Behavioural drivers of long-term land leasing adoption: application of an extended theory of planned behaviour

Abstract

Increasing agricultural productivity while also protecting the environment, requires careful stewardship of the most important resource in production – land. Most land in Northern Ireland is controlled via owner occupation and granting access to others is a matter for careful consideration. Proprietorial caution, combined with custom and values attributed to land in the region has resulted in a pattern of tenure dominated by short-term rental agreements called conacre. An insecure, short-term land rental system, such as the conacre may be a disincentive to farmers in their decision-making around investing in land management and development, consequently resulting in reduced farm-level sustainability. In this study, we analysed the psychological and behavioural factors influencing farmers’ intention to take up long-term land leasing in Northern Ireland. To achieve our objective, we employed a mixed-methods approach and statistically analysed our quantitative data using an extended theory of planned behaviour (TPB) approach in a structural equation modelling framework. The results of our analyses showed that attitude, perceived behavioural control, subjective norm and perceived risk were statistically significant determinants of farmers’ intention to adopt long-term land leasing. The study results have important implications for the design of effective policies aimed at encouraging long-term land leasing through behavioural change to ensure sustainable agricultural production and effective land management.

Decision-Making Mechanism of Farmers in Land Transfer Processes Based on Sustainable Livelihood Analysis Framework: A Study in Rural China

“…The model has the advantages of multi-factor analysis, high error accommodation, and fitting effect analysis and it is more suitable for this study [64,65]. First, previous econometric models are mostly limited to single-factor analysis; SEM breaks this constraint and can analyze and measure multiple dependent variables and factor structures as a way to clarify the complex relationships among the factors [66,67]. Second, in the face of latent variables that are difficult to measure accurately with indicators, SEM models allow for a greater degree of measurement error and prevent sample bias due to constraints such as observation difficulty [68].…”