Agricultural Problems Research Articles

THE WATER CRISIS LOOMS LARGE IN PAKISTAN WHICH MAY FACE ABSOLUTE SCARCITY

Pakistan has the fourth-highest rate of water use in the world, as reported by the IMF (International Monetary Fund), and is the 3rd greatest water-stress country in the world.Water can be scarce for many reasons: water supply is very low compared to demand, infrastructure is inadequate, and institutions fail to balance every person’s daily needs. Water scarcity is a global problem, with the most badly and worse affected communities. In this thesis we can measure Pakistan’s water scarcity with the four main indicators. The four main indicators can be measured in a country’s water scarcity: The Water Poverty Index, Water Resources Vulnerability Index, the Falkenmark indicator, and Physical and Economic Water Scarcity Indicators. Currently, Pakistan is one of the countries where demand of water exceeds over available water supply with the giving of these 4 indicators. There are many reasons for Pakistan’s water scarcity, being increased populace, citified and computerization, climate change, unsatisfactory warehouse, residues in the existing tank, corrupt system efficiency, irrigational problem and traditional methods of farming, and insufficient finance. The possible alternatives to overcome these issues are the construction of small and large dams where possible, the construction proper irrigation system, improving the surface water governance with suitable pricing, controlling the increase in population, improving transportation, changing the existing harvesting patterns by accepting low delta crops and using saline groundwater, in conjunction with independently with salt accepting crops. Nevertheless, a unified approach is required to optimally use water resources.

Predicting soil hydraulic conductivity using random forest, SVM, and LSSVM models

AbstractUnderstanding the hydraulic properties of soil is essential to solve many management problems in agriculture and the environment. Water quality affects soil hydraulic conductivity. Soil hydraulic properties play an important role in nature's water cycle and are used as basic information in designing irrigation and drainage systems, hydrological issues, and soil quality assessment. In the current study, soil sampling is performed from different areas and its hydraulic conductivity was measured using the drop load method and then predicted using support vector machine (SVM) and least‐squares support vector machine (LSSVM) models. The model inputs were: soil texture (percentage of sand, silt, and clay particles), salinity (electrical conductivity), pH, sodium adsorption ratio, soil porosity, and bulk density and the output was soil hydraulic conductivity. Correlation coefficient, root mean square error (RMSE), mean bias error (MBE), and Nash–Sutcliffe efficiency (NSE) were used to evaluate the models and compare them. Based on evaluation criteria the best performance was obtained for random forest (RF) (R = 0.89, RMSE = 0.53, mean absolute error (MAE) = 0.54, and NSE = 0.72). Following RF, the SVM with (R = 0.69, RMSE = 1.32, MAE = 0.69, and NSE = 0.48) performed better than LSSVM model.

Plant extracts to manage the parasitic weed branched broomrape (Phelipanche ramosa)

Some weeds have parasitic lifestyles, causing severe problems in agriculture. These plants include Phelipanche ramosa (L.) Pomel (branched broomrape). Greenhouse and nursery trials were carried out to assess control of P. ramosa using organic extracts from 14 plant species. The parameters recorded were counts of living and dead tubercles of P. ramosa and fresh weights of living tubercles. Organic extract of Olea europea reduced lengths of germ tubes during P. ramosa seed germination, and extracts of Bidens bipinnata and Dittrichia viscosa reduced production and development of the parasite’s tubercles, with very encouraging results in reducing seed germination rates. This research provides knowledge insights on the potential use of plant secondary metabolites to limit spread of P. ramosa, addressing an increasing challenge for organic crop production.

Long-term potato response to different irrigation scheduling methods using saline water in an arid environment

Crops’ water requirement is generally higher than the annual average precipitation in arid environments characterized by scarce freshwater resources. While using saline water for irrigation can help sustain agriculture in water-stressed regions, several challenges arises concerning productivity and soil salinization. However, adoption of efficient irrigation techniques such as drip irrigation, irrigation scheduling, and deficit irrigation can help optimize water productivity and mitigate salinity problems in irrigated agriculture. In southern Tunisia, potato is considered among the main cultivated horticultural crops due to its high economic value while it is considered as a crop sensitive to salinity. This crop (cv. Spunta) was the subject of long-term studies (2002–2020) conducted during the fall period in the arid region of Médenine. The crop response to full and deficit irrigation with saline water was assessed for several seasons under contrasting climatic conditions. Scheduling using the soil water balance (SWB) method consisted of the total and/or partial replacement of accumulated crop evapotranspiration (ETc), as derived from climatic data and crop coefficients. The impact of decreasing amounts of irrigation waters on crop yield and soil salinity with waters having a salinity ranging between 3 and 7 dS m−1 was evaluated. Results showed improvements in yield (30% to 37%) obtained with the SWB strategy under actual farming conditions, supporting the use of this strategy for irrigation. Appropriate scheduling also seems to be a key element in saving water (15%–22%) and in reducing risks of soil salinization. In the dry environment of southern Tunisia, optimum supply seems to correspond to a replacement of 100% to approximately 70%–80% of ETc. Applying such irrigation levels resulted in a lower salinity buildup in the root zone and higher crop water productivity. Natural salt leaching seems to be more effective under a more humid soil profile. Yield decreases and soil salinity increases almost linearly (r2 = 0.60) with decreasing irrigation water amounts. Future work should focus on the integration of management practices when using saline water. Investigating the relationship and interaction between irrigation amounts, cultivar, fertilizer supply, and salt leaching will help in resolving productivity and environmental issues.

CONSIDERATIONS ON THE MAIN FOOT DISEASES IN LIVESTOCK FARMING: a integrative review of the literature

Introduction: Cattle farming, whether dairy or beef, is one of the mainstays of the Brazilian economy today. Foot diseases directly affect the production chain in this agricultural area, causing production to fall and reducing the economic potential of the activity. Objective: The aim of this study was to evaluate the clinical, epidemiological and pathophysiological aspects of the main foot diseases that affect livestock, building on knowledge based on case reports and previous theoretical approaches. Materials and Methods: This is a integrative review of the literature on the main foot diseases affecting livestock. The descriptors "Foot Diseases", "Farming" and "Epidemiology" were cross-referenced in the National Library of Medicine (PubMed MEDLINE), Google Scholar and Virtual Health Library (VHL) databases. After this, Sole Ulcer, White Line Disease, Digital Dermatitis and Phlegmon were chosen for discussion. Results and Discussion: The articles included in this integrative review showed epidemiological, clinical, pathophysiological and propaedeutic aspects of the main foot diseases that affect livestock. Conclusion: Foot diseases represent a major problem for dairy cattle farming, being responsible for reduced production and the early disposal of animals. Most of them have lameness as a common symptom, which is the cause of changes in the gait of cattle and, consequently, in the physiological habits of the animals.

Phytogenic feed additives as an alternative to control postpartum problems in organic dairy farming

Organic dairy farming presents unique challenges, particularly regarding postpartum problems in dairy cows. This article explores the potential of phytogenic feed additives as an alternative solution to control such issues in organic dairy farming. Postpartum problems, including retained placenta, metritis, and mastitis, can have serious consequences for dairy cow health and productivity. The conventional treatment methods, such as antibiotics and anti-inflammatory drugs, are restricted in organic farming, necessitating the search for alternative solutions. Phytogenic feed additives, derived from plant sources, offer a promising alternative to conventional treatments. They provide anti-inflammatory, immunomodulatory, antimicrobial, and antioxidant effects, which can help prevent and manage postpartum problems in dairy cows. Studies have shown the efficacy of various plant-based remedies in treating specific postpartum issues, such as retained placenta, uterine prolapse, uterine infections, repeat breeding, mastitis, and post parturient hypocalcemia. The use of herbal remedies for postpartum problems aligns with the principles of organic farming, offering sustainable and environmentally friendly solutions. By exploring the potential of phytogenic feed additives, this article aims to contribute to the advancement of natural and eco-friendly practices in organic dairy farming, promoting the health and welfare of dairy cows while ensuring farm sustainability and profitability. In conclusion, phytogenic feed additives have shown promising results in controlling postpartum problems in organic dairy farming. They offer a safe, effective, and sustainable alternative to conventional allopathic treatments, helping to address the unique challenges faced by organic dairy farmers. However, more research and professional guidance are necessary to fully harness the potential of phytogenic feed additives in organic dairy farming.

Vitamin E Supplementation to Quail (Coturnix-coturnix Japonica) Treated by Heat Stress: Effects on Blood Hematology Parameters

Heat stress is a major problem in poultry farming. Environmental temperature and humidity greatly in fluence the performance of poultry, especially quails raised in tropical environments. Vitamin E is a fat-soluble vitamin that has an antioxidant effect. This study aimed to evaluate the effect of vitamin E supplementation on the blood profile of quails kept under heat stress. This study used 4 treatments of T0 as Control (Commercial Feed + Heat Stress 35 oC); T1 (Control + 100 mg of vitamin E/kg); T2 (Control + 150 mg of vitamin E/kg) and T3 (Control + 200 mg of vitamin E/kg). Observed parameters of leukocytes, heterophylls, lymphocytes and monocytes. The data was analyzed by Analysis of Variant (ANOVA) if there were differences between treatments, followed by the Duncan test. The observation results showed that the addition of vitamin E could increase the number of leukocytes, lymphocytes, monocytes and reduce the number of heterophytes and the H/L ratio of quail blood that experienced heat stroke. Based on the blood profile, the addition of vitamin E as much as 200 mg/kg in feed is able to reduce heat stress due to heat stress in laying quails.

A proposed plant classification framework for smart agricultural applications using UAV images and artificial intelligence techniques

Utilizing Wireless Sensor Networks (WSNs), Internet of Things (IoTs) sensors, and Unmanned Aerial Vehicles (UAVs), in conjunction with optimization techniques and machine learning algorithms, can present a novel approach to Precision Agricultural (PA) crops. Agriculture has transitioned from traditional legacy systems to incorporate advanced smart technologies. Several complex farming problems are utilized to promote a variety of UAV sensing and Artificial Intelligence (AI) algorithms in PA applications of smart agriculture, especially in developing countries. This paper proposes the design of a conceptual UAV sensing system for crop management using a novel classification framework. UAV-based image datasets can be implemented and expanded to various types of crops. In addition, it can classify various types of rice species and detect weed farms as it consists of a multistage process for identifying and monitoring different crops. The proposed classification framework is evaluated using three different real UAV image datasets compared with Naive Bayes, Decision Tree (DT), Bagging, and Random Forest (RF) techniques. The classification performance metrics obtained are as follows: i) the WeedNet dataset with 100 % F1-score, 100 % recall, 100 % precision, and 100 % accuracy; ii) the Rice Seedling dataset with 99.5 % F1-score, 99.5 % recall, 99.5 % precision, and 99.5 % accuracy; and iii) the Rice Varieties dataset with 97.99 % F1-score, 97.99 % recall, 98.14 % precision, and 97.99 % accuracy. The success rates achieved by the proposed classification framework outperform those of other recent state-of-the-art techniques, demonstrating its efficacy in crop management applications.

​Fusarium Protein Toolkit: a web-based resource for structural and variant analysis of Fusarium species

Background​​The genus Fusarium poses significant threats to food security and safety worldwide because numerous species of the fungus cause destructive diseases and/or mycotoxin contamination in crops. The adverse effects of climate change are exacerbating some existing threats and causing new problems. These challenges highlight the need for innovative solutions, including the development of advanced tools to identify targets for control strategies.DescriptionIn response to these challenges, we developed the Fusarium Protein Toolkit (FPT), a web-based tool that allows users to interrogate the structural and variant landscape within the Fusarium pan-genome. The tool displays both AlphaFold and ESMFold-generated protein structure models from six Fusarium species. The structures are accessible through a user-friendly web portal and facilitate comparative analysis, functional annotation inference, and identification of related protein structures. Using a protein language model, FPT predicts the impact of over 270 million coding variants in two of the most agriculturally important species, Fusarium graminearum and F. verticillioides. To facilitate the assessment of naturally occurring genetic variation, FPT provides variant effect scores for proteins in a Fusarium pan-genome based on 22 diverse species. The scores indicate potential functional consequences of amino acid substitutions and are displayed as intuitive heatmaps using the PanEffect framework.ConclusionFPT fills a knowledge gap by providing previously unavailable tools to assess structural and missense variation in proteins produced by Fusarium. FPT has the potential to deepen our understanding of pathogenic mechanisms in Fusarium, and aid the identification of genetic targets for control strategies that reduce crop diseases and mycotoxin contamination. Such targets are vital to solving the agricultural problems incited by Fusarium, particularly evolving threats resulting from climate change. Thus, FPT has the potential to contribute to improving food security and safety worldwide.

Advancing Agriculture: Exploring the Potential of Aeroponic Systems for Vegetable Cultivation: A Comprehensive Review

Aeroponic systems offer an efficient and sustainable approach to vegetable cutivation, highlighting advantages such as water and resource efficiency, year-round production and high quality crops. The review explores the principles of aeroponic cultivation, key factors like nutrient delivery, root oxygenation, and space optimization. It also examines the advantages and limitations of aeroponics compared to traditional soil-based methods. It also discusses recent advancements in technology and research and the future prospects for the continued development and adoption of aeroponic vegetable cultivation. Overall, this review underscores the potential of aeroponics to revolutionize vegetable farming practices to offer a promising solution for today’s agricultural farming problems viz., shrinking land, scarcity of water, declined productivity etc. and contribute to global food security in the coming years.

Preliminary In Vitro Evaluation of Silver, Copper and Gold Nanoparticles as New Antimicrobials for Pathogens That Induce Bovine Locomotion Disorders.

Lameness is a crucial problem in dairy farming. It worsens the welfare of cattle, reduces the milk yield, and causes economic losses. The etiology of lameness is varied and the cattle's condition may be infectious or non-infectious. The aim of this research was to analyze the biocidal properties of silver (AgNPs), gold (AuNPs), and copper (CuNPs) nanoparticles against bacteria causing lameness in cattle. The isolated pathogens used were Aerococcus viridans, Corynebacterium freneyi, Corynebacterium xerosis, and Trueperella pyogenes. The tested concentrations of nanoparticles were 50, 25, 12.5, 6.25, 3.125, and 1.56 mg/L. The methods used included the isolation of pathogens using standard microbiological procedures and their identification using mass spectrometry, physicochemical analysis, transmission electron microscopy, and cytotoxicity tests. Studies have shown that AgNPs at 3.125 and 1.56 mg/L concentrations, and CuNPs at 25 and 12.5 mg/L concentrations, have strong biocidal properties, while AuNPs have the weakest antimicrobial properties. The very limited number of in vivo studies focusing on lameness prevention in cattle indicate that new solutions need to be developed. However, further studies are necessary to evaluate if nanoparticles (NPs) may, in the future, become components of innovative biocides used to prevent lameness in dairy cattle.

Technology for a Food-secure Future: A Review of Technology Advances in Sustainable Agriculture

There is no doubt that advanced technologies play a very important and crucial role in driving the agricultural sustainable practices in order to satisfy the increasing demand for food production as well as to minimize environmental impacts. This review article aims to provide comprehensive points of view regarding a current state as well as the future advances in smart farming techniques for developing an agricultural sustainability. The digital sensors are considered as key techniques for agricultural transformation which provide an accurate managing and monitoring the various agricultural activities such as crop production, soil moisture, micro climate data, and others. Valuable detailed data are acquired by these sensors to be delivered to the farmers in order to make suitable decisions, ensuring the sustainable and efficient utilization of the agricultural resources. Moreover, managing irrigation using the advanced technologies is benefited wireless monitoring remotely and controlling systems. These technologies and advanced tools help the farmers to reduce water consumption and increase their benefits as well as promotes sustainable management practices of the water resources. Additionally, the drones such as unmanned aerial vehicles (UAVs) can be attached with several kinds of sensors or cameras for capturing detailed information of crop health, soil status, required fertilizers, irrigation, and pesticides. Besides these benefits of drones, they provide a function of an early detection of agricultural problems which allow the decision makers for taking suitable actions. Furthermore, the biotechnology advances such as CRISPR gene editing, and transgenic animals’ developing are very beneficial for enhancing the crop yield, disease resistance, and nutrients availability, and agricultural sustainability. The precision agriculture (PA) integration with geographic information system (GIS) and remote sensing (RS) provides site-specific management and its decisions in order to optimize the inputs utilization, waste reduce, and sustainable practices promotion. Also, advanced technologies’ application (i.e. artificial intelligence ‘AI’, machine learning ‘ML’, and the Internet of Things ‘IoT’) has been found to be a promising for achieving an agricultural sustainability. Therefore, by using these powerful technologies, the farmers can enhance an agricultural production, decrease an environmental effect and achieve the food security.

Application of nano-urea in conventional flood-irrigated Boro rice in Bangladesh and nitrogen losses investigation

Bangladesh stands third in global rice production while complete modernization of rice production is not fully enforced. The boon of nano agriculture might circumvent the challenge of increasing the yield with minimal ecological damage. Nanofertilizer might be one of the solutions to address the problem of modern agriculture confronting environmental hazards owing to the excessive use of synthetic fertilizers by farmers in Bangladesh. We synthesized nanourea by chemical co-precipitation (CP) and hydrothermal (HT) methods in an attempt to develop environmentally friendly nanofertilizers. We characterized the nanourea and confirmed the functionalization of nanohydroxyapatite (nHAP) with urea by scanning transmission electron microscopy (STEM)/EDS mapping. The CP method produced particle dimensions of 45.62 nm for length and 14.16 nm for width. In comparison, the readings obtained through the HT method were around 74.69 nm and 20.44 nm for length and width, respectively. The field application of nanourea demonstrated impressive results, indicating a significant relationship between the particle size of nanourea and its impact on several agricultural factors. The grain yield using traditional synthetic fertilizer (urea) ranged from 6.47 to 6.52 t ha−1 with a very low NUE of 35.8–36.34 %. Contrarily, the grain yield was found from 6.52 to 6.84 t ha−1 and the obtained NUE ranged from 57.58 to 71.0 % using nanourea of the same concentration calibrated with traditional urea by two methods. Additionally, nanourea treatments having 25 % less nitrogen (N) provided higher total N (TN) in grain suggesting possible nutritional enrichment while checking the yield penalty and substantial increase in N use efficiency (NUE). However, further upscaling of this research on a field scale is necessary to confirm the findings.

Influence of a New Generation Fungicide on Fusarium Head Blight in Spring Barley

Fusarium head blight is a serious problem in both traditional and intensive agriculture. The way to solve this problem is to use combined systemic fungicides. We therefore studied the effect of the new-generation fungicides Alto Super and KE (Propiconazole 250 g/l and Ciproconazole 80 g/l) combined with the foliar fertilizer Ultramag Super Sulphur-900. Laboratory studies were carried out at the Plant Pathology Laboratory of the People's Friendship University of Russia (RUDN), and field experiments were carried out in 2022 at the experimental field station of the Federal Research Centre "Nemchinovka." Fusarium head blight appeared in late July and early August. The application of the new-generation fungicide Alto Super, KE, alone and in combination with the new foliar fertilizer Ultramag Super Sulphur considerably reduced the development of the epiphytotic fungal disease. Their biological efficacy was 44.53–83.36%. The yield of Nur Spring barley in the experimental variants was higher than in the control variant. The yield gain of spring barley increased by 5.85% for Alto Super, KE, 9.40% for Ultramag Super Sulphur-900, and 12.85% for Alto Super, KE, and Ultramag Super Sulphur-900.

Allelic variation in rice blast resistance: a pathway to sustainable disease management.

Rice blast is a major problem in agriculture, affecting rice production and threatening food security worldwide. This disease, caused by the fungus Magnaporthe oryzae, has led to a lot of research since the discovery of the first resistance gene, pib, in 1999. Researchers have now identified more than 50 resistance genes on eight of the twelve chromosomes in rice, each targeting different strains of the pathogen.These genes are spread out across seventeen different loci. These genes, which primarily code for nucleotide-binding and leucine-rich repeat proteins, play an important part in the defense of rice against the pathogen, either alone or in combination with other genes. An important characteristic of these genes is the allelic or paralogous interactions that exist within these loci. These relationships contribute to the gene's increased capacity for evolutionary adaptation. The ability of resistance proteins to recognize and react to novel effectors is improved by the frequent occurrence of variations within the domains that are responsible for recognizing pathogen effectors. The purpose of this review is to summarize the progress that has been made in identifying these essential genes and to investigate the possibility of utilizing the allelic variants obtained from these genes in future rice breeding efforts to increase resistance to rice blast.

Insights into the environmental benefits of using apple pomace for biosorption of lead from contaminated water

The apple processing industry generates large quantities of organic waste, presenting a major source of organic contamination. Consequently, finding an effective solution for valorizing this waste has become a pressing issue. This study aims to address two key concerns: (i) solving an agricultural problem by efficiently using agri-food residue, and (ii) removing lead, an extremely toxic element, from contaminated waters to mitigate environmental pollution. Two biosorbents were tested: raw apple waste (RA), obtained from a mixture of apple varieties, and the same material after extracting valuable bioactive and reusable components, extracted apple (EA). The study evaluated the influence of pH, initial biosorbent mass, adsorption kinetics, and equilibrium isotherms. The results are very promising, showing a lead removal efficiency of 82 % for RA and 100 % for EA at a low initial concentration of the solution of 20 mg Pb2⁺/L and an optimal pH of 5 ± 0.5. The Langmuir model predicted a maximum adsorption capacity of 44.6 mg/g for RA and 48.6 mg/g for EA. These findings demonstrate that apple waste, even after selective extraction of valuable bioactive components, can be effectively used for environmental remediation on a practical scale.

A stacking ensemble machine learning based approach for classification of plant diseases through leaf images

Diseases and pests in plants/crops are major causes of significant agricultural losses with economic, social and ecological impacts. Therefore, there is a need for early identification of plant diseases and pests through automated systems. Recently, machine learning-based methods have become popular in solving agricultural problems such as plant diseases faced by technically-noob farmers. This work proposes a novel method based on stacking ensemble machine learning to detect plant diseases in Uradbean precisely. Two classifiers: support vector machine (SVM), random forest (RF) are trained on a dataset consists of Uradbean infected and healthy leaf images. These classifiers are stacked with logistic regression (LR) classifier. In the diverse ensemble, LR classifier is used as a meta-learner which enhanced the precision of the disease classification. The fuzzy C-Means clustering with particle swarm optimization is used for image segmentation. Haralick, Hu Moments and color histogram methods are used in feature extraction. During the tests, the proposed model is also compared with pre-trained networks: DenseNet-201, ResNet-50, and VGG19. It achieved an impressive classification accuracy of 96.82 % which is higher than the individual classifiers and pre-trained networks. To validate model performance, it is evaluated on a benchmark public dataset consists of Apple leaf images and achieved 98.30% accuracy. It is observed that ensemble method reflects an advantage over individual models in increasing the classification rates and reducing the computational overhead in comparison to pre-trained networks which struggle due to the issues such as irrelevant features, generation of pertinent characteristics, and noise

The effect of testing rate on biomechanical measurements related to stalk lodging

BackgroundStalk lodging (the premature breaking of plant stalks or stems prior to harvest) is a persistent agricultural problem that causes billions of dollars in lost yield every year. Three-point bending tests, and rind puncture tests are common biomechanical measurements utilized to investigate crops susceptibility to lodging. However, the effect of testing rate on these biomechanical measurements is not well understood. In general, biological specimens (including plant stems) are well known to exhibit viscoelastic mechanical properties, thus their mechanical response is dependent upon the rate at which they are deflected. However, there is very little information in the literature regarding the effect of testing rate (aka displacement rate) on flexural stiffness, bending strength and rind puncture measurements of plant stems.ResultsFully mature and senesced maize stems and wheat stems were tested in three-point bending at various rates. Maize stems were also subjected to rind penetration tests at various rates. Testing rate had a small effect on flexural stiffness and bending strength calculations obtained from three-point bending tests. Rind puncture measurements exhibited strong rate dependent effects. As puncture rate increased, puncture force decreased. This was unexpected as viscoelastic materials typically show an increase in resistive force when rate is increased.ConclusionsTesting rate influenced three-point bending test results and rind puncture measurements of fully mature and dry plant stems. In green stems these effects are expected to be even larger. When conducting biomechanical tests of plant stems it is important to utilize consistent span lengths and displacement rates within a study. Ideally samples should be tested at a rate similar to what they would experience in-vivo.

Prevalence and risk factors of reproductive health problems in dairy cows in Southern Ethiopia

Reproductive disorders are the leading cause of economic loss in the cattle industry, as they reduce the reproductive efficiency and lifetime productivity of cows. In Ethiopian dairy, without proper preventive measures, these disorders can have a severe impact on productivity. Therefore, it is crucial to identify the extent of these issues and the factors contributing to them in order to improve the health and productivity of dairy cows. This study aimed to assess the prevalence, type, and potential risk factors for reproductive health problems in dairy farms in Hawassa city and Wolaita Sodo town. We observed 391 pregnant and postpartum cows for reproductive disorders and used a logistic regression model to identify risk factors. The study found that 165 cows (41.6%; 95% CI: 36.8-46.5%) had at least one reproductive issue. The most common disorders included retained fetal membrane (11.6%), dystocia (10.5%), metritis (7.2%), abortion (5.6%), stillbirth (2.6%), uterine/vaginal prolapse (2.3%), endometritis (2.1%), and pyometra (0.5%). The risk of dystocia significantly increased with male calf deliveries (OR=2.9; p=0.004) and larger farm sizes (OR=2.1; p=0.039). Cows experiencing dystocia were 5.3 times more likely to have retained fetal membrane (p<0.001). Heifers were significantly more likely to develop metritis (OR=5.1; p=0.001), as were cows with retained fetal membrane (OR=11.6; p<0.001). The incidence of abortion was significantly elevated in cows from semi-intensive farms (OR= 5.3; p<0.001). The study concludes that these reproductive health issues can significantly impact future uterine health, resulting in prolonged uterine involution. It is crucial to raise awareness among dairy farmers about effective herd health management practices to minimize losses.

An in vitro evaluation of the effect of antimicrobial treatment on bovine mammary microbiota

Antimicrobial-resistant bacteria have been an increasing problem in human medicine and animal husbandry since the introduction of antimicrobials on the market in the 1940s. Over the last decades, efforts to reduce antimicrobial usage in animal husbandry have been shown to limit the development of resistant bacteria. Despite this, antimicrobial-resistant bacteria are still commonly detected and isolated worldwide. In this study, we investigated the presence of antimicrobial-resistant bacteria in bovine milk samples using a multiple approach based on culturing and amplicon sequencing. We first enriched milk samples obtained aseptically from bovine udders in the presence of two antimicrobials commonly used to treat mastitis and then described the resistant microbiota by amplicon sequencing and isolate characterization. Our results show that several commensal species and mastitis pathogens harbor antimicrobial resistance and dominate the enriched microbiota in milk in presence of antimicrobial agents. The use of the two different antimicrobials selected for different bacterial taxa and affected the overall microbial composition. These results provide new information on how different antimicrobials can shape the microbiota which is able to survive and reestablish in the udder and point to the fact that antimicrobial resistance is widely spread also in commensal species.