Search Results (1,067)

Globally, agriculture is facing significant challenges due to climate change, which is seriously affecting grain yields. This research aims to analyze the significant effect of climate change (temperature and rainfall) on cereal production in Benin. The choice of Benin is explained by its strong dependence on agriculture and its vulnerability to climatic variations. This study employed climate and agricultural data from FAO and ASECNA (1990–2020) to evaluate the impacts of climate change on cereal production. SARIMA time-series models were used for forecasting, while the PLS-SEM approach assessed the relationships between climate variables and cereal production. The findings reveal a rise in temperatures and a gradual decline in precipitation. Despite these challenges, the time-series analysis suggests that Beninese farmers are expanding cultivated areas, successfully increasing production levels, and improving yields. Projections to 2050 indicate an increase in areas and production for maize and rice, while sorghum shows a constant trend. However, even with these projections, it is recommended to explore, in more depth, the resilience strategies used by cereal producers to better understand their influence and refine the orientations of future agricultural policies. Full article

As global water scarcity intensifies, the desalination of brackish groundwater and surface water plays a critical role in augmenting water supplies. However, managing reverse osmosis concentrate (ROC) from brackish water desalination remains challenging due to silica and calcium accumulation and precipitation, which cause membrane scaling and reduce freshwater recovery. This study employed the brackish diatom Gedaniella flavovirens Psetr3 in a photobiological treatment to remove dissolved silica and calcium, offering a natural, sustainable solution to improve freshwater recovery. Optimal treatment conditions were identified, with a light intensity of 200 µmol m⁻² s⁻¹ and incubation temperatures between 23 °C and 30 °C maximizing silica uptake (up to 46 ± 3 mg/L/day) while minimizing diatom mortality. This study reports, for the first time, the silica, organic, and calcite content in diatom biomass and their production rates during the photobiological treatment of ROC using G. flavovirens Psetr3. The photobiological treatment of one million gallons (3785 m³) per day of ROC would produce 174 kg of silica, 163 kg of organics, and 314 kg of calcite daily. These findings provide valuable insights into the potential for utilizing these bioresources to offset the costs of photobiological treatment and subsequent desalination processes. Full article

Global climate fluctuations pose challenges not only to natural environments but also to the conservation and transmission of human cultural and historical heritage. World Heritage Sites are pivotal regions for studying climate change impacts and devising adaptation strategies, with remote sensing technology showcasing significant utility in monitoring these impacts, especially in the Mediterranean region’s diverse and sensitive climate context. Although existing work has begun to explore the role of remote sensing in monitoring the effects of climate change, detailed analysis of the spatial distribution and temporal trends of landscape stability remains limited. Leveraging remote sensing data and its derived products, this study assessed climate change impacts on the Causses and Cévennes Heritage Site, a typical Mediterranean heritage landscape. Specifically, this study utilized remote sensing data to analyze the trends in various climatic factors from 1985 to 2020. The landscape stability model was developed utilizing land cover information and landscape indicators to explore the landscape stability and its distribution features within the study area. Finally, we adopted the Geographical Detector to quantify the extent to which climatic factors influence the landscape stability’s spatial distribution across different periods. The results demonstrated that (1) the climate showed a warming and drying pattern during the study period, with distinct climate characteristics in different zones. (2) The dominance of woodland decreased (area proportion dropped from 76% to 66.5%); transitions primarily occurred among woodland, cropland, shrubland, and grasslands; landscape fragmentation intensified; and development towards diversification and uniformity was observed. (3) Significant spatiotemporal differences in landscape stability within the heritage site were noted, with an overall downward trend. (4) Precipitation had a high contribution rate in factor detection, with the interactive enhancement effects between temperature and precipitation being the most prominent. The present study delivers a thorough examination of how climate change affects the Causses and Cévennes Heritage Landscape, reveals its vulnerabilities, and offers crucial information for sustainable conservation efforts. Moreover, the results offer guidance for the preservation of similar Mediterranean heritage sites and contribute to the advancement and deepening of global heritage conservation initiatives. Full article

In arid regions, climatic fluctuations significantly affect vegetation structure and function. Sun-induced chlorophyll fluorescence (SIF) can quantify certain physiological parameters of vegetation but has limitations in characterizing responses to climate change. This study analyzed the spatiotemporal differences in response to climate change across various ecological regions and vegetation types from 2000 to 2020 in Xinjiang. According to China’s ecological zoning, R1 (Altai Mountains-Western Junggar Mountains forest-steppe) and R5 (Pamir-Kunlun Mountains-Altyn Tagh high-altitude desert grasslands) represent two ecological extremes, while R2–R4 span desert and forest-steppe ecosystems. We employed the standardized precipitation evapotranspiration index (SPEI) at different timescales to represent drought intensity and frequency in conjunction with global OCO-2 SIF products (GOSIF) and the normalized difference vegetation index (NDVI) to assess vegetation growth conditions. The results show that (1) between 2000 and 2020, the overall drought severity in Xinjiang exhibited a slight deterioration, particularly in northern regions (R1 and R2), with a gradual transition from short-term to long-term drought conditions. The R4 and R5 ecological regions in southern Xinjiang also displayed a slight deterioration trend; however, R5 remained relatively stable on the SPEI24 timescale. (2) The NDVI and SIF values across Xinjiang exhibited an upward trend. However, in densely vegetated areas (R1–R3), both NDVI and SIF declined, with a more pronounced decrease in SIF observed in natural forests. (3) Vegetation in northern Xinjiang showed a significantly stronger response to climate change than that in southern Xinjiang, with physiological parameters (SIF) being more sensitive than structural parameters (NDVI). The R1, R2, and R3 ecological regions were primarily influenced by long-term climate change, whereas the R4 and R5 regions were more affected by short-term climate change. Natural grasslands showed a significantly stronger response than forests, particularly in areas with lower vegetation cover that are more structurally impacted. This study provides an important scientific basis for ecological management and climate adaptation in Xinjiang, emphasizing the need for differentiated strategies across ecological regions to support sustainable development. Full article

The demand for clean-cut seafood fillets has led to an increase in fish processing side streams, which are often considered to be low-value waste despite their potential as a source of high-quality proteins. Valorizing these side streams through innovative methods could significantly enhance global food security, reduce environmental impacts, and support circular economy principles. This study evaluates the environmental sustainability of protein recovery from herring, salmon, and cod side streams using pH-shift technology, a method that uses acid or alkaline solubilization followed by isoelectric precipitation to determine its viability as a sustainable alternative to conventional enzymatic hydrolysis. Through a Life Cycle Assessment (LCA), five key environmental impact categories were analyzed: carbon footprint, acidification, freshwater eutrophication, water use, and cumulative energy demand, based on a functional unit of 1 kg of the protein ingredient (80% moisture). The results indicate that sodium hydroxide (NaOH) use is the dominant environmental impact driver across the categories, while energy sourcing also significantly affects outcomes. Compared to conventional fish protein hydrolysate (FPH) production, pH-shift technology achieves substantial reductions in carbon footprint, acidification, and water use, exceeding 95%, highlighting its potential for lower environmental impacts. The sensitivity analyses revealed that renewable energy integration could further enhance sustainability. Conducted at a pilot scale, this study provides crucial insights into optimizing fish side stream processing through pH-shift technology, marking a step toward more sustainable seafood production and reinforcing the value of renewable energy and chemical efficiency in reducing environmental impacts. Future work should address scaling up, valorizing residual fractions, and expanding comparisons with alternative technologies to enhance sustainability and circularity. Full article

The global impact of climate variability and change on agricultural production systems is a pressing concern with far-reaching implications. While substantial literature exists on these impacts, there is a notable lack of long-term studies that comprehensively analyse the relationship between climate variables and food production systems in South Africa over extended periods. This study addresses this gap by utilising longitudinal data spanning 45 years (1976–2020) and employing an ordinary least squares regression model for analysis. The findings reveal that temperature has a significant positive effect on animal and horticultural production systems. On marginal variability, a 1 °C increase in annual temperature and precipitation levels leads to an increases in animal production (244.2%), field crops (226.4%), and a decrease in horticultural crops (−116.62%). These results underscore the pronounced effects of climate variability on animal, field, and horticultural production systems. This study concludes that rising temperatures positively influence animal and horticultural production. It recommends prioritising climate-smart agricultural practices to enhance resilience and productivity, particularly in colder seasons. By implementing these strategies, South Africa can strengthen its food production systems, ensuring sustainable agricultural growth in the face of climate variability and change. Full article

Drought is one of the most severe natural disasters globally, with its frequency and intensity escalating due to climate change, posing significant threats to agricultural production. This is particularly critical in major food-producing regions, where drought profoundly impacts crop yields. Such impacts can trigger food crises in affected regions and disrupt global food trade patterns, thereby posing substantial risks to global food security. Based on historical data, this study examines the yield response characteristics of key crops—maize, rice, soybean, spring wheat, and winter wheat—under drought conditions during their growth cycles, highlighting variations in drought sensitivity among major food-producing countries. The findings reveal that maize and soybean yield in China, the United States, and Brazil are among the most sensitive and severely affected by drought. Furthermore, using precipitation simulation data from CMIP6 climate models, the study evaluates drought trends and associated crop yield risks under different future emission scenarios. Results indicate that under high-emission scenarios, crops face heightened drought risks during their growth cycles, with China and the United States particularly vulnerable to yield reductions. Additionally, employing copula functions, the study analyzes the probability of simultaneous drought occurrences across multiple countries, shedding light on the evolving trends of multicountry drought events in major food-producing regions. These findings provide a scientific basis for assessing global food security risks and offer policy recommendations to address uncertainties in food supply under climate change. Full article

Background: The wine industry in arid area serves as a crucial livelihood source at the frontiers of anti-desertification and anti-poverty. By making use of a carbon footprint (CF) management system, formerly untapped climate values can be explored, embedded, and cherished to connect rural communities with the global goals of sustainable development. However, the current standards of CF management mainly represent the traditional wine grape growing areas of Europe, Oceania, and North America. Limited study of the arid areas in lower-income regions exists, which offers a kind of potential development knowledge regarding creating climate-related livelihoods. Methods: This paper attempts to construct a cradle-to-gate CF Life Cycle Assessment (LCA) framework based on the prominent emission factors in three GHG emission phases (raw material input, planting management, and transportation) of a wine grape variety, Cabernet Sauvignon (chi xia zhu), planted at the Eastern Foothills of the Helan Mountains in the Ningxia Hui Autonomous Region of China. Results: It is found that viticulture processes (instead of wine-making, bottling, or distribution) account for a larger proportion of GHG emissions in Ningxia. Due to the large amount of irrigation electricity usage, the less precipitation wine producers have, the larger CF they produce. By using organic fertilizer, the CF of Ningxia Cabernet Sauvignon, being 0.3403 kgCO₂e/kg, is not only lower than that of the drier areas in Gansu Province (1.59–5.7 kgCO₂e/kg) of Western China, but it is even lower than that of the Israel Negev Region (0.342 kgCO₂e/kg) that experiences more rainfall. Conclusions: The measurement of CF also plays a role in understanding low-carbon experience sharing. As the largest wine grape production area in China, CF analysis of the Ningxia region and its commercial value realization might practically fill in the knowledge gap for desert areas in developing countries. It is inspiring to know that by applying green agricultural technologies, the viticulture CF can be effectively reduced. For the potential exchanges in global carbon markets or trading regulations under the Carbon Border Adjustment Mechanism (CBAM), positive variations in CF and soil organic carbon (SOC) storage volume need to be considered within financial institutional design to lead to more participation toward SDGs. Full article

In the context of global warming, climate strongly affects forest fires. With long-term and strict fire prevention policies, China has become a unique test arena for comprehending the role of climatic variables in affecting forest fires. Here, using GIS spatial analysis, Pearson correlation, and geographical detector, the climate drivers of forest fires in China are revealed using the 2003–2022 active fire data from the MODIS C6 and climate products from CHELSA (Climatologies at high resolution for the Earth’s land surface areas). The main conclusions are as follows: (1) In total, 82% of forest fires were prevalent in the southern and southwestern forest regions (SR and SWR) in China, especially in winter and spring. (2) Forest fires were mainly distributed in areas with a mean annual temperature and annual precipitation of 14~22 °C (subtropical) and 800~2000 mm (humid zone), respectively. (3) Incidences of forest fires were positively correlated with temperature, potential evapotranspiration, surface downwelling shortwave flux, and near-surface wind speed but negatively correlated with precipitation and near-surface relative humidity. (4) Temperature and potential evapotranspiration dominated the roles in determining spatial variations of China’s forest fires, while the combination of climate variables complicated the spatial variation. This paper not only provides new insights on the impact of climate drives on forest fires, but also offers helpful guidance for fire management, prevention, and forecasting. Full article

The genus of Nitraria L. are Tertiary-relict desert sand-fixing plants, which are an important forage and agricultural product, as well as an important source of medicinal and woody vegetable oil. In order to provide a theoretical basis for better protection and utilization of species in the Nitraria L., this study collected global distribution information within the Nitraria L., along with data on 29 environmental and climatic factors. The Maximum Entropy (MaxEnt) model was used to simulate the globally suitable distribution areas for Nitraria L. The results showed that the mean AUC value was 0.897, the TSS average value was 0.913, and the model prediction results were excellent. UV-B seasonality (UVB-2), UV-B of the lowest month (UVB-4), precipitation of the warmest quarter (bio18), the DEM (Digital Elevation Model), and annual precipitation (bio12) were the key variables affecting the distribution area of Nitraria L, with contributions of 54.4%, 11.1%, 8.3%, 7.4%, and 4.1%, respectively. The Nitraria L. plants are currently found mainly in Central Asia, North Africa, the neighboring Middle East, and parts of southern Australia and Siberia. In future scenarios, except for a small expansion of the 2030s scenario model Nitraria L., the potential suitable distribution areas showed a decreasing trend. The contraction area is mainly concentrated in South Asia, such as Afghanistan and Pakistan, North Africa, Libya, as well as in areas of low suitability in northern Australia, where there was also significant shrinkage. The areas of expansion are mainly concentrated in the Qinghai–Tibet Plateau to the Iranian plateau, and the Sahara Desert is also partly expanded. With rising Greenhouse gas concentrations, habitat fragmentation is becoming more severe. Center-of-mass migration results also suggest that the potential suitable area of Nitraria L. will shift northwestward in the future. This study can provide a theoretical basis for determining the scope of Nitraria L. habitat protection, population restoration, resource management and industrial development in local areas. Full article

Climate forecasting is essential for energy production, agricultural activities, transportation, and civil defense sectors, serving as a foundation for decision-making and risk management. This study addresses the challenge of accurately predicting extreme droughts in South America, a region highly vulnerable to climate variability. By employing a supervised neural network (NN) within a machine learning framework, we developed a methodology to forecast precipitation and subsequently calculate the Standardized Precipitation Index (SPI) for predicting drought conditions across the continent. The proposed model was trained with precipitation data from the Global Precipitation Climatology Project (GPCP) for the period 1983–2023. It provided monthly drought forecasts, which were validated against observational data and compared with predictions from the North American Multi-Model Ensemble (NMME). Key findings indicate the neural network’s ability to capture complex precipitation patterns and predict drought conditions. The model’s architecture effectively integrates precipitation data, demonstrating superior performance metrics compared to traditional approaches like the NMME. This study reinforces the relevance of using machine learning algorithms as a robust tool for drought prediction, providing critical information that can assist in decision-making for sustainable water resource management. Full article

Climate change is a global challenge that impacts rangeland and pastureland landscapes by inducing shifts in temperature variability, precipitation patterns, and extreme weather events. These changes alter soil and plant conditions, reducing forage availability and chemical composition and leading to nutritional stress in cattle. This stress occurs when animals lack adequate water and feed sources or when these resources are insufficient in quantity, composition, or nutrient balance. Several strategies are essential to address these impacts. Genetic selection, epigenetic biomarkers, and exploration of epigenetic memories present promising avenues for enhancing the resilience of cattle populations and improving adaptation to environmental stresses. Remote sensing and GIS technologies assist in locating wet spots to establish islands of plant diversity and high forage quality for grazing amid ongoing climate change challenges. Establishing islands of functional plant diversity improves forage quality, reduces carbon and nitrogen footprints, and provides essential nutrients and bioactives, thus enhancing cattle health, welfare, and productivity. Real-time GPS collars coupled with accelerometers provide detailed data on cattle movement and activity, aiding livestock nutrition management while mitigating heat stress. Integrating these strategies may offer significant advantages to animals facing a changing world while securing the future of livestock production and the global food system. Full article

Global climate change, characterized by nitrogen (N) deposition and precipitation reduction, can disrupt soil microbial stoichiometry and soil nutrient availability, subsequently affecting soil nutrient cycles. However, the effects of N deposition and precipitation reduction on microbial stoichiometry and the soil nutrient status in temperate forests remain poorly understood. This study addresses this gap through a 10-year field trial conducted in a Korean pine mixed forest in northeastern China where three treatments were applied: precipitation reduction (PREC), nitrogen addition (N50), and a combination of nitrogen addition with precipitation reduction (PREC-N50). The results showed that N50 and PREC significantly increased carbon-to-phosphorus (C/P) and nitrogen-to-phosphorus (N/P) imbalances, thereby exacerbating microbial P limitation, while PREC-N50 did not alter the nutrient imbalances. PREC decreased soil water availability, impairing microbial nutrient acquisition. Both N50 and PREC influenced soil enzyme stoichiometry, leading to increasing the ACP production. The results of redundancy analysis indicated that microbial nutrient status, enzymatic activity, and composition contributed to the variations in nutrient imbalances, suggesting the adaption of microorganisms to P limitation. These results highlight that N addition and precipitation reduction enhanced microbial P limitation, boosting the shifts of microbial elemental composition, enzyme production, and community composition, and subsequently impacting on forest nutrient cycles. Full article

Globally, there has been a lot of focus on climate variability, especially variability in annual precipitation and temperatures. Depending on the area, different climate variables have different degrees of variation. Therefore, analyzing the temporal and spatial changes or dynamics of meteorological or climatic variables in light of climate change is crucial to identifying the changes induced by climate and providing workable adaptation solutions. This study examined how climate variability affects tea production in Darjeeling, West Bengal, India. It also looked at trends in temperature and rainfall between 1991 and 2023. In order to identify significant trends in these climatic factors and their relationship to tea productivity, the study used a variety of statistical tests, including the Sen’s Slope Estimator test, the Mann–Kendall’s test, and regression tests. The study revealed a positive growth trend in rainfall (Sen’s slope = 0.25, p = 0.001, R² = 0.032), maximum temperature (Sen’s slope = 1.02, p = 0.026, R² = 0.095), and minimum temperature (Sen’s slope = 4.38, p = 0.006, R² = 0.556). Even with the rise in rainfall, there has been a decline in tea productivity, as seen by the sharp decline in both the tea cultivated area and the production of tea. The results obtained from the regression analysis showed an inverse relationship between temperature anomalies and tea yield (R = −0.45, p = 0.02, R² = 0.49), indicating that the growing temperatures were not favorable for the production of tea. Rainfall anomalies, on the other hand, positively correlated with tea yield (R = 0.56, p = 0.01, R² = 0.68), demonstrating that fluctuations in rainfall have the potential to affect production but not enough to offset the detrimental effects of rising temperatures. These results underline how susceptible the tea sector in Darjeeling is to climate change adversities and the necessity of adopting adaptive methods to lessen these negative consequences. The results carry significance not only for regional stakeholders but also for the global tea industry, which encounters comparable obstacles in other areas. Full article

Global precipitation products (GPPs) are vital in weather forecasting, efficient water management, and monitoring floods and droughts. However, the precision of these datasets varies considerably across different climatic regions and topographic conditions. Therefore, the accuracy assessment of the precipitation dataset is crucial at the local scale before its application. The current study initially compared the performance of recently modified and upgraded precipitation datasets, including Climate Research Unit Time-Series (CRU TS v4.08), fifth-generation ERA5-Land (ERA-5), Integrated Multi-satellite Retrievals for GPM (IMERG) final run (IMERG v07B), and Multi-Source Weighted-Ensemble Precipitation (MSWEP v2.8), against ground observations on the provincial basis across Pakistan from 2003 to 2020. Later, the study area was categorized into four regions based on the elevation to observe the impact of elevation gradients on GPPs’ skills. The monthly and seasonal precipitation estimations of each product were validated against in situ observations using statistical matrices, including the correlation coefficient (CC), root mean square error (RMSE), percent of bias (PBias), and Kling–Gupta efficiency (KGE). The results reveal that IMERG7 consistently outperformed across all the provinces, with the highest CC and lowest RMSE values. Meanwhile, the KGE (0.69) and PBias (−0.65%) elucidated, comparatively, the best performance of MSWEP2.8 in Sindh province. Additionally, all the datasets demonstrated their best agreement with the reference data toward the southern part (0–500 m elevation) of Pakistan, while their performance notably declined in the northern high-elevation glaciated mountain regions (above 3000 m elevation), with considerable overestimations. The superior performance of IMERG7 in all the elevation-based regions was also revealed in the current study. According to the monthly and seasonal scale evaluation, all the precipitation products except ERA-5 showed good precipitation estimation ability on a monthly scale, followed by the winter season, pre-monsoon season, and monsoon season, while during the post-monsoon season, all the datasets showed weak agreement with the observed data. Overall, IMERG7 exhibited comparatively superior performance, followed by MSWEP2.8 at a monthly scale, winter season, and pre-monsoon season, while MSWEP2.8 outperformed during the monsoon season. CRU TS showed a moderate association with the ground observations, whereas ERA-5 performed poorly across all the time scales. In the current scenario, this study recommends IMERG7 and MSWEP2.8 for hydrological and climate studies in this region. Additionally, this study emphasizes the need for further research and experiments to minimize bias in high-elevation regions at different time scales to make GPPs more reliable for future studies. Full article