Search Results (4,191)

Deciphering the responses of soil properties to land-use changes is of great importance for sustainable management in biogeochemically sensitive zones. This investigation examines the impacts of agricultural conversion on soil-microbial dynamics across four land-use patterns in western Xin-jiang, China: native grassland (NG), two-year paddy field (PF), one-year corn-rice rotation field (RF), and two-year sorghum field (SF). The results indicate that different land-use types significantly altered soil properties: NG retained superior soil structure, with significantly higher porosity and organic carbon (p < 0.05). Microbial communities differed distinctly across land uses. The relative abundance of Proteobacteria ranked SF > RF > PF > NG, contrasting with Bacteroidota trends. Non-metric multidimensional scaling (NMDS) revealed divergent structures of soil microbial communities under different land-use types. The results of correlation analysis and structural equation models (SEM) showed that land use could indirectly affect bacterial diversity through its influence on soil physicochemical properties, highlighting that land-use-driven shifts in bulk density, porosity, and carbon content critically shape microbial dynamics, particularly in bacteria. These results underscore the sensitivity of soil properties to land-use practices and offer actionable insights for optimizing soil quality and sustainability in vulnerable regions. Full article

Background: During the germination of soybean seeds, many biochemical metabolic reactions become extremely active, resulting in a series of physiological and biochemical activities, and the seeds being rich in nutrients. Studying the network and key genes that regulate the nutritional content of bean sprouts is particularly important. Methods: In this study, the nutrient contents of Dongnong 254 and Heze small beans were measured when the bean sprouts were 1 cm, 3 cm, 5 cm and 7 cm long, and transcriptome sequencing was performed. Results: Clustering and principal component analysis (PCA) revealed that the samples could be divided into three groups. The differences between Dongnong 254 and Heze small bean samples with sprout lengths of 5 cm and 7 cm were greater than those between materials. Through differential expression analysis, 18,472 differentially expressed genes (DEGs) in the material included 1816 unique DEGs, and a total of six clusters with statistical significance were identified, which were enriched in pathways related to photosynthesis and sugar metabolism. The 6938 DEGs among the materials included 1044 unique DEGs, and a total of nine statistically significant clusters were identified, which were mainly annotated in pathways related to photosynthesis, hormones and flavonoids. Three specific modules that were significantly related to the nutritional content of bean sprouts were identified via WGCNA. The connectivity and functional annotation of genes within the modules were calculated, and nine candidate genes were found, nine of which encoded transcription factors (Glyma.16G071900 (WD40), Glyma.17G172400 (bHLH), Glyma.18G148000 (AP2) and Glyma.01G003000 (MYB)). Conclusions: These research results provide a theoretical basis for an in-depth understanding of the molecular mechanisms of soybean sprout development and nutritional components and provide new genetic resources for the study of nutritional components in soybean sprouts. Full article

Landslides in Ecuador are one of the most common deadly events in natural hazards, such as the one on 26 March 2023. A large-scale landslide occurred in Alausí, Chimborazo province, causing 65 fatalities and 10 people to disappear, significant infrastructural damage, and the destruction of six neighborhoods. This study presents a detailed case analysis of the anthropogenic factors that could have contributed to the instability of the affected area. Field investigations and a review of historical, geological, and social information are the basis for analyzing the complex interactions between natural and human-induced conditions. Key anthropogenic contributors identified include unplanned urban expansion, ineffective drainage systems, deforestation, road construction without adequate geotechnical support, and changes in land use, particularly agricultural irrigation and wastewater disposal. These factors increased the area’s susceptibility to slope failure, which, combined with intense rainfall and past seismic activity, could have caused the rupture process’s acceleration. The study also emphasizes integrating geological, hydrological, and urban planning assessments to mitigate landslide risks in geologically sensitive regions such as Alausí canton. The findings conclude that human activity could be an acceleration factor in natural processes, and the pressure of urbanization amplifies the consequences. This research underscores the importance of sustainable land management, improved drainage infrastructure, and land-use planning in hazard-prone areas. The lessons learned from Alausí can inform risk reduction strategies across other mountainous and densely populated regions worldwide, like the Andean countries, which have similar social and environmental conditions to Ecuador. Full article

Stormwater runoff from areas with specific industrial, agricultural or logistic land use comprises a significant source of water pollution, yet research on its specific composition remains limited compared to urban stormwater pollution. This review synthesizes findings from different studies to analyze sampling methods, types of pollution parameters and their associated concentration ranges across various non-urban land use types, including industrial and commercial zones, transportation infrastructure (ports, airports, highways, railways) and agricultural areas. Studies differed in sample strategy, investigated phase (water, sediment) and analyzed chemical parameters. The latter can be grouped into sum parameters (e.g., total suspended solids (TSS), chemical oxygen demand (COD)), metals (e.g., nickel, copper, zinc, lead), nutrients (e.g., nitrogen, phosphorus), organic micropollutants (e.g., polycyclic aromatic hydrocarbons (PAH), perfluoroalkyl acids (PFAA)) and microbial contaminants. Results indicate that pollutant loads vary widely depending on land use, with industrial and railway areas showing the highest metal contamination, while agricultural and livestock farming areas exhibit elevated nutrient and microbial concentrations. The heterogeneity of the sampling, analysis and subsequent data processing hindered the statistical condensation of data from different studies. The findings underscore the need for standardized monitoring methods and tailored stormwater treatment strategies to mitigate pollution impact effectively. Full article

Climate change has a complex impact on the agricultural crop system, with knowledge of the processes being necessary to assist decisions that guide the adaptation of society to profound structural changes. This study aims to highlight the main changes generated by the modification of climatic parameters (increasing air temperature, humidity and precipitation and decreasing wind speed) on agricultural crops in a region with important changes in its economic profile due to urban extension and land use modification. The analysis methodology is based on the Cropwat software to highlight the temporal variability of crop evapotranspiration, effective rain and water requirements for different crops—strawberry, sunflower and pea—and the possibility of using other types of crops with higher yield and lower water needs. The methodology used highlights this fact, showing that major changes are needed in the choice of crop schemes and future technological processes in the current context of climate change. The current results of the study, conducted over a period of 30 years (1991–2020), showed that the climatic, land use and economic changes in the study area have led to a decrease in evapotranspiration and crop water requirements due to the amounts of precipitation that can provide for the water needs of strawberry, sunflower and pea crops. The irrigation requirements during the analysis period 1991–2020 varied from <10 mm/year to 120 mm/year for strawberry crops, and can exceed 300 mm/year for sunflower and pea crops, having higher values in years with a precipitation deficit (effective rain less than 100 mm). Analyzing the irrigation requirements during the vegetation growing seasons shows that for pea and strawberry the trend is decreasing, but without a significance level. Only for the sunflower crop is an increasing trend recorded in the initial and late stages. The results obtained provide a methodological framework as well as concrete information for decision-makers in the field of agriculture who must build adaptation mechanisms for climate challenges. Full article

Indoor farming presents a sustainable response to urbanization and climate change, yet optimizing light use efficiency (LUE) remains vital for maximizing crop yield and minimizing energy use. This study introduces an IoT-based framework for adaptive light management in controlled environments, using lettuce (Lactuca sativa L.) as a model crop due to its rapid growth and sensitivity to light spectra. The system integrates advanced LED lighting, real-time sensors, and cloud-based analytics to enhance light distribution and automate adjustments based on growth stages. The key findings indicate a 20% increase in energy efficiency and a 15% improvement in lettuce growth compared to traditional static models. Novel metrics—Light Use Efficiency at Growth stage Canopy Level (LUEP) and Lamp Level (LUEL)—were developed to assess system performance comprehensively. Simulations identified optimal growth conditions, including a light intensity of 350–400 µmol/m²/s and photoperiods of 16–17 h/day. Spectral optimization showed that a balanced blue-red light mix benefits vegetative growth, while higher red content supports flowering. The framework’s feedback control ensures rapid (<2 s) and accurate (>97%) adjustments to environmental deviations, maintaining ideal conditions throughout growth stages. Comparative analysis confirms the adaptive system’s superiority over static models in responding to dynamic environmental conditions and improving performance metrics like LUEP and LUEL. Practical recommendations include stage-specific guidelines for light spectrum, intensity, and duration to enhance both energy efficiency and crop productivity. While tailored to lettuce, the modular system design allows for adaptation to a variety of leafy greens and other crops with species-specific calibration. This research demonstrates the potential of IoT-driven adaptive lighting systems to advance precision agriculture in indoor environments, offering scalable, energy-efficient solutions for sustainable food production. Full article

Soil organic matter (SOM) is an essential factor affecting the growth and development of crops, so the establishment of an efficient and rapid method for detecting SOM content is of great significance for crop cultivation and management. The spatial distribution map of SOM content in the study area was obtained by using the optimal model, and a distribution map of aboveground wheat biomass under different fertilization conditions was drawn. The results of this study showed that the fertilization treatments significantly increased the SOM content, and its spatial distribution showed obvious heterogeneity. By plotting the spatial distribution of SOM content and wheat growth under different fertilization conditions, it was found that the wheat biomass of fertilized fields was significantly higher than that of non-fertilized fields. Further analysis showed that there was a significant positive correlation between SOM content and wheat biomass, and a quantitative model between the two was established. This study provides scientific evidence and technical support for soil nutrient management and crop productivity enhancement in precision agriculture, as well as a reference for the application of hyperspectral imagery in agroecosystem monitoring. Full article

The benefits and positive effects of urban and peri-urban agriculture (UPA) on different urban dimensions have already been extensively recognized by scholars and practitioners so far. Even possible risks of these professional and non-professional activities have been discussed, especially in the field of landscape ecology and from a social perspective. However, how these benefits are measured, monitored and assessed is still a critical point and a less investigated issue that requires further studies, especially in the research fields that aim to measure and monitor sustainability of urban practices. The evidence on quantitative approaches to estimate benefits and risks of UPA is less advanced than others. This paper aims to define spatial criteria and indicators to evaluate benefits and possible risks of UPA, using the case study of Turin and its bordering municipalities (Italy). Based on the definition and testing of indicators, embracing a spatial approach, this research aims to verify the feasibility of a set of instruments, to define methods and techniques to collect useful data, to test the replicability and transferability of this approach in other urban contexts, as well as to identify its limitations and gaps. Selected indicators showed a good level of versatility and high potential in order to collect information on UPA and its benefits. Furthermore, results of the application on the case study highlighted a significant growth of UPA benefits, especially profit initiatives and the social and economic domains. Finally, the author outlines policy recommendations to improve the evaluation and monitoring of UPA advantages and risks, as well as a possible trajectory for future research. Full article

Soil potentially toxic element (PTE) contamination remains a global concern, particularly in rural agricultural regions. This study collected 157 agricultural topsoil samples within a rural area in SW China. Combined with multivariate statistical analysis in the compositional data analysis (CoDa) perspective, the PMF model was applied to identify key contamination sources and quantify their contributions. Potential ecological risk assessment and Monte Carlo simulation were employed to estimate ecological-health risks associated with PTE exposure. The results revealed that the main exceeding PTEs (Mercury—Hg and Cadmium—Cd) are rich in urbanized areas and the GFGP (Grain for Green Program) regions. Source apportionment indicated that soil parent materials constituted the dominant contributor (32.48%), followed by traffic emissions (28.31%), atmospheric deposition (21.48%), and legacy agricultural effects (17.86%). Ecological risk assessment showed that 60.51% of soil samples exhibited higher potential ecological risk (PERI > 150), with moderate-risk areas concentrated in the GFGP regions. The elements Cd and Hg from legacy agricultural effects and atmospheric deposition contributed the most to ecological risk. Health risk assessment demonstrated that most risk indices fell within acceptable ranges for all populations, while only children showed elevated non-carcinogenic risk (THI_max > 1.0). Among PTEs, the element As, mainly from traffic emissions, was identified as a priority control element due to its significant health implications. Geospatial distributions showed significant risk enrichment in the GFGP regions (legacy agricultural areas). These findings present associated risk levels in sustainable agricultural regions, providing valuable data to support soil environmental management in regions requiring urgent intervention worldwide. Full article

Urban agglomerations serve as essential platforms for regional innovation, while agricultural technology innovation and diffusion play pivotal roles in enhancing agricultural eco-efficiency (AEE). Based on panel data from the Urban Agglomeration in the Middle Reaches of the Yangtze River (UAMRYR) (2001–2023), this study employs a super-efficiency slacks-based measure model incorporating undesirable outputs to evaluate agricultural eco-efficiency. A modified gravity model is utilized to construct agricultural technology innovation networks (ATINs) in urban agglomerations, and a spatial Durbin model is applied to examine the spillover effects of network structure on eco-efficiency. The results indicate that: (1) Higher-degree centrality within the innovation network significantly improves local agricultural eco-efficiency and produces positive spillover effects on neighboring cities; (2) both direct and spillover effects are significant in central cities, whereas sub-central cities exhibit only a significant direct effect, and peripheral cities display an insignificant direct effect but a significant spillover effect; and (3) enhanced urban informatization, agricultural financial development, and industrial scale substantially strengthen the spatial spillover effects of the innovation network, thereby further advancing agricultural eco-efficiency within the agglomeration. These findings offer theoretical and empirical support for optimizing agricultural technology pathways and enhancing eco-efficiency in urban agglomerations. Full article

Intensive farming in urban suburbs often causes habitat loss, soil erosion, wastewater discharge, and agricultural productivity decline, threatening long-term benefits for the local community. We developed a nature-based solution for sustainable land restoration by establishing “Green Treasure Island” (GTI). The aim of this study is to evaluate the ecological restoration effectiveness of GTI and explore its feasibility and replicability for future applications. The core eco-functional zone of GTI—a 7 hm² forested wetland—embedded a closed-loop framework that integrates land consolidation, ecological restoration, and sustainable land utilization. The forested wetland efficiently removed 65% and 74% of dissolved inorganic nitrogen and phosphorus from agricultural runoff, raised flood control capacity by 22%, and attracted 48 bird species. Additionally, this biophilic recreational space attracted over 3400 visitors in 2022, created green jobs, and promoted local green agricultural product sales. Through adaptive management and nature education activities, GTI evolved into a landmark that represents local natural–social characteristics and serves as a publicly accessible natural park for both rural and urban residents. This study demonstrates the feasibility of creating GTI for improving ecosystem services, providing a practical, low-cost template that governments and local managers can replicate in metropolitan rural areas worldwide to meet both ecological and development goals. Full article

Amidst global shifts in land use patterns due to urbanization, this study focuses on the rapid land use and land cover (LULC) changes in Midnapore City during the periods 2003–2014 and 2014–2024. The study employs Landsat 5 and 8 imagery with 30 m spatial resolution which were processed through Maximum Likelihood Classifier (MLC) algorithms. The results were attained through ArcGIS 10.2.2 and ERDAS IMAGINE 2014 software, with ground-truth validation using data from 117, 111, and 116 points for 2024, 2014, and 2003, respectively. For the validation, the kappa coefficient was calculated and achieved 87.3%, 88.1%, and 81.7% for 2024, 2014, and 2003, indicating substantial accuracy. Using statistical measures such as change matrix union, binary logistic regression, and correlation matrix analysis applied to classified LULC outputs and spatial drivers, the research highlights significant transformations in the region. The study reveals significant transformations, notably the conversion of 77% of forest areas and 5% of fallow land to built-up land. The increased rate of agricultural land conversion to built-up areas is evident after 2014, indicating rapid urban growth. These factors led to the reduction of LULC classes possessing substantial ecological value like forests and scrub lands which are becoming more accessible due to the increasing population. The results point out the drastic alteration of these developments and recommend a planning approach responsive to environmental needs for safeguarded ecological impacts. The research highlights the importance of reforestation, preservation of water bodies, and socio-economic surveillance in fostering urban management and sustainable development in Midnapore City. Full article

MADS-box transcription factors have undergone in-depth investigations regarding their function in regulating the development of plant floral organs. Flower type mutants serve as critical biological models for investigating the regulatory mechanisms of MADS-box genes in floral organ development, while simultaneously constituting essential genetic resources for molecular breeding programs. In this work, we examined a lip-like sepal of the peloric mutant in Phalaenopsis ‘Huayang’, which exhibited changes in both the morphology and color of the sepals. Our cryo-SEM investigations revealed that the mutation type belonged to a sepal labellum-like variation in Phalaenopsis ‘Huayang’. Nine glycosylated anthocyanins were identified and their contents were significantly upregulated in the Se-red of mutant flowers. Transcriptomic analysis identified 9408 differentially expressed genes, including 4934 upregulated and 4474 downregulated genes. In addition, 57 MADS-box genes were identified and classed into five groups (Mα, Mβ, Mγ, MIKC*, and MIKC^C) according to a phylogenetic comparison with Arabidopsis homologs. Furthermore, 29 MADS genes were screened from the MIKC^C group, and these genes may play a crucial role in the regulation of floral organ development. Through real-time PCR analysis and protein interaction analysis, we identified three genes that were upregulated in the mutant, which may be involved in sepal development. The subcellular localization results demonstrated that three genes were found within the nucleus. Taken together, our results elucidated the molecular mechanism of sepal variation in Phalaenopsis ‘Huayang’. Our results could enhance our comprehension of the regulatory mechanisms underlying floral patterning and promote the molecular breeding process of Phalaenopsis. Full article

Fire-driven land cover change has generated a paradox: while habitat fragmentation from agriculture, livestock, and urban expansion has reduced natural fire occurrences, human-induced ignitions have increased wildfire frequency and intensity. In northern Colombia’s Magdalena Department, most of the territory faces moderate to high wildfire risk, especially during recurrent dry seasons and periods of below-average precipitation. However, knowledge of wildfire spatiotemporal occurrence and its drivers remains scarce. This work addresses this gap by identifying fire-prone zones and analyzing the influence of climate and vegetation in the Magdalena Department. Fire-prone zones were identified using the Getis–Ord Gi* method over fire density and burned area data from 2001 to 2023; then, they were analyzed with seasonally aggregated hydroclimatic indices via logistic regression to quantify their influence on wildfires. Vegetation susceptibility was assessed using geostatistics, obtaining land cover types most affected by fire and their degree of fragmentation. Fire-prone zones in the Magdalena Department covered ~744.35 km² (3.21%), with a weak but significant (τ = 0.20, p < 0.01) degree of coincidence between classification based on fire density, as pre-fire variable, and burned area, as a post-fire variable. Temporally, fire probability increased during the dry season, driven by short-lagged precursors such as Dry Spell Length and precipitation from the preceding wet season. Fire-prone zones were dominated by pastures (62.39%), grasslands and shrublands (19.61%) and forests (15.74%), and exhibited larger, more complex high-risk patches, despite similar spatial connectedness with non-fire-prone zones. These findings enhance wildfire vulnerability understanding, contributing to risk-based territorial planning. Full article

Tongzhou District is the urban sub-center of Beijing, and the importance of groundwater resources is increasingly prominent. Based on groundwater level data from 1980 to 2020 and water usage data from various sectors in Tongzhou District between 2011 and 2020, this paper utilizes continuous wavelet transform (CWT), geostatistical models, and grey relational analysis (GRA) to explore the spatiotemporal evolution patterns and influencing factors of groundwater levels in Tongzhou District. The study reveals that the groundwater level evolution in Tongzhou District exhibits two primary cycles, and it predicts that the groundwater level at Liyuan Station will decrease and eventually rebound. From 1980 to 2020, the overall trend of groundwater levels in Tongzhou District showed a decline. However, the groundwater levels in the central and southern regions exhibited an upward trend from 2000 to 2020. The groundwater level is mainly influenced by spatial structural factors, with minimal impact from external random factors. Domestic water consumption, water usage in the tertiary sector, and industrial water usage have the greatest impact on groundwater levels, attributed to the rapid growth of the population and regional economy. Agricultural water usage has the least grey relational grade, which is related to changes in agricultural development planning in the study area, as well as reductions in the area of crop planting and the actual utilization area of facility agriculture. Full article