Search Results (2,289)

Pasture systems, typically composed of grasses, legumes, and forage crops, are vital livestock nutrition sources. The quality of these pastures depends on various factors, including species composition and growth stage, which directly impact livestock productivity. Remote sensing (RS) technologies offer powerful, non-invasive means for large-scale pasture monitoring and classification, enabling efficient assessment of pasture health across extensive areas. However, traditional supervised classification methods require labelled datasets that are often expensive and labour-intensive to produce, especially over large grasslands. This study explores unsupervised clustering as a cost-effective alternative for identifying pasture types without the need for labelled data. Leveraging spatiotemporal data from the Sentinel-2 mission, we propose a clustering framework that classifies pastures based on their temporal growth dynamics. For this, the pasture segments are first created with quick-shift segmentation, and spectral time series for each segment are grouped into clusters using time-series distance-based clustering techniques. Empirical analysis shows that the dynamic time warping (DTW) distance measure, combined with K-Medoids and hierarchical clustering, delivers promising pasture mapping with normalised mutual information (NMI) of 86.28% and 88.02% for site-1 and site-2 (total area of approx. 2510 ha), respectively, in New South Wales, Australia. This approach offers practical insights for improving pasture management and presents a viable solution for categorising pasture and grazing systems across landscapes. Full article

The spatiotemporal dynamics of soil nutrients in the crop row zone are critical determinants of crop yield, necessitating precision fertilization for optimal plant growth. However, previous studies have predominantly focused on plant-available nutrient status at the scale of entire cropping systems, yet a granular understanding of their distribution patterns across precise temporal and spatial dimensions remains limited. Therefore, this study investigated maize–legume intercropping systems to quantify the dynamics of soil alkaline-hydrolyzable nitrogen (AN), available phosphorus (AP), and available potassium (AK) across distinct growth stages, soil depths, and row positions. The experiment comprised five treatments: maize–soybean intercropping, maize–peanut intercropping, and monocultures of maize, soybean, and peanut. Throughout the two-year study, maize–soybean intercropping significantly enhanced the plant height of both maize and soybean relative to their respective monocultures (p < 0.05). In contrast, within the maize–peanut system, intercropping significantly promoted peanut plant height but suppressed stem diameter in both species (p < 0.05); these effects were consistent across both study years. Both systems exhibited a “benefit-sacrifice” pattern, where dry matter was preferentially allocated to maize, thereby increasing total system productivity despite suppressing legume growth. Furthermore, during the mid-to-late growth stages, intercropped maize showed an enhanced capacity for nitrogen uptake from deeper soil layers. In contrast, the alkaline-hydrolyzable nitrogen content in intercropped soybean and peanut remained lower than in their respective monocultures throughout the growth period, with reductions ranging from 8.49% to 34.79%. Intercropping significantly increased the soil available phosphorus content in the root zones of maize, soybean, and peanut compared to their respective monocultures. The available phosphorus content in the 0–20 cm soil layer was consistently higher than in monoculture systems, with a maximum increase of 41.70%. Moreover, intercropping effectively mitigated soil potassium depletion, resulting in a smaller decline in available potassium. This effect was most pronounced in the maize–peanut intercropping pattern within the 20–40 cm soil layer. The distribution of soil available nutrients (N, P, K) was also influenced by drip tape placement. The levels of these nutrients for soybean and peanut were higher at 50 cm from the drip tape than at 30 cm, while for maize, levels were higher at 80 cm than at 40 cm. Intercropping increased the thousand-kernel weight of maize and soybean but decreased that of peanut. Overall, the strategic row configuration optimized the yield performance of both intercropping systems, resulting in land equivalent ratios greater than 1, which indicates distinct yield advantages for both intercropping patterns. Full article

Schoenoplectus californicus (Totora) is a wetland plant of cultural and ecological importance, traditionally used for handicrafts and habitat conservation in Andean lakes. This study investigates its vegetative growth in two Andean lakes in Imbabura, Ecuador (Yahuarcocha and Imbacocha), which present contrasting chemical and biological conditions (total nitrogen, total phosphorus, and chlorophyll a). Vegetative growth analysis, using indices, provides tools for understanding Totora growth dynamics within a cultivation cycle. By quantifying biomass accumulation and other parameters, it is possible to infer how the plant responds to its environment and to guide its production and management. Our objective was to evaluate how physiological and morphological traits influence growth under differential nutrient conditions. A 210-day field trial was conducted with periodic sampling and analysis of physiological indices, combining classical and functional growth approaches. Key growth indices—relative growth rate (RGR), net assimilation rate (NAR), and leaf area ratio (LAR)—were calculated from photosynthetic surface area and dry biomass. Results show that plants in Yahuarcocha, a hypertrophic lake, exhibited greater biomass production (up to 2380 g m⁻²) and photosynthetic area (8.68 m²), reaching peak growth at 150 days. In contrast, plants in Imbacocha, a eutrophic lake, reached maximum growth at 180 days, with a greater dependence on NAR. Strong correlations among RGR, NAR, and LAR were observed in Yahuarcocha, highlighting the influence of higher nutrient concentrations and harvesting pressure on growth dynamics. These findings underscore the importance of considering lake trophic status when planning sustainable harvesting and cultivation strategies for Totora in Andean wetlands. Full article

A field experiment was conducted on 16-year-old ‘Wen 185’ walnut trees in Aksu, Southern Xinjiang, to identify optimal water and fertilizer management under subsurface drip irrigation. Four irrigation levels were established: 75% ET_c (W1), 100% ET_c (W2), 125% ET_c (W3), and 150% ET_c (W4). These were combined with three fertilizer levels: N 270, P 240, K 300 kg ha⁻¹ (F1), N 360, P 320, K 400 kg ha⁻¹ (F2), and N 450, P 400, K 500 kg ha⁻¹ (F3). This resulted in a total of 12 treatments. This study assessed the impact of different water and fertilizer treatments on walnut growth dynamics, yield, fruit quality, water and fertilizer use efficiency, and soil nitrate residue. Principal component analysis (PCA) was used to construct comprehensive growth and photosynthesis indices (CGI and CPI). Parameters significantly correlated with yield and quality were then screened via Pearson analysis, and a game theory-based combination weighting method was adopted to determine weights for integrating six categories of indicators: growth, photosynthesis, yield, quality, resource use efficiency, and environmental impact. A coupled TOPSIS-GRA model was developed for comprehensive evaluation. Furthermore, binary quadratic regression was employed to optimize the application ranges of water and fertilizer. The results showed that the W2F2 treatment achieved the highest rank by synergistically enhancing growth, photosynthetic performance, yield, and quality. This treatment also maintained high water use efficiency (WUE) and partial factor productivity of fertilizer (PFP) and effectively reduced nitrate accumulation in deep soil layers. The CGI and CPI, derived from PCA, effectively quantified phenological growth and photosynthetic characteristics. Correlation analysis identified seven core parameters, among which IV-CPI correlated most strongly with yield. In contrast, II-CPI was more closely associated with increased single-fruit weight and reduced tannin content. Within the comprehensive evaluation system that used game theory-based combination weighting, yield received the highest weight (0.215), while IV-CPI was assigned the lowest (0.011). The TOPSIS-GRA coupled model identified the W2F2 treatment as the highest-ranked. Furthermore, regression optimization determined the optimal total seasonal application ranges to be 5869.94–6519.81 m³ ha⁻¹ for irrigation and 975.54–1107.49 kg ha⁻¹ for fertilization. The coupled TOPSIS-GRA model enabled a balanced assessment of the objectives: high yield, superior quality, resource use efficiency, and environmental sustainability. Thus, it provides a theoretical foundation and practical guidance for enhancing the productivity and sustainability of subsurface drip-irrigated walnut orchards in Southern Xinjiang. Full article

Plants continuously adapt to dynamic environmental conditions, which include abiotic stress such as drought, salinity, and high temperature. Translocation, availability, and uptake of essential nutrients are suggested to be disrupted, thereby impairing growth, development, and productivity of the plant. The interplay between the root architecture, membrane transporters, and hormonal regulation is suggested to have efficient nutrient acquisition. For mediating nutrient uptake and redistribution under abiotic stress conditions, transporter proteins such as nitrate (NRT), ammonium (AMT), phosphate (PHT), and potassium (HAK) families play a crucial role for the major essential elements (N, P, K). Abiotic stress triggers specific transcriptional and post-transcriptional regulation of these transporters, modulating their activity in response to external nutrient availability. Under nutrient-deficient conditions, phytohormones such as abscisic acid (ABA), cytokinin, and ethylene play a pivotal role in orchestrating plant responses. Moreover, the plant stress tolerance is suggested to be influenced by stress-induced signalling mechanisms, which are mediated by reactive oxygen species (ROS). The current review synthesizes current knowledge of nutrient dynamics under abiotic stress, focusing on the molecular mechanisms governing transporter regulation and phytohormonal crosstalk. By unravelling these complex regulatory networks, this article aims to pave the way for sustainable agricultural practices. Full article

Flow cytometry typically reveals two heterotrophic prokaryote (HP) subpopulations when stained with SYBR Green: high nucleic acid (HNA) and low nucleic acid (LNA) cells. Evidence suggests these populations have distinct physiological and ecological roles with implications for mortality. We assessed HP abundance, production, the relative proportion of HNA and LNA, virus-mediated mortality, and the distribution of lytic versus lysogenic strategies within HP host communities across a latitudinal gradient in the North Atlantic during spring. The study area, characterized by dynamic physicochemical conditions consistent with the onset of seasonal stratification, was divided into three regions based on bio-physicochemical properties: Pre-bloom, Bloom, and Oligotrophic. Multivariant analysis showed these regions significantly structured HPs, as well as influenced the relative abundance and production of virus subpopulations (i.e., V1 and V2). Specifically, V1 viruses increased with the potential of encountering HNA hosts, which were elevated in the surface waters of stratified Oligotrophic and Bloom regions. In contrast, V2 abundance and production correlated with LNA cells, more prominent in DEEP samples and in surface waters of the deeper mixed Pre-bloom region. Lysogeny occurred across all regions, with the percentage of lysogens within the HP community, increasing (largely V1-driven) with HP-specific growth rate until reaching a threshold of 0.1 d⁻¹, after which it declined. We discuss the potential ecological underpinnings driving these patterns and implications for carbon flux. Full article

This paper investigates the systemic macroeconomic determinants of Pillar 2 Requirements (P2R) imposed by the European Central Bank (ECB) under the Single Supervisory Mechanism (SSM). While P2R is formally calibrated at the individual bank level through the Supervisory Review and Evaluation Process (SREP), we explore the extent to which common macro-financial shocks influence supervisory capital expectations across banks. Using a panel dataset covering euro area banks between 2021 and 2025, we match bank-level P2R data with country-level macroeconomic indicators. Those variables include real GDP growth, HICP inflation and index levels, government fiscal balance, euro yield curve spreads, net turnover, FDI inflows, construction and industrial production indices, the price-to-income ratio in real estate, and trade balance measures. We apply Principal Component Analysis (PCA) to extract latent variables related to the macroeconomic factors from a broad set of variables, which are then introduced into a Bayesian Vector Autoregression (BVAR) model to assess their dynamic impact on P2R. Our results identify three principal components that capture general macroeconomic cycles, sector-specific real activity, and financial/external imbalances. The impulse response analysis shows that sectoral and external shocks have a more immediate and statistically significant influence on P2R adjustments than broader macroeconomic trends. These findings clearly support the use of systemic macro-financial conditions in supervisory decision-making and support the integration of anticipating macro-prudential analysis into capital requirement frameworks. Full article

The slaughterhouse-treated effluent, enriched with nitrogen, phosphorus, and organic matter, presents a promising alternative for water and nutrient reuse in irrigated crop systems. This study assessed the chemical composition of the effluent, nutrient dynamics in the soil, and agronomic performance of soybean (Glycine max (L.) Merr) and sorghum (Sorghum bicolor (L.) Moench) under fertigation. A randomized block design was used, with five treatments (tap water—control—and four effluent levels: 25%, 50%, 75%, and 100%) applied to two crop species, with four replications. The effluent exhibited elevated concentrations of ammoniacal nitrogen (43.9 ± 18.7 mg L⁻¹), and potassium (13.1 ± 3.8 mg L⁻¹), confirming its potential as a nutrient source. No significant differences were observed in soybean plant height across treatments, whereas early-stage sorghum growth showed only slight variation. Irrigation with treated effluent successfully replaced 100% of tap water in both soybean and sorghum, with no significant differences in productivity across concentrations. These results demonstrate the agronomic feasibility of using treated effluent as a substitute for tap water and synthetic fertilizers. Moreover, they highlight its potential as a sustainable input for fertigation, contributing to resource efficiency and promoting more integrated and environmentally conscious agricultural practices. Full article

Agriculture significantly contributes to greenhouse gas (GHG) emissions, yet fluxes from irrigated semi-arid systems remain poorly quantified. This study investigates CO₂, CH₄, N₂O, and NH₃ fluxes in a short-term lettuce experiment under semi-arid conditions. The objective was to quantify flux variability and identify key environmental and management drivers. High-frequency soil gas flux measurements were conducted under three treatments: irrigated soil (I), irrigated soil with plants (IP), and irrigated soil with plants plus NH₄NO₃ fertilizer (IPF). Environmental factors, including solar radiation, soil temperature, water-filled pore space, and relative projected leaf area, were monitored. A Random Forest model identified main flux determinants. Fluxes varied with plant function, growth, and fertilization. IP exhibited net CO₂ uptake through photosynthesis, whereas I and IPF showed net CO₂ emissions from soil respiration and fertilizer-induced disruption of plant function, respectively. CH₄ uptake occurred across treatments but decreased with plant presence. Fertilization in IPF triggered episodic N₂O (EF = 0.1%) and NH₃ emissions (EF = 0.97%) linked to nitrogen input. Vegetated semi-arid soils can act as CO₂ sinks when nitrogen is optimally managed. Excess or poorly timed nitrogen delays CO₂ uptake and increases reactive nitrogen losses. Methanotrophic activity drives CH₄ dynamics and is influenced by plants and fertilization. Maintaining crop vigor and applying precision nitrogen management are essential to optimize productivity while mitigating GHG and NH₃ emissions in semi-arid lettuce cultivation. Full article

Population growth, urbanization, improved infrastructure, and climate change are reshaping land use systems worldwide, creating spatial trade-offs between economic development, ecosystem services, and cultural heritage. In Ladakh, Himalayan India, mass tourism and recent political changes have triggered a particularly rapid transition from traditional subsistence farming to market-oriented production, raising concerns about the sustainability of changing land management practices, cultural identity, and growing dependence on external inputs. To disentangle these concerns, we investigated land use changes, development patterns, and socio-economic drivers over the past 40 years. To this end we merged Landsat-based remote sensing data with household surveys in two contrasting, urbanizing regions—the Union Territory’s capital Leh and its more remote, third largest town of Diskit. Spatially explicit land cover maps for three periods of the 1970s, the 2000s, and the 2020s revealed an eightfold increase in residential area in Leh, with 41.7% of agricultural land converted to urban use, compared to a twofold increase and only 1.7% farmland loss in Diskit. Expansion of urban land use in Leh occurred in all directions across multiple land use types, while in Diskit, it remained localized to previously unused land. Survey data on socio-economic parameters showed a production shift toward goods demanded by tourism and the military, the latter being linked to border tensions with China and Pakistan. The divergent dynamics highlight the need for integrated spatial planning and scenario analysis to balance globalization-driven development with the conservation of cultural landscapes and ecosystem services. We recommend ecotourism-based strategies as an optimized pathway toward sustainable and multifunctional land systems in mountain regions. Full article

Bacterial synthetic microbial communities (SynCom) have exhibited significant effects for enhancing plant growth and delivering ecological benefits. However, persistent challenges, including structural instability, limited environmental adaptability, and transient efficacy, remain critical barriers to their practical application. Herein, we propose Arbuscular Mycorrhizal fungi (AMF) as the keystone component to optimize SynCom’s ecological fitness in sustainable agricultural systems. AMF modulate microbiome assembly through hyphal networks, enhancing community stability via facilitative interactions and augmenting nutrient cycling functionalities. This review systematically evaluates methodologies for AMF-based SynCom design and construction, investigates the dynamics of AMF-microbe interactions, delineates plant growth-promoting mechanisms, identifies candidate microbial taxa, and addresses implementation bottlenecks with corresponding strategies. We posit that AMF-Based SynComs represent a transformative management tool for ensuring global food security amid impending climatic perturbations and declining agricultural productivity. Full article

This study examines the relationship between energy efficiency in agricultural production and its determinants, considering technological, economic, political, and social factors. The aim was to determine the impact of the CAP on the energy efficiency of agricultural production, as well as technological, market, and social changes. The impact of time effects was also taken into account. The study focuses on the four Visegrad Group countries over the 2004–2023 period. Both fixed-effects and dynamic panel models were employed to capture structural changes over time. The significance of agriculture, as a result of structural transformations, is relatively small and hovers around 3% in these countries. The CAP was found to have a significant impact on the energy efficiency of agricultural production. However, it was not the amount of support but rather its structure that played a crucial role, particularly environmental support (0.04). The inertia effect was also of fundamental importance (0.41—elasticity in the inertia model). The total value of transfers, especially in the long term, proved to be a discouraging factor for this process. Market conditions, including energy prices (0.456), structural changes in farms (0.016), and labor input (−0.04), were also significant factors. However, it was not so much the size of support but rather the structure of support that was crucial. The total value of transfers, especially in the long term, was a demotivator for this process. Market conditions, including energy prices, structural changes on farms, and labor inputs, were also important factors. A key recommendation for agricultural financial support policy is to focus support more on environmental and low-emission issues, which are linked to improving the energy efficiency of production while maintaining its growth. Transfers related to the growing importance of renewable energy sources and support for rural development, which do not yield beneficial effects in the considered scope, require increased conditionality. Full article

Coalbed-methane (CBM) extraction involves complex processes such as desorption, diffusion, and seepage, significantly increasing the difficulty of numerical simulation. To enable efficient CBM development, this study establishes an integrated simulation workflow for CBM, encompassing geological modeling, geomechanical modeling, hydraulic fracture simulation, and production dynamic simulation. Specifically, the unconventional fracture model (UFM), integrated within the Petrel commercial software, is applied for fracture simulation, with an unstructured grid constructing the CBM production model. Subsequently, based on the case study of well pad A in the Daning–Jixian block, the effects of well spacing and hydraulic fractures on gas production were analyzed. The results indicate that the significant stress difference between the coal seam and the top/bottom strata constrains fracture height, with simulated hydraulic fractures ranging from 169.79 to 215.84 m in length, 8.91 to 10.45 m in height, and 121.92 to 248.71 mD·m in conductivity. Due to the low matrix permeability, pressure drop and desorption primarily occur in the stimulated reservoir volume (SRV) region. The calibrated model predicts a 10-year cumulative gas production of 616 × 10⁴ m³ for the well group, with a recovery rate of 10.17%, indicating significant potential for enhancing recovery rates. Maximum cumulative gas production occurs when well spacing slightly exceeds fracture length. Beyond 200 mD·m, fracture conductivity has diminishing returns on production. Fracture length increases from 100 to 250 m show near-linear growth in production, but further increases yield smaller gains. These findings provide valuable insights for evaluating development performance and exploiting remaining gas resources for CBM. Full article

Taxonomies provide essential hierarchical structures for classifying information, enabling effective retrieval and knowledge organization in diverse domains such as e-commerce, academic research, and web search. Traditional taxonomy construction, heavily reliant on manual curation by domain experts, faces significant challenges in scalability, cost, and consistency when dealing with the exponential growth of digital data. Recent advancements in Large Language Models (LLMs) and Natural Language Processing (NLP) present powerful opportunities for automating this complex process. This paper explores the potential of LLMs for automated taxonomy generation, focusing on methodologies incorporating semantic embedding generation, keyword extraction, and machine learning clustering algorithms. We specifically investigate and conduct a comparative analysis of two primary LLM-based approaches using a dataset of eBay product descriptions. The first approach involves fine-tuning a pre-trained LLM using structured hierarchical data derived from chain-of-layer clustering outputs. The second employs prompt-engineering techniques to guide LLMs in generating context-aware hierarchical taxonomies based on clustered keywords without explicit model retraining. Both methodologies are evaluated for their efficacy in constructing organized multi-level hierarchical taxonomies. Evaluation using semantic similarity metrics (BERTScore and Cosine Similarity) against a ground truth reveals that the fine-tuning approach yields higher overall accuracy and consistency (BERTScore F1: 70.91%; Cosine Similarity: 66.40%) compared to the prompt-engineering approach (BERTScore F1: 61.66%; Cosine Similarity: 60.34%). We delve into the inherent trade-offs between these methods concerning semantic fidelity, computational resource requirements, result stability, and scalability. Finally, we outline potential directions for future research aimed at refining LLM-based taxonomy construction systems to handle large dynamic datasets with enhanced accuracy, robustness, and granularity. Full article

Balancing the development of agricultural cold chain logistics with ecological conservation remains a critical challenge for green cold chain logistics in China’s three northeastern provinces. This study evaluates the efficiency of green cold chain logistics to promote synergy between logistics development and ecological sustainability. Using CiteSpace for keyword co-occurrence analysis and literature extraction, an evaluation index system comprising eight input and output indicators was constructed. The super-efficiency Slacks-Based Measure (SBM) model and the Malmquist–Luenberger (ML) productivity index were employed to assess efficiency from static and dynamic perspectives, respectively. Kernel density estimation was used to examine spatial distribution patterns, and the Dagum Gini coefficient was applied to decompose regional disparities. The results indicate that (1) overall efficiency remains relatively low, with ML index changes primarily driven by technological progress; (2) substantial regional differences exist among the three provinces in terms of distribution location, shape, and degree of polarization; and (3) inter-regional disparities are the main source of variation. A Tobit model further identified the key influencing factors, indicating that the level of economic development, growth of the tertiary industry, and informatization are the main drivers. These findings provide valuable insights for optimizing regional green cold chain logistics and promoting sustainable agricultural development. Full article