Guillermo Flichman

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.

Agricultural biodiversity, social–ecological systems and sustainable diets

The stark observation of the co-existence of undernourishment, nutrient deficiencies and overweight and obesity, the triple burden of malnutrition, is inviting us to reconsider health and nutrition as the primary goal and final endpoint of food systems. Agriculture and the food industry have made remarkable advances in the past decades. However, their development has not entirely fulfilled health and nutritional needs, and moreover, they have generated substantial collateral losses in agricultural biodiversity. Simultaneously, several regions are experiencing unprecedented weather events caused by climate change and habitat depletion, in turn putting at risk global food and nutrition security. This coincidence of food crises with increasing environmental degradation suggests an urgent need for novel analyses and new paradigms. The sustainable diets concept proposes a research and policy agenda that strives towards a sustainable use of human and natural resources for food and nutrition security, highlighting the preeminent role of consumers in defining sustainable options and the importance of biodiversity in nutrition. Food systems act as complex social-ecological systems, involving multiple interactions between human and natural components. Nutritional patterns and environment structure are interconnected in a mutual dynamic of changes. The systemic nature of these interactions calls for multidimensional approaches and integrated assessment and simulation tools to guide change. This paper proposes a review and conceptual modelling framework that articulate the synergies and tradeoffs between dietary diversity, widely recognised as key for healthy diets, and agricultural biodiversity and associated ecosystem functions, crucial resilience factors to climate and global changes.

A regional agricultural model using a plant growth simulation program as activities generator— an application to a region in Argentina

Abstract This paper presents a regional agricultural model using a plant growth simulation model (EPIC) as generator of activities. Its objective is to develop a simple tool which determines the possible effects of economic changes on regional supply and revenue as well as at the level of farm structure and farm relationships. Erosion trends can also be observed. The use of a physiological model allows us to utilize real activities of the region as well as some potential ones (such as irrigation or different fertilizer doses). Thus, if economic changes are produced, these activities may become more profitable and, so, will appear in the solution. The predictive capability of the model then, becomes more accurate. In this work the authors develop a model prototype of this kind for a region in Argentina, which considers three types of farms, including land market (sale and rent) and the market for rented machinery. The results obtained on cropping pattern are close to the actual ones with the present economic environment. When price and interest rate simulations are done, differences inter and intra farms are observed as well as in the total regional output. The sensitivity analysis shows that changes in wheat and soybean prices would induce the most important changes at the regional level, which is not the case for corn or cattle prices. On the other hand, erosion trends will generally be aggravated (except when wheat price increases), if no specific measures are taken to counterbalance the tendency.

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 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.

Impact assessment of climate change on farming systems in the South Mediterranean area: a Tunisian case study

This study considers a quantitative approach for assessing the performance of Tunisian farming systems to face climate change. It is based on the resilience concept and the calculation, with a modelling chain, of three indicators: land stock, labour stock, and income flux. Two system states, “base” and “climate change”, and a time horizon of 2010–2025, are developed and compared for representative farming systems. The study shows that 55% of the farming systems were identified as being resilient to climate change. They are diversified and mostly grow cereals, vegetables, and forage crops combined with livestock, increasing their capability to mitigate climate change by reorganizing crop activities. 35% of the farms identified as being non-resilient are dominated by orchards, or cereals and orchards. They showed an important drop in farm income (−45%), mainly due to their inability to adapt their cropping systems to water stress and soil salinity. Finally, only 10% were identified as being poorly resilient farming systems. Those farms have mainly intensified cereal cropping systems based on a strategy of purchasing land to increase the surface area of profitable activities (forage and livestock). Overall, the methodology can be adapted for other dry land areas in the Mediterranean region and help experts and policy-makers to propose and test strategies for adapting to climate change.