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BENEFITS OF IMPLEMENTING PRECISION AGRICULTURE TECHNOLOGIES IN NIGERIAN AGRICULTURAL SYSTEM: A REVIEW

Authors:

Abstract

Precision Agriculture (PA) or Information-based Management of Agricultural Production System (IMAPS) came into existence since the mid-1980s as the process through which the right treatment is given to the agricultural process at the right time. PA was employed mainly for fertilizer application to various soil conditions across agricultural fields at the onset. Since then, different use of PA has been engaged in other areas of agriculture such as farming vehicles and implements, autonomous machinery and processes, product traceability, on-farm research, and software for the overall management of agricultural production systems. To describe precision agriculture, most simplified way is the examination of the five "R"s which are the right time, right amount, right place, right source and proper manner of agriculture inputs like-water, fertilizer, pesticide, etc. The primary constraint of agricultural development in Nigeria is the use of inadequate methods of data and information acquisition on agrarian land potential, crops condition, and farming activities. It can, therefore, be concluded that the lack of decision support systems to be a significant barrier to the adoption of PA. Farmers need decision support systems that enable effective decision making based on accurate and timely data.
1st International Civil Engineering Conference (ICEC 2018)
Department of Civil Engineering
Federal University of Technology, Minna, Nigeria
BENEFITS OF IMPLEMENTING PRECISION AGRICULTURE
TECHNOLOGIES IN NIGERIAN AGRICULTURAL SYSTEM: A REVIEW
*Kutigi, I. B.1, Musa, J. J.2, Adeoye P. A.2, Adesiji, R.3 and Obasa, P.2
1. Niger State Fadama Development Project, Off Minna-Paiko Road, Behind Farm Centre, Minna.
2. Department of Agriculture and Bioresources Engineering, Federal University of Technology, P. M. B. 65,
Minna
3. Department of Civil Engineering, Federal University of Technology, P. M. B. 65, Minna
Corresponding Author email: johnmusa@futminna.edu.ng
ABSTRACT
Precision Agriculture (PA) or Information-based Management of Agricultural Production System (IMAPS) came
into existence since the mid-1980s as the process through which the right treatment is given to the agricultural process
at the right time. PA was employed mainly for fertilizer application to various soil conditions across agricultural
fields at the onset. Since then, different use of PA has been engaged in other areas of agriculture such as farming
vehicles and implements, autonomous machinery and processes, product traceability, on-farm research, and software
for the overall management of agricultural production systems. To describe precision agriculture, most simplified
way is the examination of the five “R”s which are the right time, right amount, right place, right source and proper
manner of agriculture inputs like water, fertilizer, pesticide, etc. The primary constraint of agricultural development
in Nigeria is the use of inadequate methods of data and information acquisition on agrarian land potential, crops
condition, and farming activities. It can, therefore, be concluded that the lack of decision support systems to be a
significant barrier to the adoption of PA. Farmers need decision support systems that enable effective decision
making based on accurate and timely data.
Keywords: Agriculture, Precision, GPS, GIS, Tools.
1 INTRODUCTION
Availability of food and other agricultural products in
adequate supply and quality under environmentally safe
conditions and the sustainability of the various resources
involved is of paramount importance to researchers
(Gebbers and Adamchuk, 2010). Precision Agriculture
(PA) or Information-based Management of Agricultural
Production System (IMAPS) came into existence since
the mid-1980s as the process through which the right
treatment is given to the agricultural process at the right
time. Sensitization on the variation in soil and crop
conditions combined with the various forms of
technology such as global navigation satellite systems
(GNSSs), geographic information systems (GISs), and
microcomputers, serve as the primary drivers. PA was
employed mainly for fertilizer application to various soil
conditions across agricultural fields at the onset. Since
then, different use of PA has been engaged in other areas
of agriculture such as farming vehicles and implements,
autonomous machinery and processes, product
traceability, on-farm research, and software for the
overall management of agricultural production systems.
Aside from crop production, PA technology is
employed successfully in viticulture and horticulture,
including orchards, and in livestock production, as well
as pasture and turf management (Gebbers and
Adamchuk, 2010). They further stated that application of
PA ranges from the tea industry in Tanzania and Sri
Lanka to the production of sugar cane in Brazil; rice in
China, India, and Japan; and cereals and sugar beets in
Argentina, Australia, Europe, and the United States. PA
is in three folds that is to optimize the use of available
resources to increase the profitability and sustainability
of agricultural operations. Second, to reduce negative
environmental impact. Third, to improve the quality of
the work environment and the social aspects of farming,
ranching, and relevant professions.
The idea of precision agricultural practice started
several decades ago most especially in developed
countries like the State of Israel, the United States of
America, Canada, and the Western Europe countries
(Junk et al. 2013). Precision agriculture is the application
of established technologies and principles to manage all
areas of an agricultural system for improved quality
production of farm products and the environment at large
(Pretty, 2008). In the last three decades, this has not only
matured but has moved to all other developing countries
of which Nigeria is not left out. The connection of PA to
the improvement and increment in the level of
civilization of humanity and the quest for improved gross
domestic product and national cannot be overemphasised.
To describe precision agriculture, most simplified way is
the examination of the five “R”s which are the right time,
right amount, right place, right source and proper manner
of agriculture inputs like water, fertilizer, pesticide, etc.
The success of PA is seen through the productivity and
profitability of the process. Precision agriculture is the
ability of the farmer to manage the differences that may
occur during the agricultural process for profitability and
sustainability of the farming activities. PA further
manages the low input, high yield, and environmentally
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Federal University of Technology, Minna, Nigeria
sustainable conditions. The effect of these variabilities
according to Bongiovanni et al., (2004) is grouped into
yield variability, field variability, soil variability, crop
variability, variability in anomalous factors, and
management variability.
Every section of the farmland or zones of the country
has different production capability, which is connected to
the soil type’s environmental conditions and other
geographic conditions like- such as the slop of the
farmland and topography (van Ittersum et al. 2013). This
makes the lands to respond differently to the crops grown.
Yield capacity difference can also mean yield variability.
The difference in the field topography (elevation, slope,
closeness to a water source) can be termed to be the field
variability. The variation for nutrients in the field is
termed to be soil variability (Araus and Cairns, 2014).
Soil texture to an extent could be called as a variable
factor. The plant growth rate and duration are termed crop
variability. Other factors affecting the life cycle of the
crops include weed infestation, insect infestation, disease
condition, and wind damage. These factors are known to
affect the performance of the agricultural process. It is
therefore essential to understand the variability of these
factors and their effect on agriculture. The presence of
these factors and the corrective measures are taken to
reduce their impact on the farm's aids in achieving the
objective of PA. Thus, the introduction of PA helps to
make maximum yield and profit from the sales of farm
products in spite of yield, field, soil and all another
variabilities. The practice of PA is accomplished through
the introduction of mitigative measures that control the
variabilities.
2. PRECISION AGRICULTURE IN NIGERIA
Agriculture in Nigeria since independence has played
a significant role in the country’s development at both the
food security level and income generation. This sector is
an essential occupation of the average Nigerian as about
70% of the population depends on it. The agricultural
sector provides bulk employment, income, and food for
the rapidly growing population as well as agro-based
industries. As a nation, Nigeria is still not self-sufficient
in agrarian products as most food crops are imported from
other countries. Several governments of Nigeria have
introduced different farming programmes and policies
targeted at improving agricultural productivity to meet
the growing demand by the population and agro-
industries (Olaoye, 2014). However, the hydra problems
of agriculture in Nigeria and food insecurity have taken
great contrary positions because the rate of growth in the
area of agricultural production has not in any way met the
growing demands of the population. Two significant
areas have been identified from the agrarian policies of
2016 which are the inability of products to meet the
domestic demand and the failure to achieve the quality
required for the foreign market (Lee et al. 2012). These
policies and programmes are connected to the poor
farming methods employed and the lack of essential
farming inputs such as fertilizer, herbicides, improved
seeds, irrigation crop protection and necessary support
from the various agricultural schemes (Toenniessen et al.
2008). The advent of industries and urbanization has
drastically reduced the farmlands and farming resources
largely. Population increase and limited agrarian support
resources have in recent times opened up issues like
productivity, sustainability, and profitability of
agriculture system. Currently, Nigeria is rated as one of
the leading producers of grain crops such as rice, maize,
sorghum, etc. in Africa. Thus, there is the need for
improvement using the conventional process of
agriculture and the adoption of technology.
Many challenges oppose the implementation of PA in
developing and underdeveloped countries (Scott, 2008).
These challenges do not only include the non-availability
of the much need technology but many other factors
which consist of the lack of electricity supply, insufficient
water supply, land allocation methods to farmers,
knowledge of PA among the farmers and the government
policies (Mustapha et al. 2012). With all these challenges
it becomes almost impossible for modern agricultural
technologies to strive. As one of the developing
countries, Nigeria has not started adopting the practice of
PA.
Recent developments have shown that PA system
when introduced to farmers said that they are farming
processes for the future while some others have keyed
into it and are making ways regarding the quality and
quantity of agricultural products. Precision farming
involves the use of specific soil and crop data for
improved yield. This process is targeted at optimizing
returns on investment by matching farming practice more
closely to the crop needs (Okorie, 2018). Nigeria is the
most populous black nation and is faced with the
adaptation of PA in practice due to cultural and financial
reasons. Some of these challenges are due to the types of
instruments that are involved which includes Global
Positioning System (GPS), Remote Sensing (RS), on the
go sensors, etc. Most of the agricultural systems in
Nigeria are made up of the so-called hard PA.
3. CHALLENGES OF PRECISION
AGRICULTURE
The challenges facing food insecurity, poverty,
disease and hunger in Nigeria and many other nations
have called for research in this area to forestall these
menaces. The primary constraint of agricultural
development in Nigeria is the use of inadequate methods
of data and information acquisition of agrarian land
potential, crops condition and farming activities (Harris
and Orr, 2014). The effect of this is the imperfect
knowledge and unreliable data acquisition for farm
planning and policy formulation. For instance, unguided
use of land whose consequence is often the misuse of
prime farmland. This has a significant set back on
agrarian development. It is worthy of note that substantial
agricultural production takes place under traditional
systems. This is highly dependent upon natural forces and
processes for the maintenance of yield and the quality of
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produce. Detection, identification, measurement, and
monitoring of agricultural phenomena predicated the
assumption that agrarian landscape features (such as
crops, livestock, crop infestation, and soil anomalies)
have consistently identifiable signatures on the type of
remote sensing data. These identifiable signatures are a
reflection of crop type, state of maturity, crop density,
crop geometry, crop vigor, crop moisture, crop
temperature, and soil moisture as well as soil temperature
(Chong et al. 2017). PA-based on the incorporation of
information and communication technologies into
machinery, equipment, and sensors in agricultural
production systems, allows a large volume of data and
information generated are inputted into the automation
system for processing (Rodrigues, 2013).
Demographic trends, including aging populations and
continued migration of people from rural to urban areas,
have attracted the attention of researchers, because labor
issues may become a scarcity factor in agriculture.
According to the UN data, the world’s urban population
is poised to surpass the rural total for the first time in
history. By the time this happens, more than half of the
rural population in Nigeria will be living in cities. This
kind of population growth is observed mainly in low and
middle-income nations like India, China, Nigeria and
Brazil (Prince et al. 2013). China and India have occupied
the first and second positions in the list of countries with
the fastest growing 100 cities while Nigeria is not left out
in Africa (Seto, 2011). The implications of such dramatic
shifts for economic development, urbanization, and
energy consumption are immense. In addition to these
trends, the intensification of climate change will continue
to alter growing conditions, such as temperature,
precipitation, and soil moisture, in less predictable ways
(Erwin, 2009). PA tools can help reduce these impacts,
keep them constant or reduce production costs in
agricultural activities, and they can assist in minimizing
environmental constraints (Chen, and Yada, 2011).
To meet the growing food grain demand in Nigeria
and with the increasing challenge of biotic and abiotic
stresses experienced by crops, the introduction, and
adoption of modern technology in Nigerian agriculture is
inevitable. Agriculture, like other industries, has made
entry into the knowledge-based era, leaving its previous
resource-based nature in recent times through the various
policies governing the importation of agricultural
products (Andersen, 2012). Future agriculture will be
severely competitive, knowledge-intensive and market
driven (Holt-Giménez and Altieri, 2013). Identifying
how science frames PA over time, countries and targeted
research can help drive new study with the objective of
covering areas that have received less attention; this will
develop new approaches to understand PA better and
illuminate new applications. Some of the challenges in
PA includes low technological development;
inconsistency and inept implementation of government
policies; the level of investment; crop Inputs; farm size
management practices; Optimal size zone for soil
sampling. Other technical problems include farmland
holdings, monocropping system and market imperfection
which is regarded as the most important to the farmers as
they do not have control over the market forces.
These challenges are real, and they constitute a
significant roadblock to the implementation of PA in
Nigeria agricultural development. Concerted efforts and
careful planning are required to cover these problems.
The most significant challenge is perhaps the acquisition
of relevant space technology. Remote sensing,
Geographic Information Systems, and Global Positioning
Systems are expensive tools and are currently very scarce
in Nigeria. However, with the successful launch of an
earth observation satellite, NigeriaSat-1 in March 2003,
by the Nigerian government brought this a step towards
the application of space technology to solving some of the
socio-economic problems in the country, including the
agricultural sector. The satellite will improve the
efficiency and reliability of agrarian data collection.
Several researchers have expressed in various ways the
capabilities and relevance of NigeriaSat 1 in Nigerian
agricultural development. Rilwani and Gbakeji (2009)
have demonstrated the ability of NigeriaSat 1 in farm
planning and management. He integrated data from
NigeriaSat 1 with existing soil and topographical map in
a Geographic Information System environment to assess
the current and potential agricultural land use in the
Kadawa sub-sector of the Kano River Irrigation Project.
An alternative to satellite remote sensing that could be
adopted in Nigeria is Airborne Videography. This
technology provides higher levels of spatial details
(between 0.25m and 4m pixel size) than current satellite
technology (Woodget et al., 2015; Matese et al. 2015).
This advantage in addition to the flexibility in the
frequency and time of coverage make it ideal for the site-
specific management of soil and crop conditions.
Some of the tools used in PA includes the yield
monitors which have the capability of indicating yield
(kg/ha), total kg, ha/hour; hectare worked, and grain
moisture content. They are attached to crop harvesting
equipment providing information on crop yield; global
positioning system (GPS) is a network of 24 satellites
orbiting the earth which gives exact satellite time and
location to ground receivers. Differential Global
Positioning System (DGPS) which is a way of improving
the GPS accuracy. This uses the pseudo-range errors
measured at a known location to adjust the measurements
made by the other GPS receivers within the same general
geographic area. Geographical Information System (GIS)
are computer soft and hardware’s which use feature
attributes and location data to produce maps. Remote
sensing is a useful tool for collecting lots of information
simultaneously from a distance (Zook et al. 2010).
Remotely-sensed data provide a mechanism for
evaluating crop health (Mandal and Maity, 2013).
Variable Rate Applicator which has variable rate
applicators for the three components which include a
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control computer, locator and actuator (Chopra et al.,
2008; Yuan et al., 2010; Schumann, 2010).
4. PROCESSES OF PRECISION AGRICULTURE
This is divided into two parts which include:
i. Identification and Assessment of Variable
components: This is further broken down into the
following segments:
a. Grid soil sampling: Grid soil sampling uses the same
principles of soil sampling but increases the intensity
of sampling compared to the traditional sampling.
Soil samples collected in a systematic grid also have
location information that allows the data to be mapped
(Morvan et al., 2008). The goal of grid soil sampling
is to generate a map of nutrient/water requirement,
called an application map.
b. Yield map: Yield mapping is the first step to
determine the precise locations of the highest and
lowest yield areas of the field, and to analyze the
factors causing yield variation (van Ittersum et al.,
2013). One way to determine yields map is to take
samples from the land in a 100m x 100m grid pattern
to test for nutrient levels, acidity and other factors
(Mandal and Maity, 2013). The results can then be
combined with the yield map more effective yet more
economical placement that produces higher crop
yields (Lobell et al., 2009). Researchers at Kyoto
University recently developed a two-row rice
harvester for determining yields on a micro-plot basis
(Dixit et al. 2014).
c. Crop scouting: In-season observations of crop
conditions like weed patches (weed type and
intensity); insect or fungal infestation (species and
concentrations) crop tissue nutrient status; also can be
helpful later when explaining variations in yield maps
(Huseth et al. 2018).
d. Use of precision technologies for assessing
variability: Faster and in real time assessment of
variability is possible only through advanced tools of
precision agriculture (Zhang and Kovacs, 2012).
ii. Variability Management: The management
processes of precision agriculture include:
a. Application rate: Grid soil samples are analysed in the
laboratory, and an interpretation of crop input
(nutrient/water) needs is made for each soil sample.
Then the input application map is plotted using the
entire set of soil samples. The input application map
is loaded into a computer mounted on a variable-rate
input applicator. The machine uses the input
application map and a GPS receiver to direct a
product-delivery controller that changes the amount
and kind of input (fertiliser/water) according to the
application map.
b. Yield monitoring and mapping: Yield measurements
are essential for making sound management
decisions. However, soil, landscape and other
environmental factors should also be weighed when
interpreting a yield map. Appropriately used, yield
information provides essential feedback in
determining the effects of managed inputs such as
fertiliser amendments, seed, pesticides and cultural
practices including tillage and irrigation. Since yield
measurements from a single year may be heavily
influenced by weather, it is always advisable to
examine yield data of several years including data
from extreme weather years that helps in pinpointing
whether the observed yields are due to management
or climate-induced.
c. Quantifying on- farm variability: Every farm
presents a unique management scheme. Not all the
tools described above will help determine the causes
of variability in a field, and it would be cost-
prohibitive to implement all of them immediately. An
incremental approach is a wiser strategy, using one or
two of the tools at a time and carefully evaluating the
results and then proceeding further.
d. Flexibility: Small-scale farmers often have highly
detailed knowledge of their lands based on personal
observations and could already be modifying their
management accordingly. Appropriate technologies
here might make this task more accessible or more
efficient. Larger farmers may find the more advanced
technologies necessary to collect and properly
analyse data for better management decisions
(Baumgart-Getz et al. 2012).
5. Benefits of Precision Agriculture (PA)
Precision agriculture within farmland can be managed
using different levels of inputs depending on the yield
potential of the crop in that particular area of land.
Wang et al., (2006) stated that the benefits of
precision farming are in two folds:
i. Reduction in the cost of producing a given crop in an
area of land and
ii. the risk of environmental pollution from
agrochemicals applied at levels higher than those
required by the plant can be reduced.
The following are some of the benefits of PA to the
farmers:
i. Efficient use of equipment: Information on soil
characteristics and weather can be used to plan and
improve scheduling of operations, which can increase
machinery utilisation rates and lower per- acre costs.
Also, GPS based guidance systems can allow farm
machinery operators to achieve greater field
efficiency under challenging conditions. They can
reduce overlap and missed applications of inputs (e.g.
spraying), helping fatigued operators maintain higher
field efficiency.
ii. Risk reduction: At the field level, PA provides site-
specific management that can point out problems with
growing conditions, thereby reducing variability in
net returns. At the farm level, PA information can be
used to improve variety choice, crop rotation, and
other agronomic practices that minimise risk. As well,
information on crop growth during the season can
help you make more informed market decisions.
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Federal University of Technology, Minna, Nigeria
iii. Management of different products: In the future,
precision technology may help farmers differentiate
their production within a particular field. For
example, you might segregate higher protein wheat
for marketing in more rewarding channels. Also, PA
technology will allow the additional control that is
required when you are managing the production of
differentiated products as opposed to the output of
regular bulk crops. It will allow documentation of
crops conditions and control of inputs to meet the
particular requirements of these crops.
Farmers are constantly making important decisions that
impact their business success. PA uses a continuous
cycle of data collection, data analysis and application
to maximise farm profits and protect the environment
by managing land and livestock changes over time.
The data collection process identifies areas of interest
and records the required data. The data analysis
process organises, queries and reports on the collected
data. The farmer can make effective decisions based
on the reports generated (Hochman et al. 2009).
iv. Increased farm profitability is an essential benefit of
PA for farmers. Some researchers Reviewed some
studies of Precision Agriculture published between
1988 and 2005 and found PA to be profitable for 68%
of the cases studied (Stoate et al., 2009; Ahumada and
Villalobos, 2009; Crosson et al., 2011; Zhang and
Kovacs, 2012). Arable farmers have traditionally used
a whole field approach when planting seeds, applying
fertiliser and spraying pesticides. PA allows the arable
farmer to break a field down into smaller management
zones based on crop yield rates and crop production
factors such as pest presence, soil types and soil
acidity levels. Farmers can use the knowledge gained
from management zones to develop management
plans and implement processes that ensure the best
use of resources to maximise output and profits
(Shiferaw et al., 2009).
v. Dairy farmers can use PA applications to enhance
profitability by monitoring only livestock and making
interventions at the right time to optimise outcomes.
Sensors can record critical aspects of livestock
fertility and alert farmers when an animal is ready to
reproduce. Dairy farmers can maximise the number of
calves, produce more milk, save time and reduce
artificial insemination costs by monitoring their
livestock (Hamadani and Khan, 2015).
vi. Time and labour savings are achieved through the
automation of repetitive farming tasks. In the dairy
sector, robotic milking can record valuable data on
milking performance, save time for farmers, reduce
the need for external labour, encourage greater
production, better animal health and higher quality
milk. Auto-steer systems on tractors and harvesters
can reduce driver fatigue by automating the
navigation of fields with satellite positioning.
Automated feeding systems can provide livestock
with feed at regular intervals and reduce the workload
for farmers. Animals are less stressed with automatic
feeding, and lower ranking animals have more access
to feed (Grothmann et al. 2010).
vii. PA applications can continuously monitor animal
health in real-time and alert farmers when
intervention is required. Sensors can monitor
livestock and their environment to detect changes in
livestock positioning, feeding patterns, temperature,
humidity and sounds. Pig farmers can monitor the
health of their herds by reviewing the sounds
produced by the crowd. Early detection of coughing
sounds can reduce disease transmission in the group
and save money on antibiotic purchases and
veterinary fees. Feeding patterns can be monitored for
individual animals, and farmers can be alerted when
particular animals are eating or drinking less (Banhazi
and Black, 2009).
6. APPLICATIONS OF PRECISION
AGRICULTURE
Agricultural farmlands are of diverse environments
with variable topology and microclimates. Crops grow
and produce at different rates depending on factors such
as soil quality, access to water and nutrients, altitude and
temperature. PA applications are used to increase quality,
record production levels, generate yield maps and
identify zones requiring additional irrigation or fertiliser
(Hochman et al., 2013). Drones and satellite imagery are
used to analyse the health of the crops using Normalised
Difference Vegetation Index (NDVI) to identify areas
that require attention. The NDVI uses the visible and
near-infrared bands of multispectral imagery to display
plant health information. Nutrients are applied to specific
areas using the analysed data to reduce water usage and
the cost of fertilisers.
Arable farmers in developed countries use high
precision positioning systems such as the Variable-Rate
Technologies (VRT) and Controlled Traffic Farming
(CTF) to drive efficiencies for crop production and
protect the environment (Pedersen and Lind, 2017). High
precision positioning systems enable the accurate
positioning of a farmer’s tractor in a field and facilitate
the precise seeding of crops, higher planting density and
the efficient application of pesticides, nutrients and
herbicides. VRT allows farmers to vary the use of
fertiliser on specific areas of the field according to the
needs of the crop. CTF enables farm vehicles to
accurately navigate fields which result in reduced
operator fatigue and minimised crop damage (Kroulík et
al., 2011). Tractors and combine harvesters are large
vehicles with the capacity to damage crops with poor
operator direction (Shearer et al., 2010).
Livestock farmers are using Precision Livestock
Farming (PLF) to monitor their herds and environment,
detect diseases at an early stage, record growth, food
intake and milk production (Meen et al. 2015). Farmers
can review the variation in performance within their herd
and make the necessary input changes to achieve optimal
results. Alerts can be set up to notify a farmer when a cow
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is going to calve. Time savings and better outcomes are
obtained by applying technology to herd management
(Sarac et al. 2010). Horticulture farmers are using
machine vision methods to record the size, shape, colour,
visible defects, sugar content and acidity of their products
(Kondo, 2010).
PA is used in forestry to monitor growth, produce
biomass estimates, identify diseased or infested trees,
classify different species of trees and determine areas
ready for harvesting. Remote sensing imagery captured
by satellites and drones are analysed in geospatial
systems at regular intervals to produce data that drives
planning and decision making (Matase et al. 2015).
NDVI maps can be used to identify tree health in specific
areas. Harvesting machines fitted with high precision
positioning systems can record their location and
harvesting yields to ensure that a forest is managed
appropriately (Suprem et al., 2013).
Real-time information from PA applications will lead
to changes in the monitoring and trading of crops.
Government agencies and the financial markets will be
aware of crop yields during the growing season rather
than at the end of the season. The pricing for crop markets
will become more dynamic with fluctuations occurring as
data is received during the growing season (Verchot et
al., 2007; Peltonen-Sainio et al., 2010). Government
agencies will be able to forecast crop yields more
accurately with the increased volumes of crop
performance data (Challinor, 2009; Lin, 2011).
7. REQUIREMENTS FOR IMPLEMENTING
PRECISION AGRICULTURE
Key components that can improve the implementation
of PA amongst most farmers includes scalability, low
cost, support, integration and interoperability with the
utilisation of open data standards, rule-based workflows,
automated and intuitive data processing methods, user
control over analysis and processing functions, systems
customised to meet farmer needs and an easy to user
interface (Janssen et al. 2015). Farmers need systems that
can grow over time as more PA applications come to
bear. Low-cost systems are required as farmers are not
willing to take a risk on expensive applications that may
not deliver the expected benefits. Farmers need systems
and applications with interfaces that integrate with
legacy, current and future operations.
Farming is a diverse industry, and PA applications
must be customised to suit the particular needs of the
farmer. Specific modules of PA applications can be
supplied to the farmers based on their requirements. Rule-
based workflows allow farmers to deploy their business
knowledge into a PA application. Effective
communication is ensured as regards standards and
operability of the various forms of technologies.
Usability and automated data processing methods help
the farmer manage the large volume of data generated by
PA applications (Lee et al., 2014; Fountas et al., 2015).
The educational status of the farmers as regards PA
has in the recent times been emphasised to enable them
to understand the potential benefits of the PA
technologies and practices (Fountas et al., 2015). There
are six learning processes which has been identified for
stakeholders to improve their agronomic knowledge,
information management skills and understanding of PA.
These processes include the experience of the idea of
spatial data management, spatial variability and maps; the
second is that the stakeholders gain an understanding of
sensors and how sensors can be used for benefit in
farming. Such systems that use sensors were described as
GPS, Yield Monitoring Systems, Remote Sensing and
VRT systems. The relevant stakeholders are thought IT
skills at appropriate levels and become familiar with GIS
technology. The fourth step for the stakeholders is the
creation of awareness as regards the factors that enable
the identification of flexible yield influence elements
(AkhtarSchuster et al., 2011). Here, they learn how to
analyse yield maps, yield variation patterns and
understand the difference between natural and
management-induced variation. The final step shows
stakeholders how to carry out strategic sampling and on-
farm trials to test PA technologies and practices on their
farms (Kutter et al., 2011; Mariano et al., 2012; Eastwood
et al. 2012).
8. THE ADOPTION OF PRECISION
AGRICULTURE
With the adoption of new technologies and its
practice, agriculture develops rapidly to meet the
competitive demand for its products (Hatanaka et al.
2005). The rate and diffusion of PA technology adoption
determine the impact on farm production levels. Factors
such as the farmer profile, farm type, economic
conditions, complexity and cost of the technology
influence the diffusion and speed of PA adoption (Aubert
et al. 2012). Farmers go through a five-stage decision-
making process when adopting PA technologies. In the
Knowledge stage, the farmer learns about the new
technology and its applications. At the Persuasion stage,
the farmer develops an opinion on the latest technology.
The farmer chooses to adopt the innovation at the
Decision stage. The Implementation stage is where the
farmer puts the technology into use on their farm. The
Confirmation stage is the final stage where the farmer
seeks to validate the decision to adopt the technology
(Mackrell et al. 2009).
Five significant stages of adoption of agricultural
technology were identified as the innovators, the early
adopters, the old majority, the late majority and the
laggards (Läpple and Van Rensburg, 2011). The
innovators are adventurous farmers who discover new
techniques and pay a premium to evaluate the
technologies. Innovators are a small but essential part of
a market. Early adopters are influential leaders who
observe the innovators’ findings and find practical usages
for the new technology. They communicate the benefits
of the technology to a broader audience. The early
majority adopt technologies when they are confident that
the product will be useful on their farm and there will be
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Department of Civil Engineering
Federal University of Technology, Minna, Nigeria
a good return on their investment. The late majority are
doubtful of new technology and wait until the technology
has achieved widespread adoption before deciding to
invest. The laggards are happy to continue farming in the
old way and adopt new technologies reluctantly.
PA has not achieved widespread adoption in Nigeria
due to high start-up costs, complexity, stakeholder
awareness and training, data management issues and the
size and diversity of farm structures. The average
Nigerian farm is less than 4 hectares, and many farmers
cannot afford large investments in technology products.
Nigeria with its large arable regions and intensive
farming have higher prospects of PA usage (Seck et al.
2012). Limited research and investment are on-going to
develop PA in Nigeria to ensure higher adoption rates
going forward.
9. FACTORS INFLUENCING THE ADOPTION
OF PRECISION AGRICULTURE
Research shows the primary driver of PA adoption to
be increased profitability and cost to be the primary
barrier to PA adoption (Aubert et al. 2012). Secondary
adoption drivers were environmental compliance,
availability of improved information for better decision
making and risk reduction. Nigerian farmers have
expressed frustration that PA was not a “turn-key”
technology as there are many complex interactions to be
interpreted to derive the benefits from PA. Current
research works should focus on low cost, robust and easy
to use PA technology to drive increased adoption (Tey
and Brindal, 2012). In their study, they studied the
adoption factors for PA and classified the elements found
into seven categories; socioeconomic factors, agro-
ecological factors, institutional factors, information
factors, perception factors, behavioural factors and
technological factors. Socioeconomic factors that
influence the adoption of PA were found to be the
farmer’s age, education, farming experience, attitude to
risk, market conditions and access to information. Older
farmers are less likely to adopt new technologies that
require training and investment. Farmers with higher
levels of education are more likely to take PA
technologies as they often have a more excellent
knowledge of best practice farming practices. The risk
associated with every investment and the risk-averse
farmer is more likely to continue farming traditionally.
Market conditions influence the adoption of PA and
farmers are more likely to invest in new PA technologies
and equipment when market conditions are stable, and the
return on investment is high (Tey and Brindal, 2012).
Agro-ecological factors that influence adoption
decisions include farm size, income, land tenure,
environmental compliance and crop type. Larger farms
with steady incomes are more likely to invest in PA.
Farmers who are renting land are unlikely to significantly
invest in PA technology due to uncertainty regarding
future control of the area. Farmers growing crops planted
in rows such as corn, cotton and soybeans were more
likely to adopt PA than farmers growing vegetables, fruits
and minor crops. Environmental compliance is becoming
an increasingly important adoption factor as farmers need
to meet strict environmental protection measures.
Institutional factors were found to be government
organisations and policies, distance from fertiliser and
equipment suppliers and the farm’s location. Government
organisations have a significant role to play in training
and educating farmers on the technologies driving PA
and the possible PA applications for their farms. Well
informed farmers who understand the benefits of PA are
more likely to adopt the technologies. Distance from
fertiliser and equipment suppliers is another adoption
factor as farmers located far from suppliers will be in less
contact with sales personnel that can inform farmers of
the availability of new PA equipment and possibly
convince the farmer to invest in the latest technologies
(Tey and Brindal, 2012).
Information factors included the use of consultants
and access to information sources. Farmers who work
with consultants receive information on the best practices
for their farm and are more likely to adopt PA. Access to
information sources such as industry and government
publications allows a farmer to keep informed of the
latest developments with farming. Perception factors
were the farmer’s view on the importance of PA and the
profitability of PA. The farmer’s attitude toward PA is
crucial as ultimately the farmer is the decision maker who
adopts the appropriate technologies for their farm. A
farmer who had a bad experience with early PA
technologies may be reluctant to invest in new
technologies. Behavioural factors included the farmer’s
behavioural profile and intentions (Tey and Brindal,
2012).
Technological factors found to be essential adoption
influences were the complexity of the PA technology, the
type of technology to be adopted, farm irrigation structure
and the usage of computers on the farm (Tey and Brindal,
2012). Technologies need to be understandable and
usable to achieve widespread adoption by farmers. Many
farmers are reluctant to adopt complex technologies due
to the time and training required for usage. Farmers with
previous experience of working with information
technology are more likely to take PA technologies as
they are familiar with computers. The type of technology
influences adoption decisions as there are varying costs
associated with different techniques and some
technologies may be more familiar to farmers.
Ex-post adoption factors include the farm size, quality
of the farm’s soils, farmer income, farmer education,
access to information, costs savings, desire for higher
profitability, land tenure and IT experience. The typical
PA adopter was found to be an educated farmer seeking
a competitive advantage through better agricultural
practices on their large fertile farm. The primary ex-post
driver for PA adoption was found to be farm size. Large
farms with over 500 hectares can benefit from economy
of scale when adopting PA. A secondary driver was the
farmer’s confidence with technology. Farmers with good
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1st International Civil Engineering Conference (ICEC 2018)
Department of Civil Engineering
Federal University of Technology, Minna, Nigeria
technological skills were found to be more likely to take
PA. Other ex-post drivers for PA adoption were a high
income, the farm’s location and the farmer’s education
(Kassie et al. 2011; Paustian and Theuvsen, 2017).
10. CONCLUSION
The adoption of PA has been constrained by some
barriers such as cost, complexity and weak or non-
availability of rural broadband infrastructure. The
accessibility and speed of rural broadband will need to be
improved to enhance internet connectivity between farm
systems and external providers. PA applications use
remote sensing data to identify crop health and
development patterns. Remote sensing data is delivered
in large files which require fast broadband connections
for effective communication. It can, therefore, be
concluded that lack of decision support systems to be a
major barrier to the adoption of PA. Farmers need
decision support systems that enable effective decision
making based on accurate and timely data.
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Automation is the use of mechanical and electronic equipment to reduce the need for human labour. It has been used for carrying out various farm operations like automatic identification, feeding, milking, estrus and birth detection, egg collection, exercising, barn cleaning, animal cooling, environmentally controlled housing etc in the livestock farms and grazing lands. The most salient characteristic of livestock farm automation system is the opportunity to tailor operations to the needs of each individual animal. This is only possible if there are subsystems capable of recognizing the animals as they interact with the automated systems. Automation saves time, requires less labour, improves product quality and FCR, increases production, efficiency, accuracy and safety. However, automation demands high installation and repair costs; hence is more suitable for commercial & institutional farms. With the automation of farms livestock management is shifting from being an art to an app.
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The intensification of industrial agriculture has been enabled by improved crop varieties, genetically engineered crops, fertilizers, and pesticides. Over the past 20 y neonicotinoid seed treatments have been adopted worldwide, and are used on a large proportion of U.S. field crops. Although neonicotinoids are used widely, little is known about how large-scale deployment affects pest populations over long periods. Here, we report a positive relationship between the deployment of neonicotinoid seed-dressings on multiple crops and the emergence of insecticide resistance in tobacco thrips (Frankliniella fusca), a polyphagous insect herbivore that is an important pest of seedling cotton but not soybean or maize. Using a geospatial approach, we studied the relationship between neonicotinoid resistance measured in 301 F. fusca populations to landscape-scale crop production patterns across nine states in the southeastern U.S. cotton production region, in which soybean, maize and cotton are the dominant crops. Our research linked the spatiotemporal abundance of cotton and soybean production to neonicotinoid resistance in F. fusca that is leading to a dramatic increase in insecticide use in cotton. Results demonstrate that cross-crop resistance selection has important effects on pests and, in turn, drives pesticide use and increases environmental impacts associated with their use.
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Fertilizer spreaders capable of variable rate application are increasingly important for enhancing nutrient management in horticultural crops because they improve placement and increase nutrient uptake efficiency. Matching applied fertilizer to fertilizer requirements represents a significant input cost saving for the grower and a reduction in potential pollutant loading to ground and surface water. Variable rate fertilization (VRF) is a precision agriculture technology made possible by embedded high-speed computers, accurate Global Positioning System (GPS) receivers, Geographic Information Systems (GIS), remote sensing, yield or soil maps, actuators, and electronic sensors capable of measuring and even forecasting crop properties in real time. For tree crops like Florida citrus (Citrus spp.), the most important function of the VRF spreader is to detect and avoid fertilizing spaces of the orchard not occupied by trees. Treeless spaces are becoming more common in Florida as diseases such as citrus greening (Candidatus Liberibacter asiaticus) and canker (Xanthomonas axonopodis) cause the removal of thousands of trees every year. VRF works best under those conditions. Because VRF exploits crop and soil variability, it has no value in a perfectly uniform field. VRF enables smaller trees including resets to be fertilized at lower, most appropriate rates, thus minimizing any excess application. This article examines the existing knowledge on using precision agriculture and variable rate technology to keep water and nutrients in the root zone of horticultural crops, thus facilitating maximum uptake efficiency.