Amount Of Production Research Articles

Improvement of Post-Thaw Quality and In Vivo Fertility of Simmental Bull Spermatozoa Using Ferulic Acid.

Artificial insemination and semen cryopreservation have significantly improved the quality and quantity of cattle production. Through cryopreserved semen and artificial insemination, top-breeding bull sperm can be used to inseminate thousands of cows worldwide. Our study aimed to determine the effect of adding ferulic acid (FA) to a Tris-based semen extender on frozen and thawed Simmental bull sperm. Semen samples were collected from three Simmental bulls. Pooled Simmental semen (n=34 ejaculations) were diluted with a Tris-base extender containing varying FA concentrations (0.1, 0.15, 0.25, 0.35, and 0.45mM). After the samples were frozen and thawed, the samples were tested for malondialdehyde (MDA), total antioxidant capacity (TAC), superoxide dismutase (SOD), glutathione peroxidase (GPx), total motility, progressive motility, motility characteristics, and plasma membrane functionality. The control and the groups with the best FA concentrations, 0.25 and 0.35, were compared for in vivo fertility. Fifty-one cows were inseminated 24h after the onset of oestrus. A rectal examination was used to diagnose pregnancies at least 60 days after fertilization. Results showed that adding FA-0.45, FA-0.35, FA-0.25, and FA-0.15 to the semen of Simmental bulls improved total and progressive motility, motility characteristics, and plasma membrane functionality. It also increased GPx and TAC levels, reducing MDA and DNA damage after freezing. The addition of FA did not affect SOD values. The fertility rate in the FA-0.25 and FA-0.35 groups was higher than in the control group, 35.29%, with rates of 76.47% and 70.58%, respectively. In conclusion, adding FA (0.15, 0.25, 0.35, and 0.45mM) to Tris-based semen extenders can improve the quality parameters of cryopreserved Simmental bull semen and increase in vivo fertility using 0.25 and 0.35 concentrations of FA.

Identification of Soybean Mosaic Virus Resistant Soybean Genotypes using Gene-Linked Markers

Soybean is widely consumed legumes both in the form of food and feed. In the present scenario, the quality and quantity of soybean production is largely affected by different biotic factors, Soybean Mosaic Virus being one of the major biotic stress. Plant host resistance is an effective major to develop durable resistance from viral diseases, therefore understanding the molecular mechanism and identifying the resistant genotypes can contribute to resistance breeding in soybean. In 2020, ninety genotypes collected from Grain Legume Research Program, local collection and National Agricultural Genetic Resource Center were screened for the presence of resistant genes using gene linked molecular markers. Among them, three genotypes were identified with Soybean Mosaic Virus resistant loci, Rsv1 amplified by linked marker Satt114, four genotypes were identified with resistant loci, Rsv3 with linked marker Sat_424, and eight genotypes were identified with resistant loci, Rsv4 amplified by linked marker Satt558. Thus, showing expression of dominant resistance to some SMV strains. Interestingly, two genotypes (Co 175 and TGx1990-55) possessed both Rsv1 and Rsv3 loci thus showing potential of resisting broad spectrum SMV resistance to all SMV strains. Therefore, this study has identified potential genotypes that can be used as parental materials for resistant gene pyramiding in soybean breeding for SMV resistance

Cost-Effective Salt Harvesting: Efficient and Sustainable Production through Technological Innovation

Maximizing the quantity, quality, and cost-efficiency of salt production requires innovative approaches. This study presents a novel geo-membrane (GM)-based salt production technique that significantly improves salt yield and quality for Indian salt producers. The GM method is simple to implement across various salt production facilities, requiring only basic equipment. Compared to traditional methods, statistical analyses demonstrate substantial improvements in both the quantity and quality of salt produced with the GM method. The process enhances salt purity by 12–15%, reduces production costs by 15-20%, and prevents brine leakage, optimizing solar energy utilization in crystallization ponds. Keywords: salt production, geo-membrane, salt crystallization pond, cost-efficiency,sustainability

Optimized thermal pretreatment for lignocellulosic biomass of pigeon pea stalks to augment quality and quantity of biogas production

By applying heat to the feedstock during the thermal treatment of biomass for the production of biogas, the organic material's biodegradability can be greatly increased. Biogas production is a huge research area for alternate energy production technology. Increased biodegradability, improved methane yield, pathogen, and weed seed destruction, and overall process efficiency are all benefits of this type of pretreatment. It is a useful pretreatment technique for maximizing the production of biogas because it can decrease inhibitory compounds, and increase the digestibility of biomass. This work focused on increasing the efficiency of biogas production from lignocellulosic biomass of pigeon pea stalks by a novel thermal pretreatment. The pigeon pea stalk is initially imposed to physical pretreatment (PT) by an automatic hammer mill which is considered as a base for comparing performance. Thermal pretreatment was carried out for one hour, and two hours durations at different temperatures like 100 °C, 125 °C, 150 °C, 175 °C, and 200 °C. Compared to physically pretreated pigeon pea stalks, 200ᴼC thermal pretreated pigeon pea stalks for two hours have produced 88.41 % higher biogas, 16.14 % increase of cellulose, 19.9 % higher volatile solid removal, and 3.94 % lesser lignin. The enhanced chemical characteristics were ensured by analyzing the chemical composition variations through the FTIR, XRD, and SEM images. So, this is recommended for enhanced biogas production.

Spore PCR and qPCR Methods for Rapid Detection of Five Colletotrichum Species Responsible for Pepper Anthracnose in Korea

Pepper anthracnose, caused by <i>Colletotrichum</i> spp., leads to a decrease in the quantity of pepper fruit production. Molecular diagnosis is crucial for rapid identification of pathogens and determination of fungicide resistance. However, the traditional process of isolating the pathogen, extracting genomic DNA, and analyzing the gene sequence is time-consuming, which delays rapid diagnosis. In this study, we introduced a method using conidia of <i>Colletotrichum</i> spp. instead of genomic DNA, eliminating the need for DNA extraction or special processing for diagnosis. To elucidate this method, sensitivity was assessed through polymerase chain reaction (PCR) and quantitative real-time PCR (qPCR) tests using internal transcribed spacer-based primer pairs. Both PCR and qPCR tests showed that detection is feasible with just one conidia, with over 1,000 conidia yielding results comparable to approximately 1 pg of genomic DNA. For amplifying the cytochrome b gene for quinone-outside inhibitor fungicide susceptibility testing, detection from a single conidium is achievable, but a stable PCR product is obtained by increasing the number of cycles to 35. Additionally, the addition of 10% grinding fresh chili pepper paste to V8-Juicea gar medium, which is known for inducing conidia rapidly from the isolates, resulted in 3.2 to 6.0 times more conidia compared to the commonly used potato dextrose agar medium, enhancing the potential for swift testing. Taken together, this study presents a direct utilization of pepper anthracnose conidia through PCR or qPCR, offering a valuable technique for amplifying target genes, such as the minimum conidial amount and barcode genes, for molecular identification of anthracnose disease in pepper through PCR and qPCR analysis.

Does cluster beekeeping improve the efficiency of honey production in participant households in southwestern Ethiopia?

The purpose of this study is to understand the determinants of participation in cluster beekeeping and its impact on the technical efficiency of honey production in southwestern Ethiopia. To this end, cross-sectional data from a household survey conducted in 2023 from 385 sample households in 3 districts were used for the analysis. An endogenous switching regression model with a probit model was used to analyze the impact of clustering beekeepers on the technical efficacy of honey production. The results of the study showed that honey production was significantly and favorably affected by the total number of hives, type of hive, and distance to an accessible forest. As indicated by the average technical efficiency (TE) at 72 %, the actual quantity of production differed from the desired production volume. The results show that the gender of the household head, access to training, access to credit, market information, frequency of extension contacts, and age of the household head are the most important determinants of households' decision to participate in group beekeeping. In addition, the result of the endogenous switching regression (ESR) model shows that participation in group beekeeping has a positive and significant effect on honey production efficiency as measured by technical efficiency (TE). Farmers who participated in cluster beekeeping were technically less inefficient than those who did not participate. Therefore, policies and development strategies that encourage further participation in cluster beekeeping could improve the efficiency of honey production of smallholder farmers in Jimma Zone, southwestern Ethiopia.

Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification

Manual identification of tomato leaf diseases is a time-consuming and laborious process that may lead to inaccurate results without professional assistance. Therefore, an automated, early, and precise leaf disease recognition system is essential for farmers to ensure the quality and quantity of tomato production by providing timely interventions to mitigate disease spread. In this study, we have proposed seven robust Bayesian optimized deep hybrid learning models leveraging the synergy between deep learning and machine learning for the automated classification of ten types of tomato leaves (nine diseased and one healthy). We customized the popular Convolutional Neural Network (CNN) algorithm for automatic feature extraction due to its ability to capture spatial hierarchies of features directly from raw data and classical machine learning techniques [Random Forest (RF), XGBoost, GaussianNB (GNB), Support Vector Machines (SVM), Multinomial Logistic Regression (MLR), K-Nearest Neighbor (KNN)], and stacking for classifications. Additionally, the study incorported a Boruta feature filtering layer to capture the statistically significant features. The standard, research-oriented PlantVillage dataset was used for the performance testing, which facilitates benchmarking against prior research and enables meaningful comparisons of classification performance across different approaches. We utilized a variety of statistical classification metrics to demonstrate the robustness of our models. Using the CNN-Stacking model, this study achieved the highest classification performance among the seven hybrid models. On an unseen dataset, this model achieved average precision, recall, f1-score, mcc, and accuracy values of 98.527%, 98.533%, 98.527%, 98.525%, and 98.268%, respectively. Our study requires only 0.174 s of testing time to correctly identify noisy, blurry, and transformed images. This indicates our approach's time efficiency and generalizability in images captured under challenging lighting conditions and with complex backgrounds. Based on the comparative analysis, our approach is superior and computationally inexpensive compared to the existing studies. This work will aid in developing a smartphone app to offer farmers a real-time disease diagnosis tool and management strategies.

REVITALIZING CEREAL CROP PRODUCTION: A CRITICAL REVIEW OF THE EFFECTS OF MACRO AND MICRO-NUTRIENT FOLIAR SPRAYS ON GROWTH AND YIELD

Foliar fertilizer treatment has been identified as a viable source for boosting cereal crop growth and productivity. However, some elements such as application method, nutrient supply, and timing must be taken into account to get the best results. Foliar spray is an effective and focused method of nutrient delivery since it avoids soil shortages and encourages direct absorption through leaves. This review emphasizes the significance of macronutrients for crop growth and development, including nitrogen, phosphorus, potassium, etc. It also looks at how micronutrients like zinc, iron, manganese, etc affect the quantity and quality of agricultural production. Foliar spraying can be a useful method of giving these nutrients to crops, especially during crucial growth stages. To reduce the possibility of causing harm to the crops and surrounding environment, safety precautions should be taken throughout the foliar spraying process. These safety measures include careful nozzle selection, equipment calibration, and cleaning. Overall, the application of foliar nutrients has the potential to result in the production of cereal crops that are more effective and sustainable.

Implementasi Internet of Things pada Otomasi Pemberian Pakan Ayam Ras Petelur

Adequate nutrition affects the quality and quantity of egg production which has an impact on the profitability of laying hen breeders. Rizqie Farms experienced problems with the management of feeding laying hens, when the rainy season and open cage conditions made feeding irregular. It is important to be disciplined in feeding layer chickens on time at the same time as a research ur-gency, so that the chickens do not become stressed so that discipline in feeding management increases the quality and quantity of egg production. The design thinking method as a meth-od-ology for completing the creation of Android smartphone tools and applications. The com-pletion stages start with empathize, define, ideate, prototype and test. The materials used to make the tool are ESP32 DEVKIT V1 S2, Ultrasonic Sensor, Servo driving droplet feed, Power Source/Supply 12v 1A, Stepper Motor Driver l928n, Stepper Motor Nema 17 20nm, DS3231 RTC Module (timer), Arduino Uno R3, Buzzer / Speaker, LCD 1062 16x2 with I2c port. Sensor data connected to the ESP32 DEVKIT V1 S2, Ultrasonic Sensor, Servo drive is converted to an Android smartphone as a monitoring medium. Monitoring is carried out via a mobile application, with the Internet of Things connected to the device as an internet-based communication medium. Feeding equipment testing had an average delay of 40.2 seconds and MSE 0,0514. Black box testing was carried out to test the functionality of the monitoring application on 5 users with a percentage of 100%.

A Study on The Relationship Between Organic Agriculture and Agricultural Employment in Türkiye

Organic agriculture is a sustainable agricultural system with different principles that is carried out within the scope of certain rules. Due to the substitution of organic inputs with chemical inputs, getting the products ready for sale, weed control, and similar operations by hand, enterprises engaged in organic farming require a larger labor force. All these jobs increase employment opportunities in rural areas and allow small family businesses to continue production. In this study, it has been tried to reveal the relationship between organic agriculture and agricultural employment with the help of an econometric model and to determine the effect of organic agriculture on employment. The study analyzed a 17-year time series of data spanning from 2004 to 2021 to identify the factors influencing agricultural employment, taking into account various aspects of organic agriculture, including the number of organic agricultural products, the count of farmers engaged in organic agriculture, the area dedicated to organic agricultural production, the organic wild collection area, the total area allocated for organic agricultural production, the quantity of organic agricultural production, the number of entrepreneurs involved in organic agriculture, the unit price of subsidy for organic agriculture, and the total amount of subsidy provided to organic agriculture. Based on the results of the regression analysis, it was determined that the number of organic products does not have a significant effect on agricultural employment. In addition, it was found that the wild harvesting area is positively significant for agricultural employment and that the total production area of organic agriculture and the amount of subsidy are negatively significant. The results of the research reveal that a 1% increase in the wild harvesting area related to organic agriculture would lead to an increase of more than 0.28% in the agricultural employment rate, while a 1% increase in the total production area and the amount of subsidy would lead to a decrease of 0.40% and 0.44% in the agricultural employment rate, respectively.

Abiotic stress responses in crop plants: A multi-scale approach

Population stress has compelled the whole world to think about increasing, protecting, and finding ways to develop the best plant species for assured productivity in spite of all environmental odds. In addition to climate change, fluctuations in nature and human activities pose serious environmental degradation, threatening global food security through physical stress in the environment. Significant environmental constraints on agricultural yield worldwide include salt stress, water deficiency stress, nutritional imbalances (including mineral toxicity and deficiencies), and temperature extremes. Abiotic factors, such as agronomic factors, climatic factors, and nutrient availability in the soil, influence crop yield. Plants initiate and develop various possible stress mechanisms for their survival, which can be molecular, cellular and physiological. Abiotic stress has a high impact on crop growth and productivity, either in single forms or in combined forms. For example, drought stress causes a decrease in leaf area, plant height, and crop development. Cold stress reduces plant development and crop efficiency, resulting in productivity loss. Salinity stress not only contributes to water stress in plants, but it can also adversely influence cytosolic metabolism, cell development, membrane function, and increase reactive oxygen species (ROS) generation. Higher concentrations of CO2 could potentially improve global precipitation, resulting in increased rainfall, which can adversely affect crop development. Crops under excessive water stress have a lower percentage of amylose but a higher crude protein content. This in turn, affects the quality and quantity of crop production by hindering seed germination and causing growth damage due to the combined effects of higher osmotic potential and ion toxicity. In response to abiotic stress, plants evolve a variety of escape-avoidance and tolerance mechanisms, which include physiological adaptation and integrated cellular or molecular responses. Therefore, the main purpose of the current review paper is to investigate the effect of abiotic stress on morpho-physiological, biochemical and molecular activities in various crops. Moreover, we concentrate on crop inter-relativity with abiotic stress to react to and adapt to for survival, which can be a basic roadmap for the selection of species or the development of new tolerant species in the future.

Highly enhanced photocatalytic hydrogen production via GO-modified Cu-TiO2 system for dehydrogenation of alcohols under UV & sunlight irradiation

The high efficiency of the production of hydrogen from alcohols is vital to the advancement of energy technology. This study thoroughly investigates the process of alcohol dehydrogenation utilising GO-modified Cu-TiO2 photocatalyst under UV light and sunlight exposure. GO-modified Cu-TiO2 photocatalyst was synthesised using the hydrothermal method. Various experimental techniques, including FESEM, HRTEM, XPS, and DRS, confirmed the formation of the ternary composite. The influence of different alcohols (methanol, ethanol, propanol) and their concentrations (same vol% and molarity) on the amount of hydrogen (H2) production was examined in this study. The study thoroughly investigated the impact of various parameters, such as the influence of GO and Cu loading on TiO2 and their combined effect, time course, nature of alcohol and the reaction conditions on the photocatalytic hydrogen production. The GO(0.5 wt%)/Cu(3 wt%)-TiO2 (G0.5C3T) composite demonstrated the highest quantity of hydrogen production during methanol dehydrogenation when subjected to both UV and sunlight irradiation. The composite exhibited remarkably about three-fold higher hydrogen evolution in sunlight(881 mmol) than in UV light(294 mmol). The exceptional performance of this composite can be attributed to the efficient transfer of charge carriers and the delayed recombination of electron-holes, which is a result of the cooperative effect of GO and Cu deposited over the TiO2 system. This approach offers a proactive strategy, signifying the synergetic effect of loading GO and Cu over TiO2 to enhance the photocatalytic hydrogen production, which is regarded as a green fuel solution, and turns these materials into useful energy sources by using inexpensively synthesised photocatalysts.

Ultrasensitive and on-site diagnosis of rice bakanae disease based on CRISPR-LbCas12a coupled with LAMP.

Rice bakanae disease (RBD) has longstanding challenges impacted rice production, which is predominantly induced by Fusarium fujikuroi Nirenberg. Early diagnosis of F. fujikuroi is important to control RBD and improve quality and quantity of rice production. This study presents a novel on-site diagnosis platform combined with CRISPR/LbCas12a and LAMP to detect F. fujikuroi. LAMP amplification of TEF1-α, a characteristic gene of F. fujikuroi were performed, followed with trans-cleavage reaction of LbCas12a, cleaving the single-stranded DNA reporter, which is modified by the terminal fluorophore and quencher groups, producing fluorescence signal. The platform was confirmed with high specificity and sensitivity (LOD <1 aM). Furthermore, we designed a lateral flow strip experiment based on the trans-cleavage activity of LbCas12a, which was identified with similar sensitivity and specificity to the fluorescence detection method. In summary, this study achieved a platform with remarkable sensitivity and specificity for F. fujikuroi detection and provide potential for on-site and ultrasensitive diagnostic tools for RBD. © 2024 Society of Chemical Industry.

Optimization of hydrogen production in multi-Electrolyzer systems: A novel control strategy for enhanced renewable energy utilization and Electrolyzer lifespan

In large-scale water electrolytic hydrogen production system based on renewable energy, the allocation strategy of hydrogen production power among multi-electrolyzers plays a critical role in the efficiency of renewable energy utilization and the quantity of hydrogen production, given a fixed power output from renewable energy sources. This paper considers the minimum hydrogen production power constraint of electrolyzer and proposes a novel control strategy for multi-electrolyzers. The operational phase of multi-electrolyzers is divided into two distinct phases: 1) the rapid start-up phase; 2) the equal power allocation phase. During the rapid start-up phase, by reducing the power of electrolyzer that has reached its rated power to a predetermined level and using this saved power to start another electrolyzer, this strategy ensures that electrolyzers can proactively and quickly exceed the minimum operational power threshold. Upon the activation of all electrolyzers, the system enters second phase, wherein the power is evenly allocated among all electrolyzers. Besides, this strategy employs a priority scheduling method to ensure the uniformity of operating times across all electrolyzers. Compared to the existing three control strategies, this proposed strategy demonstrates significant advantages in minimizing unused wind energy and optimizing the lifespan of electrolyzers. The effectiveness of proposed strategy is validated by the simulation result.

Sustainable practices in brazilian and global agriculture in the 21st century

With population growth, the demand for food is increasing, making sustainable practices in agriculture essential to ensure food security, reduce production costs, and mitigate environmental impacts such as climate change. It is crucial to improve the quality and quantity of food production without causing harm to the environment, preserving existing agricultural areas without expanding into new regions. The objective of this literature review is to present information on some contemporary agricultural techniques used in Brazil and around the world that promote sustainability in the 21st century. Practices such as no-till farming, crop rotation, the cover crops use, integrated crop-livestock-forestry system, and the use of beneficial microorganisms exemplify the available sustainable approaches. It is concluded that these practices have great potential for more conscious agriculture, but more studies on their application are needed. The pursuit of sustainable agriculture is a necessary path, not an option.

Impacts of El Nino on renewable energy industry and fishery and aquaculture industry around the pacific

The effects of climate change on the world economy come into view in recent years, in El Nino is one of the most intricate and solid problems in predicting its occurrence and mitigating its effects. Former studies include its impacts on fishery and aquaculture, agriculture and major crops and the renewable energy market. Possible impacts include reproductive failure of fish, worsening well-being of people in coastal consortiums, decreasing production in maize, especially in warmer regions and decreasing efficiency and quantity of wind power production. Observed regions varied from the South American Coast and Peruvian waters to Malaysia, but neither of them compared the differences between each region as their major part. In this paper, fixed effects and dummy variable regression are used to evaluate El Ninos impact on fishery and renewable energy industries and contrast the differences between Japan, the USA and Australia. Results show that El Nino has a clear restraint on both industries but a stable increase in renewable energy production makes the relationship positive. Among the countries, Japan is the most restrained in both industries while the USA is the least affected. Among the different renewable energy sources, solar energy is promoted while hydro energy is restrained.

Analisis Proses Pascapanen Kopi di Pusat Pelatihan Pertanian dan Pedesaan Swadaya (P4S) Kopi Gunung Ikamaja Kecamatan Sumberjaya Kabupaten Lampung Barat

The post-harvest coffee process is vital in determining the quality and quantity of coffee production. Problems that often occur in the post-harvest coffee process are incomplete or overcooked fermentation, improper drying process, high water content, improper roasting process, and inappropriate use of resources. For this reason, it is important to carry out proper processing in the post-harvest coffee process so that the quality of the coffee can be optimal. This research aims to analyze the post-harvest process of robusta coffee and identify post-harvest coffee facilities and infrastructure at P4S Kopi Gunung Ikamaja. The types of data used are primary data and secondary data. The data collection methods used in this research are expert interviews and active participation observation. The results of the study show that there are post-harvest coffee processes that do not comply with the provisions for post-harvest processing according to Minister of Agriculture Regulation Number 52/Permentan/OT.140/9/2012. In natural processing processes, those that are appropriate are the sorting, drying, horn stripping, roasting, and fine grinding stages, while those that are not appropriate are the packaging process. Honey process processing is in accordance with Minister of Agriculture Regulation Number 52/Permentan/OT.140/9/2012. The full wash process is not by Agriculture Ministerial Decree Number 52/Permentan/OT.140/9/2012 at the fermentation stage. Facilities and infrastructure based on building location are not yet appropriate; however, based on tools and machines and based on containers and packaging, they are by the provisions of Minister of Agriculture Number 52/Permentan/OT.140/9/2012.

Association of different classifications of hypocalcemia with quantity and quality of colostrum, milk production, and health of Holstein dams and their calves

The aim of the present study was to assess the effect of serum calcium (Ca) status in the early postpartum period on the quantity and quality of colostrum, milk production, and the health of Holstein dams and their calves. One hundred multiparous Holstein cows were enrolled in this study. To determine serum Ca status, blood samples were taken at 1 and 4 days in milk (DIM). Cows were categorized into 1 of 4 groups based on their serum Ca concentrations: normocalcemic (CON; Ca >1.87 at 1 DIM and >2.10 mmol/L at 4 DIM, n = 36), transient SCH (TSCH; Ca ≤1.87 at 1 DIM and >2.10 mmol/L at 4 DIM, n = 14), persistent SCH (PSCH; Ca ≤1.87 at 1 DIM and ≤2.10 mmol/L at 4 DIM, n = 15), or delayed SCH (DSCH; Ca >1.87 at 1 DIM and ≤2.10 mmol/L at 4 DIM, n = 35). The ROC curve was utilized to determine the optimal cut-off value for serum total Ca, maximizing both sensitivity and specificity in predicting metritis. Metabolic disorders such as dystocia, retained placenta, and culling within 60 DIM were not affected by serum Ca status in the early postpartum period (P > 0.10). However, the incidences of metritis and endometritis were different based on the serum Ca status of the cows. The PSCH cows had a higher incidence of metritis than the CON cows (P = 0.05). Also, the DSCH cows tended to have an increased risk of metritis than the CON cows (P = 0.09). However, metritis incidence in the TSCH and CON cows was similar (P = 0.83). Cows with PSCH (P = 0.07) and DSCH (P = 0.10) tended to be at a higher risk for endometritis than the CON cows. But, the incidence of endometritis in the TSCH and CON cows was not different (P = 0.50). The TSCH cows had higher milk yield than the PSCH and DSCH cows (P = 0.05). However, milk yield was not different between the TSCH and CON cows and the PSCH and DSCH and CON cows. The serum Ca status of cows had no effect on colostrum volume and its quality, age and weight of weaning, and average daily gain of calves (P > 0.10). The proportion of calves experiencing failure of passive transfer (FPT) was greater in the PSCH group than in the CON group (P = 0.03). Also, the FPT tended to be higher in the DSCH calves compared to the CON calves (P = 0.10). However, the FPT in the TSCH and CON calves was not different (P = 0.83). The incidence of diarrhea tended to be higher in the PSCH and DSCH calves compared to the CON calves (P = 0.10). However, diarrhea incidence between the TSCH and CON calves was similar (P = 0.97). Overall, the findings of this study suggest that cows with TSCH showed better adaptation to lactation, experienced a lower occurrence of diseases and produced higher milk compared to PSCH and DSCH cows. Furthermore, despite the similar quantity and quality of colostrum across all groups, the PSCH and DSCH groups displayed a greater proportion of calves experiencing FPT and diarrhea in comparison to the TSCH group.

Plant Disease: A Growing Threat to Global Food Security

The escalating global population has led to an increased demand for both quantity and quality in food production. Throughout history, plant diseases have posed significant threats to agricultural output by causing substantial food losses annually while also compromising product quality. Accurate identification of pathogens, clarifying the pathogenic mechanism of pathogens, and understanding the interaction between pathogens and hosts are important for the control of plant diseases. This Special Issue, “Research Progress on Pathogenicity of Fungi in Crops”, belongs to the section “Pest and Disease Management” of Agronomy. It contains research papers on the identification and phylogeny of fungal pathogens, the molecular genetics of plant fungal pathogens, the molecular mechanisms of fungal pathogenicity, and the molecular basis of the interaction between fungi and crops. These studies encapsulate efforts to understand disease systems within current genomics, transcriptomics, proteomics, and metabolomics studies, highlighting research findings that could be future targets for crop disease and pest control. The studies presented in this Special Issue promote the progress of fungal pathogenicity research in crops and provide a scientific basis for future disease control, which is of great significance for sustainable agricultural development and global food security.

Robust Detection of Cracked Eggs Using a Multi-Domain Training Method for Practical Egg Production.

The presence of cracks reduces egg quality and safety, and can easily cause food safety hazards to consumers. Machine vision-based methods for cracked egg detection have achieved significant success on in-domain egg data. However, the performance of deep learning models usually decreases under practical industrial scenarios, such as the different egg varieties, origins, and environmental changes. Existing researches that rely on improving network structures or increasing training data volumes cannot effectively solve the problem of model performance decline on unknown egg testing data in practical egg production. To address these challenges, a novel and robust detection method is proposed to extract max domain-invariant features to enhance the model performance on unknown test egg data. Firstly, multi-domain egg data are built on different egg origins and acquisition devices. Then, a multi-domain trained strategy is established by using Maximum Mean Discrepancy with Normalized Squared Feature Estimation (NSFE-MMD) to obtain the optimal matching egg training domain. With the NSFE-MMD method, the original deep learning model can be applied without network structure improvements, which reduces the extremely complex tuning process and hyperparameter adjustments. Finally, robust cracked egg detection experiments are carried out on several unknown testing egg domains. The YOLOV5 (You Only Look Once v5) model trained by the proposed multi-domain training method with NSFE-MMD has a detection mAP of 86.6% on the unknown test Domain 4, and the YOLOV8 (You Only Look Once v8) model has a detection mAP of 88.8% on Domain 4, which is an increase of 8% and 4.4% compared to the best performance of models trained on a single domain, and an increase of 4.7% and 3.7% compared to models trained on all domains. In addition, the YOLOV5 model trained by the proposed multi-domain training method has a detection mAP of 87.9% on egg data of the unknown testing Domain 5. The experimental results demonstrate the robustness and effectiveness of the proposed multi-domain training method, which can be more suitable for the large quantity and variety of egg detection production.