Comparative review of a system modelling approach for analysing impacts of multifunctional agriculture
Luka Juvančič, Tom Johnson, Karen Refsgaard, Holger Bergmann
Abstract
The paper brefly describes agricultural multifunctionality in terms of its impact on the design of agricultural and rural policies. and discuss the impacts . As policy commitments, and correspondingly public expenditure for supporting multiple functions of agriculture are growing, the need to develop empirical tools for checking accountability of public support is growing. The paper provides a comparative review of various modelling approaches towards analysing policy impacts on multiple functions of agriculture. Comparative review is ud to reveal strengths and limitations of the POMMARD modelling framework. Paper discuss compatibility of 'conventional’ (ctoral, macroeconomic, spatial) models into the POMMARD framework, and about its applicability to a policy evaluation tool. Keywords: multifunctional agriculture, rural development, policy impact modelling, CAP, POMMARD
Introduction
Multifunctionality is the defining element of the European model of agriculture (Buller, 2001; Swinbank, 2002; Potter and Tilzey, 2005). It recognis the multiple roles that farming plays
in the countryside and the multiple goods and bads, both private and public, that are derived from farming (Potter and Burney, 2002).
The changing configuration of the EU Common Agricultural Policy (CAP) is expected to accommodate wide-ranging and diver public expectations from agriculture. Together with growing external pressures (WTO, EU budgetary rigour) and the new involvement of non-governmental organisations (NGO), the CAP has gradually adapted to the expectations (Erjavec et al., 2009). Today, besides the provision of sufficient and safe supply of food, European agriculture is expected to accommodate multiple objectives, such as ‘safeguarding viable rural societies and infrastructures, balanced regional development and rural employment, maintaining traditional rural landscapes and bio-diversity, protecting the environment and ensuring high standards of animal welfare and food safety' (Korkeaoja 2006, p. 7). The task of the CAP is to promote the multiple functions of agriculture, which often incur additional costs without compensation through market returns. The objectives are accounted for mainly within the 'ccond pillar' (ie. the rural development part) of the CAP. Policy commitments for supporting multiple functions of agriculture are growing. This implies th
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at qualified policy planning should entail thorough understanding of how the various functions of agriculture at any given territorial scale affect the sustainable economic development and the quality of life of that territory, and how different policies affect the relationships.
The EU-funded rearch project TOP-MARD attempted to address the rearch questions (Bryden et al. 2008). Empirically, the approach undertaken in modelling territorial impacts of agriculture on rural territory took into account some ‘standard’ relationships (regional
economy, environmental indicators), alongside with some new ones (quality of life, labour market, natural and human capital, demographical indicators). A system modelling approach has been undertaken, making u of the Stella® software. The Policy Model of Multifunctional Agriculture and Rural Development (POMMARD) has been designed as a tool for understanding multifunctional role of agriculture with its effects on lected geographical areas (Johnson et al. 2008). Different policy scenarios were applied to the model. Unlike many models of economic relationships the POMMARD attempted to explore policy impacts on dynamic relationships between agricultural multifunctionality, territorial rural development and various attributes, indicating the quality of life.
The paper provides a comparative analysis of the POMMARD modelling framework with other integr
ated models asssing impacts of multifunctional agriculture. The aim of this review is to systematically compare the modelling approach and rearch scope of POMMARD with its counterparts, and to discuss its possible improvements/extensions. Methodology and results
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Comparison of different approaches towards modelling agricultural multifunctionality includes various attributes: (a) theoretical background, (b) major objective of modelling, (c) modelling approach, (d) system definition and (e) categories of multifunctionality indicators. Similarly to other modelling approaches, place bad asts (land and natural characteristics) are the primary economic driver in POMMARD. In terms of the scope and the contents, POMMARD is more complex than most of the previous modelling work on multifunctionality. Accommodating new dimensions of multifunctionality to the modelling framework, the system consists of ten inter-related modules: Initial Conditions, Policy Controls, Indicators, Land, Non- Commodities, Agriculture, Quality of Life, Human Resources, Region, and Tourism. The region is treated as a system (land, people, economy, commodities / non-commodities, policies, quality of life), and relationships between them are identified and quantified.
With the exception of two approaches (FAL, 1996, Oostermeyer and van Swaay, 1998) – the focus of the modelling of multiple functions of agriculture was at evaluation of public policies, and in. Since a dynamic impact evaluation was usually abnt in the models, their u for policy evaluation and
policy development has so far been rather limited. In contrast, POMMARD has a clear ambition to accommodate various policy and market scenarios. It is build to assist evaluating the impacts of changes in policy or market conditions in different rural contexts and conditions. Impacts of policy changes (relating mainly to agricultural, rural development and regional policies) are modelled.
As for the modelling approach, POMMARD deviates from the ‘conventional’ modelling approaches in analysing multifunctional agriculture, which are usually bad on various optimisation methods (eg. linear programming, positive quadratic programming, heuristics) have prevailed. Apart from numeric datats, the newer policy models have often ud spatial (GIS) databas. In contrast to the, POMMARD has adopted a ‘system dynamics‘ approach, a novelty in modelling of rural policies.
The modelling approaches differ in terms of spatial scales. POMMARD belongs to the group of the models that take larger areas as starting points (NUTS 3). While the approaches enable rapid analys at the regional level, the conquence of ignoring the farm houhold as
a decision making unit, is that individual decisions cannot be analyd appropriately. This is the negative, but unfortunately inevitable trade-off of the models analysing multiple functions of agriculture at ‘macro’ level’. On the other hand, a territorial approach is esntial when the focus of p
olicy shifts to sustainable rural development, including social, economic and environmental issues, and where it is the sum of all micro-behaviours that matters (Daly, 2007).
In terms of time horizon, POMMARD does not deviate significantly from other approaches to modelling of multifunctional agriculture, which mainly accommodate both short- and long-term impacts. For the time being, the smallest temporal scale of POMMARD is chon by the analyst, and in our ca was monthly for the asonality component of the tourism module, while impact analysis extended over a relatively long time-scale (20 years). But the system dynamics approach towards modelling makes POMMARD relatively easily adaptable to various (shorter or longer) time scales of analysis.
As regards the economic indicators, POMMARD deviates from the standard neo-classical micro-economic approaches, bad on eg. marginal and/or utility theory, including indicators such as costs, gross margins, rates of return etc. Explicitly, economic indicators enter the model within the ‘Regional economy’ module with a dynamic social accounting matrix (SAM). Implicitly, economic indicators are entering the model within most of the remaining modules of POMMARD (with the sole exception of the ‘Land’ module). Nevertheless, POMMARD is a regional model, and the outcome indicators include net farm income, regional per capita income and other indicators including regiona
l population which are common in regional economic modelling.
POMMARD has also initiated some novel aspects of modelling. This applies especially to the ‘quality of life’ component of the model. Five capitals have been identified and analyd within the ‘Quality of life’ component: natural, material, human, social and cultural capital, and the model also makes explicit linkages between the capitals and migration behaviour. Integration of non-commodities in the model can be pointed out as another novelty aspect, even though this needs further background rearch to improve their functional relationships. Also the demographic stratification of impacts and the inclusion of a migration module are important novel aspects of POMMARD not previously applied with reference to multifunctional agriculture and rural development issues.
Policy relevance, discussion
四月有多少天Having prented POMMARD against its modelling counterparts, the paper intends to discuss the implications for future upgrading of POMMARD (or its successor), and for accommodation of ‘conventional’ (ctoral, macroeconomic or spatial) models into the POMMARD framework.
One of the potential us of POMMARD is its application as a rural development policy evaluation tool. Due to its dynamic character and its ability to provide long-term projections, it could be particula
rly uful to explore its capabilities for ex-ante evaluation or medium-to-long-term projections of policy impacts (Bergmann and Thomson, 2008). Obviously, this would require certain amendments to the current model. It would need adaptation to integrate with the Rural Development Policy Common Monitoring and Evaluation Framework system. The relevance of POMMARD as a policy planning/evaluation tool would increa also by Territorial up-scaling of POMMARD to the level of a programming region (NUTS 2). This
would be beneficial also in terms of data-availability and resource-efficiency. However, as NUTS 2 regions are usually dominated by the influence of larger cities, the policy relevance
of the model would on the other hand result in an inevitable trade-off in terms of a loss of information on impacts on rural regions of NUTS 3 or lower levels.
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