Deliberative Processes for Comprehensive Evaluation of Agroecological Models

Deliberative processes for comprehensive evaluation of agroecological models. A review Gianni Bellocchi, Mike Rivington, Keith Matthews, Marco Acutis To cite this version: Gianni Bellocchi, Mike Rivington, Keith Matthews, Marco Acutis. Deliberative processes for comprehensive evaluation of agroecological models. A review. Agronomy for Sustainable Development, Springer Verlag/EDP Sciences/INRA, 2015, 35 (2), pp.589-605. 10.1007/s13593-014-0271-0. hal- 01151444 HAL Id: hal-01151444 https://hal.archives-ouvertes.fr/hal-01151444 Submitted on 5 Jun 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Copyright Agron. Sustain. Dev. (2015) 35:589–605 DOI 10.1007/s13593-014-0271-0 REVIEW ARTICLE Deliberative processes for comprehensive evaluation of agroecological models. A review Gianni Bellocchi & Mike Rivington & Keith Matthews & Marco Acutis Accepted: 20 November 2014 /Published online: 17 December 2014 # INRA and Springer-Verlag France 2014 Abstract The use of biophysical models in agroecology requirements of stakeholders, in respect of model use, has increased in the last few decades for two main reasons: logically implies the need for their inclusion in model the need to formalize empirical knowledge and the need to evaluation methods. We reviewed the use of metrics of disseminate model-based decision support for decision model evaluation, with a particular emphasis on the in- makers (such as farmers, advisors, and policy makers). volvement of stakeholders to expand horizons beyond con- The first has encouraged the development and use of ventional structured, numeric analyses. Two major topics mathematical models to enhance the efficiency of field are discussed: (1) the importance of deliberative processes research through extrapolation beyond the limits of site, for model evaluation, and (2) the role computer-aided tech- season, and management. The second reflects the increasing niques may play to integrate deliberative processes into the need (by scientists, managers, and the public) for simula- evaluation of agroecological models. We point out that (i) tion experimentation to explore options and consequences, the evaluation of agroecological models can be improved for example, future resource use efficiency (i.e., manage- through stakeholder follow-up, which is a key for the ment in sustainable intensification), impacts of and adapta- acceptability of model realizations in practice, (ii) model tion to climate change, understanding market and policy credibility depends not only on the outcomes of well-struc- responses to shocks initiated at a biophysical level under tured, numerically based evaluation, but also on less tangi- increasing demand, and limited supply capacity. Production ble factors that may need to be addressed using comple- concerns thus dominate most model applications, but there mentary deliberative processes, (iii) comprehensive evalua- is a notable growing emphasis on environmental, economic, tion of simulation models can be achieved by integrating and policy dimensions. Identifying effective methods of the expectations of stakeholders via a weighting system of assessing model quality and performance has become a preferences and perception, (iv) questionnaire-based surveys challenging but vital imperative, considering the variety of can help understand the challenges posed by the delibera- factors influencing model outputs. Understanding the tive process, and (v) a benefit can be obtained if model evaluation is conceived in a decisional perspective and evaluation techniques are developed at the same pace with * G. Bellocchi ( ) which the models themselves are created and improved. Grassland Ecosystem Research Unit, French National Institute for Agricultural Research, 5 Chemin de Beaulieu, Scientific knowledge hubs are also recognized as critical 63039 Clermont-Ferrand, France pillars to advance good modeling practice in relation to e-mail: [email protected] model evaluation (including access to dedicated software tools), an activity which is frequently neglected in the M. Rivington : K. Matthews The James Hutton Institute, Craigiebuckler, context of time-limited framework programs. Aberdeen AB15 8QH, UK M. Acutis Keywords Component-oriented programing . Deliberative Department of Agricultural and Environmental . Sciences—Production, Landscape, Agroenergy, approach Modeling Model evaluation Multiple metrics University of Milan, Milan, Italy Stakeholders 590 G. Bellocchi et al. Contents agroecological models are software simulation tools summa- rizing our understanding of how systems operate. They in- 1. Introduction clude sophisticated routines for simulating soil water and 2. Deliberative processes for comprehensive model nitrogen balances as well as plant phenology, biomass accu- evaluation mulation, and partitioning. The way how they evolve over 2.1 Dependence on the context time is challenging because which processes are incorporated 2.2 Model credibility into which detail in a model depends on the purposes of the 2.3 Model transparency model, which change as knowledge progresses and new ques- 2.4 Model uncertainty tions arise. For instance, abiotic stresses affecting plant growth 2.5 Modeling background and productivity (temperature, water, nutrients, etc.) only 2.6 Stakeholder approaches received attention after the simulation of non-stress conditions 2.7 Summary was satisfactory (e.g., Tanner and Sinclair 1983; Steduto et al. 2009). Also, early models were not much concerned by the 3. Computer-aided evaluation techniques effect of elevated atmospheric CO2 concentration on plant 3.1 Concepts and tools growth and transpiration and on the partitioning of dry matter. 3.2 Model coding The global carbon balance has become an issue of great 3.3 Modular simulation societal concern (and an important focus of scientific research) 3.4 Coupling between simulation and evaluation during the last decades, when the global emission of CO2 has 3.5 Summary continued to increase together with its impact on climate (IPCC 2013). This has required modeling efforts, for instance, 4. Deliberation processes in model evaluation: the examples to represent mechanistically the response of leaf photosynthe- of MACSUR and MODEXTREME sis to CO2 levels (e.g., Ethier and Livingston 2004)andso 4.1 Modelling European Agriculture with Climate Change for make the models responsive to the changing environmental Food Security—a FACCE JPI Knowledge Hub conditions (Asseng et al. 2013; Bassu et al. 2014;Lietal. 4.2 MODelling vegetation response to EXTREMe 2014). The impact of biotic stress factors (pests, diseases, and Events—European Community’s Seventh Framework parasites) has also received great attention recently because Programme their development can be modified by climate change (e.g., 4.3 Summary Maiorano et al. 2014). The control of these organisms forms 5. Conclusion an integral part of agricultural production and ecological Acknowledgements processes and represents (together with the factors that may References hamper soil fertility maintenance) a need in the development of modeling tools for the evaluation and design of sustainable agroecological systems (Colomb et al. 2013). The recent advances in scale change methodology (for scaling the infor- 1 Introduction mation provided by models from the field up to the regional level) have helped building ranges of indicators for the assess- Agroecological systems form the fundamental basis for the ment of the contribution of agricultural landscapes (mosaics of provision of food for society and thus underpin human well- agroecological systems) to a sustainable development (Cho- being. Under the increasing pressures of food demand and pin et al. 2014). declining supply of ecosystem services, their sustainable use Triggered by the need to answer new scientific questions, and management are an essential research priority. Agroeco- but also to improve the accuracy of simulations, model im- logical systems are ecosystems manipulated by anthropogenic provement is expected to continue resulting from emerging modifications, based on species diversity (Fig. 1,actualsys- new questions, better knowledge of physiological mecha- tem) in which plants, vertebrate and invertebrate animals, and nisms, and higher accuracy standards (Lizaso 2014). Howev- microbial communities cohabit with agricultural uses (such as er, the traditional paradigm in modeling for which modelers crops, livestock, and orchards) in the context of soils, and analyze the system and developers produce algorithms and provide ecosystem services (Gliessman 2007). Agroecologi- programs that they believe would do the best job has weak- cal models are representations of agroecological systems pro- nesses of its own. In so doing, in fact, scientists and experts viding an integrated perspective in which the biotic elements often drive the research focus with a narrow or incomplete (e.g., communities of closely spaced plants) dynamically in- understanding of the information needs of end users, resulting teract with soil, weather,

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