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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.

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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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