Decision Support Systems in Agriculture: Some Successes and a Bright Future - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 307

than distributing leaflets and tables of fertilizer recommendations (Attanandana et al., 2007;
Attanandana, 2004; Attanandana and Yost, 2003; Attanandana et al., 2007).
Preparing the decision-aid knowledge for the Recommendation thus requires close contact
and familiarity with the clients, or with the agents who will be the direct users of the
software. As discussed in Attanandana et al. (2008), the results of the decision-aid
consultation should be prepared in a form that enables and empowers the producer/farmer
who will be using the results. The preparation of the Recommendation thus completes the
process of close contact with the eventual user of the decision-aid results that we consider
essential for the crafting and construction of the decision-aid as well as its application
(Attanandana et al., 2007; Attanandana et al., 2006).
6.3.9 Reaction to the PDSS decision-aid among differing collaborators
The PDSS system and the knowledge contained therein was found useful in various ways
by our collaborators. For example, our Thai colleagues sought to include PDSS for the P
algorithm contained therein. They incorporated the logic and equation into their own
systems, SimCorn and SimRice (Attanandana et al., 2006). Our colleagues in the Philippines,
however, preferred to receive the PDSS algorithms in the form of the more integrated
NuMaSS software, to be discussed subsequently, which combined the nitrogen, phosphorus,
and soil acidity components (Osmond et al., 2000).
The use of the PDSS algorithms in our collaborators' software SimCorn (Attanandana et al.,
2006) reduced the recommended application of phosphorus by roughly 50% (Attanandana,
2003, personal communication) reducing the requirement for foreign exchange to purchase
fertilizer P, and limiting the accumulation of environmentally harmful levels of nutrient P.
6.3.10 Expansion and extension of the PDSS decision-aid
The PDSS decision-aid, first released in 2003, proved to be a decision-aid in development.
The development of PDSS, similar to the development of ACID4, opened up new
possibilities and suggested several additions and generated multiple research activities. The
areas where additional knowledge was prioritized included the addition of a potassium
module especially for work in Thailand. Also in Thailand and in West Africa, we needed to
help identify rock phosphate-favorable conditions as well as the amounts that should be
applied to alleviate P deficient conditions. And, lastly, we needed to diagnose and predict
nutrient requirements in perennial cropping systems such as trees, which was clear from the
intial work with decision-aid ACID4 in Sumatra, Indonesia.
6.3.11 Improving predictions of the PDSS
As a result of calculating the error in the prediction (Chen et al., 1997), which gave
confidence limits on the decision-aid prediction, we also obtained the relative ranking of
error in each of the input variables. This information was then used to identify the greatest
source of error in the prediction. This led to the identification of follow-up research
designed to reduce error and uncertainty in the predictions. Follow-up work was carried
out, for example, to better estimate and predict the buffer coefficient for phosphorus in
various project sites (George et al., 2000).
6.3.12 Potassium module
Another substantial gap in the nutrient management of crops for food and fuel in the
Tropics included the need to assess the potassium (K) status of highly weathered soils. We