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In dairy cows the body condition forms a reflection of the energy reserves of the organism. Health, welfare and productivity of dairy cows are strongly associated with changes in body condition. As lactation puts substantial demands on the metabolism of dairy cows, farm management aims at avoiding either a deficient body condition or a substantial loss of body condition within a short period of time. A body condition higher or lower than recommended (over- and underconditioning in the following) compromises dairy cow productivity. While the body condition of Holstein Friesian cows has been thoroughly explored, few is known about the consequences of deviations from a target body condition for health and productivity of cows from other breeds. This study explores the percentage of over- and underconditioned cows at different days post partum [dpp] and their association with production parameters i.e., milk yield, milk fat and milk protein content of Simmental cows on Bavarian farms, categorized by parity (primi- or multiparous). Our study displays that in Simmental cows, overconditioning is more prevalent than underconditioning. While the middle of lactation (dpp = 100-199) resulted in higher percentage of overconditioning, the dry period (dpp = < 0 & > 299) indicated a higher percentage of underconditioned cows. The dry period and the middle of lactation are therefore the most challenging lactation stages for Simmental cows. We found milk protein content to have the strongest association with over- and underconditioning in Simmental cows. The probability of overconditioning was higher with higher milk protein content for every lactation stage and the probability of underconditioning was lower with higher milk protein content in every lactation stage. This study provides a theoretical basis for potential improvements in stockbreeding, which, if implemented, could improve not only the milk yield of Simmental dairy cows, but also their health and welfare., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Fasciolosis caused by the trematode Fasciola hepatica is an important parasitosis in both livestock and humans across the globe. Chronic infections in cattle are associated with considerable economic losses. As a prerequisite for an effective control and prevention of fasciolosis in cattle fine-scale predictive models on farm-level are needed. Since disease transmission will only occur where the mollusc intermediate host is present, the objective of our research was to develop a regression model that allows to predict the local presence or absence of Galba truncatula as principal intermediate host for Fasciola hepatica in Switzerland. By implementing generalized linear mixed models (GLMMs) a total amount of 70 variables were analysed for their potential influence on the likelihood π i of finding Galba truncatula at a certain site. Important site-specific features could be considered by selecting suitable modelling procedures. The statistical software R was used to conduct regression analysis, performing the grplasso and the glmmLasso method. The selection of parameters was based on 10-fold cross validation and the Bayesian Information Criterion (BIC). This yielded a total number of 19 potential predictor variables for the grplasso and 13 variables for the glmmLasso model, which also included random effects. Nine variables appeared to be relevant predictors for the occurrence of Galba truncatula in both models. These included reed/humid area, spring water, water bodies within a 100 m radius, and trees/bushes as powerful positive predictors. High soil depth, temperatures frequently exceeding 30 °C in the year preceding the search for snails and temperatures below 0 °C especially in the second year before were identified to exert an adverse effect on the occurrence of Galba truncatula. Temperatures measured near ground level proved to be more powerful predictors than macroclimatic parameters. Precipitation values seemed to be of minor impact in the given setting. Both regression models may be convenient for a fine-scale prediction of the occurrence of Galba truncatula, and thus provide useful approaches for the development of future spatial transmission models, mapping the risk of fasciolosis in Switzerland on farm-level., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)