Decision Support Systems in Agriculture: Some Successes and a Bright Future - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 302

6.3.3 Implementing language
Delphi ® rapid application development software, Pascal language.
6.3.4 Successes
PDSS builds on the results of the structuring of the knowledge for the soil acidity decision-
aid ACID4. As a result of the meta-analysis of the soil acidity decision-making process, we
identified four components in the general process of nutrient management: 1)Diagnosis, 2)
Prediction, 3) Economic Analysis and 4) Recommendation. These components served the
basis for constructing PDSS and will now be discussed in succession.
6.3.5 Diagnosis
A diagnosis of a particular condition, in this case of a deficiency in soil and plant content of
the nutrient phosphorus (P) is critical to bringing appropriate attention to the condition and,
consequently, to its solution. A diagnosis in this sense can be observed when an expert is
confronted by a problem and asks a few quick questions and rapidly determines the
importance of further questioning or not. In this sense, the expert is exercising the “Best-
First” search strategy discussed above. Such rapid assessments were observed when
experienced scientists did field-visits, discussing with farmers the conditions of their crops.
Often during such visits and discussions a suggestion resulted that led to corrective action.
A diagnosis in this sense is our attempt to capture and implement an expert's best-first
strategy of quickly assessing the seriousness of a situation and determining the best
subsequent course of action. In another sense a diagnosis is a call to action. It is a decision
about whether to act or not. This definition and use is important in terms of problem-solving
and may be somewhat different than the classic “diagnosis” used in disease identification.
The “diagnosis” we describe in this section is most effective if carried out by the person
actually working with and intimately involved with managing the complex system (a crop-
soil production system, in our case). A frequent heuristic or rule of thumb is that if a disease
or condition is caught early then it is more likely to be successfully cured or remedied.
Likewise, in complex systems of soil and crop management, a condition can often best be
solved if it is detected early before subsequent, secondary complications, or in some cases
irreversible damage, occurs. The analogy with human medicine is clear. For these reasons, it
seems prudent for the grower, producer, or farmer to be informed and empowered with
sufficient knowledge to detect the need for action. We also, upon further analysis, learned
that there are other aspects of a good diagnosis that are important (Yost et al., 1999)
(Table 1).
Diagnostic knowledge can be useful even if it is qualitative, highly observational, and even
if a substantial amount of uncertainty is present. Highly uncertain information, when
combined with other information with a similarly large amount of uncertainty, can, when
taken together, begin to show a pattern that is typical of the disease, the condition or the
state being detected. A good diagnosis could result from multiple pieces of information,
none of which stands alone on its own, but when combined together, suggests a singular
conclusion (i.e. all tending to indicate deficiency of a particular nutrient). We implemented
this characteristic of being able to combine qualitative, quantitative, as well as uncertain
information by using a Bayesian cumulative probability framework as indicated above in a
chapter on Diagnosis (Yost et al., 1999). An example spreadsheet illustrating the calculations
is shown in Appendix 2. The combining of multiple pieces of information thus often led to a