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

Abstract

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.

Modelling and Tools Supporting the Transition to a Bioeconomy

“…Their methodology predominantly consists of mathematical linear or non-linear programming models that result in the quantity of agricultural products produced under relevant conditions. They are often developed in research projects for specific issues or locations only and are not used after the end of the project (Janssen et al 2010). However, the following models, which are exemplary of the large number of existing agricultural bottom-up models, are firmly established in research facilities and have been continuously used and developed for various economic and environmental assessments of agricultural systems.…”

“…The model is designed as a generic bioeconomic farm model. Through its flexible design, it can be used for a variety of climate zones, soil types, farm types, research applications, and data sources (Janssen et al 2010;. For instance, FSSIM has been applied to 13 regions in the EU and to different farm types.…”

Uncertainty analysis in integrated assessment: the users’ perspective

“…APES (Agricultural Production and Externalities Simulator) is a deterministic and dynamic cropping system simulation model for calculating agricultural production and its externalities in response to weather, soils and agro-management (Donatelli et al 2010); 2. FSSIM (Farm System Simulator) is a bio-economic farm model, using mathematical programming for quantifying the integrated agricultural, environmental and socioeconomic responses of farming systems, partly using the output from APES Janssen et al 2010); 3. EXPAMOD is an econometric expansion model used for up-scaling the supply responses from FSSIM to the European scale (Pérez Domínguez et al 2009); and 4.…”

Exploring opportunities for rural livelihoods and food security in Central Mozambique

“…Bio-economic models have been extensively used to explore alternatives to current systems, for example the use of legumes to increase maize yields in Tanzania (Baijukya et al, 2004;Janssen et al, 2010), to assess the trade-offs between economic and environmental objectives of Dutch dairy and arable farmers (Van de Ven and Van Keulen, 2007;Kanellopoulos et al, 2012), and to analyse the impact of alternative crop residue management practices on crop and livestock productivity at different scales (Mujaya and Yerokun, 2003).…”