Kamel Louhichi

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.

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.

Modelling the Relationship Between Agriculture and the Environment Using Bio-Economic Models: Some Conceptual Issues

In the last years there has been a significant development of bio-economic models, especially those integrating biophysical models and economic mathematical programming models. This development was enhanced by the conjunction of several factors such as the multiplicity of objectives in new agricultural policies, the increase of demand for multi-disciplinary approaches for integrated assessment, and the call for more dialogue and cooperation between scientists from various disciplines.

Modelling Agri-Food Policy Impact at Farm-household Level in Developing Countries (FSSIM-Dev): Application to Sierra Leone

This report describes the generic template of a farm-household model for use in the context of developing countries in order to gain knowledge on food security and rural poverty alleviation under different economic conditions and agri-food policy options. This model, called FSSIM-Dev (Farming System Simulator for Developing Countries), is an extension of the FSSIM model developed within the SEAMLESS project. Contrary to most well-known household models which are econometric based, FSSIM-Dev is a non-linear optimization model which relies on both the general households utility framework and the farms production technical constraints, in a non-separable regime. It is referred to as a static Positive Mathematical Programming (PMP) which optimise at farm household level, with the opportunities to simulate the exchange of production factors among farm-households. FSSIM-Dev is designed to capture five key features of developing countries or/and rural areas: (i) non-separability of production and consumption decisions due to market imperfection; (ii) interaction among farm-households for market factors; (iii) heterogeneity of farm households with respect to their both consumption baskets (demand side) and resource endowments (supply side); (iv) inter-linkage between transaction costs and market participation decisions; and (v) the seasonality of farming activities and resource use. Model use is illustrated in this report with an analysis of the combined effects of rice support policy, namely fertiliser subsidy policy, and improved rice cropping managements (practices) on the livelihood of representative farm households in Sierra Leone. Results show that, first, the improvement of rice cropping managements is a key factor to boost significantly farm household income in the studied region. Second, the amount of N fertilizer required for, mainly, upland rice appears too high and costly and could not be applied by farm households without policy support (i.e. subsidies). Third, both the simulated rice policy and the improved crop managements would increase farm productivity and boost household income but they are not sufficient to fight poverty since most of the farm household types would continue to live below the extreme poverty line of 1 USD-equivalent per day.

Modelling Farmers’ Behaviour Toward Risk in a Large Scale Positive Mathematical Programming (PMP) Model

Agricultural production is characterized for being a risky business due to weather variability, market instability, plant diseases as well as climate change and political economy uncertainty. The modelling of risk at farm level is not new, however, the inclusion of risk in Positive Mathematical Programming (PMP) models is particularly challenging. Most of the few existing PMP-risk approaches have been conducted at farm-type level and for a very limited and specific sample of farms. This implies that the modelling of risk and uncertainty at individual farm level and in a large scale system is still a challenging task. The aim of this paper is to formulate, estimate and test a robust methodology for explicitly modelling risk to be incorporated in an EU-wide individual farm model for Common Agricultural Policy (CAP) analysis, named IFM-CAP. Results show that there is a clear trade-off between the behavioural model (BM) and the behavioural risk model (BRM). Albeit the results show that both alternatives provide very close estimates, the latter increases three times the computation time required for estimation. Despite this, we are convinced that the modelling of risk is crucial to better understand farmer behaviour and to accurately evaluate the impacts of risk management related policies (i.e. insurance schemes).

Scenar 2030 - Pathways for the European agriculture and food sector beyond 2020 (Summary report)

Analysing stylised scenarios with economic modelling tools reveals complex relations, incentives and trade-offs of the different policy instruments, in particular regarding the environmental dimension. Marginal areas of the EU are most vulnerable to drastic policy changes.