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Land use change (LUC), including deforestation, agriculture, and urbanization, is a major contributor to climate change (CC). This change in land use has impacts on food, water, and energy systems, creating a complex interconnected web of issues. This study aimed to investigate the global link between LUC and CC from 1990 to 2012. Using time‐series data from the World Bank, LUC was represented by irrigated land, arable lands, and forest areas, while CC was represented by CO2 emissions. Moreover, the relationship between economic growth in high‐income and low‐income countries and LUC and CC was examined in this study. Based on the findings of this study, in low‐income countries, the intensity of LUC is higher in comparison with high‐income countries. Meanwhile, CO2 emissions are increasing in middle‐income and high‐income countries. Economic growth is closely related to CO2 emissions in countries with different levels of income. The study indicated that managing land use is of high importance to mitigate CO2 emissions globally. According to the findings, recent LUC has shown more obvious effects on ecological variables than CC. Although LUC is not inherently directly related to CC, humans change land use, especially in terms of land management, to adapt to CC, and these changes will inevitably bring many environmental impacts. This study contributes significantly to advancing the understanding of the complex relationship between CC and LUC, emphasizing the need for integrated approaches in policy development. These measures are crucial to achieving resilience and meeting global CC reduction targets. [ABSTRACT FROM AUTHOR]

The implementation of the IPARD (Instrument for Pre-Accession Assistance in Rural Development) initiative in Montenegro has advanced the agricultural sector by providing farmers with machinery and equipment, including tractors, trailers, balers, and irrigation systems. Through four public calls for support, a total of 333 investments were channelled, accompanied by infrastructural development to enhance farming operations and productivity. This paper assesses the impact and effectiveness of the IPARD initiative, particularly its contribution to modernizing the agricultural sector through machinery and equipment provision. The analyses involved a combination of data extraction from online sources, statistical analysis using tools like SWOT analysis and Technological Advancements Assessment, and qualitative insights from interviews with key stakeholders to evaluate the impact of IPARD on agricultural modernization in Montenegro. It evaluates how IPARD support has enhanced farming operations, productivity, and efficiency in Montenegro, identifying challenges and opportunities for improvement. Additionally, it provides insights and recommendations for future initiatives to foster sustainable agricultural modernization in the country. Notably, beneficiaries from Bijelo Polje received the highest support at €4,003,999, while Budva and Tivat did not receive any. Municipalities like Podgorica and Nikšić also received significant support, reflecting their larger populations and areas. The anticipated impact is transformative, empowering farmers to modernize practices and improve efficiency. Despite challenges like eligibility criteria and communication barriers, positive collaboration and efficient information dissemination have been observed. Opportunities for improvement include expanding financing mechanisms and enhancing user engagement. These insights will guide future efforts toward sustainable agricultural modernization in Montenegro. Moving forward, expanding financing mechanisms and enhancing user engagement are crucial steps to further empower farmers and ensure the continued success of agricultural development initiatives in Montenegro. [ABSTRACT FROM AUTHOR]

This study investigates the dynamics of land use and land cover (LULC) changes in the El Kssiba region of the Middle Atlas Mountains, Morocco, with a focus on soil conservation and environmental sustainability. Through a comprehensive examination of historical archives, climatic data, geological characteristics, and land use patterns, this research assesses the impacts of environmental factors and socio-economic changes on land utilization over time. The study area, covering 576.57 km², has experienced significant transformations in land use driven by government policies, population growth, land tenure, and climatic conditions. Findings reveal a notable decline in agricultural and olive cultivation, alongside a substantial increase in bare land and forest cover, suggesting both degradation and potential recovery of land. These changes highlight the urgent need for sustainable land management and soil conservation strategies to mitigate degradation and enhance land productivity. The conducted SWOT analysis highlights the intricate relationship between the traditional strengths of pastoral systems and the pronounced contemporary challenges, especially regarding environmental degradation, climate change, and socioeconomic pressures. This study offers key insights for policy development to promote balanced conservation and sustainable resource management, highlighting the need for long-term strategies to ensure sustainable land use in fragile ecosystems. [ABSTRACT FROM AUTHOR]

Organic carbon performs essential functions in soils, which act as sources or sinks of atmospheric organic carbon. Agricultural management affects the carbon cycle in the soil, with effects on climate change. One of the crops most vulnerable to climate change is coffee. Brazil is the world’s largest coffee producer, with a predominance of management under a conventional system, with sloping terrain and the absence of conservationist practices. The absence of conservationist practices increases in soil loss rates due to water management and carbon emissions, as well as a reduction in coffee production. This paper intended to estimate soil and organic carbon losses by RUSLE in coffee farms in southern Minas Gerais, south-eastern Brazil. Data were obtained from fieldwork, laboratory analysis, and cartographic products. The results indicated, exclusively for coffee crops, soil and carbon losses between 7 and 32 Mg ha−1 year−1 and 87 and 460kg ha−1 year−1, respectively. However, the highest soil losses occurred on sloping terrains with eucalyptus plantations located downhill, and the lowest losses occurred on flat land with native forests. Organic carbon losses were linked directly to soil losses, as a result from the land practices, slope and agricultural management adopted. These results can be used for the planning and priority definition of areas needing conservationist practices, such as green manuring, planting in contour and maintaining of vegetation between coffee rows, which are already used in some sites of the study area. [ABSTRACT FROM AUTHOR]

Nowadays, the interest in research to determine the healthy compounds in fig fruits has increased, as many of them have been found to be beneficial to human health. This study aimed to determine the sugars, organic acids, total phenolic content, antioxidant capacity and volatile compounds in the fruits of 19 fig (Ficus carica L.) genotypes sampled from the Kahramanmaras province in Türkiye in 2018 and 2019. In the fruit of the fig genotypes, the total phenolic content was determined by the Folin-Ciocalteu method, the total antioxidant capacity was determined by the DPPH (2,2-diphenyl-1-picrylhidrazil) method, the sugars, organic acids and volatile aroma compounds were determined chromatographically (HPLC/RID detector), (HPLC/UV detector) and headspace gas chromatography-mass spectrophotometry (HS-GC/MS), respectively. The results showed that most of the biochemical contents and antioxidant capacities of the genotypes significantly differed from each other (P < 0.05). The total phenolic content and antioxidant properties of the fruits ranged from 50.29 to 580.59 mg gallic acid equivalent per 100 g fresh weight base (and 15.98 to 36.77% DPPH, respectively. Regarding the sugar content of the fig genotypes, the main sugar is fructose ranging from 3.35 to 7.37 g per 100 g. The highest fructose content of 7.37 g per 100 g was found in the genotype KMF12. A total of 58 volatile compounds were detected in the fruits of the 19 fig genotypes, including 18 aldehydes, 3 ketones, 6 esters, 2 terpenes, 17 alcohols, 1 acid and 11 other compounds. According to the obtained results, aldehydes, esters and ketones were found to be the major volatile compounds in the fig fruits. The genotypes with the highest values of the phytochemical and antioxidant properties among the genotypes were selected as candidates as a source of variation for breeders who want to develop new commercial varieties beneficial to human health. [ABSTRACT FROM AUTHOR]

Morocco's sustainable water resource management is critical for its development, particularly in the water-rich Loukkos Hydraulic Basin. However, climate change projections indicate impending impacts, necessitating integrated planning. This study analyzes precipitation, temperature, and runoff data from 1950-2000 to establish comprehensive datasets, including mean areal precipitation (MAP) and mean areal temperature (MAT). Seasonal and annual regression equations linking runoff to MAP and MAT were developed, yielding historical runoff series (Qbaseline). Insights gained are pivotal for adaptive water management amidst climate change. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) is gaining attention in civil engineering especially within operational phase of building life cycle. This phase is crucial for managing every energy aspect while ensuring occupant comfort. Previous ML experiments have explored occupant behavior, occupancy estimation, load prediction, defect detection, and Heating, Ventilation, and Air Conditioning (HVAC) system diagnostics. However, challenges such as ML transferability and limited literature on ML components for the operational phase hinder broader industry adoption. This critical review aims to assess the potential of ML in building operations, focusing on energy consumption, big data control, reinforcement learning, and thermal comfort modeling. By identifying knowledge gaps, the study recommends further research to leverage ML for sustainable energy consumption and occupant comfort. It highlights ML's promising role in striking a balance between energy efficiency and occupant wellbeing. [ABSTRACT FROM AUTHOR]

The port of Tangier Med is essential due to its strategic location, as it is an important trading center linking Europe, North America, and Africa. However, the increased rates of downstream sediment transportation put pressure on the sustainable future of the port. Thus, assessing the existing erosion rates and future improvement scenarios is imperative for planning sustainable management at the catchment level. We utilize the Erosion Potential Model (EPM) combined with the Intensity of Erosion and Outflow (IntErO) algorithm to assess the erosion and outflow intensity and to distinguish the sediment-producing areas in the R'mel watershed. The port's proximity at the bottom of the slope opposite the R'mel Dam is relevant in this context. Initial results show an average erosion rate of 13 t/ha/year. Quarry operations were identified as the primary sediment source, as indicated by the factors contributing to erosion. The qualitative PAP/RAC (Priority Actions Program/Regional Activity Center) model was used to assess the development trends in the watershed, confirming a clear tendency toward irreversible degradation in the quarry areas. Considering that the mined carbonate lithology represents 23.77% of the total area of the catchment, the situation in the region could deteriorate if quarry operations continue. The simulation of quarry rehabilitation scenarios through land use and land cover change (LULC) with IntErO shows that reforestation of quarries can significantly reduce erosion rates (4.78 t/ha/year) compared to their conversion to agricultural land. This study underlines the effectiveness of IntErO, based on the EPM model, in quickly and effectively mapping and quantifying water erosion. [ABSTRACT FROM AUTHOR]

Coastal zones are characterized by a complex interaction between continental and marine ecosystems. They are often subject to anthropogenic and natural pressures such as urbanization, pollution, erosion, storms, and the impact of climate change. It is an environment that requires sustainable management to preserve its biodiversity and ecosystem services. This study focuses on assessing coastal vulnerability to erosion in the Bay of Agadir, one of Morocco's main tourist attractions. The methodology adopted is based on geomatic science such as GIS and geospatial remote sensing. This approach involved the preparation of a database for the study area, relating mainly to geomorphological parameters, topography, sea level rise, and shoreline change data. The latter was assessed using DSAS (Digital Shoreline Analysis System) tools and then all data were integrated into a GIS software to evaluate the Coastal Vulnerability Index (CVI). The first result shows that the EPR (endpoint rate) index reveals an erosion trend over the 8.3 km studied area. Over the 262 transects analyzed, the variations show a predominant erosion (92%) with rates of up to -13.5 m/year over 46 years. This justifies the use of the LRR (Linear Regression Rate-of-Change) index rather than the EPR for kinematic analysis of the coastline. The results of the CVI index show that 2.58 km of the coastal zone, and 1.68 km of the Bay are successively highly and very highly vulnerable to coastal erosion (20-60%). This study can be considered as a basic document, providing crucial information to guide decision-makers and planners in the implementation of effective coastal conservation strategies. [ABSTRACT FROM AUTHOR]

Located in the Brazilian Amazon biome, the Chico Mendes Extractive Reserve, Acre state, is an important area for the conservation of biodiversity and ecosystem services in the region. Despite its importance, it faces challenges such as illegal deforestation, mining, and forest fires, which increase water erosion processes and generate environmental and socioeconomic negative impacts. The need to understand these impacts motivate this research, with the objective of evaluating the influence of forest fires on water erosion and quantify soil losses at this site. For this purpose, we employed the Revised Universal Soil Loss Equation (RUSLE), utilizing parameters obtained from scientific literature and remote sensing data, such as the Normalized Difference Vegetation Index (NDVI), enabling a temporal analysis of vegetation cover. Our results indicate low variations in average soil loss rates, ranging from 3.00 to 3.74 Mg ha-1 yr-1 from 2019 to 2021. In 2021, an increase in soil loss rates was observed due to a higher incidence of forest fires, especially in pasture areas. It is concluded that the preservation and adequate management of vegetation cover are essential for the protection of natural resources. The need to adopt and develop conservation and sustainable management strategies through public policy should contribute to the mitigation of environmental impacts. Furthermore, the results obtained can highlight the importance of environmental conservation. [ABSTRACT FROM AUTHOR]

Drought indices are one of the most widely used methods for drought monitoring because of their ease of application and interpretation. The most commonly used drought index method is the Standardized Precipitation Index (SPI) method, which uses precipitation as an input. The performance of the Standardized Precipitation Index (SPI) is affected by the choice of an incorrect probability distribution function, which can distort index values, exaggerating or minimizing drought severity. This study aims to test the suitability of the statistical distribution functions proposed by the Bootstrap model, which estimates the closest probability distribution for calculating the SPI at time scales (TS) of 3, 6, 9, and 12 months. Daily rainfall data collected at the Dar Chaoui meteorological station were used for the period 2000-2021. Distribution function parameters were estimated using the maximum likelihood (ML) method and the Kolmogorov-Smirnov method and then confirmed using the Bootstrap method. The results show several extremely dry picks, especially in the 9- and 12-month time scales. [ABSTRACT FROM AUTHOR]