The fate of Cu and Zn along the feed-animal-excreta-effluent continuum in swine systems according to feed and effluent treatment strategies.

Copper (Cu) and zinc (Zn) have negative environmental impacts as they accumulate in the soil after pig manure is spread. Cu and Zn are essential elements in pig nutrition but due to their low retention rate, more than 90% of ingested Cu and Zn are excreted. A better understanding of the behaviour of these elements throughout the animal-manure-soil continuum according to feed composition and manure management chain is thus required to propose alternative ways to reduce these environmental impacts. The aim of this study was to determine the fate of Cu and Zn throughout this continuum by studying the effect of Cu and Zn contents in animal feed and in the manure management chain based on anaerobic digestion and composting. Faeces were collected from 24 finishing pigs fed with 4 different Cu and Zn dietary levels and sources of supplementation. Samples of faeces were exposed to mesophilic anaerobic digestion or to 5-week composting with straw. Concentrations of Cu and Zn in the faeces were highly dependent on dietary supplies and ranged from 38 to 188 mg Cu/kg DM and from 191 to 728 mg Zn/kg DM. Degradation of a significant fraction of organic matter during treatment led to a significant increase in Cu and Zn concentration relative to the product's dry matter (DM) content, which. Cu and Zn concentrations relative to DM content were twice as high after treatment whatever the content and the form of Cu and Zn dietary supplementation. Otherwise, effluent treatment tended to reduce the possible availability of Cu and Zn in final organic products according to water-extractible contents. This study clearly shows that feed management is the main lever for reducing the amount of Cu and Zn amount in pig effluents and hence in the soil. Appropriate treatment could also facilitate the supply of organic fertilisers to areas with soil deficiency, but feed strategies need to be adapted to the treatment chain to enable the production of good quality organic products that respect EU regulations.

Data quantifying the behaviour of macro and trace elements along the feed - manure - treated waste continuum in pig production.

Manure from animal production is commonly spread on agricultural soil as an organic fertiliser to provide macro and trace elements to crops. However, some trace elements can accumulate in the soil and become toxic to plants and microorganisms. These elements include copper (Cu) and zinc (Zn), which can be applied in large quantities when pig manure is spread. The feeding strategy and manure management (e.g. through treatment chains) are two mechanisms identified to better control the use of these elements, but their fate from the feed to the soil in pig production remains poorly documented. Better understanding the fate of Cu and Zn, as well as that of other trace and macro elements, along the feed - excreta - waste chain is required to develop alternative ways to reduce their environmental impacts. This dataset provides insight into the composition (Cu, Zn and other trace and macro elements) of organic products along two contrasting manure management chains: (1) only storage or (2) in-building separation, anaerobic digestion (AD) of solids, and digestate drying. Feed, raw slurry, liquid and solid phases after separation of the manure and AD products were sampled and then analysed to measure their total compound contents.

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.

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.

New technologies, such as sensors and automatons, are being developed in agriculture to reduce workload or help farmers make management decisions. The most common approach to the analysis of the huge amount of data generated by these technologies is to use machine-learning algorithms, to detect health or welfare problems for example. The hypothesis was that these automatically collected data and algorithms could also serve to predict the nutrient requirements of gestating sows, usually calculated based on complex models that require a lot of on-farm input data. The predictions of 22 scenarios were compared based on different combinations of sensor data, with the prediction of a nutritional model for gestating sows. The results of nine algorithms applied to the different scenarios were also compared. The results suggested that feeder data, alone or in combination with another sensor, predicted nutrient requirements with high accuracy. Data from other sensors combined with additional information about the sow (i.e., age and body weight) also led to high prediction accuracy. The difference between the algorithms evaluated was relatively significant, but all showed acceptable prediction results, especially non-linear algorithms. In conclusion, this work demonstrated the possibility of accurately predicting daily nutrient requirements for each sow using sensor data and machine-learning algorithms.

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.

Ambient temperature may influence the energy requirement of gestating sows, but this factor is not yet integrated daily in the calculation of this requirement. The objective of this study was to quantify the effect of temperatures on sow's behavior, physical activity, and energy requirements on gestating sows. The 37 gestating sows were housed in two groups for which the temperature of each room was maintained at different temperatures during four consecutive weeks: the first and third weeks at 18 °C on average (medium temperature), the second week at 15.5 °C (low temperature), and the last one at 32 °C (high temperature). The sows modified their behavior regarding the room temperature even though these changes differed regarding the group of sows. Compared to medium temperature, high temperatures may induce an increase of water consumption or of the time spent lying, and of the rectal temperature of some sows. Low temperatures may induce huddling and/or an increase in aggressiveness. Low and high temperatures seem to impact energy costs even though it depends on the group of sows. Therefore, ambient temperature and individual activity are relevant information to add into nutritional models to improve their accuracy of energy requirement prediction.

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.

In lactating sows, nutrient requirements among individual animals vary greatly. With a single diet, lactating sows are likely to be either underfed, which results in body reserve mobilization, or overfed, which results in nutrient excretion. Precision feeding (PF) is a new feeding strategy that aims to provide the right amount of nutrients at the right time for each animal. In this study, we focus on the implementation and the evaluation of a decision support system (DSS) that delivers daily tailored diets to lactating sows. Two experimental treatments were compared: a precision feeding strategy based on the DSS (PRE treatment; 239 sows), and a conventional feeding strategy (STD treatment; 240 sows). Digestible lysine intake and digestible phosphorus intake were reduced by 23% and 14% in PRE sows, respectively, and feed cost by 12%, compared to STD sows. Excretion of nitrogen and phosphorus also decreased for PRE sows by 28% and 42%, respectively. Sow body weight loss was low, although slightly higher in PRE sows, which might be due to insufficient amino acid supply in some sows. PF appears to be a very promising strategy for matching nutrient supply to the specific nutrient requirements of lactating sows.

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.

Environmental Impacts and Their Association With Performance and Excretion Traits in Growing Pigs.

The selection of pigs for improved production traits has been, for a long time, the major driver of pig breeding. More recently, because of the increasing concern with the environment, new selection criteria have been explored, such as nitrogen (N) excretion. However, many studies indicate that life cycle assessment (LCA) provides much better indicators of environmental impacts than excretion. Therefore, the objective of this study was to investigate, using a modeling approach, the relationships between production traits and LCA impacts of individual growing pigs calculated at the farm gate for 1 kg of body weight gain. Performances of pigs were simulated for 2-phase (2P) and precision feeding (PR), using the InraPorc population model (on 1,000 pigs). Nitrogen excretion was positively correlated with feed conversion ratio (FCR; r = +0.96), climate change (CC; r = +0.96), acidification potential (AC; r = +0.97), eutrophication potential (EU; r = +0.97), and land occupation (LO; r = +0.96), whatever the feeding program. However, FCR appeared to be a better indicator of LCA impacts, with very high and positive correlations (r > +0.99) with CC, AC, EU, and LO for both feeding programs. The CC, AC, and EU impacts of pig production for PR feeding were 1.3, 10, and 7.5% lower than for 2P, respectively, but the correlations within each outcome were very similar among feeding programs. It was concluded that the use of FCR as a selection criterion in pig breeding seems to be a promising approach to associate improved performance and low environmental impact of pig fattening.

Eco-Friendly Feed Formulation and On-Farm Feed Production as Ways to Reduce the Environmental Impacts of Pig Production Without Consequences on Animal Performance.

Animal feeding has a major contribution to the environmental impacts of pig production. One potential way to mitigate such effects is to incorporate an assessment of these impacts in the feed formulation process. The objective of this study was to test the ability of innovative formulation methodologies to reduce the impacts of pig production while also taking into account possible effects on growth performance. We compared three different formulation methodologies: least-cost formulation, in accordance with standard practices on commercial farms; multiobjective (MO) formulation, which considered feed cost and environmental impacts as calculated by life cycle assessment (LCA); and MO formulation, which prioritized locally produced feed ingredients to reduce the impact of transport. Ninety-six pigs were distributed between three experimental groups, with pigs individually weighted and fed using an automatic feeding system from 40 to 115 kg body weight. Based on the experimental results, six categories of impacts were evaluated: climate change (CC), demand in non-renewable energy (NRE), acidification (AC), eutrophication (EU), land occupation (LO), and phosphorus demand (PD), at both feed plant gate and farm gate, with 1 kg of feed and 1 kg of live pig as functional units, respectively. At feed level, MO formulations reduced CC, NRE, AC, and PD impacts but sometimes increased LO and EU impacts. These formulations reduced the proportion of cereals and oil meals into feeds (feed ingredients with high impacts), while the proportion of alternative protein sources, like peas, faba beans, or high-protein agricultural coproducts increased (feed ingredients with low impacts). Overall, animal performance was not affected by the dietary treatment; because of this, the general pattern of results obtained with either MO formulation at farm gate was similar to that obtained at feed level. Thus, MO diet formulation represents an efficient way to reduce the environmental impacts of pig production without compromising animal performance.

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.

Dynamic modeling of nutrient use and individual requirements of lactating sows1.

Nutrient requirements of sows during lactation are related mainly to their milk yield and feed intake, and vary greatly among individuals. In practice, nutrient requirements are generally determined at the population level based on average performance. The objective of the present modeling approach was to explore the variability in nutrient requirements among sows by combining current knowledge about nutrient use with on-farm data available on sows at farrowing [parity, BW, backfat thickness (BT)] and their individual performance (litter size, litter average daily gain, daily sow feed intake) to estimate nutrient requirements. The approach was tested on a database of 1,450 lactations from 2 farms. The effects of farm (A, B), week of lactation (W1: week 1, W2: week 2, W3+: week 3 and beyond), and parity (P1: 1, P2: 2, P3+: 3 and beyond) on sow performance and their nutrient requirements were evaluated. The mean daily ME requirement was strongly correlated with litter growth (R2 = 0.95; P < 0.001) and varied slightly according to sow BW, which influenced the maintenance cost. The mean daily standardized ileal digestible (SID) lysine requirement was influenced by farm, week of lactation, and parity. Variability in SID lysine requirement per kg feed was related mainly to feed intake (R2 = 0.51; P < 0.001) and, to a smaller extent, litter growth (R2 = 0.27; P < 0.001). It was lowest in W1 (7.0 g/kg), greatest in W2 (7.9 g/kg), and intermediate in W3+ (7.5 g/kg; P < 0.001) because milk production increased faster than feed intake capacity did. It was lower for P3+ (6.7 g/kg) and P2 sows (7.3 g/kg) than P1 sows (8.3 g/kg) due to the greater feed intake of multiparous sows. The SID lysine requirement per kg of feed was met for 80% of sows when supplies were 112 and 120% of the mean population requirement on farm A and B, respectively, indicating higher variability in requirements on farm B. Other amino acid and mineral requirements were influenced in the same way as SID lysine. The present modeling approach allows to capture individual variability in the performance of sows and litters according to farm, stage of lactation, and parity. It is an initial step in the development of new types of models able to process historical farm data (e.g., for ex post assessment of nutrient requirements) and real-time data (e.g., to control precision feeding).

Life cycle assessment as a tool to evaluate the impact of reducing crude protein in pig diets / Análise do ciclo de vida como ferramenta para avaliar o impacto da redução do conteúdo de proteína bruta da dieta de suínos

ABSTRACT: Environmental impacts of livestock systems, especially pig production, have come under increasing debate in recent years. The challenge is in meeting the growing demand for food at an affordable cost, without compromising environmental integrity. Previous studies have shown that feed production is responsible for the majority of CO2-eq. emission resulting from pig farming systems. This seems to indicate that feed strategies could be an effective tool to achieve the sustainability of the pork chain. Therefore, dietary crude protein reduction, through the addition of industrial amino acids, lessens the nitrogen excretion by pigs and, consequently, could mitigate the effects on the environment of pig production. In this sense, to effectively evaluate the environmental impacts of pig production systems, life cycle assessment has been widely used in agriculture, but the effects of feed are still understudied in Brazilian conditions. Owing to the importance and the great concern in this research area, we presented in this paper an updated review focusing on the nutritional techniques and their potential to reduce the global warming potential of pig production, considering both the direct effects, related to the choice of feed ingredients and the indirect effects, related to changes in the efficiency of use of nutrient by the animals.

RESUMO: O impacto ambiental da produção animal, especialmente da suinocultura, vêm sendo objeto de crescente debate nos últimos anos. O desafio é atender a crescente demanda por alimentos a um custo acessível, mas sem comprometer a integridade ambiental. Estudos vêm demonstrando que a produçao de rações é responsável pela maior parte das emissões de CO2-eq. do sistema de produção de suínos. Isso parece indicar que estratégias de alimentação podem ser ferramentas eficientes para alcançar a sustentabilidade da cadeia suinícola. Dessa forma, a redução do conteúdo de proteína bruta da dieta através da inclusão de aminoácidos industriais, leva à diminuição da excreção de nitrogênio em suínos e, consequentemente, pode mitigar o impacto ambiental do sistema de produção de suínos. Neste sentido, para avaliação efetiva do impacto ambiental do sistema produtivo, a análise do ciclo de vida vem sendo amplamente utilizada na agricultura, mas os efeitos da alimentação ainda são pouco estudados nas condições brasileiras. Devido a importância e a grande preocupação nesta área de pesquisa, este artigo trás uma revisão atualizada com foco nas técnicas de nutrição e principalmente no seu potencial em reduzir o potencial de aquecimento global da produção de suínos, considerando ambos os efeitos diretos, relacionados à escolha dos ingredientes da ração, e os efeitos indiretos, relacionados às mudanças na eficiência de uso de nutrientes pelos animais.

Concept and application of ideal protein for pigs.

Knowledge about the amino acid requirements and the response of pigs to the amino acid supply is essential in feed formulation. A deficient AA supply results in a reduction in performance while an oversupply is costly and leads to excessive nitrogen excretion with a potentially negative environmental impact. Amino acid requirements are determined to a large extent by the protein deposition in the body and, for lactating sows, by the protein exported in the milk. The concept of ideal protein was developed more than 50 years ago and refers to a protein with an amino acid profile that exactly meets the animal's requirement so that all amino acids are equally limiting for performance. Because Lys typically is the first-limiting amino acid, the ideal amino acid profile is often expressed relative to Lys. Although the ideal protein profile is often assumed to be constant for a given production stage, (small) changes in the ideal protein profile can occur within a production stage. This can be caused by changes in the relative contribution of the different components of amino acid requirements during the productive life on the animal (e.g. changes in the relative contribution of growth and maintenance). Amino acids requirements can be determined experimentally using dose-response studies. The design of the study, the chosen response criterion, and the statistical model affect the requirement estimate. Although considerable experimental work has been carried out to determine the requirements for Lys, Met, Thr, and Trp in growing pigs (and to a lesser extent in sows), little is known about the requirements for the other essential amino acids. Experimental dose-response studies generally focus on the requirement and less on the overall response (i.e. what are the consequences of an amino acid deficiency?). This latter aspect is, to some extent, accounted for in modelling approaches that quantify the response of the animal to the amino acid supply in a dynamic way. The paper describes the origin of ideal protein and illustrates how fundamental concepts of amino acid nutrition have been integrated in practical modeling approaches for the nutrition of growing pigs and sows.

Dietary protein restriction during lactation in primiparous sows with different live weights at farrowing: I. Consequences on sow metabolic status and litter growth.

The hypothesis that the restriction of dietary protein during lactation has different impacts on sow metabolic status and milk production according to body weight was evaluated. From 5-months of age until farrowing, the gilts were fed to achieve body weights of 180 or 240 kg at farrowing. At this time, 38 sows were assigned to one of three groups: " 180 kg" sows not restricted in dietary protein during lactation (180CP); "180 kg" restricted in protein (180LP), or "240 kg" sows restricted in protein (240LP). Catheters were fitted in the jugular vein of 24 sows and serial blood samples were collected 1 d before and 1 d after weaning. Amongst the protein-restricted animals, heavy sows (240LP) had a smaller appetite than light sows in early lactation, resulting in lower energy and protein intakes in the 240LP than in the 180LP sows. Body protein losses were 8, 11 and 13.5% of calculated body protein mass at farrowing in the 180CP, 180LP and 240LP sows, respectively. At the end of lactation, IGF-I concentrations were lower in the 180LP than in the sows from the other groups, probably because of the uncoupling between GH and IGF-I secretions. Low IGF-I concentrations likely promote lean tissue mobilization. Glucose and insulin profiles suggested an insulin resistance state in the 240LP sows compared with the 180LP sows, which may explain, at least in part, the lower feed intake and body reserve mobilization in these sows. Plasma pre- and post-prandial concentrations of amino acids in late lactation differed among the three treatment groups. Throughout lactation, litters from the 180LP and 240LP sows had a slower growth rate than litters from sows which were not restricted, suggesting that endogenous protein mobilization throughout lactation does not completely compensate for a low protein intake.

Dietary protein restriction during lactation in primiparous sows with different live weights at farrowing: II. Consequences on reproductive performance and interactions with metabolic status.

The hypothesis that the restriction of dietary protein during lactation has different impacts on reproductive performance in light and heavy sows at farrowing was investigated, as well as the relationships between reproductive parameters and sow metabolic data. At farrowing, 38 primiparous sows were assigned to one of three groups: sows weighing 180 kg not restricted in dietary protein during lactation (180CP); sows weighing 180 or 240 kg restricted in protein (180LP and 240LP). Twenty-four sows were catheterized and serial blood samples were collected 1 d before and 1 d after weaning. The sows were inseminated at the first estrus after weaning and slaughtered at d 30 of gestation. Protein restriction reduced the proportion of sows that returned to estrus within 8 d after weaning in the 180LP sows (P < 0.03), but not in the 240LP sows. It also induced a reduction in ovulation rate in the 180LP sows (P < 0.05) and, to a lesser extent, in the 240LP sows (P = 0.12). When the sows were categorized according to return to estrus (WOI < or = 8 or > 8 d), basal and mean concentrations of LH increased after weaning only in sows with a short WOI. Sows with a delayed estrus exhibited a higher ratio of plasma tyrosine to large neutral amino acids (AA, P < 0.01). In conclusion, large body reserves at farrowing buffer, at least in part, the detrimental effect of a strongly negative nitrogen balance on reproduction. We suggest that the alteration of AA profiles induced by dietary protein restriction and body protein loss alters LH secretion via modifications of the neurotransmitters involved in GnRH secretion.