Climate Change and Agricultural Risks: Perception of Farmers from a Socio-Economic Sustainability Perspective

Abstract

Agriculture is an integral part of human development and sustainability. The agricultural sector has seen critical and severe challenges in recent decades due to the impacts of climate change. Agriculture faces significant challenges, especially in developing countries and geographic regions that are characterized as arid or semi-arid. To ensure the sustainability of agriculture, farmers need to engage in adaptive strategies to tackle climate change and achieve environmentally sustainable farming. Libya is characterized as both a developing country and an arid-to-semi-arid geographic location. Hence, the country’s agricultural sector is critically challenged by climate change and an inadequate institutional infrastructure for sustainable agricultural development in the country. This study investigated the climate change adaptations and sustainable agricultural approaches of farmers in Libya, including the impact of the socio-economic dynamics of Libyan farmers on their climate change beliefs, awareness, risk perception, adaptive strategies, and maladaptive strategies. This study carried out a quantitative-style investigation on a sample size of 506 farmers across all regions of Libya. Paired t-tests, an ANOVA, and a correlation analysis were applied to the collected primary data. An analysis of the research results showed a significant correlation among all the research variables and varying degrees of relationships between the socio-economic factors and the research constructs.

Abstract

1. Introduction

Climate change is among the most severe challenges facing the future of our planet, and the severity of its impact on the future will be largely determined by current-day actions and adaptations [1]. Adaptation to climate change is particularly important in the agricultural sector due to this sector’s high sensitivity and dependency on the climate and environmental factors [2].

Climate change presents an escalating threat to global agricultural systems, with an extended impact on global food security, ecological sustainability, and human livelihoods. According to a report by the Intergovernmental Panel on Climate Change (IPCC), the frequency and severity of hazards associated with climate change have intensified in recent years. These hazards include extreme heat, droughts, floods, and irregular precipitation patterns, and these phenomena can have a severe impact on agricultural systems [3]. The IPCC report warns that, unless urgent agricultural adaptation measures are implemented, critical disruptions to agricultural systems are imminent, especially in arid and semi-arid regions [4].

The climate is changing rapidly, and there is an urgent need for agricultural stakeholders, particularly farmers in developing countries, to act and adapt fast to ensure the mitigation of critical risks such as food insecurity [5]. Adapting to climate change in the right way is a process that requires an adequate perception of what climate change is and its impacts on agricultural processes among other sectors of human development and sustainability [6]. Throughout history, farmers have adapted to changing social, economic, and environmental conditions [7]. However, the current trend of climate change poses a significant risk to farmers, and it is not clear if farmers will be able to keep up with the swift changes in the environment due to climate change [8].

Climate change poses a significant threat to the global agricultural sector, but especially so in the developing regions of the world. Adaptive strategies are required to enable farmers and the agricultural sector to secure the future of the sector and ensure sustainable development [9]. Adaptive strategies involve the use of sustainable methods of agricultural practices that complement the changing climate and ensure productivity [10]. There is a need for farmers to adapt their agricultural practices towards sustainable practices to secure agricultural productivity for current and future generations and positively impact the climate [11].

The adaptation of farmers to climate change is significantly associated with their awareness and perceptions of climate change [12]. Hence, comprehensively understanding the factors leading to adaptive strategies towards sustainable agriculture is critical for securing the agricultural sector and promoting the adaptation of farmers to climate change [13]. Individual adaptive behavior of farmers is reportedly strongly influenced by the perception of farmers relative to their appraisal of risks and their capacity to adapt [14]. The perceptions of farmers’ risks and their capacity or self-efficacy is affected by their cognitive biases, such as biases on optimism and low perceptions of risks [15].

Climate change perception is a critical factor that impacts the adaptive behavior of farmers, but there are also other external factors that contribute to and impact adaptive behaviors [16]. External factors that impact adaptive behaviors include economic factors, technology, information, and systemic support. These external factors can either support or challenge adaptive behaviors in farmers [17]. Farmers’ behaviors are strongly impacted by socio-economic contexts and environmental factors. In order to understand the adaptive behaviors of farmers, it is necessary to investigate the internal and external factors that influence their adaptive behavior towards climate change [18].

Research Contribution

Extreme global weather events are caused by climate change and have a critical impact on agricultural sectors worldwide. The Mediterranean region is primarily defined by its arid or semi-arid climate. This naturally hot and agriculturally challenged region continues to see more challenges due to the effect of climate change. Farmers in this region are being critically impacted by climate change, and there is a limited body of empirical studies on farmers’ perceptions and adaptation strategies, particularly in Libya.

The objective of this research was to analyze the socio-economic factors that influence the perceptions of farmers and adaptive strategies to climate change. Hence, this study aimed to answer the following research questions:

• How do Libyan farmers perceive climate change and its potential impact on their agricultural activities?
• What are the perceived risks associated with climate change among Libyan farmers?
• What socio-economic factors significantly influence farmers’ climate risk perceptions and adaptation behaviors?
• To what extent do socio-economic characteristics determine the level and type of climate change adaptation strategies adopted by Libyan farmers?

2. Research Design

This study used a quantitative and cross-sectional survey approach to investigate the risk awareness, perceptions, and adaptation strategies of Libya’s farmers towards climate change. This study was designed as an attempt to gain insight into the current beliefs, attitudes, and behavioral responses of Libya’s farmers towards climate change. This study integrated the protection motivation theory (PMT) [19] and the cognitive behavioral theory (CBT) [20] for a theoretical framework. The PMT explains how individuals respond to perceived threats relative to their cognitive appraisal of the threat in terms of their perceived severity, perceived vulnerability, self-efficacy, and response efficacy. According to the PMT, these perceptions influence the motivation of individuals towards adaptation behaviors [21]. The CBT contributes to the PMT by emphasizing the role of cognitive processes such as self-efficacy, awareness, and personal beliefs in influencing behavioral change [22]. These theories emphasize the point that behavioral change towards climate change, such as adaptation or maladaptation, is influenced not only by external stimuli such as climate events, but also by internal individual perceptions and attitudes [23]. Maladaptive strategies in the context of climate change and agriculture refer to actions and behaviors of farmers in response to climate change that are intended to be adaptive, but result in an increased vulnerability [24]. Maladaptive responses include, but are not limited to, reliance on chemical inputs and resignation to faith-based resolutions. Both the PMT and the CBT emphasize the fact that behavioral changes towards climate change, such as adaptation or maladaptation, are influenced by the primary climate change events. However, they are also influenced by internal cognitive patterns such as belief systems and emotional responses, which influence decision-making biases, as described in the CBT.

This study accounted for perceived threats and the coping mechanisms of Libyan farmers to investigate the behavioral responses of farmers in the agricultural context of a climate-vulnerable region. This research recognized that the adaptive behaviors of farmers are significantly influenced by socio-economic factors [25]. Hence, the research emphasized the socio-ecological perspective of farmers’ adaptive behaviors. This approach underscores the interdependence of environmental challenges and human behavior [26]. This research utilized a quantitative approach to enable an adequate statistical analysis, which determined the extent of the impact of socio-economic and psychological factors on farmers’ perceptions and adaptive behaviors towards climate change.

2.1. Study Area and Population

This study was conducted in selected agricultural communities across the climate-vulnerable regions of Libya, which are primarily characterized as having a semi-arid or arid climate. These areas were identified and selected purposely for their economic dependence on agricultural activities and exposure to climate risks such as irregular rainfall, extreme heat, and droughts [27]. The study population comprised subsistence and smallholder farmers who are primarily the backbone of agricultural activities in Libya [28]. The inclusion criteria for the study population were farmers actively involved in farming across the regions of Libya with an adequate knowledge of the local climate and agricultural dynamics.

2.2. Sampling Technique and Size

This study employed a multistage random sampling approach to ensure randomization within the relevant agricultural communities. Agriculturally intensive zones were purposefully identified through farmers’ cooperatives and local agricultural communities. Among the identified communities, farmers were selected at random using a simple random approach. The research sample size was determined using Cochran’s formula for adequate sample size estimation, with a 5% margin of error and a 95% confidence level [29]. The final research sample included 506 farmers, ensuring sufficient data for a reliable statistical multivariate analysis.

2.3. Data Collection Tool

A structured questionnaire was developed using relevant literature and theoretical constructs; this served as a data collection tool that used eight distinct sections to collect data on different variables for measurement. The first section of the tool consisted of socio-economic characteristic items to enable an adequate definition of the surveyed farmers [30]. Following the theoretical basis of the PMT, the data collection tool collected data on farmers’ threat appraisal and coping appraisal. Assessing the threat appraisal involved determining the likelihood and/or severity of an identified threat [31]. Relative to the farmers’ threat appraisal, this involved an evaluation of the potential risks and farmer vulnerabilities towards climate change. The variables in threat appraisal are as follows:

• Belief in climate change: This is characterized as a farmer’s acknowledgement of the climate change phenomenon and the associated risks [32]. This forms a prerequisite for the farmer’s adaptive strategies [33]. A five-item scale was adapted from the study by Ghanaian et al. [34].
• Climate change awareness: This is characterized as a farmer’s awareness of the causes and impacts of climate change [35]. Climate change awareness is a reported factor in influencing the perceived risks and associated vulnerabilities of farmers towards climate change [36]. A twelve-item scale was adapted from the study by Sen et al. [37].
• Climate change risk perception: This is characterized as the perceived susceptibility of farmers to climate change effects such as droughts and floods [38]. The risk perception of climate change influences the adaptation and maladaptation of farmers towards climate change [39]. A ten-item scale was adapted from the study by Ghanaian et al. [34].

The data collection tool used a five-point Likert scale measurement for all the research variables, which consisted of the following levels: (1) strongly disagree, (2) disagree, (3) neutral, (4) agree, and (5) strongly agree. The exception to this was the socio-economic variables, which were scored using multiple-choice questions. The variables and measurement scales used in this study were carefully selected based on an extensive and comprehensive literature review. The research variables and scales enabled the capturing of the critical dimensions of how farmers perceive, assess, and respond to climate change risks.

To ensure the validity of the data collection instrument and the collected data, the instrument was reviewed by academic experts. Their feedback was used to refine ambiguous terms. A pilot test was conducted on a small sample; this provided feedback to verify and improve the clarity and to ensure internal consistency and adequate respondent understanding. Revisions were made to the instrument based on the provided feedback for the optimization of the instrument.

2.4. Data Analysis

All the statistical analyses were conducted using IBM SPSS statistics 27. The internal consistency of the adopted scales was confirmed with a Cronbach’s alpha of 0.923, which indicated an excellent scale reliability. Descriptive statistics and a frequency analysis were used to summarize the socio-economic demographic characteristics and the research scale variables. A Kolmogorov–Smirnov (K-S) test and the skewness/kurtosis values were used to assess normality. The K-S test revealed statistical significance, likely due to the large sample size; the skewness and kurtosis coefficients were within the acceptable range of −1 to +1. This indicated no serious deviations from normality.

3. Results

According to

Table 1. Socio-demographic characteristics of farmers.

	Sample (n)	Percentage (%)
Sex
Male	423	83.60
Female	83	16.40
Age
Younger than 30 years	64	12.60
31–45 years	272	53.80
46–55 years	132	26.10
Older than 55 years	38	7.50
Education status
No formal education	72	14.20
Primary education	48	9.50
Secondary education	114	22.50
Tertiary education	272	53.80
Farmers’ cooperative membership
Yes	314	62.10
No	192	37.90
Main occupation
Farming	266	52.60
Civil servant	176	34.80
Trader	33	6.50
Artisan	31	6.10
Major source of funding
Personal	388	76.70
Money lender	40	7.90
Bank loan	44	8.70
Farmers’ cooperative	34	6.70
Years of experience
Fewer than 10 years	109	21.50
11–20 years	318	62.80
21–30 years	79	15.60
Total	506	100.00

, 83.60% of the farmers were male, while 16.40% were female. In terms of age, 12.60% were under 30 years old, 53.80% were between 31 and 45 years, 26.10% were between 46 and 55 years, and 7.50% were older than 55. Regarding education status, 14.20% had no formal education, 9.50% had completed primary education, 22.50% had secondary education, and 53.80% had tertiary education. Concerning farmers’ cooperative membership, 62.10% of the participants were members of a cooperative, while 37.90% were not. When it came to their main occupation, 52.60% identified farming as their primary job, followed by 34.80% who were civil servants, 6.50% who were traders, and 6.10% who were artisans. In terms of major sources of funding, 76.70% relied on personal funds, 7.90% borrowed from money lenders, 8.70% obtained bank loans, and 6.70% received funds from farmers’ cooperatives. Lastly, regarding years of experience, 21.50% had fewer than 10 years of farming experience, 62.80% had between 11 and 20 years, and 15.60% had between 21 and 30 years.

Table 2. Farmers’ climate change adaptation strategies scale scores.

	Sample (n)	$\mathbf{Mean}(\overline{\mathit{x}})$	Std. Dev.	Min	Max
Belief in climate change	506	14.07	3.21	4	20
Climate change awareness	506	50.23	9.92	21	69
Risk perception of climate change	506	36.15	6.93	10	49
Adaptation assessment towards climate change	506	16.15	3.76	5	24
Self-efficacy towards climate change	506	21.61	4.05	9	30
Maladaptation to climate change	506	12.26	1.57	7	15
Ecological policies on climate change perception	506	15.89	3.24	4	20

presents the descriptive statistics scores obtained from the farmers’ adaptation strategies scale. The scale includes different dimensions measured with Likert scale items to assess the different aspects of farmers’ perceptions and responses to climate change. The mean score for belief in climate change was 14.07 ± 3.21 (range: 4–20); this indicates a generally strong recognition of climate change among the respondents. Climate change awareness scored a mean of 50.23 ± 9.92 (range: 21–69), suggesting a moderate to high level of awareness among the surveyed farmers. The dimension of the risk perception of climate change scored a mean of 36.15 ± 6.93 (range: 10–49), suggesting a moderate degree of perceived vulnerability and concern regarding the impact of climate change. The assessment of the adaptation of farmers towards climate change scored a mean of 16.15 ± 3.76 (range: 5–24); this indicates that most of the research respondents fell within the moderate-adaptation category. This demonstrates the motivation and capability of Libyan farmers to adapt to climate change.

Self-efficacy towards climate change scored a mean of 21.61 ± 4.05 (range: 9–30); this indicates a relatively strong confidence in the ability of Libyan farmers to address climate change risks in their farming activities. In contrast, the assessment of maladaptation to climate change recorded a mean of 12.26 ± 1.57 (range: 7–15), which suggests persistent and ineffective behaviors as forms of adaptations towards climate change. Finally, the ecological policies on climate change perception assessment scored a mean of 15.89 ± 3.24 (range: 4–20); this suggests a generally favorable perception of the policy efforts by Libyan farmers.

Table 3. Normality tests of farmers’ climate change adaptation strategies scale scores.

	Kolmogorov–Smirnov
	Statistic	Degrees of Freedom (df)	Significance Level (p)	Skewness	Kurtosis
Belief in climate change	0.149	506	0.000	−0.548	0.255
Climate change awareness	0.104	506	0.000	−0.525	−0.314
Risk perception of climate change	0.099	506	0.000	−0.647	0.060
Adaptation assessment towards climate change	0.090	506	0.000	−0.231	−0.614
Self-efficacy towards climate change	0.128	506	0.000	−0.626	−0.049
Maladaptation to climate change	0.179	506	0.000	−0.311	0.366
Ecological policies on climate change perception	0.158	506	0.000	−0.585	0.025

shows the normality tests for the climate change adaptation strategies scale scores of the farmers, and it was determined that the data were normally distributed, since the skewness and kurtosis values were ±1.5.

Table 4. Comparison of farmers’ climate change adaptation strategies scale scores by sex.

	Sex	Sample (n)	$\mathbf{Mean}(\overline{\mathit{x}})$	Std. Dev.	t-Statistic	Significance Level (p)
Belief in climate change	Male	423	14.30	3.12	3.621	0.000 *
	Female	83	12.92	3.45
Climate change awareness	Male	423	51.59	9.20	7.314	0.000 *
	Female	83	43.30	10.60
Risk perception of climate change	Male	423	36.69	6.80	4.049	0.000 *
	Female	83	33.37	6.98
Adaptation assessment towards climate change	Male	423	16.53	3.67	5.356	0.000 *
	Female	83	14.18	3.60
Self-efficacy towards climate change	Male	423	21.99	3.90	4.891	0.000 *
	Female	83	19.66	4.24
Maladaptation to climate change	Male	423	12.30	1.54	1.271	0.204
	Female	83	12.06	1.73
Ecological policies on climate change perception	Male	423	15.89	3.20	0.055	0.956
	Female	83	15.87	3.42

* p < 0.05.

shows the results of the t-test applied for the comparison of the climate change adaptation strategies scale scores of the farmers in the study by gender. According to Table 4, there were statistically significant differences (p < 0.05) between the scores of male and female farmers in terms of the following sub-dimensions of the adaptation strategies scale: belief in climate change, climate change awareness, risk perception of climate change, adaptation assessment towards climate change, and self-efficacy towards climate change. The scores of female participants for the belief in climate change, climate change awareness, risk perception of climate change, adaptation assessment towards climate change, and self-efficacy towards climate change sub-dimensions were lower than those of male participants. Figure 1 illustrates the distribution of the farmers by gender.

Table 5. Comparison of farmers’ climate change adaptation strategies scale scores by age.

	Age	Sample (n)	$\mathbf{Mean}(\overline{\mathit{x}})$	Std. Dev.	Min	Max	F-Statistic	Significance Level (p)	Pairwise Diff.
Belief in climate change	Younger than 30 years	64	13.09	3.21	4	20	4.433	0.004 *	1–4
	31–45 years	272	14.01	3.15	4	20
	46–55 years	132	14.28	3.19	4	20
	Older than 55 years	38	15.39	3.30	6	20
Climate change awareness	Younger than 30 years	64	44.58	10.68	23	67	8.714	0.000 *	1–2
	31–45 years	272	51.12	9.36	21	69			1–3
	46–55 years	132	50.50	9.72	24	65			1–4
	Older than 55 years	38	52.47	10.31	27	65
Risk perception of climate change	Younger than 30 years	64	34.63	6.90	20	49	3.803	0.010 *	1–4
	31–45 years	272	35.99	6.73	15	49
	46–55 years	132	36.30	7.18	10	49
	Older than 55 years	38	39.32	6.80	20	47
Adaptation assessment towards climate change	Younger than 30 years	64	15.83	3.67	6	24	4.520	0.004 *	1–4
	31–45 years	272	15.89	3.63	5	24			2–4
	46–55 years	132	16.23	4.07	7	24
	Older than 55 years	38	18.21	3.06	10	24
Self-efficacy towards climate change	Younger than 30 years	64	20.73	4.35	9	29	2.978	0.031 *	1–4
	31–45 years	272	21.48	3.79	10	29
	46–55 years	132	21.87	4.10	9	30
	Older than 55 years	38	23.08	4.83	9	29
Maladaptation to climate change	Younger than 30 years	64	12.39	1.90	7	15	2.146	0.094
	31–45 years	272	12.14	1.46	7	15
	46–55 years	132	12.30	1.55	7	15
	Older than 55 years	38	12.79	1.74	9	15
Ecological policies on climate change perception	Younger than 30 years	64	14.75	3.71	4	20	3.222	0.022 *	1–2
	31–45 years	272	16.14	3.19	8	20
	46–55 years	132	15.89	2.89	7	20
	Older than 55 years	38	15.97	3.54	6	20

* p < 0.05.

shows the ANOVA results for the comparison of the climate change adaptation strategies scale scores of the farmers in the study according to age group. When Table 5 was examined, statistically significant differences were found among the scores obtained by different age groups of farmers for the following sub-dimensions of the scale: belief in climate change, climate change awareness, risk perception of climate change, adaptation assessment towards climate change, self-efficacy towards climate change, and ecological policies on climate change perception (p < 0.05). Farmers aged 55 and over had higher scores for the belief in climate change, risk perception of climate change, and self-efficacy towards climate change sub-dimensions than participants aged 30 and under. Farmers aged 30 and below had lower climate change awareness scores than other farmers. Farmers aged 55 and over had higher scores for the adaptation assessment towards climate change sub-dimension than those aged 30 and under and those aged 31–45. Farmers aged 30 years and below had lower scores for the ecological policies on climate change perception sub-dimension than those aged 31–45 years.

Farmers demonstrating a higher awareness of climate risks are associated with accumulated agricultural experiences and long-term exposure to environmental variability. This, however, does not guarantee better adaptation outcomes, as this may also be associated with increased risk aversion, a limited openness to innovation, and an over-reliance on traditional practices. Figure 2 illustrates the distribution of farmers by age.

Table 6. Comparison of farmers’ climate change adaptation strategies scale scores by education status.

	Education Status	Sample (n)	$\mathbf{Mean}(\overline{\mathit{x}})$	Std. Dev.	Min	Max	F-Statistic	Significance Level (p)	Pairwise Diff.
Belief in climate change	No formal education	72	13.36	3.84	4	20	2.164	0.091
	Primary education	48	13.83	2.82	8	20
	Secondary education	114	14.56	3.54	4	20
	Tertiary education	272	14.09	2.92	4	20
Climate change awareness	No formal education	72	42.54	9.94	21	66	19.389	0.000 *	1–2
	Primary education	48	49.81	9.62	32	68			1–3
	Secondary education	114	51.21	9.49	24	66			1–4
	Tertiary education	272	51.93	9.22	25	69
Risk perception of climate change	No formal education	72	33.31	7.69	15	49	5.106	0.002 *	1–2
	Primary education	48	35.83	8.43	20	49			1–3
	Secondary education	114	36.92	7.05	10	47			1–4
	Tertiary education	272	36.63	6.19	18	49
Adaptation assessment towards climate change	No formal education	72	14.65	3.34	8	24	12.690	0.000 *	1–3
	Primary education	48	14.00	3.48	6	20			1–4
	Secondary education	114	16.88	4.00	7	24			2–3
	Tertiary education	272	16.61	3.57	5	24			2–4
Self-efficacy towards climate change	No formal education	72	20.35	3.88	10	27	5.815	0.001 *	1–3
	Primary education	48	20.40	3.75	9	27			1–4
	Secondary education	114	22.46	4.34	9	30			2–3
	Tertiary education	272	21.79	3.91	9	29			2–4
Maladaptation to climate change	No formal education	72	12.38	1.73	7	15	0.848	0.468
	Primary education	48	11.96	1.17	8	15
	Secondary education	114	12.35	1.85	7	15
	Tertiary education	272	12.25	1.47	7	15
Ecological policies on climate change perception	No formal education	72	15.11	3.12	7	20	3.649	0.013 *	1–4
	Primary education	48	15.63	2.60	8	20			2–4
	Secondary education	114	15.48	2.87	8	20			3–4
	Tertiary education	272	16.31	3.45	4	20

* p < 0.05.

shows the ANOVA results for the comparison of the climate change adaptation strategies scale scores of the farmers in the study according to their education status. When Table 6 was examined, statistically significant differences were found among the scores obtained by farmers with different education levels for the following sub-dimensions of the scale: climate change awareness, risk perception of climate change, adaptation assessment towards climate change, self-efficacy towards climate change, and ecological policies on climate change perception (p < 0.05). The scores of the uneducated participants for the climate change awareness, risk perception of climate change, and ecological policies on climate change perception sub-dimensions of the climate change adaptation strategies scale were lower than those of the other participants. In addition, the climate change awareness, risk perception of climate change, adaptation assessment towards climate change, self-efficacy towards climate change, and ecological policies on climate change perception scores of farmers with no education and primary school graduates were lower than those of farmers with secondary education or tertiary education. Figure 3 illustrates the distribution of farmers by level of education.

Table 7. Comparison of farmers’ climate change adaptation strategies scale scores by farmers’ cooperative membership.

	Farmers’ Cooperative Membership	Sample (n)	$\mathbf{Mean}(\overline{\mathit{x}})$	Std. Dev.	t-Statistic	Significance Level (p)
Belief in climate change	Yes	314	1397	3.21	−0.847	0.397
	No	192	14.22	3.22
Climate change awareness	Yes	314	51.20	10.17	2.833	0.005 *
	No	192	48.65	9.31
Risk perception of climate change	Yes	314	36.46	6.75	1.289	0.198
	No	192	35.64	7.21
Adaptation assessment towards climate change	Yes	314	16.30	3.70	1.149	0.251
	No	192	15.90	3.85
Self-efficacy towards climate change	Yes	314	21.87	3.82	1.894	0.059
	No	192	21.17	4.37
Maladaptation to climate change	Yes	314	12.31	1.54	0.878	0.380
	No	192	12.18	1.63
Ecological policies on climate change perception	Yes	314	16.32	3.24	3.963	0.000 *
	No	192	15.17	3.10

* p < 0.05.

shows the t-test results of the comparison of farmers’ climate change adaptation strategies scale scores according to their cooperative membership status. According to Table 7, there were statistically significant differences (p < 0.05) between farmers’ scores for the climate change awareness and ecological policies on climate change perception sub-dimensions of the scale according to their cooperative membership status. Farmers with a cooperative membership had higher scores for the climate change awareness and ecological policies on climate change perception sub-dimensions of the scale. Figure 4 illustrates the distribution of farmers’ cooperative membership status.

According to the ANOVA results given in

Table 8. Comparison of farmers’ climate change adaptation strategies scale scores by main occupation.

	Main Occupation	Sample (n)	$\mathbf{Mean}(\overline{\mathit{x}})$	Std. Dev.	Min	Max	F-Statistic	Significance Level (p)	Pairwise Diff.
Belief in climate change	Farming	266	14.03	3.25	4	20	1.725	0.161
	Civil servant	176	14.31	2.72	8	20
	Trader	33	12.94	4.21	4	20
	Artisan	31	14.23	4.03	4	19
Climate change awareness	Farming	266	49.82	10.36	21	68	8.982	0.000 *	1–3
	Civil servant	176	52.40	8.46	25	69			2–3
	Trader	33	43.24	8.98	23	62			3–4
	Artisan	31	48.97	11.07	25	64
Risk perception of climate change	Farming	266	36.09	7.41	10	49	1.490	0.216
	Civil servant	176	36.64	6.01	18	46
	Trader	33	33.88	6.64	21	46
	Artisan	31	36.26	7.67	20	47
Adaptation assessment towards climate change	Farming	266	15.70	3.85	6	24	4.973	0.002 *	2–3
	Civil servant	176	16.90	3.63	5	24			3–4
	Trader	33	15.06	3.47	8	24
	Artisan	31	16.81	3.19	12	22
Self-efficacy towards climate change	Farming	266	21.33	3.91	9	30	2.488	0.060
	Civil servant	176	22.19	3.94	12	29
	Trader	33	20.52	4.56	9	28
	Artisan	31	21.81	4.94	11	29
Maladaptation to climate change	Farming	266	12.14	1.56	7	15	2.952	0.032 *	3–4
	Civil servant	176	12.37	1.44	7	15
	Trader	33	12.06	1.98	8	15
	Artisan	31	12.94	1.82	9	15
Ecological policies on climate change perception	Farming	266	15.93	3.08	4	20	2.951	0.032 *	2–4
	Civil servant	176	16.19	3.42	4	20
	Trader	33	15.18	3.00	11	20
	Artisan	31	14.52	3.41	8	20

* p < 0.05.

, there were statistically significant differences (p < 0.05) among the scores obtained by farmers with different occupations for the following sub-dimensions of the climate change adaptation strategies scale: climate change awareness, adaptation assessment towards climate change, maladaptation to climate change, and ecological policies on climate change perception. Participants who were traders scored lower for the climate change awareness sub-dimension than the others. Farmers who were traders scored lower than civil servants and artisans for the adaptation assessment towards climate change sub-dimension. Trader respondents scored lower than artisan respondents for the maladaptation to climate change sub-dimension. Artisan farmers scored lower than civil servants for the ecological policies on climate change perception sub-dimension. Figure 5 illustrates the distribution of the main occupations of the sampled farmers.

Table 9. Comparison of farmers’ climate change adaptation strategies scale scores by major sources of funding.

	Major Source of Funding	Sample (n)	$\mathbf{Mean}(\overline{\mathit{x}})$	Std. Dev.	Min	Max	F-Statistic	Significance Level (p)	Pairwise Diff.
Belief in climate change	Personal	388	14.32	3.17	4	20	7.464	0.000 *	1–4
	Money lender	40	13.13	2.62	8	20			2–4
	Bank loan	44	14.34	3.21	7	20			3–4
	Farmers’ cooperative	34	11.91	3.41	4	19
Climate change awareness	Personal	388	51.04	10.13	21	69	4.967	0.002 *	1–4
	Money lender	40	47.30	6.49	30	62			2–4
	Bank loan	44	49.59	9.69	27	66			3–4
	Farmers’ cooperative	34	45.32	9.39	24	62
Risk perception of climate change	Personal	388	37.05	6.72	15	49	13.969	0.000 *	1–4
	Money lender	40	32.35	5.26	22	47			3–4
	Bank loan	44	35.82	7.46	10	43
	Farmers’ cooperative	34	30.79	6.62	20	42
Adaptation assessment towards climate change	Personal	388	16.29	3.84	5	24	5.194	0.002 *	1–4
	Money lender	40	15.68	3.48	10	24			3–4
	Bank loan	44	17.00	3.25	8	23
	Farmers’ cooperative	34	13.94	2.83	7	20
Self-efficacy towards climate change	Personal	388	21.97	3.86	9	29	9.122	0.000 *	1–4
	Money lender	40	19.80	3.98	12	29			3–4
	Bank loan	44	22.09	4.07	12	28
	Farmers’ cooperative	34	18.97	4.79	9	30
Maladaptation to climate change	Personal	388	12.38	1.50	7	15	6.714	0.000 *	1–2
	Money lender	40	11.25	1.50	8	15			2–3
	Bank loan	44	12.07	1.81	8	15			2–4
	Farmers’ cooperative	34	12.32	1.79	7	15
Ecological policies on climate change perception	Personal	388	16.05	3.14	4	20	4.558	0.004 *	1–2
	Money lender	40	14.20	3.55	8	20			2–3
	Bank loan	44	16.32	3.39	8	20			2–4
	Farmers’ cooperative	34	15.41	3.19	10	20

* p < 0.05.

shows the ANOVA results for the comparisons among the climate change adaptation strategies scale scores according to the main funding source of the participants in this study. According to Table 9, there were statistically significant differences (p < 0.05) among the scores of the participants with different funding sources for the following sub-dimensions of the scale: belief in climate change, climate change awareness, risk perception of climate change, adaptation assessment towards climate change, self-efficacy towards climate change, maladaptation to climate change, and ecological policies on climate change perception. The participants whose main source of funding was a farmers’ cooperative scored lower for the belief in climate change and climate change awareness sub-dimensions than the others. These participants also scored lower for the risk perception of climate change, adaptation assessment towards climate change, and self-efficacy towards climate change sub-dimensions than those whose main source of funding was personal or bank loans. The respondents whose main source of funding was money lenders scored lower for the maladaptation to climate change and ecological policies on climate change perception sub-dimensions. Figure 6 illustrates the distribution of the farmers’ sources of funds.

The ANOVA results for the comparisons among the scores of the climate change adaptation strategies scale according to the professional experience of the farmers are shown in

Table 10. Comparison of farmers’ climate change adaptation strategies scale scores by years of experience.

	Years of Experience	Sample (n)	$\mathbf{Mean}(\overline{\mathit{x}})$	Std. Dev.	Min	Max	F-Statistic	Significance Level (p)	Pairwise Diff.
Belief in climate change	Fewer than 10 years	109	14.04	3.82	4	20	0.033	0.968
	11–20 years	318	14.06	3.05	4	20
	21–30 years	79	14.15	2.96	6	19
Climate change awareness	Fewer than 10 years	109	47.32	11.51	21	68	6.674	0.001 *	1–2
	11–20 years	318	50.77	9.49	23	69			1–3
	21–30 years	79	52.08	8.44	29	68
Risk perception of climate change	Fewer than 10 years	109	35.55	7.67	20	49	1.057	0.348
	11–20 years	318	36.13	6.83	10	49
	21–30 years	79	37.04	6.22	20	47
Adaptation assessment towards climate change	Fewer than 10 years	109	16.32	3.93	6	24	0.336	0.715
	11–20 years	318	16.04	3.70	5	24
	21–30 years	79	16.33	3.77	7	24
Self-efficacy towards climate change	Fewer than 10 years	109	21.23	4.54	9	29	1.622	0.199
	11–20 years	318	21.57	3.75	10	29
	21–30 years	79	22.29	4.45	9	30
Maladaptation to climate change	Fewer than 10 years	109	12.61	1.84	7	15	4.291	0.014 *	1–2
	11–20 years	318	12.11	1.41	7	15			1–3
	21–30 years	79	12.38	1.74	8	15
Ecological policies on climate change perception	Fewer than 10 years	109	14.88	3.57	4	20	7.986	0.000 *	1–2
	11–20 years	318	16.28	2.93	7	20			1–3
	21–30 years	79	15.68	3.61	6	20

* p < 0.05.

. According to Table 10, there were statistically significant differences (p < 0.05) among the scores obtained by participants with different levels of professional experience for the following sub-dimensions of the scale: climate change awareness, maladaptation to climate change, and ecological policies on climate change perception. Farmers with fewer than 10 years of experience scored lower than the others for the climate change awareness, maladaptation to climate change, and ecological policies on climate change perception sub-dimensions of the climate change adaptation strategies scale. Figure 7 illustrates the farmers’ years of farming experience.

Table 11. Correlation among farmers’ climate change adaptation strategies scale scores.

		Belief in Climate Change	Climate Change Awareness	Risk Perception of Climate Change	Adaptation Assessment Towards Climate Change	Self-Efficacy Towards Climate Change	Maladaptation to Climate Change	Ecological Policies on Climate Change Perception
Belief in climate change	r	1
	p
	N	506
Climate change awareness	r	0.477 **	1
	p	0.000
	N	506	506
Risk perception of climate change	r	0.332 **	0.601 **	1
	p	0.000	0.000
	N	506	506	506
Adaptation assessment towards climate change	r	0.315 **	0.441 **	0.487 **	1
	p	0.000	0.000	0.000
	N	506	506	506	506
Self-efficacy towards climate change	r	0.327 **	0.440 **	0.419 **	0.480 **	1
	p	0.000	0.000	0.000	0.000
	N	506	506	506	506	506
Maladaptation to climate change	r	0.165 **	0.134 **	0.134 **	0.217 **	0.325 **	1
	p	0.000	0.002	0.003	0.000	0.000
	N	506	506	506	506	506	506
Ecological policies on climate change perception	r	0.123 **	0.215 **	0.173 **	0.177 **	0.271 **	0.278 **	1
	p	0.006	0.000	0.000	0.000	0.000	0.000
	N	506	506	506	506	506	506	506

** Correlation is significant at p < 0.05.

shows the results of the Pearson test for the correlations among the scores obtained for the sub-dimensions of the climate change adaptation strategies scale. When Table 11 was examined, statistically significant and positive correlations were found among the scores of the farmers for the belief in climate change, climate change awareness, risk perception of climate change, adaptation assessment towards climate change, self-efficacy towards climate change, maladaptation to climate change, and ecological policies on climate change perception sub-dimensions of the adaptation strategies scale (p < 0.05).

The Pearson correlation analysis results presented in Table 11 reveals statistically significant relationships among the study variables. Notably, climate change awareness showed a strong positive correlation with risk perception and a moderate correlation with belief in climate change, adaptation assessment, and self-efficacy. The risk perception, self-efficacy, and adaptation assessment had a moderated interrelationship; this suggests that awareness and self-efficacy are associated with better adaptive behaviors in farmers. However, despite the correlation found between maladaptation and ecological policy perception, the relationship was weak. This indicates that the relationship may be less direct and context-dependent. Figure 8 illustrates the Pearson correlation heatmap for all the variables.

4. Discussion

This study explored the influence of the socio-economic dimension on sustainable farming relative to climate change in Libya. The investigated sample of this study was predominantly made up of male farmers aged between 31 and 45 years with a tertiary-level education. The gender imbalance in the study sample reflects a broader structural pattern within the agricultural sector of Libya. Men traditionally dominate land ownership, agricultural resource control, and decision-making in farming activities. Female participation is limited and predominantly in small-scale farming and agricultural labor. Hence, the underrepresentation of women in this study is a mirror of the participation limitations in Libya’s agricultural sector with regard to women. The majority of the investigated farmers also had access to some form of agricultural or farming cooperative and were primarily farmers by occupation, with farming being their main source of income. In addition, the majority of the farmers’ primary source of funding was personal funding.

The results of this study revealed that older Libyan farmers aged 55 years and above exhibit stronger climate change beliefs, have a heightened perception of climate change risks, and have more proactive climate change adaptation assessments. These findings align with the studies by Mertz et al. [40], which suggests that these findings were exhibited due to older farmers having extensive experiences and exposure to historical environmental fluctuations and changes. This makes them more aware and responsive to changes. The findings also indicate a generational gap, which was observed in the fact that farmers under the age of 30 exhibited a lower awareness of climate change and had weaker perceptions of ecological policies. A similar finding was seen by Bryan [41], who reported that younger farmers have inadequate experiential learning, which contributes to an adequate perception of the long-term impacts of climate change and environmental deterioration.

This study did not find significant differences in the impact of farmers’ level of education on their perceptions. However, the farmers with a lower level of education were notably associated with reduced climate change awareness, risk perceptions, self-efficacy, and understanding of ecological policies. Full-time farmers were found to have a higher perception of climate change risks with a higher awareness of climate change. Farmers who listed other occupations as their main occupation showed significantly lower awareness and adaptation scores compared to full-time farmers. These findings align with the findings of Tambo and Abdoulaye [42], who reported that part-time farmers exhibit weaker climate change adaptations compared to full-time farmers in Nigeria.

Farmers’ access to funding emerged as a critical determinant of climate-related awareness and adaptation. The farmers in this study who relied on farmers’ cooperative funding showed lower scores for all the climate perception scales used in this study. This could be the result of an inadequate cooperative framework for climate change and environmental knowledge dissemination. A study by Di Falco [43] reported similar findings, where farmers were found to be challenged with their adaptive behaviors in Kenya and Ethiopia. These findings point to the need for adequate capacity building in Libya’s rural farmer finance systems.

The findings of this research indicate that farmers who are members of farmers’ cooperatives have a higher awareness and adaptive cognition; on the contrary, farmers who relied on cooperative funding reported lower adaptation scores. These contrasting findings could be associated with the dual role of cooperatives in rural communities. Cooperatives facilitate access to knowledge and enable peer learning and information sharing; these enhance the understanding of members and their engagement with climate change risks. On the other hand, farmers’ reliance on cooperative funding as their main financial resource may reflect a lack of or inadequate access to diversified sources of capital, which indicates economic vulnerability. Cooperatives are important channels of agricultural literacy and peer support; however, an over-dependence on these sources may signal financial vulnerability, which is a challenging factor in the adaptation and implementation of adaptive strategies among farmers.

Farmers’ experiences were significantly associated with their climate change awareness, maladaptive behavior, and ecological policy perceptions. Farmers with fewer than 10 years of farming experience exhibited a weaker awareness and a strong tendency towards maladaptive farming practices. These findings are consistent with the study by Nhemachena and Hassan [44], in which experienced farmers in Southern Africa were reported to have a more adequate adaptive behavior towards climate change. This highlights the value of the farmer-to-farmer transfer of knowledge, especially in the context of Libya’s environmentally fragile climate, for agricultural practices. Farmers in arid and semi-arid regions have been reported to engage in adaptive strategies that are accessible to them and that they perceive as effective [45]. Sharing experiences via cooperatives and institutional climate change-adaptive initiatives significantly mitigates the climate change risks for farmers in arid and semi-arid regions [46,47].

5. Conclusions

The findings of this study reveal that the responses of Libyan farmers towards climate change are not only influenced by environmental factors and realities, but also significantly influenced by socio-economic factors and institutional relationships. This study revealed that there is a significant knowledge and experience gap among less experienced, younger, and economically challenged Libyan farmers. Hence, it is crucial to adequately promote sustainable and adaptive agricultural practices to all farmers in all socio-economic dimensions. There is also a critical need to embed climate and environmental education into farmer support frameworks in Libya. This will ensure that Libya’s agricultural sector is more climate-resilient and socio-economically sustainable.

5.1. Recommendations and Future Work

Based on the findings of this study, the following are practical recommendations to enhance sustainable farming and strengthen climate change adaptations among Libyan farmers.

• Localized environmental and sustainable agricultural programs that address context-specific climate challenges faced by Libyan farmers should be developed and implemented.
• Farming cooperatives should be strengthened and empowered as key drivers of environmental education and information dissemination to farmers. Cooperatives should be equipped with the information and skills required based on current climate change trends.
• Environmental education should be integrated into agricultural extension services in rural training initiatives. This should include practical agricultural workshops demonstrating the strategies and skills required for adequate adaptation.

Additionally, future research should explore regional disparities and the roles of environmental institutions and their impact on the adaptations of farmers towards climate change in Libya.

5.2. Limitations of the Study

This study has some limitations. One such limitation is the cross-sectional design approach used for the research, which limited the ability to establish causal relationships and observe changes over time. This study lacked control over external variables that may have had an influence on the responses of the research sample. This study’s reliance on self-reported data introduced potential biases such as recall and social desirability biases. In addition, the significant disparity in gender demographics investigated in this study limits the generalizability of the gender-based analysis due to the limited number of female farmers in Libya.