Decision Support Systems in Agriculture: Some Successes and a Bright Future - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 300

these highly acid soils that differed so greatly from those of their experience. The system
had several disadvantages including the following:
- The goal-driven, rule-based system proved rather unsuited to capture some of the
information. In particular, social science information did not necessarily fit well in the
rule-based knowledge representation system (Colfer et al., 1989).
- Many on-farm production limitations were due to multiple constraints occurring
together. Acid soils in particular are characterized by multiple constraints. In addition
to high acidity with toxic levels of aluminum and manganese. Levels of pH itself,
calcium, magnesium, and phosphorus are to be expected to be insufficient and possibly
yield limiting as well (Fox et al., 1985).
- A subsequent decision-aid was developed that attempted to address this problem (see
section 6.4 NuMaSS, (Nutrient Management Decision Support System), later in this
chapter).
- The system required a computer.
- This could be overcome by technicians and scientists running the software for the
specific site or farm and communicating the results to the producer / grower.
- We later explore and propose a type of decision-aid that is completely graphic.
- Modification and updating of the software required rather expensive, proprietary
software.
- One copy of the software could develop many systems (Le Istiqlal, 1986.)
- A small, free copy of the essential software was provided such that copies of the
decision-aid could be copied and distributed inexpensively (run-time version).
- For subsequent decision-aids we used a procedure languages such as Pascal or
declarative languages such as Prolog and hired programmers.
- Although the rules were given a numeric score of uncertainty, this uncertainty was
combined in an inflexible way that often neither represented good practice nor the
scientifically verifiable behavior.
- This effort led to subsequent improved representations of multiple sources of evidence
(Bayesian cumulative probability) (Yost et al., 1999)---an implementation of evidence
accumulation described in Pearl (1988).
Subsequent decision-aids included the cumulative probability to generate approximate
confidence limits of numeric predictions of fertilizer needs using first order uncertainty
analysis (Chen et al., 1997). This remains an area requiring more accurate representation of
evidence accumulation as well as the appropriate handling of contradictory evidence. What
are the most successful ways to carry out such calculations and accumulate evidence? It is
likely that some of the methods recently used by IBM's Watson (Watson-Jeopardy, 201)
would lead to better approaches than those described here. It also is not yet clear how
successful experts make such estimates, if they do.
6.2 Propa (Papaya expert system)
That agricultural knowledge is highly interdisciplinary presents a challenge to the classical
concept of an expert in a single discipline. When a grower or producer contacts the
University with an issue they sometimes are referred to several experts before determining
which expert is the right one for the specific problem. Confusion and failure to succeed in
the diagnostic effort may occur. The goal of the Propa decision-aid was to explore this
dynamic by attempting to construct a decision-aid that would identify and solve typical
problems possibly requiring multiple disciplines (Itoga et al., 1990).