A framework to introduce flexibility in crop modelling: from conceptual modelling to software engineering and back

Keywords: model structure, uncertainty, modularity, software design patterns, good modelling practices, crop growth and development. This thesis is an account of the development and use of a framework to introduce flexibility in crop modelling. The construction of such a framework is supported by two main beams: the implementation and the modelling beam. Since the beginning of the 1990s, the implementation beam has gained increasing attention in the crop modelling field, notably with the development of APSIM (Agricultural Production Systems sIMulator) in Australia, OMS (Object Modelling System) in the United States, and APES (Agricultural Production and Externalities Simulator) in Europe. The main focus of this thesis is on the modelling beam and how to combine it with the implementation beam. I first explain how flexibility is adopted in crop modelling and what is required for the implementation beam of the framework, namely libraries of modules representing the basic crop growth and development processes and of crop models (i.e. modelling solutions). Then, I define how to deal with this flexibility (i.e. modelling beam) and more specifically I describe systematic approaches to facilitate the selection of the appropriate model structure (i.e. a combination of modules) for a specific simulation objective. While developing the framework, I stress the need for better documentation of the underlying assumptions of the modules and of the criteria applied in the selection of these modules for a particular simulation objective. Such documentation should help to point out the sources of uncertainties associated with the development of crop models and to reinforce the role of the crop modeller as an intermediary between the software engineer, coding the modules, and the end users, using the model for a specific objective. Finally, I draw conclusions for the prospects of such a framework in the crop modelling field. I see its main contribution to (i) a better understanding in crop physiology through easier testing of alternatives hypotheses, and (ii) integrated studies by facilitating model reuse. </p

Agricultural Production and Externalities Simulator (APES) prototype to be used in Prototype 1 of SEAMLESS-IF

The Agricultural Production and Externalities Simulator is a modular simulation system targeted at estimating the biophysical behaviour of agricultural production systems in response to the interaction of soil-weather and different options of agro-technical management. APES is currently meant to be used at field scale, simulating 1-D fluxes (future version will also use 2-D fluxes to account for multiple cropping). All modules of this release are first prototypes linked to test the hypothesis on the component based structure and to evaluate consequent modelling and technical issues; outputs should not be analyzed to evaluate model performance at this stage

Effects of modelling detail on simulated potential crop yields under a wide range of climatic conditions

Crop simulation models are widely applied at large scale for climate change impact assessment or integrated assessment studies. However, often a mismatch exists between data availability and the level of detail in the model used. Good modelling practice dictates to keep models as simple as possible, but enough detail should be incorporated to capture the major processes that determine the system's behaviour. The objective of this study was to investigate the effect of the level of detail incorporated in process-based crop growth models on simulated potential yields under a wide range of climatic conditions. We conducted a multi-site analysis and identified that by using a constant radiation use efficiency (RUE) value under a wide range of climatic conditions, the description of the process of biomass production may be over-simplified, as the effects of high temperatures and high radiation intensities on this parameter are ignored. Further, we found that particular attention should be given to the choice of the light interception approach in a crop model as determined by leaf area index (LAI) dynamics. The two LAI dynamics approaches considered in this study gave different simulated yields irrespective of the characteristics of the location and the light interception approaches better explained the differences in yield sensitivity to climatic variability than the biomass production approaches. Further analysis showed that differences between the two LAI dynamics approaches for simulated yields were mainly due to different representations of leaf senescence in both approaches. We concluded that a better understanding and modelling of leaf senescence, particularly its onset, is needed to reduce model uncertainty in yield simulations

Building crop models within different crop modelling frameworks

Modular frameworks for crop modelling have evolved through simultaneous progress in crop science and software development but differences among these frameworks exist which are not well understood, resulting in potential misuse for crop modelling. In this paper we review differences and similarities among different developed frameworks and identify some implications for crop modelling. We consider three modelling frameworks currently used for crop modelling: CROSPAL (CROp Simulator: Picking and Assembling Libraries), APES (Agricultural Production and Externalities Simulator) and APSIM (Agricultural Production Systems sIMulator). The frameworks are implemented differently and they provide more or less flexibility and guidance, to facilitate assembly of crop model from model components. We underline the importance of systematic approaches to facilitate the selection of appropriate model structure and derive suggestions to facilitate it. We particularly stress the need for better documentation of the underlying assumptions of the modules on simulated processes and on the criteria applied in the selection of these modules for a particular simulation objective. Such documentation should help to point out the sources of uncertainties associated with the development of crop models and to reinforce the role of the crop modeller as an intermediary between the software engineer, coding the modules, and the end users, agronomists or crop physiologists using the model for a specific objective. Finally, the key contributions of modelling frameworks in the crop modelling domain are discussed and we draw conclusions for the prospects of such frameworks in the crop modelling field which should continue to reside on the principles of systems analysis but combined with up-to-date advances in software engineering technique