CAMAR

Competitiveness of Agriculture & Management of Agricultural resources

Introduction

Farms in lagging regions of the Community are faced by low incomes and an economic disadvantage in the production of most agricultural products. The objective of the research is to identify ways in which farm resources (land, labour and capital) can be used in developing alternative farming systems (AFS) so as to maintain or improve the comparative economic advantage of such farms in the future. Attention is focused on using farm resources on rather than off the farm, while AFS are defined in terms of (1) conversion, (2) diversification, and (3) extensification in the use of resources.

Farms in lagging regions of the European Union have low incomes and an economic disadvantage in the production of most agricultural products. Research has identified ways in which farm resources (land, labour and capital) can be used in developing alternative farming systems (AFS) so as to maintain or improve the comparative economic advantage of such farms in the future. The research has been applied to five lagging regions in the EU.

Questionnaires have been used in both farm and institutional surveys. Working papers have been produced the definition of alternative farming systems and the study regions; theory and methodology in the use of survey data, farm business change and the development of alternative farm enterprises through an expert system; institutional networking.

The research programme has three related parts : (a) the construction of normative models of the farm economy so as to project the future competitiveness of farms under different policy scenarios, including the contribution that different AFS can make to the farm economy; (b) the construction of behavioural models of the farm household to identify the socio-economic resistances to the development of different AFS; (c) the construction of institutional models of the external farm environment to define the assistance and constraints on the development of AFS by the structures and policies of institutional organisations. The research also aims to show the inter- relationship between these three parts.

There will be parallel investigations in five study areas located within the lagging regions of the Community : West of Ireland, Highlands of Scotland, Northern Pennines of England, Massif Central and Central Greece. Farm surveys, institutional surveys and RICA/FADN data will be employed.

This paper draws attention to the potential of CAMAR - DSS by reporting research on modelling farm diversification in two French Regions: Auvergne and Limousin. Similarily to SEAMLESS-IF (SEAMLESS), CAMAR-DSS was designed with a set of procedures and computer tools supplied by scientists, which can provide ‘information’ to policy makers, in order to facilitate the assessment of likely short term impacts of a suggested policy, and the exploration of alternative strategies for achieving longer-term goals through influencing land management decisions.

Problem and objectives

Which could be the main future strategies of the farmers of these two regions? and which could be the frequency of some alternative farming enterprises 2 in these new strategies?

Material and method ( prospective model )

Considering, (a) a farm and its economics environment constitute a complex system piloted by a decision maker (BROSSIER, J. et al. ,1990), whose the strategy aims to reduce the perceived «gaps» among its objectives and the vision he has of its future situation (MARTINET, A., 1983), and (b) the economic environment evolution is not predictable, the possible strategies of tomorrow of Auvergne and Limousin have been identified by simulating, for each farm type of the regional FADN, a strategic planning process with many different hypotheses about the cap reform (scenarios) 

This decision-making process includes 4 stages (see Figure 1):
Stage 1: a forecasting process, that consists of running the relationships that lie the economic results of a farm and the economic environment, so as to identify the possible future state of each farm type in plausible economic environments of tomorrow, with their current management strategies.

Stage 2: a diagnosing process, that consists of comparing the previous highlighted possible future states to norms defined by the experts, so as to identify the possible problems and stimuli that could be generated by the current strategies in the simulated environments, as well as their causes.

Stage 3: a planning process, that consists of searching for the under-new strategies that could solve the highlighted problems (or that could be generated by the «positive» stimuli), by using a rationality suited to these stimuli and to the «behavioural profiles» of the farmers type:
(a) by searching for a solution to each partial problem which takes into account its causes,
next (b) by searching for the optimal combination of current and alternative enterprises that could maximise the gross margin in the case of the «entrepreneurial farmers» ( educator6.webnode.fr/decidons/definitions/ ).

Figure 1: Inference structure of the prospective model

Stage 4: an evaluating and reviewing process, that consists : (a) of comparing the results of the under-new strategies and the problems to be solved, so as to appreciate the degree of the efficiency of each new strategy, and (b) of simulating an other economic environment, so as to study the sturdiness of the highlighted new strategy.

Next, a gathering process has draw up a regional account of the impact of the simulated political and economic environment evolution, by taking into account the statistical «weight3» of each farm type in the FADN sample. The main regional farm types of Auvergne and Limousin have been identified by building a typology of the FADN sample of these two regions in 1990, by using a multicriteria analysis. This statistical analysis has highlighted 60 farm types. Each farm type has been described by 85 social, physical and economic criteria (Busselot, A. et al., 1994), whose the values define the (initial) state of the farms by 1990, as
well as the behaviour of the farmers between 1984 and 1990. The possible economic environments of tomorrow have been described by using 40 criteria, whose the values define the possible evolutions of prices and CAP subsidies, the attribution thresholds of the subsidies, etc. (see Figure 2).

Figure 2: Evolution of the economic environment in the basis scenario

Results

Basis scenario

The simulation of the decision-making process of the main farm types of Auvergne and Limousin, in the economic environment that most French public institutions attach to the new CAP (see Figure 2), shows this new environment could be more favourable than the economic context of 1990 for the cattle and sheep farms of this two regions. Consequently : (a) 50% of the farmers of this two regions could make no change in their current activities (see Figure 3, lines S0 and S1), and (b) 25% of the farmers could increase the area of their grassland and the meat production (lines S2 et S3). Finally, the number of farms interested by the installing of an alternative farming enterprise could be lesser than in 1990 (see evolution of the lines S6, S9 and S10). However, this simulation also shows a large part of the farms «at risk» or «under pressure» could continue to use and to install alternative farming enterprises needing little
capital or little area, so as to solve a lack of money or a lack of milk, suckle cows, or cheeps quotas (see Figure 3, lines S6, S7, S10).

Figure 3: Frequency of the main strategies by 1996 (basis scenario), comparison with the frequency of these strategies by 1990.

 

(*) some farms combining several strategies, the sum is greather than 100%

Alternative scenarios

Beside, these simulations show that the impact of this new politico-economic environment may be strongly dependent of the value of some of its parameters:
1. The decrease of the maximum threshold of stocking rate for extensive premium from 1.4 to 1.0, could give a more incitative context for extensive cattle farming systems (see Figure 4, line S2), for installing alternative farming enterprises lied to the tourism (S7), for foodprocessing enterprises (S10), and for red dear production (S11). But, it could also increase by 25 % the number of farms « in economic difficulty » in Limousin.

2. The decrease by 25% of the alternative enterprises gross margins, could strongly decrease the output of the new products (by more 50%, comparatively to the basis scenario); and, in corollary, could increase the economic interest of the classical cropping, milk, cattle and sheep enterprises.

3. The impossibility for the members of the family to access to an off-farm employment could increase the economic interest of alternative enterprises needing much work and little capital, as well as the interest of extensive cattle activities.

Figure 4: Frequency of the main strategies in 1996 in the case of a decrease of the threshold of
stocking rate, comparison with (1990) and basis scenario

 

(*) Some farms combining several strategies, the sum is greater than 100


Conclusion

In summary, by adding a farms typology to a planning process by scenarios, in which farmers are viewed as men that rationalise their choices by using an inductive logic rather than as "Homo economicus" perfectly rational, we have attempt to identify the possible consequences of the CAP Reform on the farming systems perspective of Auvergne and Limousin, by using a rationality suited to the main farm types of these two regions.

However, although obtained from a statistically representative sample of the farms of the two studied regions, the thus obtained results can not expect to represent the « reality of tomorrow »: they must be viewed as some qualitative arguments for managing the CAP Reform in the two studied region, not as a prediction of the future. Firstly, because the used prospective model takes into account only a part of the reality: the alternative farming enterprises do not enjoy the same institutional and policy supports that the classical activities, and most of them can only be flowed on a local or regional market. Secondly, because the reality (and overall the reality of tomorrow) is never given: it's always built. And this prospective model notably shows that the frequency of several alternative farming enterprises could be increased by a policy that would give the priority to the work, rather than to the competitiveness of the bovine breeding.


1 This publication arises out of the Competitiveness of Agriculture and Management of Agricultural Resources
research programme (Program 8001-CT91-0119). A programme of collaborative research by the following :
Department of Geography at the Universities of Leicester, Caen and Trinity College Dublin ; Scottish
Agricultural College (Aberdeen) ; CEMAGREF (Clermont - Ferrand), TEAGASC (Dublin), and Department of
Agricultural Economics at the University of Patras.

2 diversification of building, cropping, grass land usings (B&B, farm food-processing, reforestation plants, dry
fruits or medicinal plants, horses, goats, ducks, ...), extensification of cattle enterprises, off-farm employment, ...
306 J.P. Bousset et al.

3 The FADN is a statistically representative sample of the full-time farms of a region (sampling rate : 1.5%).Each
farm is assigned a weight wi=Nj/nj, according to the number of farms Nj using the same farming system, and
that are the same economic size (strate(j))

References
Brossier, J.; Vissac, B. et J.L. Le Moigne (1990): Modélisation systémique des systèmes
agraires. INRA
Martinet, A. (1983): Stratégie. Collection Vuibert Gestion, Paris, 320 p
Busselot, A.; Bousset, J.-P. et G. Baud (1994) In: Regional report of the CAMAR program.
Cemagref Clermont-Ferrand, CERVIR, Université de Caen.