Reducing crude protein and supplementing amino acids in growing pig (50-70 kg) diets reduce nitrogen excretion but promotes different environmental impacts when using life cycle assessment

Reducing crude protein and supplementing amino acids in growing pig (50-70 kg) diets reduce nitrogen excretion but promotes different environmental impacts when using life cycle assessment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Reducing crude protein and supplementing amino acids in growing pig (50-70 kg) diets reduce nitrogen excretion but promotes different environmental impacts when using life cycle assessment Lucas Antônio Costa Esteves, Alessandra Nardina Tricia Rigo Monteiro, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3001759/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The objective of this study was to evaluate the performance, digestibility, and environmental impact of pigs in the growth phase receiving diets with reduced crude protein and supplementation of amino acids. In the metabolism experiment, 20 crossbred barrows with an initial average weight of 63.62 ± 2.21 kg were housed in metabolic cages, with four treatments and five replications, one animal per experimental unit. In performance experiment, 40 crossbred barrows were used, with an initial average weight of 49.92 ± 0.92 kg, with four treatments, ten replications. The treatments used in both experiments consisted of four diets containing 16, 15, 14, and 13% of CP, and supplementation with amino acids to meet the requirements of all digestible amino acids. For performance, backfat thickness, and depth of the longissimus lumborum muscle, no differences were observed. Plasma urea was lower in animals fed diets with protein reduction as well as the excretion of N urine and total N, but no differences were observed for retained N, P absorbed, P ingested, and P feces. Through the life cycle assessment, for the categories of eutrophication potential and land occupation, the protein reduction mitigated the impacts when referring to soybean meal produced in the southern region, but the protein reduction provided an increase in impact when the category evaluated was cumulative energy demand, considering the soybean produced in the south and that produced in the central west region. amino acid cumulative energy demand eutrophication global warming potential Figures Figure 1 Figure 2 1. Introduction The increase in the world population, combined with the urbanization process and better family income, will promote a greater demand for food of animal origin. Meeting this demand will be a challenge to the production sector and may pose a threat to the environment (Sajeev et al., 2018 ), if production is poorly conducted. Many studies have highlighted the negative effects of the animal production sector, which are often correlated with gas emissions such as ammonia (NH 3 ), which is responsible for the eutrophication of lakes and rivers and soil acidification (Sajeev et al., 2018 ). NH 3 is formed from nitrogen (N) present in animal feces and urine. In addition to N, another element found in manure is phosphorus (P), which is capable of promoting environmental eutrophication. The concern with the environmental impacts caused by the excess of N is worldwide, as a survey carried out in China showed that the disposal of N carried out in water sources is approximately 14.5 megatons per year, a value 2.7 times higher than the estimated as “safe” (approximately 5.2 megatons per year). Crops and animal production are responsible for about to one third of these effects, respectively (Yu et al., 2019 ). Improvements in production processes (diet, animal management, manure management, and use of nutrients) have been identified as ways to mitigate these impacts. The reduction of dietary crude protein (CP) in pig production is a way to reduce N excretion without harming animal performance (Monteiro et al., 2019 ). The moderate reduction of dietary protein in pigs can reduce not only the excretion of N but also the emission of NH 3 ; however, the sharp reduction can promote a decrease in the performance of the animals (Wang et al., 2020 ). To avoid a drop in performance, the daily amino acid requirement of animals must be met with industrial amino acid supplementation (IAA); thus, there will be no excess of amino acids and will promote better use of N. Currently, there is a tendency to reduce the protein content, which can be confirmed through the tables of nutritional requirements for pigs (Rostagno et al., 2011 ; Rostagno et al., 2017 ). In addition to the lower excretion of N, protein reduction can alter the intestinal flora, produce metabolites, gene expression, performance, and pig carcass composition (He et al., 2016 ; Monteiro et al., 2016 ; Fan et al., 2017 ; Zhao et al., 2019 ; Wang et al., 2020 ). However, the lower excretion of N is not a guarantee of lower environmental impact, either in the production of pigs, as changes in nutritional composition can cause different impacts along the production chain. The swine production chain involves several stages, including the production of grains, fertilizers, and additives, as well as transportation, drying, storage, handling, and application of manure. As the emission of substances occurs throughout the chain, they must be assessed in an integrated manner, and an effective tool for this type of assessment is life cycle assessment (LCA) (Dourmad et al., 2014 ). Although there are studies evaluating protein reduction, there are still questions about whether it can impair the performance of animals, since, even with IAA supplementation, the reduction may promote changes in the composition of the intestinal flora and metabolism of nutrients. In addition, the reduction has the main benefit of lower environmental impact, which is provided by the lower excretion of N, however this assessment must be carried out holistically. Thus, the objective of this work was to evaluate the environmental impact, through LCA, of diets with reduction of crude protein and supplementation of IAA for crossbred barrows in the growth phase (50–70 kg). 2. Material and methods 2.1 Goal and scope This study was conducted at the State University of Maringá (UEM), which is located in the northwest region of Paraná, a state of great representativeness in pig production in Brazil. This study aimed to evaluate the environmental impact of diets with reduced crude protein and supplementation of industrial amino acids for crossbred barrows in the growing phase (50–70 kg) and to generate information that can be used by the pig industry, researchers, and society in the search for the sustainability of the productive system. 2.2 Description of the system Two experiments were conducted to perform the LCA. Experiment I: digestibility of four diets with protein reduction to evaluate the digestibility of nutrients and to determine the excretion of manure (feces and urine). Experiment II: performance, plasma urea, backfat thickness (BT) and the depth of the Longissimus lumborum (LL) muscle of animals that received the same diets used in experiment I. 2.3 System and functional unit limits To perform the LCA, the stages of animal production, cultivation, drying, and processing of grains, transportation of ingredients to the factory and from the factory to the farm, production of feed, storage, transport, and application of manure to the soil were considered. The impacts were considered in a specific phase (50–70 kg); therefore, the impacts related to the process of transport and slaughter of the animals were not evaluated. According to Reckmann et al. ( 2013 ), the slaughter process has a low environmental impact compared to the stages of feed production and animal production, representing less than a tenth of the total impacts for categories such as acidification and eutrophication potential, respectively. Although the area necessary for the construction of the facilities was not used in the analysis, the area necessary for the production of grains was measured in the system. The manure generated during the animal housing phase was accounted for and used as a source of N, P, and K in the fertilization of crops, thereby reducing the use of chemical fertilizers. The equivalence factor (EF) used for N was 75%, assuming an extra loss of 5% in the form of nitrate (Garcia-Launay et al., 2014 ; Nguyen et al., 2010 ). The EF used for the P and K present in the manure was 100% (Sommer et al., 2008 ). All the impacts related to manure management, including storage and application, were allocated and accounted for. The functional unit was one kg of live weight gain (LWG) during the 50–70 kg growth phase. The definition of the system limits is shown in Fig. 1 , and is based on Nguyen et al. ( 2010 ). 2.4 Life cycle inventory analysis The resources used and emissions associated with the production and transport of raw materials for grain production were obtained from the Ecoinvent version 3 database (SimaPro LCA software 8.0, Pré Consultants). The use of electrical energy for lighting and ventilation of warehouses was considered; however, the emissions and resources used for the construction of the facilities, vaccines, veterinary drugs, detergents, and disinfectants used to clean the facilities were not evaluated (Arrieta and González, 2019 ). 2.5 Grain Production As they are the most important regions for soy production in Brazil, it was assumed that the soy used was produced in the south and mid-west (CONAB, 2018). The inventory for soy cultivation was based on Silva et al. ( 2010 ), with an economic allocation to determine the impact of oil and soybean meal (Garcia-Launay et al., 2014 ). The allocation is the partition of environmental impacts between different co-products and can be carried out based on physical characteristics (mass and energy) or other factors, such as economics (Arrieta and González, 2019 ). The inventory for maize cultivation was based on the report by Alvarenga et al. ( 2012 ). 2.6 Uncultivated raw materials The inventories for salt, dicalcium phosphate, sodium bicarbonate, limestone, and the premix were obtained from Wilfart et al. ( 2016 ). For the production of antioxidants, the same demand for resources and energy necessary for the production of the premix was considered. For the production of the amino acids L-lysine HCl, DL-methionine, and L-threonine, the inventory was obtained by Mosnier et al. ( 2011 ), who considered chemical and biological processes in the synthesis of these products. For the production of L-tryptophan, L-valine, and L-isoleucine, the demand for resources and energy is twice as high as that for the production of L-lysine (Garcia-Launay et al., 2014 ). 2.7 Transport specifications To transport food and feed, the methodology proposed by Silva et al. ( 2010 ). Transportation of imported products was initially established by the sea and later by road. 2.8 Feed formulation and production The treatments consisted of four diets with decreasing levels of CP. The levels used were 16, 15, 14 and 13% of CP (Table 1 ), and the requirements for digestible amino acids proposed by Rostagno et al. ( 2017 ) were met with the amino acids L-lysine, DL-methionine, L-threonine, L-tryptophan, L-valine and L-isoleucine. The amino acid compositions of corn and soybean meal were determined by Evonik Industries. To calculate digestible amino acids contained in foods and industrial amino acids, the standardized ileal digestibility coefficients proposed by Rostagno et al. ( 2017 ). Sodium bicarbonate was used to adjust the electrolyte balance of the diets. Table 1 Ingredients, chemical composition of diets with reduction on crude protein level. Ingredients Crude Protein (%) 16 15 14 13 Maize g/kg 730.75 759.80 789.65 820.34 Soybean meal g/kg 216.27 185.33 153.25 120.44 Soybean oil g/kg 22.09 20.95 19.47 7.65 Dicalcium phosphate g/kg 10.60 10.82 11.05 1.29 Limestone g/kg 6.03 6.15 6.27 6.39 Sodium bicarbonate g/kg 1.79 3.36 4.99 6.12 Salt g/kg 2.92 1.86 0.76 - Premix 1 g/kg 4.00 4.00 4.00 4.00 L-Lysine HCl 78.0% g/kg 3.09 4.08 5.10 6.15 DL-Methionine 99.0% g/kg 0.81 1.09 1.39 1.69 L-Threonine 98.5% g/kg 1.06 1.52 1.99 2.47 L-Tryptophan 98.0% g/kg 0.30 0.47 0.65 0.83 L-Valine 98.0% g/kg - 0.28 0.87 1.47 L-Isoleucine 100.0% g/kg - - 0.26 0.86 Antioxidant 2 g/kg 0.10 0.10 0.10 0.10 Growth promoter 3 g kg − 1 0.020 0.020 0.020 0.020 Calculated Composition Cálcium g/kg 5.75 5.75 5.75 5.75 Available phosphorus g/kg 2.81 2.81 2.81 2.81 Sodium g/kg 1.76 1.76 1.76 1.76 Potassium g/kg 6.30 5.82 5.33 4.83 Chlorine g/kg 3.10 2.68 2.23 1.99 SID lysine g/kg 9.27 9.27 9.27 9.27 SID methionine g/kg 3.02 3.17 3.31 3.47 SID met + cys g/kg 5.74 5.47 5.47 5.47 SID threonine g/kg 6.03 6.03 6.03 6.03 SID tryptophan g/kg 1.85 1.85 1.85 1.85 SID valine g/kg 6.67 6.40 6.40 6.40 SID isoleucina g/kg 5.95 5.41 5.10 5.10 SID histidine g/kg 3.86 3.57 3.26 2.96 SID phenilalanine g/kg 6.97 6.43 5.86 5.29 Metabolizable energy MJ 14.02 14.02 14.02 14.02 Net energy MJ 10.74 10.81 10.87 10.93 E. Balance mEq 150.02 150.03 150.01 143.89 Ratio AAE:AANE 45:55 46:54 46:54 47:53 1 Premix should provide at least the following nutriente amounts per kg of feed:: vitamin A – 4000 UI; vitamin D3–600 UI; vitamin E – 12 UI; vitamin K3–3 mg; vitamin B1–0.6 mg; vitamin B2–3.5 mg; vitamin B6–1; vitamin B12–18 mg; niacin – 20 mg; pantothenic acid – 10 mg; folic acid – 1 mg; biotin − 0.03 mg; choline chloride – 0.16 g; iron – 35 mg; copper – 15 mg; manganese – 25 mg; zinc – 0.075 g; iodine – 1 mg; selenium 0.3 mg, 2 BHT; 3 leucomycin. The amino acids lysine, methionine, threonine, tryptophan, valine, isoleucine, arginine, leucine, phenylalanine and histidine were considered essential amino acids in the calculation of the relationship between essential amino acids (AAE): non-essential amino acids (AANE). For this calculation, the total concentration of each amino acid and the amount of N present in its composition were considered. The N values used in this study were those proposed by Rostagno et al. ( 2017 ). The N for the AANE was obtained from the difference between the total N of the diets and the N present in the AAE, as adapted from Toledo et al. ( 2014 ). Data related to the production process of the feeds were based on those proposed by Garcia-Launay et al. ( 2014 ). 2.9 Animal production The procedures performed in Experiments I and II were approved by the Animal Use Ethics Committee of the State University of Maringá (CEUA no. 2846260819). 2.10 Experiment I Twenty crossbred barrows, with an initial average weight of 63.62 ± 2.21 kg were used, were housed in metabolic cages and distributed in a randomized block design, with four treatments and five repetitions, one animal per experimental unit. The amount of feed provided daily was calculated based on the metabolic weight (kg^0.75) and average consumption recorded during the adaptation period. To avoid losses and facilitate ingestion, the rations were moistened with water (approximately one third of the ration) and supplied twice daily (7:30 am and 3:30 pm). After each meal, water was provided in the feeder in the proportion of 3 mL of water/g of feed to avoid excessive water consumption. To determine the period of beginning and end of fecal collection, 2% ferric oxide (Fe2O3) was added to the diets. Feces were collected daily, packed in plastic bags, and stored in a freezer (-18°C) for later analysis. Urine was filtered and collected daily in plastic buckets containing 20 mL of HCl 1:1, to avoid nitrogen volatilization and bacterial proliferation. Aliquots of one fifth of the total volume were removed, packed in plastic bottles, and frozen (-18°C). To determine the period of beginning and end of the collection of feces, 2% ferric oxide (Fe 2 O 3 ) was added to the diets. Feces were collected daily and packed in plastic bags and stored in a freezer (-18°C), to be later analysed. The urine was filtered and collected daily in plastic buckets containing 20 mL of HCl 1: 1, to avoid nitrogen volatilization and bacterial proliferation. Aliquots of one fifth of the total volume were removed and packed in plastic bottles and frozen (-18°C). 2.11 Experiment 2 2.11.1 Performance Forty crossbred barrows, with an initial average weight of 49.92 ± 0.92 kg were used, and distributed in a randomized block design, with four treatments and ten repetitions, one animal per experimental unit. The animals were weighed at the beginning and end of the experimental period to determine the daily weight gain (DWG). The ratios were weighed to determine the daily feed intake (DFI) and feed conversion (FC). 2.11.2 Longissimus lumborum and backfat thickness When the animals reached an average weight of 70.50 ± 3.19 kg, the backfat thickness (BT) and the depth of the longissimus lumborum (LL) muscle were evaluated, using equipment consisting of an ecocamera (Aloka® SSD- 500 Vet) coupled to a 14.5 cm and 3.5 MHz probe. For this measurement, the animals were shaved between the tenth and eleventh ribs (Dutra Jr et al., 2001). 2.11.3 Plasma urea At the end of the experiment, the animals were fasted for 6-hour fast to collect the blood. The samples were collected, transferred to a tube containing EDTA, and subsequently centrifuged at 3000 rpm for 15 min. Plasma was transferred to polyethylene microtubes and stored in a freezer. The urea analysis was performed by the colorimetric method, using a commercial kit, following the standard operating procedures described therein. 2.12 Laboratory analysis The ratios were analyzed according to the Association of Official Analytical Chemists AOAC (2005), in terms of dry matter (method 950.46), ash (method 942.05), crude fiber (method 962.09), and nitrogen (method 984.13). The values of phosphorus, Cu, Zn, and K were obtained using a UV-Vis spectrophotometer. The nitrogen content of the urine was also evaluated. The levels of nitrogen, phosphorus, dry matter, ash, and crude fiber in the feces were determined following the same methodology used in the analysis of the rations. 2.13 Life cycle impact assessment 2.13.1 Emissions from pig production Emissions were calculated for the stages of animal housing, storage, and manure application, as proposed by Monteiro et al. ( 2016 ). Through laboratory analysis, the amounts of N, P, and excreted organic matter were obtained for later determination of the amount of each nutrient available for application. The excretion of the minerals copper (Cu), zinc (Zn) and potassium (K) were determined using the equations proposed by Rigolot et al. ( 2010a ). The NH3 emissions resulting from the stages of accommodation and manure management were determined considering the temperature of the shed and were calculated according to the equations proposed by Rigolot et al. ( 2010b ) and the IPCC ( 2006 ). 2.13.2 Characterization factors The LCA was based on the CML 2001 (baseline) version 3.02 method, implemented in Simapro version 8.05 (PRé Consultants), adding the following categories: land occupation from CML 2001 (all categories) version 2.04 and cumulative energy demand version 1.8 (non-renewable fossil + nuclear). The characterization factors used to calculate the impact of growing pig production were: global warming potential GWP (kg CO2-eq.), acidification potential (AP, g SO2-eq.); eutrophication potential (EP, g PO4- eq), terrestrial ecotoxicity (TE, g 1,4-DCB-eq.), cumulative energy demand (CED, MJ-eq.) and land occupation (LO, m2-year) for the GWP category, the global warming potential over a 100-year horizon was considered. 2.14 Interpretation and statistical analysis Retention coefficients of N and P were obtained for each experimental diet. These coefficients were used to determine the amounts excreted by each element during the growth period using the data obtained from the performance evaluation of the animals. The LCA calculations were evaluated for each animal, and according to the consumption and excretion data of the animals, the environmental profile of each system was constructed, with the aid of the SAS software (SAS Inst. Inc., Cary, NC). The performance, excretion, and environmental impact data were subjected to analysis of variance using the SAS GLM procedure. The statistical model included the treatment and block effects. Significant data were subjected to a regression analysis. The degrees of freedom related to the CP levels were divided into polynomials. All analyses were performed using SAS software version 9.2. 3. Results 3.1. Life cycle assessment of feed The formulated diets assess the environmental impacts of soy produced in different regions of Brazil, including the southern and midwestern regions. The impact categories that presented the greatest divergence between the results observed for these two regions were GWP and CED. The reduction in CP in diets formulated considering soybean meal from the southern region increased the impacts of GWP, AP, EP, CED, and TE (Table 2 ). The only exception was observed for LO, in which the lowest protein concentration resulted in mitigation of the impact for this category. Table 2 Life cycle assessment, per kg of feed, in pig diets (50–70 kg), containing decreasing levels of crude protein (CP) and supplementation of synthetic amino acids Parameters Crude protein (%) 16 15 14 13 Soybean meal from South GWP kg CO 2 -eq. 411 425 443 464 AP g SO 2 -eq. 11.47 11.92 12.40 12.93 EP g PO 4 -eq 4.39 4.40 4.41 4.43 CED MJ-eq. 5.18 5.47 5.89 6.38 TE g 1,4-DCB-eq. 4.24 4.36 4.51 4.69 LO m 2 -year 1.04 1.01 0.98 0.96 Soybean meal from Midwest GWP kg CO 2 -eq. 712 683 657 632 CED MJ-eq. 6.30 6.43 6.68 7.00 Global warming potential (GWP), acidification potential (AP), eutrophication potential (EP), cumulative energy demand (CED), terrestrial ecotoxicity (TE) and land occupation (LO). For diets formulated considering soybean meal from the midwest region, protein reduction also resulted in an increased environmental impact on CED; however, for GWP, an inverse behavior was observed, since the reduction of CP provided a reduction in GWP. Regardless of the protein level evaluated, diets formulated considering soybean meal produced in the midwest region resulted in a greater impact when compared to diets produced considering the meal obtained in the southern region. 3.2. Metabolism and performance The higher concentration of N in the diet promoted a linear increase in the ingested N (P = 0.002), excretion of N in the urine (P = 0.006), and total N excreted (P = 0.001). However, no significant differences were observed for N in feces, N retained, P ingested, P in feces, or P absorbed (Table 3 ). Table 3 Nitrogen and phosphorus balance of growing pigs (50–70 kg) fed diets with crude protein reduction and amino acids supplementation. Parameter Crude Protein (%) S.E.M P-value 16 15 14 13 N Intake g/d 46.78 46.59 45.08 39.22 1.267 0.021 N Feces g/d 6.10 5.59 5.37 5.64 0.186 0.452 N Urine g/d 13.33 12.85 10.16 9.32 0.527 0.027 N Excreted g/d 19.43 18.44 15.53 14.96 0.541 0.004 N Retention % 58.18 60.28 65.28 61.25 1.101 0.217 P Intake g/d 8.81 8.90 9.26 7.93 0.244 0.145 P Feces g/d 4.87 4.63 4.49 4.52 0.109 0.299 P Absorbed % 44.44 47.81 51.06 42.88 1.405 0.163 Parameter Equation Model Regression P-Value R 2 N intake Y = 9.31198 + 2.42113X Linear 0.002 0.89 N Urine Y = 9.90543 + 1.47037X Linear 0.006 0.88 N Excreted Y = -6.53338 + 1.62907X Linear 0.001 0.93 In the performance evaluation, there was no significant difference for the evaluated variables (DWG, FCR and DFI), as well as for the carcass characteristics (BT and LL). However, the lowest protein concentration promoted a linear reduction (P = 0.001) in the plasma concentrations of urea in growing pigs (Table 4 ). Table 4 Final weight, daily weight gain (DWG), feed conversion rate (FCR), daily feed intake (DFI), backfat thickness (BT), depth of Longissimus lumborum (LL) muscle and plasma urea of growing pigs fed diets with crude protein reduction and amino acid supplementation. Parameter Crude Protein (%) S.E.M P-Value 16 15 14 13 Final Weight kg 70.57 71.36 71.39 68.64 0.505 0.253 DWG kg 1.14 1.17 1.19 1.05 0.031 0.379 FCR kg 2.18 2.20 2.11 2.19 0.028 0.640 DFI cm 2.43 2.51 2.50 2.30 0.043 0.234 BT cm 0.98 0.94 0.96 0.96 0.017 0.987 LL cm 4.50 4.48 4.50 4.48 0.048 0.996 Urea mg/dL 33.50 28.45 25.67 20.94 1.300 0.002 Parameter Equation Model Regression P-Value R 2 Urea Y = -31.5122 + 4.04500X L 0.001 0.99 3.3. Life cycle assessment of growing pig production As mentioned previously, the impact categories evaluated were GWP, AP, EP, CED, TE, and LO, but the categories of GWP and CED were evaluated under two productive contexts, with soybean meal originating from soy produced in the south or midwest. GWP and CED showed the greatest divergence between the observed results; therefore, they were presented in this way. The results related to animal production, calculated using data obtained from diets that used soybean meal from the southern region, ranged from 3.06 to 2.93 kg CO2-eq, 41.41 to 37.91 g SO2-eq and 13.31 to 12.29 g 1,4-DCB eq, per kg of LWG for categories GWP, AP and TE, respectively. Although, no statistically significant differences were observed between the treatments evaluated (P > 0.050) for these impact categories (Table 5 ). Table 5 Potential environmental impacts, per kg of body weight gain, of growing pigs from 50 to 70 kg, with crude protein reduction and amino acid supplementation. Parameter Crude Protein (%) S.E.M P-value 16 15 14 13 Soybean meal from south GWP kg CO 2 -eq. 3.01 3.05 2.93 3.06 0.057 0.623 AP g SO 2 -eq. 41.41 40.83 37.91 40.68 0.768 0.110 EP g PO 4 -eq. 13.35 13.06 12.01 12.56 0.245 0.050 CED MJ-eq. 17.88 18.52 18.86 21.33 0.494 0.003 TE g 1,4-DCB eq. 12.29 12.80 12.41 13.31 0.247 0.165 LO m 2 -year 2.26 2.22 2.06 2.10 0.041 0.063 Parameter Equation Model Regression P-Value R² EP Y = 8.02464 + 0.322171X Linear 0.036 0.55 CED Y = 34.6684–1.07034X Linear 0.001 0.84 Soybean meal from Midwest 160 150 140 130 S.E.M P-Value GWP kg CO 2 -eq. 3.67 3.62 3.38 3.43 0.068 0.100 CED MJ-eq. 20.31 20.64 20.53 22.69 0.485 0.044 Parameter Equation Model Regression P-Value R 2 CED Y = 31.2237–0.702253X Linear 0.028 0.67 However, EP (P = 0.036) showed a lower impact when the diet changed from 16 to 13% of CP when considering soy from the southern region. The opposite effect was observed for the CED (P = 0.001) category, since the reduction in dietary CP promoted a increase in environmental impact (Table 5 ). Reducing the CP concentration in diets provided an increase in CED considering soybean meal from the Midwest region; however, no significant difference was observed for GWP. The results obtained for GWP and CED, per kg of LWG, were higher when considering the production of feed the soybean meal from grown in the central-west region when compared to that obtained for GWP and CED per kg of LWG of the animals produced considering the bran from the southern region. 4. Discussion 4.1. Feed For diets formulated considering soybean from the southern region, protein reduction promoted an increase in the kg of feed produced for the GWP category by up to 53 CO 2 -eq (Table 2 ). Industrial amino acids are among the foods used to formulate diets with the greatest environmental impact in this category (Mosnier et al., 2011 ). Thus, it is expected that protein reduction promotes an increase in the inclusion of amino acids in diets, and consequently, the environmental impact for this category is higher in diets with lower protein concentrations, since the requirement for essential amino acids must be met to ensure optimal performance of pigs. The same behavior was observed for GWP in relation to CED. According to Mosnier et al. ( 2011 ), the production of one kg of amino acids, such as L-lysine-HCl and L-threonine, involves an energy expenditure almost twice higher than the production of the same amount of soybean meal. Thus, reducing CP promoted an increase in CED from 5.18 to 6.38 MJ-eq and 6.30 to 7.00 MJ-eq for diets using soybean meal from the south and mid-west, respectively. 4.2. Metabolism and performance The reduction in CP provided lower consumption of N by the animals, which reduced from 46.78 g/d to 39.22 g/d for animals that received diets of 16 and 13% CP, respectively (Table 3 ). The consumption of N influenced the total excretion of N, which was higher in animals that received a 16% CP diet than in those that received a 13% CP diet. These results are in agreement with those obtained by Toledo et al. ( 2014 ), who concluded that the reduction in CP in the pig diet was responsible for decreasing and excreting N, mainly through urine. The N intake, total and urine N excretions were directly related to the N concentrations in diets, but no effects were observed for N retention (Table 3 ). As in this study, Monteiro et al. ( 2017 ) also observed that the reduction in CP significantly reduced the intake and excretion of N, without effects on retained N. The lower inclusion of soybean meal in pig diets, in order to reduce the CP concentration, may reduce the phytic phosphorus, thereby improving the absorption of phosphorus by animals, but no effects were observed for absorbed P in this study (Table 3 ). Protein reduction decreased the excretion of N to the environment without impairing animal performance, which proves that the supplementation of industrial amino acids met the daily requirements of the animals. The DWG ranged from 1.17 kg to 1.05 kg, and the treatment with the lowest protein consumption provided nutrients for the animals to reach the expected DWG for this phase (1.05) (Rostagno et al., 2017 ). FCR and DFI were also not influenced by dietary protein reduction. These results differ from those obtained in other studies, in which protein reduction worsened DWG, DFI, and FCR (Li et al., 2018 ; He et al., 2016 ). The difference between the results found in our study and those mentioned above can be explained by the marked protein reduction proposed by the authors, as well as by amino acid supplementation, since they chose not to supplement the diets with valine and isoleucine. This results in a deficiency of these amino acids in diets with low protein concentrations. In the work developed by He et al. ( 2016 ), the consumption of valine in the growth phase was 7.72 g/d and 13.25 g/d for animals that received diets with 12.35 and 18.27% CP, respectively. This large difference in amino acid consumption may have limited animal performance. In the present study, digestible valine consumption was approximately 16.23 g/d, referring to animals that received diets containing 16% CP; and 14.71 g/d for animals that received diets with 13% CP. The digestible valine requirement for this phase (50–70 kg) was 14.98 g/d (Rostagno et al., 2017 ). Although the animals in the treatment with the lowest protein concentration did not consume the requirement of 14.98 g/d of the digestible valine requirement proposed by Rostagno et al. ( 2017 ), this deficiency did not compromise their performance. Other studies have observed that the daily requirement for amino acids may be slightly overestimated in the tables proposed by Rostagno et al. ( 2011 ) (Pasquetti et al., 2015 ; Monteiro et al., 2017 ), however this higher demand for amino acids may be a guarantee margin that aims to meet the requirement of the national herd (Monteiro et al., 2017 ). The results obtained for plasma urea and excretion of N in the urine corroborate what was observed in the performance evaluation, as the animals that consumed diets with a higher protein concentration consumed amino acids above the daily requirement. As demonstrated for digestible valine, other amino acids (isoleucine, histidine, phenylalanine, among others) were also consumed in greater amounts in diets with a higher protein concentration. This resulted in greater deamination of these amino acids and in the linear increase of plasma urea and N in the urine, because in addition to the greater amount of some amino acids in the diet with higher protein concentrations, there is also a greater imbalance of essential amino acids providing greater deamination of these amino acids. The AAE: AANE ratio of the diets ranged from 45:55 to 47:53 for the diets with the highest and lowest protein concentrations, respectively. Diets with reduced crude protein and high inclusion of industrial amino acids can promote an increase in this relationship and limit the synthesis of AANE, according to Wang and Fuller et al. (1989) the ideal ratio between AAE: AANE is approximately 45:55. The data referring to the performance of the animals allowed us to conclude that although there was a variation in the AAE: AANE ratios between diets, this variation did not limit the synthesis of AANE, as there was no difference in the performance of the animals. 4.3. LCA of animal production The values found in relation to GWP ranged from 2.93 to 3.06 kg CO2-eq. per kg of LWG and from 3.67 to 3.38 kg CO2-eq. per kg of LWG for animals fed diets that used soybean meal from the southern and central-western regions, respectively. There was no difference between the treatments evaluated. The difference in relation to the environmental impact of the production of the diets was what determined the highest result for kg of LWG of the animals that received diets with soybean meal from the central-west region. Soybean grown in the central-west region has the aggravating factor of being a food produced in an area of recent deforestation, which implies accounting for CO2-eq. emitted during cultivation of this plus CO2-eq. referring to land use changes (Silva et al., 2010 ; Reckmann et al., 2016 ). In the central-west region, transportation is another aggravating factor compared to the distances traveled in the south. Although the main differences found in the environmental impact between soybeans grown in the south and mid-west are due to deforestation and transportation, another factor that should be highlighted is the greater use of fertilizers in the cultivation of soybeans produced in the mid-west region (Silva et al., 2010 ; Silva et al., 2014 ). The manure management stage contributed the most to the impact observed in the GWP category. This stage represented 64.03% (diets with bran from the southern region) and 54.78% of the impacts observed (diets with bran from the central-west region) for this category (Fig. 2 ). The food production stage also had a significant impact on this category, with 31.78 and 41.64% of kg CO2-eq emissions per kg of LWG for animals fed diets containing soybean meal in the southern and midwestern regions, respectively. The results obtained agree with those observed by Bandekar et al. ( 2019 ), who concluded that the stages of waste management and food production contributed the most to the GWP category. The authors also reported that management that results in a drop in the performance of the animals may enhance the environmental impacts for this category, as the animal will have to consume a greater amount of food and excrete a greater amount of manure until reaching the ideal slaughter weight. The most important element emitted during the manure management stage for the GWP category was CH4, and the emission of this element can be intensified or mitigated according to the temperature of the environment (IPCC, 2006 ). The average temperature recorded in the experimental period was 24.63°C; if we use the temperature obtained in an experiment in the previous year, 26.03°C, the average value obtained for the four treatments that used soybean meal from the southern region, 3.01 kg CO2-eq. per kg of LWG would be 3.25 kg CO2-eq. per kg of LWG. For the AP category, no significant difference was observed (P = 0.110) between the treatments evaluated, which varied between 41.4 to 37.9 g SO2-eq. per kg of LWG. Studies have shown that protein reduction can promote a reduction in AP through lower excretion of N in manure and, consequently, lower NH3 emissions (Reckmann et al., 2016 ; Wang et al., 2020 ). NH 3 is emitted from the moment the feces and urine mix, as this gas is formed by the hydrolysis of urea present in the urine and is catalyzed by the enzyme urease, which is present in the feces. This enzyme is produced by bacteria present in the digestive system of animals (Philippe et al., 2011 ). In this way, NH 3 is the main element responsible for the AP process during the animal housing and manure handling stages. According to a study by Reckmann et al. ( 2013 ), NH 3 was responsible for most of the impacts obtained for this category, with a large part of this element being emitted during the animal housing stage. Although no difference was observed in AP, the reduction in dietary CP was effective in reducing NH 3 emissions. There was a reduction in the emission of this element during the animal housing and manure management stages when compared to diets with higher and lower protein concentrations. This value is close to that proposed by Philippe et al. ( 2011 ), according to the authors, for each 10 g/kg less in the protein concentration of the diet, it is possible to reduce the NH 3 emission by almost 1/10. It is possible that the lowest protein concentration was not effective in significantly reducing the environmental impact of AP, as can be seen in Table 2 and Fig. 2 , although the reduction of CP in diets reduced the impact on the stages of animal housing and manure management, for the feed production stage, the effect was opposite. There was an increase in the environmental impact of AP when the kg of feed produced was compared between the diets with the lowest and highest protein values. Another factor that had an influence on the results was the animals' performance, since the impact was calculated on the kg of LWG and there were no significant differences for the DWG, FCR, and DFI. As with AP, the N emitted through NH3 is also responsible for the environmental EP. Another element that can affect this category is P (Guinée et al., 2002 ). The results obtained for the EP varied between 13.35 and 12.56 g PO4-eq. per kg of LWG, and a linear reduction (P = 0.036) was observed in the EP when comparing the lower CP diet with higher protein diet. AP and EP showed similar behaviors in relation to the impacts observed during the stages of accommodation and manure management, since the lower protein concentration reduced NH 3 emissions; consequently, the impact obtained for AP and EP during the stages of accommodation and management of the waste was mitigated. A large difference was observed in relation to the impact related to the production of the diets, since for EP, the difference between the impact per kilogram of feed produced was low. Feed production was the stage that contributed the most to EP regarding animal production (kg of LWG), representing values between 77.34 and 71.57% of the observed impacts. Similar results were obtained by Monteiro et al. ( 2016 ), in which for the EP category the production of diets was the stage that most impacted LCA, and they also reported that the use of amino acids was efficient in reducing the impacts for the category. The use of IAA combined with phytase supplementation can significantly reduce the impact of EP on pig and poultry production (Kebread et al., 2016). For the CED category, protein reduction promoted a linear increase in the impacts obtained, both for those using soybean meal from the southern region (P < 0.001), which ranged from 21.33 to 17.88 MJ-eq. per kg of LWG, and for the impacts assessed using soybean meal from the midwest region (P = 0.028), between 22.69 to 20.31 MJ-eq. per kg of LWG. The CP reduction requires an increase in dietary IAA to meet the pig’s requirements. As CED is higher for amino acids production than for the production of corn and soybean meal (Ogino et al., 2013 ), the higher inclusion provided an increased CED, both for kg of feed produced and for kg of LWG. In addition to GWP, CED also showed representative differences when diets were formulated using soybean meal from different regions. The cultivation of soybeans in the Midwest has a higher impact on CED due to the more recent deforestation, transportation and the use of fertilizers for the cultivation in that region (Silva et al., 2010 ). According to the author, the distance traveled for the transportation of equipment, seeds, and fertilizers, among others, as well as the transportation of the grains to the regional storage facilities, is greater in the Midwest than in the southern region. Evaluating the pig and poultry production chains, Arrieta and Gonzales (2019) highlighted that the production of fertilizers and pesticides contributes to more than half of the impacts observed in the CED for the production of pig diets. Thus, the use of waste to produce energy and biofertilizers is an alternative to mitigate these impacts (Nguyen et al .2010). The results obtained for TE varied between 13.31 and 12.29 g 1,4-DCB eq. per kg of LWG, however, there was no significant difference (P = 0.165) between the treatments. These values are higher than those obtained by Monteiro et al. ( 2019 ); however, the LCA performed by these authors evaluated the environmental impact of pig production in the nursery phase (15–30 kg). As the production of diets is the stage that contributes the most to the impact observed for TE, it is expected that a production phase with higher feed consumption and worse feed conversion will have a greater impact. As in the aforementioned work, the production of diets was the stage that contributed the most to the impacts observed for this category. Protein reduction was achieved through the lower inclusion of soybean meal and greater inclusion of amino acids in the diets. As the productivity per hectare of soybean cultivation is lower than that of corn, the lower inclusion of soybean meal in the diet results in a reduction in the environmental impact of the LO category (Silva et al., 2014 ). A lower concentration of protein in the diet reduced the environmental impact of the LO category in 0.16 m 2 /year. Because we do not consider the area necessary for the construction of the facilities, the impacts observed for this category are due to the production of the feed. Another factor that could alter the observed results could be related to animal performance. As there was no significant difference in performance, we can say that the linear reduction (P = 0.019) in the impact for LO, in relation to animal production (kg of LWG), was due to the impacts observed in the production of diets. The reduction of dietary CP, associated with supplementation of industrial amino acids for growing pigs, reduced the excretion of N without compromising animal performance. The effects related to LCA showed that protein reduction reduced the impacts for the categories eutrophication potential and land occupation, but the impacts for the category cumulative energy demand, both for the diets that used soybean meal from the southern region and those that used soybean meal from the midwest region, were intensified with the reduction of crude protein. Declarations Authors’ Contribution Lucas Antônio Costa Esteves: conceived and designed the study, conducted data gathering, performed statistical analysis and wrote the article. Alessandra Nardina Tricia Rigo Monteiro: conceived and designed the study. Gabriel Amaral de Araujo: wrote the article. Leandro Dalcin Castilha: conceived and designed the study. Alice Eiko Murakami: conceived and designed the study. Paulo Cesar Pozza: conceived and designed the study, performed statistical analysis and wrote the article. Funding This research was supported by CNPQ (Conselho Nacional de Pesquisa e Desenvolvimento) and CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior). Data Availability The dataset used and/or analyzed during the current study is available from the corresponding author on reasonable request. Ethical approval The procedures performed in Experiments I and II were approved by the Animal Use Ethics Committee of the State University of Maringá (CEUA no. 2846260819). 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Effect of different dietary protein levels and amino acids supplementation patterns on growth performance, carcass characteristics and nitrogen excretion in growing-finishing pigs. J Anim Sci Biotechnol, 10, 75. https://doi.org/10.1186/s40104-019-0381-2. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Maringa","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alice","middleName":"Eiko","lastName":"Murakami","suffix":""},{"id":207522506,"identity":"e1f32ca8-694a-461d-9801-6aaf9aa25c2b","order_by":5,"name":"Paulo Cesar Pozza","email":"","orcid":"","institution":"Universidade Estadual de Maringa","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Paulo","middleName":"Cesar","lastName":"Pozza","suffix":""}],"badges":[],"createdAt":"2023-05-30 19:09:00","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3001759/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3001759/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":38242434,"identity":"baf33ee8-c868-42d6-bc84-27546c3e98c2","added_by":"auto","created_at":"2023-06-08 16:23:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":69696,"visible":true,"origin":"","legend":"\u003cp\u003eSystem boundaries of growing pigs production.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3001759/v1/5054c862c32f123f3cb18730.png"},{"id":38242435,"identity":"8c22094f-82de-4060-b63b-ea7cc3d1bb01","added_by":"auto","created_at":"2023-06-08 16:23:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53253,"visible":true,"origin":"","legend":"\u003cp\u003eRelative contribution of the low protein and high protein system to the categories global warming potential (GWP), acidification potential (AP), eutrophication potential (EP), cumulative energy demand (CED), terrestrial ecotoxicity (TE) and land occupation (LO).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3001759/v1/fdd883e09f008a5000e8b703.png"},{"id":39154749,"identity":"9b0694cf-eb83-401c-9426-d0645ed25eb2","added_by":"auto","created_at":"2023-06-27 09:35:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":660656,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3001759/v1/40d24278-ce40-498d-89f5-c342284af669.pdf"}],"financialInterests":"","formattedTitle":"Reducing crude protein and supplementing amino acids in growing pig (50-70 kg) diets reduce nitrogen excretion but promotes different environmental impacts when using life cycle assessment","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe increase in the world population, combined with the urbanization process and better family income, will promote a greater demand for food of animal origin. Meeting this demand will be a challenge to the production sector and may pose a threat to the environment (Sajeev et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), if production is poorly conducted. Many studies have highlighted the negative effects of the animal production sector, which are often correlated with gas emissions such as ammonia (NH\u003csub\u003e3\u003c/sub\u003e), which is responsible for the eutrophication of lakes and rivers and soil acidification (Sajeev et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). NH\u003csub\u003e3\u003c/sub\u003e is formed from nitrogen (N) present in animal feces and urine. In addition to N, another element found in manure is phosphorus (P), which is capable of promoting environmental eutrophication.\u003c/p\u003e \u003cp\u003eThe concern with the environmental impacts caused by the excess of N is worldwide, as a survey carried out in China showed that the disposal of N carried out in water sources is approximately 14.5 megatons per year, a value 2.7 times higher than the estimated as \u0026ldquo;safe\u0026rdquo; (approximately 5.2 megatons per year). Crops and animal production are responsible for about to one third of these effects, respectively (Yu et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Improvements in production processes (diet, animal management, manure management, and use of nutrients) have been identified as ways to mitigate these impacts.\u003c/p\u003e \u003cp\u003eThe reduction of dietary crude protein (CP) in pig production is a way to reduce N excretion without harming animal performance (Monteiro et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The moderate reduction of dietary protein in pigs can reduce not only the excretion of N but also the emission of NH\u003csub\u003e3\u003c/sub\u003e; however, the sharp reduction can promote a decrease in the performance of the animals (Wang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To avoid a drop in performance, the daily amino acid requirement of animals must be met with industrial amino acid supplementation (IAA); thus, there will be no excess of amino acids and will promote better use of N.\u003c/p\u003e \u003cp\u003eCurrently, there is a tendency to reduce the protein content, which can be confirmed through the tables of nutritional requirements for pigs (Rostagno et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Rostagno et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In addition to the lower excretion of N, protein reduction can alter the intestinal flora, produce metabolites, gene expression, performance, and pig carcass composition (He et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Monteiro et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fan et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhao et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the lower excretion of N is not a guarantee of lower environmental impact, either in the production of pigs, as changes in nutritional composition can cause different impacts along the production chain. The swine production chain involves several stages, including the production of grains, fertilizers, and additives, as well as transportation, drying, storage, handling, and application of manure. As the emission of substances occurs throughout the chain, they must be assessed in an integrated manner, and an effective tool for this type of assessment is life cycle assessment (LCA) (Dourmad et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough there are studies evaluating protein reduction, there are still questions about whether it can impair the performance of animals, since, even with IAA supplementation, the reduction may promote changes in the composition of the intestinal flora and metabolism of nutrients. In addition, the reduction has the main benefit of lower environmental impact, which is provided by the lower excretion of N, however this assessment must be carried out holistically. Thus, the objective of this work was to evaluate the environmental impact, through LCA, of diets with reduction of crude protein and supplementation of IAA for crossbred barrows in the growth phase (50\u0026ndash;70 kg).\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Goal and scope\u003c/h2\u003e \u003cp\u003eThis study was conducted at the State University of Maring\u0026aacute; (UEM), which is located in the northwest region of Paran\u0026aacute;, a state of great representativeness in pig production in Brazil. This study aimed to evaluate the environmental impact of diets with reduced crude protein and supplementation of industrial amino acids for crossbred barrows in the growing phase (50\u0026ndash;70 kg) and to generate information that can be used by the pig industry, researchers, and society in the search for the sustainability of the productive system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Description of the system\u003c/h2\u003e \u003cp\u003eTwo experiments were conducted to perform the LCA. Experiment I: digestibility of four diets with protein reduction to evaluate the digestibility of nutrients and to determine the excretion of manure (feces and urine). Experiment II: performance, plasma urea, backfat thickness (BT) and the depth of the Longissimus lumborum (LL) muscle of animals that received the same diets used in experiment I.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 System and functional unit limits\u003c/h2\u003e \u003cp\u003eTo perform the LCA, the stages of animal production, cultivation, drying, and processing of grains, transportation of ingredients to the factory and from the factory to the farm, production of feed, storage, transport, and application of manure to the soil were considered. The impacts were considered in a specific phase (50\u0026ndash;70 kg); therefore, the impacts related to the process of transport and slaughter of the animals were not evaluated. According to Reckmann et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), the slaughter process has a low environmental impact compared to the stages of feed production and animal production, representing less than a tenth of the total impacts for categories such as acidification and eutrophication potential, respectively.\u003c/p\u003e \u003cp\u003eAlthough the area necessary for the construction of the facilities was not used in the analysis, the area necessary for the production of grains was measured in the system. The manure generated during the animal housing phase was accounted for and used as a source of N, P, and K in the fertilization of crops, thereby reducing the use of chemical fertilizers. The equivalence factor (EF) used for N was 75%, assuming an extra loss of 5% in the form of nitrate (Garcia-Launay et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Nguyen et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The EF used for the P and K present in the manure was 100% (Sommer et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). All the impacts related to manure management, including storage and application, were allocated and accounted for. The functional unit was one kg of live weight gain (LWG) during the 50\u0026ndash;70 kg growth phase. The definition of the system limits is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and is based on Nguyen et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Life cycle inventory analysis\u003c/h2\u003e \u003cp\u003eThe resources used and emissions associated with the production and transport of raw materials for grain production were obtained from the Ecoinvent version 3 database (SimaPro LCA software 8.0, Pr\u0026eacute; Consultants). The use of electrical energy for lighting and ventilation of warehouses was considered; however, the emissions and resources used for the construction of the facilities, vaccines, veterinary drugs, detergents, and disinfectants used to clean the facilities were not evaluated (Arrieta and Gonz\u0026aacute;lez, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Grain Production\u003c/h2\u003e \u003cp\u003eAs they are the most important regions for soy production in Brazil, it was assumed that the soy used was produced in the south and mid-west (CONAB, 2018). The inventory for soy cultivation was based on Silva et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), with an economic allocation to determine the impact of oil and soybean meal (Garcia-Launay et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The allocation is the partition of environmental impacts between different co-products and can be carried out based on physical characteristics (mass and energy) or other factors, such as economics (Arrieta and Gonz\u0026aacute;lez, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The inventory for maize cultivation was based on the report by Alvarenga et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Uncultivated raw materials\u003c/h2\u003e \u003cp\u003eThe inventories for salt, dicalcium phosphate, sodium bicarbonate, limestone, and the premix were obtained from Wilfart et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For the production of antioxidants, the same demand for resources and energy necessary for the production of the premix was considered. For the production of the amino acids L-lysine HCl, DL-methionine, and L-threonine, the inventory was obtained by Mosnier et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), who considered chemical and biological processes in the synthesis of these products. For the production of L-tryptophan, L-valine, and L-isoleucine, the demand for resources and energy is twice as high as that for the production of L-lysine (Garcia-Launay et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Transport specifications\u003c/h2\u003e \u003cp\u003eTo transport food and feed, the methodology proposed by Silva et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Transportation of imported products was initially established by the sea and later by road.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Feed formulation and production\u003c/h2\u003e \u003cp\u003eThe treatments consisted of four diets with decreasing levels of CP. The levels used were 16, 15, 14 and 13% of CP (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and the requirements for digestible amino acids proposed by Rostagno et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) were met with the amino acids L-lysine, DL-methionine, L-threonine, L-tryptophan, L-valine and L-isoleucine. The amino acid compositions of corn and soybean meal were determined by Evonik Industries. To calculate digestible amino acids contained in foods and industrial amino acids, the standardized ileal digestibility coefficients proposed by Rostagno et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Sodium bicarbonate was used to adjust the electrolyte balance of the diets.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIngredients, chemical composition of diets with reduction on crude protein level.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIngredients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e \u003cp\u003eCrude Protein (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e730.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e759.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e789.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e820.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoybean meal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e216.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e185.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e153.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e120.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoybean oil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e22.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e20.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e7.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDicalcium phosphate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e10.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e10.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimestone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e6.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e6.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e6.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium bicarbonate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e6.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePremix\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Lysine HCl 78.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e6.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDL-Methionine 99.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Threonine 98.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Tryptophan 98.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Valine 98.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eL-Isoleucine 100.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntioxidant\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth promoter\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalculated Composition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u0026aacute;lcium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e5.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvailable phosphorus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSodium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePotassium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e4.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChlorine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSID lysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e9.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e9.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e9.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSID methionine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSID met\u0026thinsp;+\u0026thinsp;cys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e5.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSID threonine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e6.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e6.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e6.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSID tryptophan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSID valine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e6.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSID isoleucina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e5.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e5.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e5.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSID histidine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSID phenilalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/kg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e6.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e6.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e5.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolizable energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e14.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e14.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e14.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNet energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e10.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e10.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e10.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE. Balance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emEq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e150.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e150.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e150.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e143.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatio AAE:AANE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e45:55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e46:54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46:54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e47:53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003ePremix should provide at least the following nutriente amounts per kg of feed:: vitamin A \u0026ndash; 4000 UI; vitamin D3\u0026ndash;600 UI; vitamin E \u0026ndash; 12 UI; vitamin K3\u0026ndash;3 mg; vitamin B1\u0026ndash;0.6 mg; vitamin B2\u0026ndash;3.5 mg; vitamin B6\u0026ndash;1; vitamin B12\u0026ndash;18 mg; niacin \u0026ndash; 20 mg; pantothenic acid \u0026ndash; 10 mg; folic acid \u0026ndash; 1 mg; biotin \u0026minus;\u0026thinsp;0.03 mg; choline chloride \u0026ndash; 0.16 g; iron \u0026ndash; 35 mg; copper \u0026ndash; 15 mg; manganese \u0026ndash; 25 mg; zinc \u0026ndash; 0.075 g; iodine \u0026ndash; 1 mg; selenium 0.3 mg, \u003csup\u003e2\u003c/sup\u003eBHT; \u003csup\u003e3\u003c/sup\u003eleucomycin.\u003c/p\u003e \u003cp\u003eThe amino acids lysine, methionine, threonine, tryptophan, valine, isoleucine, arginine, leucine, phenylalanine and histidine were considered essential amino acids in the calculation of the relationship between essential amino acids (AAE): non-essential amino acids (AANE). For this calculation, the total concentration of each amino acid and the amount of N present in its composition were considered. The N values used in this study were those proposed by Rostagno et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The N for the AANE was obtained from the difference between the total N of the diets and the N present in the AAE, as adapted from Toledo et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eData related to the production process of the feeds were based on those proposed by Garcia-Launay et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Animal production\u003c/h2\u003e \u003cp\u003e The procedures performed in Experiments I and II were approved by the Animal Use Ethics Committee of the State University of Maring\u0026aacute; (CEUA no. 2846260819).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Experiment I\u003c/h2\u003e \u003cp\u003eTwenty crossbred barrows, with an initial average weight of 63.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.21 kg were used, were housed in metabolic cages and distributed in a randomized block design, with four treatments and five repetitions, one animal per experimental unit.\u003c/p\u003e \u003cp\u003eThe amount of feed provided daily was calculated based on the metabolic weight (kg^0.75) and average consumption recorded during the adaptation period. To avoid losses and facilitate ingestion, the rations were moistened with water (approximately one third of the ration) and supplied twice daily (7:30 am and 3:30 pm). After each meal, water was provided in the feeder in the proportion of 3 mL of water/g of feed to avoid excessive water consumption.\u003c/p\u003e \u003cp\u003eTo determine the period of beginning and end of fecal collection, 2% ferric oxide (Fe2O3) was added to the diets. Feces were collected daily, packed in plastic bags, and stored in a freezer (-18\u0026deg;C) for later analysis. Urine was filtered and collected daily in plastic buckets containing 20 mL of HCl 1:1, to avoid nitrogen volatilization and bacterial proliferation. Aliquots of one fifth of the total volume were removed, packed in plastic bottles, and frozen (-18\u0026deg;C).\u003c/p\u003e \u003cp\u003eTo determine the period of beginning and end of the collection of feces, 2% ferric oxide (Fe\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e) was added to the diets. Feces were collected daily and packed in plastic bags and stored in a freezer (-18\u0026deg;C), to be later analysed. The urine was filtered and collected daily in plastic buckets containing 20 mL of HCl 1: 1, to avoid nitrogen volatilization and bacterial proliferation. Aliquots of one fifth of the total volume were removed and packed in plastic bottles and frozen (-18\u0026deg;C).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Experiment 2\u003c/h2\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.11.1 Performance\u003c/h2\u003e \u003cp\u003eForty crossbred barrows, with an initial average weight of 49.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92 kg were used, and distributed in a randomized block design, with four treatments and ten repetitions, one animal per experimental unit. The animals were weighed at the beginning and end of the experimental period to determine the daily weight gain (DWG). The ratios were weighed to determine the daily feed intake (DFI) and feed conversion (FC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.11.2 Longissimus lumborum and backfat thickness\u003c/h2\u003e \u003cp\u003eWhen the animals reached an average weight of 70.50\u0026thinsp;\u0026plusmn;\u0026thinsp;3.19 kg, the backfat thickness (BT) and the depth of the longissimus lumborum (LL) muscle were evaluated, using equipment consisting of an ecocamera (Aloka\u0026reg; SSD- 500 Vet) coupled to a 14.5 cm and 3.5 MHz probe. For this measurement, the animals were shaved between the tenth and eleventh ribs (Dutra Jr et al., 2001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.11.3 Plasma urea\u003c/h2\u003e \u003cp\u003eAt the end of the experiment, the animals were fasted for 6-hour fast to collect the blood. The samples were collected, transferred to a tube containing EDTA, and subsequently centrifuged at 3000 rpm for 15 min. Plasma was transferred to polyethylene microtubes and stored in a freezer. The urea analysis was performed by the colorimetric method, using a commercial kit, following the standard operating procedures described therein.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Laboratory analysis\u003c/h2\u003e \u003cp\u003eThe ratios were analyzed according to the Association of Official Analytical Chemists AOAC (2005), in terms of dry matter (method 950.46), ash (method 942.05), crude fiber (method 962.09), and nitrogen (method 984.13). The values of phosphorus, Cu, Zn, and K were obtained using a UV-Vis spectrophotometer. The nitrogen content of the urine was also evaluated. The levels of nitrogen, phosphorus, dry matter, ash, and crude fiber in the feces were determined following the same methodology used in the analysis of the rations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Life cycle impact assessment\u003c/h2\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.13.1 Emissions from pig production\u003c/h2\u003e \u003cp\u003eEmissions were calculated for the stages of animal housing, storage, and manure application, as proposed by Monteiro et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Through laboratory analysis, the amounts of N, P, and excreted organic matter were obtained for later determination of the amount of each nutrient available for application.\u003c/p\u003e \u003cp\u003eThe excretion of the minerals copper (Cu), zinc (Zn) and potassium (K) were determined using the equations proposed by Rigolot et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010a\u003c/span\u003e). The NH3 emissions resulting from the stages of accommodation and manure management were determined considering the temperature of the shed and were calculated according to the equations proposed by Rigolot et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010b\u003c/span\u003e) and the IPCC (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e2.13.2 Characterization factors\u003c/h2\u003e \u003cp\u003eThe LCA was based on the CML 2001 (baseline) version 3.02 method, implemented in Simapro version 8.05 (PR\u0026eacute; Consultants), adding the following categories: land occupation from CML 2001 (all categories) version 2.04 and cumulative energy demand version 1.8 (non-renewable fossil\u0026thinsp;+\u0026thinsp;nuclear).\u003c/p\u003e \u003cp\u003eThe characterization factors used to calculate the impact of growing pig production were: global warming potential GWP (kg CO2-eq.), acidification potential (AP, g SO2-eq.); eutrophication potential (EP, g PO4- eq), terrestrial ecotoxicity (TE, g 1,4-DCB-eq.), cumulative energy demand (CED, MJ-eq.) and land occupation (LO, m2-year) for the GWP category, the global warming potential over a 100-year horizon was considered.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e2.14 Interpretation and statistical analysis\u003c/h2\u003e \u003cp\u003eRetention coefficients of N and P were obtained for each experimental diet. These coefficients were used to determine the amounts excreted by each element during the growth period using the data obtained from the performance evaluation of the animals. The LCA calculations were evaluated for each animal, and according to the consumption and excretion data of the animals, the environmental profile of each system was constructed, with the aid of the SAS software (SAS Inst. Inc., Cary, NC).\u003c/p\u003e \u003cp\u003eThe performance, excretion, and environmental impact data were subjected to analysis of variance using the SAS GLM procedure. The statistical model included the treatment and block effects. Significant data were subjected to a regression analysis. The degrees of freedom related to the CP levels were divided into polynomials. All analyses were performed using SAS software version 9.2.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Life cycle assessment of feed\u003c/h2\u003e \u003cp\u003eThe formulated diets assess the environmental impacts of soy produced in different regions of Brazil, including the southern and midwestern regions. The impact categories that presented the greatest divergence between the results observed for these two regions were GWP and CED.\u003c/p\u003e \u003cp\u003eThe reduction in CP in diets formulated considering soybean meal from the southern region increased the impacts of GWP, AP, EP, CED, and TE (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The only exception was observed for LO, in which the lowest protein concentration resulted in mitigation of the impact for this category.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLife cycle assessment, per kg of feed, in pig diets (50\u0026ndash;70 kg), containing decreasing levels of crude protein (CP) and supplementation of synthetic amino acids\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eCrude protein (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eSoybean meal from South\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGWP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekg CO\u003csub\u003e2\u003c/sub\u003e-eq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg SO\u003csub\u003e2\u003c/sub\u003e-eq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg PO\u003csub\u003e4\u003c/sub\u003e-eq\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMJ-eq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg 1,4-DCB-eq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003em\u003csup\u003e2\u003c/sup\u003e-year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eSoybean meal from Midwest\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGWP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekg CO\u003csub\u003e2\u003c/sub\u003e-eq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e632\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMJ-eq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGlobal warming potential (GWP), acidification potential (AP), eutrophication potential (EP), cumulative energy demand (CED), terrestrial ecotoxicity (TE) and land occupation (LO).\u003c/p\u003e \u003cp\u003eFor diets formulated considering soybean meal from the midwest region, protein reduction also resulted in an increased environmental impact on CED; however, for GWP, an inverse behavior was observed, since the reduction of CP provided a reduction in GWP. Regardless of the protein level evaluated, diets formulated considering soybean meal produced in the midwest region resulted in a greater impact when compared to diets produced considering the meal obtained in the southern region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Metabolism and performance\u003c/h2\u003e \u003cp\u003eThe higher concentration of N in the diet promoted a linear increase in the ingested N (P\u0026thinsp;=\u0026thinsp;0.002), excretion of N in the urine (P\u0026thinsp;=\u0026thinsp;0.006), and total N excreted (P\u0026thinsp;=\u0026thinsp;0.001). However, no significant differences were observed for N in feces, N retained, P ingested, P in feces, or P absorbed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNitrogen and phosphorus balance of growing pigs (50\u0026ndash;70 kg) fed diets with crude protein reduction and amino acids supplementation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eCrude Protein (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eS.E.M\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN Intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e46.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e39.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN Feces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e6.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN Urine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e13.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN Excreted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e19.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN Retention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e58.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP Intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e8.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP Feces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eg/d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP Absorbed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e44.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eEquation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRegression P-Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;9.31198\u0026thinsp;+\u0026thinsp;2.42113X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLinear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN Urine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;9.90543\u0026thinsp;+\u0026thinsp;1.47037X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLinear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN Excreted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eY = -6.53338\u0026thinsp;+\u0026thinsp;1.62907X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLinear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the performance evaluation, there was no significant difference for the evaluated variables (DWG, FCR and DFI), as well as for the carcass characteristics (BT and LL). However, the lowest protein concentration promoted a linear reduction (P\u0026thinsp;=\u0026thinsp;0.001) in the plasma concentrations of urea in growing pigs (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFinal weight, daily weight gain (DWG), feed conversion rate (FCR), daily feed intake (DFI), backfat thickness (BT), depth of \u003cem\u003eLongissimus lumborum\u003c/em\u003e (LL) muscle and plasma urea of growing pigs fed diets with crude protein reduction and amino acid supplementation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eCrude Protein (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eS.E.M\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinal Weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDWG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFCR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eEquation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRegression\u003c/p\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eY = -31.5122\u0026thinsp;+\u0026thinsp;4.04500X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Life cycle assessment of growing pig production\u003c/h2\u003e \u003cp\u003eAs mentioned previously, the impact categories evaluated were GWP, AP, EP, CED, TE, and LO, but the categories of GWP and CED were evaluated under two productive contexts, with soybean meal originating from soy produced in the south or midwest. GWP and CED showed the greatest divergence between the observed results; therefore, they were presented in this way.\u003c/p\u003e \u003cp\u003eThe results related to animal production, calculated using data obtained from diets that used soybean meal from the southern region, ranged from 3.06 to 2.93 kg CO2-eq, 41.41 to 37.91 g SO2-eq and 13.31 to 12.29 g 1,4-DCB eq, per kg of LWG for categories GWP, AP and TE, respectively. Although, no statistically significant differences were observed between the treatments evaluated (P\u0026thinsp;\u0026gt;\u0026thinsp;0.050) for these impact categories (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePotential environmental impacts, per kg of body weight gain, of growing pigs from 50 to 70 kg, with crude protein reduction and amino acid supplementation.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"22\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"10\" nameend=\"c15\" namest=\"c6\"\u003e \u003cp\u003eCrude Protein (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c20\" namest=\"c16\"\u003e \u003cp\u003eS.E.M\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c14\" namest=\"c11\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c22\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"22\" nameend=\"c22\" namest=\"c1\"\u003e \u003cp\u003eSoybean meal from south\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGWP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ekg CO\u003csub\u003e2\u003c/sub\u003e-eq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e3.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c14\" namest=\"c11\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.623\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eg SO\u003csub\u003e2\u003c/sub\u003e-eq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e41.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e40.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c14\" namest=\"c11\"\u003e \u003cp\u003e37.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e40.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eg PO\u003csub\u003e4\u003c/sub\u003e-eq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e13.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e13.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c14\" namest=\"c11\"\u003e \u003cp\u003e12.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e12.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eMJ-eq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e17.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e18.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c14\" namest=\"c11\"\u003e \u003cp\u003e18.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e21.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eg 1,4-DCB eq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e12.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e12.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c14\" namest=\"c11\"\u003e \u003cp\u003e12.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e13.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003em\u003csup\u003e2\u003c/sup\u003e-year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c14\" namest=\"c11\"\u003e \u003cp\u003e2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c21\" namest=\"c17\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c22\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c13\" namest=\"c3\"\u003e \u003cp\u003eEquation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c20\" namest=\"c16\"\u003e \u003cp\u003eRegression P-Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c13\" namest=\"c3\"\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;8.02464\u0026thinsp;+\u0026thinsp;0.322171X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eLinear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c20\" namest=\"c16\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c13\" namest=\"c3\"\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;34.6684\u0026ndash;1.07034X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eLinear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c20\" namest=\"c16\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"22\" nameend=\"c22\" namest=\"c1\"\u003e \u003cp\u003eSoybean meal from Midwest\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c17\" namest=\"c13\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003eS.E.M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c22\" namest=\"c20\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGWP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ekg CO\u003csub\u003e2\u003c/sub\u003e-eq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e \u003cp\u003e3.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c18\" namest=\"c14\"\u003e \u003cp\u003e3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c22\" namest=\"c20\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMJ-eq.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c8\" namest=\"c4\"\u003e \u003cp\u003e20.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e \u003cp\u003e20.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e20.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c18\" namest=\"c14\"\u003e \u003cp\u003e22.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c19\"\u003e \u003cp\u003e0.485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c22\" namest=\"c20\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c13\" namest=\"c3\"\u003e \u003cp\u003eEquation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c20\" namest=\"c16\"\u003e \u003cp\u003eRegression P-Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c13\" namest=\"c3\"\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;31.2237\u0026ndash;0.702253X\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e \u003cp\u003eLinear\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c20\" namest=\"c16\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHowever, EP (P\u0026thinsp;=\u0026thinsp;0.036) showed a lower impact when the diet changed from 16 to 13% of CP when considering soy from the southern region. The opposite effect was observed for the CED (P\u0026thinsp;=\u0026thinsp;0.001) category, since the reduction in dietary CP promoted a increase in environmental impact (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eReducing the CP concentration in diets provided an increase in CED considering soybean meal from the Midwest region; however, no significant difference was observed for GWP. The results obtained for GWP and CED, per kg of LWG, were higher when considering the production of feed the soybean meal from grown in the central-west region when compared to that obtained for GWP and CED per kg of LWG of the animals produced considering the bran from the southern region.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Feed\u003c/h2\u003e \u003cp\u003eFor diets formulated considering soybean from the southern region, protein reduction promoted an increase in the kg of feed produced for the GWP category by up to 53 CO\u003csub\u003e2\u003c/sub\u003e-eq (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Industrial amino acids are among the foods used to formulate diets with the greatest environmental impact in this category (Mosnier et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Thus, it is expected that protein reduction promotes an increase in the inclusion of amino acids in diets, and consequently, the environmental impact for this category is higher in diets with lower protein concentrations, since the requirement for essential amino acids must be met to ensure optimal performance of pigs.\u003c/p\u003e \u003cp\u003eThe same behavior was observed for GWP in relation to CED. According to Mosnier et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), the production of one kg of amino acids, such as L-lysine-HCl and L-threonine, involves an energy expenditure almost twice higher than the production of the same amount of soybean meal. Thus, reducing CP promoted an increase in CED from 5.18 to 6.38 MJ-eq and 6.30 to 7.00 MJ-eq for diets using soybean meal from the south and mid-west, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Metabolism and performance\u003c/h2\u003e \u003cp\u003eThe reduction in CP provided lower consumption of N by the animals, which reduced from 46.78 g/d to 39.22 g/d for animals that received diets of 16 and 13% CP, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The consumption of N influenced the total excretion of N, which was higher in animals that received a 16% CP diet than in those that received a 13% CP diet. These results are in agreement with those obtained by Toledo et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), who concluded that the reduction in CP in the pig diet was responsible for decreasing and excreting N, mainly through urine.\u003c/p\u003e \u003cp\u003eThe N intake, total and urine N excretions were directly related to the N concentrations in diets, but no effects were observed for N retention (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). As in this study, Monteiro et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) also observed that the reduction in CP significantly reduced the intake and excretion of N, without effects on retained N.\u003c/p\u003e \u003cp\u003eThe lower inclusion of soybean meal in pig diets, in order to reduce the CP concentration, may reduce the phytic phosphorus, thereby improving the absorption of phosphorus by animals, but no effects were observed for absorbed P in this study (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eProtein reduction decreased the excretion of N to the environment without impairing animal performance, which proves that the supplementation of industrial amino acids met the daily requirements of the animals. The DWG ranged from 1.17 kg to 1.05 kg, and the treatment with the lowest protein consumption provided nutrients for the animals to reach the expected DWG for this phase (1.05) (Rostagno et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFCR and DFI were also not influenced by dietary protein reduction. These results differ from those obtained in other studies, in which protein reduction worsened DWG, DFI, and FCR (Li et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; He et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The difference between the results found in our study and those mentioned above can be explained by the marked protein reduction proposed by the authors, as well as by amino acid supplementation, since they chose not to supplement the diets with valine and isoleucine. This results in a deficiency of these amino acids in diets with low protein concentrations. In the work developed by He et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), the consumption of valine in the growth phase was 7.72 g/d and 13.25 g/d for animals that received diets with 12.35 and 18.27% CP, respectively. This large difference in amino acid consumption may have limited animal performance.\u003c/p\u003e \u003cp\u003eIn the present study, digestible valine consumption was approximately 16.23 g/d, referring to animals that received diets containing 16% CP; and 14.71 g/d for animals that received diets with 13% CP. The digestible valine requirement for this phase (50\u0026ndash;70 kg) was 14.98 g/d (Rostagno et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the animals in the treatment with the lowest protein concentration did not consume the requirement of 14.98 g/d of the digestible valine requirement proposed by Rostagno et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), this deficiency did not compromise their performance. Other studies have observed that the daily requirement for amino acids may be slightly overestimated in the tables proposed by Rostagno et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) (Pasquetti et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Monteiro et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), however this higher demand for amino acids may be a guarantee margin that aims to meet the requirement of the national herd (Monteiro et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results obtained for plasma urea and excretion of N in the urine corroborate what was observed in the performance evaluation, as the animals that consumed diets with a higher protein concentration consumed amino acids above the daily requirement. As demonstrated for digestible valine, other amino acids (isoleucine, histidine, phenylalanine, among others) were also consumed in greater amounts in diets with a higher protein concentration. This resulted in greater deamination of these amino acids and in the linear increase of plasma urea and N in the urine, because in addition to the greater amount of some amino acids in the diet with higher protein concentrations, there is also a greater imbalance of essential amino acids providing greater deamination of these amino acids.\u003c/p\u003e \u003cp\u003eThe AAE: AANE ratio of the diets ranged from 45:55 to 47:53 for the diets with the highest and lowest protein concentrations, respectively. Diets with reduced crude protein and high inclusion of industrial amino acids can promote an increase in this relationship and limit the synthesis of AANE, according to Wang and Fuller et al. (1989) the ideal ratio between AAE: AANE is approximately 45:55. The data referring to the performance of the animals allowed us to conclude that although there was a variation in the AAE: AANE ratios between diets, this variation did not limit the synthesis of AANE, as there was no difference in the performance of the animals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.3. LCA of animal production\u003c/h2\u003e \u003cp\u003eThe values found in relation to GWP ranged from 2.93 to 3.06 kg CO2-eq.\u0026nbsp;per kg of LWG and from 3.67 to 3.38 kg CO2-eq.\u0026nbsp;per kg of LWG for animals fed diets that used soybean meal from the southern and central-western regions, respectively. There was no difference between the treatments evaluated.\u003c/p\u003e \u003cp\u003eThe difference in relation to the environmental impact of the production of the diets was what determined the highest result for kg of LWG of the animals that received diets with soybean meal from the central-west region. Soybean grown in the central-west region has the aggravating factor of being a food produced in an area of recent deforestation, which implies accounting for CO2-eq.\u0026nbsp;emitted during cultivation of this plus CO2-eq.\u0026nbsp;referring to land use changes (Silva et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Reckmann et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the central-west region, transportation is another aggravating factor compared to the distances traveled in the south. Although the main differences found in the environmental impact between soybeans grown in the south and mid-west are due to deforestation and transportation, another factor that should be highlighted is the greater use of fertilizers in the cultivation of soybeans produced in the mid-west region (Silva et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Silva et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe manure management stage contributed the most to the impact observed in the GWP category. This stage represented 64.03% (diets with bran from the southern region) and 54.78% of the impacts observed (diets with bran from the central-west region) for this category (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The food production stage also had a significant impact on this category, with 31.78 and 41.64% of kg CO2-eq emissions per kg of LWG for animals fed diets containing soybean meal in the southern and midwestern regions, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe results obtained agree with those observed by Bandekar et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who concluded that the stages of waste management and food production contributed the most to the GWP category. The authors also reported that management that results in a drop in the performance of the animals may enhance the environmental impacts for this category, as the animal will have to consume a greater amount of food and excrete a greater amount of manure until reaching the ideal slaughter weight.\u003c/p\u003e \u003cp\u003eThe most important element emitted during the manure management stage for the GWP category was CH4, and the emission of this element can be intensified or mitigated according to the temperature of the environment (IPCC, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The average temperature recorded in the experimental period was 24.63\u0026deg;C; if we use the temperature obtained in an experiment in the previous year, 26.03\u0026deg;C, the average value obtained for the four treatments that used soybean meal from the southern region, 3.01 kg CO2-eq.\u0026nbsp;per kg of LWG would be 3.25 kg CO2-eq.\u0026nbsp;per kg of LWG.\u003c/p\u003e \u003cp\u003eFor the AP category, no significant difference was observed (P\u0026thinsp;=\u0026thinsp;0.110) between the treatments evaluated, which varied between 41.4 to 37.9 g SO2-eq.\u0026nbsp;per kg of LWG. Studies have shown that protein reduction can promote a reduction in AP through lower excretion of N in manure and, consequently, lower NH3 emissions (Reckmann et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNH\u003csub\u003e3\u003c/sub\u003e is emitted from the moment the feces and urine mix, as this gas is formed by the hydrolysis of urea present in the urine and is catalyzed by the enzyme urease, which is present in the feces. This enzyme is produced by bacteria present in the digestive system of animals (Philippe et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In this way, NH\u003csub\u003e3\u003c/sub\u003e is the main element responsible for the AP process during the animal housing and manure handling stages. According to a study by Reckmann et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), NH\u003csub\u003e3\u003c/sub\u003e was responsible for most of the impacts obtained for this category, with a large part of this element being emitted during the animal housing stage.\u003c/p\u003e \u003cp\u003eAlthough no difference was observed in AP, the reduction in dietary CP was effective in reducing NH\u003csub\u003e3\u003c/sub\u003e emissions. There was a reduction in the emission of this element during the animal housing and manure management stages when compared to diets with higher and lower protein concentrations. This value is close to that proposed by Philippe et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), according to the authors, for each 10 g/kg less in the protein concentration of the diet, it is possible to reduce the NH\u003csub\u003e3\u003c/sub\u003e emission by almost 1/10.\u003c/p\u003e \u003cp\u003eIt is possible that the lowest protein concentration was not effective in significantly reducing the environmental impact of AP, as can be seen in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, although the reduction of CP in diets reduced the impact on the stages of animal housing and manure management, for the feed production stage, the effect was opposite. There was an increase in the environmental impact of AP when the kg of feed produced was compared between the diets with the lowest and highest protein values. Another factor that had an influence on the results was the animals' performance, since the impact was calculated on the kg of LWG and there were no significant differences for the DWG, FCR, and DFI.\u003c/p\u003e \u003cp\u003eAs with AP, the N emitted through NH3 is also responsible for the environmental EP. Another element that can affect this category is P (Guin\u0026eacute;e et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The results obtained for the EP varied between 13.35 and 12.56 g PO4-eq.\u0026nbsp;per kg of LWG, and a linear reduction (P\u0026thinsp;=\u0026thinsp;0.036) was observed in the EP when comparing the lower CP diet with higher protein diet.\u003c/p\u003e \u003cp\u003eAP and EP showed similar behaviors in relation to the impacts observed during the stages of accommodation and manure management, since the lower protein concentration reduced NH\u003csub\u003e3\u003c/sub\u003e emissions; consequently, the impact obtained for AP and EP during the stages of accommodation and management of the waste was mitigated. A large difference was observed in relation to the impact related to the production of the diets, since for EP, the difference between the impact per kilogram of feed produced was low.\u003c/p\u003e \u003cp\u003eFeed production was the stage that contributed the most to EP regarding animal production (kg of LWG), representing values between 77.34 and 71.57% of the observed impacts. Similar results were obtained by Monteiro et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), in which for the EP category the production of diets was the stage that most impacted LCA, and they also reported that the use of amino acids was efficient in reducing the impacts for the category. The use of IAA combined with phytase supplementation can significantly reduce the impact of EP on pig and poultry production (Kebread et al., 2016).\u003c/p\u003e \u003cp\u003eFor the CED category, protein reduction promoted a linear increase in the impacts obtained, both for those using soybean meal from the southern region (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which ranged from 21.33 to 17.88 MJ-eq.\u0026nbsp;per kg of LWG, and for the impacts assessed using soybean meal from the midwest region (P\u0026thinsp;=\u0026thinsp;0.028), between 22.69 to 20.31 MJ-eq.\u0026nbsp;per kg of LWG.\u003c/p\u003e \u003cp\u003eThe CP reduction requires an increase in dietary IAA to meet the pig\u0026rsquo;s requirements. As CED is higher for amino acids production than for the production of corn and soybean meal (Ogino et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), the higher inclusion provided an increased CED, both for kg of feed produced and for kg of LWG.\u003c/p\u003e \u003cp\u003eIn addition to GWP, CED also showed representative differences when diets were formulated using soybean meal from different regions. The cultivation of soybeans in the Midwest has a higher impact on CED due to the more recent deforestation, transportation and the use of fertilizers for the cultivation in that region (Silva et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). According to the author, the distance traveled for the transportation of equipment, seeds, and fertilizers, among others, as well as the transportation of the grains to the regional storage facilities, is greater in the Midwest than in the southern region.\u003c/p\u003e \u003cp\u003eEvaluating the pig and poultry production chains, Arrieta and Gonzales (2019) highlighted that the production of fertilizers and pesticides contributes to more than half of the impacts observed in the CED for the production of pig diets. Thus, the use of waste to produce energy and biofertilizers is an alternative to mitigate these impacts (Nguyen et al .2010).\u003c/p\u003e \u003cp\u003eThe results obtained for TE varied between 13.31 and 12.29 g 1,4-DCB eq.\u0026nbsp;per kg of LWG, however, there was no significant difference (P\u0026thinsp;=\u0026thinsp;0.165) between the treatments. These values are higher than those obtained by Monteiro et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e); however, the LCA performed by these authors evaluated the environmental impact of pig production in the nursery phase (15\u0026ndash;30 kg). As the production of diets is the stage that contributes the most to the impact observed for TE, it is expected that a production phase with higher feed consumption and worse feed conversion will have a greater impact. As in the aforementioned work, the production of diets was the stage that contributed the most to the impacts observed for this category.\u003c/p\u003e \u003cp\u003eProtein reduction was achieved through the lower inclusion of soybean meal and greater inclusion of amino acids in the diets. As the productivity per hectare of soybean cultivation is lower than that of corn, the lower inclusion of soybean meal in the diet results in a reduction in the environmental impact of the LO category (Silva et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA lower concentration of protein in the diet reduced the environmental impact of the LO category in 0.16 m\u003csup\u003e2\u003c/sup\u003e/year. Because we do not consider the area necessary for the construction of the facilities, the impacts observed for this category are due to the production of the feed. Another factor that could alter the observed results could be related to animal performance. As there was no significant difference in performance, we can say that the linear reduction (P\u0026thinsp;=\u0026thinsp;0.019) in the impact for LO, in relation to animal production (kg of LWG), was due to the impacts observed in the production of diets.\u003c/p\u003e \u003cp\u003eThe reduction of dietary CP, associated with supplementation of industrial amino acids for growing pigs, reduced the excretion of N without compromising animal performance. The effects related to LCA showed that protein reduction reduced the impacts for the categories eutrophication potential and land occupation, but the impacts for the category cumulative energy demand, both for the diets that used soybean meal from the southern region and those that used soybean meal from the midwest region, were intensified with the reduction of crude protein.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthors\u0026rsquo; Contribution\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eLucas Ant\u0026ocirc;nio Costa Esteves:\u003c/strong\u003e conceived and designed the study, conducted data gathering, performed statistical analysis and wrote the article. \u003cstrong\u003eAlessandra Nardina Tricia Rigo Monteiro:\u003c/strong\u003e conceived and designed the study. \u003cstrong\u003eGabriel Amaral de Araujo:\u003c/strong\u003e wrote the article. \u003cstrong\u003eLeandro Dalcin Castilha:\u0026nbsp;\u003c/strong\u003econceived and designed the study. \u003cstrong\u003eAlice Eiko Murakami:\u0026nbsp;\u003c/strong\u003econceived and designed the study.\u003cstrong\u003e\u0026nbsp;Paulo Cesar Pozza:\u0026nbsp;\u003c/strong\u003econceived and designed the study, performed statistical analysis and wrote the article.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research was supported by CNPQ (Conselho Nacional de Pesquisa e Desenvolvimento) and CAPES (Coordena\u0026ccedil;\u0026atilde;o de Aperfei\u0026ccedil;oamento de Pessoal de N\u0026iacute;vel Superior).\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe dataset used and/or analyzed during the current study is available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003ch2\u003eEthical approval\u003c/h2\u003e\n\u003cp\u003eThe procedures performed in Experiments I and II were approved by the Animal Use Ethics Committee of the State University of Maring\u0026aacute; (CEUA no. 2846260819).\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArrieta, E.Z., Gonz\u0026aacute;lez, A.D. 2019. 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J Anim Sci Biotechnol, 10, 75. https://doi.org/10.1186/s40104-019-0381-2.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"amino acid, cumulative energy demand, eutrophication, global warming potential","lastPublishedDoi":"10.21203/rs.3.rs-3001759/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3001759/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe objective of this study was to evaluate the performance, digestibility, and environmental impact of pigs in the growth phase receiving diets with reduced crude protein and supplementation of amino acids. In the metabolism experiment, 20 crossbred barrows with an initial average weight of 63.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.21 kg were housed in metabolic cages, with four treatments and five replications, one animal per experimental unit. In performance experiment, 40 crossbred barrows were used, with an initial average weight of 49.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92 kg, with four treatments, ten replications. The treatments used in both experiments consisted of four diets containing 16, 15, 14, and 13% of CP, and supplementation with amino acids to meet the requirements of all digestible amino acids. For performance, backfat thickness, and depth of the \u003cem\u003elongissimus lumborum\u003c/em\u003e muscle, no differences were observed. Plasma urea was lower in animals fed diets with protein reduction as well as the excretion of N urine and total N, but no differences were observed for retained N, P absorbed, P ingested, and P feces. Through the life cycle assessment, for the categories of eutrophication potential and land occupation, the protein reduction mitigated the impacts when referring to soybean meal produced in the southern region, but the protein reduction provided an increase in impact when the category evaluated was cumulative energy demand, considering the soybean produced in the south and that produced in the central west region.\u003c/p\u003e","manuscriptTitle":"Reducing crude protein and supplementing amino acids in growing pig (50-70 kg) diets reduce nitrogen excretion but promotes different environmental impacts when using life cycle assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-06-08 16:23:48","doi":"10.21203/rs.3.rs-3001759/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"04b1c5b5-6f63-4fec-b0a6-e3612ad48aa3","owner":[],"postedDate":"June 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-06-27T09:34:47+00:00","versionOfRecord":[],"versionCreatedAt":"2023-06-08 16:23:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3001759","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3001759","identity":"rs-3001759","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}