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Figure 1. Location and counties of the SJP 
The goal of this study is to set up an AEIS for the SJP for 
promoting sustainable agriculture. The AEIS is developed to 
provide information about agriculture in the region, to 
investigate the impact of agricultural practices on the 
environment, and to provide spatial input parameter for regional 
modeling approaches. Based on GIS, spatial data will be 
purchased, captured, stored, managed, analyzed, provided, and 
presented. Furthermore, regional modeling approaches will be 
linked to or integrated within the AEIS based on GIS 
technologies. Consequently, the AEIS for the SJP delivers 
information for spatial decision making and therefore could be 
regarded as a Spatial Decision Support System (SDSS) in a 
regional agricultural context (Bareth, 2009). 
2. SET UP OF THE AEIS 
For the implementation of an AEIS, parameters of atmosphere, 
hydrosphere and pedosphere that are related to crop growth 
such as climate data, pH value and soil texture are needed. 
Human activities like nitrogen fertilizer input, animal waste 
input, use of irrigation water and dates of sowing and harvest 
are important in an AEIS. These parameters are required for the 
modeling of the carbon- and nitrogen-cycles in agro- 
ecosystems. Proper methods for such data analysis and available 
agro-ecosystem models must be available. According to Bareth 
(2009) and Bareth and Yu (2002), an AEIS for sustainable 
agriculture includes six different information systems which 
are: 
e Base Geo Data Information System (BGDIS) 
Soil Information System (SIS) 
Climate Information System (CIS) 
Land Use Information System (LUIS) 
e 
e 
e Hydrological Information System (HIS) 
In this study, macro, meso and micro scale are defined as 
following: macro scale: > 1:100 000; meso scale: from 
<1:100 000 to >1: 1 000 000; micro scale: < 1:1 000 000. 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
Agricultural Management Information System (AMIS) 
     
  
   
   
  
  
    
    
  
    
   
   
   
   
   
  
   
   
   
   
  
   
    
   
    
   
  
    
  
2.1 Base Geo Data Information System (BGDIS) 
The BGDIS is the core of the AEIS to provide different spatial 
levels in a holistic framework. A variety of unique spatial 
characteristics is utilized by the BGDIS to perform spatial 
analysis. Not only topographical datasets with detailed 
information such as roads, rivers, settlements etc., but also 
administrative boundary datasets both for the county boundaries 
in the PCTV system and for the farms in the NAFV system are 
needed. All these datasets should be in macro scale and either in 
vector or raster format. 
In this study, one administrative county boundary vector dataset 
in an independent coordinate system was provided by the local 
official bureau. Unfortunately, the scale of this data is not 
clearly defined. Detailed boundary datasets both for the 23 
counties and the 52 farms are needed and sub-units will be 
defined to solve the problem of overlapping county and farm 
areas. Detailed topographic datasets for Fujin County on a 
macro scale and for the whole SJP on a meso scale will be 
purchased from the National Geomatics Center of China 
(NGCC). 
Many digital maps are available on the internet. The 
1:4 000 000 data for all of China is available for free on the 
NGCC website (NGCC, 2011). Websites such as the China 
Survey Data Network (CSDN, 2012) and the DIVA-GIS 
website (DIVA-GIS, 2012) provide lots of digital datasets. 
However, usually the scale of these datasets cannot satisfy our 
data requirements. 
2.2 Soil Information System (SIS) 
Soil parameters are very often the most sensitive input 
parameters in agro-ecosystem models. Spatial information on 
soils is necessary to target disaggregated agro-ecosystem 
modeling. A SIS is essential for providing agro-ecosystem 
model input parameters. Therefore, spatial soil information (e.g. 
pH value, soil texture and organic carbon content) in form of 
digital maps in macro scale or at multiple scale levels is needed. 
  
  
  
  
  
  
  
Soil dataset A Soil dataset B 
Data format grid raster vector shape file 
Projection WGS84 Albers 
Spatial 1 km 1:200 000 
resolution/scale 
Soil FAO-90 Chinese Genetic Soil 
classification (Nachtergaele et Classification System 
system al., 2008) (Xi et al., 1998) 
Data source and SNSGS, 1979- SNSGS, 1979-1985 
years 1985 
Soil properties soil names; soil names 
e.g. soil texture, 
bulk density, 
pH value, SOM 
(all in layers of 
0-30 cm and 
  
  
  
  
30-100 cm) 
  
Table 1. Comparison of the soil data sets 
In this study, 1:1 000 000 soil data (soil dataset A) was provided 
by the Environmental and Ecological Science Data Center for 
West China, National Natural Science Foundation of China 
(EESDC, 2011). This data was sampled, analyzed and mapped 
during the Second National Soil General Survey (SNSGS) 
during 1979 to 1985. Although the soil properties are very 
detailed, the scale is not accurate enough. One larger scale 
  
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