A comparison of machine learning and logistic regression in modelling the association of body condition score and submission rate

Metabolomic evolution of the postpartum dairy cow uterus

High rates of early pregnancy loss are a critical issue in dairy herds, particularly in seasonal, grazing systems. Components of the uterine luminal fluid (ULF), on which the early embryo depends for sustenance and growth, partly determine early pregnancy losses. Here, changes in ULF from early to mid-postpartum in crossbred dairy cows were explored, linking them with divergent embryo development. For this, the uteri of 87 cows at Day 7 of pregnancy at first and third estrus postpartum were flushed to collect ULF. Eighteen metabolites (chiefly organic acids and sugars) significantly varied in abundance across postpartum, indicating a molecular signature of physiological recovery consistent of the upregulation of pyrimidine metabolism and glycerophospholipid metabolism, and downregulation of pentose phosphate and taurine metabolism pathways. Joint pathway analysis of metabolomics data and a previously generated proteomics data set on the same ULF samples suggests key links between postpartum recovery and subsequent successful embryo development. These include upregulation of VEGFA and downregulation of metabolism, NRF2, T-cell receptor, which appear to improve the ULF's capacity of sustaining normal embryo development, and a putative osmo-protectant role of beta-alanine. These relationships should be further investigated to develop tools to detect and reduce early pregnancy loss in dairy cows.

Non-linear change in body condition score over lifetime is associated with breed in dairy cows in Germany

Optimal body condition is crucial for the well-being and optimal productivity of dairy cows. However, body condition depends on numerous, often interacting factors, with complex relationships between them. Moreover, most of the studies describe the body condition in Holstein cattle, while condition of some breeds, e.g. Simmental (SIM) and Brown Swiss (BS) cattle, have not been intensively studied yet. Body condition score (BCS) proved to be one of the most effective measures for monitoring body condition in dairy cows. Alterations in BCS were previously mainly studied over a single lactation period, while changes over the lifetime were largely ignored. This study was designed to report BCS of German SIM and BS cows in the light of the broadly accepted BCS in German Holstein (GH) cows and to explore patterns of change in BCS over the productive lifetime of animals. BCS was modeled via linear mixed effects regression, over- and undercondition of animals were studied using mixed effects logistic regressions and condition of animals was explored with the multinomial log-linear model via neural networks. All models included an interaction between breed and age. We found BCS of SIM and BS to be higher than BCS of GH. Our results show that BCS of BS cows did not change over the lifetime. In contrast, the BCS of GH and SIM was found to have a non-linear (quadratic) shape, where BCS increased up to the years of highest productivity and then decreased in aging cows. Patterns of change between SIM and GH, however, differed. GH do not only reach their highest BCS earlier in life compared to SIM, but also start to lose their body condition earlier. Our dataset revealed that 23% of the animals scored were over- and 14% underconditioned. The proportion of cows that were overconditioned was high (>10% of cows) for every breed and every age, while severe underconditioning (>10% of cows) occurred only in middle aged and old GH. Moreover, we found that the probability of underconditioning of animals over lifetime increases, while the overconditioning decreases from the middle to older ages. Our findings highlight the importance of understanding the non-linear nature of BCS, and uncover the potential opportunity for improving the performance and welfare of dairy cows by adjusting their nutrition, not only during lactation, but also highly specific to breed and age.

Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study

Machine learning applications are becoming more ubiquitous in dairy farming decision support applications in areas such as feeding, animal husbandry, healthcare, animal behavior, milking and resource management. Thus, the objective of this mapping study was to collate and assess studies published in journals and conference proceedings between 1999 and 2021, which applied machine learning algorithms to dairy farming-related problems to identify trends in the geographical origins of data, as well as the algorithms, features and evaluation metrics and methods used. This mapping study was carried out in line with PRISMA guidelines, with six pre-defined research questions (RQ) and a broad and unbiased search strategy that explored five databases. In total, 129 publications passed the pre-defined selection criteria, from which relevant data required to answer each RQ were extracted and analyzed. This study found that Europe (43% of studies) produced the largest number of publications (RQ1), while the largest number of articles were published in the Computers and Electronics in Agriculture journal (21%) (RQ2). The largest number of studies addressed problems related to the physiology and health of dairy cows (32%) (RQ3), while the most frequently employed feature data were derived from sensors (48%) (RQ4). The largest number of studies employed tree-based algorithms (54%) (RQ5), while RMSE (56%) (regression) and accuracy (77%) (classification) were the most frequently employed metrics used, and hold-out cross-validation (39%) was the most frequently employed evaluation method (RQ6). Since 2018, there has been more than a sevenfold increase in the number of studies that focused on the physiology and health of dairy cows, compared to almost a threefold increase in the overall number of publications, suggesting an increased focus on this subdomain. In addition, a fivefold increase in the number of publications that employed neural network algorithms was identified since 2018, in comparison to a threefold increase in the use of both tree-based algorithms and statistical regression algorithms, suggesting an increasing utilization of neural network-based algorithms.