Factors Affecting Fish Production in Saudi Arabia

Abstract

Governmental organizations, projects, and initiatives in Saudi Arabia have focused specifically on the fisheries and the aquaculture sector to reduce reliance on imports, achieve self-sufficiency, and significantly contribute to food security. To accommodate the annual population increase, Saudi Arabia needs to enhance its fish production. This study aims to illustrate the impact of credit on the fisheries sector by examining the factors that affect fish output in Saudi Arabia, both in general and in specific contexts. The research employed annual time series data to estimate the Cobb–Douglas production function. The study computed the Cobb–Douglas model in an error correction format due to the stationarity characteristic of the data. The results show that fish production in Saudi Arabia is significantly enhanced by the number of fishermen, marine fisheries, aquaculture farms, and financial resources. Furthermore, the results reveal that economies of scale play a crucial role in the Saudi fishing industry. Nevertheless, since the data indicates that the influence of marine fisheries on fish output in Saudi Arabia in the long run surpasses that of aquaculture farms, the researchers recommend an increase in aquaculture production. Sustainable methods for fish production, such as minimizing overfishing and bycatch, improving water and environmental quality, and promoting the traceability of fish populations, should be prioritized in the advancement of the fisheries sector.

1. Introduction

Historically, fishing has been a major source of livelihood for coastal and inland fishing communities, as well as a source of healthy food for humanity at large. Aquaculture has emerged as a potential key contributor to global food production, particularly in Asia, which accounts for more than 90% of the world’s aquaculture production [1]. The growth in aquaculture has been significant, outpacing red meat growth and playing a critical role in addressing food insecurity and improving a healthier human lifestyle [2]. Given the stagnation in growth of capture fisheries and the increasing demand for seafood, global aquaculture production must strive to reach 106 million tons by 2030, which is an ambitious target [3]. Facing increasing demand for animal protein and climate change, aquaculture is considered to have rich potential to enhance the resilience of the food system [2]. Aquaculture’s significant contribution to achieving the Sustainable Development Goals is widely acknowledged [4].

In recent decades, aquaculture in Saudi Arabia has experienced a significant transformation. Motivated by the ambitious goals laid out in Saudi Vision 2030, the sector has observed significant progress and is well on its way to achieving the government’s target of producing 600,000 tons of seafood yearly [5]. This progress is mostly due to supportive government strategies, vital infrastructure growth, and significant investments in infrastructure and informative programs. This study seeks to assess the role of credit in the performance of the fisheries sector in Saudi Arabia by analyzing the key determinants that influence total fish production, both in the short and long run.

Despite the Kingdom of Saudi Arabia’s unique location, which includes a vast coastline of roughly 2640 km on the Arabian Gulf to the east and the Red Sea to the west (nearly 2060 km on the Red Sea and 580 km on the Arabian Gulf), its domestic fish production falls short of individual needs, resulting in a reliance on imports to fill the nutritional gap. The average period for this reliance was estimated to be around 112.7 thousand tons (2020–2022). Furthermore, Saudi people consume an average of 8.0 kg of fish annually per person, while the global average for the same period (2020–2022) was 20.0 kg [6]. As a result, the kingdom’s government is working hard to create policies and initiatives that can satisfy the growing demand. It is also guiding plans for investment and development in fish farming projects in a way that maximizes and sustainably uses the available agricultural resources. Only by paying attention to this important sector and supporting its development and sustainability through easily accessible loans and the necessary credit facilities will this be possible. This will encourage and motivate investors, fish producers, and the technical staff working in this sector to invest in and establish productive projects and to move toward fish farming projects that will result in a qualitative and quantitative leap for this vital sector in the kingdom. Through its emphasis on training initiatives and the granting of essential soft loans, the kingdom has recently aimed to motivate all aquaculture workers, producers, and investors to adhere to the highest standards of care and comprehend appropriate nutrition practices. Additionally, the kingdom has aimed to strengthen its ability to detect fish diseases and enhance the methods used for their treatment and management. With an annual growth rate of roughly 5.1%, fish production rose from about 61,335 tons in 2001 to nearly 184,759 tons in 2022. About 40.4% of the entire fish production, which averages about 161.7 thousand tons over the years 2018–2022, comes from marine fisheries, while the remaining 59.6% comes from aquaculture. The Arabian Gulf accounts for around 26.0% of the production of marine fisheries, followed by the Red Sea at 14.4% and fishing in international seas at a negligible percentage [7]. On average, during the period in question, freshwater aquaculture accounted for 12.0% of aquaculture production, while saltwater aquaculture accounted for 47.6% [6]. The number of fishing boats has dropped from approximately 12,113 boats in 2005 to approximately 10,737 boats in 2022, despite the Kingdom of Saudi Arabia’s interest in expanding fish production and diversifying its sources. The most significant of these measures include the creation of fishery regulations, the issuance of more fishing licenses, the establishment of the General Directorate of Fish Wealth at the Ministry of Agriculture in 1988 to oversee fishing activities, and the establishment of centers for fishery research along the kingdom’s coasts. Furthermore, aquaculture presents promising investment opportunities in the kingdom, as demonstrated by a shift in the sector’s labor dynamics. The number of fishermen declined from approximately 9207 in 2005 to about 8746 in 2022, while the number of fishery workers increased from around 27,986 to 30,099 over the same period [6]. Government policies have strongly influenced the geographic distribution of aquaculture growth, as well as the types of species, technology, management practices, and infrastructure adopted in different locations.

Saudi Arabia’s arid climate and limited freshwater availability pose substantial challenges to domestic aquaculture growth. Moreover, extreme salinity levels in some coastal and groundwater sources further limit the range of feasible aquaculture practices. These environmental restrictions are combined with the effects of climate change, including increasing sea surface temperatures, which threaten the sustainability of marine habitats and fish stocks. Some studies have stated indicators of high fishing pressure in Saudi Arabia, which resulted in low stock availability of some species, changes in behavior, and the decrease or removal of some predators [8,9,10,11,12]. Fishermen also face challenges in market access and price volatility. Moreover, the quantity demanded for fish in Saudi Arabia is insensitive to changes in price and elastic with respect to consumers’ income [13].

Several studies have explored the economic, technical, and social dimensions of fisheries and aquaculture across different regions. In the reviewed literature, several investigations considered the economics of the fisheries sector in terms of various segments, such as lending, fish production trends, the environment, credit, food security, and marketing, using different statistical and econometric approaches [14,15,16,17,18,19]. A study conducted an integrated assessment of the United Kingdom’s aquatic food system by synthesizing data across the fisheries, aquaculture, trade, health, welfare, and environmental sectors. In an evaluation of the system’s ability to deliver aquatic food that is sufficient, safe, sustainable, resilient to shocks, and ethically sound, the results confirmed that a unified, ongoing assessment framework is essential for understanding and enhancing aquatic food security in the face of social, economic, and environmental changes [20]. Another study employed cluster analysis to examine data from 26 United States–based mitigation banks that generate credit for freshwater species and ecosystems. The analysis identified two main credit generation approaches: (1) barrier removal and (2) whole-community targeting. The findings revealed that while both strategies address critical freshwater conservation objectives, each carries significant risks [21].

Global cross-sectional studies revealed a broad spectrum of under- to overregulated aquaculture systems that correspond, respectively, to high- and low-growth areas for aquaculture [2]. Credit is needed not only for investment in fishing craft and gear, fishponds, fish handling, processing, and marketing facilities and services, but also, or even more, for the smooth day-to-day capture, culture, handling, processing, and distribution of fish [22]. Financial inclusion can help reduce the different vulnerabilities of poor fishing households and rural communities and lead to improved economic resilience [23].

Several studies have been conducted in the African continent concerning the economics of fisheries and the aquaculture sector. Inoni [24] investigated how effectively resources are used in pond fish farming in Delta State, Nigeria. Data were collected from 72 farms (about 31% of the state’s estimated 232 fish farms) through interviews. Using descriptive and regression analyses, the researchers found that labor negatively impacts fish production—that is, more labor leads to lower yields. The study also revealed inefficiencies in how fish farmers allocate productive resources. Njagi et al. [25] aimed to analyze the factors affecting the profitability of fish farming under Kenya’s Economic Stimulus Program in East Tigania. A descriptive analytical approach with data from 132 fish farmers focused on the roles of marketing, advisory services, cultural practices, and pond management in influencing aquaculture profitability. The results revealed that marketing has a positive impact on fish farming profitability. Wetengere and Kihongo [26] assessed the constraints hindering access to credit facilities among fish farmers in rural Morogoro, Tanzania. After using descriptive statistical methods, the results revealed several key barriers to credit access, including limited access to information, unfavorable lending terms, inadequate support services, and low levels of financial literacy.

In the Asian continent, Keshavanath et al. [27] conducted experimental and analytical research on artificial substrates for periphyton growth in Indian freshwater ponds by assessing fish production in varying substrate types and fish densities. The results included the survival, growth, and production rates of fish, which revealed that during the fish harvest, the mortality rate associated with the treatment of sugarcane pulp reaches 100%. Goswami [28] used a purposive stratified random sampling approach involving 120 farmers to investigate the attitudes of fish farmers in West Bengal toward scientific aquaculture, focusing on the socioeconomic and psychological factors influencing these attitudes. The study emphasized the importance of risk orientation and economic motivation in shaping farmers’ acceptance of new aquaculture technologies. The research revealed that risk orientation has a positive and statistically significant correlation with farmers’ attitudes toward scientific aquaculture. Moreover, a study was conducted to understand how well fish farmers grasp critical aquaculture practices and to identify gaps in technology adoption to inform development efforts in the fisheries sector. A descriptive analytical method was applied, with a binary questionnaire distributed among 90 fish farmers in Jabalpur, India. The results showed that 64% of the farmers exhibited a low attitude toward fish farming, suggesting the need for awareness and training to improve technology adoption [29]. Several studies investigated the role of credit in fish production, with one investigating the impact of collateral-free microcredit provided by nongovernmental organizations on the household food expenditure of credit-constrained, poor fish farmers in Bangladesh. The empirical approach revealed that while credit access alone has a limited effect, household assets significantly influence the capacity of fish farmers to increase their food expenditure [30]. The link between credit access, food security, and dietary diversity was examined in Bangladesh by applying both descriptive and econometric methods to data from the Household Income and Expenditure Survey. The findings indicated that access to financial resources positively influences household food stability and contributes to more varied and balanced diets [31]. A study analyzed the impact of credit constraints on aquaculture productivity in Bangladesh using an endogenous switching regression model. The results established that farmers without credit constraints exhibit significantly higher productivity levels [32]. Another study examined the effect of access to credit on the food security of small fishermen in East Java, Indonesia, using ordinary least squares and an ordered probit model. The outcome of this study showed that fishermen’s food security is included in the borderline category [33].

The literature concerning fisheries production and consumption in Egypt is rich. Kassem and Meglla [34] conducted a mixed-method economic and technical assessment of mechanical fishing boats in the Alexandria governorate, Egypt, and identified key productivity factors and challenges based on field data analysis. The study revealed that the primary factors affecting fish production for mechanical boats include the crew’s efficiency level, the distance to fishing areas measured in kilometers, the quantity of fishing nets utilized in kilograms, the frequency of net repairs throughout the season, and the total variable costs. Abu Alainin [35] highlighted the critical role of lending in advancing Egypt’s fishing economy and the limitations of safe borrowing. The study found that the current financial support was insufficient and emphasized the success of proactive cooperative boards in securing funding through the Social Development Fund. It recommended strategies to improve credit access, reduce interest rates, and enhance institutional engagement with the fishing sector. Albasyouni and Maglad [36] performed an economic analysis using time-series fishery data from Egypt to examine fish production trends and seasonal consumption patterns. The results indicated that the overall pattern of seasonal fluctuations in total fish production from three fisheries combined is largely consistent with that of freshwater fisheries, followed by lake fisheries. Salim [37] utilized descriptive economic analysis to evaluate fish production sources and the contribution of different species in Egypt, emphasizing the growing role of aquaculture. Among the most significant fish species harvested from Egyptian fisheries, the study identified tilapia as the leading species, followed by catfish, sardines, and mullet. Barrania [38] used a qualitative analytical method and emphasized the role of cooperative organizations in Arab fisheries, calling for a redefinition of roles and stronger member engagement supported by financial tools. The study revealed that cooperatives have untapped potential in advancing fishery development and highlighted the importance of member participation in governance, rulemaking, and economic planning.

Few economic studies have examined the productivity of the fisheries and aquaculture sector in Saudi Arabia. Alhindi and Aldwis [39] used maximum sustainable yield modeling and econometric analysis to assess Saudi Arabia’s fishery resources. They found declining Red Sea yields and increased contributions from the Arabian Gulf, fish farms, and international waters. They showed that more fishing days improve yield, while more trips reduce efficiency. Optimal sustainable production was estimated at 53,000 tons, supporting better resource use policies. Alnafissa et al. [40] also estimated the maximum sustainable yield of fish in the Red Sea and Arabian Gulf to be 24,646.9 and 48,610.8 tons, respectively. Furthermore, Alnaim and Shehata [41] employed descriptive and statistical analysis to assess marine fish production in Saudi Arabia and identified species trends, productivity shifts, and regional variations in labor and boat use. They evaluated various aspects of marine fish production in Saudi Arabia to support policymaking aimed at improving fishery efficiency, boosting production, reducing food supply gaps, and promoting exports. The researchers covered fisheries in the Arabian Gulf and the Red Sea, aquaculture, and international waters and highlighted production trends and influencing factors across regions and fishing methods. Their results indicated that Arabian Gulf fisheries focused on kan’ad (threadfin), hamour (crustaceans), and sha’ri (redfish), while Red Sea fisheries targeted Trachurus indicus, rubian (shrimp), khanaq (squid), and beyadh (white mullet). Elhendy and Alzoom [42] collected a cross-sectional sample representing 23 tilapia farms in the central region of Saudi Arabia. Their results showed that fish feed cost accounts for the largest proportion of the total production cost. Moreover, the estimated cost elasticity was less than one, indicating that tilapia farms are achieving economies of scale.

The reviewed studies underscore the importance of farmer education, resource optimization, technology adoption, and institutional support for advancing sustainable and profitable aquaculture systems. Building on this foundation, the present study contributes a dissimilar perspective by employing a Cobb–Douglas production function to quantitatively assess the impact of credit on the fisheries sector by examining the factors that affect fish production in Saudi Arabia. Unlike earlier research focused on credit access constraints or group-based productivity comparisons, this approach captures the studied variables as being cointegrated in the long run and short run alongside other variables. It offers practical insights for optimizing the factors and informing the design of targeted credit programs in the aquaculture sector.

2. Materials and Methods

The sources of the required data on the fisheries and aquaculture in Saudi Arabia were the Ministry of Environment, Water, and Agriculture; the Agricultural Development Fund; and the General Authority for Statistics. The collected data are publicly available online on the General Authority for Statistics website [6]. Time series data from 2000 to 2022 were collected. Only one observation was missing in 2000 for the following variables: marine fisheries (Red Sea and Arabian Gulf), aquaculture production, and total production. The missing values were recovered using the trend extrapolation method.

In this paper, the two-step approach known as the Engle–Granger method was used. The first step involved estimating the following Cobb–Douglas model:

$${\ln \mathrm{Q}}_t={\mathsf{\beta }}_0+{\mathsf{\beta }}_1\ln {\mathrm{S}}_{\mathrm{t}} +{\mathsf{\beta }}_2\ln {\mathrm{L}}_{\mathrm{t}}+{\mathsf{\beta }}_3\ln {\mathrm{Aq}}_{\mathrm{t}}+ {\mathsf{\beta }}_4\ln {\mathrm{Sea}}_{\mathrm{t}} +{\mathsf{\beta }}_5\ln {\mathrm{F}\mathrm{V}}_{\mathrm{t}}+ {{\mathsf{\beta }}_6\mathrm{T}}_t+{\mathrm{e}}_{\mathrm{t}}$$

(1)

where Q stands for total production, S is the total number of ships, L is the total number of fishermen, AQ stands for fish quantity from aquaculture, Sea is the quantity of fish from the Red Sea and the Arabian Gulf, FV is the value of financing, and T is the linear time trend. The independent variables in Equation (1) were selected because they represent inputs of production or the most important factors affecting total fish production in Saudi Arabia. However, other factors affecting fish production, such as water temperature, water pollution, and water quality, were not considered in this study due to data availability constraints. Furthermore, $\mathrm{l}\mathrm{n}$ is the natural logarithm, ${\mathsf{\beta }}_0\dots {\mathsf{\beta }}_n$ are the parameters to be estimated, and $e$ is the error term. The Cobb–Douglas production function was selected because it has been widely used in the literature to estimate the influence of production inputs as explanatory variables on the output or yield in the fisheries and aquaculture sector [43,44,45,46,47,48,49].

After estimating model (1), the existence of cointegration was established by conducting a unit root test on the residuals, as indicated below:

$$∆{\widehat{\mathrm{e}}}_{\mathrm{t}}=\mathsf{\gamma }{\widehat{\mathrm{e}}}_{\mathrm{t}-1}+{\mathrm{v}}_{\mathrm{t}}$$

(2)

If the residuals in (2) were (stationary) white noise, we concluded that a long-run relationship among the variables existed. Thus, we moved to the next step, which was estimating the following Cobb–Douglas error correction model [50,51,52]:

$$∆\ln Q_t=a_0+a_1∆\ln Q_{t-1}+∆a_2\ln S_t+∆a_3\ln L_t+∆a_4\ln {Aq}_t+∆a_5\ln {sea}_t+∆a_6\ln {Fv}_t+\lambda {\widehat{e}}_{t-1}+u_t$$

(3)

where $a_1\dots a_n$ represent the short-run parameters, and $\lambda {\widehat{e}}_{t-1}$ are lagged residuals from Equation (1), which represents the error correction term. The long-run parameters could be computed using the partial adjustment formula proposed by [51,53], which involved dividing the negative of the short-run coefficient by the error correction term.

3. Results

Table 1. Summary Statistics.

Variable	Mean	Standard Deviation	Minimum	Maximum
Total fishing boats	10,960.04	963.2141	9224	12,195
Total fishermen	27,854.78	2126.47	22,091	30,370
Red Sea fisheries (tons)	23,920.62	1878.778	20,448	27,514
Arabian Gulf fisheries (tons)	39,161.69	4870.422	27,090	45,261
Freshwater and saltwater aquaculture (tons)	36,724.77	35,529.36	2950.619	120,495
Number of loans granted	751.2821	912.0136	68	3699
Value of granted loans (million SAR)	92.86914	163.0674	2.253825	773.8355
Total production (tons)	99,708.84	37,926.57	55,369.31	184,759

shows that the total number of fishing boats from both the Red Sea and the Arabian Gulf reached as high as 12,195, and the average number of the total workforce in the fisheries sector during the observation of this study reached 27,855. According to [54], the length of the Arabian Gulf coastline is 880 km, with 2986 fishermen. Thus, the number of fishermen per km of the Arabian Gulf coastline is 3 (2986 ÷ 880 = 3.4). Meanwhile, the coastline length of the Red Sea is 2400 km, with 5846 fishermen. Thus, the number of fishermen per km of the Red Sea equals 2 (5846 ÷ 2400 = 2.4).

On average, Arabian Gulf fisheries production was higher than Red Sea fisheries production, while the average combined production of freshwater and saltwater aquaculture was slightly less than the Arabian Gulf fisheries. For more details regarding annual marine production and aquaculture production, please see Appendix A.

Figure 1 shows that fish production in the Arabian Gulf was higher compared to the Red Sea. Marine fisheries were almost invariant from 2010 to 2022, while aquaculture production had an increasing trend. Indeed, aquaculture production exceeded Arabian Gulf fish production since 2017. In 2018, aquaculture production surpassed total marine production in Saudi Arabia.

With time series data, the most important step is the unit root test. This enabled us to select the right estimation methodology for our data.

Table 2. Dicky–Fuller Unit Root Test.

Variable	ADF Statistics I (0)	ADF Statistics I (1)
Total fishing boats	−2.461	−5.522 ***
Total fisherman	−2.491	−3.877 **
Red Sea fisheries	−2.133	−3.694 **
Arabian Gulf fisheries	−1.601	−5.205 ***
Freshwater and saltwater aquaculture	−0.179	−5.242 ***
Value of granted loans	−1.310	−3.910 **
Total production	−0.535	−5.864 ***

Note: *** p < 0.01, ** p < 0.05.

shows the results of the augmented Dickey–Fuller (ADF) unit root test.

The unit root test was performed on the logarithmic form of the key variables, as the study employed a log-log model specification. The ADF test results showed that we failed to reject the null hypothesis of a unit root at the level of the variable. However, after taking the first difference in the variables, the results allowed us to reject the null hypothesis of the unit root, and all the variables were stationary after taking the first difference.

The results of the next step, the cointegration test (2), as shown in

Table 3. Cointegration Test.

Test Statistic	Critical Value 1%	Critical Value 5%
−4.669	−3.98	−3.42

, confirmed that the variables were cointegrated in the long run—that is, the test statistic (absolute) value exceeded the critical value at the 1% level [55].

4. Discussion

After confirming the existence of a long-run relationship, we moved on to estimate model (3), as shown in

Table 4. Estimated Parameters of the Cobb–Douglas Error Correction Model.

Variable	Short-Run Coefficient		Long-Run Coefficient
Intercept	−0.039 *** (0.012)		-
$∆\ln {\mathrm{Q}}_{\mathrm{t}-1}$	−0.021 (0.061)		-
$∆\mathrm{l}\mathrm{B}\mathrm{o}\mathrm{a}\mathrm{t}\mathrm{s}$	0.099 (0.065)		0.270 (0.263)
$∆\mathrm{l}\mathrm{F}\mathrm{i}\mathrm{s}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{m}\mathrm{e}\mathrm{n}$	0.184 *** (0.045)		0.502 * (0.293)
$∆\mathrm{l}\mathrm{S}\mathrm{e}\mathrm{a}$	0.702 *** (0.066)		1.911 ** (0.773)
$∆\mathrm{l}\mathrm{A}\mathrm{q}\mathrm{u}\mathrm{a}\mathrm{c}\mathrm{u}\mathrm{l}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{e}$	0.210 *** (0.020)		0.571 *** (0.202)
$∆\mathrm{l}\mathrm{C}\mathrm{r}\mathrm{e}\mathrm{d}\mathrm{i}\mathrm{t}$	0.009 ** (0.004)		0.027 ** (0.012)
Trend	0.004 *** (0.001)		-
Error correction term	−0.367 ** (0.144)		-
Adjusted R-squared = 0.96	Residual standard error = 0.022		F-statistic = 61.94
Test of constant returns to scale
F-statistic		Decision
9.318		Reject the null hypothesis

Note: *** p < 0.01, ** p < 0.05, * p < 0.10.

. The standard errors reported in parentheses in Table 4 are heteroscedasticity-autocorrelation robust standard errors. The adjusted R-squared value showed that the model explained 96% of the variation in the dependent variable. The F-statistic allowed us to reject the null hypothesis, which states that all the parameters are equal to zero. We accept the alternative hypothesis, as at least one variable was significantly different from zero.

The error correction term (ECM) was significant at the 5% level and had the expected negative sign. The ECM showed that the speed of adjustment back to equilibrium was 37% annually. Although the number of fishing boats positively affected fish production, their impact was statistically insignificant. This could be attributed to several reasons, such as boats aging, GPS dysfunction, and an obligation to use boats with gasoline engines rather than diesel engine, which incurs larger cost per fishing trip compared to diesel engine [56]. Other factors that affect fishing boats efficiency include the crew’s experience level, the distance to fishing areas, the quantity and quality of fishing nets, and periodical maintenance [34]. Fishermen positively affected fish production in both the short and long run. This result was consistent with [40], who found a positive relationship between the number of fishermen and fish production. This outcome was expected because fishermen rely on fish catch as their main source of income [57]. Fish from the sea have the largest positive impact in the short run generally and in the long run specifically. In the long term, a 1% increase in fisheries production from the sea increased Saudi fish sector production by 1.91%. However, marine fisheries in Saudi Arabia face key challenges, including but not limited to wind speed, environmental pollution, and overfishing [40,58,59,60]. The quantity produced by aquaculture farms affects the Saudi fisheries sector positively; nonetheless, its impact is not as strong as marine production because it only contributes approximately 10% to the kingdom’s average annual fish production [61]. In line with Saudi Vision 2030, aquaculture production in Saudi Arabia is increasing annually and has exceeded sea production starting from 2018. The quantity produced by aquaculture farms in 2023 reached 139,949 tons, while the quantity produced from traditional marine fisheries reached 74,700 tons [7]. Although the quantity from aquaculture farms has surpassed that from the sea in recent years, some species are still only producible through traditional marine fishing due to the economic costs of production and biological factors.

Table A1. Red Sea Annual Fish Production (tons).

Red Sea Aggregated	2012	2013	2014	2015	2016	2017	2018	2019	2020
Milkfish	48.955	58.000	36.000	48.731	48.805	52.718	54.922	47.577	53.723
Flatfish	56.975	55.740	63.982	63.360	58.899	61.579	59.107	63.738	60.000
catfish	352.212	152.304	103.418	194.631	265.065	324.904	321.409	223.210	319.889
Brushtooth lizardfish	295.159	207.283	119.406	212.815	207.608	233.279	220.545	227.163	221.793
Greasy grouper	3179.532	2786.315	4049.338	3558.849	3527.109	3466.918	3255.601	3466.393	3234.113
Snapper	942.108	671.498	828.251	867.921	882.027	883.412	885.484	796.075	882.972
Threadfin	265.386	171.897	78.408	190.037	171.897	208.874	291.000	223.924	208.010
Blackspotted rubberlip	437.520	385.496	507.670	473.634	452.441	440.594	428.143	465.351	433.649
Spangled emperor	3128.452	3213.977	3469.810	3377.621	3242.605	3220.708	3466.304	3452.077	2999.092
Black seabream	161.419	272.517	389.581	309.974	260.642	240.068	225.441	323.970	225.279
Goatfish	38.518	42.288	71.638	59.208	48.099	47.094	41.265	82.432	46.004
Unicornfish	188.000	200.926	267.072	230.606	216.404	223.515	212.621	232.868	211.261
Napoleonfish	60.400	20.000	177.460	123.851	102.701	103.970	90.250	107.104	86.009
Squirrelfish	109.400	13.339	183.916	125.128	119.885	124.217	108.437	107.461	106.527
Barbel	26.878	21.212	15.546	29.099	21.212	28.544	24.878	27.000	27.745
Silver-biddy	246.337	272.467	164.785	223.422	235.995	245.040	260.534	323.703	264.747
Parrotfish	351.464	359.114	541.480	451.773	417.132	495.418	402.570	451.143	400.018
Rabbitfish	164.268	268.746	217.302	228.710	219.935	216.488	219.354	246.035	214.324
Grey triggerfish	12.000	11.532	15.051	9.964	10.643	17.982	14.876	12.182	14.653
Anglerfish	0.000	0.000	0.000	0.976	0.650	0.488	0.772	0.325	0.589
Catalufa/bigeye	0.000	0.000	0.000	1.463	0.976	1.220	1.524	0.488	1.189
Angelfish	28.000	0.000	0.000	0.000	9.333	4.500	5.708	0.000	7.868
Needlefish	115.871	42.926	50.812	73.919	87.151	81.839	88.412	56.107	88.001
Sphyraena putnamae	1371.460	1028.092	983.747	1131.879	1250.167	1242.595	1277.299	1178.339	1256.189
Gray mullet	173.607	249.206	95.429	150.638	158.294	160.930	174.073	165.091	175.128
Cobia	120.681	694.090	17.863	71.556	85.699	95.537	104.926	261.826	137.457
Moon wrasse	18.899	15.871	12.843	22.937	15.871	21.928	18.899	18.899	20.817
Bagrus	2953.521	2850.392	3174.073	3117.561	2996.675	3110.360	3164.773	3299.217	2916.915
Thunnus tonggol	257.527	327.290	698.628	473.010	408.195	378.095	325.857	512.630	339.762
Globefish	22.000	0.000	0.000	4.390	10.260	9.195	10.559	1.463	11.000
Silver pomfret	1.995	2.835	7.676	19.988	3.827	3.148	2.485	5.324	4.206
Sardine	92.439	37.693	14.444	38.681	53.138	62.550	63.812	38.852	66.554
Kingfish	2206.472	2945.000	2044.339	2155.318	2488.325	2403.280	2597.684	2429.158	2410.105
Tuna	1022.026	614.559	396.546	677.117	782.711	874.256	916.306	564.910	799.448
lndian scad	4453.781	2952.212	2312.277	1532.366	1951.534	1928.388	2270.072	2123.784	2409.080
Sailfish/seahorse	1.000	0.000	0.000	0.976	0.984	0.488	0.856	0.325	0.770
Kyphosinae	64.000	4.000	3.063	12.751	29.834	23.577	27.563	6.604	29.765
Shark	649.707	443.732	174.683	370.134	480.901	504.324	588.621	334.980	156.097
Whipray/stingray	10.375	9.828	9.282	22.324	17.308	16.532	19.257	18.740	17.963
Marine crab	203.516	191.716	178.871	212.485	200.960	113.180	157.491	140.154	188.440
Shrimp	897.774	650.000	601.780	808.437	669.000	501.000	535.000	568.333	668.085
Cuttlefish/squid	723.329	625.197	527.065	454.000	329.000	319.000	304.000	317.333	304.000
Mix	648.643	1572.846	1310.595	895.025	792.008	744.169	748.515	1213.092	740.365

Source: General Authority for Statistics.

,

Table A2. Arabian Gulf Annual Fish Production (tons).

Arabian Gulf Aggregate	2012	2013	2014	2015	2016	2017	2018	2019	2020
Sturgeon	11.696	6.367	9.758	8.062	15.901	12.829	15.107	10.163	12.264
Flatfish	29.326	25.806	28.485	27.146	24.697	26.591	24.790	26.645	26.145
catfish	586.936	614.726	655.842	635.284	660.705	658.274	590.207	643.353	651.421
Brushtooth lizardfish	64.805	68.641	45.790	57.216	59.730	52.760	60.472	56.892	56.568
Greasy grouper	2691.297	2202.027	2660.704	2431.366	2619.990	2640.347	2985.160	2497.633	2563.901
Snapper	642.111	531.581	444.388	487.985	514.611	479.500	516.025	491.008	494.032
Threadfin	321.128	277.228	417.706	347.467	427.218	422.462	414.719	373.258	399.049
Blackspotted rubberlip	373.580	304.440	571.564	438.002	444.470	508.017	432.800	450.749	463.496
Spangled emperor	5606.034	4767.395	5534.897	5451.146	5967.201	5751.049	5950.550	5587.139	5723.132
Black seabream	4619.186	4362.530	4671.695	4317.113	4396.439	4534.067	4426.947	4366.493	4415.873
Goatfish	41.568	70.270	95.052	82.661	99.253	97.153	96.838	87.842	93.022
Silver-biddy	313.288	236.652	178.962	207.807	240.942	209.952	240.584	213.687	219.567
Parrotfish	121.819	128.311	83.528	105.920	151.018	117.273	149.126	115.328	124.737
Rabbitfish	1752.454	1738.470	3007.667	2973.068	2977.036	2992.351	2907.156	2976.943	2980.819
Anglerfish	0.000	0.704	0.138	0.421	0.384	0.261	0.410	0.388	0.355
Amberjack	1.549	1.132	1.132	1.132	3.024	2.078	2.867	1.605	2.078
Angelfish	30.436	54.211	41.001	47.606	37.485	39.243	38.879	44.525	41.444
Needlefish	62.704	106.018	56.431	81.225	82.432	69.432	84.398	79.460	77.696
Sphyraena putnamae	844.417	844.741	662.220	753.481	688.208	675.214	634.586	729.558	705.634
Gray mullet	286.758	95.723	101.802	98.763	151.713	126.758	147.047	112.254	125.744
Cobia	54.921	63.573	72.207	67.890	80.670	76.439	79.245	91.445	75.000
Bagrus	3677.180	3036.905	3942.972	3590.000	3448.630	3695.801	3697.643	3584.072	3578.144
Thunnus tonggol	421.787	718.290	997.531	857.910	777.658	887.595	706.044	849.482	841.054
Silver pomfret	0.000	3.871	8.658	6.264	3.730	6.194	3.742	5.830	5.396
Sardine	51.108	27.019	22.089	24.554	71.159	46.624	67.481	36.000	47.446
Kingfish	4695.150	3756.430	5074.799	4516.000	4491.087	4780.000	4945.977	4555.848	4595.696
Tuna	0.000	0.000	0.000	0.000	165.880	82.940	152.057	81.470	82.940
lndian scad	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000
Sailfish/seahorse	0.000	0.000	0.000	0.000	0.244	0.122	0.224	0.061	0.122
Desert fish	13.371	26.202	31.042	28.622	21.481	26.262	11.000	26.811	25.000
Shark	361.852	432.597	614.426	523.511	622.429	608.428	604.943	554.150	584.789
Whipray/stingray	0.000	0.000	0.000	0.000	0.244	0.122	0.224	0.061	0.122
Lobster	9.000	0.000	0.000	0.000	11.000	5.500	9.131	2.750	5.500
Mix	252.460	365.341	497.000	431.171	382.640	439.820	381.199	424.524	417.877
Brachyura	4620.156	4651.961	4439.310	4639.000	4911.078	4370.039	4900.982	4643.000	4645.000
Shrimp	10,669.921	6497.315	7711.943	7209.000	7366.577	7327.900	7338.850	6899.000	6586.000
Cuttlefish/squid	2033.707	1285.903	1457.210	1372.770	1265.468	1362.274	1370.051	1423.000	1262.000

Source: General Authority for Statistics.

,

Table A3. Fresh Water Aquaculture Production (tons) in 2023.

Type	Quantity
Nile tilapia	45,200
Grass carp	453
Ctenopharyngodon idellus	1788
Sturgeon	17

Source: General Authority for Statistics.

and

Table A4. Salt Water Aquaculture Production (tons) in 2023.

Type	Quantity
Caridea	66,450
Sea bass	13,102
Gilt-head bream	11,223
Other fish	1716

Source: General Authority for Statistics.

show that the species produced by marine fisheries are varied, while the species produced by aquatic farms are limited. The Saudi aquaculture sector also faces several challenges that may hinder its growth, such as environmental and climate challenges, water stress, and water scarcity [62,63]. Credit facilities granted to fisheries have proven to have a positive impact on the sector’s output. Indeed, a 1% increase in credit facilities for the fisheries sector can increase fish production by 0.03% in the long run. Providing unconstrained credit to fish farmers not only impacts productivity but also increases fish output [32,64]. The linear trend coefficient was positive and statistically significant at the 1% level, indicating technical progress in the Saudi fisheries sector.

To judge the performance of the Saudi fishing sector, testing the existence of economies of scale in the sector is important. Table 4 illustrates that we tested the hypothesis of constant returns to scale in the Saudi fishing sector by assessing whether all the coefficients were equal to one. The results show that we rejected the null hypothesis of constant returns to scale at the 5% level, which implicitly confirms that the Saudi fisheries sector operates under economies of scale, consistent with [42]. Evidence of increasing returns to scale in the fisheries and aquaculture sectors was also found in Nigeria and Ghana [65,66].

5. Conclusions

The national transformation program and Saudi Vision 2030 have given special attention to the fisheries and aquaculture sector in their initiatives and programs in order to increase per capita fish consumption, reduce fish imports, achieve self-sufficiency, and contribute to food security. Reducing fish imports and achieving self-sufficiency in the fisheries sector require increasing fish production in Saudi Arabia. This paper examined the factors that affect fish production in Saudi Arabia by using annual time series data from 2000 to 2022. Unit root tests indicated that all the variables were stationary after first differencing. Thus, the paper estimated the Cobb–Douglas production function in the error correction format by applying the Engle–Granger two-step methodology. The estimated model consisted of total fish production as the dependent variable and marine fisheries, aquaculture farms production, fishing boats, fishermen, and credit as the independent variables. The cointegration test revealed a long-run relationship among the variables, and the estimated model’s error correction term was significant and had the expected negative sign. The results show that fishermen, marine fisheries, aquaculture farm production, and credit are significant determinants of fish production in Saudi Arabia. In addition, the effect of marine fisheries outweighs aquaculture farm production because the average annual sea production is higher than aquaculture production. This necessitates the development and expansion of the aquaculture industry in Saudi Arabia to include fish species that are not currently producible via aquaculture farms. The paper also revealed that the Saudi fisheries sector operates with increasing returns to scale. We recommend increasing financial and marketing support to the aquaculture industry to boost its share in the total domestic fish supply and promote consumers’ acceptance of aquaculture farm products. This can be done through advertising using various traditional and social platforms to increase consumer awareness about aquaculture products’ nutritional and health benefits. Moreover, aquaculture products should be priced reasonably compared to marine fisheries to encourage their consumption. In addition, we suggest the adoption of sustainable fishing practices that reduce overfishing, monitor fish populations, and increase water and environmental quality. This is achievable through conducting extensive extension programs designed specifically to target fishermen and aquatic farmers. Furthermore, credit incentives should be linked with adopting sustainable production methods by giving more credit and lower interest to farms and aquaculture enterprises that adopt sustainable practices. Furthermore, this study recommends raising financial support to aquaculture farms that invest in producing fish species that are currently not producible via aquaculture farms in Saudi Arabia.