3.3.3.3 Agricultural Management Models Using GIS
Improving agricultural efficiency is an important issue for the sustainability in
agriculture for the entire world, which needs greater use of information technology
on the farm (Nishiguchi and Yamagata, 2009). An important concept of sustainable
agriculture is to model the farmers’ practice to improve the resource efficiency and
farming profit (Rao et al. 2000). The GIS technology provides the potential to help
farmers to determine the relationship between management and production for
predicting yield with the consideration of spatial and temporal difference (Kalita
et al. 1992; Rao et al. 2000).
The EPIC model was developed to simulate the impact of different agricultural
management practices on crop yield and nutrient loss and pesticide (Williams et al.
1983; Xie et al. 2015). Other commonly used models to assess the agricultural
management practices include the SWAT (Soil and Water Assessment Tools),
AGNPS (Agricultural Nonpoint Source), HSPF (Hydrological Simulation
Program-FORTRAN), APEX (Agricultural Policy/Environment eXtender),
GLEAMS (Groundwater Loading Effects of Agricultural Management Systems),
PLOAD (Pollution Loan), USLE (Universal Soil Loss Equation), etc. (Xie et al.
2015). Although many models incorporate spatial data, few agricultural producers
are utilizing the powerful analytical power of GIS due to the “difficulty” in
informing farming community with the real-time data. This transformation is necessary and achievable through data-driven spatial decision support systems which
push the agro-informatics from “lab” to “land.”
3.3.4 Decision Support System
3.3.4.1 Traditional Decision Support Systems
With a given climate and weather condition, different crop production/management
practices, such as the introduction of later-maturing crop varieties or species,
switching cropping sequences, sowing earlier, adjusting timing of field operations,
conserving soil moisture through appropriate tillage methods, improving irrigation
efficiency, and changing pest management practices, will have different effects on
crop yield and environmental footprint. An integrated system with advanced remote
sensing and GIS as well as the artificial techniques leads to an intelligent system to
help local farmers and policy planners in making complex practice decisions. The
traditional decision support systems (DSS), such as APSIM (Agricultural Production
Systems sIMulator) and DSAT (Decision Support System for Agrotechnology
Transfer), provide the crop simulation scenarios to finalize farmers’ practices. For
example, the irrigation schedule, sowing time, and fertilize dose can be simulated
and best adopted for better crop yields.
The spatial decision support system (SDSS) was designed by integrating the
spatial component to the traditional DSS which can overlay all the spatial datasets
3 GIS Fundamentals for Agriculture
33
Improving agricultural efficiency is an important issue for the sustainability in
agriculture for the entire world, which needs greater use of information technology
on the farm (Nishiguchi and Yamagata, 2009). An important concept of sustainable
agriculture is to model the farmers’ practice to improve the resource efficiency and
farming profit (Rao et al. 2000). The GIS technology provides the potential to help
farmers to determine the relationship between management and production for
predicting yield with the consideration of spatial and temporal difference (Kalita
et al. 1992; Rao et al. 2000).
The EPIC model was developed to simulate the impact of different agricultural
management practices on crop yield and nutrient loss and pesticide (Williams et al.
1983; Xie et al. 2015). Other commonly used models to assess the agricultural
management practices include the SWAT (Soil and Water Assessment Tools),
AGNPS (Agricultural Nonpoint Source), HSPF (Hydrological Simulation
Program-FORTRAN), APEX (Agricultural Policy/Environment eXtender),
GLEAMS (Groundwater Loading Effects of Agricultural Management Systems),
PLOAD (Pollution Loan), USLE (Universal Soil Loss Equation), etc. (Xie et al.
2015). Although many models incorporate spatial data, few agricultural producers
are utilizing the powerful analytical power of GIS due to the “difficulty” in
informing farming community with the real-time data. This transformation is necessary and achievable through data-driven spatial decision support systems which
push the agro-informatics from “lab” to “land.”
3.3.4 Decision Support System
3.3.4.1 Traditional Decision Support Systems
With a given climate and weather condition, different crop production/management
practices, such as the introduction of later-maturing crop varieties or species,
switching cropping sequences, sowing earlier, adjusting timing of field operations,
conserving soil moisture through appropriate tillage methods, improving irrigation
efficiency, and changing pest management practices, will have different effects on
crop yield and environmental footprint. An integrated system with advanced remote
sensing and GIS as well as the artificial techniques leads to an intelligent system to
help local farmers and policy planners in making complex practice decisions. The
traditional decision support systems (DSS), such as APSIM (Agricultural Production
Systems sIMulator) and DSAT (Decision Support System for Agrotechnology
Transfer), provide the crop simulation scenarios to finalize farmers’ practices. For
example, the irrigation schedule, sowing time, and fertilize dose can be simulated
and best adopted for better crop yields.
The spatial decision support system (SDSS) was designed by integrating the
spatial component to the traditional DSS which can overlay all the spatial datasets
3 GIS Fundamentals for Agriculture
33