Dynamic modeling of nutrient use and individual requirements of lactating 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.

Review: Fundamentals, limitations and pitfalls on the development and application of precision nutrition techniques for precision livestock farming

Precision livestock farming (PLF) concerns the management of livestock using the principles and technologies of process engineering. Precision nutrition (PN) is part of the PLF approach and involves the use of feeding techniques that allow the proper amount of feed with the suitable composition to be supplied in a timely manner to individual animals or groups of animals. Automatic data collection, data processing, and control actions are required activities for PN applications. Despite the benefits that PN offers to producers, few systems have been successfully implemented so far. Besides the economical and logistical challenges, there are conceptual limitations and pitfalls that threaten the widespread adoption of PN. Developers have to avoid the temptation of looking for the application of available sensors and instead concentrate on identifying the most appropriate and relevant information needed for the optimal functioning of PN applications. Efficient PN applications are obtained by controlling the nutrient requirement variations occurring between animals and over time. The utilization of feedback control algorithms for the automatic determination of optimal nutrient supply is not recommended. Mathematical models are the preferred data processing method for PN, but these models have to be designed to operate in real time using up-to-date information. These models are therefore structurally different than traditional nutrition or growth models. Combining knowledge- and data-driven models using machine learning and deep learning algorithms will enhance our ability to use real-time farm data, thus opening up new opportunities for PN. To facilitate the implementation of PN in farms, different experts and stakeholders should be involved in the development of the fully integrated and automatic PLF system. Precision livestock farming and PN should not be seen as just being a question of technology, but a successful marriage between knowledge and technology.

Precision feeding of lactating sows: implementation and evaluation of a decision support system in farm conditions

Precision feeding (PF) aims to provide the right amount of nutrients at the right time for each animal. Lactating sows generally receive the same diet, which either results in insufficient supply and body reserve mobilization, or excessive supply and high nutrient excretion. With the help of online measuring devices, computational methods, and smart feeders, we introduced the first PF decision support system (DSS) for lactating sows. Precision (PRE) and conventional (STD) feeding strategies were compared in commercial conditions. Every day each PRE sow received a tailored ration that had been computed by the DSS. This ration was obtained by blending a diet with a high AA and mineral content (13.00 g/kg SID Lys, 4.50 g/kg digestible P) and a diet low in AAs and minerals (6.50 g/kg SID Lys, 2.90 g/kg digestible P). All STD sows received a conventional diet (10.08 g/kg SID Lys, 3.78 g/kg digestible P). Before the trial, the DSS was fitted to farm performance for the prediction of piglet average daily gain (PADG) and sow daily feed intake (DFI), with data from 1,691 and 3,712 lactations, respectively. Sow and litter performance were analyzed for the effect of feeding strategy with ANOVA, with results considered statistically significant when P < 0.05. The experiment involved 239 PRE and 240 STD sows. DFI was similarly high in both treatments (PRE: 6.59, STD: 6.45 kg/d; P = 0.11). Litter growth was high (PRE: 2.96, STD: 3.06 kg/d), although it decreased slightly by about 3% in PRE compared to STD treatments (P < 0.05). Sow body weight loss was low, although it was slightly higher in PRE sows (7.7 vs. 2.1 kg, P < 0.001), which might be due to insufficient AA supply in some sows. Weaning to estrus interval (5.6 d) did not differ. In PRE sows SID Lys intake (PRE: 7.7, STD: 10.0 g/kg; P < 0.001) and digestible P intake (PRE: 3.2, STD: 3.8 g/kg; P < 0.001) declined by 23% and 14%, respectively, and feed cost decreased by 12%. For PRE sows, excretion of N and P decreased by 28% and 42%, respectively. According to these results, PF appears to be a very promising strategy for lactating sows.

ASAS-NANP symposium: mathematical modeling in animal nutrition: limitations and potential next steps for modeling and modelers in the animal sciences

The field of animal science, and especially animal nutrition, relies heavily on modeling to accomplish its day-to-day objectives. New data streams ("big data") and the exponential increase in computing power have allowed the appearance of "new" modeling methodologies, under the umbrella of artificial intelligence (AI). However, many of these modeling methodologies have been around for decades. According to Gartner, technological innovation follows five distinct phases: technology trigger, peak of inflated expectations, trough of disillusionment, slope of enlightenment, and plateau of productivity. The appearance of AI certainly elicited much hype within agriculture leading to overpromised plug-and-play solutions in a field heavily dependent on custom solutions. The threat of failure can become real when advertising a disruptive innovation as sustainable. This does not mean that we need to abandon AI models. What is most necessary is to demystify the field and place a lesser emphasis on the technology and more on business application. As AI becomes increasingly more powerful and applications start to diverge, new research fields are introduced, and opportunities arise to combine "old" and "new" modeling technologies into hybrids. However, sustainable application is still many years away, and companies and universities alike do well to remain at the forefront. This requires investment in hardware, software, and analytical talent. It also requires a strong connection to the outside world to test, that which does, and does not work in practice and a close view of when the field of agriculture is ready to take its next big steps. Other research fields, such as engineering and automotive, have shown that the application power of AI can be far reaching but only if a realistic view of models as whole is maintained. In this review, we share our view on the current and future limitations of modeling and potential next steps for modelers in the animal sciences. First, we discuss the inherent dependencies and limitations of modeling as a human process. Then, we highlight how models, fueled by AI, can play an enhanced sustainable role in the animal sciences ecosystem. Lastly, we provide recommendations for future animal scientists on how to support themselves, the farmers, and their field, considering the opportunities and challenges the technological innovation brings.

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.

Providing domperidone throughout lactation enhances sow lactation performance

The goal of this project was to determine the effects of domperidone given throughout lactation on hormonal and metabolic status, lactational performance, and gene expression in mammary epithelial cells of sows. Second parity sows were divided in two treatment groups: 1) daily intramuscular injections with canola oil (Control, CTL, n = 24), or 2) daily intramuscular injections with 0.5 mg/kg body weight (BW) of domperidone (DOMP, n = 23). Injections were given at 08h05 starting the day after farrowing until weaning. Over the first 4 d of treatment, DOMP sows also received 0.5 mg/kg BW of domperidone per os twice daily, whereas CTL sows were fed the vehicle. Litter size was standardized to 11 ± 1 within 24 h of birth and piglets were weighed at birth, 24 h postpartum, and on days 7, 22 (weaning on day 23), 35, and 56. Sow feed intake was recorded daily. Representative milk samples were obtained aseptically on day 21 of lactation from 15 sows per treatment for compositional analyses and milk fat globules were used to measure mRNA abundances of various genes. Jugular blood samples were obtained from all sows on days 2, 8, 16, and 23 of lactation to measure concentrations of prolactin, insulin-like growth factor-1 (IGF-1), leptin, adiponectin, insulin, glucose, urea, and free fatty acids (FFA). Concentrations of prolactin (P < 0.001) and FFA (P < 0.01) were increased in DOMP compared with CTL sows, whereas concentrations of insulin were decreased (P < 0.05). Urea concentrations were increased by treatment (P < 0.05) on days 16 and 23 of lactation, and those of IGF-1 were increased (P < 0.01) on day 16. Piglets from DOMP sows were heavier than those from CTL sows on day 22 (P < 0.01). Milk composition was unaffected by treatment. The mRNA abundance in milk fat globules for casein beta and whey acidic protein were lower (P ≤ 0.05) in DOMP than CTL sows. The long form of the prolactin receptor and the signal transducer and activator of transcription 5A mRNA abundances tended to be lower (P < 0.10) in DOMP than CTL sows. In conclusion, hyperprolactinemia induced by domperidone during lactation affected the endocrine and metabolite status of sows and stimulated growth of their suckling piglets.

Free-range system and supplementation of 25-hydroxicholecalciferol increases the performance and serum vitamin levels in mixed-parity sows

Improvements in sow productivity have raised questions regarding dietary vitamin D recommendations. The present study aimed to evaluate the effects of the housing system with access to sunlight exposure and supplementation of 25-hydroxicholecalciferol on performance and serum levels of 25(OH)D₃ in sows during gestation and lactation. Sows were distributed in an experimental design with two housing systems: gestation crates or gestation free-range system with external area for sunlight exposure; and two diets: 0 or 50 μg of 25-hydroxicholecalciferol kg^-1 . The use of 25-hydroxicholecalciferol tended (P = 0.052) to improve total born and influenced (P = 0.046) on number of born alive. Litter weight at birth was also increased (P = 0.01) by 25-hydroxicholecalciferol supplementation; 25-hydroxicholecalciferol supplementation and housing system (free-range with sunlight exposure) tended to increase weaning weight (P = 0.07) and litter daily gain (P = 0.051) during lactation. Exposure to sunlight and 25-hydroxicholecalciferol supplementation increased 25(OH)D₃ serum levels when compared with control treatment during gestation (136.95 vs. 113.92 ng mL^-1 ; P = 0.035) and lactation (120.29 vs. 88.93 ng mL^-1 ; P = 0.026). In conclusion, the association of 25-hydroxicholecalciferol supplementation with exposure to sunlight during gestation improved significantly 25(OH)D₃ serum levels and consequently performance traits in gestation and lactation.

A Novel Estimation of Unobserved Pig Growth Traits for the Purposes of Precision Feeding Methods

Recent technological advances make it possible to deliver feeding strategies that can be tailored to the needs of individual pigs in order to optimise the allocation of nutrient resources and contribute toward reducing excess nutrient excretion. However, these efforts are currently hampered by the challenges associated with: (1) estimation of unobserved traits from the available data on bodyweight and feed consumption; and (2) characterisation of the distributions and correlations of these unobserved traits to generate accurate estimates of individual level variation among pigs. Here, alternative quantitative approaches to these challenges, based on the principles of inverse modelling and separately inferring individual level distributions within a Bayesian context were developed and incorporated in a proposed precision feeding modelling framework. The objectives were to: (i) determine the average and distribution of individual traits characterising growth potential and body composition in an empirical population of growing-finishing barrows and gilts; (ii) simulate the growth and excretion of nitrogen and phosphorus of the average pig offered either a commercial two-phase feeding plan, or a precision feeding plan with daily adjustments; and (iii) simulate the growth and excretion of nitrogen and phosphorus across the pig population under two scenarios: a two-phase feeding plan formulated to meet the nutrient requirements of the average pig or a precision feeding plan with daily adjustments for each and every animal in the population. The distributions of mature bodyweight and ratio of lipid to protein weights at maturity had median (IQR) values of 203 (47.8) kg and 2.23 (0.814) kg/kg, respectively; these estimates were obtained without any prior assumptions concerning correlations between the traits. Overall, it was found that a proposed precision feeding strategy could result in considerable reductions in excretion of nitrogen and phosphorus (average pig: 8.07 and 9.17% reduction, respectively; heterogenous pig population: 22.5 and 22.9% reduction, respectively) during the growing-finishing period from 35 to 120 kg bodyweight. This precision feeding modelling framework is anticipated to be a starting point toward more accurate estimation of individual level nutrient requirements, with the general aim of improving the economic and environmental sustainability of future pig production systems.

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.

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

Sows often receive the same feed during gestation even though their nutrient requirements vary during gestation and among sows. The objective of this study was to report the variability in nutrient requirement among sows and during gestation, in order to develop a precision feeding approach. A data set of 2,511 gestations reporting sow characteristics at insemination and their farrowing performance was used as an input for a Python model, adapted from InraPorc, predicting nutrient requirement during gestation. Total metabolizable energy (ME) requirement increased with increasing litter size, gestation weeks, and parity (30.6, 33.6, and 35.5 MJ/d for parity 1, 2, and 3 and beyond, respectively, P < 0.01). Standardized ileal digestible lysine (SID Lys) requirement per kg of diet increased from weeks 1 to 6 of gestation, remained stable from weeks 7 to 10, and increased again from week 11 until the end of gestation (P < 0.01). Average Lys requirement increased with increasing litter size (SID Lys: 3.00, 3.27, 3.50 g/kg for small, medium and large litters, P < 0.01) and decreased when parity increased (SID Lys: 3.61, 3.17, 2.84 g/kg for parity 1, 2, and 3++, P < 0.01). Standardized total tract digestible phosphorus (STTD-P) and total calcium (Total-Ca) requirements markedly increased after week 9, with litter size, and decreased when parity increased (STTD-P: 1.36 vs. 1.31 g/kg for parity 1 and parity 3 and beyond; Total-Ca: 4.28 vs. 4.10 g/kg for parity 1 and parity 3 and beyond, P < 0.01). Based on empirical cumulative distribution functions, a 4-diets strategy, varying in SID Lys and STTD-P content according to parity and gestation period (P1 from weeks 0 to 11, P2 from weeks 12 to 17), may be put forward to meet the requirements of 90% of the sows (2 diets for multiparous sows: P1: 2.8 g SID Lys/kg and 1.1 g STTD-P/kg; P2: 4.5 g SID Lys/kg and 2.3 g STTD-P/kg; and 2 diets for primiparous sows: P1: 3.4 g SID Lys/kg and 1.1g STTD-P/kg; P2: 5.0 g SID Lys/kg, 2.2 g STTD-P/kg). Better considering the high variability of sow requirement should thus make it possible to optimize their performance whilst reducing feeding cost and excretion. Feeding sows closer to their requirement may initially be achieved by grouping and feeding sows according to gestation week and parity, and ultimately by feeding sows individually using a smart feeder allowing the mixing of different feeds differing in their nutrient content.

Optimal lysine in diets for high-yielding lactating sows1

The objective of the current study was to determine the optimal concentration of dietary standardized ileal digestible (SID) Lys required to maximize litter gain and minimize sow BW loss in modern high-yielding lactating sows when SID CP was kept constant across dietary treatments. A total of 396 parity 1 to 5 sows (L × Y, DanBred, Herlev, Denmark) were included in the study from day 3 after farrowing until weaning at day 26. Sows were allocated to 6 dietary treatments increasing in SID Lys concentration (6.19, 6.90, 7.63, 8.33, 9.04, and 9.76 g/kg). Diets were isoenergetic (14.04 MJ ME/kg as-fed). Litters were standardized to 14 piglets at day 3 ± 2 d postpartum. At day 3 ± 2 d and at day 26 ± 3 d in lactation, litter weight, and sow BW and back fat were registered. On a subsample of 72 parity 2 to 4 sows, litters were additionally weighed at days 10 and 17 ± 3 d, and milk and blood were sampled at day 3 ± 2 d, and 10, 17 and at 24 ± 3 d in lactation. For the 72 sows, body pools of fat and protein were also determined at days 3 ± 2 and 26 ± 3 d using the D2O dilution technique. All data were analyzed as a randomized complete block design using PROC MIXED in SAS. Furthermore, data were subjected to linear and quadratic polynomial contrasts. Variables with quadratic or linear effects or days in milk × treatment interactions were selected for analysis in PROC NLMIXED using linear broken-line models to evaluate optimal SID Lys concentrations. Only models that converged and the best fitting models were included. Average daily litter gain increased until a breakpoint at 8.11 g/kg of SID Lys (as-fed). At and above the breakpoint, multiparous and primiparous sows had litter gains of 3.36 and 2.93 kg/d, respectively. Weaning litter size (13.0 ± 0.1) was similar between the 6 dietary treatments (P = 0.28). Lactation sow BW loss was minimized to 0.17 kg/d at 9.05 g/kg of SID Lys and sow body protein loss was minimized to 0.23 kg at 9.22 g/kg of SID Lys. Linear broken-line analyses showed that for 3, 10, 17, and 24 DIM, plasma urea was minimized at 7.02, 8.10, 8.73, and 8.32 g/kg of SID Lys, respectively, and milk fat was maximized at 7.80 g/kg of SID Lys. In conclusion, in our conditions, high-yielding lactating sows required 8.11 g/kg of SID Lys to maximize litter gain and 9.05 g/kg of SID Lys to minimize sow BW loss. Based on plasma urea, the optimal dietary concentration of SID Lys was lowest in week 1, intermediate in week 2 and 4, and greatest in week 3 of lactation.