446
use different kinds of seeds or what yields to expect in different parts of their
fields in different fields. An illustration of such visualizations can be seen in
Sect. 17.6.2.
Finally, it should be noted that precision agriculture is highly dependent on the
decision support component of the data cycle. Farmers rarely deal with, nor are they
interested in the details of data collection, management, mining, etc. but they do
make decisions using projections of future events. Their primary concern is support
for decisions using projections of not only the projected amount of yield but also the
possible alternative types of crops for the next year.
The spatial computing techniques outlined here are also applicable to many FEW
problems, including but not limited to the efficient and sustainable use of resources
by precision agriculture practices, better resource management by virtual water
trading (Oki & Kanae 2004), supply chain relocation (Allan 2003; Melo et al. 2009;
Min & Zhou 2002), etc.
16.6.2 Case Study: Geodesign for Landscape Modeling
Collaborative geodesign (Minnesota 2016) aims to involve stakeholders in landscape design using a tool which interactively lets them test and receive feedback for
different design decisions. The benefit of this approach is that the stakeholders can
immediately see the impact of their design decisions on biophysical and social indicators. Collaborative geodesign is a step forward to initiate discussions among
stakeholders and domain scientists. Thus, the actual parties (i.e., stakeholders) that
are affected by the decisions have the opportunity to communicate their concerns
with the scientific community as well as the policymakers to make better and more
realistic design decisions.
Collaborative Geodesign is a network scale (see Sect. 12.3) example of how
computing tools can be used to improve food and water resources without posing a
negative impact on stakeholders. Basically, it explores a computational framework
that brings stakeholders from a wide range of societal sectors together to tackle the
landscape redesign challenge (Fi. 16.3b). Stakeholders will divide the study area
into partitions and assign management practices to the partitions to improve multiple
ecosystem service objectives (e.g., water quality, soil quality, revenue).
The final land allocation is achieved through an iterative process of design, evaluation, and comparison to find a better balance among these conflicting ecosystem
service objectives.
In the design step, the interface of Geodesign system provides a variety of
supporting data layers to help stakeholders to make design decisions (Fig. 16.3a).
Stakeholders work together to delineate partitions on the interactive touch display and choose the best management practice for each partition
(Slotterback et al. 2016).
In the evaluation step, the Geodesign system involves state-of-the-art soil and
water physics modeling tool (Gassman et al. 2007; Nelson et al. 2009) to generate
E. Eftelioglu and S. Shekhar
use different kinds of seeds or what yields to expect in different parts of their
fields in different fields. An illustration of such visualizations can be seen in
Sect. 17.6.2.
Finally, it should be noted that precision agriculture is highly dependent on the
decision support component of the data cycle. Farmers rarely deal with, nor are they
interested in the details of data collection, management, mining, etc. but they do
make decisions using projections of future events. Their primary concern is support
for decisions using projections of not only the projected amount of yield but also the
possible alternative types of crops for the next year.
The spatial computing techniques outlined here are also applicable to many FEW
problems, including but not limited to the efficient and sustainable use of resources
by precision agriculture practices, better resource management by virtual water
trading (Oki & Kanae 2004), supply chain relocation (Allan 2003; Melo et al. 2009;
Min & Zhou 2002), etc.
16.6.2 Case Study: Geodesign for Landscape Modeling
Collaborative geodesign (Minnesota 2016) aims to involve stakeholders in landscape design using a tool which interactively lets them test and receive feedback for
different design decisions. The benefit of this approach is that the stakeholders can
immediately see the impact of their design decisions on biophysical and social indicators. Collaborative geodesign is a step forward to initiate discussions among
stakeholders and domain scientists. Thus, the actual parties (i.e., stakeholders) that
are affected by the decisions have the opportunity to communicate their concerns
with the scientific community as well as the policymakers to make better and more
realistic design decisions.
Collaborative Geodesign is a network scale (see Sect. 12.3) example of how
computing tools can be used to improve food and water resources without posing a
negative impact on stakeholders. Basically, it explores a computational framework
that brings stakeholders from a wide range of societal sectors together to tackle the
landscape redesign challenge (Fi. 16.3b). Stakeholders will divide the study area
into partitions and assign management practices to the partitions to improve multiple
ecosystem service objectives (e.g., water quality, soil quality, revenue).
The final land allocation is achieved through an iterative process of design, evaluation, and comparison to find a better balance among these conflicting ecosystem
service objectives.
In the design step, the interface of Geodesign system provides a variety of
supporting data layers to help stakeholders to make design decisions (Fig. 16.3a).
Stakeholders work together to delineate partitions on the interactive touch display and choose the best management practice for each partition
(Slotterback et al. 2016).
In the evaluation step, the Geodesign system involves state-of-the-art soil and
water physics modeling tool (Gassman et al. 2007; Nelson et al. 2009) to generate
E. Eftelioglu and S. Shekhar