Technology and Policy Implementation Effects on Youth Agricultural
Robert Brenya
1,* a
, Zhu Jing
, Agyemang Kwasi Sampene
, Fredrick Oteng Agyemang
and John Wiredu
College of Economics and Management, Nanjing Agricultural University, Nanjing, China
School of Management, Jiangsu University, Zhenjiang, China
School of Management, Northwestern Polytechnical University, Shaanxi-Xi’an, China
Keywords: Agriculture Farming, Agriculture Technology, Policy, SDGs, Youth.
The adoption and patronage of the internet by the current generation has propelled enormous endorsement of
agriculture technology applications. Agriculture technology should be prioritized by policymakers to ensure
its development and orientation of the youth towards radical amendments to agricultural farming, and food
sustainability. Notwithstanding, little or no empirical evidence is there to support policy implementation
toward agriculture technology in Ghana. This study brings to discovery the imperativeness to institute and
implement policies to support youth technological farming as it serves as the cornerstone to mitigate
unemployment, improve food security, and sustainable economic development. We used a content analysis
approach and generalized linear models, based on the technology acceptance model theory for analysis. This
study outlines policies such as agriculture technology and integration strategies that are required for effective
implementation to curtail the constraints within the agricultural sector such as dynamics in climate patterns,
inadequate policy enforcement measures, and integration strategies. The study addresses this research gap
and elucidates factors that contribute to youth participation in technological farming and the potential
achievement of the Sustainable Development Goals. We justify the need to establish a framework such as
digital advertisement, formidable implementation, and integration policy to mitigate the misconception about
The economic development and sustainability of
most developing countries are closely linked to youth
participation in agricultural activities. Empirical
research predicts that the world’s population is
increasing, hence, the demand for food and daily
necessities from the agricultural sector will continue
to soar. According to Betcherman and Khan (2015),
the African continent is having the youngest populace
with an average age of 18, expected to reach an age
range of 21-24 by 2035 and 2050 respectively.
Ghana’s National Youth Policy in 2010 defined youth
as ‘persons who are within the age range of fifteen
(15) and thirty-five (35) (Kidido et al., 2017). Getting
them involved in agriculture technology farming
(ATF) will promote the quest in accomplishing food
security, youth employment, and sustainable growth.
Furthermore, the liveliness and innovativeness of the
youth are principal assets to every country that works
towards economic and sustainable development, food
security, and agricultural farming (AF)
diversification (FAO, ILO, and UNESCO, 2009). In
addition, the youth possess dynamic ideas with a keen
interest in utilizing AT to address basic problems
encountered in achieving the Sustainable
Development Goals (SDGs), inter alia, goal 1(no
poverty), goal 2 (no hunger), goal 15 (life on land),
and the like. This unswerving interest provides the
foundation for using ATF to safeguard household
Brenya, R., Jing, Z., Sampene, A., Agyemang, F. and Wiredu, J.
Technology and Policy Implementation Effects on Youth Agricultural Farming.
DOI: 10.5220/0011595900003430
In Proceedings of the 8th International Conference on Agricultural and Biological Sciences (ABS 2022), pages 64-75
ISBN: 978-989-758-607-1; ISSN: 2795-5893
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
food security, and generate income to cater to the
basic needs, among others. Kwakye et al. (2021)
conceptualized that, ATF is the principal solution to
sustain the practice of producing food and raising
domesticated animals (livestock).
As established, for developing economics to
eradicate hunger via AF, a technological application
must be the principal bedrock to this audacious target
(Tomchek, 2020). Yet, agriculture technology policy
(ATP) integration is slacking in Ghana. There is little
empirical research to showcase the implications of the
lack of technological policy implementation in the
agricultural sectors. Most developing economies
including Ghana do not have the established policies
to facilitate the requisite technology and resources
needed to acquire them. This situation indirectly
sends the youth away from AF since the current
generation of youth are in a modern era where the use
of traditional tools such as cutlass, hoe, and other
small equipment for farming does not encourage them
to engage in agriculture activities. Kwakye et al.
(2021) confirmed in their studies that the youth are
moving away from the agriculture sector towards
both the industrial and service sectors due to causal
factors such as education, family encouragement,
unavailability of land, non-lucrativeness of
agriculture, urbanization, administrative job, lack of
agricultural policies enforcement, and inadequate
technology. These factors cause detrimental effects
on food sustainability in every developing country
(Brenya et al., 2022).
As a result, this study adopts four objectives to
investigate this predicament. First, to determine how
technology and policy implementation influence
youth farming in Ghana. Again, the present study
sought to examine the impact of technological policy
applications on the sustainable development of
Ghana. Next, to determine how it can contribute to
poverty alleviation in the country. Finally, it
attempted to establish the correlation of current
research principal dependable variables’ impact on
youth farming in the future. To do this, we employed
the technology acceptance model (TAM) for theory
interpretation, content analysis, and the probit and
logit modeling approach for empirical findings. The
study further adopts a section of technology
acceptance modeling theory to ascertain the attitudes
of the youth in applying AT. The theory was first
introduced by Davis (1985) based on the theory of
reasoned action by Fishbein and Ajzen (1975). The
theory elaborates on the reason why people employ a
particular technology when working in their habitat.
ATPs that are stipulated must house the key elements
of Fred Davis’s theory, in order to influence the youth
to participate in AF. The principal component of
Davis’s theory is Attitude Toward Using a particular
AT, which is made up of Perceived Usefulness and
Perceived Ease of Use, all in the neighborhood of
other relevant characteristics contributing to the
youths’ attitude. Although the theory implies less
about a particular technology itself. It provides the
fundamental consensus that does not relate to user-
friendliness and/or usefulness but is a matter of
perception which gives an upper hand to the current
technological youth. TAM is widely used in subjects
and/or disciplines in agriculture, biotechnology, and
food sustainability, among others (Ogwuike et al.,
2021; Venkatesh and Davis, 2000). The study used
this theory, as it provides the platform for better
assessment and understanding of the youth in
utilizing agriculture technologies, inter alia, climate-
smart agriculture, robotics, nitrogen modeling, and
smart greenhouse.
Furthermore, as the current generation has the
acumen to receive new knowledge, the youth
harnessing innovative means can facilitate crops and
animal production, efficient extension programs,
reduction in risk assessment, and improved decision
making. In line with this, the study stressed the
imperativeness of policy implementation towards
youth technological farming by asking the following
relevant questions; (i) poor technology and policy
implementation, what is the way out for youth
farming in Ghana? (ii) What impact does technology
policy application have on the sustainable
development of Ghana? (iii) How can it contribute to
the poverty alleviation of the country? We answer
these questions in subsequent sections, indicating the
novel contribution of the study toward ATP
implementation in Ghana. The study fills in the
literature gap, especially from the angle of policy
implementation in Africa toward agriculture. Next,
TAM theory provides the platform for technology
acceptance and future understanding of technology
integration and usage in the agrarian sectors.
Likewise, other contribution stems from the thorough
reflection on the knowledge of policy implementation
towards AT with its related obstacles that prevent its
implementation. More so, the content analysis used
not only enables the nearness of data to be statistically
analyzed but also provided an unobtrusive means of
evaluating interactions. The rest of the study is
arranged as follows. First, the study presents ATP
integration, sustainable development, and methods
used for the plausible analysis. Next are the results
and a detailed discussion answering the research
questions and establishing a correlation of studies that
looked into the themes that directly and indirectly
Technology and Policy Implementation Effects on Youth Agricultural Farming
promote youth farming. In the end, the study
concludes with theoretical and practical implications.
1.1 Agriculture Technology and Policy
Recent technological policy orientation showcases a
direct logical influence on youths’ decisions if
properly integrated into their acumen. According to
the United Nations (2015), effective policy
integration is all the more important given the range
of expertise from different institutions and sectors
required to tackle the SDGs, as well as demands for
more innovative, responsive, and equitable service
delivery, which transcend the competencies of
individual ministries. In this regard, various policy
implementations right from the angle of innovative
food policy, social policy, and climate-smart
agriculture policy purposely to sustain the agrarian
and economic growth of Ghana have been instituted
(Agyepong and Adjei, 2008; Thow et al., 2014) to
remedy the prevailing agriculture trend.
In this study, ATP integration is elaborated as the
use of direct and indirect directives toward the
steering of technological applications in AF.
Technology integration in youth AF will not only
induce their interest but also increase the cost-
effectiveness of agricultural production while
reducing the drudgery of farm activities. As predicted
in 2017, Ghana Statistical Service asserted over a
third (38.8%) of Ghana’s population is made of
youth, this constitutes a major boost to the
agricultural sector if technology is integrated to direct
their path (United Nations, 2017). Yet, the ATP
integration in Africa and Ghana not being exempted
has been a bottleneck for policymakers. Empirical
documentation to prove the essence of policy
application toward AT is inconclusive. This study
points out challenges such as climate change due to
excessive carbon emission into the atmosphere,
financial constraints that reduce the youth’s degree of
capabilities in acquiring the necessary machinery,
gender bias in technological designs, inadequate
technology utilization, lack of effective policy
implementation, and the plethora of scientific studies
as the principal causes of less youth participation in
AF (Sampene et al., 2021; Kwakye et al., 2021).
Hence, policy reforms and integration backed by
formidable regulations towards agriculture
technological sectors have proven to be the valuable
keystone to withhold food sustainability. For
example, Mwaijande and Lugendo (2015) indicated
that policy reformation and access to technology
serve as a key remedy to ensure cheaper products
while maintaining quality standards, and food
security. With technology at the core of AF, the youth
will actively embrace the opportunity to enhance crop
fertilization and mechanized production, animal
breeding, and dairy farming. And that, technology-
driven policies seek to effect skill changes to young
farmers in developing new agricultural operations,
enhancing productivity, fertilizer application,
precision irrigation, digitalized drone spraying, and
the like (Groot et al., 2019; Marinello et al., 2016).
1.2 Sustainable Development via
Agriculture Technology Policy
This section answers the second question, what
impact do technological-driven policies have on the
sustainable development of Ghana? In responding to
this, we linked the impact of ATP implementation in
Ghana, and its associated benefits in achieving the
Sustainable Development Goals (SDGs) endorsed by
member states under the United Nations (UN). The
2030 SDGs remain vital, forming the basis for several
socio-economic policies, formulated by governments
around the world since its inception in 2015 (Ghana
Statistical Service, 2017). As denoted in Figure 1, this
study elaborates on AT-driven applications’
influence on three-dimensional achievable goals from
the angle of social, economic, and environmental
perspectives. The social dimension houses the
eradication of poverty, ensuring food sustainability in
both developed and developing economies via
technological applications. Furthermore, the
economic dimension perceives AF as decent work
and an active contributor to economic and sustainable
growth in most developing countries. Likewise, cost-
effective production and reasonable food prices also
fall under the umbrella of the economic dimension of
the SDGs when effective AT-driven policies are
properly enacted in the various geographical regions
(Tomchek, 2020). Moreover, technological
agriculture ensures efficient technological machinery
used on farmlands which indirectly regulates the
emission of CO
, manages land degradation, enables
climate-smart farming, soil-water system, and
peatland farming which facilitates carbon
sequestration, hence promoting the environmental
dimension of SDGs, (Visser et al., 2019; Sampene et
al., 2021). In this regard, the sustainable development
of a country is assured if the government is able to
implement agricultural policies toward a well-
coordinated operation to support nutritional
efficiency, social definition, and fiscal backing while
practicing climate-resilient technology.
ABS 2022 - The International Conference on Agricultural and Biological Sciences
Figure 1: ATP benefits to Sustainable Development Goals.
2.1 Area of Respondents
As denoted in Figure 2, Ghana is located in West
Africa covering 238535 km
crossing the Gulf of
Guinea and the Atlantic Ocean in the south and
sharing borders with Burkina Faso in the north, Togo
in the east, and Ivory Coast in the west. According to
the World Population Review, Ghana is situated a few
degrees above the equator with a latitude of 7.9465°
N, and a longitude of 1.0232° W. As of the end of
February 2022, Ghana’s contemporary population is
32145660 as indicated by the Worldometer
embellishment of the newest United Nations data.
Presently, there are 16 demarcated regions that house
the various forms of natural resources, inter alia,
gold, petroleum, natural gas, diamonds, silver, and
salt. Likewise, it is the most stable country in West
Africa since the country’s independence in 1957, and
the economy is gaining the paybacks of political
stability through the improvement of agriculture
production, prudent healthcare facilitation, food
security, and the like. Notwithstanding, Ghana must
enhance its integration of technology in the agrarian
sector in order to entice the youth into AF.
1  310(0.05)
2.2 Data Collection
Furthermore, we applied a cross-sectional survey in
2020 to collect data from Ghanaian youth who are
between the ages of 17 and 35. A well-vetted
questionnaire was randomly administered to the
youth via social networks such as WeChat,
WhatsApp, and so on. We had a total of 310 youth
who willingly partook in the data collection. Scholars
such as Henson and Roberts (2006) and Worthington
and Whittaker (2006) recommend a sample size of at
least 300 for efficient and effective empirical
analysis. The data was coded by assigning 1 to
strongly agree (SA), 2 to agree (A), 3 to neutral (N),
and 4 to disagree (N) for the analysis of the variables
using STATA application software. This study
certified both the validity and reliability of the survey
data to ensure accuracy and consistency of the output
as a direct implication of the expected output via us
the content validity index (CVI) for accuracy and
Cronbach’s alpha score for reliability (Westland
2019; Kwakye et al. 2021). This enabled the study to
define the descriptive analysis which houses the
mean, standard deviation, and distribution percentage
of each variable as postulated in Table 1. Using
Yamane (1967) arithmetical approach, the study
determined the sample size of the respondents,
(Equation (1) indicates);
n = sample size
N= sample frame
e= margin of error is 5%, therefore, (equation 2)
= 174.647887
approximately 175 (2)
Technology and Policy Implementation Effects on Youth Agricultural Farming
Figure 2: Map of Ghana. (Source: Yeboah et al. (2021)).
Table 1: Variable, definition, mean and coded numbers to percentage.
Variable Definition (%) Code = Class = Percent Mean Std. Dev.
Gender 1= male =69.68;0=female =30.32 1.303 .460
Age 1=18-24=27.42;2=25-29=45.48; 3=30-34 =27.10 1.996 .739
Education 1=Diploma=16.13;2=Degree=36.45;3=Master
2.406 .867
WhiteCJob 1=(SA)=55.48;2=(A)=35.81;3=(N)=7.74;4=(DA)=.97 1.541 .680
Fiunemploy 1=(SA)=27.42;2=(A)=40.65;3=(N)=23.23;4=(DA)=8.71 2.132 .916
Ppolicies 1=(SA)=28.71;2=(A)=49.35;3=(N)=11.94;4=(DA)=10.00 2.032 .898
Technology 1=(SA)=19.35;2=(A)=45.48;3=(N)=22.26;4=(DA)=12.90 2.287 .923
Famsup 1=(SA)=6.13;2=(A)=18.06;3=(N)=35.81;4=(DA)=40.00 3.096 .905
Genencourage 1=(SA)=11.94;2=(A)=35.16;3=(N)=27.42;4=(DA)=25.48 2.664 .987
0=High=17.42;1=Low=82.58 .825 .379
0=High=12.90;1=Low=87.10 .870 .335
2.3 Data Analysis Technique
The study employed a generalized linear model to
back the content analysis via the use of the logit
model to estimate and interpret the binary outcome of
youth participation in AT. Research theories from
Gujarati (2004), and Wooldridge (2006) were
adopted to clarify the correlation among the variables
via the Stata application software. Logit has been
extensively used to assess the dual impact of a
particular variable on another. For instance, a unit
increase in the explanatory variable increases the
explained variable by the interaction unit. So, the
hypothesis is that the likelihood of youth participation
in AT increases as the policy enacted becomes
executable. As signified in equation (3), the binary
response variable (participate or otherwise) ‘1’
represents i
respondents select to participate and ‘0’
if otherwise.
𝑌𝑜𝑢𝑡ℎ 𝐴𝐹
1, 𝑖𝑓 𝑝𝑎𝑟𝑡𝑖𝑐𝑖𝑝𝑎𝑡𝑒
0 , otherwise
For the logit model, F(x
β) in equation (4) is used
for the Cumulative Distribution Function of the
logistic distribution.
ABS 2022 - The International Conference on Agricultural and Biological Sciences
β) =Λ(x
β) =
 
 (
 (
This implies that the binary outcome model
estimates the probability that y = 1 as a function of
the independent variables, (Bolang and Isumanu
2019; Katchova 2013) as indicated in Equation (5)
with the probability limited between 0 and 1;
p = pr [ y=1|X] = F(x
β) (5)
In ascertaining the likelihood of the independent
variables, marginal effects, (Equation 6) is employed
after the reports on the coefficients, this reflects the
change in the probability of y = 1 given a 1-unit
change in an independent variable X, (Katchova
2013). Comparatively, the odds ratio is difficult to
interpret while marginal effects give a clearer
understanding, a direct measure, and a more
enlightening statistic since it has the ability to bring
change in the variable of interest while holding the
remaining variables in the model constant.
= Λ (x
β) [1- Λ (x
β)] β
( 
Furthermore, the marginal effects depend on X, so
we need to estimate the marginal effects at a specific
value of X (typically the means). The model (Eq.
(7)), was used as the determining measure to quantify
how the youth perceived AF.
In (
 
) = β
+ β
Technology + β
Ppolicy +
WhiteCJob + β
Fiunemploy + β
Genencourage + β
Attitude + β
Knowledge +
Age + β
Education (7)
The coefficients of the explanatory variables vary
from each other. The study expects factors such as
technology, policy, whitecjob, fiunemploy, famsup,
genencourage, attitude, knowledge, age, and
education to have an effect on youth farming via ATP
The study observed the determinants which may or
may not skew the youths’ participation in AT
farming. The analysis was built on these variable
observations such as technology, policy (ppolicy),
white color jobs (whitecjob), forced farming or farm
rather than unemploy (fiunemploy), family support
(famsup), generational encouragement
(genencourage), attitude towards farming (attitude),
knowledge about farming (knowledge), age in years
(age), and the number of years spent in school
(education). As indicated in Tables 1 and 2, the study
received answers from both male and female youth
with 69.68 % representing the males, whilst the
remaining 30.32% constituted females. A greater
fraction of the youth was between 25-29 years with
45.48%, followed by the youngest age group (18-24)
denoting 27.42%, and lastly, the 30-34 age group
(27.10%). In every society, the education of the youth
plays a vital role in the socio-economic development
and sustainability of the country. As a result, the
model analysis of education gave an arithmetic
average mean of 2.406, and the standard deviation
equals 0.868; diploma as the lowest qualification
represented 16.13 %, next is the degree with 36.45%.
The youth pursuing master’s constituted 38.08% and
9.35% for Ph.D. at the time of data collection (Table
Table 2: Logistic regression details for variables.
Variable Coef. Std. Err. z P>z [95% Conf. Interval] Marginal Effect (dy/dx)
Age .008 .172 .050 .962 -.330 .346 .002
Education -.096 .147 .650 .513 -.192 .385 .019
WhiteCJob*** .634 .184 3.45 .001 .274 .994 .121
Fiunemploy** -.461 .151 -3.05 .002 -.758 -165 -.089
Ppoliciesi* .221 .139 1.59 .112 -.051 .493 .044
Technology*** -.549 .154 -3.57 .000 -.851 -.247 -.105
Famsup*** -.627 .142 -4.39 .000 -.907 -.347 -.116
Genencourage*** -.606 .140 -4.32 .000 -.881 -.332 -.113
Attitude** -.745 .313 -2.38 .017 -1.35 -.132 -.145
Knowledge** -.908 .348 -2.61 .009 -1.59 -.226 -.177
Technology and Policy Implementation Effects on Youth Agricultural Farming
As energetic as the Ghanaian youth are, most of
them prefer to work in administrative offices. Out of
the 310 observations, 55.48% strongly agree to work
in an office instead of going to the farm. 35.81%
assigned themselves to agree in working in offices,
whilst less than 9% remained neutral. Consequently,
the youth’s interest in white color jobs has a 0.01
statistically significant impact on their decision to
participate. The results indicate that not only are they
actively willing to boycott AF, but also very much
willing to join the administrative jobs. Thus, a
marginal effect of 0.121 presents a 12.1% decision
toward white color jobs upon a high degree of policy
inefficiency toward ATF. Likewise, the youth
responded that, instead of being unemployed, they
often resort to agriculture as the last means of
employment. This indicates a negative coefficient (-
0.461), and a p-value of 0.02 within the 5%
significance threshold. This proves as the rate of
unemployment in the country goes down, the youth
are 8.9% less likely to drop off from AT, whether the
policy instituted is favorable or not. Likewise, the
respondents had an average mean of 2.032 and a
standard deviation of 0.8989 against ATP
implementation. Within this, 28.71% strongly agreed,
49.35% confirmed by agreeing to the inadequate
policies application, whilst the remaining 11.94% and
10% represented neutral and disagreement
respectively. The regression further indicated 0.221
positive coefficients, this infers that as inadequate
policies toward farming continue, the youth move
away from technology farming by 4.4%.
Figure 3: Current Agriculture Mechanization Tools.
Surveying and spraying assistance in
hazardous environmen
Enable automatic and/or manual valve
control of moistenin
Enable precision location, up-to-date map
and weather information
Mitigation of manual drudgery via labor
Stockpile information to enable geneticists
to add more traits to a plant
Enable identification of weak crops on the
field via image capturing
Enable comparison of plant data analysis
for proper treatment
Ensure the creation of microclimate
conditions for
Enable a better nutrient efficiency to soil
health and crop development
Enable climate resilient practices to drought,
climate-related risks and shocks
Climate Smart
GIS Software
and GPS
Farm Data
Marinello et al. (2016)
Grabowski et al. (2021)
Marinello et al. (2016)
Marinello et al. (2016)
Grabowski et al. (2021)
Goyal et al. (2009)
Carolan (2017)
Walker et al. (2009)
Lee et al. (2018)
Groot et al. (2019)
ABS 2022 - The International Conference on Agricultural and Biological Sciences
According to Nafeo and Awal (2020) technology
inclusion in any subject has proven to be an
indispensable component to back progress. However,
in the biodata of respondents, the study observed a
low application of technology in the field of farming
with 19.35% and 45.48% denoting they strongly
agree and agree, respectively. On the other hand, less
than 36% disagree with this statement. This correlates
negatively to effectively enacting policies, toward
youth utilization of AT. Forecasting signifies that the
low application of technology is likely to drive the
youth away by 10.5% at its unit of change. Family
support had an average mean of 3.097 with less than
25% of respondents agreeing to have received support
from their families. On the other hand, more than 65%
asserted they do not receive any agricultural
technological motivation from their families. The
regression analysis also indicated that family support
was statistically significant with a marginal effect of
-0.116. This implies the rate of youth participation
decreases by 11.6%, as a result of limited family
support within a household.
Globally, the current generation has been
described as the technological generation that cannot
live without the internet (Kwakye et al. 2021). With a
p-value less than 0.001, the result further indicates
that 11.94% and 35.16% of the respondents agreed
with the generational encouragement, whereas the
remaining percentage opined receiving no support
from the previous generation. Likewise, the youths’
attitude toward farming led to a negative coefficient
(-0.745) with a statistically significant figure (0.017),
and a marginal effect of (-0.145). This implies that
there is a 14.50% likelihood decrease as to how the
youth attitude impacts their decision to join AF. Also,
the youth’s knowledge of AT applications had 12.9%
equal to 0.871. Moreover, the knowledge regression
provided a negative and statistical significance figure
of 0.009, and a marginal effect of -0.177, denoting a
17.7% decrease in the probability of the youth
participating in ATF.
Farming in recent years has been adjusted towards the
application of AT as the populace sees how it
enhances productivity and increases the profit of
farmers. Yet, this study observed that not only do the
youth have a negative perception of agriculture but
also witness the consistent failure of policies towards
it, this has exacerbated their reluctance in practicing
AT. As presented in Table 2, the youth’s degree of
knowledge in AT is limited with a coefficient of -
0.096 and a marginal effect of 0.019. This indicates
that education has an indirect relationship with policy
implementation. Thus, as the youth continue to
improve their level of education in technology, there
is a 1.9% for them to venture into ATF. Thus, the
primary question; is poor technology and policy
implementation, what is the way out for youth
farming in Ghana? This study elaborated the only
way out for youth participation in ATF is to enact and
implement a robust policy that will directly and
indirectly, enforce AT adoption. Ogwuike et al.
(2021) postulated, that an undeniable approach to
encourage youth farming is to institute policies that
initiate extra modern AT equipment that inspires and
pushes the youth’s interest in farming. This directly
confirms respondents’ formal education influence on
technological farming (Kwakye et al. 2021; Ogwuike
et al. 2021). Technologies denoted in Figure 3 have
proven to be the panacea for agriculture sustainability
and economic growth facilitator in most developed
countries (Nafeo and Abdul-Rahaman 2020) and can
serve as the springboard for inspiring youth to
embrace AF in Ghana. Robotics and drone utilization
have advanced the 20
-century harvesters, and
tractors use for unmanned agricultural operational
activities in chemical-induced environments; this
preserves farmers’ health and reduces labor
intensiveness (Marinello et al., 2016). Furthermore,
not only can AT produce the exact position of a
missing animal but also remains a significant factor
in promoting vivid satellite images over the farm area.
Over the decades, the internet of things has facilitated
easy accessibility of mobile-phone software
applications in areas via remote controlling of
irrigation systems to transport water to the farmland.
Still, technological advancement in agriculture has
given birth to genetic engineering, where Goyal et al.
(2009) enunciated minichromosomal technology as
an extremely small version of a chromosome that has
been produced by de novo construction using cloned
components of chromosomes or through telomere-
mediated truncation of endogenous chromosomes
and this technology promote plant development and
drought-tolerant. Likewise, in the case of nitrogen
modeling, it increases the yield of farmers due to its
ability to fertilize the land and efficiently emit the
amount of CO
(Walker et al. 2009). In this regard,
farm technology application enables reliable data
analysis that tracks plant creation via heating,
ventilation, and air conditioning (HVAC), light-
emitting diode, and climate-resilient practice
conditions, (Groot et al. 2019; Lee et al. 2018). Food
and Agriculture Organization confirmed in 2009 the
relevance of these technologies and its target to
Technology and Policy Implementation Effects on Youth Agricultural Farming
reduce the mammoth impact of food insecurity
expected to be inexperienced by 9.6 billion people by
2050.Moreover, the policy applied to these
technologies greatly initiates fiscal backing towards
farming activities that sprout reimbursements in
prices and cost reduction during production and
consumption and provide precision dispersal of
chemicals and fertilizers that promote farm
sustainability (Brenya et al. 2022; Makate et al.
2017). However, during the analysis, the study
observed that farmers in the household lack the
essential benefits derived from technology which
indirectly prevents their pursuit of income and
sustainable dependency. The youth in these farmers’
households avoid farming outright since they use the
living conditions as a proxy to determine their future
living status should they engage in farming. Thus,
among the vital question; what impact does
technology policy application have on the sustainable
development of Ghana? As indicated the impact of
technology policy application in agriculture is
countless in terms of food production and storage
decisions. Moreso, technology has turned AF into a
commercial business that increases revenue to sustain
families while providing youth employment.
Furthermore, due to the challenges encountered in the
acquisition of these technologies, poor households
can only be able to alleviate themselves from poverty
if the government and non-government agencies can
mitigate the cost and/or hiring processes (Brenya et
al. 2022). Evidence shown in the analysis indicated
that the youth are more interested in the white color
jobs, however, if better policies are instituted, then it
will skew them towards technological farming. This
will indirectly increase production and income, and
gradually lessen the degree of poverty among the
farmers in the various regions. Furthermore,
contemporary technology in agriculture ensures the
growth of large quantities of crops in the shortest
period of time. As asserted, recent farmers’ level in
education has propelled them to include AT
applications, this has enabled the farmers to cut out
middlemen and sell to consumers directly. This
approach has significantly increased the farmers’
income thereby alleviating them from poverty, which
may inspire and deter the youth from moving to
urbanized cities to earn income. In addition, poverty
reduction has been significantly reduced since
farmers can preserve the harvest during and after
production compared to the previous decades of food
deterioration. This study’s policy integration benefit
is in line with Yeboah (2018) studies, where study
enunciated that, with proactive programs,
innovations, and investment that can meet food and
nutrition security goals and support job growth, a
booming youth population has the potential to
transform entire regions, making them more
prosperous, stable, and secure.
In a nutshell, Table 3 enables the study to achieve
the last objective by tabulating previous empirical
studies that linked the principal dependable variables
to agriculture. During the analysis, the correlation
among studies further justifies the imperativeness for
policymakers on the African continent to integrate all
the dependable variables in Table 3 as it serves as the
benchmark for promoting food sustainability.
Furthermore, those mentioned earlier principal
dependable variables must be investigated further to
promote Africa AF.
This current study reveals a holistic approach to the
adoption of technologies in AF as an imperative tool
for the youth and sustainable development in Ghana.
Technology contributes to a greater percentage of
food production, employment, and agricultural
sustainability. Findings indicated from the present
study have proven that policy implementation
towards ATF does not only reward farmers with food
sustainability but trickles down youth unemployment
while enriching them with income to eradicate
poverty. However, youth encounter challenges such
as inadequate policy implementation towards ATF,
and financial constraints in acquiring technology,
among others hindering the youth agricultural
participation. The study answered three key questions
which brought to light the relevance to enforce
constructive policies toward youth ATF.
5.1 Theoretical Implication
The study contributes to theory application by
elaborating on TAM which expands the
comprehension of how technology application can be
adopted to increase youth participation in agriculture.
The study also analyzed some causal variables such
as technology, policy, generational encouragement,
white color job, education, age, and other factors that
the youth considered for having a positive or negative
impact on ATF. The analysis also confirms these
immediate statements above with statistical
significance of most of the variables at a 5%
significance level. This implies the study presents
enough evidence through the sample size among the
general population, especially on the part of the youth
who opt to adopt technology in agricultural activities.
ABS 2022 - The International Conference on Agricultural and Biological Sciences
Thus said, the study established a correlation that
exists among previous studies with principal
dependable variables that can promote African food
sustainability upon further investigation.
Table 3: Functional correlating variables for AT Policy and future works.
e Variable
Functional Measurement Source
Estimate the field and production quantity
of harvest filtered by variations and the
application of technology
Davis (1985); FAO, ILO, and UNESCO. (2009); Goyal et
al. (2009); Groot et al. (2019); Lee et al. (2018); Makate et
al. (2017); Marinello et al. (2016); Nafeo and
(2020); Ogwuike et al. (2021); Walker et al. (2009)
Estimate by indexing of agriculture farmer
price and the producer subsidy equivalent
Brenya et al. (2022); FAO, ILO, and UNESCO. (2009);
Groot et al. (2019); Lee et al. (2018); Makate et al. (2017);
Marinello et al. (2016); Mwaijande and Lugendo (2015);
Ogwuike et al. (2021)
The use of sustainable agriculture practice
index to determine the degree of
sustainability by summing up the
ted within the househol
Nafeo and Awal (2020); Ogwuike et al. (2021); Tomchek
(2020); Visser et al. (2019); Walker et al. (2009)
y Policy
Estimate by monitoring the performance of
technology adopted farmers to non-adopted
Davis (1985); Grabowski et al. (2021); Groot et al. (2019);
Lee et al. (2018); Marinello et al. (2016); Mwaijande and
Lugendo (2015)
nt Goals
Estimate using the SDGs indicators as
about the populace access to work, income,
consumption pattern and sustainability
Brenya et al. (2022); Ghana Statistical Service (2017);
FAO, ILO, and UNESCO. (2009); Marinello et al. (2016);
Nafeo-Abdulai and Abdul-Rahaman (2020); Ogwuike et
al. (2021); Spaiser et al. (2017); Tomchek (2020); United
Nations (2015)
Estimated youth population density
matched with the agricultural production
Betcherman and Khan (2015); Brenya et al. (2022); FAO,
ILO, and UNESCO. (2009); Kwakye et al. (2021); Nafeo
and Awal (2020); Ogwuike et al. (2021); Clark (1986);
Yeboah (2018)
5.2 Practical Implication
Consequently, the study recommends to all
stakeholders examine the underpinning problem,
thus, the lack of policy implementation towards
technological use in agricultural activities. The
implementation could secure the youth’s
participation via agricultural education that will be
included in the primary school education curriculum
to encourage the adolescent, especially the girls as
they grow. Additionally, the government must create
a conducive environment for sustainable agricultural
innovative models with collaboration from higher
education institutions. Furthermore, a digital
advertisement must be established and enhanced to
sensitize the youth about the benefits of participating
in ATF, aimed to address the misconception about the
deprived nature of farmers who engage in farming.
Also, the government must invest more funds through
scholarship provisions, credit facilities, scientific
research centers, and the development of agriculture
in the country. This, in essence, will sprout out the
lucrativeness of the industry and further deter the
youth from opting to travel abroad, instead of
venturing into agriculture. Likewise, the youth who
have already adopted AT in their activities must
receive a fair remuneration to prevent them from
losing interest. Similarly, excellent and reliable
agricultural policies must be drafted to replace
dormant, shallow, and ineffective policies. Policies
must be tailored, feasible, dynamic and proactive to
ensure effective implementation, without incurring
unintended consequences. Lastly, regular youth
development programs must be implemented since
the youth form a large demographic percentage of the
populace, hence, excluding them will be detrimental
to the economy.
5.3 Limitations and Future Research
Just like any other empirical result, this study also
encountered a challenge. The data that was used for
the analysis were only limited to 310 participants,
although earlier scholars have already established
that; at least 300 participants are enough for scientific
analysis. In spite of these constraints, further studies
Technology and Policy Implementation Effects on Youth Agricultural Farming
are recommended for future researchers to consider
using a larger dataset and diving deep into youth
technology policy implementation from the angle of
gender disparity. This research question may help,
could gender disparity or difference/ratio influence
the study’s results or findings?
This research is financially supported by China's
Food Security and Safety with Agriculture in
Transition - Overseas Expertise Introduction Center
for Discipline Innovation (B20074), and College of
Economics and Management, Nanjing Agricultural
University, Jiangsu, China.
Agyepong, I. A. and Adjei, S. (2008). Public social policy
development and implementation: A case study of the
Ghana National Health Insurance scheme, Health
Policy and Planning, 23(2):150–160.
Betcherman, G, and Khan T. (2015). Youth employment in
Sub-Saharan Africa taking stock of the evidence and
knowledge gaps. International Development Research
Centre (IDRC)/MastercCard Foundation: Ottawa, ON,
Canada, 35.
Bolang, P. D. and Issaka K.O. (2019). Formal sector
workers participation in urban agriculture in Ghana:
Perspectives from the Wa Municipality. Heliyon
Brenya, R., Akomea-Frimpong I., Ofosu D. and Adeabah
D. (2022). Barriers to sustainable agribusiness: a
systematic review and conceptual framework. Journal
of Agribusiness in Developing and Emerging
Economies ahead-of-print (ahead-of-print).
Carolan, M. (2017). Publicising food: big data, precision
agriculture, and co-experimental techniques of
addition. Sociologia Ruralis 57 (2):135–54.
Davis, F. (1985). A technology acceptance model for
empirically testing new end-user information systems:
theory and results. Unpublished Doctoral Dissertation,
MIT Sloan School of Management, Cambridge, MA.
FAO, ILO. and UNESCO. (2009). Training and
employment opportunities to address poverty among
rural youth: a synthesis report. FAO, ILO, UNESCO,
Fishbein, M. and Ajzen I. (1975). Belief, attitude, intention,
and behavior: an introduction to theory and research.
Reading, MA: Addison-Wesley.
Ghana S.S. (2017). Data production for SDG indicators in
Ghana. Accra.
Goyal, A., Innotech Alberta Canada, P. B. and Saikat K. B.
(2009). Minichromosomes: the second-generation
genetic engineering tool microspore transfection and
the production of transgenic doubled haploid plants
view project developing integrated crop management
technologies for food legumes view project.
Grabowski, P.P., Ida D., Leo Z., Judith K., Kampanje-Phiri
J., Akosua D., Irene E. and Gundula F. (2021). Gender-
and youth-sensitive data collection tools to support
decision making for inclusive sustainable agricultural
intensification. International Journal of Agricultural
Sustainability 19(5–6): 359–75.
Groot, A. E., J. S. Bolt, H. S. Jat, M. L. Jat, M. Kumar, T.
Agarwal, and V. Blok. (2019). Business models of
SMEs as a mechanism for scaling climate smart
technologies: the case of Punjab, India. Journal of
Cleaner Production 210 (February): 1109–19.
Gujarati, D. N. (2004). Basic econometrics. MaGraw-Hill
Institute London (4th Ed.).
Henson, R.K. and Roberts J. K. (2006). Use of exploratory
factor analysis in published research: common errors
and some comment on improved practice. Educational
and Psychological Measurement 66(3):393–416.
Katchova, A. (2013). Probit and Logit Models Example Ani
Kidido, J. K., John T.B. and Raphael K. K. (2017).
Dynamics of youth access to agricultural land under the
customary tenure regime in the techiman traditional
area of Ghana. Land Use Policy 60: 254–66.
Kwakye, B. D., Brenya R., Acquanah C. D., Agyemang
K.S., and Oteng A.F. (2021). AT as a tool to influence
youth farming in Ghana.
Open Journal of Applied
Sciences 11 (08): 885–98.
Lee, J.G., Young K. J., Sung W. Y., Man K. C., Hyeon T.
K., and Yong C. Y. (2018). Field survey on smart
greenhouse. Protected Horticulture and Plant Factory
27(2): 166–172.
Makate, C., Marshall M., and Nelson M. (2017).
Sustainable agriculture practices and livelihoods in pro-
poor smallholder farming systems in Southern Africa.
African Journal of Science, Technology, Innovation
and Development 9(3): 269–79.
Marinello, F., Andrea P., Alessandro C. and Luigi S.
(2016). Technical analysis of unmanned aerial vehicles
(drones) for agricultural applications.
Mwaijande, F. A. and P. Lugendo. (2015). Fish-farming
value chain analysis: policy implications for
transformations and robust growth in Tanzania. The
Journal of Rural and Community Development 10(2):
Nafeo A. A and Awal A.R. (2020). Does conservation at
reduce farm household poverty? evidence from rural
Zambia. African Journal of Science, Technology,
Innovation and Development 12 (4): 477–87.
Ogwuike, P. C. A., Aminou A. and Clinton O. O. (2021).
Assessment of rice threshing technology characteristics
for enhanced rice sector development in Senegal.
African Journal of Science, Technology, Innovation
and Development.
Sampene, A., Cai L., Agyeman F. and Brenya R. (2021).
Analysis of the BRICS countries pathways towards a
ABS 2022 - The International Conference on Agricultural and Biological Sciences
low-carbon environment. BRICS Journal of Economics
2 (4): 77–102.
Thow, A. M. et al. (2014). Development, implementation
and outcome of standards to restrict fatty meat in the
food supply and prevent NCDs: Learning from an
innovative trade/food policy in Ghana, BMC Public
Health, 14(1), pp. 1–9.
Tomchek, M. (2020). Sustainable technology impact on
agricultural production. In, 1–14.
United Nations, (2015). Policy integration in government in
pursuit of the sustainable development goals. New
United Nations, (2017). Adolescents and youth dashboad -
- Ghana, United Nations Population Fund [Preprint]
Venkatesh, V., and Davis F. D. (2000). A theoretical
extension of the technology acceptance model: four
longitudinal field studies. Management Science 46 (2):
Visser, S., Saskia K., Gilbert M., Margot de C., and Co
Molenaar. (2019). Soil as a basis to create enabling
conditions for transitions towards sustainable land
management as a key to achieve the SDGs by 2030.
Sustainability (Switzerland) 11 (23).
Walker, S.M., Conrad D.H., Mary L.W., and Marcus M.A.
(2009). Nitrogen modeling of potato fields in the
bolivian andes using GLEAMS.
Westland, J. C. (2019). Structural Equation Models: From
Paths to Networks. Studies in Systems, Decision and
Control. Vol. 22.
Wooldridge, J.M. (2006). Unobserved heterogeneity and
estimation of average partial effect. in: andrews, D. W.
K., Stock, J.H. (Eds.), Identification and Inference for
Econometric Models: Essays in Honor of Thomas
Rothenberg. Cambridge University Press, Cambridge,
UK, 27–55.
Worthington, R.L., and Tiffany A. W. (2006). Scale
development research: a content analysis and
recommendations for best practices. The Counseling
Psychologist 34 (6): 806–38.
Yamane, T. 1967. Statistics: an introductory analysis. New
York: Harper and Row, no. 2nd Edition.
Yeboah, E. et al. (2021) COVID-19 contact tracing: ghanas
efforts in the application of geospatial technology in
minimizing the impact of the pandemic, European
Journal of Development Studies, 1(4), pp. 8–21.
Yeboah, F.K. (2018). Youth for growth: transforming
economies through agriculture. Chicago Council on
Global Affairs, Chicago.
Technology and Policy Implementation Effects on Youth Agricultural Farming