Decision Support Systems in Agriculture: Some Successes and a Bright Future - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 314

6.7 Decision-aids to assist learning / education
Use of the decision-aids was found to stimulate and enable learning in several ways.
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Operating the decision-aid by entering the requisite data. Carrying out the calculations
and recording or transmitting the results helped users gain familiarity with the software
and elements of the decision-making process. Merely operating the software did not
result in the users gaining the knowledge embedded in the software, but to the curious
it led to questions and sometimes sparked curiosity.

- Observant users gained some measure of the types of information needed and could

test the system integrity by checking test cases. A feature of the early expert system
shells was back-tracking that demonstrated the logic that led to a specific conclusion.
With later decision-aids this feature was not present so supplementary information
surrounding the consultation had to be selected and specifically added at the
Recommendation stage.

- The rigor imposed by the decision-aid on the user, i.e. having to provide an answer in

every run or consultation, reinforced and reminded the users of the need to have
information from a specified group of interdisciplinary knowledge bases. The input
from farmers as local experts, both improved the system and provided positive
feedback to them.

- For some users the rigor of having to answer, in a precise way, was difficult and

resulted in loss of interest and failure to complete the consultation.

- Use of a decision-aid can illustrate to users the need for and value of

interdisciplinarity.

- Exposure to the way knowledge can be organized provides an exposure to a meta-level

appreciation of the problem-solving techniques, which can support learning.

- The structure of the decision-making process illustrates one type of problem-solving

that users can adopt or modify for themselves in the future as needed.

6.8 Summary
This chapter illustrates that complex agricultural knowledge could be captured and
implemented so that numerical predictions and informed recommendations could be
produced. In the example given, soil and crop management technology and knowledge on
acid soils was captured from successful practice in the Southeast US, Central and South
America and implemented in analogous soils in Indonesia.
Use of the decision-aid by users in a new location where soil acidity or phosphorus was
limiting helped users identify data needs for solving such problems. For example, improved
management of acid soils acid soils in the uplands of the Philippines was stimulated by the
introduction of the decision-aid NuMaSS. The improved management included introducing the
practice of liming. As a result producers and growers in the region were expected to benefit over
45 million $US, according to the impact analysis conducted in 2007.
The knowledge engineering process led to a meta-analysis of the process, i.e. some thought
about how to best solve nutrient management problems. The result of the meta-analysis was
the identification of a structure in nutrient management decision-making. A structure of
Diagnosis, Prediction, Economic Analysis, Recommendation was proposed. This structure
guided the formulation of PDSS, NuMaSS, and NuMaSS-PDA.

6.9 Challenges
Improved knowledge structures are needed that better match the nature of the knowledge.