Exploration of individual variability to better predict the nutrient requirements of gestating sows1

A review of calcium and phosphorus requirement estimates for gestating and lactating sows

Calcium (Ca) and phosphorus (P) are minerals involved in biological functions and essential structural components of the skeleton. The body tightly regulates Ca and P to maintain homeostasis. Maternal needs for Ca and P increase during gestation and lactation to support conceptus growth and milk synthesis. Litter size and litter average daily gain (ADG) have a large effect on Ca and P requirements for sows because as they increase, the requirements increase due to a greater need from the sow. The objective of this review was to summarize published literature on Ca and P requirements in gestating and lactating sows derived from empirical data and factorial models. A total of nine empirical studies and seven factorial models were reviewed for determining the Ca and P requirements in gestation. For lactation, there were six empirical studies and seven factorial models reviewed. Empirical studies determined requirements based on the observed effect of Ca and P on bone mineralization, sow and litter performance, and milk characteristics. Factorial models generated equations to estimate Ca and P requirements using the main components of maintenance, fetal and placental growth, and maternal retention in gestation. The main components for factorial equations in lactation include maintenance and milk production. In gestation, the standardized total tract digestible phosphorus (STTD P) requirement estimates from empirical studies range from 5.4 to 9.5 g/d with total Ca ranging from 12.9 to 18.6 g/d to maximize bone measurements or performance criteria. According to the factorial models, the requirements increase throughout gestation to meet the needs of the growing fetuses and range from 7.6 to 10.6 g/d and 18.4 to 38.2 g/d of STTD P and total Ca, respectively, on day 114 of gestation for parity 1 sows. During lactation, STTD P requirement estimates from empirical studies ranged from 8.5 to 22.1 g/d and total Ca ranged from 21.2 to 50.4 g/d. For the lactation factorial models, STTD P requirements ranged from 14.2 to 25.1 g/d for STTD P and 28.4 to 55.6 g/d for total Ca for parity 1 sows with a litter size of 15 pigs. The large variation in requirement estimates makes it difficult to define Ca and P requirements; however, a minimum level of 6.0 and 22.1 g/d of STTD P during gestation and lactation, respectively, appears to be adequate to meet basal requirements. The limited data and high variation indicate a need for future research evaluating Ca and P requirements for gestating and lactating sows.

Importance of Selected Nutrients and Additives in the Feed of Pregnant Sows for the Survival of Newborn Piglets

This systematic review analyzed the effect of selected nutrients and additives in the feed of pregnant sows on the survival of newborn piglets. We analyzed 720 peer-reviewed publications in English in PubMed® and Web of Science®, dated July 2023 to January 2024, related to the effect of dietary supplementation with fatty acids and various percentages of protein, amino acids, and/or sources of dietary fiber on the offspring of gestating sows. While several papers evaluated the effect of nutrition on gestating sows, only a few delved into the distinct feeding strategies required at each stage of gestation to meet the NRC's nutritional requirements for maternal tissue gain and postnatal neonatal survival and growth. This body of research suggests that as gestation progresses the sow's nutritional requirements increase, as the NRC established, to satisfy their own metabolic needs and those of their fetuses. Additional research is needed to determine an optimal feeding strategy.

Estimation of gestating sows' welfare status based on machine learning methods and behavioral data

Estimating the welfare status at an individual level on the farm is a current issue to improve livestock animal monitoring. New technologies showed opportunities to analyze livestock behavior with machine learning and sensors. The aim of the study was to estimate some components of the welfare status of gestating sows based on machine learning methods and behavioral data. The dataset used was a combination of individual and group measures of behavior (activity, social and feeding behaviors). A clustering method was used to estimate the welfare status of 69 sows (housed in four groups) during different periods (sum of 2 days per week) of gestation (between 6 and 10 periods, depending on the group). Three clusters were identified and labelled (scapegoat, gentle and aggressive). Environmental conditions and the sows' health influenced the proportion of sows in each cluster, contrary to the characteristics of the sow (age, body weight or body condition). The results also confirmed the importance of group behavior on the welfare of each individual. A decision tree was learned and used to classify the sows into the three categories of welfare issued from the clustering step. This classification relied on data obtained from an automatic feeder and automated video analysis, achieving an accuracy rate exceeding 72%. This study showed the potential of an automatic decision support system to categorize welfare based on the behavior of each gestating sow and the group of sows.

Two-Stage Fermented Feather Meal-Soybean Meal Product Improves the Performance and Immunity of Lactating Sows and Piglets

This study aimed to investigate the effects of a two-stage fermented feather meal-soybean meal product (TSFP) on the performance, clinical blood biochemistry, and immunity of sows and piglets. TSFP was fermented by Saccharomyces cerevisiae Y10 for three days in the second stage, which showed similar results to the five-day fermentation of B. coagulans (p > 0.05). Fifty hybrid sows (Duroc × KHAPS black pig) were randomly assigned into dietary supplementation groups of 2% fish meal or different levels of TSFP at 0%, 1%, 2%, or 3%. The results showed that body weight gain and feed conversion ratio of 2% and 3% TSFP groups were better than the control group and fish meal group during the gestation period (d 80–114) (p < 0.05). During the lactation period, the 3% TSFP group showed the best weaning litter weight (p < 0.05). In sows, interferon-γ and immunoglobulin G (IgG) of 2% and 3% TSFP groups were higher than the control group and fish meal group (p < 0.05). In piglets, in groups of 2% and 3% TSFP blood urea decreased (p < 0.05). The IgG of fermented groups was superior to the control group (p < 0.05). The oxidative burst of phagocytes in the 3% TSFP was higher than those of the control and fish meal groups (p < 0.05). In conclusion, TSFP supplementation exhibits the advantages of performance and immunity of lactating sows and piglets. Furthermore, adding 3% TSFP in the feed showed the best performance.

Prediction of the daily nutrient requirements of gestating sows based on sensor data and machine-learning algorithms

Precision feeding is a strategy for supplying an amount and composition of feed as close that are as possible to each animal's nutrient requirements, with the aim of reducing feed costs and environmental losses. Usually, the nutrient requirements of gestating sows are provided by a nutrition model that requires input data such as sow and herd characteristics, but also an estimation of future farrowing performances. New sensors and automatons, such as automatic feeders and drinkers, have been developed on pig farms over the last decade, and have produced large amounts of data. This study evaluated machine-learning methods for predicting the daily nutrient requirements of gestating sows, based only on sensor data, according to various configurations of digital farms. The data of 73 gestating sows was recorded using sensors such as electronic feeders and drinker stations, connected weight scales, accelerometers, and cameras. Nine machine-learning algorithms were trained on various dataset scenarios according to different digital farm configurations (one or two sensors), to predict the daily metabolizable energy and standardized ileal digestible lysine requirements for each sow. The prediction results were compared to those predicted by the InraPorc model, a mechanistic model for the precision feeding of gestating sows. The scenario predictions were also evaluated with or without the housing conditions and sow characteristics at artificial insemination usually integrated into the InraPorc model. Adding housing and sow characteristics to sensor data improved the mean average percentage error by 5.58% for lysine and by 2.22% for energy. The higher correlation coefficient values for lysine (0.99) and for energy (0.95) were obtained for scenarios involving an automatic feeder system (daily duration and number of visits with or without consumption) only. The scenarios including an automatic feeder combined with another sensor gave good performance results. For the scenarios using sow and housing characteristics and automatic feeder only, the root mean square error was lower with gradient tree boosting (0.91 MJ/d for energy and 0.08 g/d for lysine) compared with those obtained using linear regression (2.75 MJ/d and 1.07 g/d). The results of this study show that the daily nutrient requirements of gestating sows can be predicted accurately using data provided by sensors and machine-learning methods. It paves the way for simpler solutions for precision feeding.

Effects of thermal conditions on gestating sows' behaviors and energy requirements

Room temperature and individual behavior may influence the energy requirements of gestating sows. These factors are not yet integrated on a daily and individual basis in the calculation of these requirements. The objective of this study was to quantify the effect of temperatures on the sows' behaviors, especially on the level of physical activity, and on the energy requirements of gestating sows. Over four consecutive weeks, the temperature of two gestation rooms was maintained at medium temperatures (16.7 °C and 18.5 °C, respectively, for room 1 and room 2) for the first and third week, at low temperatures (14.4 °C and 15.3 °C) for the second week, and at high temperatures (31.6 °C and 31.9 °C) for the fourth week. Individual behavior was manually recorded based on videos and the data used to estimate the physical activity and social interactions of 37 gestating sows separated into two groups. The videos were analyzed over two periods of 5 h ("Feeding period" from 2300 to 0400 hours, "Resting period" from 1330 to 1830 hours). The energy requirements were calculated by the InraPorc model, modified for gestating sows, on the basis of a thermo-neutral situation and an average activity of 4 h standing per day for all the sows. The sows of one group were less active in high than low temperatures (83 vs. 103 min standing or walking over 5 h, P < 0.001). Isolation for high temperatures or huddling for low temperatures could be observed when sows were lying down. The sows spent more time lying laterally with high temperatures than low temperatures (66% vs. 52% of time spent lying, respectively, P < 0.001). Both groups reacted differently to high temperatures, in one the sows changed their activity (lying more) whereas in the other they drank more water compared to medium temperatures (11 vs. 8.5 L/d, P = 0.01). In one group, with high temperatures the sows were fed above their requirements (they should have received 110 g of feed per day per sow less, P < 0.001) and with low temperatures the same group should have received 50 g/d per sow more to fulfill their requirements. For the second group of sows, the temperatures did not significantly affect the feed requirements. In conclusion, daily ambient temperature and individual physical activity seem to be relevant information to add in nutritional models to improve precision feeding.

ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: Opportunities and Challenges of Confined and Extensive Precision Livestock Production

Modern animal scientists, industry, and managers have never faced a more complex world. Precision livestock technologies have altered management in confined operations to meet production, environmental, and consumer goals. Applications of precision technologies have been limited in extensive systems such as rangelands due to lack of infrastructure, electrical power, communication, and durability. However, advancements in technology have helped to overcome many of these challenges. Investment in precision technologies is growing within the livestock sector, requiring the need to assess opportunities and challenges associated with implementation to enhance livestock production systems. In this review, precision livestock farming and digital livestock farming are explained in the context of a logical and iterative five-step process to successfully integrate precision livestock measurement and management tools, emphasizing the need for precision system models (PSMs). This five-step process acts as a guide to realize anticipated benefits from precision technologies and avoid unintended consequences. Consequently, the synthesis of precision livestock and modeling examples and key case studies help highlight past challenges and current opportunities within confined and extensive systems. Successfully developing PSM requires appropriate model(s) selection that aligns with desired management goals and precision technology capabilities. Therefore, it is imperative to consider the entire system to ensure that precision technology integration achieves desired goals while remaining economically and managerially sustainable. Achieving long-term success using precision technology requires the next generation of animal scientists to obtain additional skills to keep up with the rapid pace of technology innovation. Building workforce capacity and synergistic relationships between research, industry, and managers will be critical. As the process of precision technology adoption continues in more challenging and harsh, extensive systems, it is likely that confined operations will benefit from required advances in precision technology and PSMs, ultimately strengthening the benefits from precision technology to achieve short- and long-term goals.

Dietary Phosphorus and Calcium Utilization in Growing Pigs: Requirements and Improvements

The sustainability of animal production relies on the judicious use of phosphorus (P). Phosphate, the mined source of agricultural phosphorus supplements, is a non-renewable resource, but phosphorus is essential for animal growth, health, and well-being. P must be provided by efficient and sustainable means that minimize the phosphorus footprint of livestock production by developing precise assessment of the bioavailability of dietary P using robust models. About 60% of the phosphorus in an animal's body occurs in bone at a fixed ratio with calcium (Ca) and the rest is found in muscle. The P and Ca requirements must be estimated together; they cannot be dissociated. While precise assessment of P and Ca requirements is important for animal well-being, it can also help to mitigate the environmental effects of pig farming. These strategies refer to multicriteria approaches of modeling, efficient use of the new generations of phytase, depletion and repletion strategies to prime the animal to be more efficient, and finally combining these strategies into a precision feeding model that provides daily tailored diets for individuals. The industry will need to use strategies such as these to ensure a sustainable plant–animal–soil system and an efficient P cycle.

Evaluation of a decision support system for precision feeding of gestating sows

Precision feeding (PF) with the daily mixing of 2 diets with different lysine content (high (H) or low (L)) was previously reported for growing pigs to reduce protein intake and N excretion compared with a conventional feeding (CF) based on a single diet (C). Using a simulation approach based on farm data, the objective of the present paper was to describe and evaluate a decision support system for the PF of gestating sows allowing the daily distribution of a tailored ration to each sow. Two datasets, 1 of 2,511 gestations (farm A) and 1 of 2,528 gestations (farm B), reporting sows’ characteristics at insemination and objectives at farrowing were used as inputs for a Python model. This model, mainly based on InraPorc, calculates the nutrient requirements of each sow over gestation and simulates the impact of PF in comparison to CF. Simulated diets L, H, and C contained 3.0, 6.5, and 4.8 g/kg of standardized ileal digestible lysine (SID Lys) and 2.0, 3.3, and 2.5 g/kg of standardized total tract digestible phosphorus (STTD-P), respectively. The influence of farm, parity, gestation week, and their interactions, on calculated SID Lys and STTD-P requirements was analyzed applying a mixed model. The calculated SID Lys and STTD-P requirements increased markedly in the last third of gestation (P < 0.01) and were higher for primiparous than for multiparous sows, unless after week 14 for STTD-P requirement. The calculated SID AA and mineral requirements were lower for farm B than farm A (respectively, 2.94 vs. 3.08 g/kg for SID Lys and 1.30 vs. 1.35 g/kg for STTD-P, P < 0.01). On average, feed L represented 86% and 92% of the feed projected to be delivered by the PF strategy in farms A and B, respectively. Compared to CF, average calculated dietary SID Lys content was lowered by 27% and 32% with PF, for farms A and B, respectively, while average calculated dietary phosphorus content was lowered by 13% and 16%. The simulated proportions of sows in excess and deficient in SID Lys were reduced with PF. Compared to CF, the PF strategy allowed for a 3.6% reduction in simulated feed cost per sow during gestation, and reduced nitrogen and phosphorus intake (by 11.0% and 13.8%, respectively) and excretion (by 16.7% and 15.4%, respectively). To conclude, these simulations indicate that PF of gestating sow appears to be relevant to meet the amino acid requirement while reducing feed cost, and supplies and excretion of nitrogen and phosphorus.