Bio-Economic Models applied to Agricultural Systems

Bio-Economic Models applied to Agricultural Systems

Guillermo Flichman Editor Bio-Economic Models applied to Agricultural Systems

Editor Guillermo Flichman Centre International de Hautes Etudes Agronomiques Méditerranéennes Montpellier, France Flichman@iamm.fr ISBN 978-94-007-1901-9 e-isbn 978-94-007-1902-6 DOI 10.1007/978-94-007-1902-6 Springer Dordrecht Heidelberg London New York Library of Congress Control Number: 2011937552 # Springer Science+Business Media B.V. 2011 No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface This book has the purpose of providing the state of the arts concerning bio-economic modelling dealing with agricultural systems. In most cases, the contributions use a methodology combining the use of biophysical and economic models, in all cases, an engineering production function approach is totally or partially applied. This practice is being developed in the last years as a response to concrete policy matters: agricultural policies are increasingly combined with environmental and natural resources policies, and this reality involves the need of an integrated assessment, that current economic models are not able to provide. But at the same time this type of approach involves the use of a multidisciplinary approach, extremely difficult to develop taking into account on one side the difficulty of communication between different disciplines and on the other the fact that in terms of scientific evaluation, the existing system is an obstacle to the development of this research orientation, as long as researchers are evaluated on a strictly disciplinary criteria. Part I deals principally with theoretical and methodological issues, as well as a presentation of biophysical models, an important source that provides engineering production functions appropriate to be used by bioeconomic agricultural models. Chapter 1 discusses the relations between bioeconomic modelling and economic theory. It is clear that all bioeconomic modellers do not share the points of view that are presented in this chapter, the intention is to open discussions that can be fruitful for future research development as well as for reminding the young generations of economists about some old theoretical issues that can be extremely useful for very new practical matters. Chapter 2 is about production functions; it gives a wide perspective on the matter and provides a strong justification for using biophysical models outputs as engineering production functions. Chapter 3 presents a typology of dynamic modelling approaches as well as an application of an innovative method for analyzing the problem of soil degradation by salinization in a small irrigated region of Tunisia. The methodological part of this chapter may be of interest for modellers dealing with this type of problem. v

vi Preface Chapter 4 presents in a quite detailed manner the structure and principal characteristic of biophysical models. Economists willing to use the results of these models need to understand how they are able to represent the complexity of agricultural systems integrating in a single framework the relations between multiple inputs and multiple outputs. Part II presents applications of bioeconomic models analysing different issues at regional levels - both small and big regions. Chapter 5 Is an application at the level of a big region (California), of a calibrated agricultural production model presenting an important innovation concerning the use of outputs generated by a biophysical model to infer adoption of a new bio energy crop. As prior information on supply elasticities is not available to calibrate non-linear terms in the objective function, yield variation at the regional level a piece of information typically available from highly disaggregated biophysical models of plant growth is used to construct such terms. Chapter 6 presents the use of an Agricultural Model working at European level providing indicators of nitrate pollution obtained by the output of nitrate balances and different innovative indicators of Nitrogen use at a country level for all European Union Countries. The calculation of the N-cycle follows a mass-flow approach. The model keeps track of the nitrogen available at each step net of all emissions that occurred at an earlier step and uses this as the basis for the estimation of emissions. Chapter 7 is an application of a dynamic multiobjective model of animal production (dairy sector) at a specific regional level, Reunion Island. The principal objective is to provide policy assessment. It allows simulating the effects of alternative management practices on Nitrogen emissions. The alternative management that are proposed are (a) and increase of spreadable land area for manure, (b) the transformation of manure to other forms as compost and (c) to utilise manure as a source of energy. Chapter 8 The aim of this chapter is to provide a better understanding about onfarm risk reducing strategies encompassing both risk anticipation strategy and risk modify the production system and profit distribution of French suckler cow enterprises. The method used in this case is a sequence of recursive discrete stochastic model, close to the method applied on Chap. 4 in a completely different context. The advantage of this approach compared to the standard one of dynamic stochastic programming appears in a very clear way in both cases. Chapter 9 This chapter presents an application of a bio-economic model to the Lunan Water catchment in Scotland to assess the relative cost-effectiveness of measures against agricultural nitrate pollution. The model used for this work is FFSIM-MP, integrating the outputs of a biophysical model, COUP. This contribution explores the challenges related to bio-economic modelling applications, presents the methodology and results, and evaluates the overall appropriateness of the approach for integrated policy impact assessment. Chapter 10 integrates three models to provide a spatialised assessment of the relationships between alternative agricultural management and biodiversity. The farm optimization model FAMOS[space], the crop rotation model CropRota,

Preface vii and the bio-physical process model EPIC are used in this contribution. Crop rotations and crop yields are inputs to FAMOS[space], which explicitly considers alternative land use intensities as well as landscape elements. Biodiversity effects of land use choices are evaluated with a set of field and landscape indicators. The specific interest of this chapter is the explicit introduction of the spatial dimension in the bioeconomic model. A short conclusion presents the principal achievements of this approach and the main obstacles for further development, considering the difficulties of multidisciplinary approaches, and also the complicated issue of the information needed to use this type of models, even when applied to relatively small study cases. Guillermo Flichman

Acknowledgements I want to gratefully acknowledge all the authors that accepted to give a contribution to this book. It took a lot of time and effort, but the possibility of putting together the work of different researchers developing and applying bio-economic models allows providing a better knowledge about this type of approach. ix

Contents Part I 1 Modelling the Relationship Between Agriculture and the Environment Using Bio-Economic Models: Some Conceptual Issues... 3 G. Flichman, K. Louhichi, and J.M. Boisson 2 Bio Physical Models as Detailed Engineering Production Functions... 15 J.M. Boussard 3 Dynamic Optimisation Problems: Different Resolution Methods Regarding Agriculture and Natural Resource Economics... 29 M. Blanco-Fonseca, G. Flichman, and H. Belhouchette 4 Biophysical Models for Cropping System Simulation... 59 M. Donatelli and R. Confalonieri Part II 5 Incorporating Yield Information from a Biogeochemical Model into an Agricultural Production Model to Infer Adoption of a New Bioenergy Crop... 89 P. Mérel, F. Yi, S. Bucaram, J. Lee, R. Howitt, and J. Six 6 Agri-Environmental Nitrogen Indicators for EU27... 109 A. Leip, F. Weiss, and W. Britz 7 Modelling Nitrogen Balance for a Regional Scale Livestock-Pasture System as a Discussion Support Tool... 125 U.B. Nidumolu, M. Lubbers, V. Alary, P. Lecomte, and H. van Keulen xi

xii Contents 8 On-Farm Weather Risk Management in Suckler Cow Farms: A Recursive Discrete Stochastic Programming Approach... 137 C. Mosnier, J. Agabriel, M. Lherm, and A. Reynaud 9 Using a Bio-Economic Model to Assess the Cost-Effectiveness of Measures Against Nitrogen Pollution... 155 I. Mouratiadou, D. Tarsitano, C. Topp, D. Moran, and G. Russell 10 Integrated Bio-Economic Farm Modeling for Biodiversity Assessment at Landscape Level... 185 M. Schönhart, T. Schauppenlehner, and E. Schmid Conclusions... 215 Index... 217