LCE: An Augmented Combination of Bagging and Boosting in Python

lcensemble is a high-performing, scalable and user-friendly Python package for the general tasks of classification and regression. The package implements Local Cascade Ensemble (LCE), a machine learning method that further enhances the prediction performance of the current state-of-the-art methods Random Forest and XGBoost. LCE combines their strengths and adopts a complementary diversification approach to obtain a better generalizing predictor. The package is compatible with scikit-learn, therefore it can interact with scikit-learn pipelines and model selection tools. It is distributed under the Apache 2.0 license, and its source code is available at https://github.com/LocalCascadeEnsemble/LCE

A milk urea model to better assess nitrogen excretion and feeding practice in dairy systems

A milk urea model to better assess nitrogen excretion and feeding practice in dairy systems. 20. Nitrogen Worksho

An individual reproduction model sensitive to milk yield and body condition in Holstein dairy cows

To simulate the consequences of management in dairy herds, the use of individual-based herd models is very useful and has become common. Reproduction is a key driver of milk production and herd dynamics, whose influence has been magnified by the decrease in reproductive performance over the last decades. Moreover, feeding management influences milk yield (MY) and body reserves, which in turn influence reproductive performance. Therefore, our objective was to build an up-to-date animal reproduction model sensitive to both MY and body condition score (BCS). A dynamic and stochastic individual reproduction model was built mainly from data of a single recent long-term experiment. This model covers the whole reproductive process and is composed of a succession of discrete stochastic events, mainly calving, ovulations, conception and embryonic loss. Each reproductive step is sensitive to MY or BCS levels or changes. The model takes into account recent evolutions of reproductive performance, particularly concerning calving-to-first ovulation interval, cyclicity (normal cycle length, prevalence of prolonged luteal phase), oestrus expression and pregnancy (conception, early and late embryonic loss). A sensitivity analysis of the model to MY and BCS at calving was performed. The simulated performance was compared with observed data from the database used to build the model and from the bibliography to validate the model. Despite comprising a whole series of reproductive steps, the model made it possible to simulate realistic global reproduction outputs. It was able to well simulate the overall reproductive performance observed in farms in terms of both success rate (recalving rate) and reproduction delays (calving interval). This model has the purpose to be integrated in herd simulation models to usefully test the impact of management strategies on herd reproductive performance, and thus on calving patterns and culling rate

Système d'élevage, un concept pour raisonner les transformations de l'élevage

Le concept de «système d’élevage» a été développé pour rendre compte et modéliser des interactions entre dimensions humaines et dimensions biotechniques de l’activité d’élevage. Après avoir rappelé l’origine et les bases des approches intégrées de l’élevage, et les applications centrées sur les liens décisions-pratiques-performances de troupeau, nous montrons comment ce concept évolue pour traiter des transformations de l’élevage, tant des attentes des éleveurs (pour un travail maîtrisé) que celles de la société (pour un plus grand respect de l’environnement). La problématique du développement durable appelle un renouvellement des approches des systèmes d’élevage vers l’étude des capacités adaptatives des systèmes socio-écologiques à l’échelle de territoires