Search Results (40)

Interest in the production of bioethanol from inedible biomass is growing worldwide because of its sustainable supply and lack of competition with food supplies. Candida krusei (also known as Pichia kudriavzevii or Issatchenkia orientalis) is one of the most suitable thermotolerant yeasts used in the simultaneous saccharification and fermentation process for bioethanol production. In the production of bioethanol from lignocellulosic biomass as a feedstock, various environmental conditions occur, and the stress tolerance capacity of C. krusei, especially its low pH and tolerance to inhibitors, limits its practical application. In this study, to select a suitable second-generation bioethanol-producing strain, the tolerance capacity of five available C. krusei strains (NBRC0584, NBRC0841, NBRC1162, NBRC1395 and NBRC1664) was characterized. Spot assay and growth experiment results showed that among the five C. krusei strains, C. krusei NBRC1664 showed superior tolerance capacity for low pH and inhibitors. Furthermore, this strain efficiently produced ethanol from glucose under low pH conditions with or without sulfate. A comparative analysis of the draft genome sequences suggested that Opy2, Sln1 and Cdc24 in the HOG pathway are conserved only in C. krusei NBRC1664, which may contribute to its superior tolerance to low pH levels. Moreover, amino acid sequence alignment showed that aldehyde dehydrogenase family proteins, which catalyze the degradation of cyclic aldehydes, are commonly conserved in C. krusei. In addition, the increased transcription levels in C. krusei NBRC1664 could play a role in its higher tolerance to inhibitors. These results suggest that C. krusei NBRC1664 is a more suitable strain for application in industrial processes for second-generation bioethanol production. Full article

African swine fever (ASF) is a highly lethal, infectious, hemorrhagic fever disease, characterized by an acute mortality rate approaching 100%. It is highly contagious, and results in significant losses to the global hog industry as it spreads. Despite incremental progress in research on the African swine fever virus (ASFV), a safe and effective commercial vaccine has yet to be developed. Vector vaccines, a promising type of vaccine, offer unique advantages, and are a primary focus in ASFV vaccine research. This paper focuses on the characteristics of viral, bacterial, and yeast vector vaccines; elucidates the immunological mechanisms associated with antigens; lists the types of antigens that have significant potential; discusses the feasibility of using exogenously expressed cytokines to enhance the protective power of vector vaccines; and, finally, discusses the types of vectors that are commonly used and the latest advances in this field. Full article

The food industry continuously prioritizes methods and technologies to ensure product quality and safety. Traditional approaches, which rely on conventional algorithms that utilize predefined features, have exhibited limitations in representing the intricate characteristics of food items. Recently, a significant shift has emerged with the introduction of convolutional neural networks (CNNs). These networks have emerged as powerful and versatile tools for feature extraction, standing out as a preferred choice in the field of deep learning. The main objective of this study is to evaluate the effectiveness of convolutional neural networks (CNNs) when applied to the classification of chicken meat products by comparing different image preprocessing approaches. This study was carried out in three phases. In the first phase, the original images were used without applying traditional filters or color modifications, processing them solely with a CNN. In the second phase, color filters were applied to help separate the images based on their chromatic characteristics, while still using a CNN for processing. Finally, in the third phase, additional filters, such as Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP), and saliency, were incorporated to extract complementary features from the images, without discontinuing the use of a CNN for processing. Experimental images, sourced from the Pygsa Group databases, underwent preprocessing using these filters before being input into a CNN-based classification architecture. The results show that the developed models outperformed conventional methods, significantly improving the ability to differentiate between chicken meat types, such as yellow wing, white wing, yellow thigh, and white thigh, with the training accuracy reaching 100%. This highlights the potential of CNNs, especially when combined with advanced architectures, for efficient detection and analysis of complex food matrices. In conclusion, these techniques can be applied to food quality control and other detection and analysis domains. Full article

The Silway River has historically failed to meet safe fecal coliform levels due to improper waste disposal. The river mouth is located in General Santos City, the tuna capital of the Philippines and a leading producer of hogs, cattle, and poultry. The buildup of contaminants due to direct discharge of waste from chicken farms and existing water quality conditions has led to higher fecal matter in the Silway River. While there were technical reports in the early 2000s about poultry farming, this is the first study where fecal coliform from poultry farming was detected in the Silway River using highly sensitive protocols like qPCR. This study characterized the effect of flow velocity and physicochemical water quality parameters on chicken fecal contamination. Gene markers such as Ckmito and ND5-CD were used to detect and quantify poultry manure contamination through microbial source tracking (MST) and environmental DNA (eDNA) profiling. The results of this study showed the presence of chicken fecal bacteria in all stations along the Silway River. The results revealed that normal levels of water quality parameters such as temperature, pH, and high TSS concentrations create favorable conditions for chicken fecal coliforms to thrive. Multiple regression analysis showed that flow velocity and DO significantly affect chicken fecal contamination. A lower cycle threshold (Ct) value indicated higher concentration of the marker ND5-CD, which means higher fecal contamination. It was found that there was an inverse relationship between the Ct value and both velocity (R² = 0.55, p = 0.01) and DO (R² = 0.98, p = 0.2), suggesting that low flow velocity and low DO can lead to higher fecal contamination. Findings of fecal contamination could negatively impact water resources, the health of nearby residents, and surrounding farms and industries, as well as the health and growth of fish. Full article

The development of animal husbandry directly affects climate change and the ecological environment. This study aims to explore the relationship between carbon emissions from animal husbandry and economic growth in Inner Mongolia and to provide theoretical and countermeasure support for sustainable development. Based on the environmental Kuznets theory, the present situation of animal husbandry and economic growth in this region is analyzed. By analyzing slaughter and storage data for cow, sheep, hog, and poultry from 2000 to 2022, we calculated carbon emissions using the IPCC coefficient method. The environmental Kuznets curve is used to control variables such as human capital, government intervention, openness, technological innovation, and environmental protection expenditure. The findings show that carbon emissions from cows and sheep have risen significantly over the past 23 years, while the hog industry has remained stable. Both the number of poultry farms and their carbon emissions have declined. Economic growth is one reason for the increase in carbon emissions, while government intervention and openness have had mixed results. To ensure sustainable development, Inner Mongolia should strengthen government supervision, increase investment in environmental protection, expand opening-up, improve rural education, and promote low-carbon growth. Full article

Efficient transcriptional regulation of the stress response is critical for microorganism survival. In yeast, stress-related gene expression, particularly for antioxidant enzymes like catalases, mitigates reactive oxygen species such as hydrogen peroxide (H₂O₂), preventing cell damage. The halotolerant yeast Debaryomyces hansenii shows oxidative stress tolerance, largely due to high catalase activity from DhCTA and DhCTT genes. This study evaluates D. hansenii’s response to oxidative stress caused by H₂O₂ under saline conditions, focusing on cell viability, gene expression, and catalase activity. Chromatin organization in the promoter of DhCTA and DhCTT was analyzed, revealing low nucleosome occupancy in promoter regions, correlating with active gene expression. Stress-related motifs for transcription factors like Msn2/4 and Sko1 were found, suggesting regulation by the DhHog1 MAP kinase. Analysis of a Dhhog1Δ mutant showed DhHog1’s role in DhCTA expression under H₂O₂ or NaCl conditions. These findings highlight DhHog1’s critical role in regulating the stress response in D. hansenii, offering insights for enhancing stress tolerance in halotolerant yeasts, particularly for industrial applications in saline wastewater management. Full article

The sustainable development of the hog industry has significant implications for agricultural development, farmers’ income, and the daily lives of residents. Precise hog supply forecasts are essential for both government to ensure food security and industry stakeholders to make informed decisions. This study proposes an integrated framework for hog supply forecast. Granger causality analysis is utilized to simultaneously investigate the causal relationships among piglet, breeding sow, and hog supply, as well as to ascertain the uncertain time lags associated with these variables, facilitating the extraction of valuable time lag features. The Seasonal and Trend decomposition using Loess (STL) is leveraged to decompose hog supply into three components, and Autoregressive Integrated Moving Average (ARIMA) and Xtreme Gradient Boosting (XGBoost) are utilized to forecast the trends, i.e., seasonality and residuals, respectively. Extensive experiments are conducted using monthly data from all the large-scale pig farms in Chongqing, China, covering the period from July 2019 to November 2023. The results demonstrate that the proposed model outperforms the other five baseline models with more than 90% reduction in Mean Squared Logarithm (MSL) loss. The inclusion of the piglet feature can enhance the accuracy of hog supply forecasts by 42.1% MSL loss reduction. Additionally, the findings reveal statistical time lag periods of 4–6 months for piglet and 11–13 months for breeding sow, with significance levels of 99%. Finally, policy recommendations are proposed to promote the sustainability of the pig industry, thereby driving the sustainable development of both upstream and downstream sectors of the swine industry and ensuring food security. Full article

Hog insurance and rural environmental protection are complementary to each other. Studying the environmental effects of hog insurance is imperative for safeguarding food safety and promoting the long-term development of the agricultural insurance industry. Informed by the risk management theory and sustainable development theory, this paper constructs a theoretical framework for the impact of policy-supported hog insurance on the green total factor productivity (GTFP) of hog farming. Utilizing panel data from China’s hog-dominant production areas spanning from 2005 to 2021, the slacks-based measures of directional distance functions (SBM-DDF) model and multiple-time-point difference-in-differences (DID) approach were used to measure GTFP and explore the effects of hog insurance on GTFP and the underlying mechanisms. The findings indicate a substantial enhancement in GTFP due to hog insurance. The conclusion drawn was robust to various tests. The mechanism is that hog insurance fosters GTFP by expanding the breeding scale, adjusting the planting–breeding structure, and promoting technological progress. Furthermore, the environmental effects of hog insurance policy are more pronounced in economically developed regions, with significant effects observed on the GTFP of free-range, small-scale, and medium-scale hog-farming households. This study contributes new evidence to the field of assessing the environmental impact of agricultural insurance policies and provides valuable insights for furthering green transformation and development in the hog insurance-supported breeding industry. Full article

Timely prediction of bearing faults is essential for minimizing unexpected machine downtime and improving industrial equipment’s operational dependability. The Q transform was utilized for preprocessing the sixty-four vibration signals that correspond to the four bearing conditions. Additionally, statistical features, also known as attributes, are extracted from the Histogram of Oriented Gradients (HOG). To assess these features, the Explainable AI (XAI) technique employed the SHAP (Shapely Additive Explanations) method. The effectiveness of the GRU, LSTM, and SVM models in the first stage was evaluated using training and tenfold cross-validation. The SSA optimization algorithm (SSA) was employed in a subsequent phase to optimize the hyperparameters of the algorithms. The findings of the research are rigorously analyzed and assessed in four specific areas: the default configuration of the model, the inclusion of selected features using XAI, the optimization of hyperparameters, and a hybrid technique that combines SSA and XAI-based feature selection. The GRU model has superior performance compared to the other models, achieving an impressive accuracy of 98.2%. This is particularly evident when using SSA and XAI-informed features. The subsequent model is the LSTM, which has an impressive accuracy rate of 96.4%. During tenfold cross-validation, the Support Vector Machine (SVM) achieves a noticeably reduced maximum accuracy of 84.82%, even though the hybrid optimization technique shows improvement. The results of this study usually show that the most effective model for fault prediction is the GRU model, configured with the attributes chosen by XAI, followed by LSTM and SVM. Full article

China’s large-scale hog farmers are playing an increasingly important role in promoting the stable development of the hog industry. Taking large-scale hog enterprises as samples, based on hog sales data from January 2019 to July 2022, this paper adopts a two-way fixed-effects model to test the impact, mechanism, and heterogeneity of hog futures on the production stability of large-scale hog farmers. The study found that hog futures help promote stable production of large-scale farmers. This finding still holds after a series of robustness tests. The mechanism analysis found that, first, hog futures help large-scale farmers expand their risk management factor inputs. Second, hog futures help reduce the impact of hog price risk on production. Finally, hog futures help stabilize farmers’ production expectations. The moderating effects analysis found that the stabilizing effect of hog futures will enhance as farmers’ share of hog farming operations increases. Heterogeneity analysis found that when hog prices fluctuate negatively, hog futures help promote the stable production of large-scale farmers. When hog prices fluctuate positively, the production stabilization effect of hog futures is not obvious. Therefore, hog enterprises should be encouraged to participate in hog futures hedging transactions to promote stable hog production. Full article

The drastic fluctuations in pork prices directly affect the sustainable development of pig farming, agriculture, and feed processing industries, reducing people’s happiness and sense of gain. Although there have been extensive studies on pork price prediction and early warning in the literature, some problems still need further study. Based on the monthly time series data of pork prices and other 11 influencing prices (variables) such as beef, hog, piglet, etc., in China from January 2000 to November 2023, we have established a project pursuit auto-regression (PPAR) and a hybrid PPAR (H-PPAR) model. The results of the PPAR model study show that the monthly pork prices in the lagged periods one to three have an important impact on the current monthly pork price. The first lagged period has the largest and most positive impact. The second lagged period has the second and a negative impact. We built the H-PPAR model using the 11 independent variables (prices), including the prices of corn, hog, mutton, hen’s egg, and beef in lagged period one, the piglet’s price in lagged period six, and by deleting non-important variables. The results of the H-PPAR model show that the hog price in lagged period one is the most critical factor, and beef price and the other six influencing variables are essential factors. The model’s performance metrics show that the PPAR and H-PPAR models outperform approaches such as support vector regression, error backpropagation neural network, dynamic model average, etc., and possess better suitability, applicability, and reliability. Our results forecast the changing trend of the monthly pork price and provide policy insights for administrators and pig farmers to control and adjust the monthly pork price and further enhance the health and sustainable development of the hog farming industry. Full article

Wood pellets are a versatile ingredient to produce bioenergy and bioproducts. Wood pellet manufacturing in Canada started as a way of using the excess sawdust from sawmilling operations. With the recent dwindling availability of sawdust and the growth in demand for wood pellets, the industry uses more non-sawdust woody biomass as feedstock. In this study, woody biomass materials received from nine wood pellet plants in British Columbia (BC) and Alberta were analyzed for their properties, especially those used for fractionating feedstock to make pellets. Half of the feedstock received at the plants was non-sawdust. Moisture contents varied from 10 to 60% wet basis, with the hog having an average of 50%. Ash contents ranged from 0.3 to 4% dry basis and were highest in the hog fraction. Bulk density varied from 50 to 450 kg/m³, with shavings having the lowest bulk density. Particle density ranged from 359 kg/m³ for infeed mix to 513 kg/m³ for sawdust. In total, 25% of particles received were larger than 25 mm. The extraneous materials (sand, dirt) in the infeed materials ranged from 0.03% to 1.2%, except for one hog sample (8.2%). Plant operators use mechanical fractionation and blending to meet the required ash content. In conclusion, further instrumental techniques to aid in fractionation should be developed. Full article

In the pursuit of establishing a more environmentally sustainable and low-carbon hog farming system, the accurate quantification of emissions of greenhouse gas emanating from these systems, especially within the context of China, becomes imperative. Here, drawing insights from a life cycle approach, exhaustive field surveys, and context-specific analyses, we establish an emission measurement index system tailored to hog farming enterprises in China’s Shaanxi Province. Using this methodology, we probed the emission profiles and characteristics of three emblematic hog farming enterprises in the region. Our key findings are as follows: (1) The carbon dioxide emissions per kilogram of pork, factoring in feed cultivation, processing, and transportation, for Pucheng Xinliu Science and Technology, Baoji Zhengneng Farming, and Baoji Zhenghui Farming were quantified as 0.80298 kg, 1.52438 kg, and 0.81366 kg, respectively. (2) Presently, the methane emission coefficient due to enteric fermentation in large-scale hog farms in Shaanxi surpasses the default value set by the Intergovernmental Panel on Climate Change (IPCC). There appears to be a consistent underestimation of enteric methane emissions from live pigs in the province, as gauged against the IPCC metrics. Notably, the emission factor for fattening pigs averaged 2.61823 kgCH₄/head/year, while that for breeding pigs stood at 2.96752 kgCH₄/head/year. (3) When examining methane and nitrous oxide outputs from manure across various production stages, we observed that emissions from lactating pigs significantly outweigh those from other stages. Interestingly, nitrous oxide emissions from breeding pigs during fattening, finishing, and gestation remained nearly the same, regardless of the manure treatment method. (4) Under the management protocols followed by Pucheng and Baoji, the total carbon emissions from an individual fattening pig amounted to 328.5283 kg and 539.2060 kg, respectively, whereas for breeding pigs, these values were 539.2060 kg and 551.6733 kg, respectively. Further calculations showed that the average carbon footprint CF of large-scale pig farms in China was 3.6281 kgCO₂/kg pork. In conclusion, optimizing feed cultivation and transportation logistics, promoting integrated breeding and rearing practices, refining feed formulation, and advancing manure management practices can collaboratively attenuate greenhouse gas emissions. Such synergistic approaches hold promise for steering the hog industry towards a greener, low-carbon, and sustainable trajectory. Full article

African swine fever has damaged the foundation of China’s hog industry, caused a serious decline in hog production, highlighted the contradiction between supply and demand in the pork market, and led to major economic and social impacts. The industrial resilience of 31 Chinese provinces to African swine fever shock and its spatial and temporal differentiation characteristics from 2018 to 2021 were measured in this study from the two dimensions of resistance and recoverability. Using Geodetector, the key factors influencing the resilience of China’s hog industry were explored. The results showed that 2018–2019 and 2020–2021 represented the resistance and recovery periods of the hog industry under African swine fever shock, respectively, with poor resilience characterizing the resistance period and improved resilience characterizing the recovery period. At the early stages of the African swine fever outbreak, the hog industries in Tianjin, Shanxi, Guangxi, and Yunnan had robust resistance due to the slaughter rate, economic level, mortality rate, carcass weight, and culling rate in those areas. At the most severe stage of the outbreak, resistance was generally poor in all provinces due to the slaughter rate, per capita consumption, and scale level at the time. During the period of rapid recovery in hog production, the recoverability of each province was very strong due to the industrial structure, culling rate, economic level, and resource carrying capacity at that time. During the reasonable adjustment period of hog production capacity, the recoverability based on the breeding sow inventory in 13 provinces, including Henan, Shandong, and other large hog-breeding provinces, was negative due to the scale level, slaughter rate, per capita consumption, and resource carrying at that time. Taking measures to enhance the resilience of the hog industry, strengthen the prevention and control of hog epidemics, improve the monitoring and early warning mechanisms, and enhance the ability of the hog industry to cope with major animal epidemics is recommended. Full article

Total polyphenol and total flavonoid assays were performed to characterize the relationships between the color of Peucedanum japonicum (PJ) seed coat and stem and the content of phytochemical compounds. The samples were divided into two groups based on their stem and seed coat color, with each group containing 23 samples. The stem color group was subdivided into green, light red, and red, whereas the seed coat color group was divided into light brown, brown, and dark brown. In the stem color group, the light red stems exhibited the highest content of phytochemical compounds, with levels over 10% higher than those of the stems of the other colors. Moreover, among the top ten samples with the highest total polyphenol content, eight samples were light red, and the light red group also exhibited the highest total flavonoid content among the examined color groups. In terms of the seed coat color, the plants grown from dark brown seeds exhibited the highest contents of both total polyphenols and total flavonoids. In conclusion, PJ plants with dark brown seeds and light red stems contained the highest levels of phytochemical compounds. Collectively, our findings provide a valuable basis for future seed selection of PJ for pharmaceutical purposes. Full article