Toward Adoption of Drip Irrigation and Soil-moisture Sensors by Small-scale Farmers

Abstract

The semi-arid region of the world is occasionally affected by erratic rainfall and drought which threatens agricultural production and food security. This paper presents the outcome obtained from a review to provide proactive measures that will combat the problems of water scarcity through the adoption of sensor-based drip irrigation by small-scale farmers. The small-scale farmers constituted the larger proportion of the farming population in the region. The paper is centred on the general overview of irrigation practices, advances in irrigation systems, modelling irrigation and cropping Systems, coupling soil sensors with drip irrigation and their adoption. Factors that hinder the acceptance and adoption of sensor-based drip irrigation systems were reviewed and synthesized which include initial capital investment, farmers' awareness, risk perception and uncertainties, technical know-how, farm size and capital recovery. A simple framework for adopting a sensor-based drip irrigation system was developed. The building blocks of the framework include the dissemination of sensor-based irrigation to farmers, the creation of awareness among farming communities, and the provision of subsidies and credit. Others include the provision of policies and environmental standards and review of the price of water charges. This study will be useful to farmers, agricultural extension agents and policymakers in making decisions about the water resources planning and farming activities in the region.

Full text

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8
TOWARD ADOPTION OF DRIP IRRIGATION AND SOIL-MOISTURE SENSORS BY SMALL-SCALE
FARMERS
*1,2Lawal, A., 1Shanono, N. J., 1Nasidi, N. M.
1Department of Agricultural and Environmental Engineering, Bayero University Kano, Nigeria
2Department of Agricultural and Bioenvironmental Engineering, Waziri Umaru Federal Polytechnic, Birnin-Kebbi
*Corresponding authors’ email: engrlawalahmad@gmail.com; Phone: 08039295947
ABSTRACT
The semi-arid region of the world is occasionally affected by erratic rainfall and drought which threatens
agricultural production and food security. This paper presents the outcome obtained from a review to provide
proactive measures that will combat the problems of water scarcity through the adoption of sensor-based drip
irrigation by small-scale farmers. The small-scale farmers constituted the larger proportion of the farming
population in the region. The paper is centred on the general overview of irrigation practices, advances in
irrigation systems, modelling irrigation and cropping Systems, coupling soil sensors with drip irrigation and
their adoption. Factors that hinder the acceptance and adoption of sensor-based drip irrigation systems were
reviewed and synthesized which include initial capital investment, farmers’ awareness, risk perception and
uncertainties, technical know-how, farm size and capital recovery. A simple framework for adopting a sensor-
based drip irrigation system was developed. The building blocks of the framework include the dissemination
of sensor-based irrigation to farmers, the creation of awareness among farming communities, and the provision
of subsidies and credit. Others include the provision of policies and environmental standards and review of the
price of water charges. This study will be useful to farmers, agricultural extension agents and policymakers in
making decisions about the water resources planning and farming activities in the region.
Keywords: drip irrigation system, semi-arid, small-scale farmers, soil moisture sensors
INTRODUCTION
The semi-arid region of the world is characterized by high
temperatures and low rainfall. The region has continued to
experience erratic rainfall and occasional drought caused by
climatic change impact (Medugu et al., 2011; Zakari et al.,
2019) Also, the rate at which water demand is increasing in
the region is frightening due to the ever-increasing human
population and competition among water users. Moreover,
the dry season farming activities (irrigation) in the region are
mostly practised by small-scale farmers with low capital and
technical skills. This led the farmers to resort to the
conventional surface irrigation method known to have the
lowest water use efficiency. These among other reasons
further exacerbate the problem of water scarcity in the region.
Small-scale farmers as defined by the International Fund for
Agricultural Development (IFAD) are farmers with farmland
size of less than 2 hectares. These farmers cultivate over 80%
of the world’s estimated 500 million ha of small farms (IFAD,
2013). Small-scale farmers provide about 50% of food
production in the world and more than 70% in Latin America,
sub-Saharan Africa, and South and East Asia (Samberg et al.,
2016). According to Samberg et al., (2016) small-scale
farmers contribute immensely to the economy of the farming
communities, rural development and food security. Similarly,
in Africa, small-scale farming constitutes about 80% of the
farming activities with 33 million fragmented farmlands that
are less than 2 hectares and are mostly cultivated by family
members (NEPAD, 2013). In Nigeria, small-scale farmers
form the majority of the farming population which also make-
up about 80% of the Nigerian farmers and produced about
98% of the food consumed in the country except for Wheat
(Mgbenka & Mbah, 2016).
Despite their population and contribution to both food
security and gross domestic products (GDP), Nigerian small-
scale farmers are faced with different problems that include a
lack of awareness of technology of the modern farming, high
cost of farm inputs, and lack of credit facilities just to mention
but few (Mgbenka & Mbah, 2016). These problems make
the majority of the Nigerian small-scale farmers produce
crops mainly on rainfed farming which is seasonal and the
farmers become idle during the dry seasons (Enete & Amusa,
2010). Rainfed farming exclusively depends on low, erratic
and inadequate rain received in the region which resulted in
low yield and income for the farmers and hence, low social
wellbeing (Sanni et al., 2012). Presently, Nigerian small-
scale farmers only irrigate 1% of their croplands which
contributed to the ongoing food insecurity and exorbitant cost
of farm produce as rainfed farming is affected by erratic
rainfall and climatic change impact (LiangzhiI et al., 2018).
Adoption of micro irrigation systems known to have the
highest water use efficiency such as drip irrigation by small-
scale farmers in the semi-arid region of northern Nigeria
would reduce the risks and uncertainties associated with
rainfed farming. Moreover, this will also allow more lands to
be put into cultivation thereby, boosting food production and
improving the economy of the country. Modelling developed
by the International Food Policy Research Institute shows that
about 1 million hectares of land can be adopted for irrigation
by small-scale farmers in Nigeria and crops like Maize, Rice
and Vegetables can generate more than $600 million
increased income for small-scale farmers in the dry season
alone (LiangzhiI et al., 2018). Adoption of drip irrigation by
small-scale farmers in the semi-arid region of northern
Nigeria which has been declared a water-stressed region will
help farmers to make the best use of the limited available land
and water resources. The drip irrigation system coupled with
soil sensors (sensor-based drip irrigation system) ensures
effective irrigation scheduling and improves the efficiency of
the irrigation system through reliable and optimum water
application to the crop that can precisely meet the crop water
requirement. This paper reports the outcome and knowledge
gained from an extensive review of the relevant literature.
The primary aim of this review paper was to propose a way
forward for the adoption of a sensor-based drip irrigation
FUDMA Journal of Sciences (FJS)
ISSN online: 2616-1370
ISSN print: 2645 - 2944
Vol. 6 No. 3, June, 2022, pp 259 - 270
DOI: https://doi.org/10.33003/fjs-2022-0603-961

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system in the semi-arid region of northern Nigeria. In
addition, the paper recommended some measures and
strategies on how sensor-based drip irrigation systems can be
used by small-scale farmers since about 80% of Nigerian
farmers are classified as small-scale thereby, addressing
water scarcity issues and improving agricultural production
and hence, food security in the region.
An Overview of the Irrigation Practices
The development of irrigated agriculture has undoubtedly
boosted agricultural productivity and contributed to economic
stability and made it possible to feed the world’s growing
population (Rosegrant et al., 2009; Shanono, et al., 2021).
Irrigated crops are usually higher in yields than the crops
produced under rainfed agriculture because water is optimally
applied by putting climatic conditions into consideration (Li
& Troy, 2018). According to Li & Troy, (2018) crop yields
obtained through irrigation crop yields are on average 2.3
times higher than the yield produced under rainfed
agriculture. Such an increment in yields of irrigated crops
compared to rainfed crops demonstrates that irrigated
agriculture will continue to play an important role as a
significant contributor to the world’s food security (Dowgert,
2010). Irrigated agriculture covers 275 million ha of land
which is about 20% of cultivated land in the world and
irrigation provides about 40% of global food production
(UNESCO, 2017). Irrigation in Africa has the potential of
providing food security to the entire African countries but
food production in the continent is mainly rainfed and the area
cultivated under irrigation is about 13 million ha of land
which is only about 6% of the total cultivated area in the
continent (Liangzhi et al., 2010). According to FAO, (2005)
Africa can irrigate up to 42.5 million ha, based on available
land and water resources and by far the greatest potential is
found in Nigeria, which accounts for more than 2.5 million
ha.
Irrigation development has long been considered essential to
the sustainable growth of agricultural production in Nigeria.
The country has an estimated 3.1 million ha of potentially
irrigable area, of which over 1 million ha is in the Northern
part of the country (NINCID, 2021). Out of 624,408 ha
planned for irrigation in 2004, only an estimated 293,117 ha
(47%) has been equipped for irrigation and only 218,840 ha
(35%) has been cropped. The Federal Government of Nigeria
recently released a long-term irrigation development strategy
for the period of 2016 to 2030 and to be implemented in three
phases with a total of 5 million ha by 2030. According to
projections, land under irrigation has increased at less than
1% per annum in the last decade or so. It is not foreseen that
the situation would change significantly because of many
reasons. The harvested irrigated areas are expected to increase
from the estimated current figure of 1.17 million ha to about
2.35 million ha in 2025 (NINCID, 2021).
Irrigation is usually practised in arid and semi-arid regions
where rainfall is sparsely distributed and could not sustain
agricultural production. The arid region receives an average
annual precipitation of less than 250 mm per annum with an
aridity index of between 0.03 and 0.2 while the semi-arid
region receives an average annual precipitation of 250 mm to
500 mm per annum with an aridity index between 0.20 and
0.50 (Whitford & Duval, 2020). Drylands cover almost 54
million 𝑘𝑚2 of the total land area of the world and include all
the land areas where there are limited and insufficient amount
of rainfall which limits the agricultural activities. Semi-arid
areas have the largest percentage followed by arid areas and
then dry sub-humid lands. These aridity zones spread across
all continents, but are predominantly found in Asia and Africa
(White & Nackoney, 2003). Figure 1 below shows the map of
the dryland in the world.
Figure 1: Map of dryland in the world (UNEP-WCMC, 2007)
Table 1 below shows areas equipped for irrigation across different continents of the world as in the 2018 report by World Food
and Agriculture - Statistical Yearbook 2020.

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Table 1: Area equipped for irrigation across different continents of the world (FAO, 2020)
Continent
Irrigated Area (ha)
Per cent of the world's total area (%)
Asia
238,406,000
70
America
54,819,000
16
Europe
26,219,000
8
Africa
15,959,000
5
Oceania
3,308,000
1
World
338 ,711,000
100
Irrigation practice is categorized into two, the gravity
(surface) and pressurized (micro) irrigation systems. Surface
irrigation is the conventional and the most commonly adopted
irrigation method in which water is applied and distributed
over the soil surface by gravity. Surface irrigation is being
practised on about 76 % (255,784,630 ha) of the 338,711,000
ha of irrigated cropped area in the world as of 2018. India as
a country, has the largest surface irrigation method in the
world with irrigated areas of 68,172,000 ha which accounts
for about 27% of the world's surface irrigation. Other five top
countries that practice surface irrigation methods in the world
are China, Pakistan, the United States of America, and Iran
(Knoema, 2021). Although surface irrigation is the most
commonly and widely practised by farmers, it is also known
to have the lowest water use efficiency (Shanono et al., 2020).
About 90% of the freshwater consumed by agriculture is
applied through surface irrigation using furrow, basin and
border irrigation methods in which a significant amount is lost
to conveyance losses, runoff and deep percolation. (Akbari et
al., 2018). Water losses in surface irrigation can be minimized
by proper land grading, monitoring and controlling the
inflow, improving the irrigation scheduling and adopting
micro-irrigation systems known to have high water use
efficiency.
A pressurized irrigation system otherwise known as a micro-
irrigation system is a modern irrigation method in which
irrigation water is conveyed and precisely applied to the soil
under pressure through a system of pipes. The pressurized
system provides improved water distribution and application
efficiency, control over the timing of application, reduced
wastage reduced labour and efficient use of limited water
resources (ICID, 2022). The pressurized irrigation system is
categorized into drip and sprinkler irrigation systems. Drip
irrigation is a water-saving irrigation method that is typically
designed to wet only the soil zone occupied by plant roots and
to maintain this at or near an optimum moisture level, using
emitters spaced along drip lines. Drip irrigation has improved
crop production systems in different parts of the world by
increasing yields and water use efficiency in many crops
(Zaccaria et al., 2017). Whereas sprinklers use mechanical
and hydraulic devices to apply irrigation water to the soil
surface and can also apply fertilizer and pesticides together
with the correct amount and frequency (Kulkarni, 2011).
Advances Achieved So Far in Irrigation Sector
Advances in the irrigation sector are regularly introduced into
irrigated agriculture as this is necessary to overcome the
environmental impacts associated with irrigation (Levidow et
al., 2014). In addition, an improved irrigation system can help
address the problems of the limited water resources,
particularly in arid and semi-arid (Ghanisanij et al., 2006;
Zakari et al., 2020). Some of the advances introduced recently
into irrigated agriculture include modelling, simulation and
optimization of irrigation systems. Others include the use of
an irrigation controller coupled with soil moisture sensors for
optimum irrigation scheduling. These advances are
developed to improve high water productivity, increase crop
yields, reduce the drudgery, conserve energy and reduce the
leaching of nutrients caused by over-irrigation ( Perry et al.,
2017). The advances make it possible to automate irrigation
systems using soil tension meters incorporated with
transducers that can be connected with a solenoid valve or
irrigation controller to keep the soil moisture at field capacity
(Al-Ghobari et al., 2017).
Irrigation and crop models are promising tools that are now
used to support the design of irrigation and cropping systems
for sustainable agricultural practice. Crop models are
software developed using a set of mathematical equations
describing physical system relationships (soil-water-plant-
atmosphere). The crop model simulates or imitates the
behaviour of a real crop by predicting the growth of its
components, such as leaves, roots, stems and yields. Thus, a
crop growth simulation model not only predicts the final state
of total biomass or harvestable yield but also contains
quantitative information about major processes involved in
the growth and development of a plant (Jame & Cutforth,
1996). Crop modelling has developed extensively over the
past decades in parallel with advances in crop and
environmental sciences and computing technologies.
Different models have been developed by different
researchers encompassing different approaches and levels of
complexity and emphasizing different aspects of the soil-
water-plant-atmosphere system (Singels et al., 2013) as well
as human-water interaction (Shanono & Ndiritu, 2021). Crop
models are also used as tools for assessing agricultural
management strategies and their interaction with climatic
risk.
Thus, crop modelling has greatly contributed to a better
understanding of crop performance and yield gaps, better
prediction of pest and insect outbreaks, and improving the
efficiency of crop management systems and optimization of
planting dates (Reynolds et al., 2018). Recent developments
in agricultural products include the use of remotely sensed
data and mobile phone technology linked to crop
management decision support models, data sharing in the new
era of big data, and the use of genomic selection and crop
simulation models linked to environmental data to help make
crop breeding decisions (Antle et al., 2017). Decision Support
Systems (DSS) are irrigation models that were developed to
improve crop water use efficiency at farm and water basin
scales. The application of DSS in irrigated agriculture for
irrigation water management has greatly increased across the
globe in the last 2 decades. The DSS has been widely used to
balance the water use between the field and the district levels,
allocation of irrigation water, reduce environmental pollution
and improve nutrient-use efficiency (Ara et al., 2021).
Crop simulation models are the act of imitating the processes
of plant growth and development. These models are based on
equations that describe the processes involved in crop growth
and development, amongst others ( Wallach et al., 2006;
Seidel, 2012 ). Crop models are nowadays used to evaluate
different irrigation and fertilization management or climate
scenarios and hence allow generalised predictions of crop
production. Moreover, they are powerful tools to test
hypotheses and describe as well as understand complex
systems and processes. For instance, crop simulation models

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are used to understand and establish the relationship between
the input and output of the irrigation systems such as the water
and Nitrogen applied and the yield ( Semenov, 2009; Seidel,
2012 ). Crop simulation modelling offers an opportunity for
exploring cultivar potential for new areas before establishing
expensive and time-consuming field experiments in the field
which saves time and resources (Kephe et al., 2021).
Calibrated and validated simulation models can provide very
reliable results in developing agricultural land management
strategies. Most simulation models have some forms of
limitations and therefore no model applies to every situation,
for instance, DSSAT (Decision Support System for Agro-
technology Transfer) and EPIC (Environmental Policy
Integrated Climate) models can be used in different
geographical locations and in various agro-environmental
conditions for estimating soil moisture, crop water
requirements and crop evapotranspiration. However, these
models cannot be used for planning irrigation water
management. The CROPWAT for example can be used in
estimating water requirements and planning irrigation
scheduling (Khan & Walker, 2015). The EPIC simulation
model was used by Ko et al., (2009) in South Texas to
determine the relationship between the crop yield and crop
water use parameters such as crop evapotranspiration (ETc)
and water use efficiency (WUE) in the irrigated cotton and
maize farms respectively. The researchers also used EPIC to
simulate the variability and crop yield response under
different irrigation regimes and their findings show that EPIC
can be used as a decision support tool for the crops under full
and deficit irrigation conditions. Walser et al., (2011) used
Soil–Vegetation–Atmosphere Transfer (SVAT) Daisy
models to simulate irrigation experiments conducted on a
field rain-out shelter for wheat and barley and in a container
greenhouse experiment for barley to maximize the water
productivity. The researchers concluded that DAISY
performed well with simulating lightly drought-stressed crop
growth and water balance.
Optimization is the search for the best solution concerning
certain criteria from a set of variables and parameters. It
involves maximizing or minimizing an objective function by
choosing values from an allowed set of decision variables and
determining the value of that objective function. Different
optimization algorithms are available for the optimization of
different processes of different methods of irrigation under
different conditions( Li et al., 2020). The general objective
for crop growth optimization models is to improve crop water
productivity. Through crop optimization models, the
optimum amount of water and fertilizer and other farm input
can be applied thereby, ensuring little or zero water wastage
while at the same time drought-stress crop is avoided (Kloess
et al., 2012). The economic and optimization (OPTIMEC)
tool developed in Spanish is one of the irrigation optimization
models that uses a heuristic technique and the genetic
algorithm (GA) to find a quasi-global optimum combination
of irrigation events. The model is defined by irrigation date,
water cut-off time and inflow rate that maximizes net profit
(Akbari et al., 2018). Global Evolutionary Technique for
Optimal Irrigation Scheduling (GET-OPTIS) is also another
optimization algorithm for optimal irrigation scheduling. The
optimization of irrigation scheduling using GET-OPTIS starts
with a set of solutions called the population of a random set
of schedules. Every member of the set has a fitness value
assigned to it which is directly related to the objective
function (e.g. crop yield). In sequential steps, the population
of schedules is modified by applying four steps, aiming to
imitate biological evolution which includes selection,
crossover, mutation, and reconstruction. The procedure is
then repeated until a convergence criterion is reached, or the
maximum value of steps is exceeded (Schütze & Schmitz,
2010).
The combination of any simulation model with any of the
optimization algorithms is known as simulation-based
optimization otherwise known as simulation-optimization
modelling. Simulation-optimization is a process of finding
the best input variable values from among all possibilities
without explicitly evaluating each possibility. The objective
of simulation- optimization is to minimize the resources spent
while maximizing the information obtained in simulation
experiments. For instance, the SVAT model can be employed
to simulate irrigation experiments for the growth and
development of different crops and the simulated values can
then be subjected to optimization algorithms to find the
optimal values of the simulated results for any performance
indicators. Research on the development and application of
simulation-optimization Research on the development and
application of simulation-optimization models for the
management of irrigation systems are still very few (
McCarthy et al., 2013; Akbari et al., 2018 ). Soundharajan &
Sudheer (2009) developed an optimal irrigation schedule for
rice crops (Oryza sativa) under water deficit conditions in
southern India. The ORYZA2000 simulation model was used
to identify critical periods of growth that are highly sensitive
to the reduced crop yield and coupled with a genetic
algorithm to develop optimal water allocations during the
crop growing period. The study revealed that a significant
improvement in total yield can be achieved by the model
compared to other water-saving irrigation methods. The
results of their study also suggested that employing a
calibrated crop growth simulation model combined with an
optimization algorithm can lead to achieving maximum water
use efficiency. A study was conducted by Saberi et al., (2020)
to improve the efficiency of the furrow irrigation method by
simulating and optimizing the design parameters (flow rate,
furrow length and cut-off time) using the simulation-
optimization framework FURDEV. The FURDEV is a
combination of SURDEV and AMALGAM shows that the
framework plays an effective role in improving irrigation
efficiency. Kloss et al., (2014) use a simulation-optimization
approach combined with an irrigation experiment to improve
water productivity in sensor-based deficit irrigation systems
and the results of their study were found to be effective.
Coupling Soil Sensors with Irrigation System
The soil moisture sensors measure the soil or plant water
status by either measuring the amount of water or its energy
content in the soil. The amount of soil-water contents is
measured using a soil moisture meter while the soil-water
potential (the energy contents of the water in the soil) is
measured using a tensiometer. Measurement of soil-water
contents by soil moisture meters only indicates the volume of
water in the soil but does not indicate the amount of water
available for plants ( Thompson & Gallardo, 2005; Kloss et
al., 2014; Montesano & Parente, 2015 ). The measurement of
soil matric potential using a tensiometer is used to overcome
the problem encountered in measuring soil-water contents by
soil moisture meters as it indicates the volume of water
available for plants. Moreover, tensiometers are not affected
by salinity as they respond purely to moisture tension and
thus, measure the force that the plants have to overcome to
extract water from the root zone (Lieth & Oki, 2008). A
simple tensiometer consists of a tube fitted with a porous
ceramic tip on one end and a pressure/suction gauge on the
other end. In automated systems, the gauge is supplemented
with or replaced by a transducer to convert the tension

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(suction) to an electrical signal that can be sensed by a
computer or irrigation controller. The basic operation is to
have one tensiometer coincide with each irrigation valve. This
sensor is usually installed in the root zone of a plant in an
experimental plot to represent the whole experimental plot
(Lieth & Oki, 2008).
Sensor-based irrigation is an irrigation method that used soil
sensors for irrigation scheduling (how much and when to
irrigate). Soil moisture sensors (SMS) are widely available
and used in full and deficit irrigation systems (Chen et al.,
2019). The SMS are devices for monitoring Spatio-temporal
variations of soil moisture and hence can be an effective tool
for precisely managing irrigation scheduling for various
crops. These sensors have the advantage of allowing site-
specific crop management which is the most crucial part of
precision and smart agriculture (Badewa et al., 2018). Sensor-
based irrigation is very conventional, easy to use, and cost-
saving less amount of labour is required. With an automated
technology of irrigation adopted, human labour and other
interventions can be minimized. Sensor nodes enable
environment sensing together with data processing, sensors
can network with other sensors and can exchange data with
external users (Wei et al., 2020). Sensor networks are used
for a variety of applications, including wireless data
acquisition, environmental monitoring, irrigation
management, safety management, and many other areas
(Dubey & Dubey, 2018). Irrigation manager who use soil
sensors to monitor soil moisture levels in the field greatly
increase their ability to conserve water and energy, optimize
crop yields, and avoid soil erosion and water wastage and
pollution (Chaware et al., 2015). According to the Muñoz-
carpena (2004), a sensor-based irrigation system may greatly
facilitate the successful employment of low volume-high
frequency irrigation systems for commercial vegetable crops.
This assumption is in line with the work of Dukes et al (
2003) who reported a 50% reduction in water use when using
a soil moisture sensor-based automated drip irrigation system
for bell pepper as compared to a once-daily manually irrigated
system without affecting yield.
In sensor-based irrigation, the water is applied based on
maintaining soil water between two limits of the tensiometer.
The first limit is the lower limit (drier value) or threshold that
indicates when to start an irrigation event whereas the second
limit is the upper (wetter value) which indicates when to stop
the irrigation event. The difference between the two limits is
an indication of the volume of irrigation water required
(Thompson & Gallardo, 2005). In practice, threshold soil
matric potentials (SMP) values are commonly recommended
by extension services, consultants, or suppliers. As general
guidelines, Irrometer Co., a major manufacturer of soil matric
potential sensors for commercial use, suggested lower limits
of -30 to -60 kPa for most soils and of -60 to -100 kPa for
heavy clay soils, and upper limits of -10 to -30 kPa which
represent Field Capacity (Thompson & Gallardo, 2005).
Recommended SMP values appear to be based on experience
and experimental studies conducted with open field crops and
show a wide range of threshold SMP values. Thus, some other
site-specific factors can also influence results. A study
conducted in silty-clay soil by Montesano & Parente, (2015)
with a threshold value of -10 kPa for tomato and cucumber
shows a water-saving of 35% and 46%, on average, for
tomato and cucumber respectively. Also, an earlier study
conducted by Wang et al., (2005) compare three irrigation
treatments of tomatoes in loamy soil which are irrigated using
tensiometer values set at -10 kPa, -20 kPa and -30 kPa shows
that irrigation at -30 kPa as a threshold value for tomato crop
used 85% less water than that set at -5 kPa and the study
recommended and an optimal value of -30 kPa for tomato
crop.
Several researchers and soil sensor suppliers have continued
to recommend upper and lower limits of the tensiometer to
define adequate soil water matric potential in the crop's root
zone. The recommended upper and lower limits differ
depending on the soil texture, crop species and evaporative
conditions. The most commonly recommended ranges, for
high-frequency drip irrigated crops, are between -10 and -20
kPa, -10 and -30 kPa, and -20 and -40 kPa, for coarse, medium
and fine-textured soils respectively (Thompson & Gallardo,
2005). Generally, with high-frequency drip irrigated crops,
standard Refill Point values are used regardless of crop type
and evaporative conditions. Adjustments may be made for
crops considered to be very sensitive to over-irrigation (e.g.
pepper), and to impose controlled moderate water stress to
improve fruit quality (e.g. melon, fresh tomato) (Thompson
& Gallardo, 2005).
The Need to Adopt Sensor-based Drip Irrigation System
Sensor-based drip irrigation can be adopted by small, medium
and large-scale farmers, particularly in the semi-arid regions
for proper irrigation scheduling, water-saving and the overall
water use efficiency. Irrespective of the size of the farms and
the types of crops grown, soil sensors can be installed to
monitor irrigation and can be coupled with an irrigation
controller and /or solenoid valve to automate the system
thereby saving time, energy and cost and the overall
productivity of the irrigation systems.
Sensor-based drip irrigation systems have been installed in
several large-scale farming to improve irrigation water
efficiency in different parts of the world and have shown
promising results in saving water. A study was conducted by
Lea-Cox et al., (2018) at Mellano southern California using
sensor-based drip irrigation and achieved a 25% reduction in
water use without adversely affecting the yield Generally,
sensor-based drip irrigation can save about 40-60% of water
without compromising crop yield or quality. A similar study
was also conducted in Commercial Floriculture Production to
determine the benefit of sensor-based automated drip
irrigation systems in herbaceous ornamental producers. The
study tends to compare sensor-based drip irrigation systems
with traditional grower-managed irrigation systems for two
years growing seasons. Although, the findings of their study
show that there is no difference in water consumption
between the two systems studied and compared but the
number of plants produced under sensor-based drip irrigation
was scaled up which indicates the capability of the system for
irrigation (Wheeler et al., 2018). According to Wheeler et al.,
(2018) sensor-based irrigation reduces and facilitates the
reallocation of labour from irrigation management, which
was especially valuable during peak production and shipping
periods. The financial return period calculated from labour
savings would be roughly 1.5 years if the sensor-based
irrigation system was implemented throughout the facility
(Wheeler et al., 2018).
Medium-scale farming is a farming system that produces
crops of land size of 5 - 100 ha and is mostly practised by
professionals, entrepreneurs or retired civil servants. These
types of farmers can easily adopt agricultural innovations and
technologies to their farms without any resistance considering
their level of education. Sensor-based drip irrigation along
with a mulch was adopted on the cotton farm of 18.5 ha in
Gujarat, India and the system was able to save about 60% of
water when compared to surface irrigation (Mavani &
Prajapati, 2019). In another study conducted by Barkunan et
al., (2019) a sensor-based automatic drip irrigation system

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264
was adopted for paddy cultivation in Tamilnadu and the
results of the study show that the system saves nearly 41.5%
and 13% of water compared to the conventional flood and
drip irrigation methods respectively. Sensor-based drip
irrigation systems can also be incorporated into small-scale
farming to improve both irrigation efficiency and the crop
productivity of the irrigation systems. Most of the
experiments, researches, studies on sensor-based drip
irrigation systems were conducted on small experimental
plots which indicate that the systems can be easily adopted
into a small-scale farming.
Factors Affecting the Adoption of Sensor-based Drip
Irrigation
Several factors can affect the adoption of sensor-based drip
irrigation systems by small, medium and large-scale farmers.
Some of these factors include initial capital investment,
farmers’ level of awareness, expertise and experience, capital
recovery, technical know-how and farm characteristics as
well as risk and uncertainties associated with the system.
According to the Organization for Economic Cooperation and
Development (OECD), the characteristics of technologies
such as relative advantage, complexity, divisibility,
observability and compatibility affect their diffusion and
adoption by the farmers (OECD, 2001). Diffusion is the
process by which a new idea, practice or technology (such as
sensor-based drip irrigation) spreads in a given population
(farming communities in this case).
Initial capital investment, farm size and capital recovery
Capital investment is an expenditure for acquiring, leasing, or
improving the property that is used in operating a business
and it includes land, buildings, machinery and fixtures.
Although sensor-based drip irrigation requires a high initial
capital investment but may offer cost savings and higher
yields in long run through more precise and proper irrigation
scheduling and fertigation (Sharma et al., 2019). A study was
conducted to assess the economic performance of a drip
irrigation system for growing tomato in Egypt in which 100
tomato farmers were interviewed. The study revealed that
investment in the drip irrigation system is profitable, with an
increase of 67% in the net return per hectare, compared with
results using non-drip irrigation. The benefit-cost ratio
amounted to 1.35, the net present value was US$1720, and
the internal rate of return was 44% (Ali et al., 2020). A similar
study was conducted by Narayanamoorthy et al., (2018) to
assess the techno-economic potential of drip irrigation
systems in vegetable crops using survey data from the Indian
state of Tamil Nadu. The findings of the study revealed that
the drip irrigation system has brought about 54% higher net
returns than the conventional method of irrigation. Another
study conducted by Kinyangi, (2014) to examine how capital
and credit facilities influence the adoption of agricultural
technology among small-scale farmers in Kakamega North
Sub-County, Kenya shows that both capital and credit
facilities had a positive and significant influence on the
adoption of agricultural technologies and innovations.
The size of the farm influences the decision of the farmers to
adopt sensor-based drip irrigation. The larger farms may have
access to higher equity and monetary resources to invest in
water-saving equipment (Wang et al., 2010). A field study
conducted to determine the major factors influencing farmers’
adoption of drip irrigation in two districts in Kerala, India
shows that land holding size has a positive influence on drip
irrigation adoption index by farmers (Chandran & Surendran,
2016).
Capital recovery is simply the return on an initial investment.
It is the earning back of the initial funds put into an
investment. The number of years required to recover the
capital cost of sensor-based drip irrigation may affect the
farmers’ decision to adopt or reject the technology
(Viswanathan et al., 2016). As against the general perceptions
of many farmers that the capital cost recovery of drip
irrigation investment takes a long time, a year-wise
computation of the net present worth (NPW) for sugarcane,
banana, grapes and cotton grown under drip irrigation
suggested that farmers could recover the entire capital cost of
drip-set from the net profit within one year (Viswanathan et
al., 2016).
Farmers’ awareness and experience
Farmers’ awareness and level of experience are also key
determinants of adopting or rejecting agricultural
technologies and innovations such as sensor-based drip
irrigation systems. Several studies indicate that there is a
relationship between the farmers’ awareness and their
adoption of improved agricultural technologies and
innovations (Acheampong et al., 2018; Zakari et al., 2021).
Farmers with a high level of education are well-informed
about the development and performance of different irrigation
technologies and are more likely to accept and adopt these
technologies than those with a non or low level of education
(Abdulai et al., 2011; Shanono, et al., 2021) This is also
supported by the works of Bagheri & Ghorbani, (2011) in
which 160 farmers were surveyed for the adoption and non-
adoption of micro-irrigation technology in Ardabil Province
of Iran. The results of their show that the adopters are farmers
with low farming experience but a high level of education
(Bagheri & Ghorbani, 2011). A study conducted to find the
determinants of Farmers’ awareness and adoption of
extension recommended Wheat varieties in the rainfed areas
of Pakistan shows that there is a strong relationship between
the farmers’ awareness of a technology (improved Wheat
varieties) and its adoption. The study further revealed that the
extension contacts of the farmers, income from agriculture,
and access to credit positively affected the farmers’
awareness, whereas their low level of education and
experience, as well as household sizes, negatively affected
their awareness (Ullah et al., 2022).
Risk perception and technical expertise
In agriculture, the risk is defined as the probability of
occurrence of hazards and shocks that negatively impact
agricultural production and other value-chain operations.
Farmers in the whole world particularly in Africa, face
several interconnected risks (FAO, 2016). These risks
discourage farmers to adopt new agricultural technologies on
their farms for fear of the possible negative outcome
associated with new technology. All agricultural technologies
and innovations have some subjective and objective risks
associated with them. The adoption and implementation of
these technologies are typically influenced by the farmers’
individual’s perception of risk and uncertainties, and their
ability to bear the risk of a new and uncertain endeavour
(Parvan, 2011). The capability of sensor-based drip irrigation
in increasing the water efficiency of irrigated agriculture is
not known to most farmers, particularly small-scale farmers
and therefore there is a need to undertake awareness-raising
campaigns to enlighten and encourage them to the adoption
of the technology.
Technical know-how refers to the information and knowledge
relating to the design, development, installation, operation
and maintenance of any system. Technical know-how is one

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265
of the impediments affecting the adoption of sensor-based
drip irrigation systems by farmers (Dace, 2020). The design,
layout, installation, operation and maintenance of sensor-
based drip irrigation required technical skills. These skills
become barriers for many farmers in adopting the drip
irrigation systems despite their excellent performance as
water, labour and energy-saving irrigation systems. A study
conducted to find out the factors that drive the adoption of
drip irrigation in Erode district in Tamil Nadu, India listed
technical skill among other factors that hinders the adoption
of drip irrigation by the farmers in the study area (
Viswanathan et al., 2016; Kaarthikeyan & Suresh, 2019)
Why adopting Sensor-based Drip Irrigation System
The adoption of sensor-based drip irrigation is essential for
irrigated agriculture, particularly in arid and semi-arid arid
regions of the world where water is a limiting factor. This
emerging technology of sensor-based drip irrigation is
environmentally-friendly as all issues associated with over-
irrigation such as waterlogging, salinity and soil erosion are
eliminated (Ismai’il et al., 2014; Levidow et al., 2014). The
system produces more food with less water making the
irrigated agriculture highly profitable through water, energy
and labour saving and also minimizing the leaching of
fertilizers and other chemicals applied to the soils (Ncube et
al., 2018).
In addition, sensor-based drip irrigation is the solution to the
problems caused by climatic uncertainty and erratic rainfall
that occasionally affect agricultural production. The adoption
of sensor-based drip irrigation by farmers is a promising
investment that has been found to improve the profitability of
their farms (Munoth et al., 2016). The system is also
beneficial to the larger society as the pressure on agricultural
water demand will be reduced which means more water will
be released to the society for domestic and other uses. The
adoption of sensor-based drip irrigation will also serve as a
guide for policy and decision-makers in the planning of water
resources as wide adoption of this system of irrigation will
eliminate the need for the construction and development of
large dams and reservoirs for surface irrigation.
Framework for the Adoption of Sensor-based Drip
Irrigation System
Farmers can easily adopt sensor-based drip irrigation systems
despite the major impediments affecting their decisions when
the following ideas, suggestions and recommendations are
fully implemented.
Dissemination of sensor-based irrigation to farmers
The extension agents can disseminate information on sensor-
based drip irrigation to the farming communities and
persuade them to adopt the system for efficient water
management and the overall productivity of their irrigation
systems. Extension workers are usually the middlemen
between the research institutes and the farmers. These
professionals take and promote any agricultural innovations
developed by the researchers to the farmers. Sensor-based
drip irrigation can equally be promoted to the farmers by
extension agents and irrigation engineers. The technical
know-how associated with the layout, installation, operation
and maintenance of sensor-based drip irrigation can be easily
explained and demonstrated to the farmers by the extension
agents and irrigation engineers. Also, the advantage of
sensor-based drip irrigation in comparison to both drip and
conventional irrigation systems can be explained to the
farmers. The system will be accepted and adopted by the
farmers once they are aware of these advantages.
Creation of awareness among farming communities
Awareness-raising campaigns can be created among farming
communities and farmers’ associations on the importance of
the adoption of sensor-based drip irrigation for proper and
precise water application. The demonstrations can be done to
the farmers on how the systems work. This can be done
occasionally to farmers and comparisons can also be made
between the sensor-based drip irrigation and conventional
irrigation systems for farmers to see practically how the
system saves water, energy and labour. The awareness can go
a long way in convincing the farmers to adopt the systems on
their farms.
Provision of subsidy and credit facilities
Government and other non-governmental agricultural
organizations can subsidize drip sets and soil moisture
sensors to farmers that are willing to adopt the technology on
their farms. This will encourage and motivate the farmers to
adopt the systems. The credit facilities can also be made
available to the farmers particularly small-scale who are
interested in adopting the technology but have no initial
capital to invest in the systems.
Provision of policies and environmental standards
Government should enact and implement laws and policies
on sustainable agricultural production. Any agricultural
activities that result in significant environmental degradation
should be banned. Taxes and serious sanctions should be
imposed on the defaulters. This will make farmers adopt
sensor-based drip irrigation that is environmentally friendly
and sustainable.
Review the price of water charges
The current price of water of #5,000.00 which is a charge to
the farmers per hectare by the river basin development
authority of Nigeria needs to be reviewed. This price is good
as free and makes farmers waste water injudiciously. The
price of the water needs to be reviewed and should be based
on the amount of water consumption by the farmers, not a
fixed rate. The flow meters can be installed at various farms
to measure the actual quantity of water consumed by the
farmers. This will change the perceptions of the farmers and
will look for the adoption of water-saving irrigation systems
such as sensor-based drip irrigation systems.

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Figure 2: Framework for the adoption of sensor-based drip irrigation
CONCLUSION
The adoption of sensor-based drip irrigation by small-scale
farmers in the semi-arid region of Nigeria will boost
agricultural production and food security in the region
through precise and proper irrigation scheduling. The
adoption of the system can also help in addressing the
problems of water scarcity that are being experienced
occasionally due to erratic rainfall and climatic change
impact. As the water resources in the region are reducing
gradually while their demand is rapidly increasing, thus, the
adoption of sensor-based drip irrigation to ensure efficient
water application while maintaining high crop yield is no
longer an option but a necessity. The factors identified to
hinder the adoption of sensor-based drip irrigation systems by
small-scale farmers include initial capital investment,
farmers’ awareness, risk perception and uncertainties and
capital recovery. Others include technical know-how and
farm characteristics. These issues can be overcome by
dissemination of sensor-based irrigation to farmers, creation
of awareness among farming communities, provision of
subsidy and credit facilities, provision of policies and
environmental standards and review of the price of water
charged to the farmers among others.
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