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Consumption of an adequate volume of high-quality colostrum is vital to a dairy calf's ability to survive and become a productive herd member. However, some dairy herds have reported a deficiency of colostrum production, which ranges from a low volume to no colostrum produced, by cows during fall and winter. Little information regarding this phenomenon exists. The purpose of this study was to characterize the syndrome and identify potential risk factors for low colostrum yield. A 2,500-cow Jersey dairy farm was enrolled in a prospective cohort study in May 2016, to evaluate possible effects of photoperiod, temperature, and cow factors on colostrum production. Dairy personnel were trained to collect, weigh, and evaluate colostrum quality. Information on parity, previous lactation length, previous 305-d mature-equivalent milk production, and dry period length were collected through the farm's dairy management software. Weather and photoperiod data were also collected. Over the year of enrollment, 2,988 eligible cows calved and had colostrum weights recorded and 38% were primiparous (n = 1,143), 25% were in their second lactation (n = 752), and 37% were in their third or greater lactation (n = 1,093). The overall average colostrum yield was 6.6 kg/cow in June 2016, 2.5 kg/cow in December 2016, and 4.8 kg/cow in May 2017. Multiparous cows had a larger decline in colostrum production between June and December (6.6 to 1.3 kg/cow) compared with primiparous animals (6.5 to 4.2 kg/cow). Overall, average colostrum production decreased by 0.17 kg/cow per week during this time, 0.22 kg for multiparous cows and 0.08 kg for primiparous cows. A logistic regression model was constructed for all cows to evaluate effects of cow factors on low colostrum production (<2.7 kg at first milking). Dry period length, calf sex, singleton or twin, age at freshening, month of calving and previous lactation length were significantly associated with the probability of low colostrum yield (<2.7 kg at first milking). A cross-correlation function analysis between the time series for colostrum yield and photoperiod revealed a high correlation at the time of calving and 1 mo prior, particularly for multiparous cows. A pedigree analysis showed that extreme colostrum yield (low vs. high) followed some sire lines. Low colostrum production in this herd could have an economic effect on the dairy and calf health and appears to have a strong seasonal and, potentially, a genetic component., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Wildlife-originated zoonotic diseases in general are a major contributor to emerging infectious diseases. Hantaviruses more specifically cause thousands of human disease cases annually worldwide, while understanding and predicting human hantavirus epidemics pose numerous unsolved challenges. Nephropathia epidemica (NE) is a human infection caused by Puumala virus, which is naturally carried and shed by bank voles (Myodes glareolus). The objective of this study was to develop a method that allows model-based predicting 3 months ahead of the occurrence of NE epidemics. Two data sets were utilized to develop and test the models. These data sets were concerned with NE cases in Finland and Belgium. In this study, we selected the most relevant inputs from all the available data for use in a dynamic linear regression (DLR) model. The number of NE cases in Finland were modelled using data from 1996 to 2008. The NE cases were predicted based on the time series data of average monthly air temperature (°C) and bank voles' trapping index using a DLR model. The bank voles' trapping index data were interpolated using a related dynamic harmonic regression model (DHR). Here, the DLR and DHR models used time-varying parameters. Both the DHR and DLR models were based on a unified state-space estimation framework. For the Belgium case, no time series of the bank voles' population dynamics were available. Several studies, however, have suggested that the population of bank voles is related to the variation in seed production of beech and oak trees in Northern Europe. Therefore, the NE occurrence pattern in Belgium was predicted based on a DLR model by using remotely sensed phenology parameters of broad-leaved forests, together with the oak and beech seed categories and average monthly air temperature (°C) using data from 2001 to 2009. Our results suggest that even without any knowledge about hantavirus dynamics in the host population, the time variation in NE outbreaks in Finland could be predicted 3 months ahead with a 34% mean relative prediction error (MRPE). This took into account solely the population dynamics of the carrier species (bank voles). The time series analysis also revealed that climate change, as represented by the vegetation index, changes in forest phenology derived from satellite images and directly measured air temperature, may affect the mechanics of NE transmission. NE outbreaks in Belgium were predicted 3 months ahead with a 40% MRPE, based only on the climatological and vegetation data, in this case, without any knowledge of the bank vole's population dynamics. In this research, we demonstrated that NE outbreaks can be predicted using climate and vegetation data or the bank vole's population dynamics, by using dynamic data-based models with time-varying parameters. Such a predictive modelling approach might be used as a step towards the development of new tools for the prevention of future NE outbreaks., (© 2012 Blackwell Verlag GmbH.)

The connection between nephropathia epidemica (NE) and vegetation dynamics has been emphasized in recent studies. Changing climate has been suggested as a triggering factor of recently observed epidemiologic peaks in reported NE cases. We have investigated whether there is a connection between the NE occurrence pattern in Belgium and specific trends in remotely sensed phenology parameters of broad-leaved forests. The analysis of time series of the MODIS Enhanced Vegetation Index revealed that changes in forest phenology, considered in literature as an effect of climate change, may affect the mechanics of NE transmission.