Pd/a Crsp Seventeenth Annual Technical Report

Decision support systems (DSSs) are potentially valuable tools for assessing the economic and ecological impacts of alternative decisions on aquaculture production. This report discusses the latest 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. 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. POND© contains representations for manipulating pond aquaculture and utilizes 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) and their management settings together with appropriate experts (e.g., an aquaculture engineer, an aquatic biologist, and an economist), and projecting changes in the facility over time. Most recent efforts have focused on improving the economic analysis capabilities of POND© and improving the usefulness of the software for addressing specific needs of the education and extension community. SEVENTEENTH ANNUAL TECHNICAL REPORT 176 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: a) Basic time-flow synchronization of system components; b) Data storage, collection, display, and output; c) Linear programming tools for optimization; and d) Parameter estimation methods (for determining best-fit model parameters). The framework also provides a generic simulation object class, which was subclassed into specific simulation components relevant to pond modeling and decision support. The framework also relieves the developer of much of the management of simulation details and instead allows focus on the specific components of the (physical) system to be modeled. This approach has proven to be an effective and powerful approach for model and/or DSS development and has provided the ability to effectively share simulation objects between applications. Because the underlying framework takes care of simulation details, the primary task in developing POND© was to specify important factors controlling the dynamics and decisionmaking processes of an aquaculture facility and to define a corresponding set of simulation objects. It was important that these components 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, include aquatic biology, aquatic chemistry, fish biology, fish culture, aquacultural engineering, and economics. In an aquaculture 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. 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 characteristics. 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 include 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 calculations, among others, and 5) an economist, capable of performing enterprise budget analyses and managing costs of various facility operations. 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 organized 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 userspecified, but the model optionally generates supplementary feeding schedules. Level 2 models provide a substantially more sophisticated view of pond dynamics, allowing prediction of phytoplankton, zooplankton, and nutrient dynamics (carbon, nitrogen, and phosphorus) in addition to fish growth and water temperature. This modeling level is intended for detailed pond analysis, management optimization, and numerical experimentation. Fish can feed from natural and/or artificial food pools. Consumption of natural food (phytoplankton and zooplankton pools) by fish is predicted on the basis of a resource competition model and also depends on fish appetite. At this level, a constant, user-specified concentration of pond nitrogen, phosphorus, and carbon is assumed. Mass balance accounting for each of these variables is maintained, allowing estimation of fertilizer requirements necessary to maintain steady state levels. Level 2 models generate both fertilization and feeding schedules. Further details regarding the models in POND© and their verification can be found elsewhere (e.g., Nath, 1996).