Full text: Proceedings, XXth congress (Part 7)

Zongyao Sha* *, Fuling Bian* 
* Center of Spatial Information and Digital Engineering, Wuhan University, Hubei Province, China, 430079 - 
(zongyaosha, fulingbian)@eyou.com 
Commission VI, WG 1 1/2 
KEY WORDS: GIS; Knowledge; Representation; Digital; Agriculture; Decision Support 
A main task in agriculture production is field management of water and fertilizer. Excessive fertilizer and water application not only 
waste resource but also pollute the environment. The traditional principle of digital agriculture can only apply in flat or plain place 
where a large field usually should be partitioned evenly into many little grids and thus decisions on grid-specific agronomic 
operation can be recommended. This mode of digital agriculture is called grid-level decision-making. But this seldom happens in 
farming system in mountainous region due to the complex landform where even grids partition cannot be implemented and so this 
mode is called farm-level decision-making. In this paper, we develop a web-based decision support system that integrates the expert 
knowledge, analysis model and GIS to assist farm-level agronomic decision-making and that is fit for any circumstances in 
agriculture production region with efficient knowledge support. The approach adopted involves general GIS spatial data 
management (geo-referenced digital map, spatial agriculture decision unit, etc.), agronomic diagnosis and decision-making with 
integration of expert knowledge and analysis model, so that the variable-rate application of water and fertilizer to any regular or 
irregular cultivated field can be addressed. The core technology involved includes expert knowledge representation, model 
organization, software data exchange standard and integration of GIS, expert knowledge and analysis model. With this approach and 
the basic principle of the traditional digital agriculture, it is possible to tap the variable-rate water and fertilizer application to 
agronomic fields even in the mountainous and remote region and gain maximum benefit with minimum purchased input, which is 
very useful in mountainous countries with scattered and small-scale agriculture production. The framework of the developed system 
is a hybrid structure model composed of B/S (Browser/Server) and C/S (Client/Server), which not only extends the capability of 
decision support service space but also makes the system easy to maintain. A case study is done in Guangzhou city, located in inter- 
tropical belt in the South of China and covered by mountainous landform, and shows an exciting result. 
the actual need even GIS are capable of spatial data 
management and displaying. Spatial models, as an import tool 
to enhance spatial analysis ability, can be integrated with GIS to 
solve more sophisticated and special problems, thus spatial 
model integrated system, also known as decision support 
system (SDSS), is widely researched and used in practice. 
Many domain models are developed by specialists and used 
widely in varied fields, including agriculture, environment 
management. Digital agriculture uses irrigation or fertilizer 
application model to give decision-makers how and how much 
water should be irrigated or fertilizer should be applied and uses 
GIS to express information about where and how much those 
elements lack. In some cases, model should also be connected 
with expert knowledge to solve semi- or ill-structured problems. 
Those semi- or ill-structured problems are known as complexity 
of decision tasks, difficulty in prediction of decision process, 
multi stages and intersection of those stages in decision process. 
Knowledge representation. is the key factor in knowledge 
utilization and is an important focus in knowledge engineering. 
Even Chinese farmers have accumulated rich experience and 
knowledge in agriculture production (Guo, 1988; Chinese 
Academy of Agricultural Science, 1993), they still lacks 
scientific guide. With the development of economy, it is very 
urgent to make Chinese farmers free from onerous agriculture 
operation. Building a usable, widely suitable expert knowledge- 
A main task in agriculture production is water and fertilizer 
management. Excessive fertilizer application not only wastes 
limited resource but also pollutes the environment. Deficit 
application, however, may limit the growth of crop. Due to the 
large spatial variation of agriculture field environment (e.g. soil, 
climate, terrain, etc.), location specific farm-level management 
is critical in crop growth management. The choice of cropping 
pattern, when and how much to apply fertilizers, water, 
pesticides etc., the choice of the period for planting, plant 
population density, etc., are all factors that should be considered, 
for which the appropriate choice (associated with maximum 
production or minimum risk) depends upon the soil feature, 
crop feature, climate, besides social requirement. As farm field 
is location-varied, different field should take different 
management measures. Runquist et al. (2001) proposed a 
compact, raster-based geographic information system designed 
specifically field-level management, realizing numerical data 
representing either circular (center-pivot irrigated) or irregularly 
shaped crop fields. Agricultural operation is closely connected 
with natural resources that have an obvious spatial character 
which is considered essential character of Geographic 
Information Systems (GIS) (Josef B., et al. 2002), thus GIS has 
an important function to play in agriculture production, esp. in 
field irrigation and fertilizer application. But it is still far from 
* Corresponding author. 
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