Development of decision support tools for
aquaculture: the POND experience
John Bolte
a,*, Shree Nath
b, Doug Ernst
aaDepartment of Bioresource Engineering,Oregon State Uni
6ersity,Cor6allis,OR 97331, USA
bSkillings–Connolly,Inc.,5016 Lacy Boule
6ard S.E.,Lacey,WA 95803, USA
Received 17 March 1999; accepted 29 September 1999
Abstract
Decision support systems (DSS) are potentially valuable tools for assessing the economic and ecological impacts of alternative decisions on aquaculture production. In this paper, we discuss the philosophy of design, functional modules and application areas of POND, a decision tool that has been developed to allow analysis of pond aquaculture facilities by the use of a combination of simulation models and enterprise budgeting. We focus less on the details of POND’s internal models, and more on the experiences we have gained from going through the process of the designing, developing and using the POND software. POND was designed and implemented using object-oriented programming principles. The software makes use of a simulation framework to provide much of the generic simulation, data handling, time flow synchronization and communication features necessary for complex model-based DSSs. Additionally, an architecture suitable for representing and manipulating pond aquaculture facilities was developed in order to meet the design specifications of POND. This architecture includes a series of mini-databases, a number of knowledge-based components (‘experts’), models of the pond ecosystem, and various decision support features (e.g. assembling alternate management scenarios, economic analysis, and data visualization). A typical POND simulation consists of assembling a number of appropriate objects or entities (e.g. multiple ponds and fish lots), their management settings together with appropri-ate experts (e.g. an aquaculture engineer, an aquatic biologist, an economist, etc.), and projecting changes in the facility over time. Our experience with the development of POND and other simulation-based tools indicates that the object-based approach provides a robust foundation for developing tools which allow code reusability, facilitate maintenance of complex software, and enable partition of program development among multiple
pro-www.elsevier.nl/locate/aqua-online
-* Corresponding author. Tel.: +1-541-7374021; fax:+1-541-7372082. E-mail address:boltej@engr.orst.edu (J. Bolte)
grammers. Experience gained with POND users suggests that there are largely two groups of aquaculture personnel interested in such applications, namely commercial growers and educators. These two groups have substantially different interests and needs. Consequently, a single tool such as POND may not optimally meet the requirements of both groups. Recent development work on POND, and the need to involve users in the design process of such tools are discussed. © 2000 Elsevier Science B.V. All rights reserved.
Keywords: Aquaculture; Decision support; Object-orientated modeling; Pond dynamics; Simulation; Tilapia
1. Introduction
Ponds used for aquacultural production are typically complex systems that can be driven by a wide range of inputs and interactions. This complexity can make designing and managing these systems challenging: successful fulfilment of these tasks can often be assisted by the application of tools that capture important system drivers and their interactions. These drivers can be both ecological and economic in nature. Within the realm of pond aquaculture planning and management, decisions must be made regarding site locations, target fish species and appropriate practices such as fish feeding, pond fertilization and liming, stocking densities, aeration, and water exchange (Hickling, 1962; Boyd, 1979; Allen et al., 1983; Colt, 1986; Hepher, 1988). These decisions typically have considerable effects on resource use efficiency and therefore the economics of an aquaculture facility (Allen et al., 1983). The decision-making process typically requires some expertise on the part of the planner, manager or extension agent. Such expertise includes an understanding of the principles of pond aquaculture and the implications of various decisions on facility-level economics (Shang, 1981; Allen et al., 1983). In certain situations, it may also be necessary to address socio-economic issues such as receptivity of farmers to new technology, and alternative uses of available resources (Chambers et al., 1989; Harrison, 1994; Molnar et al., 1996). Decision-makers usually acquire the required knowledge via a combination of formal education and experience.
The complexity of decision-making for an aquaculture facility suggests the need for computerized analytical tools that can integrate biological, physical, environ-mental, economic, and social components of the knowledge base required to arrive at a decision. Such tools, termed decision support systems (DSS), integrate knowl-edge in the form of mathematical models, rule-based (expert) systems, and/or databases into user-friendly software systems focused on developing, analyzing and optimizing management strategies. These tools have emerged as powerful tools for capturing expert knowledge about particular domains and providing that knowl-edge in a friendly, easy-to-use manner to end users. In a broader sense, DSSs address the problem of packaging a large domain of scientific and technical knowledge into a form that is of practical value to a diverse audience, including non-scientists (Lannan, 1993). The power of such systems results from their capability for representing and manipulating both quantitative and qualitative knowledge that describe objects in the domain of interest and their inter-relationships.
A key component of any DSS is the knowledge base(s) upon which decisions are made. Expertise exists in many forms, ranging from highly qualitative ‘rule of thumb’ approaches useful for representing subjective information, to databases containing historical data, to more rigorous and quantitative mathematical al-gorithms that describe explicit relationships among components of the domain in question (Hopgood, 1991).
In agriculture, DSSs have been developed for the diagnosis of plant diseases (Michalski et al., 1982), crop production (Smith et al., 1985), analyzing marketing alternatives (Uhrig et al., 1986), selection of appropriate crop cultivars (Lodge and Frecker, 1989; Bolte et al., 1990), pesticide application (Ferris et al., 1992) and many other applications. DSSs that have been developed for aquaculture can be classified into two broad categories: farm management/planning tools (Gempesaw et al., 1992; Ernst et al., 1993; Lannan, 1993; Silvert, 1994; Itoga and Brock, 1995; Ernst et al., 2000) and macro-economic tools (Pedini et al., 1995; El-Gayar and Leung, 1996). DSSs that fall into the former category deal primarily with decisions relevant to farm management operations (e.g. fertilizer and lime recommendations for ponds as in Lannan, 1993; site selection for marine fish culture operations as in Silvert, 1994; tilapia disease diagnosis and treatment as in Itoga and Brock, 1995), as well as long-term planning tasks that may be required during the initial design phase of a farm (e.g. financial assessment relevant to the target fish species as in Gempesaw et al., 1992). Macro-economic tools, on the other hand, have been developed to evaluate project proposals (e.g. Pedini et al., 1995) and to examine the economic consequences of aquaculture development of one or more culture species in larger regions varying in size from districts to perhaps entire countries (El-Gayar and Leung, 1996). The development of both categories of aquaculture DSSs is relatively recent, and the presently available tools are essentially first generation products.
analysis of these facilities under various management scenarios. This paper provides an overview of the design aspects (with an emphasis on object-oriented program-ming principles), functional modules and application areas of POND, a decision support software that has been developed to specifically enable analysis of pond aquaculture facilities via a combination of simulation models and enterprise budgeting.
2. POND: design rationale and approach
The rational design of DSS software is critical to determining the ultimate usefulness and maintainability of the software. Although a number of software design paradigms have come and gone over the years, a few themes relevant to software development generally, and DSS software specifically, have demonstrated their utility over relatively long time spans. These themes can be classified based on (1) their usefulness at defining the operational structure of the software, and (2) their utility in implementing, maintaining and upgrading the software at the code level.
Because of the nature of DSS software, it is important that the operational structure of the software effectively addresses user requirements for defining available input data, executing knowledge bases, displaying outputs (e.g. in tabular and/or graphical format), and providing an effective, readily understandable user interface for manipulating the software. In the case of POND, input data require-ments were initially determined in part from the datasets typically collected during experimental protocols established by the Pond Dynamics/Aquaculture Collabora-tive Research Support Program (PD/A CRSP). This program has provided the principal development support for the POND software. Datasets collected during field experiments conducted by the PD/A CRSP include weather, water quality, soil type, and fertilizer/feed inputs, and are available on the Internet (Ernst et al., 1997). Although such input datasets are unlikely to be available to many users, it was felt that they would provide a reasonable starting point for model development, especially because the most important target audience initially identified for POND were aquaculture researchers. Moreover, we believed that creating a situation where users could apply pond data in model evaluation (beyond their traditional use for presenting results from field experiments) would guide additional field experimenta-tion designed to test model assumpexperimenta-tions.
Windows platform was likely to become the dominant operating environment. The choice of this platform lent itself well to the development of a user-friendly interface (Fig. 1).
To implement POND, we surveyed the available software design methodologies and settled on an object-based paradigm for the program. Object-based, orobject
-oriented programming(OOP) provides a natural and intuitive method for examining
the process of decomposing problems and developing programming solutions (Meyer, 1988) It goes well beyond structured programming by establishing the concept of an object, a self-contained collection of data and algorithms which directly correspond to entities in the real world. An object is an instance, or specific realization, of aclass. The classis a useful design and implementation abstraction that defines the data members and methods of objects (Ege, 1992). Data members store an object’s state. Specific methods define an object’s behavior and functional-ity. An object-oriented application is a set of objects that work together to achieve an overall goal. Because objects in such applications mirror reality at both conceptual and implementation levels, object-based representations are well adapted to simulation model development, and result in programs that are
ally easier to design, maintain and understand than programs based on more traditional paradigms. Specific examples that demonstrate the use of OOP princi-ples in the POND architecture are discussed in a later section of this paper.
There have been many implementations of object-oriented languages. For POND, we chose C+ + as the implementation language. C+ + is currently the most widely used object language. It provides support for all the major characteris-tics defining the object-based paradigm, generates efficient executable code, and is widely available and portable across platforms. It has proven to be an excellent language for implementing large simulation programs.
2.1.Object-oriented simulation
Simulation is one of the natural applications of object-oriented programming. From an object-oriented viewpoint, simulation models are readily envisioned as an assemblage of interacting objects representing specific objects in the real world (Zeigler, 1990). Although the mechanisms of OOP can be implemented with a traditional programming language supporting data abstraction, better results can be achieved with an object-oriented programming language (Bischak, 1991) which directly incorporates orientation into the language specification. The object-based approach provides powerful tools for the representation of complex domains and results in very efficient programming environments (Rumbaugh et al., 1991). The limitations of procedural approaches in simulation are well established. One of the most significant problems of traditional simulation modeling has been the lack of reusability of the simulation models and/or components (Rumbaugh et al., 1991). This problem stems from the fact that most simulation models are developed for a specific simulation experiment objective. When a new objective is encountered, a new model is developed from scratch even though it may include elements contained in earlier models (Rumbaugh et al., 1991; Sierra and Pulido, 1991). A classic example of this type of approach can be found in the DSSAT (D6 ecision S6upport S6ystem for A6 grotechnology T6ransfer), an assemblage of models for simu-lating the production of different crops. Although all of these models use similar methods for integrating model equations, the underlying simulation code is re-peated for each model. Thus, any improvement of the simulation approach in any one of the models would need to be repeated for all of the models. In a well-designed object-oriented approach, such a change can be made (once) in the simulation framework and all of the models would benefit from it.
The benefits of an object-oriented approach to simulation modeling have led researchers to develop object-based models in a variety of domains. Several object oriented models have been reported in ecological and agricultural simulations (Folse et al., 1990; Sequeria et al., 1991; Whittaker et al., 1991; Van Evert and Campell, 1994). All of these authors indicated the primary advantage of OOP for simulation models as a basis for model conceptualization, program design, and model reuse.
message passing, dynamic binding and inheritance (Meyer, 1988; Budd, 1991).
Encapsulationis a mechanism for information hiding, whereby each object hides its
own information. Encapsulation ensures that object data can be protected and unwanted modification due to software errors is prevented. The use of encapsula-tion therefore improves the modularity, reliability and maintainability of system code (Wilde et al., 1993). Message passing is a necessary result of encapsulation. Communication between objects is possible only through messages. Every action in an object-oriented program is initiated by objects sending messages to one another (e.g. a request for the value of a certain variable). A message received by an object states ‘what’ should be done, whereas a method within an object expresses ‘how’ it will be done. A message is ‘bound’ to a method either statically (at compile time) or dynamically (at run time) (Ege, 1992). Dynamic binding is the ability to determine which method of an object will be called at run time, rather than resolving the call at compile time. This feature allows references to generic objects during compilation, with the run-time behavior of the system determined by the actual objects which are active in the software system at the time. Dynamic binding provides different objects with the ability to respond to the same message in different ways. For example, animal and plant simulation objects both might have a methodGrow(), but each one responds to the Grow() message differently.
Inheritance denotes the ability of an object to derive its data and behavior
automatically from another object (Budd, 1991). When a new class is defined as a
subclassof an existing one, data members and methods are automatically inherited
from the parent class (superclass). Inheritance allows new classes of objects to be created as descendants of existing ones, and may extend, reduce, or otherwise modify the parent’s functionality. It is a powerful concept that has proven to be an extremely valuable tool for constructing and reusing complex simulation compo-nents, because the inheritance hierarchy defines various levels of information and behavioral abstraction appropriate to the real-world system that is being represented.
2.2.Object-oriented decision support systems
and (4) mechanisms for synchronizing the sequencing of flow execution among system components.
The OOP paradigm provides potentially useful capabilities in all of these areas. For example, standardization of public interfaces is a central OOP concept, and is readily implemented through the definition of generic objects with virtualized public interfaces, which provide the fundamental building blocks from which domain-spe-cific objects can be developed through subclassing. Virtualizing the interface allows direct communication to specific subclasses without any knowledge of the type of that subclass, a critical requirement for the development of high-level, domain-inde-pendent decision support frameworks. POND simulation objects heavily utilize these virtual interfaces to allow them to be controlled and manipulated by the simulation engine, without the simulation engine having any direct knowledge about the simulation object. Similarly, high-level communications between objects can be accomplished through a virtualized public interface, in a manner that precludes the requirement that the caller has specific knowledge about those objects retrieving the communications. POND utilizes these higher-level communication pathways for allowing ‘expert’ objects to periodically monitor the state of a simulation and post messages when potential problems occur. Other objects in the system can retrieve the messages and initiate control modifications or prescribe solutions. Synchronization and data collection can also be handled at a high level, with subclasses inheriting much of the behavior necessary to handle synchroniza-tion, data flow and communication. This relieves POND simulation objects such as ponds and fish lots from most of the burden of collecting and displaying their own internal time-series data.
2.3.The POND architecture
In developing POND, we utilized an existing simulation framework (Bolte et al., 1993) which provides a wide range of simulation services for managing collections of interacting simulation objects. These services include:
1. basic time-flow synchronization of system components; 2. data storage, collection, display and output;
3. linear programming tools for optimization, and
Fig. 2. User interface of POND showing icons and results (graphs and an enterprise budget) generated after simulation model runs.
allow simulation of pond dynamics at both the individual pond-level, as well as at the facility-level. Addressing this need involved providing capabilities for simulating processes within a pond, as well as allowing the definition of multiple ponds and multiple fish lots (i.e. a population of fish stocked in a pond), each with their own characteristic data. Simulation of dynamic pond process requires expertise from a number of domain areas, including aquatic biology, aquatic chemistry, fish biology, fish culture, aquacultural engineering and economics. In an aquacultural facility, each of these domain areas is typically represented by well-defined entities; a facility is a collection of these entities, operating under a particular management context to allocate resources and produce fish.
current temperature and water quality variables, and associated fish lots were defined. For fish lots, characteristics defined for the object included species informa-tion, stocking sizes, and stocking dates and densities, among others. Methods for these objects include the ability to simulate their dynamics based on their current states and the influence of exogenous variables.
POND contains a series of mini-databases, which are accessible to the various objects in the software. For instance, databases are maintained for each lot and pond in a facility, as well as for simulation settings, economic information, soil types, fertilizers, feeds, liming materials, site information and weather characteris-tics. The software also has an experimental database that allows users to specify the combination of the above databases to be used in a model experiment. This feature has proven useful for quickly assembling and executing relatively complex scenarios of alternate pond management practices.
Additional objects representing ‘experts’ managing the facility were defined. These experts included (1) an aquatic chemist, with the ability to perform a wide range of water chemistry calculations, (2) an aquatic biologist, with the ability to perform functions related to fish growth and algal dynamics, (3) a weather manager, with the ability to estimate weather conditions for specific sites, (4) an aquacultural engineer, with the ability to perform heat and water balance calcula-tions, among others, and (5) and an economist, capable of performing enterprise budget analyses and managing costs of various facility operations. A schematic of the overall POND architecture is provided in Fig. 3.
The various experts in POND have capabilities for simulating different aspects of production. These areas include fish performance, water temperature, water quality dynamics, and primary and secondary productivity. Models in POND are orga-nized hierarchically into two levels, allowing users to perform different kinds of analyses based on data availability and output resolution requirements. Level 1 models are fairly simple, require minimal data inputs, and are intended for applied management and rapid analysis of pond facilities. At this level, the variables simulated are fish growth (based on a bioenergetics model) and water temperature. Consumption of natural food by fish is assumed to be a function of fish biomass and appetite. Fertilizer application rates are typically user specified, but the model optionally generates supplementary feeding schedules.
2.4.Economics
POND allows the incorporation of economic analyses of facilities in the form of enterprise budgets. Enterprise budgets allow for the accumulation of various types of cost and income streams, summarized and coupled with interest and depreciation expressions, to assess the overall economic viability of a particular production enterprise. POND supports three cost categories: (1) fixed, (2) depreciable and (3) variable costs. Fixed costs are those costs that do not change over the course of facility operation and are not depreciable (e.g. baseline labor costs that are fixed throughout an analysis period). Related to fixed costs are depreciable costs, which typically are used for items which require up-front expenditures, but which may have some back-end redeemable value after some period of time. POND incorpo-rates depreciation schedules describing the loss of value of the depreciable asset over time. An example of a depreciable cost is a tractor, which has an initial cost as well as a resale value after some period in use at the facility. Variable costs are those costs that are not fixed or depreciable, and typically vary according to the scale of production (e.g. seasonal labor costs, fertilizer and feed costs, and fuel and electricity costs).
To generate an enterprise budget, income sources are also required. POND allows the specification of any number of income sources, based on either a per unit area, per unit of production, or per facility basis. The facility simulator provides income sources relating to fish production. Additionally, interest rates used for calculating fixed and variable investment costs are required. After specifying each cost by an amount, a cost type (fixed, depreciable or variable), basis (per unit area, per unit of production, or per facility) and other related information, the economics module in POND summarizes costs on an areal, per unit of production or facility basis, balances those costs against income, and reports the results in a tabular form. By including and/or excluding particular costs/incomes, or adjusting cost/income details, one can quickly ‘experiment’ to determine alternative possibilities for the economic viability of various facility and management configurations. A more recent feature in the software is the ability to specify market prices for different sizes of a particular target species, and to automatically determine the time of harvest when economic returns may be optimized (where marginal costs equal marginal returns).
2.4.1. Facility simulation
To conduct a facility-level simulation in POND, an appropriate collection of class instances, each of which represents one of the objects described above is created by the framework. A typical simulation might be conducted by creating a series of experts (e.g. an aquatic chemist, an aquatic biologist, an aquatic engineer, a species biologist, and a fish culturist) and a collection of facility entities (e.g. one or more fish pond instances, each representing a pond in the facility, and one or more fish lot instances, each representing a single population of fish of the same species). The simulation then proceeds by ‘asking’ the fish culturist to manage the fish lot; it does so with the assistance of its associated experts according to the conditions present in each pond. The object paradigm makes it very straightforward to create facilities consisting of arbitrary numbers of fish ponds and/or fish lots, each maintaining its own characteristic data and simulating as a separate process. POND simulations are dynamic, providing time series analysis for a range of variables that are dependent on the level of resolution requested prior to simula-tion. During the simulation, POND accumulates time series data for each variable; these data may be viewed in plots or tables at the end of the simulation run. Data may also be exported (in comma-delimited format) for use in other programs such as spreadsheets and databases. Because POND encapsulates an economist in its collection of supported object classes, economic analysis of the facility may be conducted at any time. Such analysis may use simulation data if available, as well as other fixed and variable costs and income streams as defined by the user.
engineer, and the economist (in addition to the same underlying simulation framework).
3. Experience gained
The design, development and implementation of the POND software has pro-vided useful lessons in several areas. As the user base for POND has grown, we have had the opportunity to solicit feedback on how well POND is addressing user needs. The first lesson is that a diverse group of users has used POND to address a diverse set of needs. Although we originally anticipated a research-focused audience, our largest group of users has in fact been commercial aquaculture facility managers. The primary focus of this group has been on economic analyses, with the utility of the biological models contained within POND of secondary importance. An additional audience has been educators using POND in the classroom as a tool for examining pond dynamics, where the biological models play a more important role. Each of these groups has a different set of interests and a different user interface requirement, and the ‘one size fits all’ approach that we initially used has not been optimal in addressing their respective needs.
A primary feedback from POND users involved the ease of use of the software. Although we have spent considerable effort in developing a modern user interface for POND, because of the underlying complexity of the models POND employs and our desire to fully expose these models, the user interface proved to be burdensome for many users. Exposure of the underlying models is helpful to those focused on understanding the detailed biological dynamics of these systems, but it is less helpful or irrelevant to those focused primarily on economic analyses of facility operations. We are addressing this issue in upcoming releases of POND in two ways. First, the focus of POND, from a user interface perspective, is more on decision support and less on models of the underlying biophysical system. The underlying models continue to be an essential tool for supporting decision-making, but are less apparent to the user. In other words, they operate in the background but play a secondary role to higher-level decision-making processes. Second, we have introduced a series of ‘wizards’ into POND. These wizards are software tools that ‘walk’ the user through specific and frequently used tasks (e.g. estimating feed, fertilizer and water requirements and associated economic implications). The wiz-ards hide much of the complexity of these tasks and provide immediate help to the user in accomplishing their goals. We anticipate continued development of these wizards to address specific needs of different user groups and to improve ease of use of the program.
numerous requests is small commercial entrepreneurs, often new to aquaculture, who are looking for tools to help them explore the financial feasibility of launching an aquaculture venture. Both of the above groups tend to focus primarily on economic analyses, but require some basic understanding and consideration of the underlying biophysical processes underlying facility operation. Aquaculture educa-tors represent a final audience for POND. Their requirement is for readily accessi-ble tools that students can use in class to enhance their understanding of the biophysical processes controlling aquaculture ponds, as well as completing specific design tasks related to facility management.
From an implementation perspective, the object-based paradigm has been very successful at addressing the software development needs of POND. Although it is a relatively complex program from an implementation perspective, the modularity provided by OOP has allowed us to manage the complexity to a workable level. Additionally, most of the objects in POND can and have been reused directly in other applications we have developed, resulting in an efficient use and leveraging of resources for development. The advantages of approaching software development from an object-oriented perspective are most clearly evident in the enhancements we have made to the simulation framework as a result of needs that have emerged during the implementation of POND. These enhancements have automatically become available for other applications developed by our group. For instance, the need to estimate parameters for POND models (in turn derived from the need to use them for different fish species) resulted in our implementing a generic parameter estimation approach in the simulation framework. This approach has since found use in VSS (Virtual Systems Simulator), a generic model development application. Similarly, the need for a method to estimate least-cost fertilizer mixes in POND prompted the implementation of a linear programming module in the simulation framework that is available for use by other applications. At a more specific level, oriented towards aquaculture applications, refinements in the ‘experts’ shared by POND and AquaFarm automatically become available to both applications. Fi-nally, we have been able, with relative ease, to extract models from POND and apply them in complex Geographical Information Systems (GIS) to evaluate inland aquaculture potential over large areas in Latin America (Kapetsky and Nath, 1997) and Africa (Aguilar-Manjarrez and Nath, 1998).
less critical than figuring out how to assemble such components into an application focused on satisfying a list of user specifications. In any event, regardless of the specific language used, the representational capabilities of any of the object-based languages are very well matched with the needs of simulation and decision support software.
POND continues to evolve as the needs of the user base for such software tools, and the aquaculture community itself, evolves. A heightened interest in shrimp production is moving us in the direction of developing and utilizing bioenergetic models that are adaptable to these species. Advances in software development, algorithms and user interface design continue in computer science at an almost dizzying pace, and we are attempting to take advantage of these where appropriate. The World Wide Web provides both opportunities and challenges for decision support system developers. Ultimately, however, the success of any software development effort depends on how well the product addresses user needs. There is no substitute for getting software out in the hands of users, soliciting their feedback, and incorporating this feedback into future design and implementation efforts.
Acknowledgements
Development of POND has been supported by the Pond Dynamics/Aquaculture Collaborative Research Support Program (PD/A CRSP). The Pond Dynamics/ Aquaculture CRSP accession number for this publication is 1198.
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