Decision Support Systems in Agriculture: Some Successes and a Bright Future - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 294

4. Experiential knowledge
While the totality of agricultural knowledge is, as indicated above, exceedingly complex and
diverse, we will consider a small subset of that knowledge in this chapter. We will focus on
knowledge related to the growth and production of agricultural food crops and the role of
nutrients, either in deficit or excess in that relationship. Agricultural knowledge is extremely
descriptive with many adjectives and nouns, but few of the axioms, postulates, and
theorems enjoyed by sciences such as physics and mathematics. Also as suggested above,
agricultural knowledge tends to be encyclopedic with relatively few universal, nearly
inviolable rules. In addition to exercising relatively few universal rules it is also clearly
interdisciplinary, requiring close interaction among disciplines to adequately capture the
experience.
Acknowledging the interdisciplinarity is important because the methods and norms of the
various disciplines differ and should be respected in order to obtain the best knowledge
from each of the disciplines. A personal experience illustrates differences among social and
biological scientists, for example. Among biological scientists data almost always refers
exclusively to numerical knowledge, weights of maize, metric tons of root crops, dollars per
kilogram, kilograms of fertilizers or amendments, duration of crop cycles, while social
science data can be notes taken during an intensive interview, during a focus group
discussion, or as a result of a recollection. It is important in working with such diverse,
interdisciplinary knowledge that disciplines are respected for their methods, techniques,
approaches and culture.
4.1 Collecting and recording agricultural knowledge
Accurate collection and recording of agricultural knowledge, not surprisingly, must reflect
the complexity of the knowledge itself. Such collection is difficult and success, not
surprisingly, seems to require methods appropriate for the knowledge. Probably some of the
best methods from the point of view of completeness are those used by anthropologists.
Their holistic perspective requires unusually complete, thorough knowledge collection and
recording using the most current methods available. One good example is the Ph.D.
dissertation of Dr. Cynthia T. Fowler (Fowler, 1999), describing an agricultural community,
Kodi, West Sumba, Indonesia. The dissertation required approximately 550 pages to record
the relevant knowledge. A small portion of the dissertation was later synthesized into an
explanation of an apparent oddity - that an introduced plant from another continent came
to be a local 'sacred' plant (Fowler, 2005).
Another example of the capture of detailed agricultural knowledge is provided by the
dissertation of Dr. M. Robotham (Robotham, 1998). Again, some 550 pages were needed to
describe the agricultural system. In this case, Robotham attempted to generalize the
knowledge and capture the decision-making logic from each of three villages located in the
Philippines (ibid, 1998). Within each of the 3 sites, selected to represent variation in
Philippine agriculture, multiple households were interviewed using social science
techniques, with a total of some 17 households interviewed in all. Models of the apparent
decision-making process were synthesized into decision-trees (graphs that represent the
flow of decision-making, Appendix 1) to help compare and contrast the knowledge that had
been developed for each of the villages.
Influences of socio-economic forces on agroforestry adoption in the Dominican Republic
were modeled using a rule-based system (Robotham, 1996). Examples of a rule-based
system will be forthcoming in section 5.2