Building crop models within different crop modelling frameworks

Agricultural Systems 113 (2012) 57–63

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Agricultural Systems journal homepage: www.elsevier.com/locate/agsy

Building crop models within different crop modelling frameworks M. Adam a,b,c,⇑, M. Corbeels d, P.A. Leffelaar a, H. Van Keulen a,c, J. Wery e, F. Ewert f a

Plant Production Systems Group, Wageningen University, P.O. Box 430, 6700 AK Wageningen, The Netherlands Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), UMR System #1230, CIRAD-INRA-SupAgro, 2 Place Viala, Bât. 27, 34060 Montpellier Cedex 1, France c Plant Research International, Wageningen University and Research centre, P.O. Box 616, 6700 AP Wageningen, The Netherlands d Systèmes de Culture Annuels, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Avenue d’Agropolis, 34398 Montpellier Cedex 5, France e SupAgro, UMR System #1230, CIRAD-INRA-SupAgro, 2 Place Viala, Bât. 27, 34060 Montpellier Cedex 1, France f Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Katzenburgweg 5, D-53115 Bonn, Germany b

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Article history: Received 14 October 2011 Received in revised form 13 July 2012 Accepted 26 July 2012 Available online 8 September 2012 Keywords: Model structure Uncertainty Software design patterns Good modelling practices Crop growth and development

a b s t r a c t 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 techniques. Ó 2012 Elsevier Ltd. All rights reserved.

1. Introduction Adoption of advanced software engineering techniques in crop modelling in the past decade has led to the construction of modular frameworks, consisting of libraries of models from which selections can be made. Advantages of a modular structure include the possibilities for: (i) reusing code from existing models, (ii) testing alternative hypotheses about particular processes, (iii) choosing between simple or more comprehensive modules as required for a particular application, and (iv) sharing expertise on crop growth and development processes and their simulation. Although these advantages are undeniable (Acock and Reynolds, 1989), and have been illustrated on a few occasions, mostly within the APSIM (Agri⇑ Corresponding author at: Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), UMR AGAP, F-34398 Montpellier, France. E-mail address: [email protected] (M. Adam). 0308-521X/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.agsy.2012.07.010

cultural Production Systems sIMulator, Keating et al., 2003) framework (Van Oosterom et al., 2006; Moore et al., 2007; McMaster and Hargreaves, 2009), no research has explicitly addressed the process of module comparisons or model adaptations within such frameworks. The focus of previous investigations is often more on the outcomes of the overall model than on the description of how models are constructed and assembled from different modules. However, as modular frameworks provide technical possibilities to link modules, even if these links may be meaningless in terms of the underlying crop physiology, there is a need to focus on the model building process, and more specifically on the decisionmaking process of selecting one module rather than another and incorporating that module into the model structure. In general, the use of modular frameworks should aim at a qualified selection of modules, as governed by the objectives of a specific simulation exercise (De Wit, 1968). This selection process should be based on explicit criteria or approaches that guide model development.

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The aim of this article is to review differences and similarities among existing modelling frameworks in crop modelling, with a focus on the crop growth component level rather than the cropping system level. We particularly aim to analyse the choices made with the development of these frameworks at the implementation and modelling levels, and to identify implications and to derive suggestions for the assembly of crop models. We also discuss how such frameworks can contribute to scientific advances in crop modelling. Our analysis considers three modular crop modelling platforms: CROSPAL (CROp Simulator: Picking and Assembling Libraries, Adam et al., 2010), APES (Agricultural Production and Externalities Simulator, Donatelli et al., 2010) and APSIM (Agricultural Production Systems sIMulator, Keating et al., 2003). To start with, some technical terms of software engineering and crop modelling used in connection with modular platforms are defined in Table 1: this in order to ease the reader’s understanding of the paper.

2. Crop modelling frameworks 2.1. Brief description of the three modular frameworks CROSPAL (Adam et al., 2010) is a software tool that uses agronomic expert knowledge to assist with the selection of modules for crop growth simulation. It applies the principle of modularity Table 1 Definition of key terms. Concept

to facilitate the re-use of crop models and modules for individual plant processes, as it includes modules with different descriptions of key plant physiological processes. This software was designed to guide the user in the choice of these descriptions to support consistency in the considered processes to assemble a crop simulator. CROSPAL includes expert knowledge through the definition of selection criteria in a GUI, the Graphical User Interface. This interface is an important link between the software engineer, coding the strategies and factories, and the agronomist and crop modeller, defining the main basic crop processes and how to combine them. The crop modelling framework APES (Donatelli et al., 2010) consists of various modules for simulating crop growth and development, and soil carbon, nitrogen and water dynamics for a wide range of land use types. Its design facilitates the adjustment of model structure depending on the objective of the simulation, the data availability and the type of cropping system simulated (annual crops as well as grasslands, vineyards and agro-forestry systems under a range of soil and weather conditions and management practices). APSIM (Keating et al., 2003) is a highly advanced simulator of agricultural systems. It contains a suite of modules which enable the simulation of systems that cover a range of plant, animal, soil, climate and management interactions. It is undergoing continual development, with new capability added to regular releases of official versions over time. In this review, we give a particular emphasis on the GCROP approach, as it explicitly emphasizes the different crop physiology components and from which the component APSIM-Plant, currently used in APSIM, is derived (Holzworth and Huth, 2009).

Definition

Crop modelling terms Developer Person responsible for the implementation of the model Model Simplification of reality Modeller Person, having system analysis skills, whom can interact with the software engineer and the crop physiologist Module Conceptualisation of a specific crop or soil process implemented within a component (e.g. radiation use efficiency or photosynthesis for biomass production). User Person responsible for the use of the model for a specific objective Software engineering terms Abstract class A class that does not contain a complete implementation and can be used by another class Class A construct to define common properties of a set of objects Component Piece of software representing plant and/or soil processes that is used to compose a cropping systems model DLL: dynamic link A library is a collection of resources used to develop libraries software; a DLL is a library intended for dynamic linking Design pattern A general reusable solution to a commonly occurring problem Factory design Software design that provides an interface for creating pattern families of related or interdependent objects without specifying the concrete classes Framework Structure providing different components that can be selectively interchanged Strategy design A family of algorithms that have been abstracted and pattern designed in such a way as to make them interchangeable Wrapper Class enabling combination of other classes that could not be combined, because of incompatible interfaces Platforms acronyms or meanings APES Agricultural Production and Externalities Simulator (Donatelli et al., 2010) APSIM Agricultural Production Systems sIMulator (Keating et al., 2003) CROSPAL CROp Simulator: Picking and Assembling Libraries (Adam et al., 2010) GUI Graphical User Interface ModCOM Software framework to assemble simulation models (Hillyer et al., 2003) XML Extensible Markup Language

2.2. Software design analysis We first identify and discuss the similarities and differences between the software designs that were adopted to build the three frameworks on the basis of detailed descriptions of the modelling frameworks (Table 2). All three frameworks use the same delineation of modules based on the basic crop development and growth processes (Wery, 2005; Adam et al., 2010). CROSPAL and APES used the strategy design pattern (Table 2) to create a library of modules representing these basic crop processes, while the generic crop model template of APSIM (GCROP, Wang et al., 2002) used a generic APSIM model structure and XML files to select and parameterize modules included in a single DLL, i.e. Plant.dll (Table 1). Within APSIM-Plant, the same delineation is used in GCROP and each process is gathered in a library of plant classes (a class, in software engineer term, defines common properties of a set of objects, Table 1) to simulate the growth and development of a wide range of crops. The strategy design pattern enables implementation of alternative modules (Gamma et al., 1995) to simulate the same (crop growth and development) processes (Fig. 1). Similarly, the dynamic-link library (DLL) is a module that contains functions that can be used by another module (application or DLL). The PLANT model of APSIM consists of many classes that have been designed in such a way to facilitate their swapping in and out for different crops. The classes in APSIM-Plant correspond to the original DLL in GCROP. The use of the strategy design pattern, as well as that of separate DLLs or classes, eases the addition of new modelling approaches (i.e. alternative modules for a given process). Such delineation creates a high modularity in the modelling frameworks and represents the characteristics of crop growth and development: this delineation is similar in all three frameworks and builds on the knowledge generated in crop modelling over the past 40 years.

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M. Adam et al. / Agricultural Systems 113 (2012) 57–63 Table 2 Comparison of the different software designs adopted in different three crop modelling frameworks. CROSPAL Modules basic crop processes

Strategy design pattern

Component crop

Abstract factory and criteria with a GUI

Crop models soil–crop (i.e. crop simulator) a

a

Definition of new concrete factories

a

APES

APSIM

Strategy design pattern

Plant.dll. XMLa used to select and parameterise the different modules Generic model structure. XMLa used to select and parameterise the different modules APSIM-Plant linked to the APSIM engine (using the Common Modelling Protocol, Moore et al., 2007)

Composite strategy (IStrategy: interface) Components linked via wrappera (using the ModComa framework: Hillyer et al., 2003)

The technical terms are defined in Table 1.

The main difference among the three frameworks resides in how the overall structure of the crop model (i.e. assembly of modules) is configured. In CROSPAL, through the strategy design pattern, abstract factories and a GUI are used to relate a predefined crop model to criteria of selection (Table 2). In object-oriented computer programming, a factory is an object for creating other objects. It is an abstraction and can be used to implement various combinations of crop growth processes. In crop modelling terms, the use of the abstract factories design pattern enables the definition of the basic structure of the model (Fig. 2), and, through formulation of new concrete factories, enables the creation of a new structure of a crop model. Further, the use of an abstract class (i.e. a set of operations that must be supported by all objects that implement the protocol) provides the possibility for the future user to include physiological processes shared among different crop types, (i.e. identification of similarities among crops, or generic crop characteristics). Finally, the GUI enables to choose among different crop model structures defined in the abstract factories, through expert knowledge and iterative use of the framework. The user (e.g. expert in crop physiology) of CROSPAL can identify the key criteria for the simulation of the crop system and as a result select a particular crop model structure within the GUI (Adam et al., 2010). In APES, and more specifically in its crop component, the assembly of the various basic crop processes is attained via the use of the interface IStrategy (Table 2). ‘‘A composite strategy [IStrategy] differs from a simple strategy, because it needs other (simple) strategies to provide its output(s)’’ (Donatelli et al., 2010, p. 89). In other words, this composite strategy (Istrategy) defines the

model structure by invoking other classes defined as simple strategies. This composite strategy (defining the crop model structure) can be selected by the user through the selection of a ‘‘model option’’, either via an XML (Extensible Markup Language) configuration file in the integrated version of APES within a model chain (e.g. linked with economic models, Van Ittersum et al., 2008), or via a Graphical User Interface (GUI) in the stand-alone version (Fig. 3). This IStrategy could be compared to the abstract factory in CROSPAL (Table 2). However, in contrast with CROSPAL, neither an explicit link is made via the GUI to relate a specific composite strategy to a crop model, nor is a common behaviour to express generality in crop models (similarities among crops) included in the composite strategy. In the generic PLANT model as part of the current APSIM (Holzworth and Huth, 2009), the modules are combined directly via XML files (Table 2), to turn on or off the basic crop processes (i.e. calling or not the different classes). Therefore, the user can define the structure of the crop model him/herself, with no pre-packaged solution as in CROSPAL or APES. The use of XML files in the generic PLANT model enables to completely outsource the configuration of the crop model structure and to simplify the re-use of models (Holzworth et al., 2010). 3. Assembly of modular crop growth models 3.1. Modularity in modelling frameworks The development of the crop modelling frameworks is the result of a long running evolution of crop models that started as tools

Fig. 1. The strategy design pattern applied to the CreateDM() corresponding to the dry matter process within CROSPAL (from Adam et al., 2010). For instance, here the different modelling approaches to model dry matter production are encapsulated in various strategies, namely, Biomass_LUE and Biomass_LUE_Temp.

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Fig. 2. The abstract factory design pattern, designing model structure in CROSPAL (from Adam et al., 2010). The abstract crop class enables to include various methods (e.g. CreateDM(), CreatePheno()), which represents a way to structure a crop growth model.

Fig. 3. Screen shot of APES GUI where model options can be selected to define the model structure.

to better understand crop physiology (De Wit, 1968) and evolved as tools targeted at specific applications (Van Ittersum et al., 2003). This trend led to the development of a plethora of models that the developed frameworks aim at gathering in one platform, usable for different purposes and by different users.

The software design of all three frameworks allows flexibility in adapting the model structure, i.e. assembling a new crop growth model from the existing modules. In APSIM, the use of an XML file to configure a model (i.e. define its structure) provides complete flexibility to the user to select any module, no matter whether

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the different modules ‘‘fit’’ together conceptually. Technically, everything is possible and modifications in the configuration of the structure of the model are completely defined outside of APSIM. In APES, the use of the composite strategy provides less freedom to the user, as the software engineer (not the user) defines the possible compositions within the component (IStrategy) on the basis of his/her own opinion on the anticipated future modelling exercise or application. However, the selection of a specific model structure still remains the responsibility of the user through the use of model options. Further, the design adopted in APES also enables easy extension of any component and redefinition of the composite strategy (Donatelli et al., 2010) that could be tested by the user. In CROSPAL, the choice for the use of the abstract factory relies on the logic to assemble the crop model. This logic is the consequence of the vision of the developer on crop functioning and should correspond to the different criteria included in the graphical user interface. The selection of a specific model structure for an application is guided by criteria considered by the user through the GUI. The level of flexibility for the user is limited. The modularity developed in the three frameworks (APES, CROSPAL and GCROP) is clearly an advantage for crop modelling. It is important however, that this modularity is used properly in the context of a specific application. In the following section we suggest three methodologies that facilitate correct crop model assembly from existing modules. 3.2. Methodologies to facilitate model assembly The logic to assemble the appropriate modules in one model should be based on systematic methodologies that make explicit the choices made. We suggest the use of three main methodologies to define this logic and to facilitate the model assembly: (i) conceptual modelling of an agrosystem (Lamanda et al., 2012), (ii) the comparison of models or modules, and (iii) the use of an uncertainty matrix. The use of these methodologies helps to define the structure of the model and to explicitly formulate the reasons for the choices made to compose a model for a specific application, in agreement with the Good Modelling Practice (GMP) (Van Waveren et al., 1999). First, we advocate the use of conceptual modelling to represent the proper crop growth and development processes, and their relationships to the agrosystem’s environment to be included in the crop growth model. The use of the conceptual model protocol as proposed in Lamanda et al. (2012) helps to define what to include and what not to include in a model. It strongly relies on the involvement of crop physiologists to identify the basic crop processes needed for simulation of the crop under study. Lamanda et al. (2012) emphasised that interaction among crop modellers, software engineers and crop physiologists is essential to get a common understanding of the problem addressed and how to translate it into a simulation exercise. Through this protocol, the need for crop physiologists or software engineers to get familiar with each other disciplines is explicit. Although this may often happen naturally (e.g. in the APSIM initiative), we argue that there is the need for a strong interface between the two disciplines. Belhouchette et al. (2009) and Adam et al. (in preparation) applied the protocol of conceptual modelling to define a crop growth model for a pea crop from a wheat crop model using the APES framework. With the use of visual tools, the crop modeller was able to focus on the crop modelling aspects rather than on the implementation/technical aspects when assembling the new crop model. Adam et al. (2010) supported this point through the use of CROSPAL as a collaborative work among different disciplines. Further, given a modelling objective, there is a need for an appropriate definition of the level of modelling detail in a process-based crop model, complying with the rule that a model

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should be as simple as possible, but not simpler (De Wit, 1968). Leffelaar (1990) discussed the existence of an ‘‘optimum’’ level of detail in terms of the number of processes modelled that allows the closest approximation to system reality. Through module comparison, using the principles of the CROSPAL design, Adam et al. (2011a) reinforced this idea and demonstrated that through an integrated use of complex (i.e. detailed) and simple (i.e. summarised) modelling approaches (i.e. modules) more insight can be gained in how to model crop growth for large-scale applications. For instance, they showed that an aggregated value for radiation use efficiency (RUE), typically measured over a period of days, should not be corrected for radiation and temperature on a daily basis (as done in models such as CropSyst, Stöckle et al., 2003), but rather on a seasonal basis. Finally, in addition to the above two methodologies, we argue that documentation throughout the process of model selection is essential. Using an uncertainty matrix (Walker et al., 2003; Refsgaard et al., 2006, 2007) helps to document the main short-comings of the model structure (identified through conceptual modelling), and to reveal the main assumptions underlying the modules (Belhouchette et al., 2009) as e.g. identified through comparisons of modules’ outcomes. The uncertainty matrix distinguishes different types and sources of uncertainties helping in a systematic evaluation of the model chosen with respect to hypotheses, structure and data used. The matrix is divided in four main parts (i) the contextual part, referring to the uncertainty related to the understanding of the system under study, (ii) the input data, analysing the uncertainty related to the dataset, (iii) the parameters, associated with the calibration process of the model and the uncertainty related to the value and meaning of parameters, and finally (iv) the model structure highlighting the parts of the model where knowledge is not yet complete. By using the uncertainty matrix, we can assess the importance of considering the lack of certainty in our models (and knowledge) which also helps to recognise the ‘‘unachievable’’ goal of a universal model (Van Oijen, 2009; Affholder et al., 2012). In modular frameworks, the incomplete knowledge is often identified through testing different modules, single or in combination, and discussing different hypotheses (Chamberlin, 1965). But in addition, we propose that the considered modules and the built module combinations in a model must be reported in an uncertainty matrix. Adam et al. (2011b) used this methodology in combination with the APES framework to test different modelling approaches for simulating soil nitrogen dynamics. They drew conclusions on the need to include a rather detailed approach (Corbeels et al., 2005), to properly represent microbial activity, when a soil contains a high organic matter content. Combination of these three methodologies helps to reconstruct, repeat and reproduce the modelling process and to capitalise on the main outcomes of that process. It takes advantage of the ‘‘plug-and-play’’ facilities (Papajorgji, 2005) and enables to explicitly identify the validity domain of each of the modules, or crop models when modules are coupled. Thus, we argue that for the use of a modular framework, the process of model building should not be seen as a linear process but rather as a cyclic process (Rabbinge and De Wit, 1989), that explicitly yields the uncertainty associated with each module and model tested.

4. Roles of modular frameworks in crop modelling 4.1. Gaining insights into crop physiology by testing new hypotheses The three main methodologies identified to ease the process of model assembly remain applicable irrespective of the implementation framework used (APES, APSIM or CROSPAL). These methodologies enable the implementation of different modelling approaches

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and support the integration of current scientific knowledge (combining models in one tool) and the comparison of different modules (with underlying hypothesis) to deepen our understanding of crop physiology and cropping systems. A few examples of the application of modular frameworks in crop modelling have been reported recently in the literature. For instance, Van Oosterom et al. (2006) substituted the module describing floral initiation with a gene network module to better predict flowering time. A more recent example of the use of the modularity of APSIM is given by McMaster and Hargreaves (2009). They demonstrated the use of an object-oriented design to couple principles of 3D modelling (using the phytomer concept as a unit repeated within and among shoots) with the plant module of APSIM. Thus, the use of a crop modelling framework helps in (i) creating and supporting a dialogue with crop physiologists and software engineers (Adam et al., in preparation) and (ii) identifying the impact of major process characteristics on crop growth factors, even at global scale (Adam et al., 2011a). These examples further demonstrate that the use of modular frameworks in crop modelling can accelerate the advancements in simulating crop physiology and cropping systems, building on existing concepts. 4.2. Clarifying uncertainties on model outputs for integrated assessment of agricultural systems Crop modular frameworks are increasingly used in studies on integrated assessment of agricultural systems, such as APES that was originally designed for the larger SEAMLESS integrated modelling framework (Van Ittersum et al., 2008). The SEAMLESS framework linked individual components (models, data, indicators) that enables scenario analyses of the environmental, economic and social contributions of a multi-functional agriculture and the effects of a broad range of issues at different scales. Although integrated assessment studies also build on the advantages of modularity to create a model chain, the bio-economic farm models only need, as input variables, indicators derived from the crop models (Janssen and Van Ittersum, 2007). Consequently, it is likely that when integrating a crop modelling framework (i.e. APES) in a larger modelling chain (e.g. SEAMLESS-Integrated Framework), the outputs of the crop models will be used by researchers who are not necessarily familiar with the biophysical models and their main underlying assumptions (Jakeman et al., 2006). Use of conceptual modelling or uncertainty matrices, can help reduce these drawbacks by increasingly involving the end user (i.e. farm modeller) in the crop model development process, and explicitly formulate the related uncertainty. It should help to identify the origins of the uncertainties and to interpret the outputs of the crop model that are used as inputs in other models of the models chain. 5. Concluding remarks The development and use of modular frameworks in crop modelling has greatly contributed to the definition of guidelines that facilitate exchange of models (or parts of models, i.e. modules), representing different crop and cropping system processes, depending on user objectives and data availability to calibrate and run these models. The use of a modular framework in crop modelling helps to (i) capitalise on new knowledge by testing alternative hypotheses on particular processes without re-inventing the wheel, (ii) integrate different disciplines, and last but not least, and (iii) communicate efficiently with the user of the tool to explicitly identify the main uncertainties associated with its application. Furthermore, we want to stress the need for documentation of the model building process to facilitate model re-use. Three main

approaches have been proposed and discussed to support and document the model building process. The use of the uncertainty matrix emphasizes the importance of explicitly defining the unknown. The use of model comparison enables tackling the issue of the required level of detail and highlights the risks of over-simplification of processes when data are scarce. And finally, the integration of expert knowledge in the development of the framework emphasizes the importance of explicitly describing the underlying assumptions through the use of conceptual modelling of the agrosystems (Lamanda et al., 2012) to be simulated and the potential of visual tools such as declarative modelling. To properly use modular frameworks in crop modelling, there is a clear need to go back to the principle of conceptual modelling, either in its heuristic role (Hammer et al., 2002) or for integrated studies (Van Delden et al., 2010). While technical advances have stimulated substantial progress in the crop modelling field, especially in providing modular frameworks that allow easy coupling of different models at a higher scale for use in integrated assessment studies (Van Ittersum et al., 2008) or for further understanding of crop physiology (Hammer et al., 2002), conceptualisation of the systems remains an essential step. As an illustration, this paper demonstrated the continuing importance of the principles of systems analysis in the field of crop modelling, in combination with up-to-date advances in software engineering techniques, and proposes an approach to do so in different types of platforms.

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