Search Results (505)

Smallholder farmers in semi-arid regions worldwide face persistent water scarcity, declining soil fertility, and increasing climate variability, which constrain food production. This study investigated soil and water management practices and their effects on soil health, crop productivity, and adoption among smallholder vegetable farmers in a semi-arid area in Kenya. A mixed-methods approach was employed, combining survey data from 397 farmers with a randomized field experiment. Results showed that hand watering (88.7%) and manure application (95.5%) were prevalent, while only 5.7% of farmers used drip irrigation. Compost and mulch treatments significantly improved soil organic carbon (p = 0.03), available water capacity (p = 0.01), and gravimetric moisture content (p = 0.02), with soil moisture conservation practices strongly correlated with higher yields in leafy green vegetables (R = 0.62). Despite these benefits, adoption was hindered by high water costs (42.6%) and unreliable sources (25.7%). Encouragingly, 96.2% of respondents expressed willingness to pay for improved water systems if affordable and dependable. The findings stress the need for integrated water–soil strategies supported by inclusive policy, infrastructure investment, and gender-responsive training to enhance resilience and productivity in smallholder farming under water-scarce conditions across sub-Saharan Africa and other regions globally, contributing to global sustainability targets such as SDG 6, 12 and 15. Full article

The house cricket, Acheta domesticus, is found globally. It is an agricultural pest causing economic damage to a wide variety of crops including cereal seedlings, vegetable crops, fruit plants, and stored grains. Additionally, crickets act as mechanical vectors of pathogens by harboring bacteria, fungi, viruses, and toxins, causing foodborne illnesses. They can contaminate stored grains, packaged foods, or animal feed due to deposition of their feces, lowering the quality of the food and creating food safety risks. Synthetic insect repellents, such as pyrethroids and carbamates, have been used previously in integrated pest management practices to control crickets. Though successful as repellents, they have been associated with health and environmental risks and concerns. The use of organic green repellents, such as plant essential oils, may be a viable alternative in pest management practices. In this study, we tested the effects of 27 plant-based essential oils on the behavior of A. domesticus. A. domesticus were introduced into an open arena to allow them unrestricted movement. A transparent plastic bottle containing an essential oil treatment was placed in the arena to allow voluntary entry by the crickets. Following a predetermined observation period, the number of crickets that entered the bottle was recorded, and percent entry was calculated as the proportion of individuals inside the bottle relative to the total number in the arena. Analysis of the percentage entry into the bottles allowed for a comparative assessment of repellency of the selected essential oils examined in this study. Essential oils that elicited high levels of entry into the bottle were categorized as having weak or no repellency, while those that demonstrated reduced entry were classified as moderate or strong repellents. Our results indicated that A. domesticus responded with strong repellent behavior to nearly half of the essential oils tested, while four essential oils and two synthetic repellents evoked no significant repellent responses. Four strong repellent essential oils, namely peppermint, rosemary, cinnamon, and lemongrass, were tested at different concentrations and showed a clear dose-dependent repellent effect. The results suggest that selected essential oils can be useful in the development of more natural “green” insect repellents. Full article

Soybean is a key global crop for food and oil production, playing a vital role in ensuring food security and supplying plant-based proteins and oils. Accurate information on soybean distribution is essential for yield forecasting, agricultural management, and policymaking. In this study, we developed an Enhanced Chlorophyll Index (NRLI) to improve the separability between soybean and maize—two spectrally similar crops that often confound traditional vegetation indices. The proposed NRLI integrates red-edge, near-infrared, and green spectral information, effectively capturing variations in chlorophyll and canopy water content during key phenological stages, particularly from flowering to pod setting and maturity. Building upon this foundation, we further introduce a pixel-wise compositing strategy based on the peak phase of NRLI to enhance the temporal adaptability and spectral discriminability in crop classification. Unlike conventional approaches that rely on imagery from fixed dates, this strategy dynamically analyzes annual time-series data, enabling phenology-adaptive alignment at the pixel level. Comparative analysis reveals that NRLI consistently outperforms existing vegetation indices, such as the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Greenness and Water Content Composite Index (GWCCI), across representative soybean-producing regions in multiple countries. It improves overall accuracy (OA) by approximately 10–20 percentage points, achieving accuracy rates exceeding 90% in large, contiguous cultivation areas. To further validate the robustness of the proposed index, benchmark comparisons were conducted against the Random Forest (RF) machine learning algorithm. The results demonstrated that the single-index NRLI approach achieved competitive performance, comparable to the multi-feature RF model, with accuracy differences generally within 1–2%. In some regions, NRLI even outperformed RF. This finding highlights NRLI as a computationally efficient alternative to complex machine learning models without compromising mapping precision. This study provides a robust, scalable, and transferable single-index approach for large-scale soybean mapping and monitoring using remote sensing. Full article

In response to the urgent demands of global climate governance, China has systematically integrated the green transition into its “dual-carbon” goals. The practical exploration of cultivated land emission reduction is not only crucial for promoting green transition but also embodies the synergistic effects of emission reduction and carbon sequestration in high-carbon-emission pressure areas. Existing studies have paid relatively less attention to high-carbon-emission pressure areas, necessitating more systematic research. In this study, we selected Henan Province as the study area and quantitatively analyzed the spatial-temporal differentiation of cultivated land net carbon sink from 2000 to 2023 along with their driving factors using an integrated methodological framework including Intergovernmental Panel on Climate Change (IPCC)-based carbon accounting, spatial autocorrelation analysis, and trajectory modeling. Analysis of the results indicates that the total net carbon sink of cultivated land in Henan Province showed a fluctuating yet overall upward trend with an average annual growth rate of 2.51%. The spatial distribution exhibits a pattern of “higher in the south and lower in the north” and “higher in the east and lower in the west”. This spatial pattern was significantly shaped by the cultivation area and fertilizer application intensity of three major crops—wheat, maize, and vegetables. Specifically, the net carbon sink contributions from these crops increased from 82.12% in 2000 to 85.93% in 2023, while the share of carbon emissions attributable to fertilizer use in the net carbon sink increased from 4.61% in 2000 to 5.22% in 2023, representing the activity with the largest contribution ratio among carbon emission activities. These findings provide valuable scientific evidence for further optimizing the green transition in high-carbon-emission areas and promoting the synergistic effects of emission reduction and carbon sequestration. Full article

Microbial contamination of food is a crucial cause of food spoilage and foodborne diseases, posing a severe threat to global public health. Although chemical preservatives are effective, their potential hazards to human health and the environment, coupled with the growing demand for “clean label” products, have driven the search for natural alternatives. Lactic acid bacteria (LAB), recognized as the Generally Recognized as Safe (GRAS) microorganisms, have emerged as the promising bio-preservatives due to their safety, effectiveness, and multifunctionality. This review systematically summarized the core antimicrobial properties of LAB, including their inhibitory spectrum against foodborne pathogens, spoilage microorganisms, viruses, parasites, and their ability to degrade toxic substances such as mycotoxins, pesticides, and heavy metals. Key inhibitory mechanisms of LAB are highlighted, encompassing the production of antimicrobial metabolites, leading to metabolism disruption and cell membrane damage, nutrition and niche competition, quorum-sensing interference, and anti-biofilm formation. Furthermore, recent advances in LAB applications in preserving various food matrices (meat, dairy products, fruits and vegetables, cereals) are integrated, including their roles in enhancing food sensory quality, extending shelf life, and retaining nutritional value. The review also discusses critical factors influencing LAB’s inhibitory activity (medium composition, culture conditions, ionic components, pathway regulator, etc.) and the challenges associated with the application of LAB. Finally, future research directions are outlined, including the novel LAB and metabolites exploration, AI-driven cultural condition optimization, genetic engineering application, nano-encapsulation and active packaging development, and building up the LAB-based cellular factories. In conclusion, LAB and their antimicrobial metabolites hold great promise as green and safe food preservatives. This review is to provide comprehensive theoretical support for the rational improvement and efficient application of LAB-based natural food preservatives, contributing to the development of a safer and more sustainable food processing and preservation systems. Full article

Street livability is widely recognized as a fundamental indicator of urban livability. Despite growing global agendas advocating human-centered, sustainable, and smart cities, the microscale implementation of streetscape interventions remains limited and non-integrated. This gap is particularly evident in developing cities’ contexts where policy-level frameworks fail to translate into tangible street-level transformations. Responding to this challenge, this paper investigates how streetscape components can enhance everyday street livability. The study aims to explore opportunities for improving street livability through the utilization of three core streetscape components: vegetation, street furniture, and lighting. The discourse on street livability identifies vegetation, street furniture, and lighting as the primary drivers of high-quality urban spaces. Scholarly research suggests that these micro-interventions are most effective when viewed through the combined lenses of human-centered design, environmental sustainability, and smart city technology. While the literature indicates that integrating climate-responsive greenery and renewable energy systems can enhance social interaction and safety, it also highlights significant implementation hurdles. Specifically, researchers point to policy limitations, technical feasibility in developing nations, and the socio-economic threat of green gentrification. Despite these complexities, microscale streetscape improvements remain a vital strategy for fostering inclusive and resilient cities. Full article

This study, conducted in the Lifang Dryland Experimental Area in Jinzhong, Shanxi Province, China, aimed to develop a method to accurately predict maize yield by combining UAV multispectral data with meteorological information. A DJI Mavic 3M UAV was used to capture four-band imagery (red, green, red-edge, and near-infrared), from which 16 vegetation indices were calculated, along with daily meteorological data. Among eight machine learning algorithms tested, ensemble models, Random Forest and Gradient Boosting Trees performed best, with R² values of 0.8696 and 0.8163, respectively. SHAP analysis identified MSR and RVI as the most important features. The prediction accuracy varied across growth stages, with the jointing stage showing the highest performance (R² = 0.7161), followed by the flowering stage (R² = 0.6588). The yield exhibited a strip-like spatial distribution, ranging from 6450 to 9600 kg·ha⁻¹, influenced by field management, soil characteristics, and microtopography. K-means clustering revealed high-yield areas in the central-northern region and low-yield areas in the south, supported by a global Moran’s I index of 0.4290, indicating moderate positive spatial autocorrelation. This study demonstrates that integrating UAV multispectral data, meteorological information, and machine learning can achieve accurate yield prediction (with a relative RMSE of about 2.8%) and provides a quantitative analytical framework for spatial management in drought-prone areas. Full article

In the context of global climate change and intensified water resource constraints, studying the evolution of the urban–agricultural–ecological spatial structure and the water–heat–vegetation responses driven by large-scale irrigation and drainage projects in arid and semi-arid regions is of great significance. Based on multitemporal remote sensing data from 1985 to 2015, this study takes the Inner Mongolia Hetao Plain as the research area, constructs a “multifunctionality–dynamic evolution” dual-principle classification system for urban–agricultural–ecological space, and adopts the technical process of “separate interpretation of each single land type using the maximum likelihood algorithm followed by merging with conflict pixel resolution” to improve the classification accuracy to 90.82%. Through a land use transfer matrix, a standard deviation ellipse model, surface temperature (LST) inversion, and vegetation fractional coverage (VFC) analysis, this study systematically reveals the spatiotemporal differentiation patterns of spatial structure evolution and surface parameter responses throughout the project’s life cycle. The results show the following: (1) The spatial structure follows the path of “short-term intense disturbance–long-term stable optimization”, with agricultural space stability increasing by 4.8%, the ecological core area retention rate exceeding 90%, and urban space expanding with a shift from external encroachment to internal filling, realizing “stable grain yield with unchanged cultivated land area and improved ecological quality with controlled green space loss”. (2) The overall VFC shows a trend of “central area stable increase (annual growth rate 0.8%), eastern area fluctuating recovery (cyclic amplitude ±12%), and western area local improvement (key patches increased by 18%)”. (3) The LST-VFC relationship presents spatiotemporal misalignment, with a 0.8–1.2 °C anomalous cooling in the central region during the construction period (despite a 15% VFC decrease), driven by irrigation water thermal inertia, and a disrupted linear correlation after completion due to crop phenology changes and plastic film mulching. (4) Irrigation and drainage projects optimize water resource allocation, constructing a hub regulation model integrated with the Water–Energy–Food (WEF) Nexus, providing a replicable paradigm for ecological effect assessment of major water conservancy projects in arid regions. Full article

The intensifying Urban Heat Island (UHI) effect exacerbates urban heat stress. While vegetation is a key mitigation strategy, the quantitative effects of its spatial configuration are not fully understood. This study employed ENVI-met simulations to systematically evaluate how three design parameters—tree spacing (8–18 m), canopy structure (single/multi-layer, sparse/dense), and horizontal layout (enclosed, semi-enclosed, linear)—regulate summer microclimate in urban parks. Results demonstrated that reduced spacing and denser canopies significantly enhanced cooling and humidification. The multi-layer dense canopy and an enclosed “mouth-shaped” layout yielded the optimal performance, achieving a maximum daytime air temperature reduction and a corresponding humidity increase. Furthermore, layout orientation was identified as a critical modulating factor. Spatial configuration exerted a stronger influence on microclimate outcomes than structural complexity itself. This study provides a predictive, evidence-based framework for optimizing urban green space design. The framework and the derived design principles are directly transferable to other cities in humid subtropical climates, offering generalizable strategies to enhance microclimate regulation and climate resilience globally. Full article

Current urban green volume quantity and equity evaluations primarily rely on two-dimensional (2D) indicators that capture the planar distribution characteristics but overlook vertical structure variations. This study constructed a three-dimensional (3D) evaluation system for green volume quantity and equity by introducing Lorenz curves and Gini coefficients. Using multi-source data, including a 10 m global vegetation canopy height dataset, land cover, and population distribution data, an automated calculation workflow was established in ArcGIS Model Builder. Focusing on regional and neighborhood scales, this study calculates and analyzes two-dimensional green volume (2DGV) and three-dimensional green volume (3DGV) indicators, along with the spatial equity for 413 Chinese cities and residential and commercial areas of Wuhan, Suzhou, and Bazhong. Meanwhile, a green volume quantity and equity type classification method was established. The results indicated that 3DGV exhibits regional variations, while Low 2DGV–Low 3DGV cities have the highest proportion. Green volume in built-up areas showed a balanced distribution, while park green spaces exhibited 2DGV Equitable Only. At the neighborhood scale, residential areas demonstrated higher green volume equity than commercial areas, but most neighborhood areas’ indicators showed low and imbalanced distribution. The proposed 2DGV and 3DGV evaluation method could provide a reference framework for optimizing urban space. Full article

Alfalfa (Medicago sativa) is a globally important forage crop; however, improvements in its biomass yield have stagnated due to its complex genetic architecture and the costly, labor-intensive phenotyping. This study evaluated the potential of the normalized difference vegetation index (NDVI) to predict biomass yield and enhance selection efficiency in alfalfa breeding programs. Specifically, nineteen alfalfa experimental populations (AEXP 1–19) and one control cultivar (OS 66) were evaluated over two growing seasons in Croatia. NDVI was measured at four development stages using a GreenSeeker sensor and compared with forage yield, dry matter yield, and plant height. NDVI values varied significantly among genotypes, years, and growth stages, ranging from 0.23 to 0.87, and increased consistently from early to late vegetative phases. Strong positive correlations were observed between NDVI and forage yield (r = 0.543–0.843) and plant height (r = 0.537–0.738) at early vegetative, late vegetative, and early bud stages. Conversely, NDVI at the mid-vegetative stage correlated negatively with yield and height (r = –0.622 to –0.794). High-performing populations (AEXP 2, AEXP 15, AEXP 18) also exhibited the highest NDVI values. NDVI is a reliable, non-destructive indicator for early selection of high-yielding alfalfa genotypes, although multi-location validation is advised to confirm its broader applicability. Full article

Amid accelerating climate change, intensifying urban heat island effects, and rising public demand for livable, walkable streets, there is an urgent practical need for interpretable and actionable evidence on streetscape quality. Yet, research on streetscape quality has often relied on single data sources and linear models, limiting insight into multidimensional perception; evidence from temperate monsoon cities remains scarce. Using Tianjin’s main urban area as a case study, we integrate street-view imagery with remote sensing imagery to characterize satellite-derived environmental indicators at the point scale and examine the following five perceptual outcomes: comfort, aesthetics, perceived greenness, summer heat perception, and willingness to linger. We develop a three-step interpretable assessment, as follows: Elastic Net logistic regression to establish directional and magnitude baselines; Generalized Additive Models with a logistic link to recover nonlinear patterns and threshold bands with Benjamini–Hochberg false discovery rate control and binned probability calibration; and Shapley additive explanations to provide parallel validation and global and local explanations. The results show that the Green View Index is consistently and positively associated with all five outcomes, whereas Spatial Balance is negative across the observed range. Sky View Factor and the Building Visibility Index display heterogeneous forms, including monotonic, U-shaped, and inverted-U patterns across outcomes; Normalized Difference Vegetation Index and Land Surface Temperature are likewise predominantly nonlinear with peak sensitivity in the midrange. In total, 54 of 55 smoothing terms remain significant after Benjamini–Hochberg false discovery rate correction. The summer heat perception outcome is highly imbalanced: 94.2% of samples are labeled positive. Overall calibration is good. On a standardized scale, we delineate optimal and risk intervals for key indicators and demonstrate the complementary explanatory value of street-view imagery and remote sensing imagery for people-centered perceptions. In Tianjin, a temperate monsoon megacity, the framework provides reproducible, actionable, design-relevant evidence to inform streetscape optimization and offers a template that can be adapted to other cities, subject to local calibration. Full article

Enhancing the carbon sequestration (CS) capacity of urban green spaces is crucial for mitigating global warming, environmental degradation, and urbanisation-induced issues. This study focuses on the urban community unit to establish a system of determining factors for the CS capacity of green space, considering the built-up spatial pattern and green space morphology. An interpretable machine learning approach (Random Forest + Shapley Additive exPlanations) is employed to systematically analyse the non-linear relationship of built-up spatial pattern and green space morphology factors. Results demonstrate significant urban zonal heterogeneity in green space CS, whereas southern suburban area communities exhibited higher capacity. In terms of green space morphology factors, higher fractional vegetation cover (FVC) and cohesion were positively correlated with green space CS capacity. Leaf area index (LAI), canopy density (CD), and the evergreen-broadleaf forest ratio additionally further enhanced the positive effect of two-dimensional green space factors on CS. For built-up spatial pattern factors, communities with a high green space ratio and low development intensity exhibited higher CS capacity. And the optimal ranges of FVC, LAI and CD for effective facilitation of community green space CS were identified as 0.6–0.75, 4.85–5.5 and 0.68–0.7, respectively. Moreover, cohesion, LAI and CD bolstered the CS capacity in communities with a high building density and plot ratio. This study provides a rational basis for planning and layout of green space patterns to enhance CS efficiency at the urban community scale. Full article

In the context of global climate change and rapid urbanization, the Urban Heat Island (UHI) effect has become a pressing environmental and public health concern, particularly in semiarid regions. This study evaluates the microclimatic performance of various urban design strategies aimed at enhancing thermal comfort along a densely built-up street in Van, a medium-sized city located in Turkey’s semiarid climate zone. Using ENVI-met 5.7.2, nine alternative scenarios were simulated, incorporating different configurations of vegetation cover (0%, 25%, 50%, 75%), ground surface materials, and green roof applications (0%, 25%, 50%, 75%). Physiological Equivalent Temperature (PET) and other thermal comfort indicators were assessed at multiple time intervals on the hottest summer day. Results indicate that increasing vegetation cover substantially reduces PET values, with a maximum reduction of 3.0 °C observed in the 75% vegetation scenario. While the scenario with no vegetation but light-colored pavements achieved a 1.8 °C reduction in air temperature at 2:00 p.m., the maximum PET value remained unchanged. Conversely, using dark-colored asphalt decreased the average air temperature by 1 °C and improved the thermal comfort level by reducing the PET by 0.4 °C compared to a non-vegetated scenario. The scenario with the highest overall greenery led to a 2.9 °C drop in air temperature and a 12.8 °C reduction in average PET at 2:00 p.m. compared to other scenarios. The study provides evidence-based recommendations for human-centered urban planning and advocates for the integration of microclimate simulation tools in the early stages of urban development. Full article

Over the last fifteen years, the Google Earth Engine (GEE) has become a pivotal tool for large-scale geospatial analysis, with growing applications in urban environmental monitoring. This review examines the peer-reviewed literature, published between 2015 and 2024, that utilizes GEE to evaluate urban environments through remote sensing-derived indices. The literature search strategy was guided by predefined search terms, which were applied to online databases including Scopus and Google Scholar. The inclusion criteria for this review comprised English-language publications, limited to articles only from journals, while book series, books, and conference articles were excluded. The eligibility criteria applied aimed to identify peer-reviewed studies that applied GEE to urban contexts using vegetation, thermal, greenness, or density indices. Studies without a clear urban focus or not employing GEE as a primary tool were excluded. The selection process followed a structured methodological flow, where a total of 291 studies were identified that fulfilled the applied criteria. This review indicates that key methodological trends encompass both conventional techniques, such as Random Forests (RFs), Support Vector Machines (SVMs), and classification/regression trees, as well as emerging machine learning algorithms, with Landsat, Sentinel, and MODIS as the most commonly used satellite datasets. The articles included in this review show a geographic focus, with over 44% of publications from China, 11% from the United States, and 9% from India, while the rest of the countries identified in this review contribute fewer than 5% each, suggesting that there is a significant opportunity for research in underrepresented regions. The main result of this review is that GEE proves to be an effective, scalable, and reproducible platform for urban environmental analysis, with most studies focusing on vegetation and thermal indices using Landsat, Sentinel, and MODIS data. As GEE has become one of the most widely used platforms for urban environmental monitoring, future research should focus on addressing challenges such as the standardization of indices, the consistency of methodological approaches, and the expansion of global coverage through advanced cloud-based geospatial frameworks. Full article