different interpretation of data, the missing and uncertainty of data, or even
no-available of data (Janssen et al. 2009). As the spatial data related to agricultural
systems are collected across different scientists, the integrating data is critical to
overcome these problems. Many other techniques have been developed to improve
the interoperability in multiple GIS database from sematic heterogeneity, schematic
heterogeneity, and syntactic heterogeneity (Bishr 1998). We believe the barriers to
use multiple GIS database in agriculture will become more trivial in the near future.
The SEAMLESS (System for Environmental and Agricultural Modeling: Linking
European Science and Society) is a research project that aims to build a computerized framework to address economic, environmental, and social issues from microand macro-level analyses (Van Ittersum and Donatelli 2003).
3.3.3 GIS-Based Modeling in Agricultural Application
In the previous section, we descripted the GIS data and database used to perform
agricultural analysis. These data layer will also be used as input sources for various
models. GISs are incredibly helpful in mapping the current and project future
fluctuations in the environmental change, crop output, and management adjustment
through cross-disciplinary communication.
3.3.3.1 Environment Models Linked to GIS
As a broad definition, agricultural environment here represents the energy and
climate, nutrient and water, as well as the ecosystem service and its environment
impact. Since the mid-1980s, these researches have significantly increased to involve
various spatial data to develop a new generation of environmental simulation models
(Steyaert 1996). Richardson and Wright (1984) modified the weather generator and
used it to generate daily precipitation and evapotranspiration values for agricultural
application. The climate surface generated by Corbett and Carter (1996) was used as
input data into the crop growth model for the risk assessment in different crop types.
Wilson et al. (1993) adopted the Chemical Movement in Layered Soils Model
(CMLS) to combine it with the unique soil and climate characteristics overlaid by
the STATSGO and Montana Agricultural Potentials System (MAPS) database.
Many agricultural decisions and prediction depend on not only the current status
of crop and environment but also the future status of weather condition, soil
moisture, and evapotranspiration. Numerous operational weather and water forecast
model and products are currently available, such as the 3-km 15-hour HighResolution Rapid Refresh (HRRR) derived by the National Centers for Environmental Prediction (NCEP) (Weygandt et al. 2009), Climate Forest system’s (CFS)
56-km 9-month ensemble seasonal forecast, National Water Model’s (NWM)
WRF-Hydro 1-km 10-day hydrological forecast (Gochis et al. 2013), 9-km Soil
3 GIS Fundamentals for Agriculture
31
no-available of data (Janssen et al. 2009). As the spatial data related to agricultural
systems are collected across different scientists, the integrating data is critical to
overcome these problems. Many other techniques have been developed to improve
the interoperability in multiple GIS database from sematic heterogeneity, schematic
heterogeneity, and syntactic heterogeneity (Bishr 1998). We believe the barriers to
use multiple GIS database in agriculture will become more trivial in the near future.
The SEAMLESS (System for Environmental and Agricultural Modeling: Linking
European Science and Society) is a research project that aims to build a computerized framework to address economic, environmental, and social issues from microand macro-level analyses (Van Ittersum and Donatelli 2003).
3.3.3 GIS-Based Modeling in Agricultural Application
In the previous section, we descripted the GIS data and database used to perform
agricultural analysis. These data layer will also be used as input sources for various
models. GISs are incredibly helpful in mapping the current and project future
fluctuations in the environmental change, crop output, and management adjustment
through cross-disciplinary communication.
3.3.3.1 Environment Models Linked to GIS
As a broad definition, agricultural environment here represents the energy and
climate, nutrient and water, as well as the ecosystem service and its environment
impact. Since the mid-1980s, these researches have significantly increased to involve
various spatial data to develop a new generation of environmental simulation models
(Steyaert 1996). Richardson and Wright (1984) modified the weather generator and
used it to generate daily precipitation and evapotranspiration values for agricultural
application. The climate surface generated by Corbett and Carter (1996) was used as
input data into the crop growth model for the risk assessment in different crop types.
Wilson et al. (1993) adopted the Chemical Movement in Layered Soils Model
(CMLS) to combine it with the unique soil and climate characteristics overlaid by
the STATSGO and Montana Agricultural Potentials System (MAPS) database.
Many agricultural decisions and prediction depend on not only the current status
of crop and environment but also the future status of weather condition, soil
moisture, and evapotranspiration. Numerous operational weather and water forecast
model and products are currently available, such as the 3-km 15-hour HighResolution Rapid Refresh (HRRR) derived by the National Centers for Environmental Prediction (NCEP) (Weygandt et al. 2009), Climate Forest system’s (CFS)
56-km 9-month ensemble seasonal forecast, National Water Model’s (NWM)
WRF-Hydro 1-km 10-day hydrological forecast (Gochis et al. 2013), 9-km Soil
3 GIS Fundamentals for Agriculture
31