Short- and long-run policy evaluation: support for grassland-based milk production in
Switzerland
Gabriele Mack and Andreas Kohler∗ published in: Journal of Agricultural Economics
Abstract
Following the abolition of the milk quota in 2008, farmers in Switzerland strongly increased the use of concentrate feed in milk production. Against this background, the Swiss government introduced the voluntary grassland-based milk and meat (GMF) programme in 2014, which combines economic incentives with feeding restrictions to reduce the reliance on concentrate feed and increase the use of grass feed. We analyse the economic and ecological impacts of the GMF programme at the farm and at the sector levels in the short- and long-run. We use a difference-in-differences approach (ex-post) and an agent-based simulation model SWISSland (ex-ante) to construct counterfactual states to evaluate the programme’s impacts. We assess how sensitive results are in terms of the different model assumptions. We find that the GMF programme reduces the use of concentrate feed and increases the use of grass feed in Swiss milk production. Whereas the programme has a positive effect on economic indicators such as the farm income, we find no effect on ecological indicators such as the N surplus. Our analysis suggests that feeding restrictions on concentrate feed are not enough to achieve a reduction in the N surplus. Additional feeding restrictions on grassland are necessary. Furthermore, the GMF programme has a dampening effect on sectoral milk supply, and leads to higher milk prices.
Keywords: Policy evaluation; agent-based sector model; difference-in-differences;
agri-environmental programme; grassland-based milk production; Switzerland
JEL classification: Q12, Q18, Q58
∗Andreas Kohler (contact: andreas.kohler@agroscope.admin.ch) and Gabriele Mack are both with the Federal Department of Economic Affairs, Education and Research EAER, Agroscope, Ettenhausen, Switzerland. The authors thank the editor, David Harvey, and two anonymous referees for their helpful comments and suggestions, Ali Ferjani for his support with the market model in SWISSland, and Sabrina Moebius for excellent research assistance. The authors are also grateful to the seminar participants at the EAAE 2017 conference in Parma for their useful feedback.
1 Introduction
Swiss dairy farmers have steadily increased their milk yields and concentrate supplementation since the 1990s. Especially, following the abolition of the milk quota in 2008, a strong increase in the proportion of concentrates has been observed in milk production (Erdin and Giuliani 2011).
Against this background, Switzerland introduced a voluntary grassland-based milk and meat (GMF) programme in 2014, with the goal of supporting ruminant production systems based on grassland that are characterized by low concentrate and maize supplementation (Bundesrat 2012).1 The GMF programme adopts a new approach to sustainable livestock production based on an ecological consistency strategy that is conditional on feeding restrictions. The consistency strategy aims at “shifting the focus from livestock’s role in the food system as a source for high-quality protein, to another role, which is to use resources that cannot otherwise be used for food production” (Schader et al. 2015). It is regarded as a complement (Huber 2000) to both the sustainable intensification strategy (Garnett et al. 2013, Godfray and Garnett 2014) and the sufficiency strategy (Alcott 2008, Figge et al. 2014).
We provide a comprehensive evaluation of the GMF programme focusing on both the short- and long-run impacts. Since the goals of the GMF programme are only very broadly defined by the policy maker, we formulate a rigorous conceptual framework where we clearly define economic and ecological indicators at both the farm and dairy sector levels. We also discuss the implications for society as a whole, in particular, for consumers and taxpayers. Based on our conceptual framework, we use both ex-ante and ex-post methods to construct counterfactual states in order to evaluate the causal impacts of the programme.
Policy evaluation can be either ex-post or ex-ante. On the one hand, there exists a literature on ex-post policy evaluation using empirical methods such as difference-in-differences (DiD), fixed-effects models or instrumental variables (e.g., Falconer et al. 2001, Petrick and Zier 2010, Sielawa and Helfand 2015, Liu and Henningsen 2016). On the other hand, there is also a literature on ex-ante policy evaluation using (computational) simulation models (e.g., Brady et al. 2009, Cortignani and Severini 2012, Deppermann et al. 2014, Schroeder et al.
2001). However, the two strands of literature are usually independent. Our paper complements the small body of literature using both ex-ante and ex-post analyses in policy evaluation (e.g., Finger et al. 2017). Furthermore, since Switzerland is one of the first countries to implement an agri-environmental programme based on the ecological consistency strategy that is conditional on feeding restrictions, our analysis also fills a gap in the literature on the evaluation of policy instruments intended to support grassland-based milk and meat production. In particular, we can discuss the effects of direct payments for grassland conditional on environmental regulations when compared to the effects of market-based policy measures such as concentrate taxes (Hecht et al. 2014) or unconditional area payments (Hecht et al. 2015). Since the recent abolition of milk quotas may have similar effects on the use of concentrate supplementation in EU dairy production, the conclusions drawn in this paper might be of particular interest to policy makers in the European Union (EU).
We formulate a conceptual framework based on a micro-economic approach describing the
1GMF is short for “graslandbasierte Milch- und Fleischproduktion” in German.
behaviour of individual dairy farmers. The GMF programme uses economic incentives to achieve its goals by rewarding farmers who limit their use of concentrate supplementation in milk production. The farmers who participate in the programme receive an annual payment of CHF 200 per ha of grassland as compensation for compliance costs reflecting forgone income.
The economic incentives determine the decision to participate and therefore, the change in the feeding regimes of individual farms, which in turn affect the economic and ecological impacts of the programme. We study the changes in the feeding regimes of individual farms by analysing how participating farms change their concentrate, maize and grass feed input. We measure the economic impacts by looking at the farm income, which is not only affected by the feeding costs, but also by changes in the milk yield. We deduct the ecological impacts of the GMF programme from the ecological consistency strategy, which reflects the rationale behind the programme. In particular, we analyse the programme’s impact on the nitrogen (N) surplus, conservation of biodiversity and land occupation by grassland. Although a comprehensive welfare analysis is beyond this paper, we assess the welfare implications for wider society by estimating the change in the Swiss consumer’s expenditure on dairy products and the costs for the Swiss taxpayers.
Based on our conceptual framework, we use a difference-in-differences (DiD) model (Blundell and Costa Dias 2000) to estimate the causal short-run impacts of the GMF programme at the farm level, based on Farm Accountancy Data Network (FADN) panel data from 2011 to 2015. To assess the short- and long-run impacts of the GMF programme at the farm and sector level, we use the recursive-dynamic agent-based sector model SWISSland (M¨ohring et al. 2016a). Both methods carefully construct counterfactual states based on different assumptions. Hence, our approach allows us to compare the sensitivity of our results for the short-run with respect to the underlying assumptions and thus, it increases the confidence in the reliability of the results. Furthermore, using both approaches allows us to evaluate the short- and long-run impacts of a policy programme shortly after its introduction, when data are only available for a short period of time. This reduces the time lag between the introduction and a comprehensive evaluation of the programme.
The remainder of this paper is organised as follows. Section 2 provides a brief overview of the dairy sector in Switzerland, and it describes the GMF programme in detail. In Section 3, we discuss our conceptual framework including the short- and long-run impacts and indicators (outcomes) at both the farm and sector levels. Section 4 explains the motivation behind the methodological choice, and describes both the ex-post and ex-ante methods used to analyse the GMF programme’s impact on economic and ecological indicators. In Section 5, we discuss the results of the ex-post and ex-ante analyses, and we compare the short-run impacts at the farm level between the two methods. Section 6 concludes.
2 The Swiss dairy sector and the GMF programme
Dairy production represents the most important branch of Swiss agriculture. In 2015, approximately 583,000 dairy cows on 28,600 small-sized family farms produced more than 4 million tons of milk (Bundesamt f¨ur Statistik 2016). Due to the climatic and topographic
conditions, about two-thirds of the agricultural area in Switzerland can only be used for grassland, not to mention the approximately 500,000 ha of alpine summering pastures. In particular, the areas north of the Swiss Alps are ideal grassland areas characterized by adequate temperature and rainfall, which result in high per-hectare rate pasture productivity (Thomet et al. 2011). Even though the average level of concentrate supplement in Swiss dairy production (100 g per kg milk) is less than half that seen in France and three times lower than that seen in Germany, the Netherlands and Denmark (Schweizer Bauer 2014), the trends are similar.
From 1990 to 2009, concentrate supplements increased by, on average, 23 kg per cow per year, whereas the average milk yield increased by 100 kg per cow per year (Erdin and Giuliani 2011).
During the last 15 years, the tariffs on concentrate feed imports have fallen, which led to lower concentrate prices. These developments in turn increased the profitability of concentrate feed when compared to roughage-based feed in dairy production. A comprehensive assessment of the environmental impacts of intensive (8,900 kg milk yield; indoor feeding systems) and pasture-based (6,074 kg milk yield; seasonal full-pasture) Swiss dairy production systems using a life-cycle assessment show that the pasture herd performed better than the indoor herd in seven of the thirteen impact categories considered (Sutter et al. 2013).
Against this background, the Swiss government introduced the grassland-based milk and meat (GMF) programme in 2014. The programme’s aim is to promote ruminant production systems based on grassland that are characterised by low concentrate and maize supplements (Bundesrat 2012). Dairy farmers who adopt the programme have to meet two feeding restrictions. First, the proportion of concentrates they use in the total feed for all ruminants must be lower than 10 percent throughout the year.2 Second, the proportion of grass in the total roughage feed for all ruminants must be higher than 75 percent for farms located in the lowlands and higher than 85 percent for farms located in the mountains.3 In terms of receiving payments, it is not relevant where the grass feed is produced, for example, on the farmer’s own farm, on any farm in Switzerland, or abroad. Only ruminant farms whose stocking rate is higher than a minimum stocking rate are eligible for the subsidies. Farmers must also fulfil the documentation obligations necessary for programme inspections. Based on the farmers’
self-declarations (quantities of purchased concentrate and roughage feed for ruminants, grass and maize area, and number of ruminants) a software programme calculates the dry matter feeding ratios for concentrates, maize and grass.4
The GMF inspections are part of the overall inspections of Swiss farms that receive direct payments. Every year, 25 percent of those farms are randomly selected for inspections.
Interviews conducted with inspectors reveal that controlling the animals’ feed intake over the
2The GMF programme guidelines define those feed components that count as roughage-based feedstuff (Der Schweizerische Bundesrat 2017; Anhang 5, Ziffer 1): Permanent grassland/meadows and temporary leys/pastures (fresh/ensiled/dried); whole-plant maize (fresh/ensiled/dried); mixture of rachis and corn-cob kernels; coarse corn-cob meal and corn-cob silage without husks (CornCobMix (CCM) for cattle fattening only;
in all other cases classified as concentrate), cereal-whole plant silage; fodder beet; sugar beet; sugar-beet pulp (fresh/ensiled/dried): beet leaves; chicory roots; potatoes; waste from fruit and vegetable processing; draff;
straw for feeding. Thus, other animal feedstuffs not included in this list of roughage-based feedstuff count as concentrates.
3According to the GMF programme guidelines only fresh, ensiled or dried permanent grassland/meadows count as grass-based feedstuffs (Der Schweizerische Bundesrat 2017; Anhang 5, Ziffer 1).
4The software is provided by the Swiss Federal Extension Centre (Agridea 2015).
whole year is difficult. In particular, the detection of incorrect self-declarations is a problem (Wunderlich and Mann 2016).
The on-farm compliance costs of dairy farms participating in the GMF scheme were very heterogeneous during the first year following the introduction of the programme (Mack and Huber 2017). Approximately one-third of farms in the lowland region and almost 60 percent in the mountain region have zero on-farm compliance costs because these farms already met the programme’s feed requirements before the programme was introduced. Further, about 50 percent of dairy farms in the lowlands and 40 percent in mountain regions have non-negative compliance costs below CHF 20,000. Finally, there is a group of farms with high compliance costs in both regions (exceeding CHF 20,000), which have to reduce their concentrate ratio by almost 40 percent and their maize ratio by almost 20 percent in order to comply with the programme’s feed requirements.
3 Conceptual framework and indicators
Our conceptual framework is based on a micro-economic approach describing the behaviour of individual dairy farmers with regard to their decision to participate in the GMF programme and to change their feeding regime. At the farm level, we analyse the impact of the GMF programme based on both economic and environmental indicators. Notice that in our ex-post analysis, we follow the literature on (economic) policy evaluation and refer to the indicators as outcomes. We also provide results for the Swiss dairy market sector by aggregating the micro-level results to the macro level. Furthermore, we discuss the implications for wider society and, especially, for consumers and taxpayers. Figure 1 provides an overview of our conceptual framework including the impacts and indicators (outcomes).
First, we measure at the micro level how effective the design of the GMF programme is in terms of achieving a significant reduction in the use of concentrate and maize feed as well as an increase in the use of grass feed in Swiss dairy farms. Second, we evaluate at the farm level the economic impacts of the GMF programme, with a focus on milk production and farm income. With regard to milk production, we expect that the change in the feeding regime due to the GMF feeding guidelines reduces the potential nutrient intake of dairy cows, which in turn causes lower milk yields per cow. This follows, given a limit to dry matter intake, dairy cows’ loss of nutrients caused by a reduction in the proportion of nutrient-rich concentrate feed can hardly be compensated by an increase in the proportion of nutrient-poor roughage feed. We further expect that dairy farmers behave rationally and only participate in the GMF programme when the additional direct payments exceed the forgone income resulting from the lower milk yields due to feeding restrictions.
Furthermore, we evaluate at the micro level the grassland-related environmental impacts of the GMF programme that are relevant in the context of the ecological consistency strategy (Schader et al. 2015). Due to data restrictions, we focus on the programme’s impact on land allocation to grassland as well as on its impact on extensively used grassland. The latter serves as an indicator of the grasslands’ capacity to conserve biodiversity (Dahms et al. 2008). We expect that the GMF programme leads to an increase in the grassland area at the expense
Change in feeding regime
Farm level
GMF policy program
Economic and ecological impacts
Sector level
Economic and ecological impacts
Feeding indicators / outcomes
Concentrate feed
Maize feed
Grass feed
Economic indicators / outcomes
Milk yield
Farm income Ecological indicators / outcomes
Extensive grassland
N surplus
Economic indicators Dairy market
Milk production
Milk prices
Milk sales Agricultural sector
Dairy farms
Dairy cows
Revenue milk sales
Agricultural sector income
GMF budget outlay Ecological indicators
Grassland area
Extensive grassland
N surplus ex-post / short-run
ex-ante / short- and long-run
Figure 1: Conceptual framework including the impacts and indicators
of arable land. Furthermore, we expect that the GMF programme has a negative impact on the grasslands’ capacity to conserve biodiversity, since the feeding guidelines might cause an increase in intensively used grassland at the expense of extensively used grassland. We also assess the environmental impacts on the nitrogen (N) surplus because improved N management is seen as a key factor in maintaining the production of food while at the same time reducing environmentally harmful emissions (Dalgaard et al. 2012). We suppose that a reduction in the use of concentrate feed reduces both the nitrogen input and surplus of farms. Due to data restrictions, the environmental impacts of the GMF programme on the soil erosion potential, non-renewable energy demand, greenhouse gas emissions, pesticides use, and water use cannot be analysed.
An integral part of our conceptual framework is the aggregation of the micro-level results to the macro level. This enables us to assess the impacts of the programme for the Swiss dairy sector as a whole, including the market changes (dairy farms, number of cows, milk production, and milk prices at the farm gate), environmental effects and impacts on the income of the agricultural sector. We focus on raw milk, taking into account changes in the supply and demand quantities, whilst the milk-quality-driven price effects cannot be measured. On the one hand, we expect that lower milk yields per cow decrease the total milk supply in Switzerland, which might in turn lead to higher milk prices. On the other hand, the direct payments made as part of the GMF programme might influence the number of dairy farms as
well as the number of dairy cows in Switzerland.
A comprehensive welfare analysis is beyond our conceptual framework. However, we discuss the implications for the Swiss society as a whole in terms of consumer expenditures and tax costs. The GMF programme’s impacts on consumers’ expenditure on milk and the GMF programme’s tax costs can be roughly estimated based on the changes in the farm-gate milk price and direct payments resulting from the GMF programme.
In addition to the short-run impacts, our ex-ante approach also permits us to study how technological progress within dairy production affects the impact of the GMF programme in the long-run. We expect that impacts increase in the long-run because the GMF programme’s feeding restrictions reduce technological progress as measured by the milk yield.
4 Methods
The fundamental problem associated with policy evaluation is identifying a good counterfactual state (Khandker et al. 2009). The counterfactual state is defined as the (hypothetical) situation a subject affected by the policy change would have experienced had she not been exposed to that policy change (see Roy 1951, Rubin 1974). Since the counterfactual state is not observed, the identification of the treatment effect is essentially a problem of missing data (Blundell and Costa Dias 2000). In general, the construction of the counterfactual state is usually complicated by self-selection (in terms of both observables and unobservables). Save for the case of experimental data, assignment to treatment is probably not random, and an individual’s decision to participate may be based on personal characteristics that also affect the outcome. Experimental data provide the correct missing counterfactual state because random assignment of treatment renders the participation decision independent of all observables and unobservables affecting the outcome.5 Non-experimental data, as in our case, are more difficult since it is always possible that there are unobservables affecting programme participation.
The literature distinguishes between two different approaches to quantitative policy evaluation (Khandker et al. 2009), namely (i) simulation models and (ii) empirical models.
Essentially, they reflect different approaches to impute missing data or in other words, construct the counterfactual state. Simulation models explicitly describe how policies interact with agents and markets, and thus allow researchers to simulate counterfactual scenarios. In particular, in order to construct the counterfactual state in simulation models, the participation decisions of agents must be explicitly modeled. The use of simulation models relies on assumptions regarding the (economic) behaviour of agents (e.g., objective of income maximization, adaptive vs. rational expectations) and the organisation of markets (e.g., perfect vs. oligopolistic competition). Furthermore, simulating such structural models describing agents’ behaviour and market interactions requires the estimation of behavioural parameters. While simulation models depend on assumptions regarding agents’ behaviour and interactions, econometric
5Note that contrary to a randomized controlled trial (RTC) conducted in clinical research, social experiments are almost never double-blind. Thus, even if the allocation of the GMF programme had been random, the participants would still be aware of their participation. This should be kept in mind when assessing the internal validity and interpreting the results (see, e.g., Bulte et al. 2014).
models depend on identification (i.e., variation in the policy used to identify the policy’s effect on outcomes) and distributional assumptions (i.e., statistical properties of the estimator).
Since we likely cannot control for all the characteristics affecting the outcome and participation decision simultaneously, the standard econometric approach of regressing the outcome on the available covariates (observables), including a participation dummy, is not valid (Blundell and Costa Dias 2000). Blundell and Costa Dias (2000) discuss the following popular evaluation methods for non-experimental data. If only cross-sectional data are available, the instrumental variables (IV) and two-step Heckman selection estimators are popular estimators using exclusion restrictions to make the participation decision independent of the unobserved factors (for a detailed discussion, see Blundell and Costa Dias 2000). If, as in our case, panel data are available, Blundell and Costa Dias (2000) propose the difference-in-differences (DiD) estimator and methods of matching that can be combined with DiD. The key identifying assumption of DiD is that both the treated and non-treated groups follow a common trend during the pre-treatment period. The advantage of the DiD approach is that no exclusion restriction is needed, and it is possible to control for certain individual- and time-specific unobservables. The disadvantage is the relatively strong assumption that both groups follow a common trend.
4.1 Ex-post analysis: empirical methods 4.1.1 Data
We use FADN data of 675 Swiss dairy farms with milk sales observed over the period 2011 to 2015 (Hoop and Schmid 2014).6 Our panel contains farm-level data on concentrate feed (percent), maize feed (percent), grass feed (percent), milk yield per cow (kg), extensive grassland (percent), and farm income (CHF), as well as the total direct payments w/o GMF payments (CHF), area (ha), number of ruminants (livestock units), milk price (CHF/kg), location (valley, hill, mountain; canton), farm type (crop, animal, combined), and age and the education of the farmers. All monetary variables are measured in nominal Swiss Francs.
The quantities of concentrates and roughage are unavailable in the FADN data. Therefore, the amount of concentrates and the amount of purchased roughage were estimated by dividing the costs for ruminants by the average prices (Schmid and Lanz 2013).7 The quantities of owned-produced maize and grass feed are computed based on FADN data (grassland and maize area, number of ruminants) as well as the specifications provided in the application form collected by the Swiss Federal Extension Centre (Agridea 2015). Data concerning the N surpluses are only available for a subsample of 62 dairy farms for the four-year period from 2011 to 2014. For those farms, the data on the N surpluses have been collected by the Central Evaluation of Agri-Environmental Indicators (CEAEI 2017). Table A1 in the on-line Appendix
6The Farm Accountancy Data Network (FADN) is the institution responsible for summarizing and analysing data from farm accountancy departments and supplementary surveys of various data processors for research, education, consultation, determination of the economic status of agriculture, agricultural-policy decision-making and evaluation, as well as agricultural valuation, including valuation for tax purposes.
7The data on concentrate costs in the FADN data also include expenses for the use of concentrates produced on-farm, which provides a sound basis for estimating the quantities of concentrate supplementation.
A presents the summary statistics for the observation period 2011 to 2015.
4.1.2 Empirical model
Following Blundell and Costa Dias (2000), we identify the causal short-run effects of the GMF programme in a difference-in-differences (DiD) model, which compares the participating and non-participating farms before (2011-2013) and after (2014-2015) the introduction of the GMF programme. The key identifying assumption of DiD is that both groups, that is, treated and non-treated, follow a common trend during the pre-treatment period. Additionally, the following two standard assumptions are made in causal studies: (i) it is assumed that there are no relevant interactions between the members of the population (Stable Unit Treatment Value [SUTVA] assumption), and (ii) that the treatment is exogenous conditional on covariates (exogeneity assumption). While the SUTVA and exogeneity assumptions cannot be tested, we (graphically) verify the common trend assumption by comparing the trend in the outcomes of the treatment and control groups during the pre-treatment period. If these assumptions hold, the group of non-participating farms is a good control group that reflects the correct counterfactual state.
Our baseline model is the following regression formulation of the DiD model:
Yit =α+δGM Fi+X
k
βkF ARMki+X
j
γjY EARjt+x0itη+εit, (1)
whereY on farmiin yeartstands for one of the following outcomes: concentrate feed (percent), maize feed (percent), grass feed (percent), milk yield per cow (kg), ecological area (percent), income (CHF), or N surplus (kg/ha). The dummy variable GM F takes the value of one if farm iparticipated in the GMF programme starting in 2014. The expressions P
βkF ARMki
andP
γjY EARjt are full sets of dummy variables capturing unobserved farm-specific factors that are constant over time (e.g., location) as well as unobserved year-specific events that are common to all farms (e.g., weather conditions, concentrate costs).8 The vector xit includes the following farm-year varying covariates: education (dummy variables for no education, vocational training, vocational training completed, further education, and tertiary education), age, farm type (dummy variables for arable crops, special crops, dairy, suckling cows, other cattle, horses/sheep/goats, pigs/poultry, combined dairy/arable crops, combined suckling cows, combined pigs/poultry, and combined others), government support in the form of total direct payments w/o GMF payments (CHF), total area (ha), number of ruminants (livestock unit), and milk prices (CHF/kg). We include all time-varying covariates that are available in our data that we think affect the outcomes as well as the participation decision. In a sensitivity analysis, we show how the inclusion of covariates (i.e., selection on observables) affects the estimated effects of the GMF programme.
We are interested in the coefficient δ on the variable GM F, which identifies the average
8Note that the full set of dummy variables capturing farm-specific factors also controls for the different feeding restrictions across regions in the GMF programme that a farm must comply with in order to qualify for the annual payment of CHF 200 per ha. Since no farm changes its location during the observation period, farm-specific factors also capture location effects such as the different feeding restrictions of the GMF programme.
treatment effect on the treated (ATT) of participating in the GMF programme (see Blundell and Costa Dias 2009, Lechner 2010). Following the literature, we estimate our baseline model (1) using the ordinary least squares (OLS) estimator clustering standard errors at the farm level (Bertrand et al. 2004). Note that the OLS estimator can be interpreted as a parametric matching estimator in this context (Blundell and Costa Dias 2000, Angrist and Pischke 2015).
The combination of methods of matching (PSM) and DiD allows us to control for selection on observables as well as unobservables. As a robustness check, we follow Blundell et al. (2004) and combine non-parametric propensity score matching (PSM) with DiD.
4.2 Ex-ante analysis: simulation model
The Swiss agent-based agricultural sector model SWISSland follows our conceptual framework.
SWISSland uses a micro-economic approach by simulating a heterogeneous agent population of 3,300 Swiss FADN farms (base year 2011 to 2013) including 2,208 dairy farms. The model’s heterogeneous dairy agent population is almost representative with respect to the total agricultural area, intensively and extensively used grassland area, number of dairy cows, regions, milk yields and feeding regimes (M¨ohring et al. 2010). SWISSland models the changes in the feeding regime as well as the economic indicators (milk yield and farm income) and environmental indicators (grassland area, extensive grassland and N surplus) for every dairy farm. The model aggregates the micro-level results to the sector level, and it determines the raw milk price through market clearing.
The general design, the farm-optimisation model including the objective functions, and the constraints of SWISSland are described in detail in M¨ohring et al. (2016b). In the following, we only discuss those aspects relevant to our analysis, that is, the modelling of the feeding regime, the participation of the agents in the GMF programme, the environmental indicators, and the aggregation from the micro to the sector level.
4.2.1 Modelling the agents’ feeding regime
Each agent maximises its annual household income based both on price and yield expectations as well as direct payments for various animal and crop production activities. The resource endowments, prices and yields were estimated for each agent on an individual-farm basis from the FADN data of the base year. In the base year, we constrain the feed ratios (concentrate, maize and grass) in the farm-level optimisation models to the corresponding values observed in the FADN data. The agents are only allowed to reduce their concentrate supplementation they use in dairy production if they participate in the GMF programme. In that case, additional modelling constraints take into account the fact that a reduction in concentrate supplementation in dairy production needs to be equally substituted with high-quality grass.
In order to comply with the feeding restrictions, we take into account the most relevant farm strategies: (a) reduction in the stocking rate of ruminants, (b) increase in the grassland area at the expense of arable land, (c) intensification of the grassland from one to more additional cuts per year (provided that a potential for intensification exists), and (d) purchase of grass or hay. However, substituting grass for concentrates cannot fully compensate for a cow’s nutrient
loss. We therefore assume a decrease in the milk yield per cow. The milk yield reductions are calculated based on the feed recommendations for Swiss farmers made by Agroscope (2016). In the case of non-participation, the agents increase their concentrate feed every year in order to maximise their farm income through higher milk yields. This behaviour is based on historical trends over the past 20 years.
If the land use or livestock numbers change as a consequence of a change in the feeding regime, the labour demand is also affected. In that case, non-family labour is adjusted during the optimisation process. Family labour and land capacities, however, remain at their initial levels. All the ecological obligations that Swiss farmers have to meet in the context of the current Swiss Agricultural Policy (AP 2014-2017) in order to receive direct payments (nitrogen balance, requirement for 7 percent of land to serve as ecological compensation areas and an upper limit on livestock densities per ha) are also taken into account in the optimisation models.
4.2.2 Modelling the agents’ decision to participate in the GMF programme In principal, there exist two different options for determining an agent’s decision to participate in the voluntary GMF programme. First, using the farm optimisation model, an individual agent’s participation decision can be determined endogenously by an income-maximising agent comparing her participation costs (foregone income) with her benefits (direct payments for the GMF programme). Second, an exogenous individual agent’s probability of participating in the GMF programme can be estimated based on the farmers’ observed behaviour. In order to be as close as possible to observed behaviour, we model the participation of an individual agent based on estimates from an empirical model using FADN data.
We therefore estimate the probability of an agent participating in the GMF programme based on the following linear probability (LP) model using OLS:
P r(GM Fi,post= 1|Xi,pre=xi,pre) =x0i,preβ (2) where the vectorxi,preincludes the following covariates (varying at the farm level): concentrate feed (conc), maize feed (maize), milk yield per cow (milk), ruminants (ruminants), stocking rate (stocking), ruminants with high a concentrate intake (high), age, crop area (area), and region (hill and mountain) as well as a constant.9 All the independent variables are averages computed for the pre-treatment period 2011 to 2013, whereas the dependent variableGM Fi,post refers to whether farmiparticipates in the GMF programme during the post-treatment period 2014 to 2015. Altogether, we have observations for 740 farms from the FADN database.
9It is important to understand the conceptual difference between the DiD model (1) and the LP model (2).
The DiD model (1) estimates thecausal effect of the GMF programme on various outcomes by comparing the participating and non-participating farms beforeand after the introduction of the programme. The LP model (2) predicts whether a particular farm participates in the programme after its introduction, based on farm characteristics observedbefore the programme’s introduction. Hence, there is no identification problem (i.e., simultaneity) in the causal model since future participation in the programme has no effect on past outcomes (characteristics). However, since participation in the LP model (2) depends on lagged outcomes, we need to control for those outcomes in the DiD model (1), see also the discussion on selection issues in Section 4. It is further important to stress that here we are only interested in having a model that does a good job of predicting the probability of participating in the GMF programme, and we are not interested in inference. In other words, we are looking for predictive power and not causal effects.
Based on the LP model (2) estimated by OLS, we obtain
P rc(GM F = 1|x) = 1.33−0.02conc−0.02maize−0.0003ruminants−0.04stocking + 0.03high+ 0.01milk−0.002age+ 0.02area
−0.08hill−0.07mountain (3)
n= 740, R2 = 0.42
where P rc(GM F = 1|x) denotes the estimated probability of participating in the GMF programme.
In SWISSland, for each individual agent we compute her probability of participating based on the estimated equation (3). The LP model represents the most parsimonious way of computing for each individual agent her probability of participating in the GMF programme. Of course, we are well aware of the shortcomings of using a linear probability model, for example, heteroskedasticity of the error term and predictions outside the (0,1) interval (Winkelmann and Boes 2009). We address the issue of heteroskedastic errors by computing robust standard errors. The issue of predictions outside the (0,1) interval remains a potential problem. However, we only use the information if the predicted probability is below 50 percent (non-participation) or above 50 percent (participation) in SWISSland. Thus, predictions outside the (0,1) interval are either set to zero for predictions below zero, or to one for predictions above one. In 87 percent of all cases the LP model correctly predicts (in-sample) participation. The on-line Appendix B provides detailed results from the LP, Probit and Logit models (see Table B1 and Figure B1).
4.2.3 Linking the micro to the sector level
After solving the optimisation models of all the agents, we aggregate (upscaling) the farm supply to the sector supply (see Figure 2). While the supply module models the decisions made by farmers, the demand module models the decisions made by consumers, and it determines equilibrium quantities and prices based on market clearing. The SWISSland demand module is a reduced-form model based on the economic behaviour of consumers (as reflected by price and income elasticities), the processing of agricultural products, and Swiss trade policy, as well as on demographic and economic trends (i.e., population and GDP). Since the Swiss market is small relative to the world market and in particular, the EU market, EU prices are assumed to be exogenous from a Swiss point of view. If there is no international trade as in the case of raw milk, prices are determined through market clearing in Switzerland. This implies that an increase in the milk price reduces domestic milk consumption. An individual agent maximises her farm income based on adaptive price expectations. In other words, the market prices for the current year represent the agent’s price expectations when determining her production decisions for the following year. In order to prevent mainly technically driven supply and price fluctuations over the years we iterate two times per simulation year as shown in Figure 2, and we use supply, demand, and price averages over the two iterations.
External Input Data
Upscaling
Supply of the agricultural sector in the current year
Optimization models Agent’s supply in the current year
Partial equilibrium model
Domestic demand in the current year
Agents’ price expectations for the next simulation run
First iteration Market prices (𝑝1)
First iteration Market prices (𝑝2)
First iteration Market prices (𝑝̅)
Figure 2: Structure of the SWISSland model (Source: M¨ohring et al. 2016a) 4.2.4 Modelling environmental indicators
The N surpluses are computed based on the nitrogen farm-gate balances implemented in the farm optimisation models (Oenema et al. 2003). The farm-gate balance accounts for the nitrogen input and output, which are both estimated based on the results of the optimisation model. The N surplus measures the overall difference between the input and the output. The nitrogen input on the farm scale originates from purchased nitrogen (i.e., fertilizer, feed, or animals) as well as from nitrogen fixation and deposition. The nitrogen output accounts for the amount of nitrogen leaving the farm through sold agricultural products. The data-base for modelling the N surplus is described in detail in the study by Mack and Huber (2017).
4.2.5 Policy scenarios
We simulate two policy scenarios from 2015 to 2025: (1) a scenario without the GMF programme (counterfactual) and (2) a baseline scenario with the GMF policy (baseline). In the counterfactual scenario, we assume that all dairy farm agents continue to increase their concentrate feed in order to achieve higher milk yields (1 percent per year) until the year 2025, since they are not subject to the GMF programme’s feeding restrictions. The annual increase in the milk yield of 1 percent reflects the average growth trend over the last 15 years in Switzerland. In order to achieve an increase of 1 kg in milk, the agents must increase the use of concentrate feed by 0.18 kg. This assumption is based on the positive correlation between concentrate supplementation and milk yields observed in the data (Erdin and Giuliani
2011). In the baseline scenario, we assume no further increase in the milk yields for agents participating in the GMF programme due to the binding GMF feeding restrictions (concentrate and maize limits).
For both scenarios, we further assume an annual increase in crop yields (wheat: 0.25 percent; barley: 0.43 percent; corn: 0.1 percent; potatoes: 0.35 percent; sugar beet: 1 percent and rape seed: 0.8 percent) along with higher mineral fertilizer input (amount of mineral fertilizer divided by crop yield in the base year 2012/2013), and a decrease in labour demand due to (labour-saving) technological progress (M¨ohring et al. 2016b). Moreover, we assume for both scenarios that the Swiss agricultural policy AP 2014-2017 remains unchanged until 2025 (M¨ohring et al. 2016b).
The difference between the baseline and the counterfactual scenario for the participating farms represents the average treatment effect on the treated (ATT), whereas the difference between both scenarios for the group of non-participating farms is the average treatment effect on the untreated (ATU). The average treatment effect (ATE) is computed based on the differences between the counterfactual and baseline scenarios across both the non-participating and participating farms.
5 Results and discussion
5.1 Ex-post analysis: the short-run perspective 5.1.1 Verification of the DiD assumptions
We provide a comparison of means showing in what respects the participating and non-participating farms differed prior to the introduction of the GMF programme in 2014.
Table A2 in the on-line Appendix A compares the mean values for the outcomes and available covariates for the farms participating and not participating in the GMF programme. As discussed in Section 4, we use non-experimental data, meaning that the selection (participation) into the GMF programme was non-random. Indeed, the farms participating in the GMF programme exhibit significantly lower use of concentrate and maize feed, lower milk yields, as well as a lower farm income, whereas their use of grass feed and extensive grassland area are significantly higher. Furthermore, we note that the farms participating in the GMF programme are on average slightly smaller in terms of total area. They have fewer ruminants, with each producing less milk. The farms participating in the GMF programme are more often located in mountainous regions, and they specialize in animal production. The farmers participating in the GMF programme are more likely to have completed vocational education, and they are less likely to have further education. This shows that the participating and non-participating farms were already different before entering the GMF programme.
However, systematic differences in the levels are not a principal concern, since they can be controlled for in the DiD model (see Blundell and Costa Dias 2000). More important are the common trends in the outcomes observed across both groups. Nevertheless, since these farm and farmer characteristics probably affect the participation decision as well as the outcome, failing to control for selection on observables would result in biased estimators of the
programme effect on outcomes. By controlling on all observables available in our dataset, we hope to eliminate bias from selection on observables.
The DiD approach controls for all unobserved factors that are farm-specific and constant over time, as well as unobserved year-specific events that are common to all farms by taking the differences between the participating and non-participating farms before and after the introduction of the GMF programme. DiD relies on the key identifying assumption that both groups follow a common trend prior to the introduction of the GMF programme. To verify the key assumption, we look at the evolution of all the outcomes over time for both groups.
Figure 3 shows that, prior to the programme’s introduction in 2013, all the outcomes for the participating and non-participating farms do indeed follow a common trend. After 2013, the majority of the outcomes of the farms participating in the programme start to deviate from those of the non-participating farms. Together with our sensitivity analysis in Section 5.1.3, this strengthens our confidence in the assumption that there are no important differences in unobserved factors between the two groups, which are also correlated with the decision to participate in the GMF programme.
9.6 9.8
10.3 10.2 10.0
14.3 14.4
15.2
16.1
15.4
10121416
percent
2011 2012 2013 2014 2015
GMF non-GMF
Sources: FADN (Hoop and Schmid 2014)
Concentrate feed
4.1 4.1 3.9 3.8 3.8
16.7
17.5 17.2 17.0 17.2
5101520
percent
2011 2012 2013 2014 2015
GMF non-GMF
Sources: FADN (Hoop and Schmid 2014)
Maize feed
86.3 86.1 85.8 86.0 86.2
69.0 68.1 67.6
67.0 67.4
6570758085
percent
2011 2012 2013 2014 2015
GMF non-GMF
Sources: FADN (Hoop and Schmid 2014)
Grass feed
12.1 12.1
12.4
12.7
12.9
10.6
10.5 10.5
10.8
11.5
10.511.011.512.012.513.0
percent
2011 2012 2013 2014 2015
GMF non-GMF
Sources: FADN (Hoop and Schmid 2014)
Extensive grassland
6,521
6,677
6,511
6,598 6,583
7,644
7,732
7,599
7,792 7,839
6,5007,0007,5008,000
kg
2011 2012 2013 2014 2015
GMF non-GMF
Sources: FADN (Hoop and Schmid 2014)
Milk yield per cow
67,487
63,787
70,555
78,227
67,515 76,174
67,767
84,144 84,306
63,383
65,00070,00075,00080,00085,000
CHF
2011 2012 2013 2014 2015
GMF non-GMF
Sources: FADN (Hoop and Schmid 2014)
Farm income
115
92 92
86 166
128
134
126
80100120140160
kg/ha
2011 2012 2013 2014
GMF non-GMF
Sources: FADN (Hoop and Schmid 2014)
N surplus
Figure 3: Verification of the common trend assumption - evolution of the outcomes/indicators for the participating and non-participating farms
5.1.2 Main results of the DiD model
Table 1 presents the results of estimating the DiD model for our various outcomes (indicators), as specified in equation (1) by OLS, controlling for farm and farmer characteristics as well as government support. We argue that since both groups follow a common trend prior to the introduction of the programme, there are no unobservable factors affecting the outcomes that are also correlated with an individual farmer’s decision to participate in the programme.
However, there may be spillovers through market interactions. Since our ex-ante analysis explicitly models such interactions, comparing the results of the ex-post analysis with those of the ex-ante analysis offers a means of assessing the potential bias.
Table 1: Short-run effects (ATT) at the farm level based on the DiD model Feeding outcomes Economic outcomes Ecological outcomes Concentrate Maize Grass Milk yield Farm Extensive N surplus
feed feed feed income grassland
(percent) (percent) (percent) (kg/cow) (CHF) (percent) (kg/ha)
Mean 11 7 82 6839 69442 12 108
GMF -1.0∗∗∗ -0.3 1.2∗∗∗ -135∗∗ 6521∗∗∗ 0.3 -3.3
(0.2) (0.3) (0.4) (54) (2300) (0.5) (34)
N 3375 3375 3375 3375 3375 3375 224
Clustered standard errors in parentheses (at farm level)
∗p <0.1,∗∗ p <0.05,∗∗∗ p <0.01
Notes: ATT is short for average treatment effect on the treated. The “mean”-row refers to the mean of the dependent variables for the pre-treatment period 2011 to 2013. All models control for time-varying farm and farmer characteristics (age, education, farm type, total area, number of ruminants, and milk prices) as well as government support (total direct payments w/o GMF payments).
Sources: FADN (Hoop and Schmid 2014) and CEAEI (2017)
The ATT of the GMF programme on the proportion of concentrate feed is 1 percentage point, statistically significant at the 1 percent level. The programme has no effect on the proportion of maize feed, although it increases the share of grass feed by 1.2 percentage points.
However, the GMF programme appears to have strong effects on the economic outcomes. It increases the farm income by approximately CHF 6,500 (almost 10 percent) and decreases the milk yield by about 135 kg per cow (all coefficients are statistically significant). The average farm income increases by more than the average GMF payment (CHF 200× 22.6 ha = CHF 4,500). Due to the lower feed costs, the positive effect of the GMF programme on farm income during the years 2014 and 2015 also reflects the income smoothing effect of the programme in terms of sheltering farmers from milk price fluctuations. However, the GMF programme has no effect on ecological outcomes such as the share of extensive grassland and the N surplus.
We conclude that the GMF programme is partly successful in changing farmers’ feeding regimes by substituting grass feed for concentrate feed. While we find relatively strong effects on the economic outcomes in the short run, we do not find any short-run effects on the ecological outcomes.
5.1.3 Sensitivity analysis of the DiD model
Table 2 shows how sensitive our results of the DiD model are with respect to selection on observables, Models (1)-(3), as well as selection on unobservables, Models (4)-(6). Columns (1)-(6) refer to the the size and standard error of the coefficient δ on the variable GM F identifying the ATT in the following models.
Model (1) shows the results of the simple DiD estimator without controlling for farm- and time-varying observables. Models (2)-(3) step-wise include covariates for the farm and farmer characteristics, and government support, respectively. Note that Model (3) is the main model specification discussed in Sections 4.1.2 and 5.1.2. The comparison of the results across Models (1)-(3) shows how sensitive our results are with respect to selection on observables.
Model (4) includes the same covariates as Model (3), although it additionally includes canton as well as location/zone specific (time) trends, that is, P
sθsCAN T ONsi ×t and P
sθsZON Esi×twhere tdenotes time (i.e., year). Model (4) probes the DiD assumption of common trends between the farms participating and not participating in the GMF programme by allowing farms in different cantons and zones (i.e., valley, hill, mountain) to follow different unobserved time trends. Model (5) also includes the same covariates as Model (3), but it additionally includes a full set of canton and zone dummies interacted with year dummies P
sθsCAN T ONsi×ZON Esi×Y EARst, as well as its main effects (which will be dropped from the estimation due to the perfect collinearity with the full set of dummy variables capturing farm-specific events), capturing unobserved canton-zone-year-specific events (e.g., annual weather events that are common to all farms within a given canton and zone, or zone-specific changes in a canton’s agricultural policy). Model (6) combines propensity score matching (PSM) with DiD, where PSM is used in a first step to construct the treatment and control groups. We follow Blundell et al. (2004) and first apply PSM using the nearest neighbour method (without replacement) matching on the covariates of farm and farmer characteristics and government support (as well as canton and zone dummy variables) during the pre-treatment period, as well as the outcome itself during the pre-treatment period (for more detail see Blundell et al. 2004). In general, PSM is able to construct more comparable groups in terms of both farmer characteristics (age, education) and location (zone and canton).10 We then apply a DiD estimator using the constructed treatment and control groups based on PSM controlling for farm and farmer characteristics as well as government support and including canton and zone trends.
The comparison between Models (1)-(6) shows that our results are robust with respect to selection on observables and unobservables. However, the economic outcomes are slightly more sensitive to selection issues. This provides an indication that the economic outcomes may depend to a greater extent on the unobserved factors (that also affect the participation decision) than the feeding and the ecological outcomes.
10The tables showing the matching results from PSM are available upon request from the authors.
Table 2: Sensitivity analysis of the DiD model - short-run effects (ATT) at the farm level Main model
(1) (2) (3) (4) (5) (6)
Feeding outcomes
Concentrate feed (percent) -0.9∗∗∗ -0.9∗∗∗ -1.0∗∗∗ -1.0∗∗∗ -1.0∗∗∗ -1.0∗∗∗
(0.2) (0.2) (0.2) (0.2) (0.2) (0.2)
Maize feed (percent) -0.2 -0.2 -0.3 -0.5 -0.5 -0.2
(0.3) (0.3) (0.3) (0.3) (0.4) (0.3)
Grass feed (percent) 1.1∗∗∗ 1.1∗∗∗ 1.2∗∗∗ 1.5∗∗∗ 1.4∗∗∗ 1.4∗∗
(0.4) (0.4) (0.4) (0.4) (0.4) (0.3)
Economic outcomes
Milk yield (kg/cow) -137∗∗∗ -139∗∗∗ -135∗∗∗ -204∗∗∗ -179∗∗∗ -154∗∗
(58) (53) (54) (54) (56) (67)
Farm income (CHF) 7778∗∗∗ 8570∗∗∗ 6521∗∗∗ 6212∗∗ 6397∗∗ 7949∗∗
(2293) (2297) (2300) (2416) (2634) (3374) Ecological outcomes
Extensive grassland (percent) 0.0 0.1 0.3 0.5 0.4 0.5
(0.4) (0.4) (0.5) (0.4) (0.4) (0.4)
N surplus (kg/ha) 2.0 -2.4 -3.3 -4.7 - -
(16) (31) (34) (21) - -
Covariates and trends
Farm and farmer characteristics X X X X X
Government support X X X X
Canton and zone trends X X
Canton-zone-year effects X
Clustered standard errors in parentheses (at farm level)
∗p <0.1,∗∗p <0.05,∗∗∗p <0.01
Notes: Results for all outcomes are based on 3,375 observations except for the result for N surplus which is based on 224 observations. Rows show the average treatment effect on the treated (ATT) for the corresponding outcome. Model (6) is estimated based on propensity score matching (PSM) using the nearest neighbor method (without replacement) in order to construct treatment and control groups matching on the outcome itself, farm and farmer characteristics (including canton and zone dummy variables) and government support during the pre-treatment period before applying DiD controlling for farm and farmer characteristics (including canton and zone trends) and government support. Clustered standard errors in Model (6) are computed using bootstrapping with 50 replications.
Sources: FADN (Hoop and Schmid 2014) and CEAEI (2017)
5.2 Ex-ante analysis: the short- and long-run perspective
5.2.1 Short- and long-run effects of the GMF programme at the farm level Table 3 presents the average treatment effects at the farm level on the various indicators/outcomes (columns) in 2015, 2020 and 2025 (rows), respectively. The first panel shows the average treatment effect (ATE), the second panel the average treatment effect on the treated (ATT), and the third panel the average treatment effect on the untreated (ATU).
All the simulation results can be found in Table B2 in the on-line Appendix B.
Table 3: Short- and long-run effects at the farm level based on SWISSland Feeding outcomes Economic outcomes Ecological outcomes Concentrate Maize Grass Milk yield Farm Extensive N surplus
feed feed feed income grassland
(percent) (percent) (percent) (kg/cow) (CHF) (percent) (kg/ha) Average treatment effect (ATE)
GMF 2015 -1.8 0.1 1.5 -47 5869 -0.1 -2.8
GMF 2020 -2.2 0 1.8 -228 6869 -0.3 -3
GMF 2025 -2.6 0 2.2 -436 7103 -0.1 -3.6
Average treatment effect on the treated (ATT)
GMF 2015 -2.3 0.1 1.7 -94 6526 -0.1 -4
GMF 2020 -2.8 0 2.1 -333 6977 -0.1 -4.5
GMF 2025 -3.4 0.1 2.6 -605 6532 -0.2 -5.4
Average treatment effect on the untreated (ATU)
GMF 2015 0 0 0 -1.6 2110 0 0 .1
GMF 2020 0.1 0 0 -16 5189 0 0.4
GMF 2025 0.2 0 0 -22 8391 0 0.2
Notes: Computations based on SWISSland show differences in means between the baseline scenario (with the GMF program) and the counterfactual scenario (without the GMF program) for all the farms (ATE), farms participating in the GMF program (ATT) and farms not participating in the GMF program (ATU), respectively.
Sources: Own calculations based on SWISSland
Our discussion of the effects computed by SWISSland focuses on the average treatment effects on the treated (ATT). In 2015, the GMF programme leads to, on average, a reduction in the milk yield of about 94 kg per cow, a 2.3 percent reduction in the proportion of concentrate feed, and an increase in the proportion of grass feed of 1.7 percent. In the long run, the impact of the GMF programme on both the milk yield and the proportion of concentrate feed increases; the milk yield in 2025 is 605 kg less per cow and the proportion of concentrate feed is 3.4 percentage points lower, whereas the proportion of grass feed is 2.6 percentage points higher, ceteris paribus. The intuition is the following. As the participating farms cannot realize milk yield increases due to concentrate restrictions, the gap in the milk yields between the participating and non-participating farms grows. The GMF programme does not affect the
proportion of maize feed in either the short or the long run. However, the GMF programme has a positive effect on the farm income in the long run (approx. CHF 6,500). On the one hand, there is a direct effect on the farm income through the payments received from the programme (compensation for forgone revenue from milk sales lost due to lower milk yields). On the other hand, there are indirect effects on the farm income through lower feed costs and higher milk prices due to the lower milk supply (see the discussion on equilibrium effects at the sector level).
Here, it is interesting to note that the non-participating farms gain even more from higher milk prices, since their milk yields continue to grow. The GMF programme redistributes part of the income generated from milk sales from the participating to non-participating farms through its effect on milk prices. Finally, the programme has no effect on the extensive grassland, while it has only a small effect on the N surplus. On average, the extensive grassland remains almost unchanged, whereas the N surplus is about 5 kg per ha lower in 2025. We conclude that the GMF programme has no or only a very modest effect on the environmental indicators although it has more substantial effects on the economic indicators in the long run.
5.2.2 Short- and long-run effects at the sector level
The short- and long-run results at the sector level are presented in Table 4.
First, we see that the GMF programme has a positive effect on the number of dairy farms.
The intuition is that the increasing farm incomes seen due to the GMF programme lead to a lower farm exit rate, since the farm income is the main determinant of farm exit (M¨ohring et al.
2016a). Even though the number of dairy cows increases slightly due to the GMF programme, it does not offset the effect of lower milk yields on milk sales. This leads to an equilibrium with lower milk supply and higher milk prices. In fact, the milk prices are about 6 cents higher per kg in 2025, leading to higher revenues from milk sales (i.e., the price effect dominates the quantity effect) and a higher agricultural sector income. We also see that the increase in the agricultural sector income exceeds the budget outlay for the GMF programme.
Finally, the GMF programme increases the total grassland area while having a slightly negative impact on the extensive grassland. The GMF programme has a small positive effect on the N surplus of the Swiss agricultural sector of about 2,000 tons. In the long run, the programme’s effect on the N surplus is almost constant even though the nitrogen input due to the use of concentrate feed is decreasing. The increase in intensively used grassland at the expense of extensively used grassland, which causes a higher N input through fixation as well as through mineral fertilizer, partially compensates for the positive effects of concentrate savings.
However, the decrease in the N output due to lower milk yields also reduces the positive effects of the savings.
Existing ex-ante impact evaluations of direct payments for grassland without concentrate feed restrictions also show an extension of the grassland area and number of dairy cows in Switzerland (Hecht et al. 2014, Hecht et al. 2015). However, these studies show that direct payments for grassland lead to an increase in the milk supply with negative effects on milk prices, whereas they find no effect on the amount of concentrate feed.
Last, we consider the effects of the GMF programme on society as a whole. First,
