Search Results (135)

Background/Objectives: This review aimed to compare the efficacy of antibiotic treatment vs. non-antibiotic treatment in mild and moderate clinical mastitis in lactating dairy cows, categorized by the causative pathogen. Methods: The initial systematic review plan, which resulted in only four relevant articles, was altered due to limited available studies and significant heterogeneity among them. Consequently, five additional articles, closely meeting our criteria with minor differences, were included to ensure comprehensive analysis, resulting in nine included articles. Due to these pragmatic constraints, this review represents a hybrid between a systematic and a narrative review. The outcome of interest was the bacteriological cure (BC). Results: The findings revealed that antibiotic treatment resulted in improved BC rates for cases caused by Streptococci. For cases caused by Escherichia (E.) coli, antibiotic therapy showed no significant improvement in BC rates compared to non-antibiotic treatment, suggesting that antibiotics may be often unnecessary for these cases due to self-limiting tendencies. However, severe E. coli mastitis warrants systemic antibiotic treatment due to potentially life-threatening complications. Klebsiella spp. mastitis showed better cure rates with antibiotic therapy. Conclusions: This study underscores the importance of regular pathogen diagnostics to guide appropriate treatment, advocating for the use of on-farm rapid tests to reduce unnecessary antibiotic use while ensuring effective treatment outcomes. Full article

The integration of different agroecological practices could significantly mitigate the impact of climate change. Therefore, a 2-year field experiment on organic zucchini was carried out to study the effects of clover (Trifolium alexandrinum L.) cover crop management (green manure, GM vs. flattening using a roller crimper, RC), compared to a control without cover (CT). This agroecological practice was tested in combination with the following different fertilizer treatments: T1. compost produced by co-composting coal mining wastes with municipal organic wastes compost plus urea; T2. compost produced with the same matrices as T1, replacing urea with lawn mowing residues; T3. non-composted mixture of the industrial matrices; T4. on-farm compost obtained from crop residues. The GM management showed the highest marketable yield and aboveground biomass of zucchini, with both values higher by approximately 38% than those recorded in CT. The T1, T2, and T3 treatments showed higher SOC values compared to T4 in both years, with a gradual increase in SOC over time. The residual effect of fertilization on SOC showed a smaller reduction in T3 and T4 than in T1 and T2, in comparison with the levels recorded during the fertilization years, indicating a higher persistence of the applied organic matter in these treatments. The findings of this study pointed out that combining organic fertilization and cover cropping is an effective agroecological practice to maintain adequate zucchini yields and enhance SOC levels in the Mediterranean environment. Full article

Plastic is extensively used in nursery and greenhouse operations. Concerns are growing about the potential release of plastic byproducts, such as microplastics and per- and poly-fluoroalkyl substances (PFAS), into water resources. The purpose of this study was to (1) compare perceptions of plastic use and water quality impacts between scientists researching water contaminants and nursery/greenhouse growers, (2) identify barriers to growers reducing plastic use, and (3) explore preferred communication channels for scientists to inform growers about emerging research. An online survey was administered to collect data from scientists in a USDA-funded multi-state Hatch project (N = 20) and nursery/greenhouse growers (N = 66) across the United States. The findings indicated both groups were unsure of the impacts of plastic use. While most respondents perceived surface water pollution as a critical issue, neither scientists nor growers strongly agreed on-farm plastic use poses a significant threat. Both groups recognized the importance of regular water testing, but few believed mandatory changes to plastic use should be enacted without further evidence. Growers cited limited equipment, financial constraints, and uncertain availability of viable plastic alternatives as key barriers. Despite these barriers, growers were willing to learn more, primarily through online resources, short courses, and workshops. The findings underscore the need for targeted research that quantifies plastic byproducts in nursery/greenhouse water and identifies cost-effective alternatives. Timely dissemination of scientific findings using trusted sources will be critical to bridge knowledge gaps and support adoption of best practices to safeguard water quality in surface and groundwater. Full article

This study presents a mobile-based sperm analysis system featuring a user-friendly, droplet-loaded microfluidic chip that enables non-specialist users to perform the rapid and accurate quantitative evaluation of boar semen directly on the farm. The iSperm system integrates a tablet, optical module, heater, and real-time image analysis app to deliver automated measurements of sperm concentration, motility, and progressive motility in under one minute. Precision and user variability tests demonstrated high concordance with CASA and the hemocytometer, with minimal differences between trained and untrained users. A method comparison using 77 farm-collected samples confirmed agreement through Passing–Bablok regression and Bland–Altman analysis. ROC curve analyses further validated diagnostic accuracy for all parameters, with AUC values exceeding 0.95. The iSperm platform offers a reliable, user-friendly, and field-deployable solution for on-site semen quality assessment, improving decision-making in swine artificial insemination. Full article

Metritis is one of the most common postpartum diseases in dairy cows, associated with impaired reproductive performance and substantial economic losses. In this study, we investigated the potential of machine learning (ML) techniques applied to physiological, behavioural, and milk quality parameters for the early detection of metritis in dairy cows during the postpartum period. A total of 2707 daily observations were collected from 94 cows in early lactation, of which 11 cows (275 records) were diagnosed with metritis. The dataset included daily measurements of body weight, rumination time, milk yield, milk composition (fat, protein, lactose), somatic cell count (SCC), and feed intake. Five classification models—partial least squares discriminant analysis (PLS-DA), random forest (RF), support vector machine (SVM), neural network (NN), and an Ensemble model—were developed using standardised features and stratified 80/20 training/test splits. To address class imbalance, model loss functions were adjusted using class weights. Models were evaluated based on accuracy, sensitivity, specificity, positive and negative predictive values (PPV, NPV), area under the receiver operating characteristic (ROC) area under the curve (AUC), and Matthews correlation coefficient (MCC). The NN model demonstrated the highest overall performance (accuracy = 96.1%, AUC = 96.3%, MCC = 0.79), indicating strong capability in distinguishing both healthy and diseased animals. The SVM achieved the highest sensitivity (90.9%), while RF and Ensemble models showed high specificity (>98%) and PPV. This study provides novel evidence that ML methods can effectively detect metritis using routinely collected, non-invasive on-farm data. Our findings support the integration of neural and Ensemble learning models into automated health monitoring systems to enable earlier disease detection and improved animal welfare. Although external validation was not performed, internal cross-validation demonstrated consistent performance across models, suggesting suitability for application in multi-farm settings. To the best of our knowledge, this is among the first studies to apply ML for early metritis detection based exclusively only automated herd data. Full article

Optimising kid rearing is essential for sustainable dairy goat farming, yet validated parameters and practical benchmark data are lacking. This study aimed to develop and evaluate a set of key performance indicators (KPIs) for monitoring kid-rearing practices through a participatory approach. Researchers, veterinarians and five dairy goat farms participated developed a prototype set of KPIs covering birth, colostrum management, average daily gain (ADG), and mortality, which were stratified across four rearing phases: perinatal (first 48 h), postnatal (birth to weaning), postweaning (weaning to 12 weeks), and final rearing (12 weeks to mating). The set of KPIs was subsequently tested for its added value but also for its feasibility in practice on the five participating farms as proof of principle. On these farms, data were gathered over a six-month period (June 2020–January 2021), combining routine census data with on-farm records. Only three out of five farms returned complete datasets encompassing data from 715 kids. Results revealed significant variation in rearing outcomes across farms, particularly in birth weights and postweaning growth. Birth weight emerged as a key predictor for ADG, while differences in weaning strategies had the greatest impact on postweaning performance. Although the farmers acknowledged the added value of the developed KPIs, collection of these data during the kidding season was challenging and required further automation to simplify data collection on the farm. This study demonstrates the feasibility and value of individual-level data collection in dairy goat systems and underscores the need for practical tools to support routine monitoring and data-driven management. Full article

This study investigates the optimal water and nitrogen fertilization levels to enhance the productivity and quality of Mombasa grass (Panicum maximum cv. Mombasa) under drought-prone conditions. Four irrigation treatments were applied based on irrigation depth: high irrigation (I1 = 691.2 mm), control irrigation (I2 = 575.0 mm), moderate stress (I3 = 460.8 mm), and severe stress (I4 = 345.6 mm). Two nitrogen fertilization levels were tested: full fertilization (F1 = 300 kg N·ha⁻¹) and half fertilization (F2 = 150 kg N·ha⁻¹). Severe water stress (I4) significantly reduced growth parameters, with fresh weight (FW) decreasing by 21.9% and dry weight (DW) decreasing by 20.3% compared to the control. In contrast, higher irrigation levels (I1 and I2) notably improved FW and DW. Full nitrogen application (F1) enhanced FW, DW, and plant height, whereas the half dose (F2) resulted in lower growth performance. Water productivity (WP) was highest under moderate stress (I3) combined with F1, and under severe stress (I4) combined with F2, it was the worst. Protein percentage per irrigation water unit (PPW) increased with greater water deficits, while total protein production per irrigation water unit (TPW) peaked under higher irrigation levels. These findings indicate a trade-off between forage quality (PPW) and quantity (TPW), where PPW is more critical for marketing purposes and TPW is better suited for on-farm feeding. Economically, treatment I3F1 proved to be the most efficient option under moderate water availability. It combined reduced irrigation with a high fertilizer rate, resulting in a strong net return and the second-highest benefit-cost ratio among all treatments. This indicates its potential as a cost-effective and resource-efficient strategy in water-limited environments. Full article

Traditional horticultural information systems lack fine-grained, transparent on-farm event traceability, often providing only high-level post-harvest summaries. These systems also fail to standardise and integrate diverse data sources, ensure data privacy, and scale effectively to meet the demands of modern agriculture. Concurrently, rising requirements for global environmental, social, and governance (ESG) compliance, notably Scope 3 emissions reporting, are driving the need for farm-level visibility. To address these gaps, this study proposes a novel traceability framework tailored to horticulture, leveraging global data standards. The system captures key on-farm events (e.g., irrigation, harvesting, and chemical applications) at varied resolutions, using decentralised identification, secure data-sharing protocols, and farmer-controlled access. Built on a progressive Web application with microservice-enabled cloud infrastructure, the platform integrates dynamic APIs and digital links to connect on-farm operations and external supply chains, resolving farm-level data bottlenecks. Initial testing on Victorian farms demonstrates its scalability potential. Pilot studies further validate its on-farm interoperability and support for sustainability claims through digitally verifiable credentials for an international horticultural export case study. The system also provides a tested baseline for integrating data to and from emerging technologies, such as farm robotics and digital twins, with potential for broader application across agricultural commodities. Full article

The accurate prediction of body mass (BM) in cattle is crucial for herd monitoring, assessing biological efficiency, and optimizing nutritional management. This study evaluated BM prediction models using morphological data from 465 lactating Holstein cows, including the dorsal length (DL), thoracic width (TW), abdominal width (AW), rump width (RW), hip height (HH), body depth (BD), thoracic perimeter (TP), and abdominal perimeter (AP). Spearman’s correlation analysis identified TP (r = 0.89), AP (r = 0.88), and RW (r = 0.80) as the strongest predictors. Simple and multiple linear regression models, artificial neural networks (ANNs), and Support Vector Regression (SVR) were tested. The dataset was split into 90% for training (419 samples), 5% for validation (23 samples), and 5% for testing (23 samples). The best simple model, using only TP, achieved an R² of 0.7763 and an RMSE of 43.69 kg. A multiple regression model with TP, AP, and RW improved performance (R² = 0.9067, RMSE = 28.00 kg). The ANN outperformed all of the models (R² = 0.9125, RMSE = 25.86 kg), and was followed by SVR (R² = 0.9046, RMSE = 27.41 kg). As an indication of the evaluation of the results obtained, it is observed that, although regression models are effective, the ANNs and SVR provide greater accuracy, reinforcing their potential for herd management. However, simpler models remain viable alternatives for practical on-farm application. Full article

This study investigates grassland-based farming systems within the framework of agroecology (AE), focusing on the identification of relevant indicators for on-farm assessment. The purpose of this research is to test indicator compliance with AE at the farming system level in grassland farms, particularly in High-Nature-Value (HNV) areas. Seventeen farms in France and Italy were selected for this study, and data were collected through semi-structured interviews. These interviews explored various indicators across environmental, economic, and social dimensions. Principal Component Analysis (PCA) was employed to analyze the quantitative indicators, while qualitative data offered insights into farm management and learning practices. The results highlighted the importance of forage self-sufficiency (livestock production dimension) and revenue (economic dimension) as key indicators of successful agroecological management. The study also found that increasing forage self-sufficiency was linked to higher farmer satisfaction, an indicator related to the social dimension. Additionally, qualitative data underscored the significance of self-sufficiency, workload management, and social interaction and continuous learning as critical elements in grassland-based farming. In conclusion, this research proposes self-sufficiency as an indicator that can facilitate the assessment of grassland-based systems, aiding in the broader adoption of agroecological practices in compliance with European policies. Full article

Recent years have seen significant technological advancements in precision farming and plant phenotyping. Remote sensing along with deep learning (DL) techniques can increase phenotyping efficiency and help on-farm decision making with rapid stress detection. In this work, we use these techniques to evaluate drought stress in soybean plants, a crop whose yield is significantly affected by water availability. Images were taken from a high vantage in the field at various times throughout the day. Each image is given a wilting score ranging from 0 to 4 by expert scorers. We implement a DL method called multiple instance learning (MIL) to perform wilt classification as well as generate heat maps that highlight wilt levels in specific regions of the image. Given the significant overlap between adjacent classes in our dataset, we were able to achieve an overall classification accuracy of 64% and a one-off accuracy of 96% on our holdout test set. Our model outperformed DenseNet121 in most metrics, and provided comparable performance to a vision transformer (ViT) while having fewer parameters overall, less complexity (useful for edge implementations), and some interpretability. Furthermore, we were able to show that our model outperformed expert human annotators by predicting more consistent and accurate wilt levels when considering single-image re-annotation. The results show that our proposed methodology can be a useful approach in detecting drought stress in soybean fields to facilitate efficient crop management and aid selection of drought-resilient varieties. Full article

Commercial hazelnut (Corylus avellana L.) production is relatively new to Ontario and there are no Ontario-specific fertility recommendations for this crop. With the increasing numbers of hazelnut growers entering the industry and the number of acres coming into full production capacity, this was identified as a gap. A 3-year trial was conducted to look at how four different fertility treatment regimes impact hazelnut growth and yield: (1) Ontario’s guidelines for established tree fruits, (2) Modified Oregon’s guidelines for hazelnuts, (3) Grower’s management, and (4) Control (with no external fertilizers). Four pilot demonstration sites were also established to compare fertilized plots (e.g., Ontario’s guidelines for established tree fruits) with orchard-specific grower’s management. Location-specific soil and tissue tests were conducted to determine the amount of fertilizer to apply to each orchard. Hazelnut yields and economic returns varied with location, tree age, and market price of hazelnuts; however, fertilized treatment (e.g., Ontario’s guidelines for established tree fruits) outperformed the grower’s management by up to 75 percent with net economic returns of CAD 18–44 per tree. In the orchard where all four fertility treatments were compared, yields and economic returns from modified Oregon treatment and Ontario recommendation were not statistically different. However, they outperformed grower’s management by 44 and 42 percent, respectively. Modified Oregon and Ontario treatment yielded ~7.0 pounds (lb) per tree with a net economic return of CAD 27 per tree during the 3rd year of study, while grower’s management and control treatments yielded 4.8 and 4.0 lb per tree with net economic returns of CAD 19 and 16 per tree, respectively. Also, fertilized treatments showed higher levels of residual nutrients of N, P, and K in the soil and the leaf tissues. The project results supported that Ontario’s fertility guidelines for established tree fruits can be used for commercial hazelnut production on mineral soils in Ontario. Also, testing soils every three years or plant tissues every year could help match applied nutrients more closely with plant demand, thereby enhancing economical and ecological sustainability. Full article

Nutrient levels in liquid organic manures (LOM) vary greatly, so it is important to determine the concentrations before field application in order to ensure that fertilisation is tailored to the crop requirements. Precise knowledge of the nutrient content in LOMs is a basic prerequisite for the optimum supply of these nutrients to crops and for avoiding environmental problems caused by over-fertilisation. The constituents of LOMs can be determined on site using various methods. One possibility is near infrared spectroscopy (NIRS). This method is already a common procedure for use in the laboratory. This review deals with the suitability of the use of NIRS for the characterisation of LOMs on farm. For on-farm applications, there are many factors such as the ambient temperature or movements and vibrations of the machines which can influence the measurement with the sensors and thus also the measured values. The influencing factors should therefore be taken into account. The reliability of NIRS systems for the on-farm analysis of liquid manure is verified by the German Agricultural Society. For the tests, various LOMs from different farms are measured with NIRS sensors and the quality of the agreement of the NIRS data with laboratory tests is certified for the respective ingredients for each LOM type. In order to exploit the full potential of the NIRS technology in the future, the indispensable calibrations need to be expanded and improved so that the sensors deliver precise and reproducible results for the different LOM types in practical applications. Full article

The absence of efficient on-farm interventions against white spot syndrome viral (WSSV) infections can cause significant economic losses to shrimp farmers. With this exploratory study we aimed to test, both in vitro and in vivo, the efficacy of an organic acid mixture (Aq) against WSSV infections in shrimp. In vitro, using shrimp gut primary cells (SGP), 2% Aq significantly reduced WSSV infection and the amounts of H₂O₂ released but had no impact on CAT and SOD expression. In vivo, in a shrimp challenge test, 2% Aq significantly downregulated the expression of proteins involved in WSSV virulence, such as the lipopolysaccharide-β-1,3-glucan-binding protein (LGBP) and the TLR signalling pathway (LvECSIT), and increased the expression of HO-1 oxygenase. Additionally, at 2% Aq, the expression of the digestive-related enzyme carboxypeptidase B was upregulated in the gut, alongside a significant decrease in IL-22 expression, a cytokine usually increased during WSSV infection in shrimp. A low mortality rate (7.33%) was recorded in infected shrimp treated with 2% Aq compared to the 96.66% mortality in the absence of Aq. The peritrophic membrane (PM) was proven essential to ensure Aq efficacy, as the infected and treated PM deficient shrimp (PM−) had a mortality rate of 27.8%, compared to only 9.34% mortality in the infected shrimp at 2% Aq and in the presence of PM (PM+). Aq significantly increased the expression of mucin-1, mucin-2, mucin-5AC, mucin-5B, and mucin-19 in both PM+ and PM− shrimp. Conclusively, organic acid in mixtures can protect farmed shrimp against WSSV infection and increase their survivability through a mediated gut health effect which includes resistance to oxidative stress and improved immunity. Full article

Maize (Zea mays L.) is one of the most widely cultivated grain crops globally. In sub-Saharan Africa (SSA), it plays an important role in ensuring both food and income security for smallholder farmers. This study was conducted to (i) assess the performances of testcross hybrids constituted from maize lethal necrosis (MLN) tolerant doubled haploid (DH) lines under various management conditions; (ii) estimate the combining ability effects and determine the nature of gene action in the DH lines; and (iii) identify DH lines and testcross hybrids for resistance to MLN, high grain yield, and other important traits. Eleven DH lines were crossed with 11 single-cross testers using the line-by-tester mating design, and 115 successful testcross hybrids were generated. These hybrids, along with five commercial check hybrids, were evaluated across four optimum management conditions, two MLN artificial inoculations, and one managed drought environment in Kenya. Under each management condition, the effects of genotypes, environments, and genotype-by-environment interactions were significant for grain yield (GY) and most other traits. Hybrids T1/L3, T10/L3, and T11/L3 exhibited higher grain yields under at least two management conditions. A combining ability analysis revealed that additive gene effects were more important than non-additive effects for GY and most other traits, except for leaf senescence (SEN) and MLN disease severity score. DH line L3 exhibited a desirable general combining ability (GCA) effect for GY, while L5 was the best general combiner for anthesis date (AD) and plant height (PH) across all management conditions. DH lines L2, L6, and L7 showed negative GCA effects for MLN disease severity. Single-cross testers T11 and T10 were good general combiners for GY under all management conditions. Hybrids T2/L11, T9/L10, and T2/L10 demonstrated high specific combining ability (SCA) effects for GY under all conditions. This study identified DH lines and testers with favorable GCA effects for grain yield, MLN resistance, and other agronomic traits that can be used in breeding programs to develop high-yielding and MLN-resistant maize varieties. Better-performing testcross hybrids identified in the current study could be verified through on-farm testing and released for commercial production to replace MLN-susceptible, low-yield hybrids grown in the target ecologies. Full article