Data-driven modeling of reproductive performance: a cohort study for elevated sow efficiency and sustainability in livestock farming

The slow development of the Internet of Things (IoT) in pig production, due to the lack of high-quality data, limited large-scale models, and low hardware coverage, has hindered the widespread adoption of precision feeding practices. This study aimed to address these challenges by providing a standardized dataset as a foundation for IoT development and constructing predictive models focused on birth litter weight (BLW) and weaned litter weight (WLW). To achieve these objectives, two comprehensive datasets consisting of 10,089 sow characteristics were collected. By comparing eight different algorithms, GBDT algorithm was selected as the optimal algorithm for modeling of BLW and WLW. The datasets were divided into a 90 % sample for model derivation, with the remaining 10 % used for model validation. The models for both BLW and WLW datasets exhibited consistent performance between main and validation cohorts, with low error magnitudes and high relative accuracy (MAE: 1.8–2.5, MAPE: 2.55 %–18.41 %, R > 60 %), indicating robustness and generalizability to unseen data. Delving deeper, the SHAP summary plots illustrated that in the model for BLW, G.ADFIp2, G.ADFIp3, G.ADFIp4, G.ADF and parity had a significant impact on the prediction. In the WLW model, the key influencing factors were weaned litter size, duration of lactation, parity, and birth litter weight. SHAP force and dependence plots had uncovered intricate effects of various features on the model’s outcomes. To enhance accessibility, we developed a user-friendly visualization and prediction website using the Streamlit Python framework. These critical research findings provide decision-makers with invaluable insights, fostering advancements in precision feeding models and IoT technologies in the swine industry. Ultimately, this contributes to the overarching goal of enhancing the comprehensive sustainability of livestock farming.