Organic Farming in Europe by 2010: Scenarios for the future

How will organic farming in Europe evolve by the year 2010? The answer provides a basis for the development of different policy options and for anticipating the future relative competitiveness of organic and conventional farming. The authors tackle the question using an innovative approach based on scenario analysis, offering the reader a range of scenarios that encompass the main possible evolutions of the organic farming sector. This book constitutes an innovative and reliable decision-supporting tool for policy makers, farmers and the private sector. Researchers and students operating in the field of agricultural economics will also benefit from the methodological approach adopted for the scenario analysis

Expert System for Crop Disease based on Graph Pattern Matching: A proposal

Para la agroindustria, las enfermedades en cultivos constituyen uno de los problemas más frecuentes que generan grandes pérdidas económicas y baja calidad en la producción. Por otro lado, desde las ciencias de la computación, han surgido diferentes herramientas cuya finalidad es mejorar la prevención y el tratamiento de estas enfermedades. En este sentido, investigaciones recientes proponen el desarrollo de sistemas expertos para resolver este problema haciendo uso de técnicas de minería de datos e inteligencia artificial, como inferencia basada en reglas, árboles de decisión, redes bayesianas, entre otras. Además, los grafos pueden ser usados para el almacenamiento de los diferentes tipos de variables que se encuentran presentes en un ambiente de cultivos, permitiendo la aplicación de técnicas de minería de datos en grafos, como el emparejamiento de patrones en los mismos. En este artículo presentamos una visión general de las temáticas mencionadas y una propuesta de un sistema experto para enfermedades en cultivos, basado en emparejamiento de patrones en grafos.For agroindustry, crop diseases constitute one of the most common problems that generate large economic losses and low production quality. On the other hand, from computer science, several tools have emerged in order to improve the prevention and treatment of these diseases. In this sense, recent research proposes the development of expert systems to solve this problem, making use of data mining and artificial intelligence techniques like rule-based inference, decision trees, Bayesian network, among others. Furthermore, graphs can be used for storage of different types of variables that are present in an environment of crops, allowing the application of graph data mining techniques like graph pattern matching. Therefore, in this paper we present an overview of the above issues and a proposal of an expert system for crop disease based on graph pattern matching

Empirical models, rules, and optimization

This paper considers supply decisions by firms in a dynamic setting with adjustment costs and compares the behavior of an optimal control model to that of a rule-based system which relaxes the assumption that agents are explicit optimizers. In our approach, the economic agent uses believably simple rules in coping with complex situations. We estimate rules using an artificially generated sample obtained by running repeated simulations of a dynamic optimal control model of a firm's hiring/firing decisions. We show that (i) agents using heuristics can behave as if they were seeking rationally to maximize their dynamic returns; (ii) the approach requires fewer behavioral assumptions relative to dynamic optimization and the assumptions made are based on economically intuitive theoretical results linking rule adoption to uncertainty; (iii) the approach delineates the domain of applicability of maximization hypotheses and describes the behavior of agents in situations of economic disequilibrium. The approach adopted uses concepts from fuzzy control theory. An agent, instead of optimizing, follows Fuzzy Associative Memory (FAM) rules which, given input and output data, can be estimated and used to approximate any non-linear dynamic process. Empirical results indicate that the fuzzy rule-based system performs extremely well in approximating optimal dynamic behavior in situations with limited noise.Decision-making. ,econometric models ,TMD ,

GIS-based modeling of land use systems - Common Agricultural Policy reform and its impact on agricultural land use and plant species richness

An assessment of agricultural policy measures and their sustainability needs to consider economic, social, and ecological aspects. The current paradigm shift of the European Union’s Common Agricultural Policy (CAP) from coupled to decoupled transfer payments calls for such an evaluation. Land users have to reevaluate their production program and its spatial allocation. Consequently, agricultural policy influences regional land use patterns and shares of land use systems, which in turn influence regional plant species richness. Connecting land use and ecological models allows to assess socioeconomic and ecologic effects of policy measures by identifying interactions and estimating potential trade-offs. The paper presents the land use model ProLand and the fuzzy expert system UPAL. ProLand models the regional distribution of land use systems while UPAL predicts plant species richness. The models are connected through a GIS and applied to a study area in Hesse, Germany, in order to simulate the effects of changing conditions on land use, economic and social key indicators, and plant species richness. ProLand is a spatially explicit comparative static model that simulates a region’s land use pattern based on natural, socioeconomic, political, and technological parameters. The model assumes land rent maximizing behavior of land users. It calculates and assigns the land rent maximizing land use system for every investigated decision unit, generally a field. A land use system is characterized through crop rotation, corresponding outdoor operations, animal husbandry if applicable, and the relevant political and socioeconomic attributes. The fuzzy expert system derives the values of ecologically relevant parameters from several site specific attributes and land use operations. Land use dependent site characteristics that influence plant species richness are derived from predictions generated by ProLand. Detailed information on crop rotation, fertilization and pesticide strategy, and outdoor operations are considered. The expert system then classifies natural and land use dependent site characteristics into aggregate factors. Based on a set of rules it assigns the number of species to the classes and thus to the decision units. Simulation results for the study area show that the CAP reform causes a rise in grassland area. These land use changes mainly occur in areas currently used for arable farming but with natural conditions favoring grassland. Plant species richness is positively influenced by the increase in extensive grassland area.

Applying fuzzy theory concepts to the analysis of employment diversification of farm households: methodological considerations

The Deliverable 7.2 (D7.2) of the SCARLED project provides methodological considerations for applying fuzzy set theory to the analysis of employment diversification of farm households. It presents a Mamdani's type fuzzy inference model and describes its application within the project's framework. The model consists of ten variables that are grouped into the four factors: (i) necessity to diversify, (ii) internal preconditions, (iii) external preconditions, and (iv) attitudes. The coherence of these four factors with the integrated framework for the analysis of nonfarm rural employment is discussed. The model will be realised in the Fuzzy Logic Toolbox from MATLAB®. Forty four membership functions and 138 rules are going to be implemented, tested, and adapted with survey data from the five countries: Bulgaria, Hungary, Poland, Romania, and Slovenia. The final model will be used to assess the diversification potential of 15 regions in these countries. --

A Review on the Application of Natural Computing in Environmental Informatics

Natural computing offers new opportunities to understand, model and analyze the complexity of the physical and human-created environment. This paper examines the application of natural computing in environmental informatics, by investigating related work in this research field. Various nature-inspired techniques are presented, which have been employed to solve different relevant problems. Advantages and disadvantages of these techniques are discussed, together with analysis of how natural computing is generally used in environmental research.Comment: Proc. of EnviroInfo 201

Employment diversification of farm households and structural change in the rural economy of the New Member States

Changes in the rural labour market, especially the increase of rural non-farm employment are recognised as one dimension of structural change. Labour allocation decisions are driven by economic incentives such as wage differentials, but also non-economic motives may play a decisive role. This paper summarises theoretical insights and presents an integrated conceptual framework reflecting the drivers of employment shifts. Methodologically, the conceptual framework is implemented in fuzzy logic to analyse the household potential to diversify its income activities. The empirical analysis draws on a survey of 1,077 farm households in rural Bulgaria, Hungary, Poland, Romania, and Slovenia. [...] This report is structured as follows: Chapter 2 presents a review of theory, an integrated theoretical framework as well as an overview of current trends of employment diversification. Chapter 3 then introduces fuzzy logic methodology and presents the model that is implemented to assess the non-farm income diversification potential in the survey countries. This is followed by a brief description of the database in Chapter 4. Simulation results are presented in Chapter 5 and Chapter 6. The last chapter summarises the main outcomes and gives policy recommendations. --

Vulnerability to Globalization in India: Relative Rankings of States Using Fuzzy Models

globalization, vulnerability, fuzzy inference systems, India