
ISSN: 2637-4676
Nadeesha Dias*, Thilani Munaweera and Sidath Bandara
Received: September 02, 2020; Published: September 09, 2020
Corresponding author: Nadeesha Dias, Research officer, Hector Kobbekaduwa Agrarian Research and Training Institute, Sri Lanka
DOI: 10.32474/CIACR.2020.08.000300
The farmers’ adoption of Protected Agriculture (PA) in dry zone vegetable cultivation is considerably low in Sri Lanka. However, a few farmers have been practiced PA technologies to minimize negative impacts of climate change on vegetable production in dry part of Sri Lanka. Rain shelters were identified as the main type of PA technology used by dry zone vegetable farmers followed by poly tunnels. Understanding the factors that influencing farmers’ adoption to PA could be more useful to popularize the technology in suitable areas while formulating policies to overcome some barriers for adoption. This study analyzed the factors influencing farmers’ adoption of PA for vegetable cultivation in dry zone of Sri Lanka. A total of 120 vegetable farmers from 12 Agrarian service centers (ASC) in Anuradhapura and 2 ASC in Matale districts were selected randomly to identify the factors that would be influencing farmers’ adoption on PA technologies for vegetable production in the dry zone of the country. Pre tested structured questionnaire were used for the survey in data collection. Binary logistic regression model was applied to identify the factors affecting on adoption to PA by vegetable farmers. Results reveal that farmers age, household size, cultivated extent, access to credit, access to new technologies, access to extension services and primary employment have a significant effect on adoption to PA. These results highlighted that importance of extension services and financial support to promote PA technologies for small scale vegetable farmers in dry zone.
Keywords: Protected agriculture, Vegetables, Adoption, Binary logistic regression
Vegetables are major component of Sri Lankans’ daily diet and
there is a constant demand for vegetables in local market. Vegetable
farming system in Sri Lanka mainly divided into two farming
systems, there are up country and low country vegetable farming
systems [1]. In both farming systems vegetables are predominantly
cultivated in open fields using conventional farming methods.
However, open field vegetable cultivations are highly depending
on weather conditions mainly rainfall. In recent times vegetable
production has been undergoing rigorous fluctuations due to
unpredictable weather conditions in the country [2]. Compared to
other climatic zones in the country, dry zone is more vulnerable to
unexpected extreme climatic events such as droughts and floods
[3]. As the major vegetable producing area, it is needed to identify
and implement new farming technologies to minimize the impacts
of extreme climatic events on vegetable production to maintain
constant vegetable supply in the country. Adoption to protected
agriculture on vegetable production will be one of the effective
solution for minimize the impacts of these extreme climatic events.
Protected Agriculture (PA) is identified as one of the feasible
adaptation strategy to minimize the impacts of climate changes on
vegetable production as well as off seasons production with limited
resources [4]. PA can be defined as cropping techniques where in the
micro climate surrounding of the plant body is controlled partially
or fully as per the requirement of the plant species grown during
their period of growth [5]. PA technology involves the cultivation
of vegetables especially in designed protective structures such as
glass houses, poly tunnels, net houses and rain shelters where in the
factors like temperature, humidity, light, soil, water and fertilizers are manipulated to attain maximum produce as well as allow a
regular supply of them even during an off season [6]. Research
results have shown that by adopting PA, productivity of vegetable
crops can be increased by three to five times as compared to open
field cultivations [7].
About 115 countries in the world are using PA technologies
for vegetable production commercially. China is the major country
using PA and 90 percent of its protected cultivations used for
vegetable production [6]. Several studies showed that countries
like India, Pakistan, Afghanistan, Israel and Iran are successfully
practicing vegetable cultivation using PA under extreme climatic
conditions. Capsicum, tomato, cucumber, brinjal, leafy vegetables
and many other tropical vegetables are cultivated in these countries
using PA and it was observed higher yield when compared to open
field conditions [4,6].
PA was introduced to Sri Lanka somewhere around 1987
and it has been adopted mostly by the middle class farmers [8].
Main purposes of introduction of PA farming are to minimize the
impacts of erratic weather and provide optimum environmental
for horticultural crop production as well as attraction of younger
generation for agricultural sector with modern technologies (2,9).
PA is currently practiced mainly in up country and low country
wet zone areas and very less in dry zone areas. Most commonly
cultivated vegetables are pepper, tomato, cauliflower, lettuce,
broccoli and Japanese cucumber. Frequently used PA structures
in the country are net houses, poly tunnels and rain shelters [9].
Niranjan [8] mentioned that PA has a high potential for expansion
into different agro ecological regions in the country with suitable
protected structures for cultivation of vegetables. However, due
to the requirement of high initial capital and technical knowledge
limited number of farmers adapted to PA technologies with their
own funds.
By adopting PA, farmers can look forward to a better and
additional remuneration for high quality produce all year round
with an efficient use of resources. Farmers adoption of different
agricultural innovations and technologies is determined by different
factors. Therefore, identification of most influencing factors on
adoption to PA technologies is important in future research and
policy formulations to promote PA for vegetable cultivations in the
country as one of the possible adaptation method to minimize the
impacts of climate changes in vegetable production in dry zone
and increase the off season vegetable production in the country.
This study was mainly focused to identify the factors influencing
farmers’ adoption of vegetable cultivation under protected
agriculture technologies in dry zone.
Adoption may be defined as the integration of an innovation
into farmers’ normal farming activities over an extended period
[10]. Various theoretical perspectives explained the behavior of
adoption on new technologies. Innovation diffusion theoretical
perspective, the economic constraints theoretical perspective and
the adopter perception theoretical perspective are three different
theories commonly used to explain farmers’ adoption behavior and
factors affecting to technology adoption in previous studies [10,11].
Information dissemination is identified as a key factor influencing
adoption decision in innovation diffusion theoretical perspectives.
The adoption decision process is viewed as a series of linear
stages starting by the knowledge stage followed by a persuasion,
decision, implementation and finally confirmation stages [11]. The
economic constraints theoretical perspective states that adoption
is influenced by economic factors and economic constraints due to
the asymmetric distribution of resources. Lack of ownership and
access to capital constrain farmers from adopting new innovations
[12]. The adopter perceptions theoretical perspective identifies
farmers’ perceptions as the key to the adoption of a particular
farming technology [11]. This study integrated these three theories
in to developing a conceptual understanding of the adoption of the
PA technologies by farmers.
There is vast array of literatures on factors that determine
agricultural technology adoption. Factors affecting to technology
adoption is categorized in different ways in previous studies
and there is no clear distinguishing feature between variables
in each category. Categorization of factors is done according to
the investigating technology, the study location and researchers’
preference [13]. Kabir [14] categorized the determinants of
technology adoption in to four broad categories of economic, social,
institutional and management. Howely [15] categorized the factors
affecting farmers’ adoption to new technology in to a personal,
social, cultural, institutional and economic factors as well as on
the characteristics of the technology. In another studies factors
influencing on agricultural technology adoption were categorized
into technological, economic, institutional and household specific
factors [13,16]. In previous studies, farmers age, educational
level, gender, family size, farming experiences, farm size, primary
employment, off farm income, social networks, membership in
farmer organizations, access to credit, access to extension services
and trainings, access to new technologies, labour availability, cost
of new technology and net gain from technology are commonly
identified as factors influencing on farmers’ adoption to new
technology [13 ,14, 16-20].
Twelve Agrarian Service Centers (ASC) in Anuradhapura and
two ASC in Matale which belongs to the dry zone were selected
purposively for data collection with consideration of presence
of farmers who are practicing vegetable cultivation using PA
technologies. Among the different types of PA technologies, rain
shelters are the predominant type of protected structures used by dry zone vegetable farmers. Other than that there are small number
of poly tunnels also used by farmers for vegetable cultivations.
Therefore, hereafter in this study PA term is used to denote rain
shelters and polytunnels. Rain shelter is a simple form of protected
structure made with Galvenized Iron (GI) pipes and only roof of the
structure is covered with water resistant low density polyethylene
film. It is mainly used to minimize the impact of heavy rainfall on
cultivated crops and partially controlled the evapotranspiration
by acting like a roof. Poly tunnels are relatively high tech protected
structures and frame of structure constructed with GI pipes.
The structure is fully covered with UV stabilized transparent
polyethylene films. Microclimate within the structure is partially
or fully controlled in this structures. The study sample was divided
into two groups of farmers, those who adopted (adopters) vegetable
cultivation under PA and those who have not been adopted the PA
but practicing conventional vegetable cultivation within a same
locality. Seventy farmers who have been practiced vegetable
cultivation under PA and fifty conventional vegetable farmers were
selected randomly for data collection. Total sample size was 120
including both PA and conventional vegetable farmers.
To achieve the objective of the study primary data was
collected through household questionnaire survey using pre tested
semi structured questionnaire. Two separate semi structured
questionnaires were used to collect data from adopters and nonadopters
of PA. Data on socio economic characters and present
status of vegetable farming were collected through individual face
to face interviews and data was analyzed using descriptive statistics
and binary logistic regression method. Descriptive statistics used
to describe the socio economic characters of sample farmers. The
factors affecting to vegetable cultivation under PA technologies
were analyzed using binary logistic regression method.
Logistic regression model is one of the best analytical tool
commonly used for analyze the farmer adoption decisions on new
technologies [11,21]. Since the adoption of PA is a dichotomous
or binary dependent variable, with the option of either adoption
or rejection, the binary logistic regression model was applied
[12]. It explores the degree and direction of relationship between
independent and dependent variables in the adoption of PA at
the household level. The dependent variable in this study was
the farmer being an adopter or non-adopter of PA technology.
Accordingly, adoption of PA technologies in dry zone assumed to
be influenced by set of independent variables and is specified as
follows.
Where the subscript i means the i th observation in the sample.
P is the probability of that a farmer adopts the PA and (1-P) is
the probability that a farmer does not adopt PA. is the intercept term and , …. and are the coefficients to be estimated related to
independent variables X1, X2,…….,Xk. Based on literature, scope
of the study and the availability of data following independent
variables were selected for analysis *(Table 1).
Socio demographic characters of sample farmers show slight variations between adopters and non-adopters (Table 2). The results show that majority of PA adopted farmers (53%) belongs to 31 to 45 years’ age group while majority of non-adopters (42%) belongs to 46 to 60 years’ age group. It indicates that the younger generation has trusted more on PA technologies. Even though both groups received formal education but, the farmers who have adopted PA shows comparatively a higher education status while two graduates also within it. Engagement of female farmers for vegetable cultivation under PA (11%) is higher than the conventional vegetable cultivation (2%). Working under PA is quite easier than the working on open field and most of PA structures are constructed near to the farm houses might be the major factors for the female involvement to PA vegetable cultivation. There isn’t considerable difference between the household size in both adopters and non-adopters. In both groups majority of farm families consisted with four to five members with the 59 percent in adopters and 60 percent in non-adopters respectively. As shows in Table 02 primary employment of the majority of the households is agriculture. From the conventional vegetable famers 78 percent are relied on vegetable cultivation as main income source and this value is only 31 percent in PA farmers. It has been highlighting the diversified nature of PA farmers’ income activities than conventional farmers.
Results for the Tobit model was significant at the 0.05 percent level based on a model chi square statistics. Significant loglikelihood and LR Chi-square values imply that the model fitted well and the explanatory variables used in the model are collectively able to explain the determinants of adoption of PA technologies by dry zone vegetable farmers.
Table 03 shows the results of binary logistic regression model. The results of the model of farmers’ propensity to adopt PA technologies indicate that seven of the twelve independent variables significantly influence the farmers’ propensity to adopt PA technologies in the study area. These include age, household size, vegetable cultivation extent, access to credit, access to new technologies, and primary employment (Table 3).
Age is a primary character in adoption decision and it is
identified as one of the most important factor that influences
the probability of adoption of new technologies. Age may have a
negative, positive or not significantly influence on technology
adoption [17, 19]. The binary logistic regression model results
show that the age variable is an important factor influencing
the adoption decision on PA technologies. Age of the farmer was
negatively influenced PA technology adoption and it was significant
at the 10 percent significance level. The negative relationship
between age and adoption indicates the receptiveness toward
technology among the younger farmers. Adoption of the technology
by the younger farmers may be attributable to the fact that younger
farmers are more likely to use new technologies rather than
practicing conventional methods of farming. As cited by Mwangi &
Kariuki [13], in most studies age has been found to have a negative
relationship with adoption of technology. This negative relationship
is explained as younger farmers are typically less risk averse and
more willing to try new technologies while older farmers are
high risk averse and no interest in long term investment for new
technologies. These results confirmed findings from the work
of Alam [18], Arellanes [22] and Nyanga [12] that older farmers
were accustomed to conventional methods of farming and were
unlikely to change while younger farmers were faster to adopt new
technology.
Most of the studies of technology adoption are identified land
size as one of important determinant of technology adoption
which can be affected positively, negatively or neutral relationship
with adoption [13]. In this study also land extent cultivated with
vegetables is significantly and positively influenced the adoption
on PA technologies in sample area. This could be because farmers
with larger cultivable lands are more market oriented commercial
farmers hence they have good market linkages and access. Since
they can provide continuous supply to the markets with larger
quantities they may have develop good linkages with supermarkets
that demand quality products. Farmers with larger land parcels are
more likely to invest new technologies like PA that could minimize
the climate risks and ensure the continuous supply even in off
seasons. Further, discussion with farmers revealed that small-scale
farmers generally lack with financial resources to invest in costly
PA technologies. These findings are same with findings of Akudugu
[17], Alam [18] and Farid [20] as farmers with large land size are
more likely to adopt modern agricultural technologies.
Household size significantly relates with propensity to adopt
PA practices at 10% level of significance. The result shows that
farmers with small household will have high propensity to move
for protected agricultural technologies. Arene [23] and Bello [24]
reported a positive and significant relationship between adoption
and household size. Labour has become one of the major constraint
factors in Sri Lanka hence younger farmers with less number of
family labour are more likely to transfer for less labour demanding
technologies. However, in another study conducted by Alam [18]
identified that household size did not affect toward the level of
technology adoption.
The provision of support services, such as credit, has been
shown to increase the adoption of improved technologies [25].
Farmers without cash and no access to credit find it very difficult to
obtain and adopt new technologies. The results of this study indicate
that the variable ‘access to credit’ has a positive and significant
influence on the likelihood of adoption of PA, which is consistent
with reports from previous studies [26, 27]. As discussed earlier,
not like in conventional cultivation adoption of PA technology needs
high initial investment which prevents farmers from adopting it.
The variable that captures the subsidy effect on adopting is not
significant in the analysis by emphasizing the need of increasing
the availability of credit rather than promoting subsidies that have
dead weight losses. This validates the condition that farmers with
high accessibility to credit are more willing to use PA technologies.
The extension services have identified as very important
aspect in explaining adoption decisions. Extension officers acts as
a link between innovators and end users of technology. Previous
studies show that extension services have positive relationship
with new technology adoption [13]. The variables that measure
the effectiveness of extension service, access to extension services
measured by the number of meeting with Extension Officer in
a cultivation season and it is positively significant at 5 percent
significance level. Therefore, farmers who have more frequent
visits from extension workers are more likely to adopt technologies
or farming practices that they are exposed to through extension
services. In this case, frequent extension services increased the
farmer adopting PA agriculture as a farming practice. Similar
relationship was observed by Akugudu [17], Bello [24], and Alam
[18] as farmers have more access to extension services are highly
adopted for modern agricultural production technologies.
Access to new technology is identified as another factor that
affects positively and significantly to adoption of PA technologies
by dry zone farmers. Access to new technology is important
for farmers to identify the benefits and effectiveness of new
technologies. However, access to information of new technology
can be negatively affect for technology adoption. Therefore, it is
important to disseminate reliable and accurate information on
technology for farmers [13].
The dummy variable that explains the type of primary
employment of the farmer is significantly and negatively influencing
on the decision of using protected agricultural technologies
among the sample farmers. Farmers whose primary employment
is agriculture are poor adopters than the farmers who are not
completely rely on agriculture as income source. This could be dues
to part time farmers are more willing to use less labour intensive
technologies and they have financial stability to invest in capital
intensive technologies. Farmers with high off farm income can
overcome credit constraints for technology adoption. However, in
some technologies there is a positive relationship of technology
adoption with primary employment. Labour intensive technologies
requires more labour for the afoption and have positive relationship
with on farm income [13].
The binary logistic regression results show that factors associated with the adoption of PA technology were the age of the farmer, household size, access to credit, access to technology type of primary employment, access to extension services and the total vegetable cultivated land in hectare (ha). Working with the elder farmers with limited formal education can be a challenge when introducing new technologies such as protected agriculture. This finding highlights the need of targeting the suitable young people of the farming community to promote advanced technologies like PA. Findings of the logistic regression highlight the importance of increasing the access to credit which is one of the major constraints to adopt new technologies in agriculture among small scale vegetables farmers in dry zone of Sri Lanka. Also study has shown the importance of extension services in promoting new farming technologies. Access to extension services as well as intensity of the extension services are critical in determining the level of adoption.
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