Abstract
Optimising resource use efficiency is high on many national policy agendas. Inappropriate management in agricultural production can result in increased risk of nutrient loss to the environment. Best practice in nutrient management can help to mitigate this. However, policy initiatives aimed at encouraging farmers to follow a nutrient management plan (NMP) appear to be limited in their success. We employ a typology to classify farms/farmers based on a number of policy relevant farm and farmer characteristics. The theory of planned behaviour is applied to understand the variables which influence farmers’ intentions to follow a NMP across the Republic of Ireland. The typology resulted in a total of three classes of farmers, namely ‘traditional’, ‘supplementary income’ and ‘business-orientated’. The findings from the regression analysis reveal that attitude towards the outcomes of following a NMP is a weak predictor of intentions whereas subjective norm (social pressure) and perceived behavioural control (ease/difficulty) are strong predictors of intentions across the classes. Furthermore, contact with agricultural extension (a combination of one-to-one and group based extension) is found to be critical in determining the intentions of both traditional and supplementary income classes of farmers. The results also indicate that policy, which requires certain farmers in Ireland to develop a NMP on a mandatory basis, has consistent but mixed levels of influence on intentions. Initiatives designed to further encourage farmers to follow a NMP must account for the diversity that exists among the farming population and how different groups of farmers may respond to such initiatives.
Original language | English |
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Pages (from-to) | 280-290 |
Number of pages | 11 |
Journal | Resources, Conservation and Recycling |
Volume | 146 |
Early online date | 5 Apr 2019 |
DOIs | |
Publication status | Print publication - Jul 2019 |
Keywords
- Nutrient management plan
- Farmer decision making
- Theory of planned behaviour
- Intentions
- Latent class analysis