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.
Often, the immediate need for pond aquaculture technology may cause decision- makers to apply or recommend management practices developed and tested at one
location to a new site, without first assessing the appropriateness of the technology. The use of technology that has been found to be suitable for one location may very
well be inadequate when applied elsewhere Colt, 1986. This may be due to differences in fish production potential caused by the variability in climate, water
and soil characteristics among sites King and Garling, 1983, and because of differences in the availability and cost of resources used in pond production Shang,
1981. For example, a decision as specific as the calculation of feed requirements for a pond requires consideration of fish biomass, natural food availability, and water
temperature which vary both with time and among different locations Hepher, 1988. Similarly, calculation of fertilizer application rates requires a basic under-
standing of soil and water chemistry both of which also vary among different sites. In both cases, availability and cost of appropriate inputs should be factored into
the decision-making process Shang, 1981.
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, andor 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 bases 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 managementplanning 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.
With the exception of the system described by Ernst et al. 1993, none of the farm managementplanning DSSs were designed to serve as a framework for
representing aquaculture facilities and providing capabilities for comprehensive
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
