The study examined factors influencing adoption of improved agricultural technologies (IATs) among smallholder farmers in rural communities of Kaduna State.The study was conducted in Giwa and Sabon-gari Local Government Areas. Three objectives guided the study. The study adopted a descriptive research design. Purposive sampling technique was employed to select the farming communities for the study. Two rural communities (Bassawa and Shika) were purposely selected out of 16 villages primarily because of their age-long agricultural technologies. The sample size of the study was 200 smallholder farmers made up of 100 farmers from each of the communities which were purposively selected. Primary data were collected using a structured interview schedule, focus group discussion and in-depth interview while the secondary data which relate to the objectives of the study were collected from the office of the Kaduna State Agricultural Development Project (ADP) and National Agricultural Extension and Research Liaison Services (NAERLS), ABU, Zaria. Data were analyzed using frequency and percentages. Results from the findings of the study revealed a positive significant (p<0.05) influence on adoption of agricultural technology and farmers’ educational levels, gender and age also had a positive significant influence on the adoption of technology. Therefore, the following recommendations were made: there is need to increase farmers’ capital and credit facilities and make funds accessible to the farmers. Also, it is therefore imperative for Government to ensure that policies that support the adoption of improved agricultural technologies are put in place.

1. Factors Influencing Adoption of Improved Agricultural Technologies (IATs) among Smallholder Farmers in Kaduna State, Nigeria
Factors Influencing Adoption of Improved Agricultural
Technologies (IATs) among Smallholder Farmers in
Kaduna State, Nigeria
*Sennuga, Samson Olayemi1, Fadiji, Taiye Oduntan2 and Thaddeus, Hellen3
1School of Agriculture Food and Environment, Royal Agricultural University, Stroud Road, Cirencester, Gloucestershire,
GL7 6JS, United Kingdom
2Department of Agricultural Extension and Rural Sociology, Faculty of Agriculture, University of Abuja, FCT, P.M.B. 117,
Abuja, Nigeria
3Department of Educational Technology, School of Science and Technology Education, Federal University of Technology,
Minna, Nigeria
The study examined factors influencing adoption of improved agricultural technologies (IATs)
among smallholder farmers in rural communities of Kaduna State.The study was conducted in
Giwa and Sabon-gari Local Government Areas. Three objectives guided the study. The study
adopted a descriptive research design. Purposive sampling technique was employed to select the
farming communities for the study. Two rural communities (Bassawa and Shika) were purposely
selected out of 16 villages primarily because of their age-long agricultural technologies. The
sample size of the study was 200 smallholder farmers made up of 100 farmers from each of the
communities which were purposively selected. Primary data were collected using a structured
interview schedule, focus group discussion and in-depth interview while the secondary data
which relate to the objectives of the study were collected from the office of the Kaduna State
Agricultural Development Project (ADP) and National Agricultural Extension and Research
Liaison Services (NAERLS), ABU, Zaria. Data were analyzed using frequency and percentages.
Results from the findings of the study revealed a positive significant (p<0.05) influence on
adoption of agricultural technology and farmers’ educational levels, gender and age also had a
positive significant influence on the adoption of technology. Therefore, the following
recommendations were made: there is need to increase farmers’ capital and credit facilities and
make funds accessible to the farmers. Also, it is therefore imperative for Government to ensure
that policies that support the adoption of improved agricultural technologies are put in place.
Keywords: Improved Agricultural Technologies, smallholder farmers, community, adoption, Kaduna state.
INTRODUCTION
Agriculture plays a fundamental role in economic growth,
enhancing food security, poverty reduction and rural
development. It is the main source of income for about2.5
billion people in the developing world (Wandji, et al.,2012).
Consequently, additional sustainable agricultural
technologies such as improved agricultural technologies
remain an important part of the efforts to boost food
availability, crop production and improve soil quality in a
bid to reduce food and nutrition insecurity which is
currently threatening humans’ right to food accessibility in
developing countries (Sennuga and Fadiji, 2020).
Improved Agricultural Technologies (IATs) are a collection
of principles for on-farm production and post-production
processes, aimed at delivering in safe and healthy food
and non-food agricultural products, while taking into
account economic, social and environmental sustainability
(FAO 2010; Sennuga, et al., 2020). IATs enable farmers
to increase their productivity and it covers a range of areas
*Corresponding Author: Sennuga, Samson Olayemi,
School of Agriculture Food and Environment, Royal
Agricultural University, Stroud Road, Cirencester,
Gloucestershire, GL7 6JS, United Kingdom.
Email: dr.yemisennuga@yahoo.co.uk
Research Article
Vol. 6(2), pp. 382-391, July, 2020. © www.premierpublishers.org. ISSN: 2167-0432
International Journal of Agricultural Education and Extension

2. Factors Influencing Adoption of Improved Agricultural Technologies (IATs) among Smallholder Farmers in Kaduna State, Nigeria
Sennuga et al. 383
including improved seeds, crop protection, water modern
irrigation practices, crop land management, degraded land
restoration, integrated pest management, integrated
fertilizer management and conservation agriculture (FAO
2010; Sennuga, et al., 2020).
In addition, agricultural technologies include all kinds of
improved techniques and technologies which affect the
growth of agricultural output (Jain, et al., 2009). According
to Loevinsohn et al. (2013), the most common areas of
technology development and promotion for crops
include new varieties and management regimes, soil
as well as soil fertility management, weed and pest
management, irrigation and water management. By
virtue of improved input/output relationships, new
technology tends to raise output and reduces average cost
of production which in turn results in substantial gains in
farm income (Challa, 2013).
An improved agricultural technology that enhances
sustainable production of food and fiber has made the
dynamics of technical change in agriculture to be an
area of intense research since the early part of twentieth
century (Loevinsohn et al., 2013). These technologies
are particularly relevant to smallholder farmers in
developing countries because they are constrained in
several ways, which makes them a priority for
development efforts. These farmers for instance, live and
farm in areas where rainfall is low and erratic, and soils
tend to be infertile. In addition, infrastructure and
institutions such as irrigation, input and product markets,
and credit as well as extension services tend to be
poorly developed (Muzari et al., 2012; Sennuga, et al.,
2020).
Smallholder farmers rely on traditional methods of
production and this has lowered the level of productivity.
For instance, over 70% of the maize production in the
majority of developing countries is from smallholders who
use traditional methods of production (Muzari et al.,
2012). These farmers generally obtain very low crop
yields because the local varieties used by farmers have
low potential yield, most of the maize is grown under
rain-fed conditions and irrigation is used only in limited
areas, little or no fertilizers are used and pest control is
not adequate (Sennuga, et al., 2020). This has triggered
much need to increase productivity and sustainability in
agriculture globally but much less information is available
on specific means to achieve this aim. Similarly, the
process of adoption and the impact of adopting new
technology on smallholder farmers have been studied.
However, improved agricultural technologies are often
adopted slowly and several aspects of adoption remain
poorly understood despite being seen as an important
route out of poverty in most of the developing countries
(Bandiera and Rasul, 2010; Simtowe, 2011).
Technology is one of the resources for agricultural
production. Technology adoption refers to the acceptance
of a group or an individual to use a new product or
innovation. The process of adopting an idea or new
innovation does not happen as a single unit act, but rather
a mental process that consists of at least five stages
namely; the awareness stage, the interest stage, the
evaluation stage, trial stage and finally, the adoption stage
(Rogers, 2013, Cheteni et al. 2014; Sennuga and
Oyewole, 2020). At the awareness stage, an individual
becomes aware of the idea but lacks detailed information
about it. At the interest stage, an individual gets more
information about it and wants to know more about how it
works, what it is and its affordances. At the third mental
stage, when the user has obtained more information from
the previous stages. At the fourth mental stage, the
individual makes a small scale trial of the idea, and
requests for more specific information to answer
questions. The last mental stage, adoption, is
characterized by alarge scale adoption of the idea, and
most importantly its continued use (Cheteni et al. 2014).
Adoption of improved agricultural technologies has been
associated with higher earnings and lower poverty,
improved nutritional status, lower staple food prices,
increased employment opportunities as well as earnings
for landless laborers (Kasirye, 2010; Sennuga et al.
2020). Adoption of improved technologies is believed
to be a major factor in the success of the green
revolution experienced by developed countries (Ravallion
and Chen, 2004; Kasirye, 2010).Conversely, non-
adopters can hardly maintain their marginal livelihood
with socio-economic stagnation leading to deprivation
(Jain et al., 2009). Agricultural technology embodies a
number of important characteristics that may influence
adoption decisions. For instance, Akudugu (2012) have
classified the determinants of adoption of agricultural
technology into: social, economic and physical factors.
Physical factors such as the farm size play a critical role in
adoption process of an improved technology. Many
studies have reported a positive relation between farm size
and adoption of agricultural technology (Mwangi and
Kariuki, 2015). Small farm size provides an incentive to
adopt a technology especially in the case of an input-
intensive innovation such as a labor-intensive or land-
saving technology. Smallholder farmers with small plots of
land adopt land-saving technologies such as greenhouse
technology, zero grazing among others as an alternative
to increased agricultural production (Diro, 2013).
In addition, a key determinant of the adoption of an
improved technology is the net gain to the farmer from
technology adoption, inclusive of all costs of using the
improved technology. However, high cost of agricultural
technology has been reported as hindrance to adoption
agricultural technology (Kinyangi, 2014, Sennuga et al.
2020). This is supported by other previous studies such as
Chi and Yamada (2002), Lavison (2013) on determinants
of technology adoption. For instance, the elimination of
subsidies on prices of seed and fertilizers since the 1990s
due to the World Bank-sponsored structural adjustment
programs in sub-Saharan Africa has widened this
constraint.

3. Factors Influencing Adoption of Improved Agricultural Technologies (IATs) among Smallholder Farmers in Kaduna State, Nigeria
Int. J. Agric. Educ. Ext. 384
Acquisition of information about improved technology is
another factor that determines adoption of technology
(Foster and Rosenzweig, 2010). It enables farmers to
learn the existence as well as the effective use of
technology and this facilitates its adoption. Smallholders
will only adopt the technology they are aware of or have
heard about it. Therefore, access to agricultural
information reduces the uncertainty about a technology’s
performance hence may change smallholder’s
assessment from purely subjective to objective over time
(Sennuga et al. 2020). Similarly, a study conducted by
Muzari, et al. (2012) in Sub-Saharan Africa on the impact
of technology adoption on smallholder agricultural
productivity found out that the factors affecting technology
adoption were assets, income, institutions, vulnerability,
awareness, labour, and innovativeness by smallholder
farmers. The authors also established that technologies
that require few assets, have a lower risk premium, and
are less expensive and have a higher chance of being
adopted by smallholder farmers. However, previous
studies on adoption of improved agricultural technologies
did not focus the influence of socio-economic
characteristics of smallholders and sources of modern
technologies on adoption by smallholders. This study
therefore will attempt to address the factors influencing the
adoption of Improved Agricultural technologies among
smallholder farmers that previous studies did not address.
Improved technologies are core to agricultural
development and the improved technologies selected are
compatible to local environment of the farmers in Kaduna
State. Therefore, the purpose of this study is to find out
the factors influencing adoption of improved agricultural
technologies among smallholder farmers in Kaduna State.
The specific objectives of this study are to:
i. examine the influence of socio-economic
characteristics of the farmers on adoption of
technologies;
ii. identify the improved agricultural technologies
adopted by farmers in the study area;
iii. highlight the sources of agricultural information on
adoption of technologies by farmers.
MATERIALS AND METHODS
This study was conducted in Giwa and Sabon-gari Local
Government Areas of Kaduna State, Northern Guinea
Savannah ecological zone of Nigeria, West Africa. Kaduna
State is located between latitudes 90 03’ and 110 32’ North
of the equator and longitude 60 05’ and 80 38’ East of the
Greenwich Meridian (Kaduna State Ministry of Agriculture,
2014). However, two rural communities (Bassawa and
Shika) were purposively selected for the study due to
active engagement of the rural farmers in agricultural
production in the district and for its proximity to Ahmadu
Bello University, Zaria, which is easily accessible to the
researchers. The major economic activity conducted by
the rural dwellers in the two communities is farming. Very
few people engage in hunting and small-scale business.
The major food crops grown are yam, maize, millet,
groundnut, rice, beans, melon, sweet potato, cassava,
guinea corn and vegetables such as pepper, tomato and
carrot.
Population of the study and research design
The study was made on two rural farmers’ group (Bassawa
and Shika); both the rural communities are similar in agro-
climatic, ethnic group, religion and cultural settings. There
is no climatic or agronomic difference between these
communities; they are just 500 metres apart. The
communities are similar and have virtually everything in
common. The two communities have access to extension
agents. The study employed descriptive research design
(Gillis and Jackson, 2002; Yin, 2003) in order to explore
and obtain in-depth information related to factors
influencing adoption of Improved Agricultural technologies
among smallholder farmers in their real-life settings.
Sample Size and Sampling Techniques
Kaduna state has 23 LGAs of which all of them has equal
probability of been chosen, however two; Shika and
Sabon-gari were randomly sampled for their closeness
(about 500 meter apart) and proximity to the office Kaduna
State Agricultural Development Project (ADP) and
National Agricultural Extension and Research Liaison
Services (NAERLS), Ahmadu Bello University, Zaria.
Purposive sampling technique was employed to select the
farming communities for the study. Two rural communities
(Bassawa and Shika) were purposively selected out of
16villages primarily because of their age-long agricultural
practice and presence of adoption technologies noted
there. The two communities are similar in agro-climatic,
ethnic group, religion and cultural settings. However,
Shika community gets only public extension services with
about 3000 smallholder farmers per extension agent while
Bassawa community receives extension services plus the
research education establishment from Adopted Village
Program with estimated extension agent and farmers’ ratio
of 1:85 (Sennuga et al. 2020).
Sample size
The sample size for the study was 200 smallholder
farmers. It consists of 100 farmers from each community.
Within each community, farm families were invited to
participate in the study through community meetings, in
which 137 farmers attended from Bassawa and 142 from
Shika, and 8 extension workers were in attendance. From
this sampling frame of individuals, 100 farming households
were randomly selected from each community; primarily
on voluntary basis. Other criteria for individual participants
were as follows: age between 18 and 65 years, farming
experience, interested in participating, and permanent
resident of the community. The foremost rationale for
selecting 100 farmers per community were based largely
on the number of farming households that volunteered and
showed interest during the community meetings, as well
as conformed to the previously mentioned criteria.

4. Factors Influencing Adoption of Improved Agricultural Technologies (IATs) among Smallholder Farmers in Kaduna State, Nigeria
Sennuga et al. 385
Data collection
Primary data were collected using structured interview
schedule, focus group discussion and in-depth interview
from both rural dwellers and extension workers. Structured
questionnaires were administered to collect data and the
survey took about 1 hour 10 minutes. The key themes in
the survey included socio-economic characteristics of
smallholder farmers, household assets, extension advice,
level of awareness of improved agricultural technologies,
sources of agricultural information in the area. In order to
ascertain the appropriateness and reliability of the
questions set for the survey, the survey were pre-tested
among three smallholder farmers working with Ahmadu
Bello University, Zaria, to correct aspect related to verbal
understanding and to ensure the interviewees'
performance, and some minor corrections were effected
before administering the survey to study participants.
Three researchers and four trained extension agents
(research assistants) with professional skills in agriculture
conducted the survey and focus groups. In few cases,
additional visits were made when it was compulsory to
clarify and review incomplete information. Secondary data
which relate to the objectives of the study were collected
from the office Kaduna State Agricultural Development
Project (ADP) and National Agricultural Extension and
Research Liaison Services (NAERLS), ABU, Zaria.
Data analysis
The data collected for the study were analyzed using
descriptive statistics such as frequency- and percentages.
Spearman rank influence technique was used to test the
significant relationship between Improved Agricultural
technologies adoption and socio-demographic variables of
the respondents. With aid of Statistical Package for Social
Science (SPSS) version 24 the data were analyzed and
the descriptive statistics were used to present the results.
RESULTS AND DISCUSSION
Table 1: Demographic representation of the socio-
economic Characteristics of the smallholder farmers (n=
200)
Variables Percentage
Age (years)
20-30 15.8
31-40 31.7
41-50 27.5
51-60 17.5
61-70 6.7
> 70 .8
Gender (Sex)
Male 100
Female 0
Marital status
Single 3.3
Married 96.7
Household size
<10 50.8
11-20 36.4
21-30 12.1
>31 .7
Level of education
No education 30.8
Primary 44.3
Secondary 17.0
Tertiary 7.5
Family education
No education 3.3
Primary 55.0
Secondary 35.8
Tertiary 2.6
No Children yet 3.3
Household Asset
Poultry 58.0
Sheep and goats 61.7
Cattle 42.8
Other livestock 6.5
Pig 0
Socio-economic characteristics of the respondents in
the study area
The results of socio-economic characteristics of the
respondents were presented in Table 1. The variables
investigated in the study included: age, sex, marital status,
household size, level of education, major crops cultivated,
household assets and income level. The age of the
farmers in the households ranged from 20 to 70 years.
59.2 per cent of them fell within the middle age of 31-
50years in both communities. This suggests that the
majority of the respondents were within their economic
active age and this enhances their productivity in order to
ensure food security (Table 1).The old age group (51-70)
had the lowest impact in farm work with 17.5per
centcontributing to active farming among the sampled
population. This result reveals that the majority (65%) of
farmers who participated in the survey belong to the active
age group and still have strength to cultivate more
farmland and explore new agricultural innovations.
However, it is generally assumed that younger people tend
to be more productive than that of their older counterparts.
In the same vein, the results in Table 1 below showed that
all the respondents were males; this is because the cultural
traditions of the study area do not allow females to be
actively involved in farming activities (Sennuga and Fadiji,
2020).

5. Factors Influencing Adoption of Improved Agricultural Technologies (IATs) among Smallholder Farmers in Kaduna State, Nigeria
Int. J. Agric. Educ. Ext. 386
In terms of the marital status of the respondents,
overwhelming majorities (96.7%) of the respondents were
married with half of these households having 10 or more
members; the remainder had larger families of more than
21 members reflecting polygamy within the communities.
The result is not surprising because large family sizes are
the norm in the Northern Nigeria and large families provide
accessible workforces. Furthermore, the cultural tradition
and religion allows the men to marry at most four women.
The use of household labour for several activities was very
common in the study area with activities such as
ploughing, harrowing, planting, weeding, chasing away
straying domestic animals, irrigation activities and
harvesting. In the same vein, large household may also
help to access more agricultural information.
Educationally, 44.3 per cent of the respondents had
acquired primary education, while 17per cent had
secondary education. Only 7.5per cent of the respondents
possessed higher education (Table 1). This suggests that
the respondents in the study area obtained the basic
education required for better understanding and ability to
embrace new technologies especially the adoption of IATs
technology. In addition, it is generally thought that the level
of education enhances the ability to comprehend and
adopt relevant agricultural information, which is in
conformity. In term of household asset, 58per cent of the
household keep poultry, a greater proportion (61.7%) keep
sheep and goats. A sizeable proportion of the respondents
(42%) also indicated that they rear cattle and only 6.5per
cent specified that they keep other livestock such as
camel, duck, turkey etc. The baseline livelihood survey
shows that no single household keeps pigs in the study
area. This was attributed to the religion (Muslims) of the
respondents.
Improved Agricultural Technologies Adopted by
Farmers
Table 2: Improved Agricultural Technologies Adopted by
Farmers in the study area
Improved Agricultural Technologies Percentage
Improved seeds 88.6
Spraying of herbicide 79.5
Pesticide use/Pest control 77.3
Fertilizer application 75.8
Water management/irrigation 69.1
Crop rotation 66.5
Cover crops 50.2
Compost and Green Manure 49.7
Spacing 38.6
Mulching 35.2
Source: Survey 2018; Farmers n =200
Improved Agricultural Technologies Adopted by
Farmers
Data in Table 2 revealed the level of adoption of improved
agricultural technologies (IATs) among smallholders. The
IATs selected as appropriate for the local communities and
study area includes; improved seeds, spraying of
herbicide, pesticide control, fertilizer application, water
management/irrigation, crop rotation, cover crops,
compost and green manure, spacing and mulching.
A total of 200 questionnaires were used to obtain
information from the respondents, farmers were requested
to indicate their level of awareness and level of adoption
of improved technologies by using a three-point Likert
rating scale. The scale was as follows: High = 3, Medium
= 2 and Low = 1. The level of adoption was determined
using Spearman rank correlation. The results in Table 2
show that six agricultural technologies were highly
adopted by farmers, these includes improved seeds
(88.6%), spraying of herbicide (79.5%), pesticide control
(77.3%), fertilizer application (75.8%), water
management/irrigation (69.1%), crop rotation (66.6).
However, cover crops (50.2%), compost and green
manure (49.7%) were categorised under medium level of
adoption.
Factors Influencing Adoption of IATs Technologies
Various factors relating to the adoption of improved
agricultural technologies and farmer characteristics were
also tested using Spearman rank influence. Table 3 below
reveals a significant influence between IATs adoption and
socio-demographic variables. The results reveal that age,
gender, education attainment and farming experience had
a positive significant (P<0.05) influence on the adoption of
IATs. The findings of the study are in line with most
adoption studies such as Keelan et al. (2014); Mwangi and
Kariuki (2015) who found that farmers’ socio-economic
characteristics had an influence on the adoption of
technologies. However, the present study found that
farmers’ marital status, household size, indigenous
knowledge and household assets were not significant.
These factors are discussed in more detail in the following
sub-sections.
Table 3: Spearman rank influence of factors influencing
adoption of improved agricultural technologies among
smallholder farmers
Variable Spearman rank P-value
Age 0.641 0.001**
Gender 0.502 0.000**
Marital status 0.740 0.081
Social participation 0.342 0.000**
Household Size 0.360 0.001**
Cultural/Religious 0.497 0.001**
Education level 0.690 0.000**
Farming experiences (Year) 0.081 0.002**
Farm Size 0.062 0.001**
Weather condition -0.226 0.620
Pest and disease control 0.529 0.110
GAP participatory training 0.650 0.000**
Indigenous knowledge -0.407 0.328
Source: Survey 2017; P < 0.05 is significant

6. Factors Influencing Adoption of Improved Agricultural Technologies (IATs) among Smallholder Farmers in Kaduna State, Nigeria
Sennuga et al. 387
i. Impact of Age on Adoption of Technologies
The findings reveal a positive statistically significant
relationship between age (0.001) and technology adoption
(Table 3). Age has been considered to be a major
underlying characteristic in the adoption decisions made
by smallholders (Adesina and Baidu-Forson 1995). Age
was also found to positively influence the adoption of
Integrated Pest Management (IPM) on peanuts in Georgia
(McNamara et al., 1991) and sorghum in Burkina Faso
(Adesina and Baidu-Forson 1995) among older farmers.
However, there is a debate on the direction of the effect of
age in adoption, the older farmers find it extremely difficult
to take the risks which may result in low technology uptake
(Caswell et al. 2001).
The results of this study are supported by Mwangi and
Kariuki (2015) who found that the active age group are
characteristically less risk-averse and are keener to try
new technologies than older farmers. Furthermore,
younger farmers still have the potency to take a risk, grow
more crops and search for new agricultural innovations.
For instance, in India, Alexander and Van Mellor (2005)
established that the adoption of genetically modified maize
increased with age for the active age group farmers as
they gained experience and increased their stock of
human capital, but declined with age for older farmers
closer to retirement.
ii. The Role of Gender in the Adoption of Technologies
The study results revealed that the gender of the
respondents had positive and statistically significant
(0.05%) level influence on the adoption of IATs
technologies. This implies that male farmers are more
likely to adopt modern agricultural technologies than their
female counterparts. The reason for this is that men are
the people in the study area who make the production
decisions and also control the productive resources such
as land, labour and capital which are critical for the
adoption of new technology. However, gender issues in
agricultural production and technology adoption have been
investigated for a long time and most studies have
reported mixed evidence regarding the different roles men
and women play in technology adoption (Bonabana-
Wabbi, 2002).
However, the present study results disagree with Morris
and Doss (1999) who found no significant influence
between gender and the adoption of improved maize
technology in Ghana. The study concluded that
agricultural technology adoption decisions depend largely
on access to resources only, rather than gender. They
explained further that if adoption of improved maize
depends on access to land, labour, or other resources, and
if in particular context men tend to have better access to
these resources than women, then, they are more likely to
adopt new technologies than women. In comparison,
Lavison (2013) indicated that male farmers were more
likely to adopt organic fertiliser than their female
counterparts. This finding corroborates with that of Mwangi
and Kariuki (2015) who found that male-led households
are more likely to embrace agricultural technology,
because of their leading role; facilitating the planning and
operation of the farm to improve productivity and maintain
the well-being of the family. In Nigeria, a survey conducted
by Obisesan (2014) found that male farmers had a
significant and positive influence on the adoption of
improved cassava production techniques. Accordingly,
men are more likely to seek and adopt new knowledge and
technologies due to their access to resources (Asfaw and
Admassie, 2004; Buyinza and Wambede, 2008). This is
consistent with the results of the present study, which
found that male-led households adopted almost all the
recommended IATs technologies.
iii. Impact of Cultural/Religious on the Adoption of
Technologies
The results of spearman rank influence revealed in Table
3 show a significant influence between cultural/religious
and adoption of IATs technologies in the study area.
Cultural norms and value, religion and tribal background
may influence adoption of agricultural technology. The
belief, habits and rituals attached to religion and culture
are so deeply rooted and many influence how smallholder
farmers embrace improved technology. For instance, due
to the religion affiliations in the study area no single farmer
keep/rear pigs. Consequently, the cultural/religion affect
the ownership of certain type of livestock by the
households and may also play a significant role in the
adoption process.
iii. Impact of Education and Training on the Adoption
of Technologies
The study results presented in Table 3illustrate a
significant relationship between level of education and the
adoption of IATs technologies. According to Sennuga, et
al. (2020) it is expected that more knowledgeable farmers
will adopt more improved technologies than those less
knowledgeable. This relationship has been established by
previous studies (Caswell et al., 2001, Mwangi and Kariuki
2015). According to Deressa et al. (2011), involvement of
the educated population in farming activities is thought to
create a favourable mental attitude towards the
acceptance of new agricultural technologies especially of
information and management-intensive technologies.
Additionally, Croppensted et al. (2003) reported that more
highly educated farmers (a minimum of primary level) and
those from large households were more likely to adopt new
technologies than the less educated and those from
smaller families due to their greater exposure to new
knowledge and technologies, and having more labour
resources to carry out farming activities. Therefore, the
effect of the educational level was found to increase the

7. Factors Influencing Adoption of Improved Agricultural Technologies (IATs) among Smallholder Farmers in Kaduna State, Nigeria
Int. J. Agric. Educ. Ext. 388
probability of a smallholders’ adoption of new
technologies. Moreover, Doss and Morris (2001) and Daku
(2002) found that education positively affected the
adoption of Integrated Pest Management (IPM)
technologies among smallholder farmers in Kenya and
Nepal. This implies that the level of education is a powerful
tool in the hands of smallholder farmers enabling them to
read the labels on fertilizer bags, for example, or follow
directions on the operation of machines, tools and other
items.
Educational levels increase the ability to obtain, process
and use information relevant to the adoption of a new
technology (Mignounal, et al., 2011; Lavison, 2013). For
example, in a recent study by Mwangi and Kariuki (2015)
on the adoption of new technologies by fish farmers, and
Keelan et al. (2014) on the adoption of organic fertilisers,
it was found that education levels had a positive and
statistically significant influence on the adoption of the
related technology. The reason for this is that higher
education levels influence respondents’ attitudes, making
farmers more open, rational and able to analyse the
benefits of the new technology (Waller et al. 1998). Other
studies that have also reported a positive relationship
between education and technology adoption as cited by
Mwangi and Kariuki (2015) include; Mishra, et al. (2009)
on forward pricing methods, Putler and Zilberman (1988)
on the adoption of microcomputers in agriculture, Mishra
and Park (2005); on the use of the internet, Rahm and
Huffman (1984) on reduced tillage, Roberts et al. (2004)
on precision farming and Traoreb et al. (1998) on the on-
farm adoption of conservation tillage.
iv. The Role of Farming Experience in the Adoption of
Technologies
As reported in Table3, the level of farming experience is a
significant factor influencing the adoption of GAP
technologies in the study area. According to Petros
(2010), longer farming experience implies accumulated
farming knowledge and technical know-how and skills, all
of which contribute to technology adoption. In a study by
Melaku (2005), farming experience was found to be
positively and significantly related to adoption. Similarly,
Yishak (2005) found the difference between the mean
level of farming experience of adopters and the non-
adopters was statistically significant.
v. Impact of Household Size in the Adoption of
Technologies
The findings reveal a positive and significant relationship
between household size and technology adoption.
Household size is simply used as a measure of labour
availability for farmers with large families (Mwangi and
Kariuki, 2015). It determines the adoption process in that,
larger households have the capacity to relax labour
constraints during the introduction of new technologies
(Mignouna, et al., 2011). This implies that farmers with
large families will certainly generate more income through
large-scale production of improved technologies using
family labour. Hence, the bigger the family size, the more
economically stable the family (Mwangi and Kariuki, 2015).
vi. Impact of Farm Size on the Adoption of
Technologies
As noted from Table 3, farm size had a negative significant
influence on technology adoption. These results show that
farm size does not have an effect on the IATs adoption.
The reason may be because the respondents are small-
scale farmers who operate on small farmlands. A similar
finding was reported by Parvan (2011) who established
that farm size does not always affect adoption; rather the
literature finds that the effects of farm size vary depending
on the type of technology being introduced, and the
institutional setting of the rural community. However, in a
study undertaken by Akudugu et al. (2012), farm size was
found to have a positive relationship with the probability of
adoption of modern agricultural production technologies
among commercial farmers. This finding is consistent with
previous studies that have found that large-scale farmers
are more likely to adopt new technologies than small scale
farmers (Kasenge, 1998).In analysing the diffusion of
conservation tillage technologies, integrated pest
management (IPM) activities and soil fertiliser testing
among American farmers, Fuglie and Kascak (2003)
began with the traditional explanatory factors, including
farm size (Moser and Barrett, 2008; Parvan, 2011). They
reported that larger farms were more likely to adopt the
technology bundles sooner than small farmers (Parvan,
2011).
This presents a serious challenge to policy makers and the
government of Nigeria in promoting the adoption of
modern agricultural production technologies in the study
area. This is because an overwhelming majority of farmers
in the Kaduna state and Nigeria as a whole operate on a
small scale with the average farm sizes hardly exceeding
three hectares (Sennuga, 2019).
Sources of agricultural information on adoption of
technologies by smallholder farmers
Information has become a critical factor to increase
smallholders' production and productivity. As a result, the
most preferred sources of information by smallholder
farmers were investigated and respondents were
requested to rank the sources of agricultural information
used. As presented in figure 1a-b, revealed that
smallholder farmers preferred traditional ICT, mainly radio
(36%) as their main source of accessing agricultural
information followed by mobile phones (28%) for Shika
community, while (39 %) and (31%) of smallholder farmers
from Bassawa community indicated that they prefer radio
and mobile phone respectively.

8. Factors Influencing Adoption of Improved Agricultural Technologies (IATs) among Smallholder Farmers in Kaduna State, Nigeria
Sennuga et al. 389
The study results further indicate that agricultural
extension agents, personal sources and social media were
not considered as significance in obtaining agricultural
information by the respondents. The findings of the study
show that radio and mobile phones were relevant
agricultural information which helps farmers to make
informed decisions about what crops to plant and where to
purchase affordable farm inputs and which market to sell
their produce. In this regard, the need and choice of the
sources of information on improved agricultural
technology, and how the timely and relevant information is
disseminated to the targeted smallholder farmers should
be of paramount concern to both agricultural development
practitioners and agricultural extension workers. However,
the spearman rank influence shows that there were no
statistically significant differences between the farmer's
present sources of agricultural information.
Figure 1a-b: sources of agricultural information on adoption of technologies by smallholder farmers
Source: Survey; Shika n=100% Bassawa n=100Scale: %
CONCLUSION
The essence of this study is to dig into the various factors
affecting the adoption of improved agricultural
technologies by smallholder farmers in Nigeria rural
communities. The study had revealed factors affecting
smallholder farmers’ decision to adopt agricultural
technologies. Findings from this study had shown that
adoption of agricultural technology depends on a range of
factors which include among others: human factors, social
factor, cultural/religious factor, economic factor, education
levels, household size, access to information, utilization of
social networks and so on.
The outcome of the study revealed that smallholder
farmers in Nigeria rural communities had positive a
significant influence between age and technology adoption
of improved agricultural technology. This implies that the
older a farmer get the higher the rate of improved
agricultural technology adoption. Results also indicated a
positive significant influence between level of education
and adoption of technologies among smallholder farmers.
This means that the level of education of small holder
farmers could result to higher rate of agricultural
technology adoption.
There was a positive influence between availability of
agricultural information devices such as radio and mobile
phones and adoption of agricultural technology, which
could help farmers to make informed decisions about what
crops to plant and where to purchase affordable farm
inputs and which market to sell their produce. In
conclusion, some fundamental policy implications can be
drawn from this study in order provide managerial and
technical skills on improved agricultural technology
adoption.
RECOMMENDATIONS
The following recommendations were made based on the
findings and the conclusions of the study:
1. There is a need for Government to increase farmers’
capital and credit facilities and make these services
accessible to the farmers.
2. There is need for farmers to be trained on yield-raising
technologies and other technologies that can positively
contribute to high productivity among farmers. This will
increase awareness on the availability and usefulness
of improved agricultural technologies.
3. It is imperative for policy makers to ensure that a wider
spectrum of smallholders farmers are able to have
access to credit in order to improve their adoption level
of agricultural technology. Developers of new
agricultural technology should try to understand the
farmers need as well as their ability to adopt technology
in order to develop technology that will suit them.
1
2
7
9
18
28
36
0 10 20 30 40
Attending Village
meeting
Social Media
Personally
Extension workers
Family and friends
Mobile phone
Radio
Shika
1
1
4
8
16
31
39
0 10 20 30 40 50
Attending Village meeting
Social Media
Personally
Extension workers
Family and friends
Mobile phone
Radio
Bassawa

9. Factors Influencing Adoption of Improved Agricultural Technologies (IATs) among Smallholder Farmers in Kaduna State, Nigeria
Int. J. Agric. Educ. Ext. 390
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Accepted 9 July 2020
Citation: Sennuga SO, Fadiji TO, Thaddeus H (2020):
Factors Influencing Adoption of Improved Agricultural
Technologies (IATs) among Smallholder Farmers in
Kaduna State, Nigeria. International Journal of Agricultural
Education and Extension, 6(2): 382-391.
Copyright: © 2020 Sennuga et al. This is an open-access
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