Technology and Policy Implementation Effects on Youth Agricultural

Farming

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.

Abstract:

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

farming.

1 INTRODUCTION

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

https://orcid.org/0000-0003-1054-0871

https://orcid.org/0000-0002-0475-7700

https://orcid.org/0000-0003-4615-2555

https://orcid.org/0000-0003-3980-1289

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

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

Integration

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 METHODS

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.

𝑛

𝑁

1𝑁(𝑒)



n

310

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



()



(1)

n = sample size

N= sample frame

e= margin of error is 5%, therefore, (equation 2)

n



(.)



= 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

=38.08;4=PhD=09.35

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

Attitude

0=High=17.42;1=Low=82.58 .825 .379

Knowledge

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

(3)

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

F(x

β) =Λ(x

β) =









 







 (



)

 (



)

(4)

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.



𝑿

𝒊

= Λ (x

β) [1- Λ (x

β)] β









( 







)



βi

(6)

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

Famsup

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

application.

3 RESULTS

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

1).

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

Notes:

∗

p<0.1;

∗∗

p<0.05;

∗∗∗

p<0.01.

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

cro

Enable precision location, up-to-date map

and weather information

Mitigation of manual drudgery via labor

lacemen

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

etual

lant

rowth

Enable a better nutrient efficiency to soil

health and crop development

Enable climate resilient practices to drought,

climate-related risks and shocks

Functions

Smart

ouse

Climate Smart

Drones/Aerial

Vehicles

GIS Software

and GPS

Irrigation

Control

Robotics

Satellite

Ima

Minichromosomal

Technolo

Farm Data

Anal

tic

itrogen

Modelin

Agriculture

Technology

Marinello et al. (2016)

Grabowski et al. (2021)

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)

Source

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.

4 DISCUSION

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.

5 CONCLUSIONS

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.

Dependabl

e Variable

Functional Measurement Source

Agriculture

Technolog

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

Awal

(2020); Ogwuike et al. (2021); Walker et al. (2009)

Agriculture

Policy

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)

Sustainable

agriculture

The use of sustainable agriculture practice

index to determine the degree of

sustainability by summing up the

technolo

ado

ted within the househol

Nafeo and Awal (2020); Ogwuike et al. (2021); Tomchek

(2020); Visser et al. (2019); Walker et al. (2009)

Technolog

y Policy

Estimate by monitoring the performance of

technology adopted farmers to non-adopted

farmers

Davis (1985); Grabowski et al. (2021); Groot et al. (2019);

Lee et al. (2018); Marinello et al. (2016); Mwaijande and

Lugendo (2015)

Sustainable

Developme

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)

Youth

Farming

Estimated youth population density

matched with the agricultural production

density

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?

ACKNOWLEDGEMENTS

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.

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