bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing drought tolerance in cassava through genomic selection
Authors: da Costa Weverton Gomes , Bandeira e Souza Massaine , Azevedo Camila Ferreira , Nascimento Moyses , Morgante Carolina Vianna , Borel Jerônimo Constantino , de Oliveira Eder Jorge
Published: December 2024

The complexity of selecting for drought tolerance in cassava, influenced by multiple factors, demands innovative approaches to plant selection. This study aimed to identify cassava clones with tolerance to water stress by employing truncated selection and selection based on genomic values for population improvement and genotype evaluation per se. The Best Linear Unbiased Predictions (BLUPs), Genomic Estimated Breeding Values (GEBVs), and Genomic Estimated Genotypic Values (GETGVs) were obtained based on different prediction models via genomic selection. The selection intensity ranged from 10 to 30%. A wide range of BLUPs for agronomic traits indicate desirable genetic variability for initiating genomic selection cycles to improve cassava’s drought tolerance. SNP-based heritability (h2) and broad-sense heritabilities (H2) under water deficit were low magnitude (<0.40) for 8 to 12 agronomic traits evaluated. Genomic predictive abilities were below the levels of phenotypic heritability, varying by trait and prediction model, with the lowest and highest predictive abilities observed for starch content (0.15 – 0.22) and root length (0.34 – 0.36). Some agronomic traits of greater importance, such as fresh root yield (0.29 – 0.31) and shoot yield (0.31 – 0.32), showed good predictive ability, while dry matter content had lower predictive ability (0.16 – 0.22). The G-BLUP and RKHS methods presented higher predictive abilities, suggesting that incorporating kinship effects can be beneficial, especially in challenging environments. The selection differential based on a 15% selection intensity (62 genotypes) was higher for economically significant traits, such as starch content, shoot yield, and fresh root yield, both for population improvement (GEBVs) and for evaluating genotype’s performance per (GETGVs). The lower costs of genotyping offer advantages over conventional phenotyping, making genomic selection a promising approach to increasing genetic gains for drought tolerance in cassava and reducing the breeding cycle to at least half the conventional time.
Fuzzy backstepping controller for agricultural tractor-trailer vehicles path tracking control with experimental validation
Authors: Wang Anzhe , Wang Yefei , Ji Xin , Wang Kun , Qian Meiling , Wei Xinhua , Song Qi , Chen Wenming , Zhang Shaocen
Published: December 2024

Unmanned driving technology for agricultural vehicles is pivotal in advancing modern agriculture towards precision, intelligence, and sustainability. Among agricultural machinery, autonomous driving technology for agricultural tractor-trailer vehicles (ATTVs) has garnered significant attention in recent years. ATTVs comprise large implements connected to tractors through hitch points and are extensively utilized in agricultural production. The primary objective of current research focus on autonomous driving technology for tractor-trailers is to enable the tractor to follow a reference path while adhering to constraints imposed by the trailer, which may not always align with agronomic requirements. To address the challenge of path tracking for ATTVs, this paper proposes a fuzzy back-stepping path tracking controller based on the kinematic model of ATTVs. Initially, the path tracking kinematic error model was established with the trailer as the positioning center in the Frenet coordinate system using the velocity decomposition method. Then, the path tracking controller was designed using the back-stepping algorithm to calculate the target front wheel steering angle of the tractor. The gain coefficient was adaptively adjusted through a fuzzy algorithm. Co-simulation and experiments were conducted using MATLAB/Simulink/CarSim and a physical platform, respectively. Simulation results indicated that the proposed controller reduced the trailer's online time by 36.33%. When following a curved path, the trailer's tracking error was significantly lower than that of the Stanley controller designed for a single tractor. In actual experiments, while tracking a U-turn path, the proposed controller reduced the average absolute value of the trailer's path tracking lateral error by 65.27% and the maximum lateral error by 87.54%. The mean absolute error (MAE) values for lateral error and heading error were 0.010 and 0.016, respectively, while the integral of absolute error (IAE) values were 1.989 and 2.916, respectively. The proposed fuzzy back-stepping path tracking controller effectively addresses the practical challenges of ATTV path tracking. By prioritizing the path tracking performance of the trailer, the quality and efficiency of ATTVs during field operations are enhanced. The significant reduction in tracking errors and online time demonstrates the effectiveness of the proposed controller in improving the accuracy and efficiency of ATTVs.
Contact Parameter Calibration for Discrete Element Potato Minituber Seed Simulation
Authors: Chen Kai , Yin Xiang , Ma Wenpeng , Jin Chengqian , Liao Yangyang
Published: December 2024

The discrete element method (DEM) has been widely applied as a vital auxiliary technique in the design and optimization processes of agricultural equipment, especially for simulating the behavior of granular materials. In this study, the focus is placed on accurately calibrating the simulation contact parameters necessary for the V7 potato minituber seed DEM simulation. Firstly, three mechanical tests are conducted, and through a combination of actual tests and simulation tests, the collision recovery coefficient between the seed and rubber material is determined to be 0.469, the static friction coefficient is 0.474, and the rolling friction coefficient is 0.0062. Subsequently, two repose angle tests are carried out by employing the box side plates lifting method and the cylinder lifting method. With the application of the response surface method and a search algorithm based on Matlab 2019, the optimal combination of seed-to-seed contact parameters, namely, the collision recovery coefficient, static friction coefficient, and rolling friction coefficient, is obtained, which are 0.500, 0.476, and 0.043, respectively. Finally, the calibration results are verified by a seed-falling device that combines collisions and accumulation, and it is shown that the relative error between the simulation result and the actual result in the verification test is small. Thus, the calibration results can provide assistance for the design and optimization of the potato minituber seed planter.
Simulation of Suitable Distribution Areas of Magnolia officinalis in China Based on the MaxEnt Model and Analysis of Key Environmental Variables
Authors: Gan Tingjiang , Qian Qianqian , Liu Zhiqian , Xu Danping
Published: December 2024

Magnolia officinalis Rehder & E.H. Wilson is a deciduous tree in the Magnoliaceae family with extensive medicinal uses in China and Japan, being used to treat symptoms such as indigestion, insomnia, and anxiety. In this study, we used the MaxEnt model to (1) simulate the suitable spatial distribution areas of M. officinalis in China in the current and future periods (2050s and 2090s) and, (2) identify the key environmental variables affecting its spatial distributions by comparing the changes in the center of mass of the suitable areas under the current and projected future climate. The research results show that the current distribution range of M. officinalis is mainly between east longitude 102.2° to 122.2° and north latitude 23.7° to 33.9°, and it is located in the subtropical region of China. In the future, only the high-suitability area under scenario SSP1-2.6 and the low-suitability area under scenario SSP5-8.5 decreased in the 2050s, while the area increased under all other conditions. In the 2050s, the high- and medium-suitability areas under the SSP5-8.5 scenario increased the most, by 54.76% and 20.90%, respectively. Most of the key bio-climatic variables affecting the spatial distributions of M. officinalis are related to temperature and precipitation, and soil, terrain, chemical, and human variables that are also key environmental variables affecting the spatial distributions of M. officinalis. Currently, the suitable spatial distribution centroid of M. officinalis is at (111.71° E, 28.52° N), but it will change in the future climate; although, it will still be located in Hunan Province. This study predicts the spatial distribution areas that are favorable for the cultivation of M. officinalis with the intention of offering an objectively informed identification of suitable areas for the current and future development of this tree crop’s industry.
Differences Between Prices of Organic and Conventional Food in Poland
Authors: Smoluk-Sikorska Joanna
Published: December 2024

Consumers perceive the level of organic food prices as the major obstacle to increasing purchases. The main objective of the presented study is to recognize the differences between the prices of organic and non-organic food (so-called price premiums) in the Polish organic food market. Consequently, in order to determine these differences, prices of organic and non-organic food were noted in 45 retail outlets providing both types of food products. The investigation was performed within the Poznań agglomeration for nine months, from October 2022 to June 2023. The research showed that organic food price premiums ranged between 35% and over 270%. The lowest premiums were observed in tomato passata, cereal and dairy products, and eggs and olive oil. The highest occurred in chocolate, tea, juices, rapeseed oil, and chicken. Compared to previous studies, premiums observed in the conducted research, although high, have decreased for most investigated products. Moreover, the differences in price premiums were noted depending on the outlet type—they were lower in supermarket and hypermarket chains than in small retail shops. It should be highlighted that measures to maintain organic market development should be carried out with particular emphasis on the continued participation of retail chains in the distribution of organic food and the increased participation of large companies in processing organic food.
Recent Application of Heat Pump Systems for Environmental Control in Livestock Facilities–A Review
Authors: Han Zheyuan , Wang Kaiying , Dai Limin , Li Kui , Wang Xiaoshuai
Published: December 2024

The application of heat pump systems in agriculture, especially within livestock farms, has attracted considerable attention due to their potential for energy efficiency and improved environmental sustainability. Many studies have explored using heat pumps to optimize the indoor environments of barns. This review offers a comprehensive overview and analysis of the current applications of heat pump systems in livestock barn environmental control. Initially, it outlines the fundamental principle of heat pumps and the various types of heat pumps. Then, the technical advantages of the heat pump systems in regulating indoor temperature and humidity of livestock facilities, improving energy efficiency, and reducing environmental impacts are evaluated. Heat pump systems outperform conventional heating and cooling methods in terms of energy utilization and cost-effectiveness, and they positively contribute to reducing environmental pollution. However, some barriers obstruct the widespread adoption of heat pump systems, including policy and regulatory, economic and financial, and technological and infrastructure, as well as public perception and awareness. Future research is recommended to address these barriers. Thus, more heat pump systems in livestock farms could be extensively applied.
Experimental Investigation of Magnetic Drum Separation Techniques for Dodder (Cuscuta L.) Seed Removal from Alfalfa Seed Mixtures
Authors: Petcu Petruţa , Pruteanu Augustina , Ciobanu Valeria Gabriela , Nicolau Ana-Maria
Published: December 2024

Thousands of species of parasitic weeds, such as dodder, pose significant threats to agricultural crops due to their ability to spread rapidly through seeds. When cultivated lands become infested with dodder, the quality of production declines, leading to substantial damage. The most effective way to limit the infestation of agricultural lands by parasitic weeds, particularly dodder, is to control the quality of seeds intended for sowing. To obtain seed material free of dodder seeds, special separation machines equipped with magnetic drums are used. These machines operate on the principle of magnetic fields acting on ferromagnetic particles, which helps differentiate the physical states of the seeds intended for separation. This paper presents experimental research on magnetic drum separation techniques for removing dodder seeds from alfalfa seed mixtures. The study examines variables such as magnetic drum speed, feed rate, amounts of iron powder, water, solution (water and glycerin), and the initial content of dodder seeds. The experimental results indicated that using a water–glycerin solution at optimal concentrations for moistening the seeds enhances the separation efficiency of dodder seeds from alfalfa seed mixtures, compared to using only water. Additionally, the numerical content of dodder seeds in the A and C sorts, which primarily contain the seeds of the main crop, decreases with each pass through the machine, resulting in higher quality seed material. The research found that using appropriate parameters—drum rotation speed (20 rpm), iron powder quantity (19 g/min), and seed feed rate (25.39 g/s)—achieved a “free” classification for dodder in alfalfa seeds. These findings are valuable for evaluating the performance of separation equipment with magnetic drums to obtain high-quality seed material. They are also beneficial for designers, machine-building units, and economic agents specializing in this field.
Evaluation of Machine Learning Models for Estimating Grassland Pasture Yield Using Landsat-8 Imagery
Authors: Huang Linming , Zhao Fen , Hu Guozheng , Ganjurjav Hasbagan , Wu Rihan , Gao Qingzhu
Published: December 2024

Accurate estimation of pasture yield in grasslands is crucial for the sustainable utilization of pasture resources and the optimization of grassland management. This study leveraged the capabilities of machine learning techniques, supported by Google Earth Engine (GEE), to assess pasture yield in the temperate grasslands of northern China. Utilizing Landsat-8 data, band reflectances, vegetation indexes (VIs), and soil water index (SWI) were extracted from 1000 field samples across Xilingol. These data, combined with field-measured pasture yields, were employed to construct models using four machine learning algorithms: elastic net regression (Enet), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM). Among the models, XGBoost demonstrated the best performance for pasture yield estimation, with a coefficient of determination (R2) of 0.94 and a precision of 76.3%. Additionally, models that incorporated multiple VIs demonstrated superior prediction accuracy compared to those using individual VI, and including soil moisture data further enhanced predictive precision. The XGBoost model was subsequently applied to map the spatial patterns of pasture yield in the Xilingol grassland for the years 2014 and 2019. The estimated average annual pasture yield in the Xilingol grassland was 1042.38 and 1013.49 kg/ha in 2014 and 2019, respectively, showing a general decreasing trend from the northeast to the southwest. This study explored the effectiveness of common machine learning algorithms in predicting pasture yield of temperate grasslands utilizing Landsat-8 data and ground sample data and provided the valuable support for long-term historical monitoring of pasture resources. The findings also highlighted the importance of predictor selection in optimizing model performance, except for the reflectance and vegetation indices characterizing vegetation canopy information, the inclusion of soil moisture information could appropriately improve the accuracy of model predictions, especially for grasslands with relatively low vegetation cover.
Function and Expression Analysis on StFLA4 in Response to Drought Stress and Tuber Germination in Potato
Authors: Nie Hushuai , Lu Siqi , Wu Xiaojuan , Wang Peijie , Li Nan , Ma Yu , Wu Juan , Ma Yanhong
Published: December 2024

Drought stress is one of the main factors limiting the high yield and quality of potatoes. Arabinogalactan proteins (AGPs) are an important class of glycoproteins widely present in the cell walls, plasma membranes, and extracellular matrices of higher plants. Among them, fasciclin-like arabinogalactan proteins (FLAs) are involved in plant development, stress responses, and hormone signal regulation. However, little is known about the FLAs gene in potatoes. Based on transcriptome sequencing data, this study screened a drought stress-related candidate FLA gene (StFLA4) through bioinformatics and expression analysis in potatoes. qRT-PCR analysis showed that StFLA4 was induced by drought stress, and its expression decreased with the extension of stress time. Moreover, the relative expression level of StFLA4 in the drought-resistant variety “Kexin 1” was lower than in the drought-sensitive variety “Atlantic”. The StFLA4 protein was located in the cell membrane and interacted with nineteen proteins, mainly related to response to environmental stimulus, cellular response to abiotic stimulus, and cell maturation. After heterologous overexpression of StFLA4 in tobacco, the transgenic plants showed more withered leaves than the wild-type tobacco under drought stress. During the drought stress period, the expression level of StFLA4 in the transgenic plants significantly decreased, and the activity of SOD and POD was significantly lower than that of WT. However, the MDA content was higher than that of WT. These results indicated that StFLA4 negatively regulates the response to drought stress. In addition, in the germination test of potato “Variety V7” tubers, it was found that the variation tendency of StFLA4 expression was along with the concentration of arabinogalactan proteins, and it may participate in the regulation of potato tuber germination. This study lays the foundation for elucidating the function and expression pattern of StFLA4 response to drought stress and tuber germination in potatoes.
Effect of Temperature on the Inocula Preservation, Mesophilic Anaerobic Digestion Start-Up, and Microbial Community Dynamics
Authors: Wu Jingwei , Zhang Huan , Zhao Ye , Yuan Xufeng , Cui Zongjun
Published: December 2024

Anaerobic digestion (AD) is a well-established technology for the sustainable conversion of agricultural organic by-products and waste into bioenergy. Temperature is crucial for optimizing methane production through inocula preservation and reactor start-up in AD. The preservation of inocula induced by temperature has rarely been assessed from an engineering perspective. There has also been limited exploration of the influence of high-to-moderate temperature transition on the initiation of AD. This study employed continuous mesophilic AD reactors with potential engineering applications to conduct revival tests. These tests evaluated the methane production activity of sludge stored at different temperatures and investigated the impact of high-temperature initiation on mesophilic AD. Additionally, we elucidated the correlation between these assessments and microbial diversity as well as composition. The results indicated that bacterial diversity was higher in the inoculum stored at 35 °C compared to 15 °C, ensuring a stable start-up operation of the mesophilic AD. The richness of the bacteria and diversity of the archaea remained stable during the transition from high to mesophilic temperatures. This was conducive to enhancing methanogenic activity of mesophilic AD initiated at 55 °C. The continuously operated AD system showed significant differences in microbial composition compared to its inoculum. Increased abundance of Coriobacteriaceae and Prevotellaceae led to propionate and butyrate accumulation, respectively, reducing AD operational capacity. Methanogenic archaea were less diverse in AD initiated with low-temperature preserved inoculum compared to that with a medium temperature. Streptococcaceae induced by high temperarure could promote AD stability. Hydrogenotrophic methanogens had a competitive advantage in mesophilic AD due to their prior exposure to high-temperature initiation, possibly influenced by Thermotogaceae.
The Effects of Elite Puroindoline Gene Alleles on the Kernel Hardness of Chinese Winter Wheat
Authors: Wen Qinglin , Lu Shan , Qiao Pengfang , Chen Liang , Hu Yingang
Published: December 2024

Kernel hardness (KH) is a significant factor that influences wheat quality. In order to gain a better understanding of KH profiles and the effects of its associated genes in Chinese wheat cultivars growing under normal and latest stage drought stress conditions, 206 wheat cultivars were examined. The kernel hardness index (KHI) was measured by utilizing a single kernel hardness tester, and allelic variations of the puroindoline genes regulating KH were detected using KASP markers. The hardness test indicated that 121 (58.7%) were classified as hard wheat, 39 (18.9%) as soft wheat, and 46 (22.3%) as mixed wheat. Genotypic analysis revealed that 10 cultivars (4.9%) carried the superior Pina-D1b allele, 143 cultivars (69.4%) possessed the Pinb-D1b allele, representing the main allele for hard wheat, and 45 cultivars (21.8%) contained the Pinb-B2b allele. An analysis of the cumulative effect across five gene loci indicated that among the tested materials, none contained all five excellent gene loci simultaneously. However, materials with combinations of two, three, or four excellent gene loci exhibited significantly higher KHI values compared to those with zero or only one excellent locus. This finding suggests that the accumulation of excellent gene loci can enhance KH. Among various allelic combinations, Pina-D1 + M0159 displayed remarkably higher KH values than the others. Conversely, Pinb-D1 + M0380 exhibited significantly lower KH values. Drought stress during the late growth stage of wheat could enhance KH.
New Insights into Crop Molecular Breeding and Genetics
Authors: Su Yaqi , Cheng Zhen , Ying Jiezheng , Liu Chaolei , Li Zhiyong
Published: December 2024

As the global population continues to grow, the need to increase agricultural productivity is becoming increasingly urgent [...]
Impact of Alternative Substrates on Plant Growth and Root Exudates in Plant Interactions: A Study on Secale cereale L. and Amaranthus retroflexus L.
Authors: De Snyders Elise , Fauconnier Marie-Laure , Canelle Pauline , Wirth Judith , Gfeller Aurélie
Published: December 2024

This study investigates the effects of substrate composition on root architecture, plant growth, and allelopathic secondary metabolites, specifically benzoxazinoids (BXs), in the rhizospheres of rye (Secale cereale L.) and redroot pigweed (Amaranthus retroflexus L.). Given the complexities of root exudate analysis, including the influence of substrate on root morphology and exudation, the experiment compared plant growth and BX release in two substrates: glass microbeads and a mixture of clay beads and attapulgite. Rye, pigweed, and co-cultures of the two were grown under controlled conditions, with root and shoot parameters measured to assess substrate suitability. Additionally, UPLC-QTOF-MS was used to analyze BXs in rye and rye–pigweed co-cultures. The results demonstrated that the clay bead and attapulgite mixture provided better growth conditions and was effective for BX extraction, making it a suitable substrate for studying allelopathy in controlled environments. The findings highlight the critical role of substrate composition in both plant development and the study of root exudates, with implications for better understanding of crop–weed interactions and allelopathy.
Machine Learning and Deep Learning for Crop Disease Diagnosis: Performance Analysis and Review
Authors: Ngugi Habiba Njeri , Akinyelu Andronicus A. , Ezugwu Absalom E.
Published: December 2024

Crop diseases pose a significant threat to global food security, with both economic and environmental consequences. Early and accurate detection is essential for timely intervention and sustainable farming. This paper presents a review of machine learning (ML) and deep learning (DL) techniques for crop disease diagnosis, focusing on Support Vector Machines (SVMs), Random Forest (RF), k-Nearest Neighbors (KNNs), and deep models like VGG16, ResNet50, and DenseNet121. The review method includes an in-depth analysis of algorithm performance using key metrics such as accuracy, precision, recall, and F1 score across various datasets. We also highlight the data imbalances in commonly used datasets, particularly PlantVillage, and discuss the challenges posed by these imbalances. The research highlights critical insights regarding ML and DL models in crop disease detection. A primary challenge identified is the imbalance in the PlantVillage dataset, with a high number of healthy images and a strong bias toward certain disease categories like fungi, leaving other categories like mites and molds underrepresented. This imbalance complicates model generalization, indicating a need for preprocessing steps to enhance performance. This study also shows that combining Vision Transformers (ViTs) with Green Chromatic Coordinates and hybridizing these with SVM achieves high classification accuracy, emphasizing the value of advanced feature extraction techniques in improving model efficacy. In terms of comparative performance, DL architectures like ResNet50, VGG16, and convolutional neural network demonstrated robust accuracy (95–99%) across diverse datasets, underscoring their effectiveness in managing complex image data. Additionally, traditional ML models exhibited varied strengths; for instance, SVM performed better on balanced datasets, while RF excelled with imbalanced data. Preprocessing methods like K-means clustering, Fuzzy C-Means, and PCA, along with ensemble approaches, further improved model accuracy. Lastly, the study underscores that high-quality, well-labeled datasets, stakeholder involvement, and comprehensive evaluation metrics such as F1 score and precision are crucial for optimizing ML and DL models, making them more effective for real-world applications in sustainable agriculture.
An Enhanced Cycle Generative Adversarial Network Approach for Nighttime Pineapple Detection of Automated Harvesting Robots
Authors: Wu Fengyun , Zhu Rong , Meng Fan , Qiu Jiajun , Yang Xiaopei , Li Jinhui , Zou Xiangjun
Published: December 2024

Nighttime pineapple detection for automated harvesting robots is a significant challenge in intelligent agriculture. As a crucial component of robotic vision systems, accurate fruit detection is essential for round-the-clock operations. The study compared advanced end-to-end style transfer models, including U-GAT-IT, SCTNet, and CycleGAN, finding that CycleGAN produced relatively good-quality images but had issues such as the inadequate restoration of nighttime details, color distortion, and artifacts. Therefore, this study further proposed an enhanced CycleGAN approach to address limited nighttime datasets and poor visibility, combining style transfer with small-sample object detection. The improved model features a novel generator structure with ResNeXtBlocks, an optimized upsampling module, and a hyperparameter optimization strategy. This approach achieves a 29.7% reduction in FID score compared to the original CycleGAN. When applied to YOLOv7-based detection, this method significantly outperforms existing approaches, improving precision, recall, average precision, and F1 score by 13.34%, 45.11%, 56.52%, and 30.52%, respectively. These results demonstrate the effectiveness of our enhanced CycleGAN in expanding limited nighttime datasets and supporting efficient automated harvesting in low-light conditions, contributing to the development of more versatile agricultural robots capable of continuous operation.
Field Application of Mycorrhizal Inoculant Influences Growth, Nutrition, and Physiological Parameters of Corn Plants and Affects Soil Microbiological Attributes
Authors: Ferreira Paulo Ademar Avelar , Marchezan Carina , Scopel Gustavo , Schwab Natalia Teixeira , da Silva Emanuela Pille , Soares Cláudio Roberto Fonsêca Sousa , Brunetto Gustavo , Stürmer Sidney Luiz
Published: December 2024

Mycorrhizal inoculants can contribute to the development of corn crops by improving crop productivity. In this sense, the objective of this study was to evaluate the effects of a mycorrhizal inoculant on the dynamics of root system growth, gas exchange, corn crop productivity, and microbial activity in the rhizospheric soil in a no-till area with different levels of available soil phosphorus. The experiment was conducted during the 2019/2020 and 2020/2021 growing seasons. At 75 days after plant emergence, root morphological parameters (total root length (cm), average root diameter (mm), root surface area (cm2), and root volume), shoot biomass production, P content in the plant shoots, gas exchange, and microbiological attributes of the rhizospheric soil of corn were evaluated. At the end of the cycle, corn grain yield was determined. A beneficial effect of AMF inoculation was observed on the root and shoot parameters regardless of soil P level. Under conditions of evenly distributed rainfall during the experiment (2019/2020 season), AMF inoculation contributed to a 90% increase in acid phosphatase activity and a 76% increase in microbial biomass carbon (C-BIO), independent of soil P level. In contrast, under water deficit conditions (2020/2021 season), AMF inoculation provided a 29% increase in grain yield. We concluded that introducing a commercial mycorrhizal inoculant in corn benefits root system morphological parameters and physiological traits, and favors the activity of enzymes related to increased P availability, contributing to increased crop productivity in a no-till system.
AgriNAS: Neural Architecture Search with Adaptive Convolution and Spatial–Time Augmentation Method for Soybean Diseases
Authors: Omole Oluwatoyin Joy , Rosa Renata Lopes , Saadi Muhammad , Rodriguez Demóstenes Zegarra
Published: December 2024

Soybean is a critical agricultural commodity, serving as a vital source of protein and vegetable oil, and contributing significantly to the economies of producing nations. However, soybean yields are frequently compromised by disease and pest infestations, which, if not identified early, can lead to substantial production losses. To address this challenge, we propose AgriNAS, a method that integrates a Neural Architecture Search (NAS) framework with an adaptive convolutional architecture specifically designed for plant pathology. AgriNAS employs a novel data augmentation strategy and a Spatial–Time Augmentation (STA) method, and it utilizes a multi-stage convolutional network that dynamically adapts to the complexity of the input data. The proposed AgriNAS leverages powerful GPU resources to handle the intensive computational tasks involved in NAS and model training. The framework incorporates a bi-level optimization strategy and entropy-based regularization to enhance model robustness and prevent overfitting. AgriNAS achieves classification accuracies superior to VGG-19 and a transfer learning method using convolutional neural networks.
Developing Courses of Spanish for Specific Purposes in Agriculture to Bridge the Communication Gap Between the Hispanic Workforce and English-Speaking Veterinary and Animal Sciences Students
Authors: Salazar Leonor , Martinez Aguiriano Allen Jimena , Pietrosemoli Silvana , Garcia Arlene
Published: December 2024

This study addresses the integration of Spanish for Specific Purposes in Agriculture (SSPA) into the curricula of English-speaking veterinary and animal sciences students at three collaborating universities (Texas Tech University School of Veterinary Medicine, North Carolina State University, and Tarleton State University), to bridge communication gaps in agricultural settings. We designed and implemented three SSPA courses focused on key areas such as animal health, welfare, and food safety, using insights from industry professionals. The courses incorporated contextualized language instruction, including vocabulary, grammar, and practical communication scenarios relevant to the field. Teaching materials including interactive resources and metacognitive strategies were developed to enhance learning and engagement. The results showed that while the courses improved students’ communication skills, challenges such as technical issues and balancing academic commitments required adjustments to course delivery. Significant differences in performance were observed between institutions, with a considerable impact on the course completion rates and final test scores. The study concludes that SSPA courses significantly enhance the ability of veterinary and animal sciences students to effectively communicate with the Hispanic workforce, providing a model for similar language integration efforts in specialized fields.
Dietary Supplementation with Methylsulfonylmethane and Myo-Inosito Supports Hair Quality and Fecal Microbiome in Poodles
Authors: Zhang Jie , Guo Dan , Zhang Limeng , Li Deping , Deng Baichuan
Published: December 2024

This study aimed to investigate the effects of dietary supplementation with methylsulfonylmethane (MSM) and myo-inositol (MI) on hair quality, fecal microbiota, and metabolome in poodles. Thirty-two adult poodles categorized based on initial body weight and sex were randomly assigned to four groups. These groups (designated the CON, MSM, MI, and MSM + MI groups) received a basal diet, the same diet supplemented with 0.2% MSM + 0% MI, the same diet supplemented with 0% MSM + 0.2% MI, or the same diet supplemented with 0.2% MSM + 0.2% MI, respectively. The study lasted for 65 days. During the entire study period, body weight, average daily weight gain, feed intake, energy intake, and fecal output were normal in all the animals and did not differ significantly among the treatment groups. Hair scale thickness was lower in the MI and MSM + MI groups than in the CON group on Day 65 (p < 0.05). An amino acid analysis of the hair revealed higher sulfur content in the MI and MSM + MI groups on Day 65 than on Day 0 (p < 0.05). Moreover, the poodles in the MSM, MI, and MSM + MI groups presented significantly lower levels of Proteobacteria_unclassified and Candidatus Phytoplasma than did those in the CON group. The relative abundance of Gammaproteobacteria_unclassified was greater in the MSM and MI groups than in the CON group (p < 0.05). The MSM group presented a greater abundance of Glucerabacter than the CON group (p < 0.05). Compared with those in the CON and MSM + MI groups, the abundances of Paramuribaculum and Hafnia in the MSM group were greater (p < 0.05). The abundances of Enterobacter and Kineothrix were greater (p < 0.05) in the MI group than in the CON and MSM + MI groups. The poodles in the MI group presented significantly greater abundances of Bacteroidales_unclassified, Halanaerobium, Mycobacterium, and Erysipelotrichaceae_unclassified than did poodles in the CON, MSM, and MSM + MI groups. Fecal metabolomics analysis revealed that MSM, MI, and MSM + MI treatment markedly affected carbohydrate metabolism. MSM + MI treatment also influenced lipid metabolism. These findings suggest that dietary supplementation with MSM and MI can improve the hair quality of poodles.
A Screen of Traditional Chinese Medicinal Plant Extracts Reveals 17 Species with Antimicrobial Properties
Authors: Ellward Garrett L. , Binda Macie E. , Dzurny Dominika I. , Bucher Michael J. , Dees Wren R. , Czyż Daniel M.
Published: December 2024

Background/Objectives: Antimicrobial resistance (AMR) is a growing threat that undermines the effectiveness of global healthcare. The Centers for Disease Control and Prevention and the World Health Organization have identified numerous microbial organisms, particularly members of the ESKAPEE pathogens, as critical threats to global health and economic security. Many clinical isolates of these pathogens have become completely resistant to current antibiotics, making treatment nearly impossible. Herbal remedies, such as those found in Traditional Chinese Medicine (TCM), have been practiced for thousands of years and successfully used to treat a wide range of ailments, including infectious diseases. Surprisingly, despite this extensive knowledge of folk medicine, no plant-derived antibacterial drugs are currently approved for clinical use. As such, the objective of this study is to evaluate the antimicrobial properties of extracts derived from TCM plants. Methods: This study explores a comprehensive library comprising 664 extracts from 132 distinct TCM plant species for antimicrobial properties against gram-negative (Escherichia coli) and gram-positive (Micrococcus luteus) bacteria using liquid and solid in vitro assays. Results: Intriguingly, our results reveal 17 plant species with potent antimicrobial properties effective primarily against gram-positive organisms, including Streptococcus aureus and epidermidis. A literature search revealed that nearly 100 purified compounds from the identified TCM plants were previously isolated and confirmed for their antimicrobial properties, collectively inhibiting 45 different bacterial species. Conclusions: Our results indicate that phytobiotics from the identified plants could serve as potential candidates for novel antimicrobials.
Characterization of Metallo β-Lactamase Producing Enterobacterales Isolates with Susceptibility to the Aztreonam/Avibactam Combination
Authors: Posteraro Brunella , De Maio Flavio , Spanu Teresa , Vidal Pereira Maria Alejandra , Fasano Francesca Romana , Sanguinetti Maurizio
Published: December 2024

Background/Objectives: Metallo-β-lactamases (MBLs) in Enterobacterales and other Gram-negative organisms pose significant public health threats due to their association with multidrug resistance (MDR). Although aztreonam (AZT) can target MBL-producing organisms, its efficacy is compromised in organisms expressing additional β-lactamases that inactivate it. Combining AZT with the β-lactamase inhibitor avibactam (AVI) may restore its activity against MBL-producing isolates. Methods: AZT-AVI, along with other clinically relevant antimicrobials, was tested against thirteen MBL-producing clinical isolates of Enterobacterales (nine Klebsiella pneumoniae, three Enterobacter cloacae, and one Providencia stuartii) using whole-genome sequencing (WGS) for genetic characterization. Results: AZT-AVI demonstrated full susceptibility across all isolates, whereas aztreonam alone was ineffective. The newer β-lactam/β-lactamase inhibitor combinations imipenem/relebactam and meropenem/vaborbactam were inactive in 100% and 92.3% of isolates, respectively. WGS-based analysis revealed multiple resistance mechanisms consistent with MDR phenotypes, including high-risk K. pneumoniae clones (ST147 and ST11). Conclusions: AZT-AVI is effective against MDR MBL-producing Enterobacterales, highlighting its therapeutic potential for challenging infections. While WGS does not replace phenotypic testing, it provides valuable insights for antimicrobial stewardship and the monitoring of resistance gene dissemination.
Goondicones A–H: Spiro-Isoindolinone Heartworm Anthelmintics from an Australian Pasture-Soil-Derived Streptomyces sp.
Authors: Han Jianying , Bruhn David F. , Childs Cynthia T. , Moreno Yovany , Salim Angela A. , Wu Taizong , Capon Robert J.
Published: December 2024

Background/Objectives: There is an urgent need for new and improved anthelmintics that are not constrained by existing resistance pathways and that can safeguard the health and welfare of animals. Methods: An integrated platform of chemical, bioassay, and cultivation profiling applied to a library of microbes isolated from Australian livestock pasture soil was used to detect and guide the production, isolation, characterization, identification, and evaluation of new natural products with anthelmintic properties. Results: A global natural products social (GNPS) molecular network analysis of 110 Australian pasture-soil-derived microbial extracts prioritized for antiparasitic activity identified unique molecular families in the extract of Streptomyces sp. S4S-00185A06, a strain selectively active against Dirofilaria immitis microfilariae. UPLC-DAD analysis identified metabolites with unique UV-vis chromophores and unprecedented molecular formulas. A chemical investigation of Streptomyces sp. S4S-00185A06 yielded goondicones A–H (1–8) as new examples of a rare class of spiro-isoindolinones, with structures assigned on the basis of detailed spectroscopic analysis, ECD calculations, and biosynthetic considerations. Conclusions: While goondicones 1–8 exhibit little to no in vitro inhibitory activity against Gram-positive, Gram-negative, and/or fungal pathogens, human carcinoma cells, or the livestock gastrointestinal parasite Haemonchus contortus L1–L3 larvae, 5 and 6 (and, to a lesser extent, 1) inhibit the motility of heartworm Dirofilaria immitis microfilaria (IC50 10–11 μM). A structure activity relationship analysis based on the co-metabolites 1–8 suggests that (i) an 8-OH is preferable to 8–oxo moiety, (ii) 20-NMe and 3-OH moieties are essential, and (iii) C-9 epimerization exerts no discernible impact on in vitro potency.
The Detoxification Effects of Melatonin on Aflatoxin-Caused Toxic Effects and Underlying Molecular Mechanisms
Authors: Dai Chongshan , Li Daowen , Velkov Tony , Shen Jianzhong , Hao Zhihui
Published: December 2024

Aflatoxins (AFTs) are a form of mycotoxins mainly produced by Aspergillus flavus and Aspergillus parasiticus, which are common contaminants in various agricultural sources such as feed, milk, food, and grain crops. Aflatoxin B1 (AFB1) is the most toxic one among all AFTs. AFB1 undergoes bioactivation into AFB1-8,9-epoxide, then leads to diverse harmful effects such as neurotoxicity, carcinogenicity, hepatotoxicity, reproductive toxicity, nephrotoxicity, and immunotoxicity, with specific molecular mechanisms varying in different pathologies. The detoxification of AFB1 is of great importance for safeguarding the health of animals and humans and has increasingly attracted global attention. Recent research has shown that melatonin supplementation can effectively mitigate AFB1-induced multiple toxic effects. The protection mechanisms of melatonin involve the inhibition of oxidative stress, the upregulation of antioxidant enzyme activity, the reduction of mitochondrial dysfunction, the inactivation of the mitochondrial apoptotic pathway, the blockade of inflammatory responses, and the attenuation of cytochrome P450 enzymes’ expression and activities. In summary, this review sheds new light on the potential role of melatonin as a potential detoxifying agent against AFB1. Further exploration of the precise molecular mechanisms and clinical efficacy of this promising treatment is urgently needed.
Selenium Nanoparticles Suppressed Oxidative Stress and Promoted Tenocyte Marker Expression in Tendon-Derived Stem/Progenitor Cells
Authors: Lui Pauline Po Yee , Huang Caihao , Zhang Xing
Published: December 2024

Traumatic tendon injuries generate reactive oxygen species and inflammation, which may account for slow or poor healing outcomes. Selenium is an essential trace element presented in selenoproteins, many of which are strong antioxidant enzymes. Selenium nanoparticles (SeNPs) have been reported to promote tissue repair due to their anti-oxidative, anti-inflammatory, anti-apoptotic, and differentiation-modulating properties. However, its effects on the functions of tendon-derived stem/progenitor cells (TDSCs) and tendon healing have not been reported. This study examined the effects of SeNPs on the functions of hydroperoxide (H2O2)-stimulated TDSCs. Rat patellar TDSCs were treated with H2O2 with or without SeNPs. The viability, marker of proliferation, oxidative stress, inflammation, apoptosis, and tenocyte marker expressions of H2O2-stimulated TDSCs after SeNPs treatment were assessed. Our results showed that SeNPs increased the viability and expression of the marker of proliferation of TDSCs exposed to H2O2, while concurrently reducing oxidative stress, inflammation, and apoptosis. Additionally, the expressions of tenocyte markers were significantly elevated in H2O2-treated TDSCs after treatment with SeNPs. Furthermore, the expressions of Sirt1 and Nrf2 also increased after SeNPs treatment in H2O2-stimulated TDSCs. In conclusion, SeNPs mitigated oxidative stress, inflammation, and apoptosis while enhancing the survival and expression of the marker of proliferation of TDSCs in an oxidative stress environment. Additionally, it promoted the fate of TDSCs towards the tenocyte lineage in the presence of such oxidative stress. The increased expressions of Sirt1 and Nrf2 likely mediated the anti-oxidative and anti-inflammatory effects of SeNPs. SeNPs hold promise as a novel intervention for promoting tendon healing.
The Physicochemical Parameters, Phenolic Content, and Antioxidant Activity of Honey from Stingless Bees and Apis mellifera: A Systematic Review and Meta-Analysis
Authors: Zaldivar-Ortega Ana Karen , Cenobio-Galindo Antonio de Jesús , Morfin Nuria , Aguirre-Álvarez Gabriel , Campos-Montiel Rafael G. , Esturau-Escofet Nuria , Garduño-García Angel , Angeles-Hernandez Juan Carlos
Published: December 2024

The most common bee species used for honey production is Apis mellifera (A. mellifera), followed by stingless bees. This study included scientific articles using the PRISMA approach. A random effect model was implemented and the effect size (ES) was calculated and reported as the standardized mean difference (SMD) and raw mean difference (RMD). The mean phenolic content in A. mellifera honey was 61.21 ± 28.3 mg GAE/100 g and stingless bee honey +33.69 mg GAE/100 g; p = 0.01. The antioxidant activity, discovered by the Ferric Reducing Antioxidant Power (FRAP) method, showed a mean of 97.34 ± 7.84 μmol Fe(II)/100 g in A. mellifera and stingless bee honey +63.39 μmol Fe(II)/100 g; p = 0.009. The physicochemical properties showed significant differences in moisture (A. mellifera honey 19.54 ± 3.65%; stingless bee honey +8.02%; p = 0.0001), hydroxymethylfurfural (HMF) (A. mellifera honey 20.14 ± 16.27 mg/kg; stingless bee honey −11.25 mg/kg; p = 0.001), and free acidity (A. mellifera honey 31.32 ± 16.67 meq/kg; stingless bee honey +34.76 meq/kg; p = 0.01). The variability in the trials was explained by the heterogeneity, and a meta-regression analysis incorporated four covariates: (1) stingless bee species; (2) floral source; (3) country, and (4) latitude. This study highlights the importance of conducting further studies on stingless bee honey.
Addressing Oxidative Stress and Endothelial Dysfunction in Chronic Respiratory Diseases: The Role of Exercise and Multidisciplinary Rehabilitation
Authors: Ambrosino Pasquale , Nolano Maria , Candia Claudio , Grassi Guido , Maniscalco Mauro
Published: December 2024

Chronic respiratory diseases (CRDs) are highly prevalent conditions, causing over 4 million deaths every year and representing a major source of physical, psychological, and occupational disability [...]