Decision Support Systems in Agriculture: Some Successes and a Bright Future - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 298

milestone in terms of managing complex and uncertain information, far exceeding the
previous success of IBM's Deep Blue that excelled with the much more algorithmic game of
chess. The success by Watson included the access of 15 terabytes of information accessed by
10 racks of IBM Power 750 servers, which generated 80 teraflops of processing power.
Henshen (2011) reports that the 80 teraflops of processing power together with improved
information access methods reduced information access time from 2 hours to 3 seconds.

6. Example decision-aids

Some of the authors used an expert system development tool (expert system shell) to
implement a rule-based system that used backward-chaining to diagnose acid soil
conditions and prepare predictions of the amount of agricultural limestone needed to
remove the soil acidity limitation to selected crops. This software, ACID4, was described in
(Yost et al., 1986) and subsequent decision-aids. We now present a list of various decision-
aids developed and illustrate the range of uses, methods of implementation, purposes as
well as unexpected benefits.

6.1 ACID4 rule-based system
6.1.1 Goal
Facilitate the transfer of the acid soil management knowledgebase developed in Southeast
US, Central and South America to farmers and producers of the Transmigration area of
Indonesia in general and Sumatra in particular.

6.1.2 Objectives
Implement a set of rules that together represent both the scientific knowledge and farmer
experience in managing acid soils for food crop production. The primary source for the
knowledge was a review paper by (Kamprath, 1984), practical experience reported by
(Gonzalez-Erico et al., 1979), and firsthand experience by the authors.

6.1.3 Implementing language
EXSYS, expert system “shell”(Hunington, 1985.)

6.1.4 Successes
The ACID4 decision-aid illustrated that soil management knowledge could, indeed, be
captured and represented in a computer-based decision-aid. The system permitted non-
experts with only inputs of measured soil acidity (KCl-extractable acidity, calcium and
magnesium and a selected crop) to receive predictions of lime requirements in tons of
limestone per hectare (eq. 1).

 

Lime requirement tons / hectare
1.4 Exchangeable Acidity - CAS * ECEC / 100



(1)

 

- Where: Lime requirement is the amount of limestone of 100% CaCO 3 quality,

- Exchangeable Acidity is the KCl-extractable toxic aluminum and hydrogen,

- CAS is the Critical Aluminum Saturation, which is the maximum amount of toxic

aluminum and hydrogen the specific crop can tolerate while achieving maximum
yields.