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 intertropical
belt in the South of China and covered by mountainous landform, and shows an exciting result.
A main task in agriculture production is field management of water and fertilizer. Excessive fertilizer and water application not onlywaste resource but also pollute the environment. The traditional principle of digital agriculture can only apply in flat or plain placewhere a large field usually should be partitioned evenly into many little grids and thus decisions on grid-specific agronomicoperation can be recommended. This mode of digital agriculture is called grid-level decision-making. But this seldom happens infarming system in mountainous region due to the complex landform where even grids partition cannot be implemented and so thismode is called farm-level decision-making. In this paper, we develop a web-based decision support system that integrates the expertknowledge, analysis model and GIS to assist farm-level agronomic decision-making and that is fit for any circumstances inagriculture production region with efficient knowledge support. The approach adopted involves general GIS spatial datamanagement (geo-referenced digital map, spatial agriculture decision unit, etc.), agronomic diagnosis and decision-making withintegration of expert knowledge and analysis model, so that the variable-rate application of water and fertilizer to any regular orirregular cultivated field can be addressed. The core technology involved includes expert knowledge representation, modelorganization, software data exchange standard and integration of GIS, expert knowledge and analysis model. With this approach andthe basic principle of the traditional digital agriculture, it is possible to tap the variable-rate water and fertilizer application toagronomic fields even in the mountainous and remote region and gain maximum benefit with minimum purchased input, which isvery useful in mountainous countries with scattered and small-scale agriculture production. The framework of the developed systemis a hybrid structure model composed of B/S (Browser/Server) and C/S (Client/Server), which not only extends the capability ofdecision support service space but also makes the system easy to maintain. A case study is done in Guangzhou city, located in intertropicalbelt in the South of China and covered by mountainous landform, and shows an exciting result.
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