Decision Support Systems in Agriculture: Some Successes and a Bright Future - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 299

- ECEC is the soil effective cation exchange capacity, which is the sum of the cations (Al,
Ca, Mg, and K) as measured by a neutral salt.
The predictions of amount of limestone thus included current soil status of soil acidity, crop
needs, quality of the limestone, and ancillary needs of calcium and magnesium in the soil.
An extensive comparison of ADSS, which was a slightly improved version of ACID4,
indicated that the system made accurate predictions of lime requirements for maize ( Zea
mays, L.) and soybean ( Glycine max , L.) but predictions for rice ( Oryza sativa, L) and cassava
( Manihot esculenta . L.) needed improvement (Dierolf et al., 1999).
Results from an exploratory, rule-based system FARMSYS (Colfer et al., 1989) illustrated that it
was possible to merge multiple disciplines in a rule-based decision-aid. Ethnographic knowledge
could be combined with knowledge of soil chemistry and management, when diagnosing and
prescribing management when acid soil conditions were encountered. Local Minangkabau
farmers preferred to grow rubber on their acid soils, which required no limestone applications
and no tilling of the soil. Transmigrant Javanese and Sundanese farmers, on the other hand,
would not hesitate to ameliorate their acid soils by applying the recommended limestone and
tilling the soil for annual food crop production (Yost et al., 1992b).
Through repeated use of the decision-aid, users became familiar with typical requirements
for particular crops, given usual levels of measured soil acidity, differences among soils and
various crops. In fact, the users gained familiarity with the methodology, and learned
certain aspects of the knowledge of managing acid soils. It is likely that some measure of the
'expert' knowledge was transferred to novice users through extensive use of the system.
Perhaps the meta-level information was transferred to the decision-aid users as a result of
using the system. It is clear, also that the detailed scientific knowledge was not transferred.
Thus the mere use of the decision-aid does not replace the learning of the detailed
algorithmic knowledge.
6.1.5 Observations
Further consideration of the factors affecting lime decisions indicated selection of lime
materials could become impossibly complex. A linear programming model was developed
that evaluated limestone cost, quality (fineness, neutralization capacity, as well as calcium
and magnesium content), quantity available, and distance from the location for up to 5
limestone materials. These parameters were evaluated to provide a minimal cost choice of
one or more of the limestone materials that met the specified soil pH and Ca and Mg targets
in a spreadsheet decision-aid (Li et al., 1995).
While the main benefit of the decision-aid ACID4 was the use of the knowledge it contained,
the process of organizing and recording the knowledge led to greater scrutiny of the
knowledge and the identification of gaps and imprecision, which, in turn, led to improved
subsequent research. This is illustrated in the evaluation of ADSS (a slight improvement
over ACID4) (Dierolf et al., 1999). Thus, ironically, the preparing of the knowledge for
dissemination, rather than detracting from the research process, actually improved and
accelerated it. This meta-level analysis of the knowledge resulting from the crafting of the
knowledge and representing it in the knowledge-base later proved to be extremely
beneficial. This, in fact, may be a replication of the “patterns” and “larger framework” that
experts seem to develop over time (Glaser and Chi, 1988)
6.1.6 Disadvantages
The ACID4 system provided a hands-on introduction to capture important knowledge and,
for the Transmigrants of West Sumatra, critical knowledge about how to produce food on