GIS originally is developed to store, retrieve and display spatial
data and domain models are combined with GIS to simulate
some complex phenomena later. The use of domain models in
GIS greatly expanded its application domain and improved its
application level. Applications as environmental pollution
simulation, shortest route selection and material distribution
plan, flood submersion prediction, etc are benefited a lot from
GIS and domain models. Some special spatial tasks which are
semi- or ill structured, however, are beyond either GIS itself or
domain models. This put GIS use in difficulty. Fortunately
Artificial Intelligence (AI) shows that expert knowledge can be
used to solve semi– or ill-structured problems, thus the
integration of GIS and expert knowledge is our research
consideration. The advantage of GIS and expert knowledge
integration is its power to support people in decision-making
with reliable and comprehensible map-based format. The
critical factors in this integration include expert knowledge
representation, model organization, the integration of GIS
models and knowledge, and the proper use of model and
knowledge.
The fact that the topologic features and uneven surface of
agricultural land in most region makes farm fields small in area,irregular in shape, and scatted in distribution. The
overpopulation makes this even worse since a large farm field
usually has to be divided into bits and pieces to meet all farmers
need for sharing. This is particularly true in China and many
overpopulated countries. The mode of digital agriculture that a
large land evenly partitioned into regular grid is inapplicable in
those regions. Moreover models are the main component that
calculates fertilizer, water and pesticide application for different
grids while expert knowledge is usually fixed in models.
Knowledge lacks flexibility in maintenance. This also limits the
extension of GIS use. The approach discussed here for using
farm fields (grids) variability information and expert knowledge
for enhancement of yields and reduction of risk in farm field
management should be applicable over much of those regions.
To offer an application system accessible to location-distributed
users, a web-based spatial decision system with the integration
of GIS and expert knowledge, GZ-AgriGIS is developed. Expert
knowledge associated with different crops obtained from human
expert and analysis models can lead to appropriate field
management to any farm field no matter where the field locates.
The novelty of GZ-AgriGIS is its integrated knowledge base,
which contains information on most of agronomic knowledge.
With the system run, it is possible to tap the complex spatial
decision-making and gain an insight into the variety of options
of management practices available to each piece of farm fields.
It fits with uneven area thus it has more flexibility in practice,
esp. in mountainous regions with scattered, small area and
irregular farm fields.