







































Ecology, Economy, and Society–the INSEE Journal 8(2): 5-31, July 2025 

RESEARCH PAPER 
 

Environmental Literacy among Vegetable Farm 
Entrepreneurs and Its Influence on Green Agricultural 
Production in Ghana 
 

Charles Dwumfour Osei  
 
Abstract: A global surge in demand for vegetables such as lettuce, cabbage, 
tomatoes, onions, and eggplants has led to a significant increase in their production, 
particularly in Ghana. Consequently, many vegetable farm entrepreneurs (VFEs) 
have adopted farming practices that conflict with the principles of sustainable 
agriculture. This study investigates the role of environmental literacy in shaping 
green agricultural production (GAP) behaviour among 473 VFEs in Ghana. 
Environmental literacy is defined here as knowledge, responsibilities, and values 
related to the environment, as well as the skills required to translate these into 
practical action. Drawing on primary survey data, the study employs partial least 
squares structural equation modelling to examine the pathways through which 
environmental literacy influences GAP behaviour. The study’s findings indicate a 
generally low level of environmental literacy among participants; however, it 
significantly influenced GAP behaviour across the pre-production, production, and 
post-production phases. The study also identifies GAP willingness as a partial 
mediator between environmental literacy and GAP behaviour. These insights 
highlight the crucial role of environmental education in promoting sustainable 
agricultural practices in emerging economies. 
 
Keywords: Sustainable Agriculture, Environmental Literacy, Clean Production, 
Environmental Responsibility, Environmental Knowledge, Ghana 

 

1. INTRODUCTION 

Consuming vegetables can improve human health and help prevent chronic 
diseases. The globally increasing demand for vegetables such as lettuce, 
cabbage, tomatoes, onions, and eggplants can be attributed to this perceived 

 
 Department of Planning and Development, Christian Service University, P. O. Box 3110, 
Kumasi, Ashanti Region, Ghana; codwumfour@csuc.edu.gh   

Copyright © Osei 2025. Released under Creative Commons Attribution © NonCommercial 
4.0 International licence (CC BY-NC 4.0) by the authors.  

Published by Indian Society for Ecological Economics (INSEE), c/o Institute of Economic 
Growth, University Enclave, North Campus, Delhi 110007.  

ISSN: 2581–6152 (print); 2581–6101 (web).  

DOI: https://doi.org/10.37773/ees.v8i2.1533  

mailto:codwumfour@csuc.edu.gh
https://doi.org/10.37773/ees.v8i2.1533


Ecology, Economy and Society–the INSEE Journal [6] 

causality. However, to increase vegetable production and preservation, 
some vegetable farm entrepreneurs (VFEs) are engaging in unsustainable 
farming practices that pose risks to human health, particularly in developing 
countries in Africa (Kumah et al. 2023; Mohammed et al. 2019; Adu et al. 
2019). These practices include the unnecessary and wasteful application of 
highly toxic agrochemicals that accumulate in water, air, and land (Zhou et 
al. 2023), causing significant environmental pollution and negatively 
impacting human health (Aniah et al. 2021).  

Recent studies in Ghana have reported that VFEs use non-recommended 
insecticides, such as Polytrine, Delphos, Thiodan, Thionex, Cypercal, 
Dursban, and Fastac; their excessive application appears to be increasing 
(Adu et al. 2019; Adesuyi et al. 2018). As a result, major vegetable products 
in Ghana, including tomatoes, cabbage, carrots, and lettuce, contain 
pesticide residues above the recommended thresholds (Kumah et al. 2023; 
Mohammed et al. 2019). The impact on human health is severe; for 
instance, a study revealed that the breast milk and blood of some vegetable 
farmers in Ghana were contaminated with highly toxic agrochemicals such 
as dichlorodiphenyltrichloroethane (Afari-Sefa et al. 2015).  

To address unsafe agrochemical applications by farmers, governments and 
development practitioners in both developed and developing countries 
advocate for investing in green agricultural production (GAP) (Li et al. 2020, 
2021). Green agricultural production refers to agricultural practices that 
minimize environmental pollution and conserve energy through the use of 
environmentally friendly technologies (Li et al. 2020, 2021; Tian, Sun, and Li 
2023). Examples include the use of organic fertilizers and manure, organic 
pesticides, intercropping, zero-tillage methods, and crop rotation (Niu et al. 
2022). GAP has tremendous potential to improve healthy crop yield, 
promote sustainable livelihoods, augment farmers’ incomes, and ensure 
healthy food consumption while protecting the environment (Li and Shen 
2021). It can also enhance farmers’ capacity to adapt to climate change, 
particularly in contexts where coping strategies have become reactive, 
focusing primarily on reducing agricultural losses from climate change 
effects (Mehta 2024).  

To support this shift, governments and other stakeholders in African 
countries are encouraging farmers to invest in GAP (Asiedu-Ayeh et al. 
2022; Dapaah Opoku et al. 2020). However, despite numerous efforts to 
promote GAP, its adoption, particularly among VFEs in sub-Saharan 
African countries such as Ghana, remains low. Conversely, studies suggest 
an increase in the application of inorganic agrochemicals (Aniah et al. 2021; 
Mohammed et al. 2019).  



[7] Osei 

Well-established literature highlights a persistent knowledge gap among 
VFEs in Ghana regarding the adoption of GAP behaviour, which can be 
linked to a lack of environmental literacy (EL) (Aniah et al. 2021; Adesuyi et 
al. 2018; Kumah et al. 2023; Dapaah Opoku et al. 2020). Environmental 
literacy refers to farmers’ knowledge, values, motivations, skills, and ability 
to analyse environmental challenges and understand how to conserve and 
protect the environment (Roth et al. 1992). Theoretically and conceptually, 
EL is believed to play a crucial role in shaping farmers’ willingness (and, 
ultimately, their behaviour) to act in a way that benefits the environment. 
For instance, inadequate EL and limited knowledge of safety precautions 
regarding agrochemical use among farmers may result in their unsafe 
application (Adesuyi et al. 2018).  

This study aims to examine how GAP behaviour develops among VFEs at 
different stages of the production process. Specifically, it explores the 
processes and mechanisms through which EL influences GAP behaviour 
among VFEs in Ghana. For this purpose, VFEs are assumed to perceive 
farming as an entrepreneurial venture—aiming to scale up, maximize 
profits, take calculated risks, and drive business innovation (Osei et al. 
2024). Consequently, farm entrepreneurs are assumed to pursue farm 
business opportunities, innovation, and entrepreneurial activities (Osei and 
Zhuang 2024). This paper seeks to answer the following two research 
questions:  

(1) How does environmental literacy, conceptualized as knowledge, values, 
motivations, skills, and the ability to analyse environmental issues and 
understand how to conserve and protect the environment, contribute to 
GAP behaviour among VFEs in developing countries, particularly Ghana?  

(2) Through what mechanisms does environmental literacy influence GAP 
behaviour among these entrepreneurs?  

The study also examines the mediating effect of the willingness to engage in 
GAP on the relationship between EL and actual GAP behaviour. The 
findings contribute to a deeper understanding of the cognitive and 
motivational factors that drive GAP practices in the context of developing 
economies such as Ghana. This study’s unique approach to assessing EL 
and its influence on GAP behaviour among VFEs in Ghana advances 
existing theoretical frameworks of EL and clean production practices 
among VFEs in developing countries. The following section outlines the 
theoretical background and the process of developing the hypotheses. 
Section 3 describes the methodology, followed by the results (Section 4) 
and discussion (Section 5). The final section presents some key insights. 

 



Ecology, Economy and Society–the INSEE Journal [8] 

2. THEORY AND HYPOTHESES DEVELOPMENT 

The value–belief–norm (VBN) theory explains how individuals’ values, 
beliefs, norms, awareness of consequences, and sense of responsibility 
interact in the development of pro-environmental behaviour (Stern 2000). 
Here, belief refers to individuals’ acceptance of specific environmental 
protection values and practices as normal. Those with strong beliefs 
regarding environmental protection are more likely to develop significant 
conservation behaviours (Tuncer et al. 2009; Zhao et al. 2022) and attitudes 
towards environmental protection (Goulgouti et al. 2019; Tuncer et al. 
2009). Environmental values serve as moral frameworks that determine 
whether actions are beneficial or harmful to the environment. The study 
proposes that VFEs develop GAP behaviour based on their beliefs and 
values regarding the environment. It also suggests that EL plays a bi-
directional role. On the one hand, EL is influenced by awareness, value 
systems, and a sense of responsibility; on the other, it leads to the 
acquisition of skills necessary for implementing sustainable farming 
practices.  

The study uses the theory of planned behaviour (TPB) to examine the 
mechanisms by which EL influences GAP behaviour. Proposed by Ajzen 
(1991), TPB posits that individual behaviours are determined by 
behavioural intentions, which are shaped by three core components: 
attitude towards the behaviour, subjective norms, and perceived 
behavioural control. According to TPB, an individual’s willingness to act 
influences their behaviour (Ajzen 2011). In the context of VFEs adopting 
GAP behaviours, TPB provides a robust framework for understanding how 
EL can translate to actual behavioural change. For VFEs, a positive attitude 
towards environmental conservation and sustainable farming enhances their 
likelihood of adopting GAP. Further, their subjective norms, such as 
environmental values and awareness, significantly contribute to forming 
behavioural intentions towards practices that promote food safety, 
environmental protection, and resource efficiency. Environmental 
knowledge and practical skills are key components of EL, strengthening 
farmers’ confidence in their ability to implement GAP effectively. 

2.1. Environmental Literacy and Green Agricultural Production 
Behaviour  

Environmental literacy encompasses several dimensions, including 
environmental cognition (McBride et al. 2013), environmental skills and 
responsibility (Srbinovski et al. 2010), as well as environmental knowledge 
and values (Maurer and Bogner 2020; Yu et al. 2022). Over time, the 
concept of EL has emerged as a key driving force in policies and research 



[9] Osei 

aimed at achieving sustainable communities (Shri and Tiwari 2021). In line 
with the existing literature, this study defines EL as comprising the 
following key dimensions: environmental knowledge and skills, a sense of 
responsibility, and values regarding the protection and conservation of the 
environment (Yu et al. 2022).  

Environmental knowledge and skills refer to the ability to recognize 
environmental issues, analyse problems, and devise effective solutions to 
protect the environment. Such knowledge enables individuals to develop a 
deeper understanding of environmental challenges and enhances their 
awareness of the need for environmental protection (Chi 2022). For 
instance, farmers’ knowledge and skills training in environmental protection 
practices significantly improved the safe application of pesticides in China 
(Pan et al. 2021). Similarly, equipping farmers with knowledge and skills 
related to GAP enabled them to implement GAP practices such as green 
pest control (Qiao et al. 2022a).  

Environmental responsibility refers to individuals’ views and beliefs that 
protecting the environment is a personal duty. Such individuals are more 
likely to dedicate their time and resources to improving environmental 
quality (Yang et al. 2021). Studies suggest that individuals and organizations 
with higher levels of environmental responsibility are more likely to engage 
in environmentally friendly practices, safeguard natural resources, and adopt 
green initiatives (Lee et al. 2018). Environmental responsibility has also been 
shown to positively influence the adoption of innovative green technologies 
(Wang et al. 2021). Hence, enhancing environmental literacy can have a 
substantial impact on fostering environmental responsibility. 

Environmental values represent the beliefs and ethics individuals hold 
regarding environmental protection (Kurniawan 2021). These values 
significantly shape how individuals assess the importance of the 
environment. Pro-environmental behaviour is often driven by a sense of 
responsibility (Kurniawan 2021; Tamar et al. 2021) and the adoption of 
environmentally sustainable practices, which are closely linked to 
individuals’ level of environmental education (Cincera et al. 2022; Qiao et al. 
2022b). Based on this, we hypothesize that: 

H1: Environment literacy has a significant direct effect on GAP behaviour 
among VFEs. 

2.2. Environmental Literacy, GAP Willingness, and GAP Behaviour  

Recent studies indicate that EL is a precursor to environmental awareness, a 
sense of responsibility, and the willingness and readiness to engage in 
environmental protection, which are directly associated with pro-



Ecology, Economy and Society–the INSEE Journal [10] 

environmental behaviour (Ramdas and Mohamed 2014; Clayton et al. 2019; 
Cincera et al. 2022). TPB highlights the strong positive association between 
individuals’ intentions and their behavioural patterns. A farmer’s decision to 
adopt GAP practices is strongly influenced by their environmental 
knowledge and skills, which increase their awareness of green agricultural 
practices (Liu et al. 2023). Consequently, we hypothesize that: 

H2: EL significantly influences GAP willingness. 

Several studies have found that behavioural willingness is directly related to 
actual behaviour (Chalak et al. 2017; Li et al. 2020). For example, farmers’ 
willingness to engage in green agricultural waste disposal strongly influences 
their actual green waste disposal behaviour (Li et al. 2020). Zhu et al. (2022) 
demonstrated that the willingness of cooperative farmers in China to adopt 
GAP practices had a significant positive impact on their eventual adoption. 
Other studies have also confirmed a strong positive relationship between 
farmers’ willingness and their subsequent adoption of organic agricultural 
practices (Zhou and Ding 2022). Based on this evidence, we hypothesize 
that: 

H3: GAP willingness significantly influences GAP behaviour among farm 
entrepreneurs. 

The willingness to adopt GAP also serves as a crucial mediator between EL 
and the actual adoption of sustainable farming behaviours. Using mediation 
analysis models, Yu et al. (2022) demonstrate that farmers’ willingness to 
practise green agriculture fully mediates the relationship between their EL 
and GAP behaviour. This implies that increased knowledge and 
understanding of environmental issues motivate farmers to take action, 
provided they also have a willingness to act. Supporting this, Liu et al. 
(2024) show that while digital literacy positively influences green production 
behaviour, ecological cognition plays a critical bridging role, reinforcing the 
significance of intermediary cognitive factors such as willingness. 

H4: The willingness to adopt green agriculture significantly mediates the 
relationship between EL and the GAP behaviour of VFEs. 

 

3. METHODS 

This section presents the methodology adopted to examine the factors 
influencing the development of GAP among VFEs in Ghana. It outlines 
the research design, sampling strategy, data collection procedures, and 
analytical tools employed in the study. 

3.1. Sample and Data Collection 



[11] Osei 

A cross-sectional survey design was employed to investigate the factors 
driving the development of GAP in Ghana. The Offinso North District of 
the Ashanti region was purposively selected as the survey location, as 
Ministry of Food and Agriculture data identifies it as a geographical region 
in Ghana where vegetable farming activities are predominant. The study 
population encompassed all VFEs operating in the Offinso North District. 
The map showing the study area (Figure 1) has been sourced from 
Acheampong and Danso-Wiredu (2024). The district receives an average 
annual rainfall of 1250–1800 mm. It has approximately 30,000 farmers who 
primarily cultivate maize, yams, cashews, tomatoes, and okra. Vegetable 
farmers constitute the majority of the farming population in the study area.  

Figure 1: Map of Offinso North District 

Source: Acheampong and Danso-Wiredu (2024) 

Given the homogenous nature of vegetable farming activities in the area, 
we used convenience sampling to select 473 VFEs based on their readiness 
to participate, availability, and relevant characteristics. The questionnaire 
aimed to assess VFEs’ EL, willingness to adopt GAP, and actual GAP 
behaviour. The VFEs surveyed operate under the supervision of 
agricultural extension officers, who regularly educate them on GAP 
methods. Hence, most participants had already received some training on 
the practices, benefits, and consequences of GAP, including organic 
farming practices. During data collection, researchers personally visited the 
VFEs and administered a structured questionnaire to them.  



Ecology, Economy and Society–the INSEE Journal [12] 

3.2. Measurement of Variables 

Dependent Variable: We measured green agricultural production behaviour 
(GAPB) as a latent construct based on VFEs’ decision to adopt GAP and 
implement it across the pre-production, production, and post-production 
stages. At these three stages of production, VFEs engage in the 
procurement of green farming inputs (pre-production), the application of 
green farming inputs and agronomic practices (production), and the green 
packaging of farm outputs and waste disposal (post-production). A five-
point Likert scale, ranging from 1 (never) to 5 (always), was used to measure 
the three dimensions of the GAPB construct. The observable items used to 
measure the GAPB dimensions were adapted and modified from Zhou et al. 
(2019) and Yu et al. (2022). The individual items used to measure the GAPB 
construct are presented in Table 2.  

Independent Variable: Environmental literacy was measured based on three 
dimensions: environmental knowledge and skills, environmental values, and 
environmental responsibility, as proposed by previous scholars (Tuncer et 
al. 2009). The items used to measure these dimensions were adapted and 
modified from previous studies, including Yu et al. (2022). A five-point 
Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree), was 
used to assess each item. 

Mediating Variable: Green agricultural production willingness (GAPW) was 
measured on a five-point Likert scale, ranging from 1 (strongly disagree) to 
5 (strongly agree). The items used to measure GAPW were adapted and 
modified from previous studies, including Li et al. (2020). They captured the 
VFEs’ willingness to invest time and resources in the practice of GAP (see 
Table 2). GAPW was utilized as a mediating variable to test the indirect 
effect of EL on GAPB.  

Control Variables: We included a few control variables in the measurement 
model. Demographic factors such as age and education level were 
incorporated to control for farmer characteristics, while perceived soil 
quality was included to control for farm characteristics. Previous studies 
have confirmed the significant influence of farmers’ profiles and farm 
characteristics on the adoption of green agricultural practices and 
behaviours (Zhang et al. 2021; Cui et al. 2022; Li et al. 2021).  

 

 

3.3. Analytical Strategy  



[13] Osei 

We used the partial least squares structural equation modelling (PLS-SEM) 
approach to test the study’s hypotheses. PLS-SEM, a multivariate analysis 
technique, offers several advantages over other regression methods. 
Notably, it produces robust results even with small sample sizes, in contrast 
to other estimation techniques, such as analysis of moment structures–
structural equation modelling (AMOS-SEM), which typically require larger 
samples (Manley et al. 2020). PLS-SEM can also handle issues associated 
with non-normal data. Initially, we used SPSS to assess data normality using 
the Kolmogorov–Smirnov and Shapiro–Wilk tests. Common method bias 
was checked prior to the analysis. The results showed that all factors had 
eigenvalues greater than 1, with the maximum variance explained by a single 
factor at 24.625%, confirming the absence of common method bias. 

We applied the PLS-SEM analysis technique in two iterative steps. The first 
involved assessing the measurement model, which yielded the factor 
loadings of the constructs’ indicators and their reliability and validity. 
Scholars such as Manley et al. (2020) and Sarstedt et al. (2020) propose 
specific thresholds for assessing the measurement model based on indicator 
loadings, composite reliability (CR > 0.6), Cronbach’s alpha (CA > 0.70), 
and average variance extracted (AVE > 0.5). The second step entailed 
evaluating the structural model to test the relationships between the latent 
variables. We employed the bootstrapping approach, with 5,000 
resamplings, to estimate the significance levels of the direct and indirect 
path coefficients (Manley et al. 2020). 

The structural model was further evaluated based on its predictive power 
(R²), effect size (f²), predictive relevance (Q²), t-statistic, and p-values. The 
bootstrapping approach in PLS-SEM was applied to simulate the unknown 
data distribution. This method transforms the original small sample data 
into a larger sample with minimized standard error. The approach provides 
consistent and accurate estimates of path coefficients, even when the data is 
not normally distributed. 

The structural equations used for the analysis are as follows:  

1eXY +=       (1) 

2eXM +=        (2) 

3eMXCY ++=       (3) 

In these equations, Y denotes the main dependent variable, green 
agricultural production behaviour (GAPB), X represents the independent 
variable, environmental literacy (EL), and M denotes the mediating variable, 



Ecology, Economy and Society–the INSEE Journal [14] 

green agricultural production willingness (GAPW). Equation (1) estimates 
the direct effect of EL on GAPB, where α is the coefficient of EL. 
Equation (2) further estimates the direct effect of EL on the mediator 
(GAPW); hence, β represents the coefficient of EL. Equation (3) also 
estimates the overall outcome effect with the mediator. Hence, C' is the 
direct effect of EL on GAPB after accounting for the mediating effect, 
while δ denotes the effect of the mediator (GAPW) on GAPB. The 

expressions 
1e , 

2e , and 3e are the respective error terms. 

 

4. RESULTS 

In this section, the results of the study are presented and interpreted in 
detail. The analysis begins with the demographic profile of participants, the 
reliability and validity of the study’s measurement model, as well as the 
structural model examined. Results from the study further demonstrate that 
EL significantly influences GAPB, both directly and through GAPW. 
GAPW partially mediates the effects of EL, age, education, and land quality 
on GAPB, confirming the study’s core hypotheses. 

4.1. Demographic Characteristics of Respondents  

Table 1 presents the demographic characteristics of the respondents. 
Among the 473 VFEs who participated in the study, a significant majority 
(86.7%) were male, while 13.3% were female. Regarding age distribution, 
75.7% of the respondents were above 36 years old, whereas 24.3% were 
aged between 18 and 35 years (categorized as young VFEs). The results 
show that 73.6% of the respondents had some level of formal education, 
including primary, secondary, and tertiary education, whereas 26.4% had no 
formal education. In terms of farm characteristics, most respondents 
(41.4%) operated farms smaller than 1 hectare, 36.2% operated on 1–2 
hectares of land, and only 10.6% had farms of approximately 5 hectares or 
more. The farmers’ annual income from farming activities varied, with the 
majority (28.5%) earning between GH₵ 20,001 and GH₵ 25,000, followed 
by 18.6% earning between GH₵ 15,001 and GH₵ 20,000. Additionally, 
15.6% reported annual incomes below GH₵ 5,000, while 12.5% earned 
above GH₵ 25,000 annually. Household size also varied among 
respondents, with 31.5% having household sizes of 1–2 persons, 37.6% 
having 3–5 members, and 30.8% having 6 or more household members. 

 

 

Table 1: Demographic Characteristics of Respondents  



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Variables  Frequency  Percentage (%)  

Gender    

Male 410 86.7 

Female 63 13.3 

Age distribution 

18–25 37 7.8 

26–35 78 16.5 

36–45 114 24.1 

46–55 136 28.8 

Above 55 108 22.8 

Educational background 

No formal education 125 26.4 

Primary education 158 33.4 

JHS/O’ level education 91 19.2 

SHS/VOC/TECH/A’ level Education 76 16.1 

Tertiary education 23 4.9 

Farm size 

>1 ha 196 41.4 

1–2 ha 171 36.2 

3–4 ha 55 11.6 

5–6 ha 32 6.8 

6 ha and above 19 4.0 

Annual income level (GH₵) 

Less than 5,000 74 15.6 

5,000–10,000 56 11.8 

10,001–15,000 61 12.9 

15,001–20,000 88 18.6 

20,001–25,000 135 28.5 

 Above 25,000 59 12.5 

Household size   

1–2 149 31.5 

3–5 178 37.6 

6–8 72 15.2 

9–10 48 10.1 

Above 10 26 5.5 

               Source: Author’s analysis 

 

4.2. Reliability and Validity of the Measurement Model 

Following the recommendations of previous studies, we evaluated the 
measurement models based on item loadings, Cronbach’s alpha, composite 
reliability, and convergent validity (Hair et al. 2021). In the present study, all 
individual observed indicator loadings exceeded 0.50, as shown in Table 2, 



Ecology, Economy and Society–the INSEE Journal [16] 

which satisfies the recommended criteria (Hair et al. 2021). The results 
further reveal high item loadings ranging from 0.828 to 0.955, as shown in 
Table 2. 

Table 2: Factor Loadings, Construct Reliability, and Validity  

Constructs/indicators Loadings Mean 
Std 
dev. 

Construct 
reliability 

Environmental literacy (EL)  
 

 
CA = 0.876 
CR = 0.900 

AVE = 0.502 

Environmental knowledge and 
skills (EK) 

 
 

 

CA = 0.926 
CR = 0.953 

AVE = 0.871 

EK1 → I have an adequate level 
of knowledge on how to protect 
the environment  

0.933 3.6110 0.009 

EK2 → I possess an adequate 
level of knowledge on how to 
detect environmental problems 
as early as possible before they 
emerge  

0.940 3.6025 0.008 

EK3 → I have sufficient skills to 
solve environmental problems 
caused by agricultural activities  

0.928 3.7632 0.012 

Environmental responsibility 
(ER) 

 
 

 

CA = 0.912 
CR = 0.945 

AVE = 0.851 

ER1 → I have the responsibility 
to practise green agricultural 
production to conserve the 
environment sustainably  

0.884 3.9112 0.018 

ER2 → I have the responsibility 
to protect the ecological 
environment from pollution by 
adopting green agricultural 
practices 

0.942 3.7928 0.009 

ER3 → If I do not adopt green 
farming practices to protect the 
environment, I will feel guilty 
since I believe I have that 
responsibility 

0.941 3.7970 0.009 

Environmental values (EVALS)    

CA = 0.930 
CR = 0.955 

AVE = 0.877 

EVALS1 → I believe that a safe 
environment free from pollution 
is most important for sustainable 
development  

0.922 3.4778 0.009 



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EVALS2 → I believe that the 
environment belongs to nature 
and man is not the master of 
nature 

0.948 2.3425 0.007 

EVALS3 → I believe that 
humans must coexist 
harmoniously with nature and 
not harm it. 

0.939 2.3805 0.008 

Green agricultural production 
willingness (GAPW) 

 
 

 

CA = 0.944 
CR = 0.964 

AVE = 0.900 

GAPW1 → I am willing to 
invest some labour in the 
practice of green agricultural 
production technologies 

0.940 2.6258 0.009 

GAPW2 → I am willing to 
invest my time in the adoption of 
green agricultural production 
technologies 

0.955 2.7019 0.007 

GAPW3 → I am willing to 
invest some money in the 
adoption and practice of green 
agricultural production 
technologies 

0.951 2.7040 0.007 

Green agricultural production 
behaviour (GAPB) 

 

 

 
CA = 0.884 
CR = 0.910 

AVE = 0.592 

Pre-production (PREP)    

CA = 0.851 
CR = 0.931 

AVE = 0.870 

PREP1 → The degree to which I 
purchase low-toxic and low-
residue pesticides 

0.937 2.6660 0.006 

PREP2 → The degree to which I 
purchase organic fertilizers and 
insecticides  

0.929 2.4313 0.007 

Production (PRODUCT)    

CA = 0.810 
CR = 0.887 

AVE = 0.724 

PRODUCT1 → The extent to 
which I apply low-toxic and low-
residue pesticides in my 
vegetable farming activities  

0.843 3.2981 0.014 

PRODUCT2 → The extent to 
which I apply organic fertilizers 
and pesticides in my vegetable 
farming activities  

0.828 2.0846 0.023 

PRODUCT3 → The degree to 
which I use the technology of 
formula fertilization in my 
vegetable farming activities 

0.882 3.2727 0.013 



Ecology, Economy and Society–the INSEE Journal [18] 

Post-production (POSTP)    

CA = 0.898 
CR = 0.951 

AVE = 0.907 

POSTP1 → The extent to which 
I reuse agricultural waste 
products from my vegetable 
farm 

0.950 2.8985 0.007 

POSTP2 → The degree to which 
I apply environmentally friendly 
packaging activities for 
agricultural products 

0.955 2.9345 0.005 

Source: Author’s analysis 

Note: CA, CR, and AVE denote Cronbach’s alpha, composite reliability, and 
average variance extracted, respectively. 

The reliability of the measurement constructs was determined using 
Cronbach’s alpha and the composite reliability scores. Following the 
thresholds recommended by prior studies (Manley et al. 2020; Sarstedt et al. 
2020), thresholds of 0.70 or higher were considered acceptable. As seen in 
Table 2, the CA reliability scores for this study ranged between 0.810 and 
0.944, while the CR values of all constructs fell between 0.887 and 0.964. 
The results indicate that the study achieved sufficient reliability of all 
constructs. Additionally, the average variance extracted for all constructs 
exceeded the recommended threshold of 0.50.  

To assess discriminant validity, the Fornell-Larcker criterion was applied to 
further evaluate the convergent validity of the latent constructs used in the 
measurement model. As per the criterion, the square root of the AVE of 
the latent variables should be greater than the coefficients of the correlation 
with the latent variables. Table 3 shows that the model meets this criterion. 
These results demonstrate a strong positive relationship between the latent 
variables, indicating the adequate discriminant validity of the model. 

Table 3: Discriminant Validity  

Constructs 1 2 3 4 5 6 7  8 9 

1. Agricultural 
green 
production 
behaviour 

0.769                 

2. Agricultural 
green 
production 
willingness 

0.647 0.949               

3. Environ-
mental 
knowledge and 

0.484 0.489 0.934             



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skills 

4. Environ-
mental literacy 

0.728 0.703 0.818 0.709           

5. Environ-
mental respon-
sibility 

0.347 0.311 0.451 0.683 0.923         

6. Environ-
mental values 

0.569 0.741 0.412 0.576 0.250 0.936       

7. Post-
production  

0.687 0.621 0.482 0.603 0.397 0.488 0.952     

8. Pre-
production 

0.729 0.903 0.490 0.413 0.316 0.757 0.605 0.933   

9. Production 0.565 0.637 0.289 0.551 0.198 0.698 0.471 0.685 0.851 

Source: Author’s analysis 

We further utilized the Heterotrait–Monotrait ratio (HTMT) to assess the 
discriminant validity of the constructs. As recommended by scholars, to 
meet the acceptance threshold for discriminant validity, the HTMT values 
should not exceed 0.90 (Sarstedt, Ringle et al. 2020; Ali et al. 2018). Table 4 
indicates that all the HTMT values are less than 0.90; hence, the results 
satisfy the sufficient condition of discriminant validity. 

Table 4: Heterotrait–Monotrait Ratio 

Constructs 1 2 3 4 5 6 7  8 

1. Agricultural 
green production 
behaviour 

                

2. Agricultural 
green production 
willingness 

0.808               

3. Environmental 
knowledge and 
skills 

0.525 0.522             

4. Environmental 
literacy 

0.788 0.742 0.805           

5. Environmental 
responsibility 

0.382 0.335 0.492 0.829         

6. Environmental 
values 

0.843 0.791 0.442 0.807 0.270       

7. Post-production  0.882 0.673 0.528 0.674 0.439 0.533     

8. Pre-production 0.801 0.705 0.551 0.793 0.358 0.850 0.689   

9. Production 0.603 0.715 0.327 0.608 0.226 0.796 0.546 0.811 

Source: Author’s analysis 



Ecology, Economy and Society–the INSEE Journal [20] 

4.3. Assessment of the Structural Model 

As suggested by prior studies, the coefficient of determination (R²), 
predictive relevance (Q²), and effect size (f²) were used to evaluate the 
structural model in the present study (Manley et al. 2020; Sarstedt, Hair et al. 
2020). Regarding effect size, it is recommended that f2 exceed 0.02. As 
indicated in Table 5, all values of f2 exceed 0.02, confirming that the model 
has a meaningful effect on the dependent variables. Furthermore, the 
model’s coefficient of determination (R2 = 0.781) was substantial, as the 
model, including EL and GAPW, explains up to 78.1% of the variation in 
GAPB, the dependent variable. This indicates that EL, GAPW, and the 
control variables—such as age, perceived soil quality, and education have a 
significant impact on GAPB. Similarly, the R2 value of 0.539 (Table 4) 
demonstrates that EL and the control variables explain up to 53.9% of the 
changes in GAPW. These results further affirm that the model has an 
appreciable explanatory power. Cross-validated redundancy (Q2) measures 
the predictive relevance of the model. The Q2 value is above zero, implying 
that the model has predictive relevance. The results from Table 5 show the 
Q2 values for GAPW (0.457) and GAPB (0.429), further confirming that the 
model has adequate predictive relevance. We verified the structural equation 
model fit index using standardized root mean square (SRMR = 0.075) and 
normed fit index (NFI = 0.837) values. The results confirm that the SRMR 
and NFI values were within acceptable and satisfactory limits (see Table 5). 

Table 5: Quality Criteria of Structural Model 

Constructs GAPB GAPW SSO SSE 
Q²  

( = 1 – SSE/SSO) 
R² 

GAPW 0.747   1,419.000 770.629 0.457 0.539 

EL 0.127 0.314     

GAPB   3,311.000 1,890.733 0.429 0.781 

Note: Goodness of fit index: SRMR = 0.161. NFI = 0.837 
Source: Author’s analysis 

4.4. Direct Path Effects of EL on GAPW and GAPB  

The standard PLS-SEM bootstrapping method, employing 5,000 
resamplings, was used to estimate the significance of the path effect 
coefficients in the structural model. The results presented in Table 6 
demonstrate the direct effect of EL and the mediating effect of GAPW. 
They demonstrate that EL has a positive and statistically significant effect 
on GAPB (β = 0.290, t = 8.22, p < 0.01). The results further suggest that 
EL has a statistically significant and positive effect on GAPW (β = 0.579, t 
= 11.178, p < 0.01). In addition, GAPW was found to have a positive and 
statistically significant effect on GAPB (β = 0.595, t = 22.74, p < 0.01). The 
analysis of the control variables reveals that educational level and land 



[21] Osei 

quality statistically affect GAPB and GAPW. In contrast, age shows a 
significant but negative influence on both variables. 

Table 6: Direct Structural Path Effects   

 Hypothesized direct structural 
relationships  

β Std dev. t p-value 

 EL > GAPB 0.290*** 0.035 8.222 0.000 

 EL > GAPW 0.579*** 0.052 11.178 0.000 

 GAPW > GAPB 0.595*** 0.026 22.740 0.000 

Control variables      

Age  > GAPB −0.172*** 0.027 6.483 0.000 

Age > GAPW −0.139*** 0.034 4.099 0.000 

Education > GAPB 0.035* 0.024 1.443 0.075 

Education > GAPW 0.094** 0.046 2.065 0.020 

Land quality > GAPB 0.121*** 0.029 4.135 0.000 

Land quality > GAPW 0.212*** 0.039 5.423 0.000 

Source: Author’s analysis 
Note: ***, **,* denote p < 0.01, p < 0.05, and p < 0.10 levels of significance 

4.5. Mediating Effect of GAPW on the Relationship Between EL 
and GAPB 

This study further examines the mediating role of GAPW in the 
relationship between EL and GAPB. As shown in Table 7, GAPW 
exhibited a positive and significant partial mediating effect in the 
relationship between EL and GAPB (β = 0.345, t = 10.442, p < 0.01). The 
results indicate that GAPW also partially mediates the relationships between 
age, education, and land quality and GAPB. The results presented in Table 
7, however, show that age has a negative indirect effect on GAPB (β = –
0.083, t = 3.96, p < 0.10). 

Table 7: Mediating Effects  

Mediating effects β Std dev. t p-value 

 EL > GAPW > GAPW 0.345*** 0.033 10.442 0.000 
Control variables      
Age > GAPW > GAPB −0.083*** 0.021 3.961 0.000 
Education > GAPW > GAPB 0.056** 0.027 2.055 0.020 
Land quality > GAPW> GAPB 0.126*** 0.024 5.228 0.000 

Note: ***, **,* denote p < 0.01, p < 0.05, and p < 0.10 levels of significance  
Source: Author’s analysis 

The results in Table 8 present the decisions made on the various 
hypotheses tested in the study. The results support hypotheses H1 and H2, 
which test the direct effect of EL on GAPW. The results further 
demonstrate that hypothesis H3, which tests the significant influence of 
GAPW on actual GAPB, is also supported. Similarly, GAPW was found to 



Ecology, Economy and Society–the INSEE Journal [22] 

have a partial mediating effect in the relationship between EL and GAPB 
(H4). 

Table 8: Hypotheses Testing 

 Hypothesized direct 
structural relationships  

β 
Std 
dev. 

Decision 
hypothesis supported / 

hypothesis not supported 

 H1: EL > GAPB 0.290*** 0.035 Hypothesis supported 
H2:  EL > GAPW 0.579*** 0.052 Hypothesis supported 
 H3: GAPW > GAPB 0.595*** 0.026 Hypothesis supported 
 H4: EL > GAPW > GAPW 0.345*** 0.033 Hypothesis supported 

Note: *** denotes p < 0.01 level of significance  
Source: Author’s analysis 
 

5. DISCUSSION 

The results indicate that EL has a positive and significant effect on GAPW 
and GAPB. This suggests that VFEs with higher levels of EL are better 
equipped to analyse farming practices and incorporate environmentally 
friendly technologies in their production and post-production activities. 
Moreover, the findings highlight that EL has a statistically significant and 
positive effect on farmers’ willingness to implement GAP technologies. 

Environmental literacy is rooted in VFEs’ knowledge, skills, values, and 
sense of responsibility, which influence their willingness and intentions to 
implement GAP technologies (Zhu et al. 2022). VFEs with higher EL tend 
to exhibit strong environmental values and a greater sense of responsibility, 
which cultivates a positive attitude towards protecting the environment. 
These findings align with previous studies that argue that pro-
environmental behaviour is associated with increased environmental 
knowledge and skills (Amoah and Addoah 2021; Kurniawan 2021; Tamar et 
al. 2021).  

During the pre-production, production, and post-production stages, VFEs 
with greater EL are more likely to adopt GAP practices, such as using 
organic fertilizers or minimizing the use of inorganic pesticides (Yu et al. 
2022). The findings indicate that VFEs are willing to adopt GAP activities 
and technologies when equipped with the necessary knowledge and skills. 
Moreover, they reveal that the willingness to implement GAP technologies 
partially mediates the association between EL and GAPB. These findings 
align with previous studies, such as those by Liu et al. (2024) and Yu et al. 
(2022), which identify willingness as the cognitive pathway mechanism 
through which EL translates into actual behavioural outcomes. The results 
confirm that farmers’ willingness to implement GAP is significantly 



[23] Osei 

dependent on their environmental knowledge, skills, awareness, and 
readiness to implement GAP activities. 

Strengthening farmers’ EL is, therefore, essential to facilitate the transition 
to sustainable farming behaviours. These findings are consistent with prior 
research, including that by Zhu et al. (2022), who found that farmer 
willingness has a significant impact on GAPB due to the influence of 
environmental knowledge and skills. This study also makes an important 
theoretical contribution to both the extended value–belief–norm (VBN) 
theory and the theory of planned behaviour (TPB) by demonstrating how 
EL influences GAPB among VFEs. In the context of the VBN theory, the 
research shows that EL—encompassing knowledge, skills, and ethical 
responsibility—shapes environmental values and beliefs, which in turn help 
create personal norms that encourage pro-environmental behaviour. The 
introduction of “willingness” as a mediator between norms and behaviour 
adds depth to the VBN framework, highlighting that behavioural change 
not only arises from internal values but also from a conscious readiness to 
act. 

Furthermore, concerning TPB, the study demonstrates that EL has a 
positive influence on farmers’ attitudes towards sustainable farming and 
enhances their perceived locus of control by equipping them with the 
necessary knowledge and skills to implement green practices. Although 
subjective norms are not directly examined, the emphasis on ethical 
responsibility implies a social dimension that may influence behaviour. The 
findings confirm that EL strengthens key TPB components—attitude, 
perceived control, and intention—thereby encouraging the adoption of 
GAP practices. Overall, the study highlights the pivotal role of EL in 
driving sustainable behaviour through both normative and cognitive 
behavioural pathways. 

 

6. CONCLUSIONS 

This study assessed environmental literacy (measured through 
environmental knowledge and skills, environmental responsibility, and 
environmental values) as a key factor influencing the adoption of green 
agricultural practices across the pre-production, production, and post-
production stages among VFEs in Ghana. It expands our understanding of 
the critical factors driving GAPB during pre-production decision-making, 
production, and post-production activities, contrary to previous studies that 
focus only on non-farm production. In line with trends in agricultural 
modernization and the Sustainable Development Goals, GAP is promoted 
as a strategic approach to simultaneously protect the environment and 



Ecology, Economy and Society–the INSEE Journal [24] 

ensure food security in Africa and globally. The findings from the present 
paper reveal that while some VFEs are already engaged in GAP, others 
demonstrate a willingness to adopt GAP technologies to achieve sustainable 
agriculture in Ghana. Promoting GAP among VFEs is thus a key strategy 
for conserving the environment, improving productivity, and safeguarding 
human health through the production of safe agricultural products. 

Central to this effort is the dissemination of information on green 
agricultural practices and the strengthening of environmental literacy 
through strong policies. Empirical evidence suggests that EL among the 
respondents remains low. Respondents lack sufficient environmental 
knowledge, skills, a sense of responsibility, and values to fully implement 
GAP. This limited EL can potentially undermine ongoing government and 
stakeholder efforts. The provision of adequate environmental education 
and comprehensive information on green agriculture must be prioritized. 
Therefore, agricultural extension officers and the Environmental Protection 
Agency in Ghana should play an active role in enhancing the EL of VFEs. 
Additionally, stronger policies and legal frameworks should be developed 
and implemented to promote adherence to sustainable agricultural 
practices. 

To further support VFEs’ willingness and readiness to adopt GAP, practical 
tools, resources, and incentives should be provided. Since willingness only 
partially mediates the relationship between EL and green behaviour, 
interventions must extend beyond the dissemination of information and 
include motivational support, peer-learning opportunities, and 
demonstration farms showcasing successful GAP adoption. Strengthening 
farmers’ perceived behavioural control through hands-on training, access to 
sustainable inputs, and technical assistance will further empower them to 
transition from intention to action. Encouraging collaboration among 
government agencies, NGOs, and private-sector actors will ensure a holistic 
approach that reinforces the capacity and motivation of VFEs to adopt 
sustainable agriculture practices. 

Although the present study focuses on Ghana, the findings and policy 
implications hold broader relevance for other regions and developing 
countries with similar agricultural conditions, particularly in sub-Saharan 
Africa. Many of these areas face shared or comparable challenges, including 
low levels of EL among VFEs, limited access to sustainable farming inputs, 
and inadequate extension support for GAP activities. Therefore, the 
insights gained from this study, especially regarding the role of EL in 
shaping GAP behaviour, can inform sustainable agriculture initiatives in 
similar socioeconomic and agroecological contexts. Lessons on enhancing 
EL, strengthening institutional support, and promoting farmer willingness 



[25] Osei 

can serve as a model for other developing countries striving to balance food 
security with environmental conservation. 

Despite the rigorous analysis and empirical findings, this study has certain 
limitations. It relies on cross-sectional empirical data collected from VFEs 
in only the Ashanti region of Ghana. Future studies could benefit from 
employing panels or time series to assess how EL and participation in GAP 
evolve over time. The adoption of GAP in different regions can be 
compared. While this study examines only the perspectives of VFEs, future 
studies may extend the analysis to other agricultural enterprises, such as 
livestock farming, pisciculture, and staple crop production (e.g., rice, maize, 
and cocoa). Additionally, future investigations may focus on the decision-
making processes underlying farmers’ participation in GAP using either 
qualitative or quantitative research approaches. 

Ethics Statement: We hereby confirm that this study complies with 
requirements of ethical approvals from the institutional ethics committee 
for the conduct of this research. 

Data Availability Statement: All data used in this study are not publicly 
available due to privacy concerns. However, they may be obtained from the 
corresponding author upon reasonable request and with appropriate ethical 
clearance. 

Conflict of Interest Statement: The author declares no conflicts of 
interest. 

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