recognized and continues to increase as the advanced technology accelerates the
opportunities for the agricultural communities to acquire, manage, and analyze the
spatial data from the farmer level to global level.
3.3.1 GIS Mapping and Analytical Techniques
One distinguished advantage of GIS from other computer technologies is that it
enables various data from diverse sources to be integrated and analyzed to its
powerful analytical functionality (Luzio et al. 2017). The use of GIS techniques in
farm-related practices has evolved in the past decades at regional or national scale.
Combined with remotely sensed data, GIS techniques has been used to support land
capability assessment (Corbett and Carter 1996), crop condition and yield (Wade
et al. 1994; Carbone et al. 1996), flood and drought (Yagci et al. 2011; Yu et al.
2013), soil erosion and condition (Narasimhan and Srinivasan 2005), nonpoint
source pollution (Fitzhugh and Mackay 2000; Tang 2015), pest infestation and
control (Bellotti et al. 1999; Joshi et al. 2004), and climate change impacts
(Di et al. 1994; Morton 2007).
The ability of GIS to map and analyze the agricultural environments has proved to
be very valuable to the farming industry by combining various maps and satellite
information sources (Sood et al. 2015). Long et al. (1991) examined the potential of
GIS methods, combining with the global positioning system (GPS), in soil surveys
and found these methods more efficient than traditional mapping. Loveland et al.
(1995) generated a multilevel digital geographically reference land cover maps for
the contiguous USA based on new developed GIS method by the United States
Geological Survey (USGS) and the University of Nebraska-Lincoln (Olson and
Olson 1985). These combination generated eco-regional maps to describe the land
cover characteristics at continental and global scales.
The development of digital format in environment data, such as climate and
land cover, has promoted a new agricultural application in the past decades. For
example, the African climate surface was combined with the cumulative seasonal
erosion potential (CSEP) to derive a climate index of erosion potential (Kirkby and
Cox 1995). The GIS-based EPIC model was used to simulate the national spatial
crop yield based on the digitized climate, soil, irrigation, and topography data
(Priya and Shibasaki 2001). Wratt et al. (2006) integrated climate data with
GIS-based soil and crop information to reduce risk in agricultural decisionmaking. A systematic approach to develop practical solutions was proposed to
adapt agricultural to climate change using analogue locations in Eastern and
Southern Africa (Trincheria et al. 2015).
Generally, the GIS technology can be used to synthesis and integrate more spatial
data than previous research or application in the pre-computer era. The shift from the
traditional spatial delineation or analysis of agroecological and agroclimatological
studies toward user-specific-driven, together with the big data revolution, presents
28
J. Tang
opportunities for the agricultural communities to acquire, manage, and analyze the
spatial data from the farmer level to global level.
3.3.1 GIS Mapping and Analytical Techniques
One distinguished advantage of GIS from other computer technologies is that it
enables various data from diverse sources to be integrated and analyzed to its
powerful analytical functionality (Luzio et al. 2017). The use of GIS techniques in
farm-related practices has evolved in the past decades at regional or national scale.
Combined with remotely sensed data, GIS techniques has been used to support land
capability assessment (Corbett and Carter 1996), crop condition and yield (Wade
et al. 1994; Carbone et al. 1996), flood and drought (Yagci et al. 2011; Yu et al.
2013), soil erosion and condition (Narasimhan and Srinivasan 2005), nonpoint
source pollution (Fitzhugh and Mackay 2000; Tang 2015), pest infestation and
control (Bellotti et al. 1999; Joshi et al. 2004), and climate change impacts
(Di et al. 1994; Morton 2007).
The ability of GIS to map and analyze the agricultural environments has proved to
be very valuable to the farming industry by combining various maps and satellite
information sources (Sood et al. 2015). Long et al. (1991) examined the potential of
GIS methods, combining with the global positioning system (GPS), in soil surveys
and found these methods more efficient than traditional mapping. Loveland et al.
(1995) generated a multilevel digital geographically reference land cover maps for
the contiguous USA based on new developed GIS method by the United States
Geological Survey (USGS) and the University of Nebraska-Lincoln (Olson and
Olson 1985). These combination generated eco-regional maps to describe the land
cover characteristics at continental and global scales.
The development of digital format in environment data, such as climate and
land cover, has promoted a new agricultural application in the past decades. For
example, the African climate surface was combined with the cumulative seasonal
erosion potential (CSEP) to derive a climate index of erosion potential (Kirkby and
Cox 1995). The GIS-based EPIC model was used to simulate the national spatial
crop yield based on the digitized climate, soil, irrigation, and topography data
(Priya and Shibasaki 2001). Wratt et al. (2006) integrated climate data with
GIS-based soil and crop information to reduce risk in agricultural decisionmaking. A systematic approach to develop practical solutions was proposed to
adapt agricultural to climate change using analogue locations in Eastern and
Southern Africa (Trincheria et al. 2015).
Generally, the GIS technology can be used to synthesis and integrate more spatial
data than previous research or application in the pre-computer era. The shift from the
traditional spatial delineation or analysis of agroecological and agroclimatological
studies toward user-specific-driven, together with the big data revolution, presents
28
J. Tang