A SUSTAINABLE FUTURE FOR AQUACULTURE: A GLOBAL OVERVIEW WITH A FOCUS ON MEXICO’S POTENTIAL

Currently, aquaculture is a worldwide development activity, with an overall volume of more than 110.2 million Mg live weight and a value of approximately 243 billion USD, contributing more than 60 % of the combined production of aquatic organisms. Among major economic activities, aquaculture has grown more rapidly both internationally and domestically. Asia represents the region with the most extensive development in the cultivation of most species, with China as the dominant producer. However, in terms of growth, some countries in Africa and Latin America, such as Egypt, Nigeria, Chile, and Mexico, are emerging on the world stage. Mexican aquaculture grew by around 70 % from 2013 to 2021, and further growth is expected in the coming years. Despite its importance and growth for human livelihood, aquaculture involves significant water consumption, and its degradation represents a major threat to the future of humanity. Therefore, it is essential to establish clear notions of water quality and sustainable management, monitor social and economic variables, and intensify research on alternatives for water treatment and use control. The present work provides an overview of Mexican aquaculture in the global context, including its importance, concepts related to sustainable management and its alternatives, as well as the development of the industry.Currently, aquaculture is a worldwide development activity, with an overall volume of more than 110.2 million Mg live weight and a value of approximately 243 billion USD, contributing more than 60 % of the combined production of aquatic organisms. Among major economic activities, aquaculture has grown more rapidly both internationally and domestically. Asia represents the region with the most extensive development in the cultivation of most species, with China as the dominant producer. However, in terms of growth, some countries in Africa and Latin America, such as Egypt, Nigeria, Chile, and Mexico, are emerging on the world stage. Mexican aquaculture grew by around 70 % from 2013 to 2021, and further growth is expected in the coming years. Despite its importance and growth for human livelihood, aquaculture involves significant water consumption, and its degradation represents a major threat to the future of humanity. Therefore, it is essential to establish clear notions of water quality and sustainable management, monitor social and economic variables, and intensify research on alternatives for water treatment and use control. The present work provides an overview of Mexican aquaculture in the global context, including its importance, concepts related to sustainable management and its alternatives, as well as the development of the industry

TECHNICAL AND ECONOMIC ANALYSIS OF WHITE SHRIMP (Penaeus vannamei) CULTURE IN THREE DIFFERENT SYSTEMS BASED ON STOCKING DENSITY

Given the increasing global demand for aquaculture products and the necessity to enhance production systems, it is important to produce comparative evidence to inform decision-making in aquaculture investments. This is particularly relevant in contexts such as Nayarit, Mexico, where aquaculture is a key activity for local economic development. This study evaluated the technical and economic variables of three white shrimp (Penaeus vannamei) production systems in the San Blas region of Nayarit. The hypothesis was that the hyper-intensive (HI) system would have a higher benefit-cost ratio and better yields per unit area due to its high level of technology and stocking density, compared to the semi-intensive (SI) and intensive (I) systems. Using principal component analysis with VARIMAX rotation, 14 key variables were identified that reflected significant differences in production intensity, with yields ranging from 1124 kg ha-1 (SI) to 36 409 kg ha-1 (HI) per cycle. Results showed that the HI system had the highest total costs (USD 54 419.28 year-1) but also the highest net income (USD 14 857.36 year-1) due to its high stocking density and technification. However, the SI system stood out with the best benefit-cost ratio (B:C) (1.51) and an internal rate of return (IRR) of 30 %, surpassing HI (1.21 B:C, 11.56 % IRR) and I (1.66 B:C, 7.53 % IRR). Despite requiring a higher break-even point (588.89 kg cycle-1), HI demonstrated greater efficiency in resource utilization per kilogram of shrimp produced, reducing the environmental impact. Although hyperintensive systems require higher initial investments, their profitability improves over time due to cost amortization and higher yields per unit area.Given the increasing global demand for aquaculture products and the necessity to enhance production systems, it is important to produce comparative evidence to inform decision-making in aquaculture investments. This is particularly relevant in contexts such as Nayarit, Mexico, where aquaculture is a key activity for local economic development. This study evaluated the technical and economic variables of three white shrimp (Penaeus vannamei) production systems in the San Blas region of Nayarit. The hypothesis was that the hyper-intensive (HI) system would have a higher benefit-cost ratio and better yields per unit area due to its high level of technology and stocking density, compared to the semi-intensive (SI) and intensive (I) systems. Using principal component analysis with VARIMAX rotation, 14 key variables were identified that reflected significant differences in production intensity, with yields ranging from 1124 kg ha-1 (SI) to 36 409 kg ha-1 (HI) per cycle. Results showed that the HI system had the highest total costs (USD 54 419.28 year-1) but also the highest net income (USD 14 857.36 year-1) due to its high stocking density and technification. However, the SI system stood out with the best benefit-cost ratio (B:C) (1.51) and an internal rate of return (IRR) of 30 %, surpassing HI (1.21 B:C, 11.56 % IRR) and I (1.66 B:C, 7.53 % IRR). Despite requiring a higher break-even point (588.89 kg cycle-1), HI demonstrated greater efficiency in resource utilization per kilogram of shrimp produced, reducing the environmental impact. Although hyperintensive systems require higher initial investments, their profitability improves over time due to cost amortization and higher yields per unit area

ELICITATION OF PRIOR DISTRIBUTIONS TO ESTIMATE VARIANCE COMPONENTS

In the Bayesian context, when variance components are modeled in normal hierarchical models, the inverted gamma distribution (IG) is typically used as the prior density for each component. However, the literature indicates that this prior density is highly informative, and thus the Half Cauchy distribution (HC) is recommended. The aim of this study was to evaluate, using simulation (in the context of high-dimensional data such as in the case of genomic selection applications), the suitability of the scaled inverse chi-squared (X-2v,S) distribution, which belongs to the family of scaled inverse gamma distributions, and HC as prior densities for the variance components in the Bayesian Ridge regression model. The evaluation was carried out when the number of observations in the response variable is greater than the number of predictor variables (n > p) as well as in high dimensions (n << p). The Bayesian learning of the posterior distribution was evaluated using the Hellinger distance (HD). The results of the Bayesian analysis were also compared with those obtained with the restricted maximum likelihood (REML). Results indicate that when n > p, the REML method underestimates the variance of the random effect, whereas in scenarios in which n << p, the method overestimates the same parameter when the variance of the error is large (greater than or equal to 6.0) and gives consistent estimations when the error variance is moderate (equal to 1.0). On the other hand, under prior distribution (X-2v,S) and in both scenarios (n > p) and n << p, it was observed that the parameters can be overestimated or underestimated, depending on the fixed values used to simulate the data. For the case of the HC prior distribution, the credibility intervals for both the variance of the effects of the predictor variables and the variance of the error contain the true values of the parameters, and their precisions increase with the sample size.In the Bayesian context, when variance components are modeled in normal hierarchical models, the inverted gamma distribution (IG) is typically used as the prior density for each component. However, the literature indicates that this prior density is highly informative, and thus the Half Cauchy distribution (HC) is recommended. The aim of this study was to evaluate, using simulation (in the context of high-dimensional data such as in the case of genomic selection applications), the suitability of the scaled inverse chi-squared (X-2v,S) distribution, which belongs to the family of scaled inverse gamma distributions, and HC as prior densities for the variance components in the Bayesian Ridge regression model. The evaluation was carried out when the number of observations in the response variable is greater than the number of predictor variables (n > p) as well as in high dimensions (n << p). The Bayesian learning of the posterior distribution was evaluated using the Hellinger distance (HD). The results of the Bayesian analysis were also compared with those obtained with the restricted maximum likelihood (REML). Results indicate that when n > p, the REML method underestimates the variance of the random effect, whereas in scenarios in which n << p, the method overestimates the same parameter when the variance of the error is large (greater than or equal to 6.0) and gives consistent estimations when the error variance is moderate (equal to 1.0). On the other hand, under prior distribution (X-2v,S) and in both scenarios (n > p) and n << p, it was observed that the parameters can be overestimated or underestimated, depending on the fixed values used to simulate the data. For the case of the HC prior distribution, the credibility intervals for both the variance of the effects of the predictor variables and the variance of the error contain the true values of the parameters, and their precisions increase with the sample size

FEATHERS AS INDICATORS OF EXPOSURE TO METALS: STUDY IN Anas crecca AND Anser caerulescens IN DURANGO, MEXICO

The aim of this study was to evaluate the presence of inorganic elements in feathers of green-winged teals (Anas crecca Linnaeus, 1758) and snow geese (Anser caerulescens Linnaeus, 1758) that hibernate at Laguna de Santiaguillo in Durango. Additionally, the use of feathers as exposure indicators to metallic pollutants was determined. The hypothesis proposed was that the feathers of both bird species contain detectable concentrations of metals, indicating varying levels of exposure to environmental pollutants based on their habits and migration routes. During the 2021–2022 hunting season, a total of 30 green-winged teals and 27 snow geese were collected. The primary P9 and P10 feathers from the left wing of each bird were gathered for analysis. The feathers were cleaned, dehydrated, and analyzed using voltammetry to quantify the concentrations of Zn, Cd, Pb, Cu, Cr, Sn, Al, As, Ni, and Hg. The results revealed significant differences between species. The teals displayed higher concentrations of As, Cr, and Ni, whereas the geese had higher levels of Ni and Cu. Although essential elements like Zn and Cu were present in high concentrations, non-essential elements such as Cd and Pb were also detected. Particularly, Pb levels in some teal individuals were concerning due to their potential toxicity. Significant correlations were identified between certain metals (As-Cr and Pb-Zn), suggesting common exposures to anthropogenic sources, possibly related to agricultural and industrial activities. This study confirms that feathers serve as effective and non-invasive biomarkers to detect the exposure to metallic pollutants, providing a “chemical memory” of accumulation during growth. Consequently, the working hypothesis is accepted, establishing a foundation for future research and environmental conservation efforts focused on priority wetlands, such as Laguna de Santiaguillo.The aim of this study was to evaluate the presence of inorganic elements in feathers of green-winged teals (Anas crecca Linnaeus, 1758) and snow geese (Anser caerulescens Linnaeus, 1758) that hibernate at Laguna de Santiaguillo in Durango. Additionally, the use of feathers as exposure indicators to metallic pollutants was determined. The hypothesis proposed was that the feathers of both bird species contain detectable concentrations of metals, indicating varying levels of exposure to environmental pollutants based on their habits and migration routes. During the 2021–2022 hunting season, a total of 30 green-winged teals and 27 snow geese were collected. The primary P9 and P10 feathers from the left wing of each bird were gathered for analysis. The feathers were cleaned, dehydrated, and analyzed using voltammetry to quantify the concentrations of Zn, Cd, Pb, Cu, Cr, Sn, Al, As, Ni, and Hg. The results revealed significant differences between species. The teals displayed higher concentrations of As, Cr, and Ni, whereas the geese had higher levels of Ni and Cu. Although essential elements like Zn and Cu were present in high concentrations, non-essential elements such as Cd and Pb were also detected. Particularly, Pb levels in some teal individuals were concerning due to their potential toxicity. Significant correlations were identified between certain metals (As-Cr and Pb-Zn), suggesting common exposures to anthropogenic sources, possibly related to agricultural and industrial activities. This study confirms that feathers serve as effective and non-invasive biomarkers to detect the exposure to metallic pollutants, providing a “chemical memory” of accumulation during growth. Consequently, the working hypothesis is accepted, establishing a foundation for future research and environmental conservation efforts focused on priority wetlands, such as Laguna de Santiaguillo

YOLOV8-POWERED COMPUTER VISION FOR COFFEE CHERRY RIPENESS, DEFECT, AND MORPHOLOGICAL ASSESSMENTMEXICO’S POTENTIAL

The present investigation describes an advanced multi-task deep learning framework for automated inspection of coffee cherry quality using YOLOv8 with color-based segmentation and Vision Transformer-Convolutional Neural Network (ViT-CNN) feature extraction. The model performs ripeness stage classification, defect detection, and size and shape analysis. For ripeness detection, YOLOv8 was enhanced with a color segmentation module, achieving class-wise accuracies of 90–95 % for unripe, partially ripe, and fully ripe cherries, with moderate performance (85 %) for overripe samples. ViT-CNN feature maps improved segmentation clarity and bounding-box localization, particularly in high-density clusters. Defect detection was carried out across five categories (healthy, blackened, moldy, wrinkled, and insect-damaged), achieving F1-score values between 0.88 and 0.96 and mean average precision at 50 % intersection over union (mAP@50) values above 0.97 for key defect classes after 150 training epochs. Quantitative evaluation of morphological characteristics for size and shape assessment further demonstrated model robustness, with insect-damaged cherries reaching a contour accuracy of 0.98 and an Intersection over Union (IoU) of 0.96. Comparative analysis with YOLOv5 and Faster Region-Based Convolutional Neural Network (Faster R-CNN) showed superior performance of the proposed architecture across all metrics, including precision, recall, F1-score, and mAP. By incorporating contextual embeddings and attention mechanisms, the framework enables accurate, real-time sorting for smart agricultural systems.The present investigation describes an advanced multi-task deep learning framework for automated inspection of coffee cherry quality using YOLOv8 with color-based segmentation and Vision Transformer-Convolutional Neural Network (ViT-CNN) feature extraction. The model performs ripeness stage classification, defect detection, and size and shape analysis. For ripeness detection, YOLOv8 was enhanced with a color segmentation module, achieving class-wise accuracies of 90–95 % for unripe, partially ripe, and fully ripe cherries, with moderate performance (85 %) for overripe samples. ViT-CNN feature maps improved segmentation clarity and bounding-box localization, particularly in high-density clusters. Defect detection was carried out across five categories (healthy, blackened, moldy, wrinkled, and insect-damaged), achieving F1-score values between 0.88 and 0.96 and mean average precision at 50 % intersection over union (mAP@50) values above 0.97 for key defect classes after 150 training epochs. Quantitative evaluation of morphological characteristics for size and shape assessment further demonstrated model robustness, with insect-damaged cherries reaching a contour accuracy of 0.98 and an Intersection over Union (IoU) of 0.96. Comparative analysis with YOLOv5 and Faster Region-Based Convolutional Neural Network (Faster R-CNN) showed superior performance of the proposed architecture across all metrics, including precision, recall, F1-score, and mAP. By incorporating contextual embeddings and attention mechanisms, the framework enables accurate, real-time sorting for smart agricultural systems

CONSUMER PREFERENCES AND WILLINGNESS TO PAY FOR TRADITIONAL TORTILLA USING CONTINGENT VALUATION

The tortilla is a staple food in the Mexican diet. However, in recent years, it has been declining in quality and nutritional value due to the use of flour and the industrialization of production processes. Fresh traditional tortillas are known to possess superior nutritional and nutraceutical properties compared to commercially produced tortillas, along with unmatched flavor and texture. Consumers have increased their preferences for local, more natural products produced in an eco-friendly agricultural manner. Thus, soil degradation, water pollution, and the loss of biodiversity are avoided. However, the price consumers are willing to pay for a tortilla with these attributes is unknown. Using the contingent valuation method (CVM) in its referendum and double-bounded formats, the aim of this investigation was to estimate consumers’ willingness to pay (WTP) for the tortilla they consume with the following attributes: native maize content, organic production, and traditional nixtamalization. Moreover, the variables that explained the behavior of the WTP were determined. A total of 216 surveys were conducted between January and March 2024 in 15 municipalities of the State of Mexico belonging to the metropolitan area of the Valley of Mexico. The double-bounded CVM displayed the highest theoretical consistency. The variables of price, monetary income, gender, education level, economic dependents, and age of the respondent helped estimate the WTP. The estimated value was MXN 36.12 per kg of tortilla.The tortilla is a staple food in the Mexican diet. However, in recent years, it has been declining in quality and nutritional value due to the use of flour and the industrialization of production processes. Fresh traditional tortillas are known to possess superior nutritional and nutraceutical properties compared to commercially produced tortillas, along with unmatched flavor and texture. Consumers have increased their preferences for local, more natural products produced in an eco-friendly agricultural manner. Thus, soil degradation, water pollution, and the loss of biodiversity are avoided. However, the price consumers are willing to pay for a tortilla with these attributes is unknown. Using the contingent valuation method (CVM) in its referendum and double-bounded formats, the aim of this investigation was to estimate consumers’ willingness to pay (WTP) for the tortilla they consume with the following attributes: native maize content, organic production, and traditional nixtamalization. Moreover, the variables that explained the behavior of the WTP were determined. A total of 216 surveys were conducted between January and March 2024 in 15 municipalities of the State of Mexico belonging to the metropolitan area of the Valley of Mexico. The double-bounded CVM displayed the highest theoretical consistency. The variables of price, monetary income, gender, education level, economic dependents, and age of the respondent helped estimate the WTP. The estimated value was MXN 36.12 per kg of tortilla

INFLUENCE OF HEALTH ON THE WORK PERFORMANCE OF RASPBERRY PICKERS: A PILOT STUDY

Raspberry pickers are a specific group of agricultural workers whose labor conditions have received limited attention in research. Their working conditions, the health problems they face, and the factors affecting their performance remain scarcely explored. The aim of this pilot study was to analyze the relationship between farmworkers’ self-perceived health status and their work performance, measured by the number of buckets harvested and the income derived from piece-rate work. Data were collected through a daily self-evaluation survey administered to a group of pickers over 53 harvest days. Workers’ perceived health status was recorded at the beginning, during, and at the end of each workday, along with the number of buckets harvested per day. Simple correlation analysis yielded an r coefficient of 0.66, corresponding to a determination coefficient (R2) of 0.44, indicating a moderate relationship between self-perceived health status and work performance. The results showed that poorer self-perceived health status was associated with a 21 % reduction in the number of buckets harvested relative to the group average. This reduction led to income differences of up to 35 % between the most and least productive pickers. These findings highlight the impact of health on both productivity and income among these workers. For future research, studies with larger samples and models incorporating additional factors are recommended to further clarify the relationship between health and productivity in this type of work.Raspberry pickers are a specific group of agricultural workers whose labor conditions have received limited attention in research. Their working conditions, the health problems they face, and the factors affecting their performance remain scarcely explored. The aim of this pilot study was to analyze the relationship between farmworkers’ self-perceived health status and their work performance, measured by the number of buckets harvested and the income derived from piece-rate work. Data were collected through a daily self-evaluation survey administered to a group of pickers over 53 harvest days. Workers’ perceived health status was recorded at the beginning, during, and at the end of each workday, along with the number of buckets harvested per day. Simple correlation analysis yielded an r coefficient of 0.66, corresponding to a determination coefficient (R2) of 0.44, indicating a moderate relationship between self-perceived health status and work performance. The results showed that poorer self-perceived health status was associated with a 21 % reduction in the number of buckets harvested relative to the group average. This reduction led to income differences of up to 35 % between the most and least productive pickers. These findings highlight the impact of health on both productivity and income among these workers. For future research, studies with larger samples and models incorporating additional factors are recommended to further clarify the relationship between health and productivity in this type of work

EXOPOLYSACCHARIDE SYNTHESIS BY Bacillus thuringiensis HA1 USING CARBON SOURCES FROM THE SUGARCANE AGROINDUSTRY

Exopolysaccharides are biopolymers produced by bacteria and have characteristics that make them suitable for applications in the pharmaceutical, environmental, and food industries. However, exopolysaccharide production faces challenges like high production costs. Therefore, strategies such as culture conditions improvement, strain selection, and the use of low-cost carbon sources have emerged as alternatives to improve exopolysaccharide production. In this work, the capability of Bacillus thuringiensis HA1 to produce exopolysaccharides using low-cost carbon sources (commercial sucrose, molasses, and panela) was explored. The production conditions were evaluated as follows: fermentation time (0–86 h), initial pH (5–9), temperature (31–43 °C), carbon sources (commercial sucrose, molasses, and panela), and concentration of carbon sources (50–350 g L-1). The settled conditions to assess the carbon sources were 60 h, 37 °C, and pH 7.5. Exopolysaccharide production was higher using commercial sucrose (23.54 mg mL-1), followed by molasses (8.62 mg mL-1) and panela (6.37 mg mL-1). The sucrose sample showed similarity to a glucan-type exopolysaccharide, since the presence of peaks at 1000–1200 is characteristic of C–O–C glycosidic linkages, while the molasses sample showed similarity to the standard levan. These results were achieved without pretreating the carbon sources, thus allowing the process to be economically feasible. To date, Bacillus thuringiensis has not been reported as a producer of two types of exopolysaccharides using different carbon sources.Exopolysaccharides are biopolymers produced by bacteria and have characteristics that make them suitable for applications in the pharmaceutical, environmental, and food industries. However, exopolysaccharide production faces challenges like high production costs. Therefore, strategies such as culture conditions improvement, strain selection, and the use of low-cost carbon sources have emerged as alternatives to improve exopolysaccharide production. In this work, the capability of Bacillus thuringiensis HA1 to produce exopolysaccharides using low-cost carbon sources (commercial sucrose, molasses, and panela) was explored. The production conditions were evaluated as follows: fermentation time (0–86 h), initial pH (5–9), temperature (31–43 °C), carbon sources (commercial sucrose, molasses, and panela), and concentration of carbon sources (50–350 g L-1). The settled conditions to assess the carbon sources were 60 h, 37 °C, and pH 7.5. Exopolysaccharide production was higher using commercial sucrose (23.54 mg mL-1), followed by molasses (8.62 mg mL-1) and panela (6.37 mg mL-1). The sucrose sample showed similarity to a glucan-type exopolysaccharide, since the presence of peaks at 1000–1200 is characteristic of C–O–C glycosidic linkages, while the molasses sample showed similarity to the standard levan. These results were achieved without pretreating the carbon sources, thus allowing the process to be economically feasible. To date, Bacillus thuringiensis has not been reported as a producer of two types of exopolysaccharides using different carbon sources

ANALYSIS OF BIOECONOMY ASPECTS IN AGRICULTURE AND BIOLOGICAL SCIENCES WITHIN AN INTERNATIONAL CONTEXT

The objective of this research was to identify key aspects of the bioeconomy by examining multiple international studies, particularly in the areas of agriculture and biological sciences from 2008 to 2023. A bibliographic source analysis was conducted using Bibliometrix tools from R Studio and VOSviewer to analyze a database extracted from Scopus. During this period, the bioeconomy experienced significant growth in published research and its increasing relevance to the global scientific community. The number of citations and articles reflects the impact of bioeconomy research in academia, with countries such as Finland, Germany, and Italy standing out for their publication volume. The study identified three main categories defining current trends in the bioeconomy: sustainable development; forestry and production; and innovation, biomass, and biotechnology. There is a global pursuit of an environmentally friendly economic model. Therefore, the identified areas can inform future research and contribute to the development of public policies for specific contexts and the advancement of the bioeconomy.The objective of this research was to identify key aspects of the bioeconomy by examining multiple international studies, particularly in the areas of agriculture and biological sciences from 2008 to 2023. A bibliographic source analysis was conducted using Bibliometrix tools from R Studio and VOSviewer to analyze a database extracted from Scopus. During this period, the bioeconomy experienced significant growth in published research and its increasing relevance to the global scientific community. The number of citations and articles reflects the impact of bioeconomy research in academia, with countries such as Finland, Germany, and Italy standing out for their publication volume. The study identified three main categories defining current trends in the bioeconomy: sustainable development; forestry and production; and innovation, biomass, and biotechnology. There is a global pursuit of an environmentally friendly economic model. Therefore, the identified areas can inform future research and contribute to the development of public policies for specific contexts and the advancement of the bioeconomy

CLIMATE CHANGE AND SUSTAINABLE RESOURCE MANAGEMENT IN SAUDI ARABIA: STRATEGIC ADAPTATION

This study investigates the complex impacts of climate change on Saudi Arabia’s ecosystems, focusing on two major challenges: biodiversity loss and water scarcity. Using structural equation modeling (SEM), the research evaluates the effectiveness of national adaptation strategies that integrate biodiversity conservation, water resource management, and climate policy. The analysis examines sustainable agricultural practices, biodiversity protection programs, and advanced water conservation technologies. The results identified water scarcity as the most critical issue, with renewable water resources expected to decline by 20–30 % by mid-century. Biodiversity loss, particularly among endemic species such as the Arabian oryx, also emerged as a severe threat. Results point out the need to expand desalination capacity, promote agroecological farming, and strengthen ecosystem restoration initiatives, alongside public awareness and environmental education to foster long-term resilience. Aligning adaptation strategies with Saudi Arabia’s Vision 2030 framework is essential to support economic diversification, safeguard natural resources, and enhance ecological sustainability.This study investigates the complex impacts of climate change on Saudi Arabia’s ecosystems, focusing on two major challenges: biodiversity loss and water scarcity. Using structural equation modeling (SEM), the research evaluates the effectiveness of national adaptation strategies that integrate biodiversity conservation, water resource management, and climate policy. The analysis examines sustainable agricultural practices, biodiversity protection programs, and advanced water conservation technologies. The results identified water scarcity as the most critical issue, with renewable water resources expected to decline by 20–30 % by mid-century. Biodiversity loss, particularly among endemic species such as the Arabian oryx, also emerged as a severe threat. Results point out the need to expand desalination capacity, promote agroecological farming, and strengthen ecosystem restoration initiatives, alongside public awareness and environmental education to foster long-term resilience. Aligning adaptation strategies with Saudi Arabia’s Vision 2030 framework is essential to support economic diversification, safeguard natural resources, and enhance ecological sustainability