To achieve a sophisticated and self-sufficient production environment that aims to optimize a particular production sequence or direction, a combination of multiple interconnected IoT devices, software, and decision-making expertise is required. This is nowadays referred to as “smart” systems and can be related to almost any field. In the case of the poultry industry, “smart” stands for automatic data gathering, in-depth processing, and decision-making support. The implementation of a smart poultry concept introduces several challenges that are production related (e.g., productivity forecasting); therefore, this study focuses on hen egg production forecasting with limited data sets. Different methods and approaches used in the poultry sector for egg production forecasting were investigated. A cross-comparison was made between different models in order to determine their applicability. The models considered include a non-linear Modified Compartmental and several machine learning (ML) models, such as, Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), XGBoost, and Random Forest (RF). Selected models used only two data sets—one for training and one for testing. Furthermore, the testing data set was significantly different than the training data, therefore setting the forecasting task to be even more challenging. The ML models had significantly more inputs that allowed them to adapt more flexibly to a changing environment in comparison with the nonlinear model that expected only one input, e.g., the week of egg production. The tests showed that the machine learning models proved to be overall more accurate than the selected nonlinear model.

The aim of this study is to identify the key factors contributing to student dropout and to develop a predictive model that estimates the dropout risk of students based on their entry characteristics and enrolment registration data. Our analysis is based on the registration and academic data of 971 full-time and part-time bachelor’s students in five faculties, who were enrolled in the academic year 2021–2022 at the Latvia University of Life Sciences and Technologies (LBTU). The dropout analysis was done during the 3.5 years of study, when the students started their last semester in engineering and information technology, agriculture and food technology, economics and social sciences, and forest and environmental studies and when veterinary medicine students had completed more than half of their program of study. Survival analysis methods were used during the study. Students’ dropout risk in relation to gender, faculty, priority to study in the program, and secondary school performance (SM) was estimated using the Proportional hazard model (Cox model). The highest student dropout was observed during the first year of study. Secondary school performance was a significant predictor of students’ dropout risk; students with higher SM had a lower dropout risk (HR = 0.66, p < 0.05). As well, student dropout can be explained by faculty or study programme. Students in economics and social sciences were at lower dropout risk than the students from the other faculties. Results show the model’s concordance index was 0.59, and this indicates that additional or stronger predictors may be needed to improve model performance.

A breeding program has been developed for the Latvian warmblood heavy type horses in Latvia, with the aim to preserve this type, because the number of these horses is small and the status of the local endangered breed has been granted. The aim of the study was to analyse the genetic structure, inbreeding and effective population size of the Latvian warmblood heavy type (LSB) horse population. Horses that were living at the time of the study on January 1, 2023 were used for the study. A total of 374 genetic resources live horses were analysed. Population structure analysis, inbreeding and effective population size were investigated. Calculations were done by POPREP (v1.0) software. The effective population size (Ne) decreased, however, since 2018 the decline in the size of the effective population has not been so rapid. In 2000, the Ne was 316 animals, and in 2020, it was 101 animals. Over the years, changes in the average inbreeding level in the LSB population were observed from year to year. In 2000, inbreeding was 1.39%, and in 2020 it was 1.47% with the average inbreeding level of 1.12%. Over the last 20 years, large changes in Ne have been observed, thus, the loss of genetic diversity in the LSB breed should be controlled.