Enhancing Treated Wastewater Reuse in Saudi Agriculture: Farmers’ Perspectives

Abstract

The reuse of treated wastewater (TWW) offers a sustainable solution for water management in agriculture, particularly in arid regions like Saudi Arabia. However, its success depends on farmers’ acceptance, influenced by perceptions of economic benefits, social acceptability, environmental impacts, and health risks. This study surveys 391 farmers across five regions in Saudi Arabia to assess their attitudes toward TWW reuse in irrigation, exploring how advanced wastewater treatment technologies can improve acceptance. Results show that 65% of farmers use TWW, with usage peaking at 72% in Al-Ahsa and Qatif, driven by water scarcity and lack of alternatives. While 78% are satisfied with TWW, concerns persist regarding pests, consumer acceptance, health risks, and soil quality. Advanced technologies can mitigate these issues by enhancing water quality and safety. The highest positive impact of the use of TWW in irrigation from was the impact on productivity, reduction in the cost of fertilizers and savings in the cost of water abstraction. With only 57% of farmers receiving extension services, integrating education on these technologies could further boost confidence. This study highlights key acceptance factors, underscoring the need for technological and educational interventions to promote sustainable TWW reuse in agriculture.

1. Introduction

Saudi Arabia, an arid nation with limited freshwater resources, faces significant water management challenges [1]. Agriculture consumes approximately 84% of its annual freshwater, primarily from non-renewable groundwater, which is depleting rapidly due to over-extraction [2]. The absence of perennial water bodies, low annual rainfall (less than 100 mm), and high evaporation rates exacerbate the country’s water scarcity, making the search for alternative water sources imperative [3]. Treated wastewater (TWW) reuse has emerged as a viable and sustainable solution to alleviate this pressure and promote sustainable agricultural practices. In 2022, Saudi Arabia produced 1.95 billion m³ of TWW, yet only 22% was reused, with a mere 12% allocated to agriculture—representing only 4% of total agricultural water use [4,5]. This underutilization reflects barriers, including farmers’ perceptions of health risks, environmental impacts, and water quality.

Reusing TWW has emerged as a pivotal strategy for enhancing water sustainability, particularly in the agricultural sector. It reduces dependence on groundwater and desalination, especially in arid regions [6]. The repurposing of TWW stands as a viable and sustainable alternative, offering a means to reduce the pressure on finite freshwater resources and enhance water-use efficiency in agriculture [7]. It supports long-term resilience amid growing water demand. Recognizing the strategic importance of redirecting TWW for productive use is therefore fundamental to addressing Saudi Arabia’s water scarcity challenges and advancing a more sustainable and resource-efficient development trajectory [1].

The Kingdom’s long-standing dependence on groundwater has resulted in significant depletion, with extraction rates surpassing natural recharge capacity [3]. The unsustainable use of groundwater is further exacerbated by the absence of robust regulatory frameworks, limited monitoring, and inefficient irrigation practices over the past four decades [3,8]. Consequently, the search for alternative water sources—particularly for the agricultural sector—has become an urgent priority [9]. Treated wastewater has garnered attention as a promising solution to bridge the widening gap between water supply and demand [10]. Despite governmental efforts, including infrastructure development and policy reform, the large-scale acceptance of TWW in agriculture remains constrained [4]. In response, national authorities have initiated programs aligned with the Saudi Irrigation Organization’s strategy to expand the reuse of TWW in agriculture. These initiatives aim not only to improve water management but also to raise farmers’ awareness and foster societal acceptance of this unconventional water source.

TWW is already used in several agricultural projects across the Kingdom, including Al-Ahsa, Riyadh, and Qatif. In Riyadh, TWW is the sole source of irrigation, while in Al-Ahsa, it is the primary source. These cases demonstrate the technical feasibility of large-scale reuse. Despite ongoing infrastructure investments and national strategies promoting wastewater reuse, adoption in agriculture remains limited, largely due to farmers’ concerns regarding water quality, health risks, and environmental impacts [11].

Environmental and health concerns—such as the presence of heavy metals, residual pharmaceuticals, and microbial contamination—hinder acceptance [6]. High salinity levels in TWW may also affect soil health and crop productivity over the long term [12]. Farmers’ decisions to adopt TWW are influenced by both measured and perceived health concerns. Al-Karablieh et al. [13] reported acceptable heavy metal levels in strawberries irrigated with TWW, but noted a significant rise in microbial contamination, indicating potential health risks based on objective measurements. Similarly, Badr et al. [7] found that while TWW in Al-Ahsa was generally suitable for irrigation, spatial variability and residual contamination posed limitations. Alzahrani et al. [11] revealed that while 77% of respondents in Al-Ahsa supported TWW reuse—particularly for non-edible crops—perceived health concerns and psychological factors continued to shape public attitudes. Empirical evidence suggests that farmers are more likely to accept TWW reuse when they perceive it as safe, economically beneficial, and supported by effective policies and institutional trust [11,14,15,16]. These findings align with international efforts to ensure the safe and sustainable reuse of treated wastewater in agriculture. Recent studies have emphasized the complex nature of reuse, highlighting its implications for soil quality, crop health, and consumer safety. For example, decision-support tools such as the DSS-computer program have been developed to guide the reuse of biosolids under varying environmental and agronomic conditions [17]. Case studies across the globe highlight the need for integrated risk management frameworks and regular monitoring of chemical and bacteriological parameters to ensure the safe reuse of treated wastewater in agriculture [18,19,20]. In alignment with the Saudi Irrigation Organization’s strategy, this study aims to contribute contemporary, evidence-based insights into farmers’ acceptance of TWW reuse for irrigation. It focuses on five agriculturally significant regions historically dependent on groundwater. While the study is based in Saudi Arabia, similar challenges and opportunities for TWW reuse are evident in other arid regions, such as parts of North Africa, and the Middle East, where water scarcity has driven interest in non-conventional water sources. The research explores farmers’ information sources, understanding of water and wastewater dynamics, and socio-demographic characteristics to better inform policy design and support a transition toward more sustainable and resilient water management practices.

2. Materials and Methods

2.1. Study Area

The study was conducted in four regions of Saudi Arabia, including the Eastern Province, Riyadh, Mecca and Medina (Figure 1). However, the field survey included five cities, Al Qatif, Al-Ahsa, Riyadh, Taif and Medina (Figure 1), where questionnaire samples were collected. These sites were selected for their agricultural importance and varying TWW usage. Significant variations in terrain heights are observed between the different study sites (Figure 2), resulting in slight variations in climate between the regions. Generally, Saudi Arabia experiences a predominantly arid desert climate with significant regional variations. The summers are extremely hot and dry, while winters are mild, with cooler temperatures in coastal areas and the southwestern mountains [21]. However, in study sites, the mean surface temperature starts to increase from March and reaches its peak (more than 40 °C) by August, before starting to cool. The hottest regions in the summer months are located in eastern Saudi Arabia, with hotspots of maximum mean temperatures in the southwestern parts of the country. The Saudi Arabian land receives most of its rainfall between November and April, with two shoulder months: October, which marks the start of the wet season, and May, which signals its end. During these wet months, rainfall spreads from the southern Red Sea to the northeastern part of Saudi Arabia through the central regions, reflecting the pathway of the moisture flux [22]. The accumulated annual rainfall, which varies between 500 and 4500 mm/year over the southwest regions of Saudi Arabia is mainly associated with the Indian monsoon (summer and winter) systems, while the northwest and northeast regions of Saudi Arabia, which, respectively receive a total annual rainfall between 150–450 mm/year and 100–350 mm/year, are associated with the passage of mid-latitude systems [22].

The land-use and land-cover (LULC) of the study site [23], extracted from PROBA-V satellite data, and showed 9 LULC classes (Figure 3). These LULC data were developed by the Copernicus Global Land Service Land Cover Map at 100 m spatial resolution (CGLS-LC100) and were released in May 2019 [23].

Renewable surface water resources in Saudi Arabia are limited due to the country’s lack of rivers and lakes. Groundwater from local aquifers (mostly non-renewable) is the primary source of water for domestic, agricultural, and industrial purposes. Therefore, the four regions utilize TWW for agricultural purposes at varying levels and scales. The TWW is used to irrigate various types of fruits and vegetables on both small-scale (less than 1 hectare) and large-scale farms (greater than 1 hectare). The Saudi Irrigation Organisation (ISO) is responsible for controlling and managing the TWW in the study regions. The soil and the TWW characteristics of the study sites are shown in

Table 1. The soil and the TWW characteristics of the study sites.

Study Sites	Soil Texture (%)			pH	EC (dS/m)	TDS (ppm)	Total N (%)	Available P (%)
	Sand	Silt	Clay
Al-Qatif	88	6	6	7.8	1.3	823	0.02	5.75
Al-Ahsa	76	13	11	7.1	1.7	1106	0.02	5.80
Riyadh	72	13	15	7.2	0.7	421	0.13	2.14
Taif	68	17	15	7.3	1.5	886	0.02	3.80
Medina	68	19	13	7.1	1.7	1044	0.02	2.44
Water Quality Indicators	SAR		NO3 (ppm)		CaCO3 (ppm)		pH	EC (dS/m)
	1.0–1.2		1.8–2.3		30–36		7.3–7.7	0.7–1.1

EC = Electrical Conductivity, TDS = Total Dissolved Solids, N = Nitrogen, P = Phosphorus, SAR = Sodium Adsorption Ratio, NO₃ = Nitrate, CaCo₃ = Calcium Carbonate.

. The sandy soil is dominant in all study sites, and these soils are poor in total Nitrogen (N). However, the main cultivated crops in the study sites include date palms (Phoenix dactylifera), lemons (Citrus limon), figs (Ficus carica), grapes (Vitis vinifera), and zucchini (Cucurbita pepo).

2.2. Data

A structured questionnaire was designed to collect data from the five regions. It consisted of three main sections, each tailored to achieve the study’s objectives. The first section focused on the social and economic characteristics of farmers, the second addressed farm-specific data and irrigation methods, and the third explored farmers’ perspectives on TWW reuse. This final section examined respondents’ opinions regarding the impact of TWW use in irrigation, as well as the factors influencing their acceptance or rejection of its application.

A stratified random sampling technique was employed to ensure comprehensive representation across the five regions, resulting in a total of 391 respondents. This approach strengthened the reliability of the data by accounting for geographic and demographic diversity within the sample. To mitigate potential sources of bias in data collection, several strategies were implemented. The questionnaire included a mix of closed-ended and open-ended questions, allowing respondents to provide both structured and nuanced responses, thereby reducing response bias. Face-to-face interviews were conducted by trained enumerators. Standardized procedures were followed during data collection to minimize interviewer bias, while targeted recruitment strategies were used to avoid self-selection bias. Furthermore, cultural sensitivity was emphasized in the design of the questionnaire, and a pilot test was conducted to identify and correct any ambiguities or culturally inappropriate phrasing. Collectively, these methodological measures were intended to enhance the validity and reliability of the findings, ensuring that the data accurately reflected farmers’ attitudes and perceptions toward the reuse of TWW in irrigation across Saudi Arabia.

2.3. Statistical Analysis

Descriptive statistics (frequencies, means, and cross-tabulations) and probit regression were used to analyze socio-economic factors and perceptions influencing TWW acceptance. The binary dependent variable (use/non-use of TWW) was modelled against independent variables using Stata12^®. Unlike multiple regression or discriminant analysis, which require assumptions not met in this context, probit models allow for proper estimation of acceptance probabilities based on socio-economic and perception variables [24,25,26,27]. The model treats acceptance as a binary variable (1 = accept, 0 = reject) and estimates the likelihood of acceptance using a set of explanatory variables such as age, education, income, household size, and attitudes toward TWW reuse. A positive coefficient suggests an increased probability of acceptance, while a negative one suggests the opposite [28]. Probit models are preferred due to their statistical robustness, which ensures that predicted probabilities lie between 0 and 1 [29,30]. The probit model is specified as follows:

$$Y_i^{*}={\beta }_0+{\sum }_{j=1}^k{\beta }_j{x}_{ij}+u_i$$

(1)

where Y_i* is called a “latent” variable, x’s is a vector of explanatory variables, u_i is the error term. However, the latent variable can only be observed as a dichotomous variable as Y_i is defined by Equation (2):

$$Y_i=\left\{\begin{array}{c}1 if Y^{*}>0\\ 0 otherwise\end{array}\right.$$

(2)

where Y_i is a variable measuring the acceptance/non-acceptance of re-using TWW. If the cumulative distribution of u_i is logistic, we have what is known as the logit model, as shown in Equation (3):

$$log{\displaystyle \frac{P_i}{1-P_i}}={\beta }_0+{\sum }_j^k{\beta }_i{x}_{ij}$$

(3)

where P_i is the probability of adoption. Model performance was assessed using the percentage of correctly classified cases. This metric compares observed versus predicted binary outcomes (TWW user vs. non-user) based on a cutoff probability of 0.5. A case is classified as positive if its predicted probability is ≥0.5, and negative otherwise. A prediction is considered correct if it matches the actual binary response. In addition, a cross-sectional analysis was conducted to compare current users and non-users of TWW. Independent samples t-tests and chi-square tests were applied to assess differences in socioeconomic, contextual, and perceptual variables. The variables used in the model are outlined in

Table 2. Variable Definitions.

Category	Variable	Definition	Unit/Code
Socioeconomic	Age	Farmer’s age	Years
	Education	Formal education level	Years of education
	Family size	Number of household members	Count
	Work status	Employment status	0 = not working, 1 = retired, 2 = working
	Income level	Monthly income in Saudi Riyals	0 = <3k, 1 = 3k–5k, 3 = 5k–7k, …
	Agricultural experience	Years of farming experience	Years
Farm-specific	Holding size	Total land held	Dunam
	Irrigated area	Total irrigated area	Dunam
	Land ownership	Whether the land is owned	1 = yes, 0 = no
	Use of groundwater	Use of groundwater for irrigation	1 = yes, 0 = no
	Sufficient water	Receives sufficient water supply	1 = yes, 0 = no
	Water storage	Stores water on farm	1 = yes, 0 = no
	Store damage	Water quality affected due to storage	1 = yes, 0 = no
	Permanent labor	Number of permanent workers	Count
	Occasional labor	Number of occasional workers	Count
Attitudes and adoption willingness	TWW satisfaction	Satisfied with use of treated wastewater	1 = yes, 0 = no
	Willing to use TWW	Willing to use treated wastewater	1 = yes, 0 = no
	TWW as alternative	Perceive TWW as a substitute for their current primary water source	1 = yes, 0 = no
	TWW as new source	Consider TWW as an entirely new addition to their water sources	1 = yes, 0 = no
	TWW as complementary	View TWW as an additional source used alongside existing ones	1 = yes, 0 = no
Perceived impacts	Productivity impact	Perceived impact on crop productivity	1 = positive, 0 = no impact, −1 = negative
	Soil impact	Perceived impact on soil	1 = positive, 0 = no impact, −1 = negative
	Fruit impact	Perceived impact on fruit quality	1 = positive, 0 = no impact, −1 = negative
	Return impact	Perceived impact on total farm returns	1 = positive, 0 = no impact, −1 = negative
	Costs impact	Impact on water costs	1 = positive, 0 = no impact, −1 = negative
	Water impact	Impact on water consumption	1 = positive, 0 = no impact, −1 = negative
	Fertilizers impact	Impact on fertilizer use	1 = positive, 0 = no impact, −1 = negative
	Pest impact	Impact on pest incidence	1 = positive, 0 = no impact, −1 = negative
	Health impact	Perceived impact on human health	1 = positive, 0 = no impact, −1 = negative
	Consumer impact	Impact on final consumers	1 = positive, 0 = no impact, −1 = negative
Other	Extension services	Receives any extension services	1 = yes, 0 = no
	Public extension services	Receives public extension services	1 = yes, 0 = no
	No alternative	Uses TWW due to lack of alternatives	1 = yes, 0 = no
	GW salinity	Uses TWW due to saline groundwater	1 = yes, 0 = no
	Water scarcity	Uses TWW due to water scarcity	1 = yes, 0 = no

, including both demographic factors and farmers’ perceptions regarding the impacts and acceptability of using TWW for irrigation.

3. Results

3.1. Descriptive Analysis

Table 3. Summary statistics.

Location	Al-Ahsa	Riyadh	Taif	Qatif	Madina	Mean	SD
Age	60.13	58.06	47.4	59.37	58.24	57.34	12.05
Education	7.02	15.55	13.9	8.87	12.64	11.31	5.68
Family size	7.92	7.8	6.53	8.37	3.7	7.2	5.80
Work status	0.79	1.68	1.6	1.33	1.06	1.25	0.74
Income level	2.46	5.45	4.1	3.85	3.7	3.84	2.11
Agricultural experience	37.0	24.8	19.7	37.0	25.4	29.73	16.11
Holding size	5.5	119.5	26.4	9.6	53.0	46.4	153.15
Irrigated area	4.23	52.17	20.12	8.31	40.89	24.87	76.15
Land ownership	0.66	0.97	0.72	0.33	0.88	0.74	0.44
Use groundwater	0.22	0.17	0.84	0.3	0.4	0.33	0.47
Sufficient water	0.84	0.38	0.29	0.93	0.76	0.63	0.48
Water storage	0.34	0.84	0.5	0.13	0.18	0.46	0.50
Store damage	0.41	0.79	0.36	0.89	0.84	0.62	0.49
Permanent labor	1.23	6.74	3.19	2.22	2.78	3.34	5.25
Occasional labor	3.65	8.0	4.09	1.59	2.0	4.45	6.46
TWW satisfaction	0.93	0.96	0.36	0.39	0.88	0.78	0.72
WTU TWW	1.0	0.99	0.47	1.0	1.0	0.92	0.27
TWW as alternative	0.53	0.5	0.17	0.13	0.28	0.39	0.49
TWW as new source	0.17	0.08	0.09	0.07	0.02	0.1	0.30
TWW as complementary	0.3	0.4	0.21	0.8	0.7	0.42	0.49
Productivity impact	0.5	0.94	−0.22	−0.7	0.84	0.42	0.84
Soil impact	0.62	0.36	−0.34	−0.7	0.78	0.27	0.88
Fruit impact	0.41	0.58	−0.4	−0.7	0.7	0.24	0.86
Return impact	0.54	0.57	−0.02	−0.61	0.54	0.33	0.81
Costs impact	0.54	0.77	−0.17	−0.76	0.56	0.35	0.87
Water impact	0.51	0.65	−0.12	−0.15	0.72	0.4	0.81
Fertilizers impact	0.62	0.76	−0.33	−0.39	0.72	0.41	0.81
Pest impact	0.36	0.06	−0.55	−0.74	−0.28	−0.07	0.84
Health impact	0.17	0.09	−0.22	−0.59	0.16	0.0	0.68
Consumer impact	0.35	0.14	−0.52	−0.46	0.08	0.04	0.84
Extension services	0.54	0.94	0.22	0.61	0.22	0.57	0.50
Public extension	0.38	0.61	0.38	0.61	0.12	0.43	0.50
No alternative	0.28	0.11	0.12	0.63	0.0	0.22	0.41
GW salinity	0.02	0.0	0.02	0.0	0.34	0.05	0.22
Water scarcity	0.03	0.21	0.26	0.02	0.02	0.11	0.32
Sample size	130	107	58	46	50

presents descriptive statistics for key socioeconomic variables and farmers’ perceptions across five regions. The average age across all regions was 57 years, with the majority of respondents (73%) being over 50 years old. Regionally, the mean ages ranged from 47 years in Taif to 60 years in Al-Ahsa, indicating that the sample was composed mainly of older, more experienced farmers. Educational attainment varied substantially across locations. On average, farmers had 11.3 years of education, with Riyadh exhibiting the highest mean education level (15 years), reflecting a larger proportion of university-educated individuals (approximately 72%). In contrast, Al-Ahsa recorded the lowest educational attainment (7 years), where the majority had only primary school education. Overall, 35.2% of the respondents had completed secondary education, while 26.4% were university graduates or held postgraduate degrees, and 16.8% had only completed primary education. In terms of household structure, the mean family size was seven, with Qatif reporting the largest families and Madina the smallest. Most farmers are actively employed. This is highest in Riyadh, suggesting a more economically engaged agricultural labor force. Income levels vary across regions, with Riyadh again reporting the highest mean, indicating more favorable economic conditions. Agricultural experience averaged 29 years, highest in Qatif and Al-Ahsa (37 years) and lowest in Taif (19.7 years).

Farm-specific data show significant regional variability in terms of land access and resource use. The average holding size is 46.4 dunams, but this is highly skewed due to the large average in Riyadh (119.5 dunams), whereas Al-Ahsa has a significantly smaller average holding of just 5.5 dunams. Similarly, the irrigated area averages 24.87 dunams across the sample, with Riyadh (52.17 dunams) again leading and Al-Ahsa (4.23 dunams) on the lower end. About 74% of the sampled farmers reported owning land, although this figure drops to just 33% in Qatif, which may influence long-term investments in infrastructure like TWW systems. Access to water is also uneven. The average use of groundwater across the sample is 0.33, with Taif having the highest incidence of groundwater use (0.84). Only 63% of farmers reported receiving sufficient water for their crops, and approximately 46% store water on their farms. About 62% indicated that water storage led to quality degradation. In terms of labor use, the farms employ an average of 3.34 permanent workers and 4.45 occasional workers, with the highest labor intensity seen in Riyadh and the lowest in Al-Ahsa and Qatif.

The farmers’ perceptions regarding the use of TWW are mixed but generally favorable. The vast majority (mean = 0.92) are willing to use TWW, and over 78% express satisfaction with its use. However, fewer farmers identify TWW as a new, alternative or complementary water source, with mean values ranging between 0.10 and 0.42, indicating that while farmers are open to its use, its role as a primary source remains limited. The perceived impacts of TWW use vary across different domains. Crop productivity is viewed positively (mean = 0.42), particularly in Riyadh (0.94). Soil quality and fruit quality are also perceived somewhat positively overall, although regions like Taif and Qatif show negative mean values, indicating concerns. Farm returns and costs are generally viewed favorably, with farmers reporting a perception of reduced costs (0.35) and improved returns (0.33). Water consumption (mean = 0.40) and fertilizer use (mean = 0.41) are perceived to be positively affected, suggesting efficiency gains. However, the impact on pests (mean = −0.07), health (mean = 0.00), and consumer perception (mean = 0.04) remains more ambiguous or neutral, with near-zero averages across all regions. These areas appear to be marked by either a lack of information or greater skepticism among farmers

Extension services play a key role in shaping attitudes toward TWW. They are defined as educational outreach programs provided by governmental or institutional bodies to disseminate agricultural knowledge, improve farming practices, and support the adoption of innovations such as TWW reuse. On average, 57% of farmers reported receiving extension services, with the highest proportion in Riyadh (0.94). Public extension services reached 43% of respondents, again with notable variation between regions. Contextual drivers such as groundwater salinity and water scarcity also influence TWW adoption. About 5% of farmers indicated that saline groundwater motivated them to use TWW, and 11% cited water scarcity as a key reason. The influence of having no alternative water sources is more substantial than initially presumed, with 22% of farmers reporting this as a reason for utilizing TWW in irrigation.

3.2. Factors Affecting Farmers’ Acceptance of TWW

Table 4. Probit regression analysis of factors influencing farmers’ TWW acceptance.

Factor (Independent Variable)	Willingness to Use TWW (General)	TWW as Alternative Source	TWW as New Source	TWW as Complementary Source
Satisfaction with TWW	1.391 ***	0.374 ***	−0.400 **	-
Education level	−0.100 ***	-	-	-
Extension services	0.960 **	0.416 ***	0.418 *	−0.279 *
Public extensions	−1.132 **	-	-	-
Work kind	0.539 **	−0.162 **	-	0.162 **
Lack of alternative source	0.999 *	-	-	-
Using groundwater	−2.018 ***	−0.379 **	-	-
Fertilizers saving	0.589 ***	-	-	-
Fruit impact	-	0.298 **	-	−0.282 **
Health impact	-	0.426 ***	−0.407 **	−0.240 *
Costs impact	-	−0.220 *	0.534 ***	-
Occasional lab	-	−0.043 ***	-	0.029 **
Store damage	-	−0.492 ***	−0.346 *	0.667 ***
Holding size	-	0.001 *	−0.008 **	-
Land ownership	-	0.382 **	-	-
Return impact	-	-	0.456 **	−0.292 **
Fertilizers	-	-	−0.408 **	0.327 ***
Pest impact	-	-	0.349 **	−0.239 **
Agr. experience	-	-	-	0.011 **
Water impact	-	-	-	0.361 ***
Water store	-	-	-	−0.324 **
Constant	2.096 ***	−1.053 ***	−1.614 ***	−0.739 ***
Model Statistics
Log likelihood	−42.09	−198	−101	−212.9
Pseudo R2	0.618	0.229	0.196	0.196
LR chi2	136	118	49.6	100.4
Prob > chi2	0.000	0.000	0.000	0.000
% Cases Correctly Classified	94.82%	74.0%	89.9%	73.06%

A dash (-) indicates that the variable was not included as a significant factor in the final stepwise regression model for that specific analysis. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. These are based on the p > |z| values from the original tables.

presents the consolidated results from four separate probit regression models. These models were developed to identify the socioeconomic, operational, and perceptual factors that significantly influence farmers’ acceptance of TWW under different adoption scenarios: (1) general willingness to use TWW, (2) willingness to use TWW as an alternative source, (3) willingness to use TWW as a new source, and (4) willingness to use TWW as a complementary source. The table displays the regression coefficients (β), with statistical significance denoted by asterisks, allowing for a comparative analysis across the four models.

An examination of the model fit statistics, located at the bottom of the table, confirms the robustness of the analyses. All four models are highly significant (Prob > chi² = 0.000), indicating that the independent variables collectively provide significant explanatory power over the dependent variables. The Pseudo R² values range from 0.196 to 0.618, with the model for general willingness to use TWW demonstrating the highest explanatory power. Furthermore, the high percentage of correctly classified cases, particularly for general willingness (94.82%) and new source adoption (89.9%), underscores the models’ strong predictive accuracy.

An analysis of the individual coefficients reveals several key themes that determine farmers’ willingness to adopt TWW. Farmer satisfaction emerges as a powerful determinant of acceptance. The positive and highly significant coefficient for “Satisfaction with TWW” in the general willingness model (β = 1.391, p < 0.01) and the alternative source model (β = 0.374, p < 0.01) highlights that positive prior experience strongly encourages further adoption. Conversely, a negative coefficient in the “new source” model (β = −0.400, p < 0.05) suggests that already-satisfied farmers may not be looking to adopt TWW as a completely new source, perhaps because they have successfully integrated it into their existing system.

The availability of existing water sources also emerges as a primary driver of adoption. The models clearly illustrate that a “Lack of alternative source” significantly increases the general willingness to use TWW (β = 0.999, p < 0.10). In contrast, the current “Using groundwater” is negatively correlated with both general willingness (β = −2.018, p < 0.01) and use as an alternative source (β = −0.379, p < 0.05), indicating that farmers with access to groundwater are less inclined to adopt TWW. This underscores that TWW is often considered a solution born of necessity, driven by water scarcity.

In addition to resource availability, the economic and agronomic calculus of farming significantly influences adoption decisions. Perceived economic benefits are a crucial factor, as shown by the “Fertilizers saving” coefficient (β = 0.589, p < 0.01) positively influencing general willingness. Interestingly, the perceived impact on costs, returns, and pests varies by adoption scenario. For instance, a positive “Costs impact” perception is a strong driver for adopting TWW as a new source (β = 0.534, p < 0.01), while a negative “Pest impact” perception is a deterrent for using it as a complementary source (β = −0.239, p < 0.05). This suggests farmers weigh different economic and operational risks depending on how they intend to integrate TWW.

Finally, the role of external support systems, particularly information and education, presents a more nuanced picture. While “Extension services” positively impacts willingness for general, alternative, and new source adoption, it is negatively correlated with using TWW as a complementary source (β = −0.279, p < 0.10). This may suggest that current extension programs are effective at promoting initial adoption but may not adequately address the complexities of integrated (complementary) water management. Notably, a higher “Education level” was negatively associated with general willingness (β = −0.100, p < 0.01), a counterintuitive finding that may indicate more educated farmers are more aware of potential risks or have access to other solutions, warranting further investigation in the discussion section.

In summary, the probit models reveal that farmers’ decisions to adopt TWW are a complex interplay of necessity (water scarcity), experience (satisfaction), perceived economic outcomes (cost and fertilizer savings), and external support (extension services). The varying significance and direction of coefficients across the four models emphasize that a “one-size-fits-all” policy approach is unlikely to be effective. Instead, strategies must be tailored to the specific adoption context, whether it is introducing TWW to new users or encouraging its integrated use among existing adopters.

4. Discussion

This study provides a comprehensive and geographically diverse analysis of farmers’ perspectives on the reuse of TWW in agriculture across five major regions in Saudi Arabia based on a sample of 391 farms. The distribution of the sample (Al-Ahsa: 130, Riyadh: 107, Taif: 58, Qatif: 46, Medina: 50) marks a significant advancement over previous studies that focused on one or two regions. For instance, Al-Shenaifi et al. [31] focused on 304 farms solely in Riyadh, while Alataway et al. [32] examined 400 farmers in Al-Ahsa and Tabuk. This broader geographic scope allows for greater generalizability of the findings and provides insights into the regional heterogeneity in attitudes toward TWW reuse.

The findings provide important empirical evidence regarding the factors that shape farmers’ acceptance of TWW for agricultural irrigation in Saudi Arabia. The results demonstrate that while overall acceptance is relatively high—particularly in regions such as Al-Ahsa and Riyadh—usage patterns are highly context-dependent and shaped by both structural water scarcity and farmer-specific considerations. The analysis affirms previous studies that suggest necessity, rather than proactive sustainability considerations, is the principal driver of TWW adoption in water-stressed contexts (e.g., Alzahrani et al. [11]; Badr et al. [7]).

Compared to earlier research, the current sample exhibits greater demographic diversity. In Al-Shenaifi et al. [31], the majority of respondents (95%) were aged 60 or older, 52% had less than primary education, and 89% reported farming as their primary occupation. In contrast, our sample had a more balanced age distribution and higher average educational attainment, although significant regional disparities remain. The educational gradient, particularly high in Riyadh and lower in Al-Ahsa, allows for a more nuanced assessment of the influence of education on attitudes toward TWW. Prior literature has indicated an inverse relationship between age and acceptance of TWW reuse, suggesting younger farmers tend to be more receptive [31]. Additionally, a significant positive association has been found between education level and acceptance of TWW, implying that more educated farmers perceive lower health risks and are more open to innovation. However, our findings challenge this assumption, as the probit regression revealed a negative association between education and general willingness to adopt TWW. This counterintuitive result could reflect greater risk awareness or higher expectations of water quality among more educated farmers, warranting further qualitative investigation.

In terms of perceptions, previous studies have yielded mixed results. Al-Shenaifi et al. [31] reported that 62% of farmers agreed that TWW helps preserve non-renewable water resources, and 71% believed it could be the primary source of irrigation in the future. However, 75% opposed the expansion of TWW use in agriculture, indicating deep-seated concerns despite acknowledging its potential. Similarly, Alharbi [33] identified three attitudinal groups—negative (0.3%), neutral (74.4%), and positive (25.3%)—with major barriers related to religious and health concerns (e.g., consumer rejection: 45%, religious objections: 42%), and technical issues such as pest risks (60%) and infrastructural incompatibilities (52%). Other regional comparisons reinforce these concerns. Alataway et al. [32] found stronger public support for TWW reuse in Al-Ahsa compared to Tabuk, attributing this to greater familiarity due to a large-scale reuse project in Al-Ahsa. This aligns with our findings, where both TWW usage rates and satisfaction were highest in Al-Ahsa.

The results of this study also confirm the importance of experiential factors. Satisfaction with TWW use was the most significant predictor of general and alternative adoption willingness. Economic incentives, particularly fertilizer savings, also positively influenced attitudes, reinforcing the idea that tangible farm-level benefits are crucial to driving behavioral change. El-Sebaei et al. [8] provided evidence of increased crop productivity when using tertiary-treated wastewater, noting yield increases of 16.8–19.2% for crops such as dates and lemons compared to groundwater-irrigated farms. However, perceptions of increased pest incidence and potential health risks remained substantial deterrents. The study also identifies notable regional and perceptual heterogeneities. While productivity gains and cost reductions—particularly in relation to fertilizer use—were perceived positively by many respondents, concerns related to soil degradation, pest proliferation, and consumer acceptance remain substantial. Particularly in regions such as Taif, negative perceptions of TWW’s impact on soil and crop quality were associated with reduced willingness to adopt or expand use, suggesting the need for site-specific strategies and interventions. These findings are aligned with extant literature pointing to the influence of perceived agronomic and market risks in mediating farmers’ responses to wastewater reuse (e.g., [8,10,11,34,35,36,37]).

Finally, our findings echo those of Husain and Ahmed [38] and Massoud et al. [36], who emphasized the central role of public perception and information availability in enabling or hindering TWW adoption. Extension services emerged as a critical, albeit underutilized, mechanism for influencing farmer behavior. While extension support was positively associated with adoption across most models, its limited reach—reported by only 57% of respondents—represents a missed opportunity for scaling sustainable practices. Furthermore, the negative association between extension services and willingness to use TWW as a complementary source may reflect a lack of nuanced messaging in current outreach programs, which may focus narrowly on binary adoption rather than integrated water management strategies. Without adequate public awareness and credible institutional communication, psychological and cultural barriers may persist regardless of technological feasibility.

In sum, while this study affirms certain patterns reported in prior literature—such as the importance of water scarcity and prior experience in driving adoption—it also reveals new complexities, particularly regarding the roles of education and regional context. These insights suggest that future policies should not only invest in infrastructure and water quality but also address perceptual, educational, and trust-based dimensions of TWW reuse.

5. Conclusions

This study contributes to the growing body of literature on sustainable water resource management in arid regions by examining the socio-economic, perceptual, and institutional factors influencing farmers’ acceptance of TTW reuse in Saudi Arabia. Drawing on survey data across five agriculturally significant regions, the analysis reveals that while a majority of farmers are willing to use TWW—primarily driven by water scarcity and limited alternatives—perceptions of quality, safety, and economic benefit condition this willingness.

Positive perceptions of TWW’s impact on productivity, cost savings, and fertilizer reduction significantly enhance its acceptance. However, concerns about pest incidence, health risks, and consumer resistance persist, particularly in regions where negative experiences or limited infrastructure have shaped attitudes. These concerns, if unaddressed, could undermine broader efforts to scale the use of non-conventional water resources in agriculture. Ultimately, the successful integration of TWW into Saudi Arabia’s agricultural water portfolio will require a multi-dimensional strategy that combines technological investment, institutional support, and participatory engagement with the farming community. Only through such an integrated approach can the Kingdom move toward a more sustainable and resilient agricultural future in the face of mounting water scarcity.

The insights from this study have broader implications for global research and policy communities. The integrated framework used—linking farmer perceptions with institutional and environmental factors—can be adapted to other arid and semi-arid regions facing similar challenges. Importantly, effective reuse strategies must address not only technical feasibility but also local trust, behavioral drivers, and knowledge gaps. A holistic, multi-criteria approach—one that considers soil suitability, crop tolerance, wastewater quality, and public health—is essential to ensure safe and effective application. Only by bridging the gap between the physical science of reuse and the social science of adoption can we develop truly sustainable water management strategies that benefit farmers, consumers, and the environment alike.