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“International Journal for Science and Emerging ISSN No. (Online):2250-3641
Technologies with Latest Trends” 5(1): 1-14 (2013) ISSN No. (Print): 2277-8136
Precision Farming Technology, Opportunities and Difficulty
Amir Abbas Bakhtiari* and Amir Hematian**
Ph.D. Candidate in Precision Agriculture,
Agricultural Machinery Department, University of Tabriz, Iran.*
Young Researchers Club, Kermanshah Branch,
Islamic Azad University, Kermanshah, Iran. **
(Received 15 December 2012 Accepted 1 January 2013)
Abstract-Precision farming is a data-based management and a way of agricultural production, which
takes into account the in-field variability. Precision agricultural technologies, such as Global Positioning
Systems, Geographic Information Systems, remote sensing, yield monitors, mapping, and guidance
systems for variable rate application, made it possible to manage within-field variation on large scales.
The objectives of this perusal are to collect information about precision farming technology and its
opportunities, challenge and difficulty. Results of the study show that there are many opportunities and
challenges for adoption of precision agricultural techniques around the world. Although the form of
precision practices may be different from one place to another place, depending upon the creative mindset
of farmers, practitioners, scientists and consultants local to the area of interest.
Keywords: Precision agriculture (PA), adoption, challenge, technology.
1. INTRODUCTION
Precision Agriculture (PA) or Precision Farming
(PF) has witnessed unprecedented growth in the
last decade, especially in countries such as the
United States, Germany, Canada and others.
While the rest of the world has been relatively
slow in embracing precision agricultural
practices, the change is coming. From Australia
to Zimbabwe, PA is growing across the globe.
This is clearly evident by the number and
diversity of manuscripts published in the area of
PA in international journals and also by the
variety of papers presented at the major
international conferences on PA from different
countries around the world. Publications and
presentations may not be a scientific metric to
account for the geographical spread but it indeed
is a reflection of changing times and the
proliferation of PA techniques and concept
(Khosla, 2010). Interestingly, there are a number
of definitions and concepts that can be found in
literature pertaining to PA. The one that is most
commonly cited and used by practitioners is the
one that consist of several “R”s of Precision
Agriculture. Robert et al. (1994) proposed three
“R”s, the Right time, the Right amount and the
Right place. Later, the International Plant
Nutrition Institute added another “R” to that list,
“the Right Source”, and more recently, Khosla
(2008) proposed an additional “R”, the Right
manner.
Generally, three major components of precision
agriculture are information, technology and
management. Base of these three principles, we
can defined PA in different ways. Precision
farming is information-intense. A lot of data are
required to generate treatment maps and many
techniques are under development or have been
developed during the last decade.
Precision Agriculture is a management strategy
that uses information technologies to bring data
from multiple sources to bear on decisions
associated with crop production (National
Research Council, 1997). It allows for the
management of spatial and temporal variability
within a field, reduction of costs, improvement
of yield quantity and quality and reduction of
environmental impacts (Reichardt and Jurgens,
2008).
In other hand, PA can be defined as a set of
technologies that have helped propel agriculture
into the computerized information-based world,
and is designed to help farmers get greater
control over the management of farm operations
(Gandonou, 2005). Precision farming is based
on several separate technologies, yet
interdependent parts, which together form the
basis for individual management systems.
Precision farming technology (PFT) is designed
to provide information and data to assist farmers
when making site-specific management (SSM)
decisions. Demand for information about
technologies to manage agricultural production
systems increased with the advent of yield
monitors and global positioning systems, etc.,
and improvements in computing power and data
management (Swinton and Lowenberg-DeBoer,
1998; Griffin et al., 2004). The following section
outlines the most prevalent systems that are used
in PA.
2. SPATIAL DATA MANAGEMENT
Field is not one uniform unit. In principle, all
parameters and farming practices can be a part
of a site-specific resource management.
2.1. Geographical Positioning
Precision farming, as we know it today, started
in the late 1980s with the advent of the global
positioning system (GPS) into the agricultural
sector, when it was realized how much farming
data were spatially related (Blackmore, 2003).
Geographical positioning with GPS or DGPS
(Differential GPS) is the “backbone” of most PF
practices. It is essential for most site-specific
practices that a specific action is recorded and
positioned in order to use the information for
future treatments. With a positioning system it is
possible to gather information site-specifically.
The global positioning system (GPS) makes
possible to record the in-field variability as
geographically encoded data (Nemenyi et al.,
2003). As an alternative to the GPS-system is
developed the Russian GLONASS satellite
system, which in principle is similar to the GPS-
system and based on several satellites.
The Global Positioning System (GPS) is one of
the key technologies that make precision
agriculture possible. GPS receivers with
sufficient accuracy for yield mapping, grid
sampling, variable rate application, and other
precision activities are available at moderate
cost. GPS receivers, which provide accurate,
geo-referenced position information, are often
used with combine yield monitors, scouting
equipment, or variable rate application
machinery (Stombaugh et al., 2002). Several
factors, including the satellite and receiver clock
limitations, ephemeris variation, satellite
configuration, atmospheric interference,
multipath and selective availability (SA) can
cause errors in GPS position information.
The most common way to counteract GPS errors
is by using Differential GPS or DGPS. The
Differential Global Positioning System (DGPS)
is an integration of space-based and ground-
based segments that together comprise a radio-
navigation facility (Shearer et al., 1999). In a
DGPS system, a GPS receiver is placed at an
accurately known location. This base station
receiver will calculate GPS errors by comparing
its actual location to the location computed from
the GPS signals. This error information is sent to
the rover receiver, which uses it to correct the
position information it computes from the GPS
signals (Stombaugh et al., 2002). DGPS
corrections can be broadcast by tower-based or
satellite-based systems (Figure 1).
Figure 1. DGPS tower-based (left) and satellite-
based systems (right).
By using the geographical positioning system it
is possible to locate the information for further
analysis and make a visual presentation in a
GIS-system.
2 Bakhtiari and Hematian
2.2. Geographical Information System
In order to save your geographical position and
site-specific field characteristics it is an
indispensable to have a Geographical
Information System (GIS) platform to save and
handle spatial information. The GIS created by
computing background makes possible to
generate complex view about our fields and to
make valid agro-technological decisions (Pecze,
2001). Most GIS-systems are reliable but also
fairly expensive depending on the features and
capabilities of the program.
A GIS is a set of computer tools that allows one
to work with data that are tied to a particular
location or spatially mapped area on the earth. A
GIS is a database that is specifically designed to
work with map data (Price, 2006). GIS is the
merger of attribute and a geographic database.
This allows for multiple detailed data to be
graphically depicted on a map and utilized for
decision making. Farmers have long since
utilized maps for data collection and decision
making. However, the difference with applying
the advanced technology of GIS is that the GIS
map exhibits “intelligence” where you can ask a
question and get an answer. GIS technology
application is influencing decision making
because it avoids the shortcomings of traditional
maps, allows for the rapid computer analysis,
and applies sophisticated data structures. GIS is
used as a data synthesizing and decision-support
tool in many fields (Mickle, 2009). GIS is a
spatial decision support system not a decision
making system.
A GIS software program enables the farmer to
assemble and organize different sources of site-
specific and geographical data information in
various layers. Each layer in GIS is termed
“coverage” and consisting of topologically
linked geographic features and associated data.
2.3. Remote Sensing
Remote sensing is the science and art of
acquiring information about the earth‟s surface
without actually coming in contact with it. This
is done by recording energy, which is either
reflected or emitted from the earth‟s surface.
The information recorded is then processed and
analyzed, and the information is used to develop
a prescription map that can be used in a variable
rate application (Grisso et al., 2011). This
technology can be used to obtain various layers
of information about soil and crop conditions. It
allows detection and/or characterization of an
object, series of objects, or the landscape
without having the sensor in physical contact
with the soil. It uses aerial or satellite imaging to
sense crop vegetation and identify crop stresses
and injuries, or pest infestation. So far satellite
images have mainly been used to produce maps
and predict yield potentials on large areas. With
higher resolution and near-infrared and red-
sensing it is possible to map the relative
reflectance from the crop in a small scale
(Adamchuk et al., 2003).
Technological advances are broadening the use
of remote sensing in PA, as follows: (a)
Satellites such as Quick Bird (QB) provide high
spatial resolution images of around 2.0–2.4 m in
multi-spectral (DIGITAL GLOBE Corp, 2010);
(b) Conventional airplanes images can be used
to map the spatial variability of biotic and
abiotic parameters on agricultural plots at spatial
resolutions of 0.25–0.50 m (STEREOCARTO,
2010); (c) Unmanned aerial vehicles (UAV)
flying at low altitude have been developed by
commercial companies (UAV, 2010) to provide
high spatial resolution images, with the
advantage of autonomous management and
ability to work in cloudy days. Satellite images
are in principle commercially available but the
images require a further processing into a GIS-
format. The satellite images can be provided
with resolutions between 1 and about 100 meters
per pixel, depending on the image and number
of colors. In that respect they are not as detailed
as aerial photos (Gomez-Candon et al., 2011).
The size of the satellite image are typical
relatively large, implying that it is very costly
for the individual medium sized farm holding to
utilize the images.
Bakhtiari and Hematian 3
3. SOIL SAMPLING AND MAPPING
A variety of different soils exist across any
given field. Also, a field may contain a low level
of one nutrient and a high level of another
nutrient. Soil sampling and testing is the best
way to identify these differences and to adjust
liming and fertilization practices.
Both grid and directed sampling are used to
describe soil properties for management of
variable rates of fertility. Grid soil sampling
involves sampling at points on a square grid
throughout a field. An alternative to grid
sampling is directed sampling, which involves
dividing a field into regular or irregular
management zones based on features such as
soil type, topography, or past yield performance.
In either system, collect at least five or six
subsamples from each cell or zone and mix them
into one sample container (Stombaugh et al.,
2001). The soil samples are collected from each
section and then forwarded to a laboratory for
analysis often guided by the advisory service. It
is absolutely necessary to take care to assure that
the soil sample you send to the laboratory
accurately represents the area sampled (Thom et
al., 1997). When soil fertility data is returned
from the soil testing lab, you can enter the data
into your mapping program to create fertility
maps.
Gathering of soil data from soil samples to
create a soil map is a relatively expensive and
time consuming task but have also valuable
information for later treatments with fertilizers
and design of decision support systems. A soil
analysis can identify some of the limiting factors
regarding yield and growth potential for the
specific crop on a specific spot in the field. Soil
test results should be included in a record system
for each production field on a farm, along with
the amounts of lime and fertilizer applied each
year, the crops grown, and the yields obtained
(Thom et al., 2000). In an effective sampling
program, each production field should be tested
at least every three to four years.
3.1. Soil Electrical Conductivity Mapping
Soil conductivity mapping is a simple and cheap
method to quickly characterize soil differences
within a field. Soil conductivity correlates to soil
properties, which affect crop productivity such
as soil texture, drainage conditions, organic
matter and salinity. The spatial soil conductivity
mapping may enable the farmer to explain yield
variations, pH-levels and water holding capacity
in sub-areas of the field. In this respect,
conductivity mapping could enable the farmer to
reduce costs for liming, irrigation for certain
crops or nitrogen application. Moreover soil
conductivity mapping could be valuable
information to direct soil samples on the field in
management units to minimize the total number
of samples on the field (Nehmdahl and Greve,
2001). There are currently two methods to
measure soil conductivity; electromagnetic
induction (EMI) and contact electrode. Both
systems show similar results (Doerge et al.,
2002). Corwin and Lesch (2005) provide a
comprehensive review of the historical
development of EC measurements in agriculture
as well as a discussion of the basic principles,
including general theory and factors influencing
the EC measurement; different geophysical
techniques for measuring EC; mobilized EC
measurement equipment and applications to site-
specific crop management (SSCM).
4. YIELD MONITORING AND MAPPING
Yield monitors and maps form a very important
part of a PF system. Yield monitors are a logical
first step for those who want to begin practicing
SSCM. The yield monitor which predicting
yield from other variables, is intended to give
the user an accurate assessment of how yields
vary within a field. With a yield monitor, a
producer also can conduct on-farm variety trials
or weed control evaluations without the need of
a weigh wagon. Such on-farm comparisons help
producers fine-tune crop production practices to
their soils. Yield monitors are a combination of
several components. They typically include
several different sensors and other components,
including a data storage device, user interface
(display and key pad), and a task computer
located in the combine cab, which controls the
integration and interaction of these components.
The sensors measure the mass or the volume of
grain flow (grain flow sensors), separator speed,
ground speed, grain moisture, and header height.
The sensors are interfaced with both analog to
digital and direct digital inputs. Yield is
determined as a product of the various
parameters being sensed (Shearer et al., 1999).
4 Bakhtiari and Hematian
Grain volume flow sensor is prone to errors on
slopes as the grain can slip to one side and block
the beam. By adding slope sensors, this has been
accounted for (Moore 1998). Yield monitoring
values can be exported to a personal computer
and stored in nonvolatile memory for further
analysis or printing via specialized software
packages. Yield data were recorded and
analyzed over ten years, resulting in a rich data
set for both spatial and temporal trend analysis
and management information.
According to Blackmore et al. (1996) six main
sources of errors of yield measurement that have
been identified are unknown crop width entering
the header during harvest, time lag of grain
through the threshing mechanism, the inherent
„wandering‟ error from the GPS, surging grain
through the combine transport system, grain
losses from the combine, and sensor accuracy
and calibration. Yield mapping is one
application of data analysis where the process of
observation, interpretation, evaluation and
implementation can be applied. Yield mapping
software can be purchased as a part of an overall
investment in a combine harvester or separately.
To display the maps as contours the (effectively)
randomized combine data are converted onto a
regular grid. As these grid points rarely coincide
with existing data points, a technique called
interpolation is used to estimate the value from
surrounding values. There are many forms of
interpolators, but the two most commonly used
are called Inverse Distance to a Power (usually
Inverse Squared) and Kriging (Blackmore,
2003).
5. VARIABLE RATE APPLICATION AND
TECHNOLOGY
The concept of PF has the potential of aiding
farmers to use nutrients and chemicals in an
efficient way according to crop needs at a given
site on the field and thereby make farming more
economically competitive and sustainable.
Variable Rate Application (VRA) of nutrients
has the potential to improve the specific crop
quality in a number of rotations, for example to
obtain certain protein content in cereals; in
particular wheat for bakery flour and barley for
malting.
Variable Rate Technology (VRT) is the
equipment with the ability to change the
application rate and mixture as it moves across
the field. VRT adjust pesticide, herbicide and
seeding application rates to match soil potentials
or problem areas (Adamchuck and Mulliken,
2005). Robert s et al. (2001) reported that N
losses to the environment were lower with VRT
than with uniform rate application (URA),
except on fields with little spatial variability.
Gandonou (2005) showed that variable rate
fertilizer application results in an increased
yield.
The two basic technologies for VRA are map-
based and sensor-based. Map-based VRA
adjusts the application rate based on an
electronic map, also called a prescription map.
Using the field position from a GPS receiver and
a prescription map of desired rate, the
concentration of input is changed as the
applicator moves through the field. Canopy
management can be conducted with a
combination of crop sensors and real time
modeling. Sensor-based VRA requires no map
or positioning system. Sensors on the applicator
measure soil properties or crop characteristics
“on the go”. Based on this continuous stream of
information, a control system calculates the
input needs of the soil or plants and transfers the
information to a controller, which delivers the
input to the location measured by the sensor
(Grisso et al., 2011). Because map-based and
sensor-based VRA have unique benefits and
limitations, some SSCM systems have been
developed to take advantage of the benefits of
both methods.
6. SECTION CONTROL
Precision agriculture technology has evolved in
such a manner that it provides farmers with new
and innovative ways to possibly improve
profitability. One of these ways is a new
approach to the application of liquid chemicals
and other inputs known as automatic section
control. Automatic section control on a sprayer
has the ability to selectively manage input
application across the spray boom. This
technology utilizes a global positioning system
(GPS) to locate the position of the sprayer
within the field, and then records the areas
covered. If the sprayer traverses an area
previously covered it can automatically turn the
Bakhtiari and Hematian 5
appropriate section off, therefore eliminating
over application. In addition, automatic section
control can manage chemical application in
undesirable areas such as point rows, waterways,
and during headland turns. The largest benefit
associated with automatic section control is the
reduction in overlapped areas especially
prevalent on irregular shaped fields. As a result,
this new technology has the potential to increase
profits due to reducing input costs.
Environmental benefits are also possible due to
the ability to manage buffer zones and protect
sensitive areas in and around the field.
Furthermore, improved efficiency can occur if
coupled with a navigational aide such as lightbar
or auto-steer (Shockley, 2010). Once a prototype
was created and farm trials conducted, Dillon, et
al. (2007) examined the economic implications
of utilizing automatic section control. Mooney,
et al. (2009) conducted the most recent
economic analysis and determined automatic
section control became profitable at input saving
levels of 11% or above.
7. GUIDANCE SYSTEMS
There are two classifications of guidance
systems for agricultural production: guidance
aides, and autonomous systems. Guidance aides
are devices that provide guidance information to
the operator and still require the operator to fully
control the machines operations. The intent of
the autonomous system is to free up the operator
of the machine from the guidance task and thus
improve operating efficiency.
7.1. Guidance Aid
The term guidance aide refers to devices that
provide guidance information to the operator but
do not attempt to replace the operator (Ima and
Mann, 2004). One of the most broadly adopted
and more recently developed methods to reduce
guidance error is the lightbar technology. The
main function of lightbar is to assist the machine
operator in driving; thus decreasing operator
fatigue and minimizing application errors of
overlaps and skips. Most lightbar systems
include a Differential GPS (DGPS) receiver and
antenna, some kind of computer or
microprocessor, and a lightbar or graphics
display (Stombaugh, 2002).
From the study of cost maps Kayrouz (2009)
indicated that inaccuracy of machinery
movement, whether in the application stage or
the harvesting stag e is very costly. Lightbar
gives a visual guide that changes colors as the
operator veers off the desired path. Previous
technologies were developed with the idea of
achieving the same goal, for example, planter
markers and foam markers. Both of these
previous technologies are becoming shadowed
by lightbar and its effectiveness in improving
farm profitability. As a result, foam and planter
markers are becoming an obsolete technology
(Kayrouz, 2009).
Buick and White (1999) explained several
reasons why lightbar is replacing foam markers.
They mentioned, lightbar is more reliable and
more accurate than foam markers, lightbar
allows accuracy at higher speeds, lightbar is a
possibility with spinner spreaders, lightbar is
easy to use, lightbar provides effective guidance
over growing crops, lightbar allows operation
when visibility is poor, lightbar is less affected
by weather, and lightbar has lower recurring
costs. Also Griffin et al. (2005) indicates that
lightbar is commercially available and promises
increased efficiency of field operations.
7.2. Autonomous Field Machinery
According to Haapala et al., (2006) as human
capacity to handle simultaneous information is
limited new intelligence has been developed
between the user and the system to be
controlled. The intelligent layer decides which
kind of information is passed on for the user and
which part is used for other purposes.
Development stages of the Human-Machine
Interface are shown in Figure 2. Introduction of
small, light-weight robots that can perform
agricultural tasks autonomously may prove to be
a realistic option for farmers in the future. These
robots will likely operate in fleets and utilize
intelligent controls to cooperate with each other
to perform tasks such as scouting for weeds and
diseases, yield and field mapping, and plant
specific operations like sowing and fertilizing
(Shockley, 2010). The role of automation should
be designed so that automation helps the
individual user to simultaneously avoid stress,
and increase efficiency of work.
6 Bakhtiari and Hematian
Figure 2. Human-Machine Interface
development stages (Haapala et al., 2006).
Recently, engineers have developed various
autonomous machines capable of agricultural
production. Several studies have investigated
the mechanization and the design of autonomous
robots (Blackmore and Blackmore, 2007;
Vaugioukas, 2007; Vaugioukas, 2009). The
majority of studies have focused on the
advancement of autonomous platforms with
regard to accuracy, steering, and performance
(van Henten et al., 2009; Bak, 2004). Many
research conducted in different methods on
automated guidance and its algorithm. Bakhtiari
et al. (2011) formulated an ant colony
optimization (ACO) algorithm which can
successfully generate routes that can be followed
by farm machinery equipped with automation
systems such as auto-steering navigation
systems. Some other advancements on robotic
equipment navigation perception technology
researches include the work reported by
Subramanian et al. (2006) who used a
monocular camera to observe citrus grove
alleyways, and Nara and Takahashi (2006) who
applied a vision system to detect obstacles. Lee
and Ehsani (2008) investigated the accuracy of
two common laser range finder units. Other
studies have concentrated on autonomous weed
detection and management (Griepentrog et al.,
2009; Gottschalk et al., 2009; Pederson et al.,
2007). An overview of the activities in weed
control is given in Slaughter et al. (2008). Also
the study of agricultural robot application for
plant production presumably started with a
tomato harvesting robot (Kawamura et al.,
1984).
8. DISCUSSION
The use of new precision farming technology
allows growers to micro-manage individual
grids or management zones in a specific field
according to its unique production capabilities.
In summary, the implementation of PF schemes
offers the possibilities for farmers to:
Be more economically competitive
Improve the sustainability of farming
Improve technical functionality
Improve the crop quality and produce
more homogeneous qualities
Secure traceability of products through
documentation by the system
Be better adapted to comply with
regulations regarding use of nutrients and
chemicals.
The farming operation is one that involves a
significant level of risk and uncertainty.
Reduction in government price supports,
weather variability, and other uncontrollable
environmental factors such as insect and disease
infestation contribute to the increased concern of
risk management. The term risk refers to the
variability of the outcomes of some uncertain
activity. Consequently, risk management
involves choosing among alternatives to reduce
the effects of risk. It is hypothesized by
Lowenberg-DeBoer (1999) that precision
agriculture technologies are useful in risk
reduction. The application of such technologies
provides producers with more and better
information and increased control of crop
growing conditions. Several studies have
demonstrated the economic and ecological
benefits of PF tools over conventional
techniques (Sylvester-Bradley et al., 1999; Silva
et al., 2007; Takacs-Gyorgy, 2008). Although,
McBratney et al. (2005) conclude that drive
assist systems are the only success products in
PA since they are not requiring the farmer to
make additional decisions but help her/him to
reduce them. Drive assist systems give benefits
for the farmer since they are easy-to-use and
they solve actual important problems, and they
return investment costs immediately.
Nowadays environment protection is more and
more in focus. Most of the papers reviewed
Bakhtiari and Hematian 7
indicate that PA can contribute in many ways to
long-term sustainability of production
agriculture, confirming the intuitive idea that PA
should reduce environmental loading by
applying fertilizers and pesticides only where
they are needed, when they are needed (Wang et
al., 2003; Bonham and Bosch , 2001;
Timmermann et al., 2001; Hatfield, 2000).
The very nature of precision farming and the
GPS-system is to trace each action on the field.
As a first objective this feature has enabled the
farmer to conduct measurements of variable
application of inputs like fertilizers and
pesticides. In theory, PF also enables retailers
and the final consumer to trace and control each
action on the field.
8.1. Adoption
PF is comprised of numerous component
technologies that farmers may adopt as a system.
Adoption is a learning process where
information needs to be collected, integrated and
evaluated (Pannell et al., 2006). Organizational
arrangements (Leeuwis, 2004) and networks
(Allaire and Boiffin, 2004) are also addressed in
adoption theory. Also the agricultural advisory
centers have an important role in
implementation and adoption of PF.
To date, we are in a stationary state between the
early adopters and the early majority, mainly
since yield increases aren‟t well enough
documented to cover the cost of equipment. The
adoption of PFTs is likely to follow a normal
distribution with the innovators and early
adopters as the first to adopt the technology and
then later on will the majority of farmers follow
up (Lamb et al., 2008). The adoption of PA is
currently in a stationary phase between the early
adopters and the majority.
In other hand, Kutter et al. (2011) explained that
the adoption of PA tools is related to farm size,
forms of communication and co-operation. Also
Torbett et al. (2007) found that land tenure, age,
and computer use ability had a significant
impact on farmer„s perceptions of the
importance of PFTs. Larger farms are able to
spread PFT costs over more production acres
(Solano et al., 2003). Older farmers may be less
likely to invest resources in obtaining PF
information without the certainty of receiving
returns on their investment in the short run
(Banerjee et al., 2008). Higher educational levels
give farmers higher analytical ability to use
information and translate it into a useful input
for their decision-making processes (Just et al.,
2002). Daberkow and McBride (2003) reported
that mainly young, well-educated full-time
farmers operating large farms are interested in
PA.
Information plays an important role in the
adoption of PFTs. According to Jenkins (2009),
producers learning about PF tend to use multiple
sources of information to increase their
knowledge about precision agriculture. Arnholt
(2001) found that the single most important
motivational factor driving PF adoption is to
increase profits. A common conclusion is the
fact that the farmers want to use the information
and data gained from PF adoption to make better
informed management decisions thereby
increasing their farm business profits.
Finally, a quodlibet that needs to be considered
is mentioned by Olson (1998) who argues that
the adoption process for PA is difficult to
predict. It is not a single technology but a suite
of management strategies, technologies, and
practices used to improve agricultural decision
making that can be chosen in many different
combinations of products and services. Farmers
will use them in various combinations
depending on variations in geography,
production systems, and the farmers themselves.
8.2. Challenge
Precision farming seems to be a logical
management tool for farmers, and several
manufactures and research groups have put a lot
of effort into this technology. Despite the big
effort and many promises amongst
manufacturers many farmers are still reluctant
for the following reasons:
PFT has the potential to improve production
efficiency but adds complexity to the decision
making processes because of the large amount of
information to be processed. Thus, the large
amount of information available to farmers
through different PFTs may require guidance on
how this information is incorporated into actual
management plans (Griffin and Lambert, 2005).
A general concern among farmers is the
agronomic decision support, which so far has
been unable to cope with the comprehensive
agronomic data available today. The decision
8 Bakhtiari and Hematian
algorithms in most decision support systems rely
on one or few information sources. However, to
cope with the complexity of the agronomic
process it is necessary to use many
complementary sources of information in order
to carry out precise and site-specific application
of nutrients. Thus farmers who gather data based
on site-specific tools (GPS, yield mapping and
sensors) have a limited number of agronomic
models to evaluate this spatial information and
thereby adapt their decisions within the field
(Thomsen, 2001 as quoted in Pedersen, 2003;
Acock and Pachepsky, 1997). Consequently, the
positive impact of PF on farm economics has not
yet been demonstrated.
PA is capital intensive rather than labor
intensive. One reason that many producers
(especially in developing countries) are reluctant
to get into PA is the cost of the technology.
Specific tools for PF are costly and the
economic benefits are not clear (Bullock, 1998;
Schmerler and Basten, 1999; Swinton and
Lowenberg-DeBoer, 1998). One often
overlooked option is to custom hire the services
rather than purchasing the equipment. Smaller
operations could benefit from custom hire while
larger operations are better off purchasing the
equipment.
Profitability of PF continues to be difficult to
predict and uncertain (Atherton, 1999). Some
studies have recognized that PF has the potential
to be economically profitable, but the
profitability depends heavily on the degree of
spatial variability within the field according to
such attributes as soil types and yield response
variability (Roberts et al., 2000). Although, the
PA profitability review conducted by Lambert
and Lowenberg-DeBoer (2000) found that 73
percent of the studies done on the profitability of
PA concluded that adoption of the technology
was profitable. But in other view, according to
Haapala et al. (2006) usability issues have not
been a central issue in electronics development
in agriculture. Poor experiences of unacceptable
operation could be one reason for the customers
not relying on new electronic control systems
such as those of PFT.
Although yield maps is a well-established and
relatively low cost method to gather data about
field variability, the findings of Goodwin et al.
(2002) indicate that farmers and crop advisors
should be cautious about using yield maps solely
to develop nitrogen application maps. Other
complementary field related data are required to
optimize the application of fertilizers in plants.
PF will not make farming less complex. A
management system like PF which heavily
depends on data, maps and images is likely to
create new concerns about how to communicate
this information between the acting parties and
presumably also the ownership of data and
responsibility of different tasks (Olesen, 2002).
Another general concern among farmers is
hardware and software compatibility and to
choose the right technical systems for
conducting PF (Rehnberg, 2002; Pedersen et al.,
2001). It is important that different technologies,
especially hardware devices, are compatible
with other electronic components and systems.
The PFTs calibrations are very important and
crucial. For example in VRT any change in the
equipment performance (boom pressure or etc.)
or vehicle speed from that of the calibration
results in an application rate different from the
planned rate. Moreover, the environmental
impacts of precision farming are not yet clear
(Stafford, 2000).
There is more to PA than just buying the tools
and technologies. As mentioned, PA has the
potential to improve efficiency but also adds
complexity to the decision making processes. In
all steps of precision agriculture technology
adoption, decision management is a process or
set of processes from incorporation of technical
and economical components. Moreover,
according to Stombaugh et al. (2001) PA‟s
effectiveness is highly dependent on how much
variability exists within your fields and your
ability as a producer to identify and put into use
the best management practices for each field‟s
sub-area.
Finally as discussed above, data collected from
soil sampling, yield monitoring, crop scouting,
remote sensing, and satellite imaging are used to
create maps. Many of these maps can be
overlaid to look at interactions between yield
and topography or yield and soil N content for
example. It is the specific ability to process
multiple layers of spatial data (yield maps, soil
maps, or topography maps) that makes PA a
powerful management and decision tool. The
availability of historical data combined with
multiple layers of information for a farmer
engaged in PA improves the quality of inputs
recommendations and management decisions.
Bakhtiari and Hematian 9
9. CONCLUSION Precision farming is information-intense and
geographical positioning is the backbone and an
essential tool to record all the site-specific
information about the field, weed patches, crop
canopy, soil texture and previous yields. This
study provides an investigation into various
precision agriculture technologies, their benefits
challenges and difficulties. Benefits of PA are
widely agreed. Today, PA enables farmers to
increasingly integrate and take control of the
production process in order to improve the
profitability of the farm operation and reduce
production risk. In spite of its great potential,
there are still a significant number of obstacles
obstructing the full development of the PA
technology and its adoption by a majority of
farmers. Profitability of PA is the largest
concern listed by farmers, equipment
developers, and researchers. In near future, the
replacement of large manned machines with
small autonomous robots will be a paradigm
shift in agricultural production to a small scale
precision farming approach.
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