Abstract
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.
The bio-economic modelling framework was designed to be sufficiently generic and flexible in order to be applied for all relevant farming systems across the European Union, easily transferable between different geographic locations, and reusable for different applications. For this chapter, it was tested for a set of farms representing the arable farming systems in two European regions (Flevoland [Netherlands] and Midi-Pyrénées [France]) in order to analyse the current situation and anticipate the impact of new alternative scenarios.
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Notes
- 1.
The chosen value can vary from 0 to 1.65, as suggested by the literature.
- 2.
Percent absolute deviation (%):
$$ PAD{\text{ (\% )}} = \frac{{\sum\limits_{i = 1}^n {\left| {{{\hat X}_i} - {X_i}} \right|} }}{{\sum\limits_{i = 1}^n {{{\hat X}_i}} }}.100 $$where \( {\hat X_i} \) is the observed value of the variable i and Xi is the simulated value. The best calibration is reached when PAD is close to zero.
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Louhichi, K. et al. (2010). A Generic Farming System Simulator. In: Brouwer, F., Ittersum, M. (eds) Environmental and Agricultural Modelling. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3619-3_5
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