444
16.5.2 Numerical Approximation Models
Numerical approximation models can be applied to describe the climate-driven
impacts of changes in land management on water quality and carbon sequestration.
Each of these approaches has limitations in representing the FEW nexus systems
due to issues such as data availability, incongruence in scales at which data are collected, and processes are modeled, and the ability to represent uncertainty.
16.6 Importance of Computing in Nexus via Case Studies
16.6.1 Case Study: Precision Agriculture
Precision agriculture is a facility scale example of how computing can help scientists
from a FEW Nexus perspective. It is an agricultural decision support system whose
tools allow site-specific (i.e., location-aware) use of water, pesticides, and fertilizers
by measuring crop health, soil nutrients, and moisture. It enables farmers to improve
yields, reduce unnecessary applications of fertilizers and pesticides, preserve natural
resources, and contend with impending weather events (Dixon & McCann 1997;
McBratney et al. 2005; Press, The National Academies 1997).
Apart from the benefit of improving yields, precision agriculture improves the
efficient use of energy and water as well. In agricultural practices, one of the implicit
costs is the cost of energy. From the application of fertilizer and seeds to pumping
irrigational water, energy is widely used for agriculture. Moreover, the indirect costs
of energy use in fertilizer production and desalination of irrigational water also
increase the energy cost.
With precision agriculture, water run-offs are prevented, and water is used only
on the required locations in the field depending on the soil moisture levels measured
by soil sensors. Therefore, by using precision agriculture for food production, not
only the food aspect of FEW nexus is improved but also energy and water are used
more efficiently.
Precision agriculture uses a wide variety of computing tools to achieve its success. As explained earlier, spatial data is collected from a variety of sources including soil sensors installed in the ground, tractors that collect soil samples from a
variety of locations in the field, and unmanned aerial vehicles (UAV) and satellites
that collect remote sensing imagery. For example, Normalized Difference Vegetation
Index (NDVI) imagery, which determines the greenness of plants, is widely used in
the field by precision agriculture applications in large farms. Using these collected
remote sensing data, the locations where fertilizer and irrigations are needed are
determined. Finally, variable rate applicator devices apply fertilizer and water just
as needed.
The key technology that allows these data to be merged is their location information, which, as noted earlier, is made available by Global Navigation Satellite
Systems (GNSS) (Davis et al. 2012; GNSS 2011).
E. Eftelioglu and S. Shekhar
16.5.2 Numerical Approximation Models
Numerical approximation models can be applied to describe the climate-driven
impacts of changes in land management on water quality and carbon sequestration.
Each of these approaches has limitations in representing the FEW nexus systems
due to issues such as data availability, incongruence in scales at which data are collected, and processes are modeled, and the ability to represent uncertainty.
16.6 Importance of Computing in Nexus via Case Studies
16.6.1 Case Study: Precision Agriculture
Precision agriculture is a facility scale example of how computing can help scientists
from a FEW Nexus perspective. It is an agricultural decision support system whose
tools allow site-specific (i.e., location-aware) use of water, pesticides, and fertilizers
by measuring crop health, soil nutrients, and moisture. It enables farmers to improve
yields, reduce unnecessary applications of fertilizers and pesticides, preserve natural
resources, and contend with impending weather events (Dixon & McCann 1997;
McBratney et al. 2005; Press, The National Academies 1997).
Apart from the benefit of improving yields, precision agriculture improves the
efficient use of energy and water as well. In agricultural practices, one of the implicit
costs is the cost of energy. From the application of fertilizer and seeds to pumping
irrigational water, energy is widely used for agriculture. Moreover, the indirect costs
of energy use in fertilizer production and desalination of irrigational water also
increase the energy cost.
With precision agriculture, water run-offs are prevented, and water is used only
on the required locations in the field depending on the soil moisture levels measured
by soil sensors. Therefore, by using precision agriculture for food production, not
only the food aspect of FEW nexus is improved but also energy and water are used
more efficiently.
Precision agriculture uses a wide variety of computing tools to achieve its success. As explained earlier, spatial data is collected from a variety of sources including soil sensors installed in the ground, tractors that collect soil samples from a
variety of locations in the field, and unmanned aerial vehicles (UAV) and satellites
that collect remote sensing imagery. For example, Normalized Difference Vegetation
Index (NDVI) imagery, which determines the greenness of plants, is widely used in
the field by precision agriculture applications in large farms. Using these collected
remote sensing data, the locations where fertilizer and irrigations are needed are
determined. Finally, variable rate applicator devices apply fertilizer and water just
as needed.
The key technology that allows these data to be merged is their location information, which, as noted earlier, is made available by Global Navigation Satellite
Systems (GNSS) (Davis et al. 2012; GNSS 2011).
E. Eftelioglu and S. Shekhar