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indicated above the construction of this component required quantitative output from the
Prediction component in order to carry out the calculation of the benefit resulting from the
solution of the diagnosed problem. This, for example, required a quantitative estimate of the
amount by which the crop yield was increased as a result of supplying the necessary
nutrient phosphorus. Understandably this required more than the usual soil science solution
to increase extractable P. In PDSS, this required an estimate of crop growth response, which
required estimating crop behavior at various levels of extractable soil P. This requirement
meant that we had to incorporate plant response in addition to the simple chemical
evaluation of extractable P. Thus we had to fill yet another knowledge gap in order to link
the Prediction module with the Economic Analysis module. We found this “stretch” to
ensure module communication helpful and broadening. As a result we gained an improved
perspective of the decision-making process. The ultimate advantage was that we could
conduct sensitivity analysis of the effects of change in extractable soil P on crop yield,
profitability, and benefit/cost.
The adopted methodology for economic analysis was a simple partial budget analysis. This
type of analysis permitted a quantitative calculation of benefit versus cost, giving some
indication of economic advantage of the practice suggested in the Prediction step. The
strength of the partial budget assessment was the minimal data requirement. The weakness,
of course, was that the entire enterprise could be losing money but if the addition of
fertilizer was resulting in yield increases considering fertilizer costs, the analysis would
report a profit. Another weakness, from an anthropological point of view is that economic
analyses capture only part of people's decisionmaking logic----issues like gender/ethnic
division of labour and circular migration issues, symbolic meanings of particular crops,
distaste for handling of fertilizers, food crop taste preferences, etc. are ignored...[of varying
relevance, depending on local conditions]. In addition, the partial budget assumes no
interactions among the fertilizer variables with other factors in the enterprise. Since the
exploration of the consequences of various cost and benefit scenarios is often helpful for
decision-support, a separate form was constructed to facilitate entry and calculation of
benefit/cost given various price inputs.
6.3.8 Recommendation
The fourth and last component of the structure of nutrient management revealed as a result
of the meta-level analysis of the decision-making process was the Recommendation. The
Recommendation as identified in this analysis is the process and result of summarizing the
entire decision-making process and includes the Diagnosis, Prediction, and Economic
Analysis, and presents this information in a way that the decision-aids user can utilize.
Understandably this varies with the needs, knowledge preferences and capabilities of the
users. In the case of PDSS software a simple page is constructed that includes the specific
segments of Diagnosis, Prediction, and Economic Analysis, and concludes with a list of the
warnings (aspects of the consultation that could be seriously in error) or information notes
that supplement the conclusions of the consultation.
The Recommendation, in the case of the SimCorn decision-aid (a decision-aid developed by
scientists at Kasetsart University using the knowledge and algorithm implemented in PDSS)
for the diagnosis, prediction, and economic analysis of fertilizer quantities for maize, was a
topic of recommendations that could be used by local extension officers to interpret soil test
results and to communicate specific amounts of fertilizer blends for producers and growers
in their region. In this case, the extension officers provided the information verbally rather
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