The contribution of previous lameness events and body condition score to the occurrence of lameness in dairy herds: a study of 2 herds

It has been demonstrated that low body condition and previous occurrence of lameness increase the risk of future lameness in dairy cows. To date the population attributable fraction (PAF), which provides an estimate of the contribution that a risk factor makes toward the total number of disease events in a population, has not been explored for lameness using longitudinal data with repeated measures. Estimation of PAF helps to identify control measures that could lead to the largest improvements on-farm. The aim of this study was to use longitudinal data to evaluate the proportion of lameness that could be avoided in 2 separate herds (2 populations) through (1) reduced recurrence of previous lameness events, (2) and moving body condition score (BCS) into more optimal ranges. Data were obtained from 2 UK dairy herds: herd A, a 200-cow herd with 8 yr of data from a total of 724 cows where lameness events were based on weekly locomotion scores (LS; 1 to 5 scale), and herd B, a 600-cow herd with data recorded over 44 mo from a total of 1,040 cows where treatment of clinical cases was used to identify lameness events. The PAF for categories of BCS were estimated using a closed equation appropriate for multiple exposure categories. Simulation models were used to explore theoretical scenarios to reflect changes in BCS and recurrence of previous lameness events in each herd. For herd A, 21.5% of the total risk periods (cow-weeks) contained a lameness event (LS 3, 4, or 5), 96% of which were repeat events and 19% were recorded with BCS 16 wk before a risk period. The median PAF estimated for changes in BCS were in the region of 4 to 11%, depending on severity of lameness. Repeated bouts of lameness made a very large contribution to the total number of lameness events. This could either be because certain cows are initially susceptible and remain susceptible, due to the increased risk associated with previous lameness events, or due to interactions with environmental factors. This area requires further research

Lameness in dairy heifers; impacts of hoof lesions present around first calving on future lameness, milk yield and culling risk

The importance of lameness in primiparous dairy heifers is increasingly recognised. Although it is accepted that clinical lameness in any lactation increases the risk of future lameness, the impact of foot lesions during the first lactation on long-term lameness risk is less clear. This retrospective cohort study aimed to investigate the impacts of foot lesions occurring around the time of first calving in heifers on future lameness risk, daily milk yield and survival within a dairy herd. Records were obtained for 158 heifers from one UK dairy herd. Heifers were examined in 2 month blocks from 2 months pre-calving through to 4 months post-calving. Sole lesions and white line lesions were scored on a zero to 10 scale and digital dermatitis on a zero to 3 scale. Outcomes investigated were; lameness risk based on weekly locomotion scores, average daily milk yield and culling risk. Mixed effect models were used to investigate associations between maximum lesion scores and outcomes. Lesion scores in the highest score categories for claw horn lesions (sole lesions and white line lesions) in the 2 to 4 month post-calving period were associated with an increased risk of future lameness; heifers with white line lesion scores ≥3 compared with scores zero to 1 and heifers with sole lesion scores ≥4 compared with score 2, at this time point, had a predicted increased risk of future lameness of 1.6 and 2.6 respectively. Sole lesions ≥4 were also associated with a reduction in average daily milk yield of 2.68 kg. Managing heifers to reduce claw horn lesions during this time period post-calving may provide health, welfare and production benefits for the long-term future of those animals. A novel finding from the study was that mild lesion scores compared with scores zero to 1, were associated with a reduced risk of future lameness for white line lesions and sole lesions occurring in the pre-calving or 2 to 4 months post-calving periods respectively. Mild sole lesions in the pre-calving period were also associated with a reduced risk of premature culling. One hypothesis for this result is that a mild insult may result in adaptive changes to the foot leading to greater biomechanical resilience and so increased longevity

Police-initiated diversion for youth to prevent future delinquent behavior: a systematic review

BackgroundOverly punitive responses to youth misconduct may have the unintended consequence ofincreasing the likelihood of future delinquency; yet, overly lenient responses may fail to serveas a corrective for the misbehavior. Police diversion schemes are a collection of strategiespolice can apply as an alternative to court processing of youth. Police-initiated diversionschemes aim to reduce reoffending by steering youth away from deeper penetration into thecriminal justice system and by providing an alternative intervention that can help youthaddress psychosocial development or other needs that contribute to their problem behavior.ObjectivesThe objective of this review was to synthesize the evidence on the effectiveness of pre-courtinterventions involving police warning or counseling and release, and cautioning schemes inreducing delinquent behavior.Search methodsA combination of 26 databases and websites were searched. References of relevant reviewswere also scanned to identify studies. We also consulted with experts in the field. Searcheswere executed by two reviewers and conducted between August 2016 and January 2017.Selection criteriaOnly experimental and quasi-experimental designs were eligible for this review. All quasiexperimentaldesigns must have had a comparison group similar to the police diversionintervention group with respect to demographic characteristics and prior involvement indelinquent behavior (i.e., at similar risk for future delinquent behavior). Additionally, studiesmust have included youth participants between 12 and 17 years of age who either underwenttraditional system processing or were diverted from court processing through a police-leddiversion program. Studies were also eligible if delinquency-related outcomes, includingofficial and non-official (self-report or third-party reporting) measures of delinquency werereported.Data collection and analysisThis study used meta-analysis to synthesize results across studies. This method involvedsystematic coding of study features and conversion of study findings into effect sizesreflecting the direction and magnitude of any police-led diversion effect. There were 19independent evaluations across the 14 primary documents coded for this review. From this,we coded 67 effect sizes of delinquent behavior post diversion across 31 diversion-traditionalprocessing comparisons. We analyzed these comparisons using two approaches. The firstapproach selected a single effect size per comparison based on a decision rule and the secondused all 67 effect sizes, nesting these within comparison condition and evaluation design.ResultsThe general pattern of evidence is positive, suggesting that police-led diversion modestlyreduces future delinquent behavior of low-risk youth relative to traditional processing.Authors’ conclusionsThe findings from this systematic review support the use of police-led diversion for low-riskyouth with limited or no prior involvement with the juvenile justice system. Thus, policedepartments and policy-makers should consider diversionary programs as part of the mix ofsolutions for addressing youth crime

A physics-informed, machine learning emulator of a 2d surface water model: What temporal networks and simulation-based inference can help us learn about hydrologic processes

While machine learning approaches are rapidly being applied to hydrologic problems, physics-informed approaches are still relatively rare. Many successful deep-learning applications have focused on point estimates of streamflow trained on stream gauge observations over time. While these approaches show promise for some applications, there is a need for distributed approaches that can produce accurate two-dimensional results of model states, such as ponded water depth. Here, we demonstrate a 2D emulator of the Tilted V catchment benchmark problem with solutions provided by the integrated hydrology model ParFlow. This emulator model can use 2D Convolution Neural Network (CNN), 3D CNN, and U-Net machine learning architectures and produces time-dependent spatial maps of ponded water depth from which hydrographs and other hydrologic quantities of interest may be derived. A comparison of different deep learning architectures and hyperparameters is presented with particular focus on approaches such as 3D CNN (that have a time-dependent learning component) and 2D CNN and U-Net approaches (that use only the current model state to predict the next state in time). In addition to testing model performance, we also use a simplified simulation based inference approach to evaluate the ability to calibrate the emulator to randomly selected simulations and the match between ML calibrated input parameters and underlying physics-based simulation. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Assessment of the ParFlow-CLM CONUS 1.0 integrated hydrologic model: evaluation of hyper-resolution water balance components across the contiguous United States

Recent advancements in computational efficiency and Earth system modeling have awarded hydrologists with increasingly high-resolution models of terrestrial hydrology, which are paramount to understanding and predicting complex fluxes of moisture and energy. Continental-scale hydrologic simulations are, in particular, of interest to the hydrologic community for numerous societal, scientific, and operational benefits. The coupled hydrology-land surface model ParFlow-CLM configured over the continental United States (PFCONUS) has been employed in previous literature to study scale-dependent connections between water table depth, topography, recharge, and evapotranspiration, as well as to explore impacts of anthropogenic aquifer depletion to the water and energy balance. These studies have allowed for an unprecedented process-based understanding of the continental water cycle at high resolution. Here, we provide the most comprehensive evaluation of PFCONUS version 1.0 (PFCONUSv1) performance to date by comparing numerous modeled water balance components with thousands of in situ observations and several remote sensing products and using a range of statistical performance metrics for evaluation. PFCONUSv1 comparisons with these datasets are a promising indicator of model fidelity and ability to reproduce the continental-scale water balance at high resolution. Areas for improvement are identified, such as a positive streamflow bias at gauges in the eastern Great Plains, a shallow water table bias over many areas of the model domain, and low bias in seasonal total water storage amplitude, especially for the Ohio, Missouri, and Arkansas River basins. We discuss several potential sources for model bias and suggest that minimizing error in topographic processing and meteorological forcing would considerably improve model performance. Results here provide a benchmark and guidance for further PFCONUS model development, and they highlight the importance of concurrently evaluating all hydrologic components and fluxes to provide a multivariate, holistic validation of the complete modeled water balance. © 2021 Mary M. F. O'Neill et al.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Sandtank-ml: An educational tool at the interface of hydrology and machine learning

Hydrologists and water managers increasingly face challenges associated with extreme climatic events. At the same time, historic datasets for modeling contemporary and future hydrologic conditions are increasingly inadequate. Machine learning is one promising technological tool for navigating the challenges of understanding and managing contemporary hydrological systems. However, in addition to the technical challenges associated with effectively leveraging ML for understanding subsurface hydrological processes, practitioner skepticism and hesitancy surrounding ML presents a significant barrier to adoption of ML technologies among practitioners. In this paper, we discuss an educational application we have developed—Sandtank-ML—to be used as a training and educational tool aimed at building user confidence and supporting adoption of ML technologies among water managers. We argue that supporting the adoption of ML methods and technologies for subsurface hydrological investigations and management requires not only the development of robust technologic tools and approaches, but educational strategies and tools capable of building confidence among diverse users. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

The ParFlow Sandtank: An interactive educational tool making invisible groundwater visible

Physical aquifer models are a highly effective teaching tool for hydrology education, however they come with inherent limitations that include the high cost to purchase, the static configuration of the model materials, the time required to visualize hydrogeological phenomena, and the effort to reset and clean them over time. To address these and other limitations, we have developed an interactive computer simulation of a physical aquifer model called the ParFlow Sandtank. In this gamified interface, users run the simulation using a familiar web-app like interface with sliders and buttons while learning real hydrologic concepts. Our user interface allows participants to dive into the world of hydrology, understanding assumptions about model parameters such as hydraulic conductivity, making decisions about inputs to groundwater aquifer systems such as pumping rates, visualizing outputs such as stream flow, transport, and saturation, and exploring various factors that impact real environmental systems such as climate change. The ParFlow Sandtank has already been used in a variety of educational settings with more than 9,000 users per year, and we feel this emerging educational tool can be used broadly in educational environments and can be scaled-up to provide greater accessibility for students and educators. Here we present the capabilities and workflow of the ParFlow Sandtank, two use cases, and additional tools and custom templates that have been developed to support and enhance the reach of the ParFlow Sandtank. Copyright © 2022 Gallagher, Farley, Chennault, Cerasoli, Jourdain, O'Leary, Condon and Maxwell.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]