How Much Farm Succession is Needed to Ensure Resilience of Farming Systems?

Summary

en

Farm succession is a key policy concern of the EU's Common Agricultural Policy and the European Commission's proposals for the future. This article uses the agent-based model AgriPoliS to study the effects of the availability of potential successors in two agricultural regions, one in Belgium (Flanders) and one in eastern Germany (the Altmark). The analysis provides no indication that considerably fewer successors would threaten the ability of the farming systems to ensure an adequate provision of private and public goods. Most farm closures occur due to the low efficiency of some farms which hinders the ability of famers to cover true long-term opportunity costs, i.e. the possibility of earning a higher income outside agriculture. In both study regions, a lack of successors leads to adaptations which create new opportunities for other farms and, in Flanders, to higher economic prosperity at the regional level. The adaptations differ between regions due to existing farm structures, labour demands and costs, institutional frameworks, and the ability to exploit economies of scale. The results of the simulations challenge the notion central to many CAP policies – that more farm succession is better for European agriculture. These findings call for a contextualised reconsideration of agricultural policies which support structural change and regional growth – not hinder it.

Abstract

fr

La transmission des exploitations agricoles à des successeurs est une préoccupation essentielle de la politique agricole commune (PAC) de l'Union européenne et dans les propositions de la Commission européenne pour l'avenir. Cet article utilise le modèle d'agent AgriPoliS pour étudier les effets de la disponibilité de successeurs potentiels dans deux régions agricoles, l'une en Belgique (Flandre) et l'autre en Allemagne de l'Est (Altmark). L'analyse ne donne aucune indication qu'un nombre considérablement moindre de successeurs menacerait la capacité des systèmes agricoles d'assurer une fourniture adéquate de biens privés et publics. La plupart des disparitions d'exploitations surviennent en raison de la faible efficacité de certaines, qui entrave la capacité des agriculteurs à couvrir les coûts d'opportunité réels à long terme, c'est-à-dire la possibilité de gagner un revenu plus élevé en dehors de l'agriculture. Dans les deux régions étudiées, le manque de successeurs conduit à des adaptations qui créent de nouvelles opportunités pour d'autres exploitations et, en Flandre, à une plus grande prospérité économique au niveau régional. Les adaptations diffèrent selon les régions en raison des structures agricoles existantes, des demandes et des coûts de la main-d’œuvre, des cadres institutionnels et de la capacité d'exploiter les économies d’échelle. Les résultats des simulations remettent en question la notion centrale de nombreuses mesures de la PAC - selon laquelle un plus grand nombre de transmissions d'exploitations agricoles à des successeurs est meilleure pour l'agriculture européenne. Ces résultats appellent à un réexamen contextualisé des politiques agricoles qui soutiennent le changement structurel et la croissance régionale - et non qui le gênent.

Abstract

de

Ein zentrales politisches Anliegen der Gemeinsamen Agrarpolitik der EU und der Vorschläge der Europäischen Kommission für die Zukunft ist die Hofnachfolge. Dieser Beitrag untersucht anhand des agentenbasierten Modells AgriPoliS die Auswirkungen des Vorhandenseins einer potenziellen Betriebsnachfolge in jeweils einer landwirtschaftlichen Region in Belgien (Flandern) und in Ostdeutschland (Altmark). Die Ergebnisse der Analyse liefern keinen Hinweis darauf, dass eine mangelnde Betriebsnachfolge die Fähigkeit der landwirtschaftlichen Systeme, eine angemessene Versorgung mit privaten und öffentlichen Gütern sicherzustellen, gefährden würde. Die meisten Betriebsschließungen sind auf die geringe Effizienz einiger Betriebe zurückzuführen. Diese schränkt die Fähigkeit der in der Landwirtschaft tätigen Personen ein, langfristige Opportunitätskosten zu decken, d. h. die Möglichkeit, außerhalb der Landwirtschaft ein höheres Einkommen zu erzielen. In beiden Untersuchungsregionen entstehen durch eine mangelnde Nachfolge Anpassungen, die neue Möglichkeiten für andere Betriebe schaffen. In Flandern bewirken diese einen höheren wirtschaftlichen Wohlstand auf regionaler Ebene. Die Anpassungen unterscheiden sich aufgrund der bestehenden Betriebsstrukturen, der Arbeitsanforderungen und -kosten, der institutionellen Rahmenbedingungen und der Fähigkeit, Skaleneffekte zu nutzen, von Region zu Region. Die Ergebnisse der Simulationen stellen den zentralen Gedanken vieler GAP-Politikansätze in Frage – nämlich, dass mehr Betriebsnachfolge für die europäische Landwirtschaft besser sei. Diese Erkenntnisse erfordern ein kontextbezogenes Überdenken der agrarpolitischen Ansätze, und zwar dahingehend, dass sie den Strukturwandel und das regionale Wachstum unterstützen und nicht behindern.

Generational renewal in agriculture, or ensuring the next generation of farmers, has been the subject of much public and political debate due to the ‘young farmer problem’. The perceived ‘problem,’ that the number of young people entering agriculture is too low, has received substantial attention within the EU. Several policy measures have been included in the EU Common Agricultural Policy (CAP) in order to support young farmers, including the Young Farmer Payments, totalling 383 million euros in 2017 alone, and start-up aid for young farmers.

Understanding the ‘young farmer problem’ requires understanding its different components, like the ageing of the farmer population, restructuring of the agricultural sector, and farm succession (Zagata et al., 2017). Farm succession, defined as the transfer of managerial control of farm business assets, plays an important role in farm generational renewal. Historically, farm succession has been framed within established family traditions, which have guaranteed the continuity of farming by farmers’ descendants. However, this framing has limitations. The definition of a typical European farm has broadened. As part of EU enlargements since 2004, a number of post-communist countries entered the EU, in which corporate and co-operative farms are of great regional importance. These farms also go through generational renewal in terms of ownership, management and employees. Due to social and financial burdens, some corporate and co-operative farms are facing generational renewal difficulties. Key challenges are transferring multi-million Euro valued shares to new owners who will manage or work on the farm and securing employees willing to live where the farms are located, often in areas with limited infrastructure and low wages.

Despite the attention that the EU pays to the generational renewal ‘problem’, several scholars contest whether Europe is truly facing a farm succession crisis (Lobley et al., 2010; Matthews, 2018). Underlying the generational renewal ‘problem’ is the prevalent idea that generational renewal is needed to ensure that farming systems can fulfil their essential functions, like the provision of private and public goods, both now and in the future. It is also argued that young farmers are the source of introducing new knowledge, that they are more likely to manage sustainably, and will modernise their farms. However, an optimum or minimum level of succession has never been explicitly defined. The renewal ‘problem’ of a given region, country or particular farming system should be considered simultaneously with the continuous restructuring process of the agricultural sector, which is seen to be essential for the resilience of agricultural systems in a changing and turbulent environment (Meuwissen et al., 2019).

Against this background, and in order to explore to what extent agricultural systems can withstand varying levels of farm succession, we simulated the structural development of two contrasting agricultural regions in Europe, the Altmark in Germany and Flanders in Belgium (Table 1) with varying amounts of successor likelihood using the agent-based model AgriPoliS (see Box 1 for details). The regions reflect key features of European agricultural structures, like the very large corporate farms common in the post-communist Central and Eastern European countries (the Altmark), as well as the medium-sized family farms commonly found in Western Europe (Flanders).

Table 1. Scenario descriptions

Characteristic	Altmark	Flanders
Location	Eastern part of Germany in the federal state Saxony-Anhalt	Northern part of Belgium
Size of region	~200,000 hectares	~600,000 hectares
Farm types	Large-scale cooperative and corporate farms with roots in the communist era and newly or re-established family farms	Family farms
Farming systems	Arable cropping, dairy, a few pig farms	Dairy, pig and poultry farms, horticulture, arable cropping
Labour source	~15% family labour, ~85% hired labour	~80% family labour, ~20% hired labour
Average farm size	>200 hectares	~30 hectares
Annual farm exits	~1%	3%–4%
Key challenges	Replacement of skilled labour	Low profitability

Box 1. The Model: AgriPoliS

The Agricultural Policy Simulator, AgriPoliS, is an agent-based model used to further the understanding of agricultural structural change and the effects of agricultural policies (Happe et al., 2006; Happe et al., 2008). Agent-based models simulate the actions and interactions of agents, such as farms, and provide a bottom-up approach to simulating a complex adaptive system, such as an agricultural region. Given the dynamic interactions, agent-based models like AgriPoliS can capture path dependency, or how major outcomes are the result of historical events or previous decisions. AgriPoliS is a spatially-dynamic model adapted to match the characteristics of European agricultural regions (e.g. typical farms, production and investment options), and to differentiate by economic conditions (e.g. economies of scale, soil and market conditions) in a model region. In each region there are agents, i.e. farms, which are heterogeneous in terms of legal form, age, asset structure, location, managerial skills that act and interact based on maximising profits (if the agent is a corporate farm), or household incomes (if the agent is a family farm). In simulations, a representative set of farm types is defined in which certain variables, such as age, location, managerial skills and the availability of a potential successor are initialised randomly. The distribution of the variables can be adjusted to reflect the characteristics of the simulated region or scenario. Farms are heterogeneous and evolve endogenously based on their decisions and competitiveness. Determinants of competitiveness are farm size, managerial skills and relative location; all of which are randomly initialised. The key decisions that agents make are how much to bid on additional plots of land, what investments they will make, and what they will produce. At the end of each year (one iteration), each agent decides whether to continue to farm or to exit agriculture. Farms that are illiquid or whose operator could earn more off-farm close and the land is put onto the land rental market. Additionally, if the farmer has reached the point of retirement, and has no successor or the successor could earn more off-farm, the farm will exit agriculture with its land going onto the rental market. Interaction between farms therefore occurs through this market, in which farms bid according to the expected profits of farming additional plots. Farms with the highest bid obtain the land. This is not fully compatible with land market conditions in Flanders where some land prices are officially regulated. However, the assumption of a competitive land market is used to understand the potential effects of farm succession and structural change. Simulation results can be analysed at the farm and regional levels.

Scenarios of successor availability

Both model regions are adapted in accordance with empirical data to fit the characterisations presented in Box 1 (Pitson et al., 2020). For each model region, three scenarios are defined that vary by the likelihood that a farm has a potential successor. This simulates cases where all farms have a potential successor, or where some do not because there is no descendant able to continue a family farm or if a corporate farm fails to establish a new management or to transfer its shares to the next generation (Table 2). Once the current farmer reaches retirement age, and there is a potential successor, the farm continues to operate if expected earnings from farming are higher than after farm closure, including earnings from the rental or sale of assets. If there is no potential successor, the farm will close. Both model regions have been estimated and modelled using the last available Eurostat (2013) data at the national level with regard to farmers’ age distributions. In reality, Flanders’ rate of succession is close to the 50 per cent scenario, while the Altmark's is between the 50 per cent and 100 per cent scenarios depending on farm size and legal form. The purpose of the scenarios is not to capture reality exactly, rather to understand the effects that varying rates of successor likelihood have on different agricultural regions. Simulations start in the year 2016, to which the model regions are calibrated. Most of the analysis in this paper focuses on the state after 20 iterations, i.e. in the year 2036. All scenarios are simulated 15 times with different random initialisations of variables detailed in Box 2. The results present the average of the repetitions.

Table 2. Scenario descriptions

Name	Share of farms with a potential successor
25% Altmark	25% of family farms 50% of corporate farms
50% Altmark	50% of family farms 80% of corporate farms
100% Altmark	100%
25% Flanders	25%
50% Flanders	50%
100% Flanders	100%

Box 2. Efficiency concepts

The overall efficiency of a farm or farming system can be differentiated by subcategories. The relevant efficiency concepts are allocative, technological and scale efficiency. Allocative efficiency implies profit-maximising ratios of inputs and outputs. The agents are programmed to make their decisions on how to use their resources, including their own labour, based on allocative efficiency. Technological efficiency is the use of the most productive technology. In AgriPoliS, this means that an agent's investments in machinery are most efficient given resources and expectations and, in turn, production decisions are influenced by available machinery. Scale efficiency means producing at the optimal level, where any changes in scale will result in waste. In AgriPoliS, scale efficiency is closely linked to technological efficiency, where larger machinery which larger farms can use, saves on labour and other costs, resulting in the use of fewer inputs for the same output than with smaller machinery.

Farm succession and structural change

Figure 1 shows the development of average farm size in hectares and livestock density per hectare over time. Regardless of the availability of a successor, there is ongoing structural change. The speed of structural change increases when fewer farms have a potential successor. In both regions, farm closures allow other farms to grow. The growth can be reflected through increasing farm size in terms of area and through intensification. The difference in average size in hectares between regions remains substantial. For all scenarios, the average farm size in the Altmark continues to be more than ten times that of Flanders. Due to assumed high relative profitability, livestock density increases in both regions in all scenarios, with an inverse relationship between potential succession and livestock density.

Figure 1

Average farm size and livestock density in the Altmark and Flanders

While, on average, remaining farms grow in size, the growth is not equal for each farm. The land which becomes available on the land market, due to farm closures or size reductions, is primarily obtained by larger farms. This is shown in Figure 2 for year 2036. In the Altmark, structural change shifts a large share of the land towards the size category of more than 1,000 ha. In Flanders, land is reallocated from smaller farms towards size categories of more than 100 ha, and with fewer successors even some large farms of more than 500 ha emerge.

Figure 2

Land distribution according to size categories in the Altmark and Flanders in 2016 and 2036

Opportunity costs driving farm closure

Table 3 shows the share of farms still operating in the year 2036 and the shares of farms which closed due to four different reasons. The rows ‘operating’ and ‘no successor’ show that the absence of a potential successor has a substantial effect on operational status. However, what is interesting is that closure is not necessarily less likely for farms that have a potential successor. In the cases of farm closures due to opportunity costs or illiquidity, farms close due to: the farmer expecting to earn more outside of agriculture (significant for the Altmark), the successor expecting to earn more outside of agriculture (significant for Flanders), or the farm being bankrupt. Irrespective of whether there is a potential successor, low profitability, as indicated by the rows for opportunity costs, remains a primary cause of farm closures in both regions. Interestingly, in both regions, the shares are relatively independent of the availability of successors, with the exception of the 100 per cent Scenario in the Altmark. What the table emphasises is that successor presence has a limited effect on farm profitability, which is a driving cause of farm closure.

Table 3. Farm operational status 2036 for the Altmark and Flanders (per cent)

Status	25% Altmark	50% Altmark	100% Altmark	25% Flanders	50% Flanders	100% Flanders
Operating	47.3	55.2	67.8	52.4	56.6	68.5
Closed due to:
1) Opportunity costs	13.9	14.9	18.6	2.8	2.8	3.0
2) Opportunity costs at generational change	7.1	7.5	7.7	20.3	20.4	20.6
3) Illiquidity	5.9	5.8	5.9	7.6	7.6	7.9
4) No successor	25.8	16.6	N/A	16.9	12.6	N/A

Fewer farms, more output and factor income

Despite reductions in the number of farms, total sector income from farming from land, labour and capital increases in all scenarios. The marginal effects of successor availability are relatively small. Figure 3 shows sector factor income in both regions. In Flanders, it increases most rapidly when farms do not have a potential successor. This contrasts to the Altmark, where sector factor income increases at a slower rate when fewer farms have a potential successor. The increasing availability of land allows surviving farmers in Flanders to exploit economies of scale. In the Altmark, many of the larger farms are already able to exploit economies of scale and these farms provide a large share of regional output. When very large and efficient farms exit agriculture due to a lack of successors, there is a decrease in sector factor income compared to scenarios with higher successor presence. This is because when the remaining farms have the opportunity to grow significantly and large amounts of land become available, their investments cannot compensate for those which were devalued due to the exit of very large farms. The same effect is not present in Flanders where farms are on average 1/10^th of the size of those in the Altmark. Many of the farms in Flanders carry labour and machinery surpluses and have sufficient financial capacity to make complementary investments when more land becomes available. Although there are some differences between scenarios, the effect of successor presence on factor income is relatively small in both regions.

Figure 3

Total sector income per ha in the Altmark and Flanders

Succession, competition and the land market

The substantial positive economic effect of farm closures on factor income in Flanders reflects a lack of efficiency in 2016. In other words, the region's initial allocation of resources (land, technology, financial and human capital) is such that there is significant waste. In particular, economies of scale cannot be exploited when too many farms compete for the scarce factor – land. Despite lacking the various forms of efficiency, many farms continue to produce. Reasons for this include sunk costs (costs which have already been incurred and cannot be recovered) of existing assets and human capital, which is particularly relevant in the period before retirement. Another reason points to existing land market legislation in Flanders which partially limits rental prices. Farms with low competitiveness continue to use the land because their actual rental cost is less than what other farmers would be prepared to pay, and because the opportunity costs of renting out their land to other farmers are low due to regulation. The simulations with AgriPoliS, however, assume a competitive land market. This allows more efficient farms to outbid less efficient farms and also compensates closed farms for renting out their land through high rental rates. The effects of moving from the regulated land market prices at the initialisation to a competitive market are illustrated in Figure 4 with the economic land rent and rental prices. The economic land rent has been calculated as sector factor income minus agricultural wages, opportunity costs for family labour, interest on loans, and the opportunity costs of equity capital.

Figure 4

Average economic land rents and rental prices in the Altmark and Flanders

Whereas in the Altmark economic land rents are somewhat higher than rental prices and allow for real profits for farms, the economic land rent in Flanders is initially negative. However, after 10 years of structural change and land price adjustments, economic land rent increases rapidly. This shows how current land market restrictions in Flanders impede structural change and economic growth. The effect of farm successor availability is relatively low in both regions. Because of already widely exploited economies of scale, the Altmark does not benefit to the same extent when average farm size increases, as seen in the smaller increase in economic land rent and land rental prices in the scenarios where successor presence is not guaranteed.

How much farm succession is enough?

Our analysis has some limitations which cannot be addressed in full here. The role of securing hired labour, social and environmental constraints, volatile prices, uncertain yields, product market responses of other regions, non-agricultural operations competing on the land market, and macroeconomic developments are not addressed and their effects are assumed to remain constant in all scenarios. Nevertheless, the simulations illustrate the potentially substantial economic benefits of structural change. These benefits can be assumed to be largest in regions where existing farms cannot exploit economies of scale and where efficient land allocation is hindered by legislative friction. Additionally, the simulations shed light on the heterodox situation observed in many European agricultural regions – the continued operation of inefficient farms. Neoclassical theory teaches that these farms, which cannot exploit economies of scale or otherwise differentiate their products, would close. However, this ignores the realities of sunk costs, path dependency, and financial incentives from policies, which are captured in the model simulations.

In the process of structural change, farm succession and the availability of successors is an important determinant. The model simulations show that the same situation (the likelihood of a successor) can have contrasting outcomes in different regions. In the Altmark, the region benefits most when already highly competitive farms have a successor. In Flanders, the availability of a successor can be a positive stimulus for individual farm development, but its effects at the regional level can be negative. If producers, farming the majority of land, cannot exploit economies of scale because they are too small, the lack of successors can lead to higher economic prosperity generated by the agricultural sector.

Is a 25 per cent successor likelihood ‘enough’? From our simulations, we cannot conclude that even such a low rate of available farm successors will cause system collapse. While there are varying effects in both regions, in neither case was there a collapse in the functions provided by the agricultural sector nor any indication that one would happen. However, with continued size growth that comes with fewer farms, the availability of hired labour will play an increasingly important role.

With regard to the EU CAP, our findings raise severe doubts as to whether the current CAP, including extra payments for young farmers and extra payments for the first 90 hectares farmed, benefit agricultural development. Rather, the findings suggest that they are counterproductive. These payments incentivise inefficient farms to continue farming, particularly in regions that suffer most from technological and scale inefficiencies. The resulting farm successions come at the cost of other medium-sized and larger farms. Policy should move towards increasing the human capital base of the agricultural sector through training, trans-sectoral and transregional mobility – including the provision of adequate infrastructure in rural areas to attract young skilled labour.

“An optimum or minimum level of succession has never been explicitly defined.”

“Un niveau de transmission à des successeurs optimal ou minimum n'a jamais été défini explicitement.”

“Ein optimales oder minimales Level der Betriebsnachfolge wurde nie explizit definiert.”

Satellite image of fields in the Altmark, Germany.

Satellite image of fields in Flanders, Belgium.

A young farmer teaching his child about harvesting.

Acknowledgments

Open access funding enabled and organised by Projekt DEAL.