When to use SDBm

Database tool useful for storage of primary soils information assembled at national level, or data collected in a soil survey at subnational and local level. SDBm data are used in the computerized AEZ land evaluation systems. It is useful to soil scientists, agricultural extension officials and environmental modellers.

What is SDBm

SDBm is a collection of programs written in CLIPPER 5.2 and C languages incorporated into a menu-based interactive user interface to enter data and manage the database. The coding system used in the database conforms to FAO Guidelines for Soil Profile Description. Data storage is greatly facilitated by the multilingual function which provides "assist menus" in three languages: English, French and Spanish.

Options for simple statistical analysis: calculation of weighted averages or dominant values of selected variables by soil unit, depth range and group of soil profiles; graphic presentation of soil analysis data, such as pie chart image of relative percentages of variable groups of attributes in a given soil profile.

Read/write facility to communicate with other SDBm databases and exchange data.

Examples of SDBm applications

Global: SDBm has been used to create a global soil profile database linked with the FAO digitized soil map of the world.

National: SDBm is being used by soil and land use planning institutions in various countries, including the Netherlands, Lithuania, China, Kenya, Tanzania, Grenada, Yemen, Namibia, Ghana, Nigeria.

Availability

When to use MCDA/ARLDS

In "real world" situations, when solutions to problems are reached as compromise solutions, resulting from trade-offs between various conflicting objectives of the stakeholders through negotiations to reach a consensus. In such situations the approach is not to maximize all the objectives, but to optimize, that is, to find an acceptable balance between the requirements of the stakeholders. Different kinds of objectives can be included, expressing not only economic values of products but also addressing goals which can not always be expressed in monetary terms such as biodiversity, people's preferences, equity, or minimizing risk and uncertainty. The factors of a solution are not fixed valued, but are variable or fuzzy within certain ranges determined by resources availability and socio-economical realities. Usually models have to be run a great number of times in order to identify a "best" (or even acceptable) solution; and many options need to be examined to generate the information and knowledge required for these decisions and to quantify and display the trade-offs that must be made between conflicting objectives.

What is MCDA/ARLDS

It is a recent DSS tool developed at IIASA based on one of the most successful MCDA methods called the aspiration-reservation led decision support (ARLDS).

From the user's point of view, the critical step of MCDA is generating a part of the Pareto-optimal solution set. Efficient, or Pareto-optimal, solutions are those for which an improvement in the value of one criterion cannot be attained without worsening the value of at least one other criterion. Generating the entire Pareto-set is practically impossible and -even if done - would result in a vast amount of useless information. The MCDA/ARLDS method facilitates generation of Pareto-solutions (having properties implicitly defined by aspirations set by the decision maker (DM) and then provide tools for analysing these solutions and generating another set of Pareto-optimal solutions based on these results. Since aspirations are usually not attainable, the DM uses an interactive tool in order to adjust both aspiration and reservation levels until a solution is found which best meets the DM expectations.

The ALDS is implemented with a software package which was constructed from several modular tools designed according to the principle of re-usability. These tools include the following: 

The package is oriented towards an interactive mode of operation in which a sequence of problems is solved under varying conditions (e.g. different objective functions, reference points, values of constraints and bounds). It offers also many options useful for diagnostic and verification of a problem being solved.

Land and water application examples

a) Making land use choices in Kenya districts

The main issue here was to analyse potential population supporting capacity of a district under various land use scenarios, considering simultaneously several objectives such as maximizing revenues from crop and livestock production, maximizing district self-reliance in agricultural production, minimizing costs of production and environmental damages from erosion. Population supporting capacity, as defined here, relates the maximum potential of soil and climatic resources to produce food energy and protein, at a given level of technology.

b) Regional water quality management: the Nitra River case

The scope of the problem is a river basin or a large region composed of several basins where untreated and inadequately treated municipal and industrial wastewater emissions should be reduced in order to improve ambient water quality. At each discharge, one technology to be selected out of a set of possible technologies can be implemented in order to meet the desired water quality goals in the region. The approach is to specify ambient water quality goals and to look for a regional low cost policy, using multi-criteria optimizartion methods. The problem is handled in one step with the interactive incorporation of decision makers’ preferences.

In both situations the approach is to optimize on the basis of the various objective functions of the different actors and analysis of the extent to which the different optimization runs lead to similar land use patterns or water treatment technologies; to develop and apply interactive methods to maximize the extent of consensus in a land use pattern or a technology.

The information produced in this process can then form a common basis and tool for arriving at a negotiated solution for any remaining differences.

Availability

MCDA is available as documented software package at IIASA, International Institute for Applied Systems Analysis, Vienna, Austria. Please consult the following URLs for updated information on the availability of MCMA alone: