Precision feeding is an excellent alternative to conventional phase feeding systems for growing-finishing pigs, especially with increasing feeding costs and environmental sustainability concerns. However, precision feeding strategies sometimes require advanced technologies such as electronic devices and the modernization of pig facilities. In addition to hardware implementation, precision feeding is frequently related to mathematical difficulties due to a lack of professionals trained in decision making. Therefore, this study compares a conventional phase feeding model (CON) and a daily fit model (DFM) with a simplified approach to the conscious use of nutrients for pig production. A simulation study was conducted using growth curves of barrow pigs, following three nutritional recommendations for conventional phase feeding. Once the nutrient requirements for CON were determined, these diets were used for the DFM by anticipating a proportional percentage of the next phase diet in the current diet. This simple adjustment does not impair the growth performance of pigs. However, in this study, the DFM showed promise during the growing-finishing phases to reduce pigs’ costs and nutrient intakes, such as crude protein, lysine, and digestible phosphorus, up to 5.58, 7.11 and 9.13%, respectively. In conclusion, the DFM can effectively reduce costs, minimize environmental impact, and promote sustainable practices. Also, this industry-wide adoption of this simplified precision feeding strategy could play a vital role in swine farmers’ challenges, fostering broader environmental benefits and improved resource efficiency.

The identification of estrus at the right moment allows for a higher success of fecundity with artificial insemination. Evaluating changes in body surface temperature of sows during the estrus period using an infrared thermography camera (ITC) can provide an accurate model to predict these changes. This pilot study comprised nine crossbred Large White x Landrace sows, providing 59 data records for analysis. Observed changes in the behavior and physiological signs of the sows signaled the identification of estrus. Images of the ocular area, ear tips, breast, back, vulva, and perianal area were collected with the ITC. The images were analyzed using the FLIR Thermal Studio Starter software. Infrared mean temperatures were reported and compared using ANOVA and Tukey–Kramer tests (p < 0.05). Supervised machine learning models were tested using random forest (RF), Conditional inference trees (Ctree), Partial least squares (PLS), and K-nearest neighbors (KNN), and the method performance was measured using a confusion matrix. The orbital region showed significant differences between estrus and non-estrus states in sows. In the confusion matrix, the algorithm predicted estrus with 87% accuracy in the test set, which contained 40% of the data, when agitated behavior was combined with orbital area temperature. These findings suggest the potential for integrating behavioral and physiological observations with orbital thermography and machine learning to detect estrus in sows under field conditions accurately.

This study comprehensively explores the environmental implications of two feeding strategies in pig farming, focusing on three scenarios: Brazilian tables (BT-2017), NRC (NRC-2012), and AGPIC (AGPIC-2021). The comparison involves conventional phase-feeding (CON) and the daily fit model (DFM). The five-phase system provided the same diet to all pigs within a group during each proposed phase. In contrast, the DFM adjusted the diet based on the nutritional requirements of pigs, anticipating subsequent diets through daily adjustments. We employed a cradle-to-gate approach, with the functional unit defined as one barrow with an initial body weight of 20.61 ± 0.85 kg, raised to 138.94 ± 0.90 kg over a 120-day growing-finishing period. Input data were sourced from observed commercial records from pig farms in Brazil, including over 1,000,000 data points from pigs raised under standard industry conditions. We evaluated the impact of the life cycle by considering factors such as acidification, climate change, ecotoxicity, eutrophication, land use, resource use, and water use. The OpenLCA software (version 1.11.0) and the Environmental Footprint 3.0 impact assessment method were used. Our results indicate that the DFM consistently outperforms the CON strategy in terms of reducing environmental impacts. Among the three scenarios, BT-2017 results in higher environmental impact reductions compared with NRC-2012 and AGPIC-2021. This is due to the higher concentration of corn and soybean meal in diets. Notable reductions include in relation to land use-related climate change impacts (12.55%), freshwater eutrophication (6.21%), mineral and metal resource depletion (6.11%), and fossil resource use (4.88%). These findings highlight that even modest adjustments to feeding strategies can effectively reduce the environmental footprint of pig farming.