Van den Pol-Van Dasselaar A.1,2, Philipsen A.P.2 and de Haan M.H.A.2
1CAH Vilentum University of Applied Sciences, De Drieslag 4, 8251 JZ Dronten, the Netherlands;
2Wageningen UR Livestock Research, De Elst 1, 6708 WD Wageningen, the Netherlands;
a.van.den.pol@cahvilentum.nl
Abstract
The extent of grazing is affected by technical factors like stocking density per ha grassland. Previous research suggested that farmers themselves also play a key role in determining the extent of grazing since they decide on the day to day management on their farm. However, the effect of social factors on the extent of grazing had not been quantified. The aim of this research was to study the technical and social factors that affect the extent of grazing on commercial dairy farms. Based on the Theory of Planned Behavior, it was hypothesised that the extent of grazing is influenced by the attitude of farmers towards grazing, subjective norms about grazing, perceived behavioural control of grazing and technical possibilities for grazing. An on-line questionnaire was sent to commercial dairy farmers in the Netherlands and 212 valid responses were obtained. Results were analysed using factor analysis and linear regression analysis.
Combining technical and social factors in a multiple linear regression model could account for 47% of the variation in the extent of grazing. The results imply that future work on grazing in the Netherlands should take the mind-set of the farmer into account.
Keywords: attitude, dairy, grazing, mind-set of the farmer
Introduction
In the last decade, grazing of cattle has become a societal issue in the Netherlands. The dairy sector wants to support grazing by influencing the factors that are affecting the extent of grazing on farms.
In general, grazing is seen as an economically attractive activity (e.g. Dillon et al., 2005; Peyraud et al., 2010). The extent of grazing is obviously depending on a number of technical factors, like available land area for grazing and number of dairy cows present. Changes in those technical factors could lead to changes in the extent of grazing per cow. But technical factors are not the only influencing factors.
Farmers play a key role in determining the extent of grazing of their dairy cattle since they decide on the day to day management on their farm. From on-farm participatory research, it is known that personal values, preferences, experiences and habits of farmers are very important in the decision whether to graze or not to graze (e.g. Reijs et al., 2013; Van den Pol-Van Dasselaar et al., 2008). However, this has not been quantified. Think for example of the beliefs of farmers with respect to grazing, e.g. the benefits that farmers expect from grazing or their personal view on capabilities to practise grazing. Therefore, when studying the factors that influence the extent of grazing, the opinions and thoughts of farmers should be included. Their perception on the effect of grazing will definitely influence their decision on whether to graze and, if grazing is chosen, to what extent grazing is practised. If farmers do not perceive positive effects of grazing or if they expect too many barriers to grazing, they will not be motivated to start grazing.
The aim of this research was to study the technical and social factors that affect the extent of grazing on commercial dairy farms.
Materials and methods
A conceptual framework was developed based on the Theory of Planned Behavior (Ajzen, 1991), in which the extent of grazing is influenced by:
• the attitude towards grazing (which is a function of the belief of the dairy farmer in the effect of grazing on various topics, for example does the dairy farmer believe that grazing leads to more job satisfaction, and the importance of these topics);
• subjective norms about grazing (which is a function of the social normative beliefs with respect to grazing, for example does the dairy farmer believe that grazing leads to a better image of dairy farming, and the importance of these beliefs);
• perceived behavioural control of grazing (which deals with the perceptions about having the necessary resources available, like knowledge and infrastructure); and
• technical possibilities for grazing (here expressed as milk production per ha).
Based on this framework, an on-line questionnaire with questions on attitude, norms and perceived behavioural control was developed and sent to 1109 dairy farmers. The answers were then combined with technical and economic data from the annual accounts of these dairy farmers. This resulted in a total of 212 valid respondents. The effect of social and technical factors on the extent of grazing was studied in two steps. First, a factor analysis was carried out to understand the structure of the items of attitude, subjective norm and perceived behavioural control. Second, a multiple linear regression analysis was carried out with extent of grazing as dependent variable and attitude towards grazing, subjective norm about grazing, perceived behavioural control of grazing and technical possibilities for grazing as independent variables.
Results and discussion
The main characteristics of the 212 farms are given in Table 1. The mean herd size and the mean annual milk production were near the average of the Netherlands (LEI, 2014).
Factor analysis revealed that attitude of the farmer towards grazing consisted of two components: the component Farm Continuity Beliefs (related to the belief of the farmer with respect to the effect of grazing on income, labour, animal health, mineral losses, job satisfaction and farm development) and the component Grass Yield Beliefs (related to the belief of the farmer with respect to the effect of grazing on grass growth and grass utilisation). Only one component was extracted for subjective norm about grazing (Social Normative Beliefs) and for perceived behavioural control.
There were significant effects of grazing on the different components. On average, Social Norms were seen as a driver for grazing; dairy farmers believed that the outside world supports grazing. Grass Yield was seen as a barrier to grazing; farmers believed that grazing has a negative effect on grass yield. Farmers that grazed, associated grazing with Farm Continuity and Perceived Behavioural Control. Farmers on
Table 1. Characteristics of the 212 dairy farms in this study: total area of forages (grass and maize), total grassland area, herd size, milk production, age of the dairy farmer, number of people working in the farm.
Mean Standard deviation Minimum Maximum
Forage area (grass and maize) (ha) 45 18 11 157
Grassland area (ha) 37 15 8 111
Herd size (number of dairy cows) 87 38 22 323
Milk production (kg FPCM cow-1 yr-1)a 8,801 968 5,403 11,110
Age of the dairy farmer 52 9 27 93
People working in the farm (FTE)b 1.7 0.6 1 4
a FPCM = Fat-protein corrected milk.
b FTE = full-time equivalent.
the other hand that did not graze had the opposite association. This was consistent with their choices in grazing management.
Combining Farm Continuity Beliefs, Grass Yield Beliefs, Social Normative Beliefs, Perceived Behavioural Control and technical possibilities (expressed as Milk production in kg ha-1) in a multiple linear regression model could account for 47% of the variation in the extent of grazing. Farm Continuity Beliefs, Perceived Behavioural Control and Milk production per ha were significant at the 0.01 level in this model and Social Normative Beliefs was significant at the 0.05 level (Table 2).
Combining data on the extent of grazing, technical data and data on personal values, capabilities and preferences of dairy farmers had not been done before in a quantitative way and provides a unique first insight into the corresponding relations.
The intended use of the research results is to apply the obtained knowledge and insights in discussions with different stakeholders like farmers and policy makers in order to support grazing. For effective knowledge dissemination on grazing to specific groups especially the knowledge about drivers for grazing and barriers to grazing is important. Potential drivers for grazing can be used to stimulate grazing and actions can be defined to overcome potential barriers.
Conclusions
Combining technical and social factors in a multiple linear regression model could account for 47% of the variation in the extent of grazing. The results imply that future work on grazing in the Netherlands should take the mind-set of the farmer into account.
References
Ajzen I. (1991) The theory of planned behavior. Organ. Behav. Hum. Dec. 50, 179-211.
Dillon P.G., Roche J.R., Shalloo L. and Horan B. (2005) Optimizing financial returns from grazing in temperate pastures. In:
Proceedings of the XX International Grassland Congress. Cork Satellite, 131 pp.
LEI (2014) Land- en tuinbouwcijfers, digitaal. http://www3.lei.wur.nl/ltc/Classificatie.aspx.
Peyraud J.L., Van den Pol-Van Dasselaar A., Dillon P. and Delaby L. (2010) Producing milk from grazing to reconcile economic and environmental performances. Grassland Science in Europe 15, 865-879.
Reijs J.W., Daatselaar C.H.G., Helming J.F.M., Jager J. and Beldman A.C.G. (2013) Grazing dairy cows in North-West Europe.
Economic farm performance and future developments with emphasis on the Dutch situation. LEI Report 2013-001. The Hague, LEI Wageningen UR, 124 pp.
Van den Pol-Van Dasselaar A., Vellinga T.V., Johansen A. and Kennedy E. (2008) To graze or not to graze, that’s the question.
Grassland Science in Europe 13, 706-716.
Table 2. Results of the multiple linear regression with extent of grazing (hours cow-1 yr-1) as dependent variable and Farm Continuity Beliefs, Grass Yield Beliefs, Social Normative Beliefs, Perceived Behavioural Control and Milk production per ha as independent variables.
B Std Error β Significance
Constant -410 372 – 0.271
Farm continuity beliefs 426 79 0.332 0.000
Grass yield beliefs 93 57 0.091 0.104
Social normative beliefs 127 60 0.123 0.034
Perceived behavioural control 171 50 0.190 0.001
Milk production per ha -0.059 0.011 -0.290 0.000