY+{\Gamma }X+e$$
(1)

Where\(Y\) represents all endogenous variables, observed \(\left(y\right)\) and latent \(\left(\eta \right)\) and \(X\)represents all exogenous variables, observed \(\left(x\right)\)and latent \(\left(\xi \right)\). \(B\) and \({\Gamma }\) are the path coefficients for the structural (regression) and measurement pathways respectively. \(\alpha\) is the intercept of the endogenous variables and \(e\)is the measurement error. The general equation can be broken down into three components as presented in matrix terms in Eq. (2) to (4).

$${\text{The structural model}}\,\,\eta :\eta = B\eta + \Gamma \xi + \zeta$$
(2)

Where \(\eta\) is a vector of endogenous (criterion) latent variables; \(\xi\) is a vector of exogenous (predictor) latent variables; \(B\) is a matrix of regression coefficients of \(\eta\) variables in the structural model; \({\Gamma }\) is a matrix of regression coefficients of \(\xi\) variables in the structural model. \(\zeta\) is a vector of equation error (random disturbances) in the structural model.

The latent variables are linked to observable variables through the measurement equations for the endogenous variables and exogenous variables. The measurement equations are presented in Eqs. (3) and (4).

$${\text{The measurement model for}}\,y:y = {\Lambda _y}\eta + \varepsilon$$
(3)
$${\text{The measurement model for}}\,x:x = {\Lambda _x}\xi + \delta$$
(4)

\(y\) is a vector of endogenous variables and \(x\) is a vector of exogenous (predictor) variables. \({{\Lambda }}_{y}\) and \({{\Lambda }}_{x}\) are matrices of factor loadings, respectively. \(\epsilon\) is a vector of measurement errors in \(y\) and \(\delta\) is a vector of measurement errors in \(x\). The statistical analysis was performed using STATA 15 software.

4 Results and discussion

4.1 Land rental characteristics

Table 1 gives an overview of the way land is rented in and rented out by the farmers in Northern Ireland. The results show that the conacre land rental system remains very dominant in the region.

Table 1 Land rental characteristics
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About 34% of the farmers rented out their land and 91% of the land was rented out in conacre. Only 5% of the land was rented out on a long-term lease and 4% was a combination of conacre and long-term lease. Similarly, 78.4% of the 687 respondents that rented in land did so on conacre while only 12% rented in their land on a long-term lease. Also, farmers that rent in land on a long-term lease own larger farm size (62.6 ha) compared to farmers that rented in land in conacre. The majority of the land rented in conacre is rolled over with 95% and 97% of land rented out and rented in respectively being rolled over from one year to the other without having to sign a new contract. It should be noted that, regardless of the number of times a particular rent is rolled over, it still does not provide the assurance needed for the farmers to be able to invest in the land. The results also show that 6.3% of the farmers were not actively farming but rented out land to other farmers. The average land rented overall was 22.7 hectares while the average land rented in was 23.8 hectares. A good proportion of the farmers (48.1%) considered the closeness of the land rented to the main farm building as a very important factor in making their decision to rent land while only 6.5% considered it as not that important.

4.2 Measurements items in the TPB models

To capture farmers’ inherent views on long-term land leasing, statements, reflecting the constructs of the extended TPB model, were developed, and used as measurement indicators. Twenty-four measurement items were used for direct measurement and operationalisation of the TPB and extended TPB models. The statements were measured using a 5-point Likert scale anchored in the extreme points to capture the intention, attitude, subjective norm, perceived behavioural control, attitude to risk and environmental behaviour of the farmers with lower values (1 and 2) standing for “unlikely” and high values (4 and 5) standing for “likely” while the middle number (3) interpreted as a neutral response. Previous behavioural studies, for example, Tensi et al., (2022) have also used the 5-point Likert scale. A summary of the farmers’ responses to the measurement items, reliability coefficients, means and standard deviations (SD) for the TPB and extended TPB models are presented in Tables 2 and 3 respectively. Some of the respondents did not answer all the questions; so, denominators relating to the total number of people answering each question are reported in the results section where appropriate.

Table 2 Survey questions, reliability coefficients, means and standard deviation (SD) for the TPB model
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Table 3 Survey questions, reliability coefficients, means and standard deviation (SD) for the additional constructs to the TPB model
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The internal consistency, validity, and reliability of the set of items for each latent construct in the models were analysed using Cronbach’s alpha (α) coefficient. A construct is considered to demonstrate adequate reliability if the alpha coefficient is 0.6 or above (Jack et al., 2020; Triste et al., 2018). The Cronbach’s alpha Coefficients for all the constructs showed good-to-excellent reliability ranging from 0.60 to 0.92. This supports the argument for internal consistencies of the constructs.

In general, farmers showed a positive attitude towards long-term land leasing with a mean of 3.74 for all items used to measure the construct. When we considered only farmers that currently rent out land, the mean value for attitude towards long-term land leasing was 3.55 indicating that this group of farmers has a lower positive attitude towards long-term land leasing compared to the average for the whole sample. Among the nine measurement items used to measure attitude, two items “Long-term land leasing would make a farmer’s retirement planning easier” and “Long-term land leasing would provide security of tenure and certainty” had a mean of at least 4.0, indicating that farmers have some level of understanding of the likely benefits of long-term land leasing. This was also reflected in the focus group discussion we undertook in which farmers acknowledged that conacre with its lack of security of tenure does not encourage investment in land management. One farmer stated: “I’m very friendly with 3 or 4 guys who do regular soil analysis around the country, when you look at the results of the farms you can see the conacre land immediately because it is not in anything like as good fertility as someone’s farm, it stands out straight away. You must ask yourself “Why?” Well, why would you bring in a lime spreader to spread lime and improve the productivity of the land if you know come the 1st ofNovember next year you’re out?”. The fact that the statement “long-term land leasing would provide security of tenure and certainty” has the highest score is significant because it implies that the farmers understand that long-term land leasing can be advantageous in terms of security of tenure, and as such, would support investment in the land. However, the results also show that farmers are risk-averse given the high score of the perceived risk construct.

Results of Structural equation modelling.

The analysis of the factors influencing the intention to take up long-term land leasing was undertaken in stages. In the first stage, we conducted a separate exploratory PCA analysis for the statements in each of the constructs (Fakayode et al., 2016). A simple final solution about the statements to be included in the TPB and extended TPB models was obtained using a combination of the K1 rule, Scree plot, parallel analysis and theoretical and conceptual coherence (O’Kane et al., 2017). Statements with factor loadings greater or equal to 0.3 on their target factor were retained while statements that did not load up to 0.3 on any component were removed as had been done in previous studies (Hair et al., 2010; Hansson et al., 2012; O’Kane et al., 2017). In the second stage, we employed the structural equation modelling technique to analyse the factors influencing long-term land leasing. Taking advantage of our large data set, we first undertook the analysis for all the observations in our data set and then for only farmers that currently rent out land. The result of the structural equation modelling for the first part of the analysis is presented in Table 4. The analysis was undertaken using different estimation options of “maximum likelihood” and the “maximum likelihood with missing values”. Although the point estimates for both models were similar, we adopted the model results of the “maximum likelihood with missing values” given that it possesses lower standard errors. To assess the fitness of the structural equation model, multiple indices were used. The model indices alongside the recommended values are presented in Table 5.

Table 4 Results of Structural equation model (maximum likelihood with missing values, n = 1,684)
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Table 5 Goodness of fit measures of SEM model
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The indices for both the TPB and the extended TPB model attained the recommended Goodness-of-Fit (GOF). The root means square error of approximation (RMSEA), the goodness of comparative fit index (CFI), Tucker-Lewis index (TLI) and the coefficient of determination (CD) were all close to or better than the recommended levels. This indicates that the hypothetical model was suitable for analysing the survey data. The path diagrams for the standardized path coefficients are presented in Figs. 2 and 3. They represent the hypothesised relationship between the constructs: Intention (INT), subjective norm (SN), attitude (ATT), perceived behavioural control (PBC), perceived risk (PR) and the environmental (EV) constructs for the TPB and extended TPB models respectively. The coefficients were standardized so that all the model-implied variances were set to 1, allowing the covariances to be interpreted as correlations. In addition, the standardized coefficients make paths across the model comparable because it puts all the variables in the model (observed and latent) on the same scale. The lower value of RMSEA for the extended TPB model suggests that including additional constructs to the original TPB model helps to improve the model.

Fig. 2
figure 2

Path diagram for the TPB model for all observations in the data set; Source: Authors’ graph created with Stata 15 software

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Fig. 3
figure 3

Path diagram for the extended TPB model for all observations in the data set; Source: Authors’ graph created with Stata 15 software

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The results of our analyses showed that the results for both TPB and the extended TPB models are similar in terms of the relationships between the constructs and the intention to take up long-term land leasing. All the constructs for the TPB model, ATT, PBC and SN were positive and statistically significant at the 1% level. However, the magnitude of the effects of the constructs on farmers’ intention to take up long-term land leasing was asymmetric. ATT has the largest influence given the relative size of the path coefficient. On the overall, our TPB model explains 75.6% of the variance in behavioural intention to take up long-term land leasing. In a meta-analyses review of TPB undertaken by Armitage and Conner (2001), they had also found attitude, subjective norm and perceived behavioural control to account for significant variance in individuals’ behaviour. The study results imply that farmers are more likely to embrace long-term land leasing as they have more positive attitudes towards the system. A standard deviation increase in attitude is associated with a 0.43 standard deviation increase in intention to take up long-term land leasing. Farmers attribute more positive attitudes to long-term land leasing than negative attitudes. The positive and statistically significant influence of PBC implies that the farmers have some level of command and control over undertaking long-term land leasing contracts. The results of the analysis also show that both PBC and SN also have positive and statistically significant effects on attitude. Previous studies by Faisal et al. (2020) also found the existence of two indirect causal relationships between the TPB constructs indicating that individual farmers’ ATT is influenced by their environment.

The extended TPB model included perceived risk and pro-environmental behaviour as additional constructs to the TPB model. In general,, the extended TPB model explains 97.3% of the variance in behavioural intention to take up long-term land leasing. The results of the analysis showed that PR has a statistically significant and negative impact on the intention to take up long-term land leasing. This implies that farmers that are risk-averse are less likely to take up long-term land leasing. Precisely, one standard deviation increase in PR reduces the intention to take up long-term land leasing by 0.18 standard deviation. On the other hand, EV does not have a statistically significant relationship with the intention to take up long-term land leasing and the relationship was also negative. However, EV has a positive and statistically significant effect on ATT implying that attitude significantly mediates the effect of environmental behaviour on the intention to take long-term land leasing. Just like the TPB model the ATT PBC and SN constructs were also positive and statistically significant at the 1% level in the extended TPB model. ATT also has the greatest effect in the extended TPB model relative to the other constructs.

Result of analysis considering only farmers the rent-out land.

The results of the analyses considering only farmers that currently rent out land are presented in Table 6. The indices presented in Table 7 for both the TPB and the extended TPB model also attained the recommended Goodness-of-Fit (GOF) in the sub-sample analysis.

Table 6 Results of Structural equation model (maximum likelihood with missing values, n = 583) for farmers that rent out land
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Table 7 Goodness of fit measures of SEM model for farmers that rent out land
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We found some slight differences in the results obtained in the sub-analysis compared to when the analysis was undertaken for the whole observations in the sample. Although the results also show that ATT, PBC and SN constructs all have a positive and statistically significant effect on the intention to take up long-term land leasing, this occurred at a different level of statistical significance for PBC and SN constructs in both the TPB and extended TPB models. Just like the results for the whole sample, for the TPB model, we also found a positive and statistically significant relationship between SN and ATT, PBC and ATT as well as SN and PBC. The difference between the analyses undertaken for the whole observations in our sample and for the sub-group of farmers that currently rent out land can be observed in the extended TPB model. Although not statistically significant, we found a positive relationship between the EV and the intention to take up long-term land leasing. This is unlike for the whole sample where the relationship was negative. Also, unlike for the whole sample, the relationship between SN and ATT was not statistically significant in the extended TPB model and was only significant at the 10% level of significance for the TPB model. The path diagrams showing the path coefficients for the TPB and extended TPB models for this group of farmers are presented in Figs. 4 and 5 respectively.

Fig. 4
figure 4

Path diagram for the TPB model for farmers that rent out land only; Source: Authors’ graph created with Stata 15 software

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Fig. 5
figure 5

Path diagram for the extended TPB model for farmers that rent out land only; Source: Authors’ graph created with Stata 15 software

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5 Discussion

This paper applies the social-psychology theory of planned behaviour to explain the intention of farmers and farmland owners to take up long-term land leasing. The Structural equation analyses revealed an excellent fit between the TPB models and the data, although the extended model fits the data better when the proportion of explained variance is considered. Our study found the following: (1) Farmers have a good level of understanding of the benefits of long-term land leasing; (2) attitude, perceived behavioural control and subjective norm have a significant positive impact on the intention to engage in long-term land leasing with attitude having the greatest influence; (3) farmers’ perceived risk has a significant negative impact on their intention to engage in long-term land leasing; (4) attitude of the farmers has a mediating on the relationship between environmental management of the land and the intention to undertake long-term land leasing. This important role of attitude as a mediator between the intention to take up long-term land leasing and other constructs is fully in line with those of previous studies (Baeuml et al., 2021). The results confirm the need to take into consideration the behaviour of farmers when formulating policies relating to long-term land leasing. The study provides support for the TPB model as an appropriate model to examine farmers’ intention to undertake long-term land leasing.

. In terms of theory, the study represents a new approach to the study of farmers’ intention to undertake long-term land leasing using a behavioural economics approach. The results of the quantitative analyses were complemented by statements from the farmers in the FGDs. These results have a strong policy implication as it emphasises the importance of formulating policies that would support change in the attitude of farmers towards long-term land leasing. To increase the uptake of long-term land leasing in Northern Ireland, changing the perspectives of farmers in relation to their attitudes towards long-term land leasing should be a primary objective. This is particularly the case because the use of conacre is not just a system but has historical and political connections to agriculture on the Island of Ireland. A participant in one of our FGDs expressly stated: “Conacre is part of the culture of Northern Ireland farming” Another farmer stated that “Personally for me, I have no problem (with the system). Any land I have leased, I have had it for 12, 13 or 14 years. It’s between me and the man who owns it. Another farmer added “A third of Northern Ireland agriculture is conacre. If it isn’t broke why fix it?” There is generally a mindset among some of the farmers that they are content with the conacre system, and a significant behavioural and attitudinal change would be required to make them change from the status quo.

The positive and statistically significant relationship between subjective norm and the intention to adopt long-term land leasing is also significant as it indicates that the farmers’ decisions are particularly influenced by those around them. Previous studies have also found that farmers value the opinion of others either because they seek approval or because they do not want to be seen as deviating from the cultural norms and values (Borges & Lansink, 2016; Borges et al., 2014; Martínez-García et al., 2013). The high correlation of the influence of accountants, land agents and solicitors are an indication that these groups of stakeholders have important roles to play in developing policies to encourage long-term land leasing in Northern Ireland. It is essential that information coming from stakeholders about long-term land leasing is coherent and aligns with the current reality on the ground. There has to be a common position taken by them.

6 Conclusion

In this paper, we employed the social-psychology theory of planned behaviour together with qualitative techniques to analyse the intention of farmers to adopt long-term land leasing. Intention is modelled as a function of belief-based measures which include attitude to long-term land leasing, subjective norms, perceived behavioural control, perception of risk-taking and environmental orientation which accounts for the heterogeneity in the environmental attitude of farmers and farmland owners. The model constructs were validated and confirmed using the PCA. The study results show that farmers’ attitude has the greatest impact on their intention to adopt long-term land leasing. Conducting the analysis for only farmers that currently rent out land also shows similar results with the attitude component of the model having a greater impact on the intention of the lessors to adopt long-term land leasing. Our findings provide support for the TPB as a whole and hence adequate in examining farmers’ intention to take up long-term land leasing. Not only is the intention to take up long-term land leasing directly influenced by attitude, but the results also show that it serves as a mediator between other variables considered in the models. The result has strong policy implications as it emphasises the importance of formulating policies that would support change in the attitude of farmers towards long-term land leasing. To increase the uptake of long-term land leasing in Northern Ireland, changing the perspectives of farmers abouttheir attitudes towards long-term land leasing should be a primary objective. There is generally a mindset among some of the farmers that they are content with the conacre system, and a significant behavioural and attitudinal change would be required to make them change from the status quo. Efforts should also be made to increase the capability of the farmers through relevant training and ensure accurate and consistent information is provided to farmers by those professionals whom they look up to for advice around land leasing i.e., accountants, solicitors, and land agents.

With the UK no longer part of the Common Agricultural Policy (CAP) and the four devolved administrations having greater responsibility in relation to designing agricultural policies there is a need for legislative and structural change to support long-term land leasing, particularly in Northern Ireland where there is currently no tenancy legislation. For example, changes to tax legislation to support tax incentives for long-term land leasing. By incentivising the renting out land on a long-term basis, more land can be brought into farming through private landowners who might be less productive renting out their land rather than farming it themselves as has been observed in the Republic of Ireland. It also has the potential to increase the opportunities for young farmers and new entrants as well as increase the efficiency with which rented land is being managed.

A possible limitation of this study is the risk of social desirability bias due to the self-reported nature of the data collection process. This is because the farmers may have overestimated their positive disposition towards long-term land leasing in a bid to impress the research team. However, we believe that the assurance given to the farmers in terms of the anonymity of the survey may have mitigated this source of bias. In designing our questionnaire, we have also explained in detail the focus of the study and the need for the farmers to be as objective as possible in answering the questions. We are therefore confident that the self-reported behaviour used in this study is an appropriate proxy for intention to take up long-term land leasing. Future research should analyse the relationship between long-term land leasing and environmental sustainability of agricultural production as soon as adequate data is available to do that.