Assessing Commercial Sugarcane Irrigators’ Intentions to Adapt Water-Use Behaviour in Response to Climate Variability in South Africa

Abstract

The scarcity of water resources in South Africa remains a considerable challenge for water users. This study evaluated the impact of climate variability on the adaptive water-use behaviour of sugarcane producers by identifying the factors influencing their adaptation decisions. A survey, the Theory of Planned Behaviour (TPB), and structural equation modelling (SEM) were used to achieve this objective. The study involved 54 sugarcane producers from the Impala Irrigation Scheme, selected through random sampling. Socio-economic profiles revealed a largely male, older demographic with varied education levels and farm characteristics. Results indicated that attitude (β = 0.349, p < 0.1) and subjective norms (β = 0.281, p < 0.05) significantly influenced farmers’ intentions to adapt, while perceived behavioural control had no significant effect (β = 0.051, p > 0.1). These findings suggest that improving farmers’ attitudes and strengthening social support systems can enhance their intentions to adopt adaptive strategies. However, the model’s explanatory power (R² = 0.276) suggests that other unexamined factors may also influence farmers’ adaptive intentions, highlighting the need for further research. Overall, our findings suggest that interventions targeting attitudes, social support, and resource access can improve adaptive behaviours.

1. Introduction

Water is a vital and limited resource necessary for all human activities, utilised by various users, such as the agricultural, domestic, and industrial sectors [1]. Increased competition for water resources due to population growth, urbanization, and climate challenges is especially concerning as the agricultural industry, the largest consumer of freshwater, contributes substantially to global water stress [2,3,4]. Water is crucial for sustaining livelihoods, and its scarcity poses considerable challenges for agricultural production, particularly in areas prone to frequent droughts [5]. The changes and fluctuations in precipitation and temperatures present a severe risk to available water resources [6]. According to the World Bank, South Africa ranks among the top 30 driest countries globally and is the fifth most water-scarce in Sub-Saharan Africa, increasing its susceptibility to climate-related challenges in agriculture [7]. As a result, climate variability and change further complicate the sustainability of water use in agricultural production, affecting the region’s ability to produce essential commodities.

The performance of a country’s economic sectors can be affected by changes in climatic conditions, including variations in rainfall and temperatures, as well as droughts and floods, which could lead to decreased crop yield and productivity [8]. This is concerning because agriculture is often viewed as a catalyst for economic change and advancement, ensuring household food security and income [9]. Specifically in South Africa, agriculture plays a pivotal role in the economy by producing a wide variety of essential commodities for both human and animal consumption, while generating employment opportunities [10]. According to the South African Government (SAG) [11], South Africa typically receives about 40% less rainfall annually than the global average of 860 mm, emphasising its status as a water-stressed country. The pressure on water resources, compounded by fluctuating rainfall patterns, has led to reduced water availability in many regions across South Africa [12]. Fluctuations in rainfall intensify the necessity for efficient water use to meet demand [13]. Thus, it emphasises the water-use behaviour of water users within the agricultural sector.

The production within the agricultural sector in South Africa is threatened by changes in climatic conditions, which can affect food security and the country’s economic performance. South Africa’s susceptibility to climate variations leaves the country vulnerable, given its limited adaptive capacity to cope with climate-related challenges [14]. The variability in temperatures and rainfall could impact the success of crop production, food security, and farming profitability [13,15,16,17]. Water utilisation decisions within the agricultural sector vary among farmers in response to different contextual factors experienced in their production regions [18,19]. The behaviour of water users is crucial for agricultural production, as their decisions impact water usage, leading to various response strategies and adaptation measures to cope with climate-related challenges [20].

The scarcity of freshwater makes optimal utilisation and conservation highly important for the agricultural sector [21,22]. Farmers are the primary water users within the agricultural sector, and it is essential to understand their decision-making regarding sustainable water use [23]. Water availability is influenced by climate conditions, and climate variability impacts crop yields, contributing to food insecurity [24]. The growing importance of effective water management for food production is driven by constraints such as climate variability and climate change [25,26]. A better understanding of agricultural producers’ water-use behaviour can help develop strategies for optimising water use and ensuring sustainability. Furthermore, it can aid in better policy development.

Climatic changes endanger the agriculture industry; consequently, farmers’ decision-making regarding adaptation and their responses to climate fluctuations are vital in agriculture [27]. Farmers’ ability and willingness to adapt their farming techniques are often influenced by their understanding of climate variability and the perceived risks associated with changing climate conditions [28,29]. This highlights the critical role of effective and efficient water management in minimising the effects of climate variability [30]. Understanding farmers’ decision-making concerning land and water use is essential to managing scarce water resources, especially as climate change intensifies pressures on water availability [19,31].

Human behaviour plays a critical role in water scarcity, highlighting the need for sustainable management approaches that account for water users’ actions and decisions [32,33]. The challenge of not understanding water users’ behaviour makes it difficult to tailor policy formulation based on their actions [34]. Consequently, water management policies will prove ineffective as water users may not adhere to them [32]. The behaviour of water users, especially irrigators, substantially influences the effectiveness of measures aimed at improving water-use efficiency [34]. Several studies have assessed the effects of climate variability on various sectors and aspects of the agricultural industry [35,36,37,38,39]. However, while many studies exist on related topics (see Appendix A for a summary of similar studies and the highlighted research gap), studies have yet to explore the water-use behaviour of irrigators in South Africa, particularly in relation to sugarcane production. A lack of understanding of irrigators’ behaviour in South Africa presents a serious challenge, hindering the development of effective policies, support programs, and water-use efficiency measures, ultimately compromising sustainable irrigation practices. In the Impala Irrigation Scheme, where sugarcane production is a major economic driver, recent droughts have underscored the need for improved irrigation and adaptive agricultural practices [40]. These droughts have highlighted the sensitivity of the region’s water resources to both climatic variability and water management practices. With rainfall fluctuations directly affecting the Pongola River catchment, it is crucial to understand how farmers make decisions regarding water use in response to changing climate conditions. This study aims to address this need by examining how attitudes, subjective norms, and perceived behavioural control influence farmers’ intentions to adopt adaptive water-use practices, providing insights to support more resilient and sustainable agriculture in the face of climate variability. To achieve this objective, a survey, the Theory of Planned Behaviour (TPB), and structural equation modelling (SEM) were utilised. This aim was achieved by answering the following research questions:

i.

How do attitudes, subjective norms, and perceived behavioural control influence irrigators’ intentions to adapt to climate variability in the Impala Irrigation Scheme?

ii.

What is the relationship between these key TPB constructs and irrigators’ adaptive responses to climate variability?

iii.

To what extent do these factors explain the variation in irrigators’ intentions to adopt water-saving practices?

The remainder of the paper is structured as follows: Section 2 outlines the materials and methods, including the survey, TPB, and SEM. Section 3 presents the results, while Section 4 discusses the findings. Finally, Section 5 concludes the study, offering key insights and recommendations.

2. Materials and Methods

2.1. Study Area

The study was conducted in the Impala Irrigation Scheme, located in the northern region of the Zululand district municipality in KwaZulu-Natal, South Africa, near the Eswatini border [41]. The scheme is situated around Pongola, within the uPhongolo local municipality, where irrigation plays a crucial role in local agricultural activities. Figure 1 provides an overview of the Zululand district municipality and shows Pongola’s location within the uPhongolo local municipality.

The crops produced in the area include sugarcane, pecan nuts, macadamias, citrus, mangoes, and vegetables. Sugarcane is the primary crop produced in the scheme, with 15,439 hectares, contributing 87.75% of crops produced. Sugarcane is planted from September to April and harvested from March to December. The average yield at the Impala Irrigation Scheme is around 100 tons per hectare [43]. The climate in the Pongola River catchment varies due to differences in elevation. The evaporation rate in the Impala Irrigation Scheme ranges from 1500 mm to 1600 mm, substantially affecting irrigation water use [41]. The scheme experiences warm, humid conditions, with an average annual temperature of 20.2 °C [44].

Figure 2 presents the monthly climate data for Pongola, averaged from 1991 to 2021, showing trends in temperature (minimum, maximum, and average) and precipitation. Rainfall is highest in the summer months of January, February, and December (119 mm, 104 mm, and 124 mm, respectively), and lowest in winter, with June and July receiving 12 mm and 16 mm. The average temperature remains warm year-round, peaking at 23.7 °C in January and 23.8 °C in February, while the coolest months, June and July, average 15.5 °C to 15.9 °C. These climate patterns are essential for understanding agricultural water use in the region.

Figure 3 provides an overview of the conveyance and distribution infrastructure of the Impala Irrigation Scheme. Water is diverted from the Grootdraai Weir into the main canal, which splits into the Impala and Transvaal Canals. The system consists of primary and secondary canals, with siphons at river crossings. Branch canals distribute water to irrigators throughout the scheme. The total length of the canal system is 217 km, of which the majority is concrete-lined, except for 3 km [41].

2.1.1. Climate Variability in the Pongola Hydrological Zone and Its Impact on Agricultural Water Use

The Impala Irrigation Scheme is located in the Pongola Hydrological Zone of KwaZulu-Natal, a region that is highly vulnerable to climate change. According to South Africa’s Department of Forestry, Fisheries, and the Environment (DFFE), climate change poses considerable risks to water resources, food security, and the overall ecosystem of the province [45]. The DFFE [45] further highlighted that vulnerability is compounded by low adaptive capacity and high biophysical sensitivity in the Pongola Hydrological Zone. In response to the concern regarding the lack of information on climate variability, the DFFE [45] provided projections for the Pongola Hydrological Zone, which highlighted notable climate variability that farmers in the area are likely to face in the near future. These projections indicate changes in temperature and rainfall patterns that will directly impact water resources, making it crucial for farmers to adapt their water-use behaviour. Given the expected climate variability in the region, it further underscores the need to understand how changes in climate conditions influence farmers’ decision-making, particularly regarding their intentions to adopt sustainable water management practices.

2.2. Research Design

2.2.1. Sample Selection

The respondents were selected as a sample of farmers in the Impala Irrigation Scheme located in the KwaZulu-Natal Province of South Africa who produce sugarcane under irrigation. Literature suggests that SEM requires at least five observations per measurement item when latent variables include multiple indicators [46,47]. As illustrated in Figure 5, the model includes eight measurement items. Therefore, a minimum of 40 respondents is needed when applying the five observations per measurement item rule. However, the minimum sample size requirement should also consider the statistical power of the estimates [48]. For this reason, we applied Kock and Hadaya’s [49] inverse square root method, which is more tailored to the specific parameters of our SEM model and accounts for the expected path coefficients.

Kock and Hadaya’s [49] inverse square root method considers the probability that the ratio of a path coefficient to its standard error will exceed the critical value of a test statistic at a specific significance level. While Kock and Hadaya’s [49] formula (Equation (1)) specifies $P_{min}$ as the minimum path coefficient, we used the average of the minimum path coefficients across studies from

Table 1. Summary of path coefficients from studies using the Theory of Planned Behaviour (TPB) to measure intentions (source: authors’ compilation).

Reference	Minimum Path Coefficient	Maximum Path Coefficient	Average
Lee et al. [50]	0.243	0.578	0.411
Ayob et al. [51]	0.267	0.563	0.415
Wong et al. [52]	0.291	0.585	0.438
Imari et al. [53]	0.176	0.429	0.303
Average	0.244	0.539	0.4

, selected based on their similar model complexity.

The average minimum path coefficient from Table 1 was identified as 0.244. However, instead of using this value directly as input for calculating the required minimum sample size in our study, we followed the recommendation of Hair et al. [48]. According to Hair et al. [48], when limited information about expected effect sizes is available, researchers should consider a range of effect sizes rather than relying on a single value.

Table 2. Minimum sample sizes for different levels of minimum path coefficients ($P_{min}$) and significance levels (source: Hair et al. [48]).

${\mathit{P}}_{\mathit{m}\mathit{i}\mathit{n}}$	1% Significance	5% Significance	10% Significance
0.05–0.1	1004	619	451
0.11–0.2	251	155	113
0.21–0.3	112	69	51
0.31–0.4	63	39	29
0.41–0.5	41	25	19

provides a summary of the minimum sample sizes required for different levels of minimum path coefficients ($P_{min}$) and significance levels, as outlined by Hair et al. [48].

Hair et al. [48] suggest that when deriving the minimum sample size, it is reasonable to consider the upper boundary of the effect size range, as the inverse square root method is conservative. Given that the minimum average path coefficient from previous literature was identified as 0.244, the corresponding range lies between 0.21 and 0.3 in Table 2. Applying Kock and Hadaya’s [49] inverse square root method (Equation (1)) with the upper-bound $P_{min}$ of 0.3, the required minimum sample size was determined to be 51 at a 10% significance level. A 10% significance level was selected for this study, given the exploratory nature of our research and, to the authors’ best knowledge, as it is the first application of the TPB in the context of our study area.

$$Significance level=10\% : n_{min}>{\left({\displaystyle \frac{2.123}{P_{min}}}\right)}^2$$

(1)

where $n_{min}$ is the minimum sample size required for the analysis and $P_{min}$ is the minimum value of the path coefficient in the model. In Equation (1), 2.123 is a constant corresponding to a significance level of 10%.

The population of irrigated sugarcane producers at the Impala Irrigation Scheme is 67, and all members of this population were invited to participate in the study. Only 54 farmers indicated their willingness to participate in the study and were interviewed. This number exceeds the minimum sample size requirement of 51, as determined by the inverse square root method, and represents 80.6% of the study population in the scheme. These participants were subsequently included in the analysis.

2.2.2. Questionnaire Development

The questionnaire was developed from reviewing the previous literature and studies to identify the questions regarding climate change and variability, and also water-use behaviour [27,54,55,56,57]. The reviewed literature and studies were used as a guideline to develop the questionnaire to align with the objectives and requirements of this study. This was done to develop a questionnaire with measurement items relevant and applicable to irrigation farmers within the study area. The questions were developed to measure the behaviour of farmers towards adaptation to climate variability and climate change on their farms. The respondents had to indicate, on a Likert scale, the level to which they agreed with certain statements about their water use and adaptation to climate variability. The questionnaire addressed socio-economic factors (farm, farmer, and household characteristics), as well as measures of farmers’ attitudes, subjective norms, perceived behavioural control, and the intention to adapt to climate variability.

The questionnaire was first evaluated by the project team and reference group members to ensure its relevance and clarity. Following this, a pilot study was conducted in the Orange-Riet Irrigation Scheme in South Africa. Initially, the questionnaire was piloted with the head of the Farmers’ Association of the Orange-Riet Irrigation Scheme. Based on the feedback received, necessary adjustments were made. Subsequently, a second pilot study was conducted with 11 irrigators from the Orange-Riet Irrigation Scheme. The feedback from this second round of piloting further informed final revisions to the questionnaire before the main data collection. The data collection formed part of a Water Research Commission project (Project nr: C2022/2023-00798). The project also received ethical clearance (Ethical clearance nr: UFS-HSD2023/1327).

2.2.3. The Survey

The primary data were collected through face-to-face interviews with farmers in November 2023. These interviews took place at the head office of the Impala Irrigation Scheme in Pongola, KwaZulu-Natal.

2.2.4. Data Processing

The survey data were captured in Excel and exported to SmartPLS 4 software for further analysis. SEM was used through the SmartPLS 4 software to analyse the data and obtain the TPB results for the study. The processing entailed assigning path coefficients and variables to each construct regarding the outcome of their measurement items. These path coefficients for each construct were used to compare the different constructs and how they contributed to irrigation farmers’ intentions to adapt to climate variability. The intended results obtained were used to measure the behaviour of farmers in adapting their farming practices to climate conditions. It assisted in identifying how each construct contributed to the intention of sugarcane producers to adapt to climate variability. The factors that impacted the farmers’ water-use behaviour and decision-making were also identified.

2.3. Theoretical Framework

2.3.1. The Theory of Planned Behaviour

The TPB is a widely used theoretical framework for measuring human behaviour across various fields [58]. Developed by Ajzen [59], the TPB builds on earlier decision-making models, specifically the Theory of Propositional Control and the Theory of Reasoned Action [60]. This socio-psychological model posits that the most substantial predictor of behaviour is a person’s intention to perform it. According to the TPB, three factors influence an individual’s intention: attitude, subjective norms, and perceived behavioural control. The TPB offers a flexible framework adaptable to various behaviours [58].

Figure 4 is a flow diagram that illustrates the core components of the TPB, showing how subjective norms, attitudes, and perceived behavioural control contribute to an individual’s behavioural intention. Subjective norms are shaped by an individual’s normative beliefs, reflecting the influence of others, such as family, friends, and community members, on their decision-making. An individual’s attitude can affect the likelihood of achieving a desired outcome through specific behaviours and beliefs. Additionally, the extent to which a person perceives they have control over a behaviour will influence their intention to engage in that behaviour. Ultimately, these behavioural intentions will determine the actual behaviour performed [56,59].

The TPB is widely used in research to understand farmers’ behaviours and decision-making processes regarding water use, with various studies applying it to assess their perceptions, intentions, and adaptive behaviours [22,29,56,61,62,63]. In the present study, the TPB was employed to assess sugarcane producers’ decision-making regarding their irrigation water use in the context of climate variability. The TPB was identified as the appropriate framework to evaluate the decision-making of farmers and to have a better understanding of their water use behaviour due to the fact that several other studies have applied the TPB in their research and that it was identified as the adequate framework to achieve the objectives of this study.

To analyse the constructs of the TPB, this study focused on four key constructs: attitudes, subjective norms, perceived behavioural control, and behavioural intentions. SEM was employed to assess the relationships between these constructs, specifically examining how attitudes, subjective norms, and perceived behavioural control influence the intentions of sugarcane producers regarding their irrigation water use. The following section discusses the application of SEM in this context to obtain the results related to the TPB.

Figure 4. The TPB core components (source: modified from Pourmand et al. [64]).

Figure 4. The TPB core components (source: modified from Pourmand et al. [64]).

2.3.2. Structural Equation Modelling

SEM is a second-generation multivariate data analysis method that can test the theoretically supported linear and additive causal models [56]. SEM is often used in social science to explain and predict the behaviour of an individual or group in a scientific manner. In statistical terms, this model comprises a set of equations with accompanying assumptions about the studied system. Its parameters are inferred through statistical observations [65]. Thus, structural equations denote mathematical expressions using parameters to investigate the relationships between latent constructs and their associations with observable variables (measurement items) [65,66].

The path model estimated using SmartPLS 4 is illustrated in Figure 5. In this study, each construct is measured using two specific items. Attitudes were represented by AB1 and AB2, subjective norms by SN1 and SN2, perceived behavioural control by PBC1 and PBC2, and intentions by INT1 and INT2. Only two measurement items were used per construct, as this study was part of a larger research project that employed a comprehensive questionnaire to collect data. To avoid respondent fatigue, we limited each construct to two measurement items. This decision does not pose a limitation, as SmartPLS 4 is capable of analysing models with as few as one measurement item per construct. The empirical specifications of the TPB and the SEM are indicated in Appendix B, which was specifically developed according to the empirical requirements of the study. Appendix B also provides an overview of the steps followed in assessing the measurement and structural model of our study’s SEM path model. The data used in the SEM analysis to estimate the path coefficients and derive the t-statistics and p-values were exclusively based on the responses to the TPB constructs (attitudes, subjective norms, and perceived behavioural control). The socio-economic and demographic variables collected in the questionnaire were not included in the SEM analysis.

Several approaches can be used for SEM. The first approach is covariance-based SEM (CB-SEM), which uses software such as Analysis of Moments Structures (AMOS version 29) and Linear Structural Relations (LISREL version 9.3). The second approach is component-based SEM, or Generalised Structured Component Analysis (GSCA), implemented through VisualGSCA. The third approach is partial least squares (PLS), which focuses on variance analysis using techniques such as SmartPLS and WarpPLS [56,67]. PLS-SEM is an appropriate alternative to CB-SEM when small sample sizes are used [56]. This study applied the PLS approach using SmartPLS 4 software.

The conceptual framework and path model, as shown in Figure 4 and Figure 5, were employed to test the study’s hypotheses. These hypotheses were developed based on the research objective, which seeks to understand the factors influencing commercial sugarcane producers’ intentions to adapt to climate variability. The specific hypotheses are as follows:

Hypothesis 1 (H1): 

There is a positive and significant relationship between attitude and the intention of commercial sugarcane producers to adapt to climate variability.

Hypothesis 2 (H2): 

There is a positive and significant relationship between subjective norms and the intention of commercial sugarcane producers to adapt to climate variability.

Hypothesis 3 (H3): 

There is a positive and significant relationship between perceived behavioural control and the intention of commercial sugarcane producers to adapt to climate variability.

3. Results

3.1. Socio-Economic Characteristics

This section provides an overview of the socio-economic characteristics of the respondents, focusing on three key aspects: demographic characteristics (

Table 3. Respondents’ demographic profile (source: authors’ compilation).

Variable	Classification	Frequency	Percentage (%)
Gender	Male	54	100%
	Female	-	-
Age	Younger than 20	-	-
	21–30	-	-
	31–40	7	13%
	41–50	16	30%
	51–60	13	24%
	61 and older	18	33%
Highest education level	Primary (Grades 1–7)	-	-
	Secondary (Grades 8–12)	16	30%
	Diploma	20	37%
	Undergraduate degree	18	33%
	Master’s	-	-
	PhD	-	-
Health level	Poor	-	-
	Average	7	13%
	Good	29	54%
	Excellent	18	33%

), household characteristics (

Table 4. Respondents’ household characteristics (source: authors’ compilation).

Variable	Classification	Frequency	Percentage (%)
Number of adults (18 years and older) living in the household	One	9	17%
	Two	41	76%
	Three or more	4	7%
Total individuals living in the household (adults + children)	One	4	7%
	Two	25	46%
	Three	8	15%
	Four or more	17	32%
Gender of the head of the household	Male	54	100%
	Female	-	-
Individuals in household involved in farming activities	One	32	59%
	Two	18	33%
	Three or more	4	8%
Marital status	Married	51	94%
	Single	2	4%
	Divorced	-	-
	Widowed	1	2%
Age of the head of the household	Younger than 20	-	-
	20–30	-	-
	31–40	7	13%
	41–50	14	26%
	51–60	13	24%
	61 and older	20	37%
Primary source of livelihood	Farmer/agricultural activities	47	87%
	Self-employed/business owner	7	13%
Off-farm income	Yes	19	35%
	No	35	65%

), and farm characteristics (

Table 5. Respondents’ farm characteristics profile (source: authors’ compilation).

Variable	Classification	Frequency	Percentage (%)
The primary source of water used for irrigation	River	3	6%
	Dam	4	7%
	Canal	38	70%
	Borehole	-	-
	River and dam water	2	4%
	River and canal water	4	7%
	River and borehole	-	-
	Dam and canal water	3	6%
	Dam and borehole	-	-
	Canal and borehole	-	-
Type of farming performed	Crop production	44	82%
	Livestock production	-	-
	Mixed farming	10	18%
Amount of agricultural land deeds owned	One	4	7%
	Two to five	27	50%
	More than five	23	43%
How scattered is the farmland from the main farm	Highly clustered together (1–5 km)	24	44%
	Moderately dispersed (6–10 km)	13	24%
	Highly dispersed (more than 10 km)	17	32%
Business structure of the farm operations	Sole proprietorship	8	15%
	Corporation	21	39%
	Partnership	2	4%
	Trust	22	41%
	Closed corporation	1	1%
The land ownership type of the farm	Sole owner	43	80%
	Lease	-	-
	Combination of sole owner and lease	11	20%
	Communal	-	-
Distance of main irrigation land from the water source	Less than 1 Km	38	70%
	1–3 Km	10	18%
	3–7 Km	3	6%
	7–10 Km	1	2%
	More than 10 Km	2	4%

).

3.1.1. Demographic Characteristics of Respondents

The respondent demographic profile is shown in Table 3. All respondents (100%) identified as male, indicating a male-dominated demographic in this study. The age distribution shows that 33% of respondents were 61 years or older, with notable representation in the 41–50 years (30%) and 51–60 years (24%) age brackets. Notably, younger individuals (under 30) were not represented. Only 30% of respondents completed secondary education, while the majority have attained higher qualifications, with 37% holding diplomas and 33% obtaining undergraduate degrees. Health levels are largely positive, with 54% rating their health as good and 33% as excellent.

3.1.2. Household Characteristics

Table 4 presents the household characteristics of respondents within the Impala Irrigation Scheme. A great majority (76%) of households comprise two adults aged 18 and older, indicating a tendency towards smaller household sizes. The total number of individuals living in these households shows that 46% have two members, while 32% have four or more, suggesting a mix of small and slightly larger families. All respondents reported that the head of their household is male. Regarding involvement in farming, 59% of households reported that only one person is engaged in farming activities, and 33% have two people involved. Most heads of households are married (94%), indicating a stable demographic. The age distribution of household heads shows that 37% are 61 years or older. Additionally, 87% of respondents indicated that farming or agricultural activities are their main source of income, highlighting the importance of agriculture in the local economy. This is further supported by the fact that 65% of respondents reported having no off-farm income, indicating that many families depend solely on their agricultural earnings.

3.1.3. Farm Characteristics

The farming characteristics of the respondents are summarized in Table 5. A substantial majority, 70%, report using canal water as their primary source for irrigation. In contrast, reliance on river (6%) and dam water (7%) is minimal, with only a small percentage indicating the use of combined water sources. In terms of farming activities, crop production is the predominant focus, with 82% of respondents engaged in this practice. Notably, there are no respondents dedicated exclusively to livestock production, while 18% practice mixed farming. Regarding land ownership, 50% of respondents own between two and five agricultural land deeds, while 43% own more than five deeds. Moreover, 80% of respondents identify as sole owners of their land, reflecting a preference for individual ownership over leasing arrangements. The distance of farmland from water sources is also noteworthy, with 70% of respondents indicating that their main irrigation land is located less than 1 km from the water source. Finally, the organizational structure of farming operations varies, with 39% operating as corporations and 41% as trusts.

3.2. Empirical Results

3.2.1. Theory of Planned Behaviour

This section presents certain key findings from the assessment of the measurement and structural models, as outlined in Appendix B.3. A comprehensive overview of the results of the measurement and structural model assessment is provided in Appendix C. Figure 6 presents the standardised estimates of the model. The estimates were analysed to evaluate the adequacy of the measurement model, ensuring that the constructs accurately reflect the underlying theoretical concepts. After the analysis for the measurement model was completed, the structural model analysis was conducted, as discussed in the next section.

3.2.2. Structural Model Assessment and Reporting of the TPB and SEM Results

The bootstrapping estimates presented in Figure 7 were obtained using SmartPLS 4 software with 5000 resamples and the percentile method to determine 90% confidence intervals at a 10% significance level. The bootstrapping procedure was performed with the ‘Complete (slower)’ setting, the test type set to ‘two-tailed’, and confidence intervals were constructed by evaluating the lower 5% and upper 95% percentiles of the distribution. Path coefficients (β) were used to assess the significance of the hypotheses, with coefficients having p-values ≤0.10 considered significant, as this study is exploratory in nature. Figure 7 presents the path coefficients and p-values for the bootstrapping calculations. All factor loadings were significant, indicating that they met the inclusion criteria, as shown in Figure 7.

Table 6. The results from the bootstrapping technique (source: authors’ compilation). Note: * p < 0.1; ** p < 0.05; *** p < 0.01 and * t > 1.65; ** t > 1.96; *** t > 2.57.

Constructs	Path Coefficients	Sample Mean	Standard Deviation	t-Statistics	p-Values
Attitude → Intention	0.349	0.327	0.206	1.690 *	0.091 *
Subjective norms → Intention	0.281	0.288	0.125	2.244 **	0.025 **
Perceived behavioural control → Intention	0.051	0.099	0.191	0.265	0.791

illustrates the path coefficients, the sample mean, standard deviation, t-statistics, and p-values obtained from the bootstrapping procedure. The results show that subjective norms significantly affect the intention to adapt to climate variability (p < 0.10). Similarly, attitude significantly affects intention (p < 0.10). Interestingly, the results show that perceived behavioural control does not significantly affect intention (p > 0.10).

Table 7. The R² and adjusted R² results (source: authors’ compilation). Note: * p < 0.1; ** p < 0.05; *** p < 0.01 and * t > 1.65; ** t > 1.96; *** t > 2.57.

Construct	R²	t-Statistic of R²	p-Value of R²	Adjusted R²	t-Statistic of Adjusted R²	p-Value of Adjusted R²
Intention	0.276	2.758 ***	0.006 ***	0.233	2.193 **	0.028 **

presents the R-squared (R²) and adjusted R² results, along with their t-statistics and p-values. The R² value refers to the coefficient of determination calculated as the squared correlation between the model’s predicted and observed values for the endogenous construct, intention [48]. The results show that the R² (0.276) of intention was significant at a 0.01 significance level for both the t-statistic and p-value. The t-statistic (2.758) of R² was greater than 2.57, and the p-value (0.006) of R² was smaller than 0.01. The results show that the adjusted R² (0.233) of intention was significant at a 0.05 significance level for both the t-statistic and p-value. The t-statistic (2.193) of R² was greater than 1.96, and the p-value (0.028) of R² was smaller than 0.05. According to Anjum et al. [68], an adjusted R² value greater than 0.20 is deemed high in behavioural studies. Thus, the adjusted R² result obtained for this study was in accordance with the literature.

The estimated standard root mean square residual of 0.093 is less than 0.1, which suggests that the model is a good fit, as highlighted by Mahdavi [56], and can be used to assess the relationships between the four constructs. The measurement and structural model assessment discussed above provided a comprehensive overview of the procedures followed to obtain the results for the study in SmartPLS 4 software. Next follows a discussion of the results to put the findings into context.

4. Discussion on TPB and SEM

This section provides a detailed analysis of the TPB constructs, including attitude, subjective norms, perceived behavioural control, and intention, using the SEM procedure. Socio-economic characteristics were collected to describe the respondents and provide context, but were not part of the formal analysis and would, therefore, not be included in the discussion. Each TPB construct is discussed individually, with comparisons drawn to the relevant literature in agricultural studies. The study’s hypotheses are examined to determine whether they were supported or rejected, and the findings are compared with other studies on climate-related behaviour and agricultural research.

4.1. Attitude

A farmer’s attitude can positively or negatively affect their behaviour. The extent to which a farmer perceives that climate variability will specifically influence their intention and behaviour to adapt (H1, as indicated in the Section 2).

The results for the path coefficient, t-statistic, and p-value for attitude are presented in Table 6. The path coefficient value of 0.349 (p < 0.1) indicates a positive and significant relationship between attitude and the intention to adapt to climate variability. Consequently, if attitude increases by one standard deviation, the intention to adapt to climate variability is expected to increase by 0.349 standard deviations, assuming all other variables in the model are held constant. Sugarcane farmers with a positive attitude towards climate variability are significantly more likely to adopt strategies to withstand the effects of variable climate conditions. A positive attitude in this context means they are open to change and willing to take action to cope with climate variability. Thus, based on the results presented for attitude, H1 was accepted, meaning that a sugarcane farmer’s attitude will substantially affect the intention to adapt to climate variability.

The positive and significant effect of attitudes on farmers’ intention to adapt to climate variability aligns with the findings of similar studies within agriculture. Arunrat et al. [61] found that attitude had a path coefficient of 0.352 for farmers’ intention to adapt to climate change, aligning with the positive relationship findings of this study. Farmers’ attitudes towards climate adaptation could be influenced by their previous experiences and risks, for example, droughts and flooding, which would drive them to implement new farming practices [62]. Zhang et al. [62] found that attitude had a coefficient of 0.384 in climate adaptation, indicating a positive relationship. According to Wheeler et al. [69], farmers exposed to risks (higher debt, fluctuations in rainfall, and temperature) are more likely to have a behavioural attitude acknowledging that climate is a substantial risk for their agricultural production.

It has been found that the structure of survey questions could influence farmers’ attitudes towards the climate. Thus, farmers are more likely to agree with statements that indicate that variability in climate conditions is occurring rather than that human actions are to blame for climate change [70]. The perception of sugarcane farmers about climate conditions could lead to an intention for them to adapt, as their attitude had a positive relationship with intention. Jellason et al. [71] found that when the determinants of attitude were significant, they caused intentions to adapt to climate change. Farmers with negative attitudes towards climate are subsequently less likely to change their farming practices [72]. Furthermore, the ability to access climate information, membership in social groups, and previous experiences of adverse climate conditions would positively affect the attitude of farmers when referring to climate variability adaptation.

The studies discussed above indicate that farmers’ attitudes play a significant role in shaping their intentions to adopt certain behaviours. The attitude of farmers could drive their intentions towards making the necessary adaptations to climate variability. However, it will ultimately depend on their view of specific behaviours, such as those related to climate. This implies that there must be efforts to enhance farmers’ attitudes towards climate adaptations, which would increase their intention to implement adaptive measures. This will promote and contribute to more resilient agricultural practices and improve the water-use behaviour of commercial sugarcane farmers. The next section elaborates on the findings about subjective norms as a driver of a farmer’s intention to adapt to climate variability.

4.2. Subjective Norms

The engagement of a farmer in a specific behaviour could be impacted by the social context in which they produce sugarcane. The social pressure that a farmer experiences contributes to their intention to adapt to climate variability (H2, as indicated in the Section 2).

The results for the path coefficient, t-statistic, and p-value for subjective norms are presented in Table 4. The path coefficient value of 0.281 (p < 0.05) indicates a positive and significant relationship between subjective norms and the intention to adapt to climate variability. Thus, if subjective norms increase by one standard deviation, the intention to adapt to climate variability is expected to increase by 0.281 standard deviations, assuming all other variables in the model are held constant. Sugarcane farmers who experience social pressure towards adapting to climate variability are more likely to implement such strategies. Thus, H2 was accepted, indicating that subjective norms would substantially affect sugarcane producers’ intention to adapt to climate variability in the Impala Irrigation Scheme.

The results showed that subjective norms have a positive relationship with the intention of sugarcane farmers to adapt to climate variability. Multiple studies also found that subjective norms positively and significantly affected the intention to adapt to climate conditions. In the study of Zhang et al. [62], the path coefficient of subjective norms (0.285) was very close to the results found in this study (0.281), showing a very similar outcome. The study area investigated by Zhang et al. [62] relates to the location identified for this study, as both regions have a subtropical climate, explaining why the results are almost similar. The findings of Arunrat et al. [61] about subjective norms (0.258) showed a significant relationship between them and farmers’ intention to adapt to climate change. Nguyen and Drakou [73] also identified a significantly positive relationship between subjective norms and the intention to adapt, with a path coefficient of 0.47.

The study by Jellason et al. [71] revealed contrasting effects of subjective norms on the intention to adapt to climate change, depending on the study area. In one area, subjective norms had a significant positive effect (0.60), whereas in another, the relationship was positive but insignificant (0.31). While our study aligns with other research that shows a positive relationship between subjective norms and the intention to adapt, such as those by Zhang et al. [62] and Arunrat et al. [61], the findings of Jellason et al. [71] underscore the need to account for contextual factors when assessing behavioural intentions. Overall, our results, which show a significant positive effect of subjective norms, are consistent with the majority of studies in the field, emphasizing the importance of social pressure in the decision-making processes of sugarcane farmers in the Impala Irrigation Scheme.

The literature supported our findings that subjective norms substantially affected this study’s participants’ intention to adapt to climate variability. The social environment sugarcane farmers experience will thus play a role in adapting to climate variability. The social pressure on sugarcane farmers could subsequently determine their water-use behaviour. This finding suggests that social influences, such as the opinions of important others (family, friends, community leaders) or societal expectations, play a major role in shaping individuals’ intentions to adapt to climate change. Efforts to increase the intention to adapt to climate change could benefit from strategies that strengthen positive subjective norms, such as promoting social approval or endorsing adaptive behaviours.

4.3. Perceived Behavioural Control

The adoption of water-use behaviour by farmers is affected by how they perceive having a level of control over it. If farmers think they have influence over a specific water-use behaviour, they would be more encouraged to have intentions to adopt a particular behaviour (H3, as indicated in the Material and Methods Section 2).

The findings for the path coefficient, t-statistic, and p-value for perceived behavioural control are presented in Table 4. The path coefficient value of 0.051 indicates a slightly positive but insignificant (p > 0.1) relationship between perceived behavioural control and the intention to adapt to climate variability. This suggests that sugarcane producers’ perception of their ability to adapt to climate variability does not significantly influence their intention to adopt climate adaptation strategies.

The results showed that perceived behavioural control is the only construct that does not substantially influence sugarcane farmers’ intentions to adapt to climate variability. These findings are related to only a few other studies regarding perceived behavioural control because they also found insignificant results. Jellason et al. [71] investigated behaviour in two different study areas. They found that perceived behavioural control was insignificant in predicting both study areas’ intentions to adapt to climate change. These insignificant findings may suggest that the farmers in the Impala Irrigation Scheme feel they do not have a substantial degree of influence on their water-use behaviour. The sugarcane producers did not have substantial intentions to adapt against climate variability and perceived themselves as having a low level of behavioural control over climate conditions.

Numerous other studies, however, found that farmers had a significant intention to adapt to climate conditions. Arunrat et al. [61] found that PBC (0.503) significantly and positively affected the intention of farmers to adapt to climate conditions. Nguyen and Drakou [73] reported similar results, with their perceived behavioural control path coefficient of 0.510 indicating a positive and significant effect on intention. Renita and Anindita [74] added that perceived behavioural control had a significantly positive impact on the intentions of farmers to adapt, with a coefficient of 0.27. Lastly, Zhang et al. [62] discovered that PBC (0.183) was a major determinant of intention. Therefore, it can be concluded that results from various water-use behaviour studies regarding adaptation to climate change could have either significant or insignificant outcomes, depending on the perceived control of farmers.

The literature showed that most water-use behaviour studies reported a significant relationship between perceived behavioural control and intention. This study’s findings indicated an insignificant relationship, which showed that sugarcane farmers in the Impala Irrigation Scheme were not perceived to be likely to adapt to climate variability. The following section discusses the intention to adapt to climate variability.

4.4. Intention

The intention of farmers could be expressed as the degree to which they plan to perform and implement a specific behaviour. Numerous factors contributed to the water-use behaviour of sugarcane producers in the Impala Irrigation Scheme. As the results indicated, attitudes and subjective norms had a significant and positive relationship with the intention of sugarcane farmers to adapt to climate variability, with perceived behavioural control showing insignificant outcomes.

Table 5 shows the R² value as 0.276, with a t-statistic of 2.758 and a p-value of 0.006. R² is the value of the determination coefficient, measuring the proportion of the variance in the dependent variable predicted by the independent variables [62,75]. The R² found in this study indicated that 27.6% of the explained variance in the intention to adapt to climate variability among sugarcane farmers was explained by the independent variables in the model. The adjusted R², which accounts for the number of predictors in the model, was lower, at 23.3%, reflecting a slight penalty for model complexity. The adjusted R² was not discussed in detail, as the difference between R² and adjusted R² was minimal and did not substantially alter the explanatory power of the model. The predictive accuracy for the R² can be classified as moderate [75].

These results are consistent with findings from other water-use behaviour studies. The study by Zhang et al. [62] found an R² value of 0.421 for adaptation behaviours to climate change, indicating a moderate relationship. Arunrat et al. [61] reported an R² value of 0.518 for the intention of farmers to adapt to climate change, which was a moderate to strong relationship between the independent variables and the intention to adapt to climate change. A moderate R² value indicates that other factors may affect the adaptive intentions of sugarcane farmers in the Impala Irrigation Scheme, which can be a limitation of this study.

Usman et al. [76] found that the adaptation measures implemented by farmers were affected by various socio-economic, demographic, and agronomic factors. They included factors such as age, farming experience, education, access to credit, and climate information. The findings of Dang et al. [77] correlate with the factors mentioned because they found that access to agricultural extension, credit, and better technologies substantially affect farmers’ adaptation to climate change. Trinh et al. [78] reported that training for climate change, farm size, farming experience, access to credit, and education affected farmers’ adaptation to climate change. Lastly, Michalak [79] indicated several problems in adapting to climate change. The lack of awareness of climate change and inadequate methods to use water were some of the issues farmers faced in effectively managing water resources.

Sugarcane producers in the Impala Irrigation Scheme may face some of the factors mentioned, explaining only a moderate relationship between attitudes, subjective norms, and perceived control and farmers’ intention to adapt to climate change. A better understanding of the influence of these factors on farmers’ water-use behaviour is essential.

4.5. Sample Size Considerations and Validity

The sample size for this study was determined prospectively using the inverse square root method based on an average of the smallest path coefficients from prior studies with similar models. This approach recommended a minimum sample size of 51 respondents. However, when using the smallest path coefficient from our own results (0.051 for the relationship between perceived behavioural control and intentions), the inverse square root method suggests that a sample size of 451 is needed at the 10% significance level. In light of this, it is important to note that while the sample size required based on our own results is larger than our actual sample size of 54, Rigdon [80] argues that PLS-SEM can still be effectively used when the population is finite and additional data are not available. Rigdon [80] emphasizes that the generalisability of results in such cases is limited to the specific population being studied. In our case, the sample of 54 farmers is drawn from a finite population of 67 sugarcane farmers, and while the sample size may be smaller than typically recommended for generalising findings to a larger population, the findings remain valid and meaningful within the context of the Impala Irrigation Scheme.

5. Conclusions

Agriculture is vital to South Africa’s economy, yet it remains the largest consumer of freshwater resources, raising concerns about the efficiency and sustainability of water use. Given that agricultural producers are the primary water users, understanding how climate variability impacts their water-use behaviour is essential. This study addresses a research gap by examining how climate variability influences the intentions of commercial sugarcane irrigators in the Impala Irrigation Scheme to adapt their water-use behaviour in response to climate variability, using the TPB and formalizing it in SEM.

This study demonstrates that attitudes and subjective norms significantly influence sugarcane irrigators’ intentions to adapt their water-use practices in response to climate variability, while perceived behavioural control does not. The findings suggest that efforts to promote adaptive behaviour among sugarcane irrigators should focus on strengthening positive attitudes and social norms. These insights can assist policymakers and stakeholders within the Impala Irrigation Scheme in supporting sugarcane farmers’ adaptation to climate variability.

This study contributes to the theoretical application of the TPB in agriculture and highlights the importance of psychological factors in shaping farmers’ adaptive behaviours. From a practical standpoint, improving water-use behaviour among sugarcane producers in the Impala Irrigation Scheme requires targeted educational initiatives to enhance farmers’ knowledge about climate variability. Additionally, increasing access to financial resources and accurate climate information is essential for informed decision-making.

Stakeholder involvement in supporting water-use practices is essential for helping farmers adapt. Policymakers should focus on developing strategies that facilitate access to improved technologies and sustainable practices. Addressing these areas will improve the adaptive capacity of sugarcane producers in the Impala Irrigation Scheme.

Our study is not without limitations. The primary limitation is that we applied the standard TPB model, focusing only on its four core constructs (attitudes, subjective norms, perceived behavioural control, and intention). This approach excludes other factors that could influence farmers’ adaptive behaviour. The study focused on adaptation to climate variability, without exploring other water-use behaviours such as technology adoption or conservation practices. Furthermore, the generalisability of the findings is limited to the specific context of the Impala Irrigation Scheme, given the finite nature of the sample (67 farmers). The results should be interpreted as relevant to this population and may not be applicable to other irrigation schemes or broader farming populations.

Future research could expand the TPB model by incorporating additional constructs to gain a broader understanding of adaptive behaviours. Additionally, exploring how the constructs within the TPB interact and influence each other could provide deeper insights into the dynamics that shape irrigators’ adoption of water-saving behaviours. Future studies could also investigate other water-use behaviours, such as conservation practices and the adoption of water-saving policies.