The results of the two logistic models confirm essential hypotheses for structural change in Swiss dairy farms. The side-by-side analysis of farm exit and farm type change reveals similar and differing relations with single factors. First, we discuss results that confirm our hypotheses and former results of similar studies. Second, we discuss results from our analysis that deserve further attention.
The general relevance of the difficulty of production conditions and of general economic conditions as well as the stabilising association with direct payments is supported by our analysis. According to the model results, farm operators’ age is positively related to farm exit and negatively to the probability of a farm type change. Looking in detail at differentiated age classes, the probability of exits increases sharply for older farmers. This can be interpreted as a generally stable farm situation in the dairy sector, which is defined by farm exits of older farmers (e.g. in case no successor is available when exceeding the age threshold of 65 years to get direct payments).
Regarding the farm type change model, we observed a negative (change probability decreasing) relationship with age. The strategic orientation to change farm type is rather taken by younger farmers after farm acquisition. Anecdotal evidence that mostly small farms change to suckler cow production is supported by the number of family workers; however, herd size seems not to be related to the change probability.
When differentiating herd size, we observe that the generally decreasing relation of the dairy herd size with the exit probability is particularly important for small farms. Furthermore, lower amounts of direct payments have a stronger association with a farm exit and farm type change than high payments. These two findings express the marginal perspective that an additional dairy cow or increasing direct payments are worth more for small farms.
The adherence or fulfilment of additional standards such as organic or animal welfare schemes reduces the probability of a farm exit. As expected, these programs allow farms to tap into added value (Salvioni et al. 2013). Apart from economic motivation, however, adherence to organic or animal welfare schemes could also be an expression of openness for innovation or motivation for sustainable family farming (also in terms of family farming tradition), i.e. unobserved characteristics. Such unobserved characteristics could positively impact the possibility to stay in farm business which would bias our estimates. Hence, as discussed also at the end of this section, we are cautious about interpreting the results as causal effects, but prefer the interpretation as conditional correlations.
Other variables for which the results differ from former analyses or our hypotheses deserve further discussion: Theoretically ambiguous, a higher degree of specialisation is empirically related to significantly more exits. Specialised dairy farms in grassland areas where only limited alternative farming activities exist, in combination with barriers to growth (e.g. limited area for farm enlargement, dead end), could contribute to this result. Specialised farms could be less resilient due to their focus on a single or few outputs and increased income risk (El Benni et al. 2012).
In both regions in which internationally known quality cheeses can be produced, we observe increased exit probabilities. Milk production for these cheeses often takes place in mountain areas; however, we control for difficulty of production. Since the production involves specific requirements (such as milk delivery twice a day in case of Gruyere) and the quantity of production is controlled for, not all farms in a PDO region may benefit; farmers located in a PDO region but not belonging to a PDO production scheme could experience a more pronounced price difference. Higher exit rates in a PDO region could reflect stronger competition for land which is reflected in above average growth rates of different farm size measures. The UAA (growth rate in region Gruyère: 1.6%, Raclette: 2.3%, rest of Switzerland 1.4%), the dairy herd size (growth rate in region Gruyère: 1.7%, Raclette: 1.1%, rest of Switzerland 0.8%) as well as the farms’ standard output (growth rate in region Gruyère: 1.8%, Raclette: 1.6%, rest of Switzerland 1.2%) increase more strongly in the Gruyère and Raclette regions compared to the rest of Switzerland. In summary, we can state that structural change is more apparent in the PDO cheese regions under consideration. For policy makers, the promotion of such qualitative differentiation could therefore offer a starting point for stimulating structural change in the dairy sector. Given the high relevance of quality schemes in regional and agricultural policies, a deeper understanding of the underlying effects deserves further attention.
The average milk price declined by nearly 20% during the period of analysis, especially after the milk quota abolition. However, milk prices do not seem to be related to a dairy farm’s exit, but higher milk prices increase the probability of a farm type change. This unexpected effect could trace back to the level of aggregation of the milk price index, and there may be different findings for disaggregated data at farm level. Unfortunately, such data sources are not widely available. One may also suggest that the downward movement of the milk price may confound the coefficient in the sense that it depicts a linear trend rather than the variation of the milk price itself. However, the results from a model including a linear time trend turn out to be robust with our presented findings.
The higher probability of a farm type change for organic and free-range farms (RAUS) may be explained by growth barriers that accompany the implementation of free-range practices. Since free-range grazing is limited to areas close to the barn, such farms could imply a higher probability of diversification since herd size growth relies on additional free-range area. To empirically test this hypothesis, we estimate an additional model with interactions of herd size and RAUS or organic farming, respectively. Although the marginal plots do not systematically differ between additional quality schemes, the AMEs are negative and larger pronounced for RAUS and organic dairy farms with a large herd size. Hence, those who can tap additional value by quality schemes and grow in size experience smaller probabilities to change to suckler cow husbandry, which supports our hypothesis. Apart from economic motives, the implementation of organic and animal welfare schemes and their association with a farm type change could be linked to a farmers’ disposition with regard to moral and environmental concerns (Ferguson and Hansson 2013; Kielland et al. 2010).
Specialisation does not exhibit an association with farm type change. This implies that both specialised and diversified farms exhibit similar change probabilities (i.e. there are farms that gradually shift by diversifying and other farms that directly shift from specialised dairy to specialised suckler cow production). This finding illustrates that the effects of higher profitability and higher risk may cancel each other out.
According to the logit models’ results, structural change in the dairy sector was not stimulated during the periods of different agricultural policy reforms. Although we observed significant differences between staying and leaving (changing) farms when comparing groups of farms, the multivariate model results indicate only negative coefficients. The last period in particular reflecting the last agricultural policy reform exhibits decreasing exit and change probabilities indicating a deceleration of structural change in the Swiss dairy farm sector. Lips et al. (2016) explain the specific steadiness of Swiss dairy farms by nonpecuniary job preferences, such as passion and farm managers’ preference for self-employment.
However, agricultural policy can also drive structural change in the sector and hereby directly and indirectly influences other policy areas. The simultaneous analysis of different development options of dairy farms at the same time, using the example of Swiss dairy farming, illustrates different starting points. With regard to environmental policy, the change from dairy to suckler cow farming typically is accompanied by a lower land use intensity (lower stocking density), which can contribute to the objective to reduce nitrogen surplus. On the other hand, the lower intensity of suckler cow farms goes along with lower value-added in the agricultural sector and decreased food provision compared to more intensive farming activities. This example illustrates potential conflicts of objectives. Depending on the primary policy objective, this analysis of Swiss dairy production offers various starting points for agricultural policy. To safeguard agricultural income and milk supply, policy could focus on stabilizing viable dairy farms, e.g. by supporting their growth. This could imply exit incentives for older dairy farmers. To reduce the sector’s nutrient surplus, the change to suckler cow farm types could be stimulated, e.g. by corresponding farm advice especially during the process of intergenerational farm handover. The detailed understanding which factors of dairy farms correlate with farm exit or with farm type change to suckler cow husbandry allows governments to better control structural change in the dairy sector. Therefore, knowledge of the detailed development of the sector is important in order to be able to comprehensively assess the possibilities and the consequences of agricultural policy measures with regard to the different policy goals.
Changing to a less intensive farm type could be the first step of a longer process of farm exit. However, we could not find any descriptive evidence for this expectation given our comprehensive data set of almost 20 years.
Overall, the results show a diverse picture of factors that influence the development of Swiss dairy farms. Age is of high relevance for farm exit. Generally, the influence of other economically important factors, such as herd size (AME of − 0.0003 per cow in the exit model) or direct payments (AME of − 0.0005 per 1000 CHF), may seem of limited significance. However, such an isolated consideration of individual factors might neglect the overall effect of significant variables. The low absolute relevance of individual factors can rather be understood as expression of the complexity of change processes.
Finally, we would like to add some thoughts on further robustness checks or extensions of the analysis. The quality of the administrative data used in this article is generally high. More details would have been useful with regard to the concrete labour input (which is only documented in three rough categories), farm household’s off-farm labour and income, the existence of a potential farm successor, and to farm-related activities. A high number of family workers only roughly models the existence of a potential farm successor which is an important factor to prevent farm exit (Dong et al. 2016). Farm-related activities can offer diversification and business development opportunities and could therefore enrich such analyses. Suckler cow products in Switzerland are often marketed via direct marketing; therefore, existing farm shops could increase the probability to change from dairy to suckler cows. However, data on farm-related activities such as direct marketing, tourism, services (work as private contractor, care farming) are not yet collected systematically in the given data.
Our analysis raises some questions about the relevance of farm specialisation and PDO cheese production regions for dairy farms’ structural development. Both factors result in increased farm exit probabilities. Which farms in the PDO regions exit—those with PDO production or those without? Are barriers to growth the reason that specialised dairy farms exit? Which other reasons could contribute to the increased exit probabilities of specialised farms? Such questions should be answered by in-depth analyses.
Additionally, the outcome variables under consideration all relate to the extensive margin of farm type changes (i.e. change versus no change), and neglect the intensive margin (i.e. the number of dairy or suckler cows). Hence, it may be worthwhile to examine changes with respect to herd size. With a continuous outcome measure, the estimation of a linear fixed effects model may be suitable and would allow considering the panel structure of the data. Such a model implies the elimination of time-constant farm-specific effects.
In this context, a causal interpretation of our empirical analysis is based on the assumption that we can fully observe all relevant variables that are related to the outcome and our factors of interest. Although we have detailed data on farm characteristics that we include in the logistic regression, we are cautious about any causal interpretation and prefer the wording “conditional correlation”.