Search Results (156)

Macroalgae are increasingly recognized as a valuable source of nutrients and bioactive compounds for animal nutrition, including for aquatic species. However, the complex structure of the macroalgal cell wall limits the accessibility of intracellular components, restricting their use in feeds. To overcome this limitation, macroalgal hydrolysis using various technological treatments has been tested, often employing a low solid-to-water ratio, which complicates downstream processing due to phase separation. In contrast, high-solids loading hydrolysis has the advantage of producing a single and consolidated fraction, simplifying subsequent processing and application. The present study assessed the effectiveness of high-solids loading water or alkaline (0.5 and 1N NaOH) autoclaving for 30 or 60 min, applied alone or followed by sequential enzymatic hydrolysis, using a xylanase-rich enzymatic complex aimed at promoting cell wall disruption and increasing the extractability of intracellular components in the red macroalga Palmaria palmata with minimal free water. The 1N NaOH treatment for 30 min decreased neutral and acid detergent fiber while increasing Folin–Ciocalteu total phenolic content (GAE) (expressed as gallic acid equivalent) and the water-soluble protein fraction and decreased crude protein, indicating enhanced extractability of these components. Microscopic examination showed relatively mild structural changes on the surface of P. palmata after high-solids loading alkaline (1N NaOH) autoclaving for 30 min. Following alkaline or water treatment, the enzymatic complex hydrolysis further increased the Folin–Ciocalteu total phenolic content (GAE), with minimal effects on NDF, ADF, or crude protein. Overall, these results showed that high-solids loading alkaline autoclaving, with or without subsequent enzymatic hydrolysis, effectively disrupts P. palmata cell walls and induces substantial modifications while simplifying processing by avoiding phase separation. Full article

This study examines the short- and medium-run linkages within Mexico’s energy–economy system under conditions of limited information. The analysis is motivated by the structural relevance of hydrocarbons for fiscal stability and by the growing need to understand how energy shocks propagate through economic and environmental subsystems. Using a vector autoregression (VAR) framework, nine interdependent macroeconomic and energy variables are jointly evaluated after harmonizing mixed-frequency data, standardizing series, and ensuring stationarity through ADF and KPSS tests. Dynamic responses are assessed through impulse response functions (IRFs), generalized IRFs (GIRFs), and forecast error variance decomposition (FEVD), complemented by Granger causality tests. Results show that oil rents exert a persistent and positive influence on GDP and public expenditure, while shocks to coal-fired generation and oil prices consistently reduce economic activity and increase emissions. Renewable capacity expands pro-cyclically but displays limited autonomous effects. Overall, the evidence reveals a fiscally and environmentally constrained system dominated by hydrocarbons, underscoring the importance of improving PEMEX’s operational efficiency, accelerating fiscal diversification, and strengthening institutional conditions for renewable investment. Full article

Ensuring high-quality forage under Mediterranean conditions requires careful evaluation of genetic resources. Two perennial forage species, cocksfoot (Dactylis glomerata L.) and tall fescue (Festuca arundinacea Schreb.), were evaluated to determine the stability and broad-sense heritability of major forage quality traits across Greek environments. The objective was to identify stable, heritable traits contributing to consistent forage quality under climatic variability. Measured traits included crude protein (CP%), crude fiber (CF%), ash, acid detergent fiber (ADF), neutral detergent fiber (NDF), cellulose, hemicellulose, acid detergent lignin (ADL), digestible dry matter (DDM%), dry matter intake (DMI%), and relative feed value (RFV). Significant genotype × environment (G × E) interactions were observed for most traits, highlighting the importance of multi-environment testing, except for RFV in cocksfoot, which was non-significant. Principal Component Analysis (PCA) helped clarify how these traits covary across environments. The traits Crude Protein, Ash Content, and ADL (on PC1) are largely independent of the traits Cellulose and Hemicellulose (on PC2) in the case of cocksfoot. The pattern of loadings in the case of Tall fescue revealed that hemicellulose represents a completely separate dimension of variation, which is uncorrelated to the rest of the traits that form a unified, highly correlated group. In both cases, the first two PCs explained over 82% of the total variance, separating genotypes and environments. By integrating stability (SI) and heritability (H²) results, Cock2D and T2fes were identified as the most stable and high-performing genotypes across environments. These findings could support breeding strategies for developing resilient forage cultivars with consistent quality and adaptability to Mediterranean environments, thereby enhancing sustainable livestock production. Full article

To tackle grape branch and leaf waste and alleviate global feed shortages, this study tested silage made from Xinjiang ‘Seedless White’ grape foliage. Three treatments were established: CK (control, only grape branches and leaves), PL (inoculated with 5 × 10⁶ CFU·g⁻¹ fresh weight Lactiplantibacillus plantarum), and PLC (inoculated with 5 × 10⁶ CFU·g⁻¹ L. plantarum and 0.3% cellulase). Silages were fermented at 18–23 °C and analyzed on days 7, 15, 30, and 60. PLC reduced dry matter loss in the late fermentation stage, while lowering Neutral detergent fiber (NDF) and Acid detergent fiber (ADF) contents to solve the high-fiber issue of grape foliage silage. It also maintained a lower pH in the mid-to-late stage and higher Lactic acid (LA) content to ensure anti-spoilage. Microbiologically, PLC had the highest Lactiplantibacillus abundance on day 7; on day 60, its Simpson index was higher, meaning stronger microbial community stability. Firmicutes replaced Cyanobacteria as the new dominant phylum, with Lactiplantibacillus remaining the absolute dominant genus, and the growth of molds and yeasts was effectively inhibited. In conclusion, the combined application of L. plantarum and cellulase enhances the quality of grape branch and leaf silage. This study turns low-value grape branches and leaves into high-quality feed, providing support for grape branch and leaf resource utilization and helping alleviate global feed shortages. Full article

This study aims to investigate the effect of varying addition levels of corn steep liquor (CSL) on the fermentation quality, bacterial community, and ruminal degradation rate of corncob silage. The experiment included a control group (CON) and four treatment groups: L1 with 5% CSL (50 g·kg⁻¹ fresh matter), L2 with 10% CSL (100 g·kg⁻¹ fresh matter), L3 with 15% CSL (150 g·kg⁻¹ fresh matter), and L4 with 20% CSL (200 g·kg⁻¹ fresh matter). The water content was controlled at 65% during fermentation for a period of 45 days. The results showed that the addition of CSL significantly increased the contents of dry matter (DM), crude protein (CP), and lactic acid (LA), while decreasing the pH, neutral detergent fiber (NDF), acid detergent fiber (ADF), and ammonia nitrogen (NH₃-N). Furthermore, the addition of CSL altered the relative abundance of microbial genera. While Pediococcus was the dominant bacterium in the CON group, Lactobacillus became the prevalent species upon the addition of CSL, and its relative abundance increased in accordance with the supplemental amount. These findings suggest that CSL provides a favorable environment for lactic acid bacteria. It is worth noting that CSL addition did not significantly alter the phylum-level bacterial community structure. The dominant bacterial taxa across all treatments were Bacillota, Proteobacteria, and Bacteroidota, with their cumulative relative abundance accounting for over 95%. The rumen degradation of the tested feedstuff was determined using the in situ nylon bag method. Results revealed that incorporating CSL into corncob silage significantly enhanced the effective degradation rates of DM, CP, NDF, and ADF in the rumen of Kazakh sheep. Specifically, the effective degradation rate of DM in the CON group was only 49.10%, which increased to 53.12% following the addition of 20% CSL, along with corresponding improvements in the degradation rates of CP, NDF, and ADF. In summary, as a valuable feed additive, corn steep liquor supports the proliferation of beneficial microorganisms in fermentation systems by supplying essential growth substrates. Additionally, it improves the nutritional balance of corncob feed and further enhances the absorption and utilization of nutrients from this feed by animals. Full article

Climate variability directly influences agriculture, especially in a scenario of global change and transition to a sustainable bioeconomy. This study analyzed historical series (1994–2023) of productivity and harvested area of annual crops (corn, cassava, and beans) and perennial crops (pineapple, cocoa, annatto, avocado, and guava), in order to understand the relationship between rainfall, maximum temperature, and agricultural production in northern Brazil. To achieve this, the Augmented Dickey–Fuller (ADF) test was applied to verify the stationarity of the series, and principal component analysis (PCA) was used to identify correlation patterns between climate and production variables. The ADF test showed that annual precipitation is stationary, while maximum temperature is non-stationary, confirming a warming trend. Among the crops, only bean productivity was stationary, albeit at low levels, while corn, cassava, and cocoa showed non-stationary behavior, reflecting technological advances combined with climatic pressures. PCA indicated different responses: corn showed a positive association with temperature, but signs of recent stagnation, whereas cassava and beans depended more on precipitation, demonstrating vulnerability to drought. Among perennials, avocado and guava responded positively to increased temperature, while annatto and pineapple were more dependent on rainfall. Cocoa showed a balanced correlation with both variables. It can be concluded that climate impacts on agriculture are heterogeneous and require specific adaptive strategies. From a bioeconomy perspective, the importance of productive diversification, technological innovation, and public policies aimed at climate resilience and the sustainability of low-carbon value chains is highlighted. Full article

Air pollution, a critical environmental threat, has worsened alongside urbanization and industrialization, particularly in rapidly developing economies like India. Despite efforts to curb emissions, the concurrent rise in energy consumption, industrial activity, and digitalization complicates the fight against air pollution. This study examines the interplay between air pollution, economic growth, clean energy transition, digitalization, and urbanization in India from 1990Q1 to 2020Q4. Using advanced econometric techniques, including multivariate quantile-on-quantile regression (MQQR) and the quantile ADF and quantile KPSS tests, we investigate the complex, non-linear relationships across these factors. Our findings suggest that while economic growth exacerbates air pollution, the clean energy transition can mitigate its impact, especially when integrated with digitalization. However, the effects of digitalization are nuanced, potentially increasing pollution unless paired with green energy policies. The study demonstrates that the combined strategies of promoting clean energy and digitalization can provide a sustainable pathway for reducing air pollution in India. This work offers novel insights into the role of digital technologies in enhancing environmental sustainability and highlights the need for policy interventions that balance economic growth with climate resilience. The results present a roadmap for India’s sustainable development, emphasizing the integration of clean energy, digital innovation, and urban planning. Full article

Traditional approaches to predicting business cycles are limited by their omission of financial variables, which, in turn, leads to failures to signal financial-sector crises and to misestimate the duration and intensity of economic events. This study addresses this challenge by constructing a real-financial activity gap for South Africa and utilising it to predict the occurrence of economic recoveries. The study examines the period from 1970Q1 to 2023Q4, using real GDP, domestic credit, house prices, and share prices. The dynamic factor model and the Hodrick–Prescott filter are employed to construct the real-financial activity gap. The recursive ADF unit root test is used to assess the presence, frequency, and duration of economic recoveries. To validate the results, a Markov switching dynamic regression model is applied. The results reveal that the new gap tends to produce economic recovery predictions that are less frequent but longer in duration. In contrast, predictions based on real GDP lead to more frequent but shorter recoveries. The new gap suggests that financial variables contribute to stabilising growth over extended periods, whereas real GDP reflects quicker but more volatile economic adjustments. The latest gap offers a more stable basis for forecasting recoveries, aiding policymakers in better anticipating and mitigating economic downturns. Accordingly, the output gap and the real-financial activity gap should be used as complements. Full article

This study aimed to determine the optimal combination of forage grass and row spacing to maximize the balance between sorghum silage yield and quality in a simultaneous sowing system for integrated crop-livestock production. The experiment evaluated three cropping systems: biomass sorghum (Sorghum bicolor (L.) Moench) in monoculture, and intercropped with Urochloa brizantha cv. Marandu and Megathyrsus maximus cv. BRS Zuri. These systems were tested under two row spacings: 45 cm and 90 cm. The field trial was conducted in Vicentina, Mato Grosso do Sul State, Brazil, using a randomized complete block design in a 3 × 2 factorial arrangement with four replications. Dry matter production, fermentative parameters, and chemical composition were measured. The 45 cm spacing provided higher productivity (23.1 t/ha of TDMY), while the intercropping with Zuri grass showed lower levels of NDF (73.46%) and ADF (49.61%), indicating better nutritional quality. The silages exhibited ideal pH (4.0–4.1) and low levels of butyric acid (<0.33%), with higher total digestible nutrients (TDN) (54.33%) at the 90 cm spacing. The Sorghum + Zuri (ZS) intercropping at the narrower spacing (45 cm) is viable for quality silage production, showing a better balance between overall chemical quality and biomass production. Full article

Shaft misalignment is among the most common faults in rotating machinery, and although many diagnostic methods have been proposed, reliably detecting it under varying load conditions remains a major challenge for vibration-based techniques. To address this issue, this study proposes a new vibration-based misalignment detection framework that leverages cointegration analysis. The approach examines both the stationarity of vibration signals and the residuals derived from the cointegration process. Specifically, it combines the Augmented Dickey–Fuller (ADF) test with cointegration analysis in three stages: (1) applying the ADF test to raw vibration data before cointegration, (2) performing cointegration on the vibration time series, and (3) reapplying the ADF test to the post-cointegrated data. The method was validated using experimental measurements collected from a laboratory-scale test rig comprising a motor, gearbox, and hydraulic gear pump, tested under both healthy and misaligned states with varying degrees of severity. Vibration signals were recorded across multiple load conditions. The results demonstrate that the proposed method can successfully detect misalignment despite load variations, while also providing insights into fault severity. In addition, the residuals from the cointegration process proved to be highly sensitive to damage, highlighting their value as features for vibration-based condition monitoring. Full article

The increasing demand for sustainable ruminant feeds has driven interest in the valorization of agro-industrial wastes. Artichoke wastes are attractive in the Mediterranean region due to their availability and richness in protein (CP) and fiber (NDF), but their high lignin (ADL) and tannin contents limit their nutritional value. This experiment was conducted using a completely randomized design with four treatments—control, ozone (O₃), exogenous fibrolytic enzyme (EFE), and O₃ + EFE—tested over six runs, each including three replicates per treatment. The study evaluated the effects of ozone (O₃) and exogenous fibrolytic enzyme (EFE) treatments, applied alone or in combination, on artichoke waste chemical composition, ruminal fermentation, microbial populations, enzyme activity, and degradability. Ozone pretreatment significantly reduced fiber fractions (NDF −10%, ADF −7%), ADL (−16%), and condensed tannins (−64%), while increasing CP (+13%) and non-fibrous carbohydrates (NFC +38%). These modifications enhanced ruminal bacterial populations (+29%) and fibrolytic enzyme activities (xylanase +21%, endoglucanase +19%, exoglucanase +10%), resulting in higher dry matter degradability (DMD +11%), fiber degradability (NDFD +14%), total volatile fatty acids (VFAs +13%), and a lower acetate-to-propionate ratio. EFEs alone showed negligible effects; however, when applied after ozone, further improvements were observed in NFCs (+21%), bacterial populations (+21%), enzyme activities (xylanase +11%, endoglucanase +10%), DMD (+8%), NDFD (+7%), and VFAs (+6%) compared to ozone alone. These findings demonstrate that O₃ pretreatment facilitates the enzymatic hydrolysis of lignocellulosic structures and enhances the effectiveness of EFEs, offering a sustainable and eco-efficient strategy for the bioconversion of artichoke wastes into high-value feed for ruminants, contributing to resource efficiency and circular bioeconomy development in livestock systems. Full article

Surface soil moisture (SSM) plays a critical role in climate change, hydrological processes, and agricultural production. Decision trees and deep learning are widely applied to SSM retrieval. The former excels in interpretability while the latter outperforms in generalization, neither, however, integrates both. To address this issue, an attention decision forest (ADF) was developed, comprising feature extractor, soft decision tree, and tree-attention modules. The feature extractor projects raw inputs into a high-dimensional space to reveal nonlinear relationships. The soft decision tree preserves the advantages of tree models in nonlinear partitioning and local feature interaction. The tree-attention module integrates outputs from the soft tree’s subtrees to enhance overall fitting and generalization. Experiments on conterminous United States (CONUS) watershed dataset demonstrate that, upon sample-based validation, ADF outperforms traditional models with an R² of 0.868 and a ubRMSE of 0.041 m³/m³. Further spatiotemporal independent testing demonstrated the robust performance of this method, with R² of 0.643 and0.673, and ubRMSE of 0.062 and 0.065 m³/m³. Furthermore, an evaluation of the interpretability of the ADF using the Shapley Additive Interpretative Model (SHAP) revealed that the ADF was more stable than deep learning methods (e.g., DNN) and comparable to tree-based ensemble learning methods (e.g., RF and XGBoost). Both the ADF and ensemble learning methods demonstrated that, at large scales, spatiotemporal variation had the greatest impact on the SSM, followed by environmental conditions and soil properties. Moreover, the superior spatial SSM maps produced by ADF, compared with GSSM, SMAP L4 and ERA5-Land, further demonstrate ADF’s capability for large-scale mapping. ADF thus offers a novel architecture capable of integrating prediction accuracy, generalization, and interpretability. Full article

This study investigates the dynamic relationship between interest and noninterest income at Azer Turk Bank using quarterly data from 2016Q1–2024Q3. Unit root tests including Augmented Dickey–Fuller (ADF), Kwiatkowski–Phillips–Schmidt–Shin (KPSS), and Fourier–KPSS indicate that both variables are non-stationary in levels but become stationary after first differencing. The Hylleberg–Engle–Granger–Yoo (HEGY) test further shows that both series contain a unit root at the non-seasonal (0) frequency, while no unit roots are detected at the seasonal frequencies (π/2 and 3π/2). Johansen cointegration and the Fourier Autoregressive Distributed Lag (Fourier–ADL) framework confirm the existence of a stable long-run equilibrium. As a key methodological contribution, the study derives explicit Fourier-based Vector Error Correction Model (VECM) equations, enabling the modeling of cyclical deviations around nonlinear trends. Fourier Toda–Yamamoto and Breitung–Candelon frequency-domain causality tests reveal asymmetry: interest income consistently drives noninterest income in the short and medium run, whereas the reverse effect is weak. The results also confirm mean reversion, with deviations from equilibrium corrected within 5.9; 2.5 quarters. Overall, the findings highlight the limited diversification potential of noninterest income and the decisive role of lending in bank revenues, offering both methodological advances and practical guidance for macroprudential policy. Full article

Accurately predicting evaporation duct height (EDH) is a crucial technology for enabling over-the-horizon communication and radar detection at sea. To address the issues of overfitting in neural network training and the low efficiency of manual hyperparameter tuning in conventional evaporation duct height (EDH) prediction, this study proposes the application of Bayesian optimization (BO)-based deep learning techniques to EDH forecasting. Specifically, we developed a novel BO–LSTM hybrid model to enhance the predictive accuracy of EDH. First, based on the CFSv2 reanalysis data from 2011 to 2020, we employed the NPS model to calculate the hourly evaporation duct height (EDH) over the Yongshu Reef region in the South China Sea. Then, the Mann–Kendall (M–K) method and the Augmented Dickey–Fuller (ADF) test were employed to analyze the overall trend and stationarity of the EDH time series in the Yongshu Reef area. The results indicate a significant declining trend in EDH in recent years, and the time series is stationary. This suggests that the data can enhance the convergence speed and prediction stability of neural network models. Finally, the BO–LSTM model was utilized for 24 h short-term forecasting of the EDH time series. The results demonstrate that BO–LSTM can effectively predict EDH values for the next 24 h, with the prediction accuracy gradually decreasing as the forecast horizon extends. Specifically, the 1 h forecast achieves a root mean square error (RMSE) of 0.592 m, a mean absolute error (MAE) of 0.407 m, and a model goodness-of-fit (R²) of 0.961. In contrast, the 24 h forecast shows an RMSE of 2.393 m, MAE of 1.808 m, and R² of only 0.362. A comparative analysis between BO–LSTM and LSTM reveals that BO–LSTM exhibits marginally superior accuracy over LSTM for 1–15 h forecasts, with its performance advantage becoming increasingly pronounced for longer forecast horizons. This confirms that the Bayesian optimization-based hyperparameter tuning method significantly enhances model prediction accuracy. Full article

The potential of fungal probiotics as ruminant feed additives has not been extensively studied. This study aimed to evaluate the effect of A. oryzae and T. longibrachiatum supplementation on Angus cattle during the early stages of fattening. In this study, 80 Angus cattle aged approximately 9~10 months (40 males and 40 females), with an average initial body weight (BW) of 276.46 ± 27.92 kg, were randomly assigned to four groups. Each group included 4 replicates (2 replicates of males and 2 replicates of females). Each replicate contained 5 male or 5 female Angus cattle. Cattle in the control group received a total mixed ration (TMR) without additives, while those in test groups 1, 2, and 3 received a TMR supplemented with complex probiotics (CPs) at 1.0, 2.0, and 3.0 g·kg⁻¹ of feed dry matter (DM), respectively. The adaptation and experimental periods were 7 and 60 days, respectively. Compared with those in the control group, the apparent digestibility of ether extract, calcium, and acid detergent fiber (ADF) was higher in test group 3. Test group 2 exhibited increased apparent digestibility of hemicellulose. Meanwhile, test group 3 exhibited increased levels of acetate, propionate, butyrate, and total volatile fatty acids and decreased pH. CPs increased the abundance of the SR1 phylum, Solibacillus, Lysinibacillus, and Planococcaceae_Bacillus and decreased the proportions of Lactococcus, Ruminococcus, and Ophryoscolex. Solibacillus was associated with the apparent digestibility of CP, Ca, and ADF. Planococcaceae_Bacillus was associated with increased apparent digestibility of CP, ADF, and hemicellulose. This suggests that CPs improve crude protein and cellulose digestion by increasing the proportions of Solibacillus and Planococcaceae_Bacillus. Therefore, the optimal CP dietary supplementation dose for Angus cattle was 3 g·kg⁻¹ of DM. Full article