Search Results (223)

Despite an abundance of available research, calf mortality persists as a multifaceted phenomenon that presents ongoing challenges in practical management. This historical single-cohort study was conducted to provide a more comprehensive layer of knowledge to the existing information pool on calf mortality risk factors by using multiblock partial least squares analysis. The method reveals the contribution of several variables aggregated into thematic blocks and allows to include multiple outcome variables describing the same phenomenon. Such an analysis of the data provides valuable information to farmers, veterinarians, and advisors alike, not only about single risk factors, but also about management areas to prioritize when tackling calf mortality. Data was gathered from 118 Estonian dairy herds, each comprising ≥100 cows, via questionnaire, sample collection, and on-farm scoring and measurements. The final dataset included 147 questions divided into 13 meaningful blocks. The outcome variables were annual herd-level calf mortality risk during the first 21 days (MR21) and 22–90 days (MR90) using farm records and the national cattle database, respectively. The average MR21 was 5.9% (median 4.4%, range 0.0–26.8%) and the average MR90 was 2.7% (median 2.3%, range 0.0–12.7%). Of the 13 thematic variable blocks, the most important blocks explaining calf mortality were ‘Routine stress-inducing activities’, ‘Herd characteristics’, ‘Calving management’, ‘Calf housing during 5–21 days’, and ’External biosecurity’. The most influential single variables associated with higher overall calf on-farm mortality during the preweaning period were poorer cleanliness scores of calving animals and calves having access to an outdoor area during the first 21 days of life. Detected risk factors for MR21 were calf barn age > 20 years, allowing the calves to suckle the first colostrum, bucket feeding calves during the first three weeks, disbudding all calves (compared to only heifer calves), and disbudding at 21–29 days of age. Risk factors for MR90 included the use of automatic milk feeders and feeding waste milk during the first three weeks, early introduction of calves to large group pens and higher in-pen age differences, absence of forced ventilation during the first three weeks, opportunity for feces to spread between calf pens, and use of calving pens for sick animals. Washing and disinfection of newborn calves’ pens and testing colostrum quality were protective factors against both MR21 and MR90. Other protective practices for MR21 were related to proper colostrum feeding routines, whereas lower MR90 was mostly associated with efficient external biosecurity practices and vaccination programs. The multiblock model proved to be beneficial in providing a broader understanding of the importance of different management areas on calf mortality. Full article

Soil moisture content is an important determinant of crop productivity, especially in agricultural systems that are dependent on rainfall. Climate variability has introduced water management challenges for smallholder farmers in Southern Africa. The emergence of unmanned aerial vehicle (UAV)-borne remote sensing offers modern solutions for monitoring soil moisture, plant health and overall crop productivity in real-time. This study evaluated the utility of UAV-acquired data in conjunction with random forest regression in predicting soil moisture content and chlorophyll across different growth stages of taro. The estimation models achieved R² values up to 0.90 with rRMSE as low as 1.25%, demonstrating the robust performance of random forest in concert with different spectral datasets in estimating soil moisture and chlorophyll. Correlation analysis confirmed the association between these two variables, with the strongest correlation observed during the vegetative stage (r = 0.81, p < 0.05) and the weakest during the late vegetative stage (r = 0.78, p < 0.05). The results showed that UAV bands were crucial in predicting soil moisture and chlorophyll across all stages. These results demonstrate the utility of remote sensing, particularly UAV-borne sensors, in monitoring crop productivity in smallholder farms. By employing UAV-borne sensors, farmers can improve on-farm water management and make better and more informed decisions. Full article

The measurement of available water for agricultural plants is a crucial parameter for farmers, particularly to plan irrigation. However, an area-wide measurement is often not trivial as there are several inputs and outputs of water into the system. Here, we present a high-resolution, remote sensing-based water balance model for starch potato cultivation, combining multispectral ground station data with UAV and satellite imagery. Over a three-year period (2021–2023), data from Arable Mark 2 ground stations, DJI Phantom 4 MS drones, PlanetScope satellites, and Sentinel-2 satellites were collected in Mecklenburg–Western Pomerania, Germany. The model utilizes NDVI-based crop coefficients (R² = 0.999) to estimate evapotranspiration and integrates on-farm irrigation and precipitation data for precise water balance calculations. A correlation with reference NDVI observations by Arable Mark 2 systems can be shown for UAV (R² = 0.94), PlanetScope satellite data (R² = 0.94), and Sentinel-2 satellite data (R² = 0.93). We demonstrate the model’s ability to capture intra-site heterogeneity on a precision farming scale. Our spatially comprehensive model enables farmers to optimize irrigation strategies, reducing water and energy use. Although the results are based on sprinkler irrigation, the model remains adaptable for advanced irrigation methods such as drip and subsurface systems. Full article

The increasing adoption of digital technologies in the agriculture sector has significantly contributed to optimizing on-farm routines, especially in data-driven decision-making. This study aimed to develop an application to determine the slaughter point of lambs by predicting subcutaneous fat thickness (SFT) using pre-slaughter parameters such as body weight (BW), body condition score (BCS), and skinfold measurements at the brisket (BST), lumbar (LST), and tail base (TST), obtained using an adipometer. A total of 45 Pantaneiros lambs were evaluated, finished in feedlot, and slaughtered at different body weights. Each pre-slaughter weight class showed a distinct carcass pattern when all parameters were included in the model. Exploratory analysis revealed statistical significance for all variables (p < 0.001). BW and LST were selected to construct the predictive equation (R² = 55.44%). The regression equations were integrated into the developed application, allowing for in-field estimation of SFT based on simple measurements. Compared to conventional techniques such as ultrasound or visual scoring, this tool offers advantages in portability, objectivity, and immediate decision-making support. In conclusion, combining accessible technologies (e.g., adipometer) with traditional variables (e.g., body weight), represents an effective alternative for production systems aimed at optimizing and enhancing the value of lamb carcasses. Full article

On-farm emergency slaughter (OFES) is employed when cattle are unfit for transport but still suitable for human consumption, thereby ensuring animal welfare and reducing food waste. This study analysed OFES patterns in Northern Italy, where a large cattle population is housed but information on the practice is rarely analysed. A total of 12,052 OFES cases from 2021 to 2023 were analysed. Most involved female cattle (94%) from dairy farms (79%). Locomotor disorders were the leading reason (70%), particularly trauma and fractures, followed by recumbency (13%) and calving-related issues (10%). Post-mortem findings showed limbs and joints as the most frequent condemnation sites (36%), often linked to trauma. A significant reduction in OFES cases occurred over time, mainly due to fewer recumbency and calving issues, likely reflecting stricter eligibility criteria introduced in 2022. Weekly variations, with peaks on Mondays and lows on Saturdays, suggest that logistical constraints may sometimes influence OFES promptness. These findings suggest that on-farm management and animal handling could be improved further to reduce welfare risks and carcass waste. Due to the lack of standardised data collection and regulatory harmonisation, a multi-country investigation could improve our understanding of this topic and inform best practice. Full article

Global changes and society’s development necessitate the improvement of water use and irrigation water saving, which require a set of water management measures to best deal with the necessary changes. This study considers the framework of the change process for water management in the Hetao Irrigation District (HID) of the Yellow River Basin. This paper presents the main measures that have been applied to ensure the sustainability and modernization of Hetao, mitigating water scarcity while maintaining land productivity and environmental value. Several components of modernization projects that have already been implemented are characterized, such as the off-farm canal distribution system, the on-farm surface irrigation, innovative crop and soil management techniques, drainage, and salinity control, including the management of autumn irrigation and advances of drip irrigation at the sector and farm levels. This characterization includes technologies, farmer training, labor needs, energy consumption, water savings, and economic aspects, based on data observed and reported in official reports. Therefore, this study integrates knowledge and analyzes the most limiting aspects in some case studies, evaluating the effectiveness of the solutions used. Full article

The petiole nitrate–nitrogen concentration (PNNC) has been an industry standard indicator for in-season potato (Solanum tuberosum L.) nitrogen (N) status diagnosis. Leaf sensors can be used to predict the PNNC and other N status indicators non-destructively. The SPAD meter is a common leaf chlorophyll (Chl) meter, while the Dualex is a newer leaf fluorescence sensor. Limited research has been conducted to compare the two leaf sensors for potato N status assessment. Therefore, the objectives of this study were to (1) compare SPAD and Dualex for predicting potato N status indicators, and (2) evaluate the potential prediction improvement using multi-source data fusion. The plot-scale experiments were conducted in Becker, Minnesota, USA, in 2018, 2019, 2021, and 2023, involving different cultivars, N treatments, and irrigation rates. The results indicated that Dualex’s N balance index (NBI; Chl/Flav) always outperformed Dualex Chl but did not consistently perform better than the SPAD meter. All N status indicators were predicted with significantly higher accuracy with multi-source data fusion using machine learning models. A practical strategy was developed using a linear support vector regression model with SPAD, cultivar information, accumulated growing degree days, accumulated total moisture, and an as-applied N rate to predict the vine or whole-plant N nutrition index (NNI), achieving an R² of 0.80–0.82, accuracy of 0.75–0.77, and Kappa statistic of 0.57–0.58 (near-substantial). Further research is needed to develop an easy-to-use application and corresponding in-season N recommendation strategy to facilitate practical on-farm applications. 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

Soil health is vital for food security and ecosystem services supporting climate change mitigation. Cover crops (CCs) improve soil quality and crop yields in intensive agriculture. This study assessed the impact of Sinapis alba L. as a CC on ten physical, chemical, and biological soil indicators before maize planting. Three management systems were compared: (i) CC with conventional tillage (CT), (ii) CC under no tillage (NT), and (iii) tilled fallow without CC (TF). Measurements were taken at 60 and 90 days after sowing (DAS) at 0–6 and 0–20 cm depths. The Soil Quality Index (SQI) was higher at the surface under NT (0.69 at 60 DAS; 0.65 at 90 DAS). At 0–20 cm, SQI values increased at 90 DAS but did not differ among treatments. TF also showed improvements (up to +18% at 0–20 cm). Dissolved organic matter increased significantly (1.7–2.5 times), especially under NT and CT. NT enhanced structural stability (+70%) and reduced bulk density (−47%). All glomalin fractions decreased at 90 DAS; however, NT retained higher concentrations of recalcitrant glomalin in the 0–6 cm layer compared to the other treatments. These findings highlight S. alba under no tillage as a promising strategy to improve soil quality, though long-term studies are needed. Full article

Foot-and-mouth disease (FMD) significantly impacts global livestock industries, with raw milk transportation posing a recognized pathway for viral dissemination, particularly in endemic regions. This study aimed to evaluate the risk of FMD virus (FMDV) introduction and transmission to dairy farms via raw milk transportation in Ban Thi District, Thailand. A qualitative risk assessment methodology, adhering to WOAH guidelines, was employed. Data were collected through structured farmer surveys (n = 109), expert interviews (n = 12), and reviews of national disease surveillance data and scientific literature. The risk assessment, utilizing a scenario tree approach for domestic dairy cattle, revealed a moderate overall risk of FMDV transmission. This finding is primarily attributed to critical gaps in on-farm biosecurity practices, potential contamination at milk collection centers, and significant challenges in detecting subclinical carrier animals. While the qualitative approach presented inherent limitations and uncertainties, the study successfully highlighted key vulnerabilities. The results underscore the urgent necessity for implementing targeted biosecurity protocols, developing more robust surveillance strategies for FMDV carriers, and establishing standardized risk assessment frameworks to mitigate potential outbreaks and protect the regional dairy industry. Full article

Hoof disorders in small ruminants pose significant challenges to animal welfare and farm productivity. This study presents the first attempt to determine the prevalence of lameness and hoof disorders and their associated risk factors in goat and sheep farms in Selangor, Malaysia. Locomotion scores were collected from 226 animals (126 sheep and 100 goats) across 10 farms. A hoof examination was conducted, and hoof lesions were identified through detailed photographic evaluation. On-farm assessments and interviews were conducted to gather information on management practices from the farms. Data were analysed using descriptive statistics, bivariate analysis, and logistic regression models. The prevalence of lameness was 42.8% (95% CI 34.2 to 51.9) in sheep and 23.0% (95% CI 16.3–38.4) in goats. Significant variation (p > 0.05) in lameness prevalence was observed across farms, ranging from 26.7% to 61.5% in sheep and 7.7% to 30.8% in goat farms. The majority of lameness and hoof lesions were observed in the hindlimbs of both species. The prevalence of hoof disorders was 91.3% (95% CI 84.6–95.4) in sheep and 43.0% in goats (95% CI 21.4–58.0). The predominant hoof disorders were overgrown wall horn, white line disease, sole bruise, and wall fissures. No hoof affections of infectious origin were observed in the sampled animals. Risk factors for lameness and hoof lesions in sheep included pregnancy, semi-intensive management, and breeds other than Damara. Higher odds of lameness were observed in exotic goat breeds and those with overgrown wall horns. In conclusion, this study revealed a high prevalence of lameness and hoof disorders in goat and sheep farms, highlighting the need to address these important welfare and economic issues. While the identified risk factors could be considered for the management of hoof disorders in small ruminant farms, a larger sample size that is representative of the sheep and goat population is recommended for more generalizable results. 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

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

Among physiological parameters, total locomotor activity (TLA) and heart rate (HR) are used as physiological indicators in animal welfare evaluations. The present study aimed to simultaneously record for 24 h the TLA and HR of ten clinically healthy horses housed in conventional individual boxes subjected to a natural photoperiod and temperature. An actigraphy-based data logger was placed on the headstall, and an equine HR monitor was placed around the chest to monitor TLA and HR, respectively. Activity was monitored with 5 min sampling intervals and HR with 5 s intervals. To make the data points uniform, the means of 5 min intervals were calculated. Both investigated parameters showed a daily rhythmicity with a diurnal acrophase (locomotor activity 17:05 ± 1:15 arbitrary unit; heart rate 16.40 ± 0.30 beats/min). Robustness of the rhythm was 17.95 ± 10.53% and 37.05 ± 0.63% for the TLA and HR. A positive correlation was observed between the two investigated parameters in each horse, r = 0.48 ± 0.07, p < 0.0001. Change in TLA is a good index for success of management. Its positive correlation with daily HR monitoring confirms the use of these two physiological parameters for an objective on-farm welfare assessment. The application of new technologies for the simultaneous recording of physiological indexes of animals’ welfare can be a useful instrument. Full article