Thomas Heckelei

Agriculture, Forestry and Fisheries

Agriculture and fishing are the primary source of almost all our food, as well as of many other products, so remain vital activities even though their share in the economy is small and in continuous decline. Partly for this reason public policy in these areas is almost entirely integrated at European level in the Common Agricultural Policy (CAP) and Common Fisheries Policy (CFP). Forestry policies are also heavily affected by EU-level decision-making. As agriculture dominates in terms of direct contribution to GDP and numbers of people engaged in it, as well as accounting for the largest amount of public support expenditure, it is agriculture that will receive the greatest attention here.

Positive Mathematical Programming Approaches - Recent Developments in Literature and Applied Modelling

This paper reviews and discusses the more recent literature and application of Positive Mathematical Programming in the context of agricultural supply models. Specifically, advances in the empirical foundation of parameter specifications as well as the economic rationalisation of PMP models – both criticized in earlier reviews – are investigated. Moreover, the paper provides an overview on a larger set of models with regular/repeated policy application that apply variants of PMP. Results show that most applications today avoid arbitrary parameter specifications and rely on exogenous information on supply responses to calibrate model parameters. However, only few approaches use multiple observations to estimate parameters, which is likely due to the still considerable technical challenges associated with it. Equally, we found only limited reflection on the behavioral or technological assumptions that could rationalise the PMP model structure while still keeping the model’s advantages.

A Generic Bio-Economic Farm Model for Environmental and Economic Assessment of Agricultural Systems

Bio-economic farm models are tools to evaluate ex-post or to assess ex-ante the impact of policy and technology change on agriculture, economics and environment. Recently, various BEFMs have been developed, often for one purpose or location, but hardly any of these models are re-used later for other purposes or locations. The Farm System Simulator (FSSIM) provides a generic framework enabling the application of BEFMs under various situations and for different purposes (generating supply response functions and detailed regional or farm type assessments). FSSIM is set up as a component-based framework with components representing farmer objectives, risk, calibration, policies, current activities, alternative activities and different types of activities (e.g., annual and perennial cropping and livestock). The generic nature of FSSIM is evaluated using five criteria by examining its applications. FSSIM has been applied for different climate zones and soil types (criterion 1) and to a range of different farm types (criterion 2) with different specializations, intensities and sizes. In most applications FSSIM has been used to assess the effects of policy changes and in two applications to assess the impact of technological innovations (criterion 3). In the various applications, different data sources, level of detail (e.g., criterion 4) and model configurations have been used. FSSIM has been linked to an economic and several biophysical models (criterion 5). The model is available for applications to other conditions and research issues, and it is open to be further tested and to be extended with new components, indicators or linkages to other models.

Direct Payments, Spatial Competition, and Farm Survival in Norway

We argue that farm survival is influenced by neighboring farmers’ characteristics and, in particular, by the direct payments neighboring farmers receive. The article shows empirically that these interdependencies are crucial for an assessment of the effects of direct payments on farm survival. Using spatially explicit farm-level data for nearly all Norwegian farms, a spatial probit model is estimated to explain farm survival from 1999 to 2009 controlling for spatial farm interdependence. We show that ignoring spatial interdependencies between farms leads to a substantial overestimation of the effects of direct payments on farm survival. To our knowledge, this article is the first attempt to empirically analyze the importance of neighboring interdependencies for the effects of direct payments on farm survival.

A Generic Farming System Simulator

The aim of this chapter is to present a bio-economic modelling framework established to provide insight into the complex nature of agricultural systems and to assess the impacts of agricultural and environmental policies and technological innovations. This framework consists of a Farm System Simulator (FSSIM) using mathematical programming that can be linked to a cropping system model to estimate at field level the engineering production and environmental functions. FSSIM includes a module for agricultural management (FSSIM-AM) and a mathematical programming model (FSSIM-MP). FSSIM-AM aims to define current and alternative activities and to quantify their input output coefficients (both yields and environmental effects) using a cropping system model, such as APES (Agricultural Production and Externalities Simulator) and other sources (expert knowledge, surveys, etc.). FSSIM-MP seeks to describe the behaviour of the farmer given a set of biophysical, socio-economic and policy constraints and to predict its reactions under new technologies, policy and market changes. The communication between these different tools and models is based on explicit definitions of spatial scales and software for model integration.

A comparison of CAPRI and SEAMLESS-IF as Integrated Modelling Systems

SEAMLESS-IF and CAPRI are both integrated agricultural modelling systems for policy impact assessment at EU level, linking model components across scales and between the economic and bio-physical domains. However, the overall design, focus and representation of agricultural sub-systems vary between them. This chapter describes and compares the main characteristics of SEAMLESS-IF and CAPRI, looking at objectives, concepts for database and model linking, modelling approaches at farm level and technology representation, agri-environmental indicators and baseline construction for forward looking impact assessment. Observed differences in these areas follow from SEAMLESS-IF focusing on field and farm level components stressing bio-economic interrelations and technological innovation, whereas CAPRI adopts a more market oriented perspective with full coverage of EU policies. Software design in SEAMLESS-IF is shaped by flexible component integration and a strong client oriented graphical user interface. CAPRI instead stresses simulation performance and exploitation of results by modellers.

Regional Economic Analysis of Milk Quota Reform in the EU

This report is based on the outcome of a study carried out by the European Commissions Joint Research Centre - Institute for Prospective Technological Studies (JRC-IPTS, Spain) in cooperation with EuroCARE (Bonn, Germany) and the collaboration of the Agricultural Economics Research Institute (LEI, the Netherlands) and the Catholic University of the Sacred Heart (Unicatt, Italy). The report provides an economic impact assessment of possible implications of the Health Check of the Common Agricultural Policy (CAP), with an explicit focus on regional effects of a milk quota abolition in the EU-27 in the year 2015. For the analysis the CAPRI model was updated with econometric estimates of milk quota rents at regional level and simulation results are presented for the year 2020. The detailed spatial resolution allows identifying regions where economic changes are larger than visible from aggregated impacts at Member State or European level.

Symmetric Positive Equilibrium Problem: A Framework for Rationalizing Economic Behavior with Limited Information: Comment

In a recent contribution to this journal, Paris suggests a framework which extends positive mathematical programming (PMP)—a widely used calibration methodology for agricultural supply models—to a symmetric positive equilibrium problem (SPEP). He stresses three main contributions: (1) The PMP methodology is modified to incorporate more than one observation on production programs; (2) A solution to the “self-selection problem” with respect to the choice of crops produced by each farm is provided; (3) “Limiting inputs” are no longer considered fixed quantities as in PMP. We address several conceptual concerns with respect to the SPEP methodology and the presented application. We consider these to be substantial enough to question Paris’ claim to present “ ... a general framework of analysis that is capable of reproducing economic behavior in a consistent way ... ” (p. 1049). Our discussion is structured along Paris’ presentation: The next three sections represent the core of the comment and deal with the methodology itself. They refer to the three phases of SPEP: (i) recovery of unknown variable marginal costs and shadow prices of limited resources, (ii) use of these results to specify data constraints and parameter supports for generalized maximum entropy (GME) estimation of a cost function, and (iii) definition of a simulation model. Finally, concluding remarks are made regarding the application of SPEP to an analysis of the EU Common Agricultural Policy (CAP) based on Italian farm data. Throughout the comment we use the same

Cooperation and collapse in a communal livestock production SES model - A case from South Africa

Institutional arrangements are considered necessary for successfully governing the commons. They are considered to be most effective if they are self-organized rather than imposed from outside. However, endogenous institutional arrangements, such as local norms, are specific to a particular socio-ecological system (SES). This paper presents a SES model of communal livestock producers in South Africa. Its bio-physical component accounts for the impact of biotic and abiotic factors on livestock population. The social agent-based component models individual and socially determined behaviour, the latter of which is a social norm specific to the case. Model results show that when cooperative agents obey and sanction the norm, there is less likelihood of SES collapse in terms of livestock population crashes. However, cooperation among agents only emerges in times of ecological crisis where social reorganization is fostered. The crisis creates an opportunity for initializing a self-enforcing process of mutual cooperation. Model specifications are based on survey data, and agents were parameterized according to individual household data. A sensitivity analysis shows that this empirical heterogeneity cannot be reduced without changing model outcomes. We model a communal cattle production SES in South Africa to investigate the impact of a social norm.The SES model is a full integration of an ABM with a dynamic rangeland model.The emergence of norm compliance prevents SES collapse.A SES crisis is the opportunity for cooperative behaviour to emerge.The model is sensitive towards the parameterization of agent attributes from field data.

Tools for integrated assessment in agriculture. State of the art and challenges.

The increased interest in Integrated Assessment (IA) of agricultural systems reflects the growing complexity of policy objectives and corresponding impacts related to this sector. The paper contemplates on the status of quantitative tools for IA in agriculture, drawing on recent European experiences from the development and application of large-scale integrated modelling systems which are both multi-dimensional/disciplinary and covering multiple spatial scales. Specific challenges arise from the numerous roles of agriculture with societal relevance, the heterogeneity of farms and farming systems across a geographical region and the multitude of environmental impacts of interest associated with agricultural production. Conceptual differences between typical bio-physical and economic models as well as deficiencies regarding validation and uncertainty analysis require continued efforts to improve the tools.