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Journal of Animal Science - Animal Production

Effect of feeding strategy on environmental impacts of pig fattening in different contexts of production: evaluation through life cycle assessment1

 

This article in JAS

  1. Vol. 94 No. 11, p. 4832-4847
     
    Received: Apr 05, 2016
    Accepted: Aug 31, 2016
    Published: October 27, 2016


    2 Corresponding author(s): jean-yves.dourmad@rennes.inra.fr
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doi:10.2527/jas.2016-0529
  1. A. N. T. R. Monteiro*†,
  2. F. Garcia-Launay,
  3. L. Brossard,
  4. A. Wilfart§ and
  5. J.-Y. Dourmad 2
  1. * Animal Science Department, Maringá State University, 91540-000, Maringá, PR, Brazil
     CAPES Foundation, Ministry of Education of Brazil, 70040-020, Brasília, DF, Brazil
     INRA Agrocampus Ouest, UMR1348 Pegase, 35590 Saint-Gilles, France
    § INRA Agrocampus Ouest, UMR1069 SAS, 35000 Rennes, France

Abstract

Life cycle assessment (LCA) has been used in many studies to evaluate the effect of feeding strategy on the environmental impact of pig production. However, because most studies have been conducted in European conditions, the question of possible interactions with the context of production is still under debate. The objective of this study was to evaluate these effects in 2 contrasted geographic contexts of production, South America (Brazil) and Europe (France). The LCA considered the process of pig fattening, including production and transport of feed ingredients and feed, raising of fattening pigs, and manure storage, transport, and spreading. Impacts were calculated at the farm gate, and the functional unit considered was 1 kg of BW gain over the fattening period. The performances of pigs were simulated for each scenario using the InraPorc population model (2,000 pigs per scenario considering between-animal variability). The LCA calculations were performed for each pig according to its own performance and excretion, and the results were subjected to variance analysis. The results indicate that for some impacts there are clear interactions between the effects of the feeding program, the origin of soybean, and the location of production. For climate change, interest in phase feeding and incorporation of crystalline AA (CAA) is limited and even counterproductive in Brazil with soybeans from the South (without deforestation), whereas they appear to be efficient strategies with soybeans from the Center West (with deforestation), especially in France. Rather similar effects, as those for climate change, were observed for cumulative energy demand. Conversely, potential eutrophication and acidification impacts were reduced by phase feeding and CAA addition in a rather similar way in all situations. Individual daily feeding, the only strategy that took into account between-animal variability, was the most effective approach for reducing the life cycle impact of pig fattening in all situations, whereas the potential of phase feeding programs and CAA was dependent on soybean origin and the geographical context of pig production, in contrast with previous results.



INTRODUCTION

The environmental impacts of pig production have come under increased debate in recent years, resulting in greater focus on identifying and mitigating the environmental degradation that they may cause. The COP21 Conference is a recent example of this, where stakeholders from many countries pledged to prepare strategies, plans, and actions for mitigating greenhouse gas emissions (United Nations, 2015). The better adjustment of nutrient supply to meet the requirements of animals (Dourmad and Jondreville, 2007) may be a key factor in improving the efficiency of nutrient retention, reducing excretion, and consequently increasing the sustainability of pig production system. In recent years, life cycle assessment (LCA) has been widely used in agriculture (Guinée et al., 2002), and several studies of swine production chains have been conducted as reviewed by McAuliffe et al. (2016). Most of the LCA studies about the effect of pig feeding strategies do not usually compare results in various geographical contexts, and questions may be raised about possible interactions within the context of production. Differences in human and natural resources between Brazil and France have led to the establishment of different supply chains, which may result in different environmental impacts (Prudêncio da Silva et al., 2014). This offers contrasted situations in terms of raising animals, climatic conditions, diet formulation, and type and origin of feed resources, which can be used to evaluate whether some feeding strategies may be environmentally friendly in one situation but not in another. The aim of this study was to evaluate the environmental impact of pig fattening in contrasted geographical situations based on LCA by comparing different feeding programs (different numbers of feeding phases and individual daily feeding), protein sources (soybean meal only vs. a country-specific mix of different protein sources), and different levels of crystalline AA (CAA) inclusions.


MATERIAL AND METHODS

Goal and Scope Definition

The definitions of system and subsystem boundaries were derived from Nguyen et al. (2010) and are described in Fig. 1. The LCA considered the activity of pig fattening in 2 different geographical contexts (France and Brazil), including crop production, grain drying and processing, production and transport of feed ingredients, feed production at the feed factory, transport of the feed to the farm, growing to finishing pig production, and manure storage, transport, and spreading as displayed in Fig. 1. Impacts were calculated at the farm gate, and the functional unit considered was 1 kg of BW gain (BWG) over the fattening period. The pig production systems considered were conventional growing-finishing pig farms with animals raised indoors on slated floor and collection and storage of manure as liquid slurry. Animals were females and castrated males (50/50) that were supposed to be housed in different pens and fed separately.

Figure 1.
Figure 1.

System boundaries of swine production in Brazil and France with the main processes for the production of crop inputs, crop production, production of feed ingredients and feeds, and pig production.

 

Life Cycle Inventory

Resource use and emissions associated with the production and delivery of inputs for crop production (fertilizers, pesticides, tractor fuel, and agricultural machinery) came from the ecoinvent database version 3 (SimaPro LCA software 8.0; PRé Consultants, Amersfoort, The Netherlands). Energy use in the building for light, heating, and ventilation was considered, but the emissions and resources used for the construction of buildings and the land occupied by the buildings were not. Veterinary medicines and hygiene products were also not included. The assessment considered the growing-finishing pig production system, with 4 different feeding programs: 2 phases (2P), 4 phases (4P), daily multiphase (DMP) , and individual daily feeding (IDF). These strategies were combined with 3 formulation scenarios built with least-cost formulated feeds: 1) feeds without CAA allowed (noAA), 2) feeds with CAA (withAA) and fixed CP content corresponding to the usual local practical recommendations, and 3) feeds with CAA without any minimum CP constraint (lowCP). For each scenario, 2 types of protein sources were considered: soybean meal only (SOY) or a mix of different protein sources (MIX), including soybean meal, meat, and bone meal in Brazil and soybean meal, rapeseed meal, and pea in France. Two hypotheses were also considered for the soybean origin: Center West (CW) and South (SO) Brazil, which are contrasted in terms of recent deforestation. This resulted in a total of 96 scenarios: 4 feeding strategies tested in 12 situations of feed formulation in 2 contexts of pig production.

Crop Production

We assumed that soybean was produced in Brazil for both countries because most of the soybean meal imported to France comes from Brazil (FranceAgrimer, 2014). For Brazil, we considered that 54% of the wheat bran came from Argentina (MDIC, 2015), with the other part produced in the country. For Brazilian crops, life cycle inventory (LCI) came from de Alvarenga et al. (2012) for maize and from Prudêncio da Silva et al. (2010) for soybean, taking into account the effect of land-use change on C release due to conversion of Brazilian forest to cropland for soybean (British Standards Institute, 2011). To perform the LCI of wheat, we used data from government agencies and cooperatives to determine the mean input and output values for each production system in Brazil and Argentina. For the grain-drying and storage stage, we used data from Marques (2006). For French crops, LCA came from a national database developed by French research institutes with data on the environmental impacts of all of the main ingredients used in animal feeds (Wilfart et al., 2015). For the feed ingredients that are coproducts, i.e., soybean meal, soybean oil, rapeseed meal, rapeseed oil, and wheat bran, the resource use and emissions were economically allocated following the suggestion of Ramírez (2009).

Nonplant Feed Components

Data for phytase, salt, phosphate, sodium bicarbonate, and limestone used in the diet came from Wilfart et al. (2015). The premix was assumed to contain mainly limestone and to have the same impacts. The impacts of vitamins and trace elements were not considered. Impacts associated with the production of CAA were based on Mosnier et al. (2011). Meat and bone meal was assumed to come from poultry slaughter processes. Impacts associated with broiler production were based on Prudêncio da Silva et al. (2014), and those associated with processing were based on Wilfart et al. (2015). The environmental impacts between products and coproducts were then allocated on an economic basis.

Transport Specifications

The pig production systems considered were growing-finishing pig farms located in Brittany, West France (as described by Garcia-Launay et al. [2014]) and South Brazil (as described by Cherubini et al. [2015]); these regions are areas of intensive pig production, contributing to more than 50% of the respective national production. For Brazilian crops, we hypothesized that the grains came from the main producing areas in the SO (cereal and soybean) and the CW (soybean). For transport, we used the methodology described by Prudêncio da Silva et al. (2010), considering the distances described by Spies (2003). For France, we considered that the crop products and feed ingredients produced were mainly transported by train followed by road (Nguyen et al., 2012). The distances were based on data from Gaudré et al. (2015). Products imported into both countries were assumed to be mainly transported by sea followed by road (Mosnier et al., 2011).

Feed Specifications

The feedstuff composition was obtained from Sauvant et al. (2004) and Rostagno et al. (2011) for France and Brazil, respectively. The costs of ingredients were provided by Brazilian and French government agencies (Embrapa and IFIP Institut du Porc, respectively). We utilized the mean prices for the year 2014. All feedstuff prices were expressed in euros per ton (€/t) assuming an R$/€ ratio of 3.11. Feeds were formulated at least-cost using OpenSolver for Excel according to the methodology described by Pomar et al. (2014a). For each gender in each country, 2 experimental feeds (named A and B) were independently formulated on the basis of NE, standardized ileal digestible (SID) AA, and digestible P. Feeds A and B differed in their AA and P concentrations, with feed A being formulated with a high nutrient density to meet 110% of the mean estimated nutrient requirements at the beginning of the growing period and feed B formulated with a low nutrient density to meet the mean estimated nutrient requirements at the end of the finishing period (Table 1). Feeds A and B were blended according to the feeding program and taking into account the changes in SID lysine requirement along the pig’s growth for each gender in each country. This is illustrated in Fig. 2 for the females in France. In the IDF strategy, individual pigs were fed daily as suggested by Hauschild et al. (2012) with a blend of feeds A and B providing the expected SID lysine requirement. When the SID lysine requirement exceeded the content in feed A or was lower than the content in feed B, feeds A and B, respectively, were given alone. The feed production process at the feed factory was included in the inventory, considering that it would be held in the pig production region. According to Garcia-Launay et al. (2014), it was considered that grinding and pelleting required 41 kWh of electricity per t of feed and 20.5 kWh of natural gas per t of feed.


View Full Table | Close Full ViewTable 1.

Minimum limits of nutritional parameters for feed formulation

 
Brazil
France
Gilts Barrows Gilts Barrows
BW, kg 30 115 30 115 30 115 30 115
NE, MJ/kg 10.3 10.3 10.3 10.3 9.6 9.6 9.6 9.6
SID1 AA, g/kg
    Lysine 8.35 5.81 8.40 5.22 9.84 4.55 10.32 3.89
    Threonine 5.24 3.64 5.27 3.27 6.30 2.96 6.59 2.56
    Methionine 2.53 1.75 2.55 1.58 2.97 1.36 3.13 1.17
    Met + cystine 4.95 3.44 4.98 3.10 5.86 2.74 6.15 2.35
    Tryptophan 1.44 0.99 1.44 0.89 1.75 0.83 1.84 0.72
    Isoleucine 5.01 3.48 5.04 3.13 5.90 2.73 6.19 2.34
    Valine 5.70 3.97 5.72 3.57 6.84 3.24 7.16 2.80
    Leucine 8.35 5.81 8.40 5.22 9.84 4.55 10.32 3.89
    Phenylalanine 4.18 2.90 4.20 2.61 4.92 2.27 5.16 1.95
    Phe + tyrosine 7.94 5.52 7.98 4.96 9.35 4.32 9.80 3.70
    Histidine 2.67 1.86 2.69 1.67 3.15 1.45 3.30 1.25
    Arginine 3.51 2.44 3.53 2.19 4.13 1.91 4.33 1.64
Minerals, g/kg
    Digestible P 2.79 1.99 2.81 1.86 3.01 1.68 3.02 1.53
    Ca 5.80 4.13 5.84 3.87 8.73 4.87 8.74 4.44
1SID = standardized ileal digestible.
Figure 2.
Figure 2.

Evolution with pig BW of standardized ileal digestible (SID) lysine content in A and B feeds and in the daily feed ration according to the feeding program and in the case of females for France. 2P, 2-phase; 4P, 4-phase; DMP, 10-phase; av. req. and pop. req., curve of average pig and population requirements.

 

Pig Production

Performance data from experimental studies in Brazil (Monteiro, 2014) and France (Brossard et al., 2014), with weekly recordings of feed intake and BW, were used to adjust average animal profile parameters for growth and feed intake using InraPorc software (INRA, Saint-Gilles, France) for each gender in each country (Table 2). These profiles were used to calculate, according to InraPorc, the average nutritional requirement curves for each sex (females and castrated males), with these requirements being used for diet formulation. Because of the differences in animal profiles, nutritional requirements differed according to the growth curve; simulated pigs in France showed higher SID AA requirements at 30 kg and lower requirements at 115 kg compared to simulated pigs in Brazil, whereas as their average performances were rather similar. To take account of the variability, the nutrient requirement of the population was calculated as 110% of the mean requirement as generally recommended (Pomar et al., 2003; Brossard et al., 2009). Moreover, because variability between animals is known to affect the response of pig populations (Pomar et al., 2003; Brossard et al., 2014), we decided to use the population version of InraPorc (Vautier et al., 2013) to evaluate the animals’ response to the feeding strategies. Parameters for growth and feed intake profiles were thus defined for a population of 1,000 castrated males and 1,000 females for each country, according to the method developed by Vautier et al. (2013) using a variance-covariance matrix. Simulations for 2,000 pigs (50% female and 50% castrated male) were performed for each feeding scenario in each country to determine animal performance, nutrient balance, and excretion according to InraPorc. A total of 192,000 pigs, i.e., 96 scenarios × 2,000 pigs, were simulated on a daily basis, and a database was built from these simulations.


View Full Table | Close Full ViewTable 2.

Parameters used to describe animal profiles in InraPorc1

 
a b Mean PD (g/d) BGompertz (d)
Brazil
    Barrows 5.31 0.0159 136 0.0118
    Gilts 4.64 0.0171 131 0.0105
France
    Barrows 5.02 0.0189 150 0.0194
    Gilts 4.45 0.0216 143 0.0171
1Ad libitum NE intake was modeled as a gamma function of BW expressing daily NE intake in multiples of NE intake above maintenance with 2 parameters “a” (dimensionless) and “b” (per kilogram BW) and “c,” a fixed parameter representing the maintenance energy requirement (0.75 MJ/kg BW0.60; van Milgen et al., 2015). Protein deposition (PD) was modeled by a Gompertz function described using mean PD and precocity (BGompertz; van Milgen et al., 2008).

Manure Management

The environmental consequences of manure utilization were evaluated using system expansion as described by Nguyen et al. (2010). This approach is often used to provide credit for avoided emissions, as explained by MacLeod et al. (2013). Thus, manure produced was assumed to substitute a certain amount of mineral fertilizers by using a mineral fertilizer equivalency (MFE, %). We assumed that the MFE was 75% of total N in manure (Nguyen et al., 2010), with 5% extra loss as nitrates compared to mineral fertilizers (Garcia-Launay et al., 2014), and MFE was 100% for P (Sommer et al., 2008).

Life Cycle Impact Assessment

Emissions From Animal Production.

Emissions to air during swine production were estimated step-by-step for NH3, N2O, NOx, and CH4. The CH4 emissions from enteric fermentations were calculated according to feed digestible fiber content using equations from Rigolot et al. (2010a). The CH4 emissions from manure storage were calculated according to IPCC (2006) and Rigolot et al. (2010b) considering the average annual ambient temperature in each region (22°C for South Brazil and 17°C for West France). The NH3 emissions from the building and during manure storage were calculated according to emission factors proposed by Rigolot et al. (2010b), considering the effect of temperature. The N2O and NOx emissions from slurry storage were calculated according to IPCC (2006) and Dämmgen and Hutchings (2008), respectively. The amounts of nitrogen, P, Cu, Zn, K, and OM excreted by the pigs were obtained from InraPorc simulations. These data were used to calculate the amount of each element available at field application. During field application, NH3 emissions were based on Andersen et al. (2001), N2O emissions on IPCC (2006), and NOx emissions on Nemecek and Kägi (2007). The potential of NO3 and PO4 leaching came from Nguyen et al. (2011).

Characterization Factors.

We based our analysis on the CML 2001 (baseline) method version 3.02 as implemented in Simapro software version 8.05 (PRé Consultants) and added the following categories: land occupation from CML 2001 (all categories) version 2.04 and total cumulative energy demand version 1.8 (nonrenewable fossil + nuclear). This method was chosen because it has been used in most of the pig LCA studies in the literature. Moreover, according to de Alvarenga et al. (2012), this method has the advantage of being more robust because a larger number of environmental impact categories are analyzed, giving rise to a higher number of hotspots. Thus, we considered the potential impacts of pig production on climate change (CC; kg CO2–eq), eutrophication potential (EP; g PO4–eq), acidification potential (AP; g SO2–eq), terrestrial ecotoxicity (TE; g 1,4-DCB-eq), cumulative energy demand (CED; MJ), and land occupation (LO; m2∙yr). The CC was calculated according to the 100-yr global warming potential factors in kilograms CO2–eq.

Calculations and Statistical Analyses

The LCA calculations were performed for each pig according to its individual performance and excretion from 70 d (with 30 kg BW on average) to an average BW of 115 kg at slaughter. These calculations were performed using SAS software (SAS Inst. Inc., Cary, NC). Performance responses and environmental impacts were subjected to variance analysis using the GLM procedure (SAS Inst. Inc., Cary, NC). The statistical model included effects of country, protein source, feeding phases, and AA inclusion. Differences were considered significant if the P value was <0.05. Means were compared using the Tukey test. For LCA data, we also performed a variance analysis taking into account the effect of interactions between country and the other factors to evaluate the behavior of environmental impacts among scenarios. All analyses were performed using SAS version 9.2 (SAS Inst. Inc.).


RESULTS

Animal Performance, Nitrogen and Phosphorus Balance, and Feed Cost

The simulated performance of pigs and results of nitrogen and P excretion are presented in Table 3 with the statistical analysis in Table 4. Average dietary CP content was significantly affected by country, feeding program, and CAA inclusion. Compared to 2P, the IDF feeding program reduced dietary CP by 7 and 14% on average for Brazil and France, respectively (Table 3). According to the scenarios of CAA inclusion, the average CP content of the feed was highest for the noAA scenarios, with values about 21 and 17% higher than for lowCP scenarios, for Brazil and France, respectively. The reduction of CP content was accompanied by a decrease in feed cost, both between feeding programs (−7 and −10% between the 2P and IDF programs for Brazil and France, respectively) and between scenarios of AA inclusion (−9 and −11% between noAA and lowCP scenarios for Brazil and France, respectively).


View Full Table | Close Full ViewTable 3.

Performance and N and P balance of growing-finishing pigs fed with different protein sources, in 2-phase (2P), 4-phase (4P), daily multiphase (DMP), and individual daily feeding (IDF) feeding programs, without AA addition (noAA), with AA addition and minimal CP constraints (withAA), and with AA without any CP constraint (lowCP)

 
Protein source1
Feeding program
AA inclusion
SOY MIX 2P 4P DMP IDF noAA withAA lowCP
No. batches 96 96 48 48 48 48 64 64 64
Brazil
    CP2, g/kg 144b 147a 152a 146b 143b 141b 160a 149b 126c
    ADG, kg/d 0.847a 0.844b 0.844b 0.834c 0.827a 0.878d 0.847a 0.847a 0.843b
    G:F, kg/kg 0.369 0.368 0.368b 0.363c 0.360d 0.384a 0.370a 0.369ab 0.367b
    Feed cost, €/kg ADG 0.560a 0.537b 0.562a 0.556a 0.554a 0.523b 0.573a 0.553b 0.520c
    N excretion, kg/pig 3.30b 3.41a 3.60a 3.45b 3.39b 2.98c 3.88a 3.50b 2.69c
    P Excretion, kg/pig 0.632b 0.680a 0.665 0.658 0.657 0.642 0.696a 0.670ab 0.602b
France
    CP2, g/kg 156 156 167a 159ab 156b 144c 173a 153b 144c
    ADG, kg/d 0.876b 0.902a 0.885b 0.880b 0.875b 0.915a 0.898 0.884 0.885
    G:F, kg/kg 0.365b 0.370a 0.367ab 0.365b 0.363b 0.375a 0.368 0.364 0.371
    Feed cost, €/kg ADG 0.615a 0.597b 0.634a 0.615ab 0.607b 0.569c 0.639a 0.607b 0.572c
    N excretion, kg/pig 3.81 3.69 4.16a 3.90ab 3.79b 3.17c 4.37a 3.68b 3.22c
    P Excretion, kg/pig 0.688b 0.743a 0.752a 0.725a 0.713ab 0.673b 0.728a 0.744a 0.675b
a–dMeans followed by same or no letter within group (protein source, feeding program, and AA) do not differ (P > 0.05) according to Tukey’s test.
1SOY = soybean meal only; MIX = soybean meal, rapeseed meal, and pea (France), or soybean meal and meat and bone meal (Brazil).
2CP = average CP of the diet fed to the pigs.

View Full Table | Close Full ViewTable 4.

Statistical analysis of the effects of country, origin of soybean, protein sources, feeding program, crystalline AA supplementation, and their interactions on average dietary CP content, ADG, F:G, feed cost (Feed), N excretion (N exc.), P excretion (P exc.), and potential environmental impacts on climate change (CC), acidification (AC), eutrophication (EU), cumulative energy demand (CED), terrestrial ecotoxicity (TE), and land occupation (LO)

 
Performance
LCA1 environmental impact
CP, g/kg ADG, g/d G:F, kg/kg Feed, €/kg BWG N exc.,kg/pig P exc., kg/pig CC,kg CO2–eq AC,g SO2–eq EU,g PO4–eq CED,MJ TE,g DCB-eq LO,m2∙yr
No. batches 192 192 192 192 192 192 192 192 192 192 192 192
Mean 151 867 0.37 0.578 3.55 0.69 2.60 53.8 17.5 13.7 11.5 3.2
Root mean square error 7.5 33 0.01 0.016 0.26 0.05 0.07 2.1 0.6 0.4 0.3 0.1
Country <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Gender <0.001 0.01 0.01 0.01 0.04
Origin of soybean (OS) <0.001 <0.001 <0.001 <0.001 <0.001
Protein sources (PS) 0.05 <0.001 <0.001 <0.001 0.01 <0.001 <0.001 <0.001
Feeding program (FP) <0.001 <0.001 <0.001 <0.001 <0.001 0.01 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
AA sup. (AAS) <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 <0.001
Country × PS 0.04 0.03 0.03 <0.001 <0.001 <0.001
Country × FP 0.02 0.01 0.03 0.06 0.04 <0.001
Country × AAS 0.01 0.02 0.02 0.01 0.02 <0.001
OS × PS <0.001 <0.001
OS × FP <0.001 <0.001
OS × AAS <0.001 <0.001
Country × OS × PS <0.001
Country × OS × FP
Country × OS × AAS 0.02
1LCA = life cycle assessment.

In both countries, ADG was affected by the feeding program; the highest growth performance was obtained for IDF (896 g/d on average) and the lowest (851 g/d on average) for DMP, with the other programs being intermediate. Differences in ADG between scenarios of CAA inclusion and between protein sources were not significant. Similar effects were obtained for the G:F, which was lower for IDF compared to DMP (2.68 vs. 2.79 kg/kg). The effect of CAA inclusion on G:F was small and opposite (interaction P = 0.02) in France and Brazil. The G:F was affected by protein source in France (lower with the MIX scenario) but not in Brazil (interaction P = 0.03).

Compared with the 2P program, the IDF program reduced nitrogen excretion by 17 and 24%, and P excretion by 3 and 11% for Brazil and France, respectively, with the other feeding programs being intermediate. In the same way, increasing the level of CAA inclusion reduced nitrogen excretion by 30.5 and 26.3% and P excretion by 13.5 and 7.3% for Brazil and France, respectively. Diversifying the sources of protein (MIX vs. SOY) slightly reduced nitrogen excretion in France (−3%) but increased it in Brazil (+3%; interaction P = 0.03), whereas P excretion was higher for the MIX scenario in both countries.

Potential Environmental Impacts

Climate Change.

Climate change impact was significantly affected by country, feeding program, CAA inclusion, the origin of soybean, and protein source (Table 4 and 5). With soybean from SO, the average values for CC ranged between the feeding programs from 2.31 to 2.45 kg CO2–eq per kg of BWG in Brazil and from 2.28 to 2.35 kg CO2–eq per kg of BWG in France (Table 5). When soybean meal from CW was used, CC values increased up to 2.75 to 2.96 kg CO2–eq per kg of BWG in Brazil and up to 2.61 to 2.89 kg CO2–eq per kg of BWG in France. The lowest CC impact was reached for IDF, both for soybean from SO and from CW. With soybean meal from SO, the highest impacts among the CAA inclusion scenarios were observed for lowCP in Brazil and for withAA in France, whereas with soybean meal from CW, the highest impacts were observed for noAA in both countries. Independently of the soybean origin and the geographical context of pig production, SOY showed higher impacts than MIX scenarios.


View Full Table | Close Full ViewTable 5.

Potential environmental impacts at the farm gate per kilogram of BW gain of growing-finishing pigs fed with different protein sources in 2-phase (2P), 4-phase (4P), daily multiphase (DMP), and individual daily feeding (IDF) programs without AA addition (noAA), with AA addition and minimal CP constraints (withAA), and with AA without any CP constraint (lowCP)

 
Protein source (PS)1
Feeding program (FP)
AA inclusion
SOY MIX 2P 4P DMP IDF noAA withAA lowCP
No. batches 96 96 48 48 48 48 64 64 64
Impact category2
    CC, kg CO2–eq
        Brazil–South3 2.44a 2.36b 2.41a 2.43a 2.45a 2.31b 2.37b 2.39b 2.45a
        Brazil–Center West 2.99a 2.79b 2.96a 2.93a 2.92a 2.75b 3.00a 2.91b 2.76c
        France–South 2.34 2.32 2.34ab 2.34ab 2.35a 2.28b 2.27b 2.37a 2.35a
        France–Center West 2.93a 2.62b 2.89a 2.81ab 2.78b 2.61c 2.88a 2.78b 2.66c
    AP, g SO2–eq
        Brazil–South 58.4b 60.3a 61.2a 60.6a 60.5a 55.3b 62.4a 60.6b 55.1c
        Brazil–Center West 59.8b 61.4a 62.6a 61.9a 61.7a 56.4b 64.0a 61.9b 56.0c
        France–South 47.2 46.8 50.1a 48.3ab 47.5b 42.1c 51.0a 46.9b 43.2c
        France–Center West 48.9 47.7 51.7a 49.6ab 48.7b 43.1c 52.7a 48.0b 44.1c
    EP, g PO4–eq
        Brazil–South 17.5 17.6 18.1a 17.9a 17.8a 16.4b 18.6a 17.9b 16.1c
        Brazil–Center West 17.4 17.5 18.0a 17.8a 17.7a 16.3b 18.5a 17.7b 16.1c
        France–South 17.9a 17.3b 18.4a 18.0a 17.8a 16.3b 18.9a 17.6b 16.4c
        France–Center West 17.8a 17.3b 18.3a 17.9a 17.7a 16.3b 18.7a 17.5b 16.4c
    CED, MJ
        Brazil–South 13.3 13.1 13.3a 13.3a 13.4a 12.8b 12.6c 13.2b 13.8a
        Brazil–Center West 16.1a 15.3b 16.1a 15.9a 15.8a 15.0b 15.8ab 15.7a 15.4b
        France–South 12.2 12.2 12.5a 12.3a 12.2a 11.7b 11.5c 12.3b 12.7a
        France–Center West 14.2a 13.2b 14.4a 13.9ab 13.7b 12.9c 13.6 13.7 13.8
    TE, g 1,4-DCB-eq
        Brazil–South 9.2a 8.7b 8.9b 9.1ab 9.2a 8.4c 8.7c 8.9b 9.2a
        Brazil–Center West 9.9a 9.2b 9.6a 9.7a 9.8a 9.0b 9.5 9.6 9.6
        France–South 13.6 13.7 14.0a 13.8ab 13.7ac 13.1bd 13.6b 13.9aa 13.3b
        France–Center West 13.8 13.8 14.2a 14.0a 13.9a 13.2b 13.8a 14.1b 13.4a
    LO, m2∙yr
        Brazil–South 2.58a 2.40b 2.52a 2.52a 2.52a 2.38b 2.60a 2.49b 2.41c
        Brazil–Center West 2.52a 2.35b 2.46a 2.46a 2.47a 2.33b 2.53a 2.43b 2.33c
        France–South 4.08a 3.94b 4.03 4.04 4.05 3.93 4.15a 4.01b 3.88c
        France–Center West 4.02a 3.91b 3.97 3.98 4.00 3.89 4.09a 3.96b 3.84c
a–dMeans followed by the same or no letter within group (PS, FP, and AA) do not differ (P > 0.05) according to Tukey’s test.
1SOY = soybean meal only; MIX = soybean meal, rapeseed meal, and pea (France) or soybean meal and meat and bone meal (Brazil).
2CC = climate change; AP = acidification potential; EP = eutrophication potential; CED = cumulative energy demand; TE = terrestrial ecotoxicity; LO= land occupation.
3South = soybean meal from south region of Brazil; Center West = soybean meal from the center west region of Brazil.

The main processes contributing to CC impacts were feed production (production and transport of feed ingredients, feed processing at the feed factory, and transport to the farm), animal housing, and manure management (storage, transport, and spreading; Fig. 3). Manure management had the highest contribution to CC (50%) in the Brazilian context followed by feed production (44%), while in the French context, feed production was the main contributor (52%) followed by manure management (41%).

Figure 3.
Figure 3.

Relative contribution of the different processes (%) to impacts on climate change (CC), acidification potential (AP), eutrophication potential (EP), cumulative energy demand (CED), terrestrial ecotoxicity (TE), and land occupation (LO) in Brazilian (BR) and French (FR) scenarios of pig production. Mean results for soybean meal from South Brazil.

 

The detailed results with the interaction effects between feeding programs, AA inclusion scenarios, and protein source are presented in Fig. 4A. The variation of impacts among scenarios was highest for noAA (from 2.2 to 3.2 kg CO2–eq per kg of BWG), intermediate for withAA (from 2.3 to 3.1 kg CO2–eq per kg of BWG), and lowest for lowCP (from 2.3 to 2.9 kg CO2–eq per kg of BWG). In all scenarios of CAA inclusion and whatever the feeding program or country, scenarios based on soybean meal from CW showed higher CC impacts than scenarios based on soybean from SO (Fig. 4A). Differences between protein sources (i.e., SOY and MIX) were less pronounced for soybean from SO compared to CW and were reduced when CAA inclusion increased. There was a clear interaction between the soybean origin and CAA inclusion scenario in both countries. With soybean from CW, CC impact decreased when the incorporation of CAA increased, with the effect being more marked for SOY than for MIX protein source, whereas no effect or even the opposite was observed with soybean from SO. The effect of the feeding program on CC was mainly affected by the soybean origin and the level of CAA inclusion. With soybean from CW, increasing the number of feeding phases and IDF reduced CC impact in all situations; however, the magnitude of the effect decreased when CAA inclusion increased. The largest variation between feeding programs with soybean from CW was observed for France in the withAA scenario, with a reduction of CC impact from 3.18 kg CO2–eq per kg of BWG for 2P to 2.72 kg for IDF. In all scenarios of feed formulation based on soybean meal from SO, except for the French SOY scenario, increasing the number of feeding phases from 2P to DMP slightly increased the CC impact by about 2 to 3%, whereas IDF resulted in a reduced impact in all scenarios.

Figure 4.
Figure 4.

Interactions between effects of the feeding program, the use of AA, and the soybean origin on the environmental impact of climate change (A), acidification potential (B), and eutrophication potential (C) for the Brazilian (BR) and French (FR) contexts of pig production. 2P, 2-phase; 4P, 4-phase; DMP, multiphase; IDF, individual daily feeding; noAA, no AA; withAA, with AA; lowCP, without constraints in the CP content; SO, soybean from South region; CW, soybean from Center West Brazil; SOY, soybean meal as sole protein source; MIX, diversified protein sources.

 

Acidification Potential.

With soybean from SO, the values for AP ranged between the feeding programs from 55.3 to 61.2 g SO2–eq per kg of BWG in Brazil and from 42.1 to 50.1 g SO2–eq per kg of BWG in France, with a significant effect of country (Table 5). When we considered soybean meal from CW, the values slightly increased up to 56.4 to 62.6 g SO2–eq per kg of BWG in Brazil and up to 43.1 to 51.7 g SO2–eq per kg of BWG in France. Depending on the feeding program, the lowest AP impact was reached for IDF for soybean both from SO and from CW. The highest AP impacts among the AA inclusion scenarios were observed for noAA in both countries, and the lowest impacts were for lowCP in both countries. For the French context, SOY showed 1.7% higher impacts than the MIX protein source, whereas for the Brazilian context, MIX showed 2.9% higher AP impact than SOY.

The main processes contributing to impacts are displayed in Fig. 3. Feed production had the highest contribution to AP (53%) in the Brazilian context followed by animal housing (31%), while in the French context, animal housing was the main contributor (44%) followed by feed production (35%).

Detailed results with the interaction effects between feeding programs, AA inclusion scenarios, and protein sources are presented in Fig. 4B. The variation of impacts among scenarios was highest for noAA (from 45.6 to 67.6 g SO2–eq per kg of BWG), intermediate for lowCP (from 36.9 to 58.8 g SO2–eq per kg of BWG), and lowest for withAA (from 42.0 to 63.7 g SO2–eq per kg of BWG). In all scenarios of CAA inclusion and whatever the feeding program or country, scenarios based on soybean meal from CW showed higher AP impacts than scenarios based on soybean from SO (Fig. 4B). Differences between protein sources (i.e., SOY and MIX) in the Brazilian context were reduced when CAA inclusion increased. Whatever the country and soybean origin, AP impact decreased when the incorporation of CAA increased. The effect of the feeding program on AP was mainly affected by the level of inclusion of CAA. Increasing the number of feeding phases and IDF reduced the AP impact in all situations; however, the magnitude of the effect decreased when CAA inclusion increased. The largest variation between feeding programs with soybean from CW was observed for France in the lowCP scenario, with a reduction of AP impact from 51 g SO2–eq per kg of BWG for 2P to 37 g for IDF. In all scenarios of feed formulation whatever the soybean meal origin and the protein source, changing the feeding strategy from 2P to IDF decreased the AP impact by about 10% in Brazil and 16% in France.

Eutrophication Potential.

The values for EP were not affected by country or origin of soybean and ranged between the feeding programs from 16.3 to 18.4 g PO4–eq per kg of BWG (Table 5). According to the feeding program, the lowest EP impact was obtained for IDF in both countries (mean of 16.3 g PO4–eq per kg of BWG) and the highest for 2P (mean of 18.2 g PO4–eq per kg of BWG). The highest EP impacts among CAA inclusion scenarios were observed for noAA scenarios and the lowest for lowCP scenarios in both countries.

Main processes contributing to EP impacts are displayed in Fig. 3. Feed production had the highest contribution to EP in both contexts (64 and 60% in Brazil and France, respectively) followed by animal housing (23 and 26% in Brazil and France, respectively).

The detailed results with the interaction effects between feeding programs, CAA inclusion scenarios, and protein sources are presented in Fig. 4C. In all scenarios of CAA inclusion and whatever the feeding program or country, scenarios based on soybean meal from SO showed slightly higher EP impacts than scenarios based on soybean from CW (Fig. 4C). Differences in EP between protein sources increased when CAA inclusion increased and were less pronounced in Brazil than in France. With both protein sources, EP impact decreased when the incorporation of CAA increased, with the effect being more marked for France than for Brazil. The effect of the feeding program on EP was mainly affected by the level of inclusion of CAA. Increasing the number of feeding phases and IDF reduced the EP impact in all situations. The largest variation between feeding programs was observed for France in the lowCP scenario with soybean from SO with a reduction of EP impact from 17.7 g PO4–eq per kg of BWG for 2P to 15.0 g for IDF.

Cumulative Energy Demand.

Cumulative energy demand impact was significantly affected by country, feeding program, CAA inclusion, origin of soybean, and protein source (Table 4 and 5). With soybean from SO, the values for CED ranged between the feeding programs from 12.8 to 13.4 MJ-eq per kg of BWG in Brazil and from 11.7 to 12.5 MJ-eq per kg of BWG in France (Table 5). When we considered the soybean meal from CW, the values increased up to 15.0 to 16.1 MJ-eq per kg of BWG in Brazil and up to 12.9 to 14.4 MJ-eq per kg of BWG in France. According to the feeding program, the lowest CED impact was reached for IDF, both for soybean from SO (12.8 and 11.7 MJ-eq per kg of BWG in Brazil and France, respectively) and from CW (15.1 and 12.9 MJ-eq per kg of BWG in Brazil and France, respectively). On average, the highest CED impacts among the CAA inclusion scenarios were observed for lowCP in both countries, and the lowest for noAA. Independently of the soybean origin and the geographical context of pig production, except for the French SO scenario, for which there was no difference between protein sources, SOY showed higher CED impacts than MIX scenarios of protein source.

The main processes contributing to CED impacts are displayed in Fig. 3. Feed production had the highest contribution to CED in both Brazilian (94%) and French (95%) contexts, followed by animal housing (12.6% in both countries).

The detailed results with the interaction effects between feeding programs, CAA inclusion scenarios, and protein source are presented in Fig. 5A. In all scenarios of CAA inclusion and whatever the feeding program or country, scenarios based on soybean meal from CW showed higher CED impacts than scenarios based on soybean from SO (Fig. 5A). Differences between protein sources were reduced when there was no constraint in the CP content (lowCP), especially for soybean meal from CW. There was a clear interaction between the soybean origin and CAA inclusion scenario in both countries. The CED impact increased when the incorporation of CAA increased for diets based on soybean meal from SO, whereas it decreased for diets with soybean from CW. The effect of the feeding program on CED was mainly affected by the soybean origin and the level of CAA inclusion. With soybean meal from CW, increasing the number of feeding phases and IDF reduced the CED impact by about 8.6% in all CAA inclusion scenarios, whereas no effect or even a slight increase was observed with soybean meal from SO. For both soybean meal origins and both protein sources, moving from noAA to lowCP reduced the variation among the CED impacts. The largest variation between feeding programs with soybean from CW was observed for France in the withAA scenario, with a reduction of CED impact from 15.7 MJ-eq per kg of BWG for 2P to 12.8 MJ-eq for IDF.

Figure 5.
Figure 5.

Interactions between effects of the feeding program, the use of AA, and the soybean origin on environmental impact of cumulative energy demand (A), terrestrial ecotoxicity (B), and land occupation (C) for the Brazilian (BR) and French (FR) contexts of pig production. 2P; 2-phase; 4P, 4-phase; DMP, multiphase; IDF, individual daily feeding; noAA, no AA; withAA, with AA; lowCP, without constraints in the CP content; SO, soybean from South region; CW, soybean from Center West Brazil; SOY, soybean meal as sole protein source; MIX, diversified protein sources.

 

Terrestrial Ecotoxicity.

Terrestrial ecotoxicity impact was significantly affected by country, feeding program, CAA inclusion, origin of soybean, and protein source (Table 4 and 5). With soybean from SO, the values for TE ranged between the feeding programs from 8.45 to 9.19 g 1,4-DCB-eq per kg of BWG in Brazil and from 13.1 to 14.2 g 1,4-DCB-eq per kg of BWG in France (Table 5); the range of values was rather similar with soybean from CW (from 9.02 to 9.80 g 1,4-DCB-eq per kg of BWG in Brazil and from 13.2 to 14.1 g 1,4-DCB-eq per kg of BWG in France). According to the feeding program, the lowest TE impact was reached for IDF, both for soybean from SO and from CW. The highest impacts among the CAA inclusion scenarios were observed for lowCP in Brazil (with a mean impact of 9.39 g) and withAA for France (mean of 14.0 g). Independently of the soybean origin and the geographical context of pig production, SOY showed higher impacts than MIX scenarios.

The main processes contributing to TE impact are displayed in Fig. 3. Feed production had the highest contribution to TE in the Brazilian (70%) and French (69%) contexts, followed by manure management (30 and 31% for Brazil and France, respectively), while animal housing did not contribute to TE.

The detailed results with the interaction effects between feeding programs, CAA inclusion scenarios, and protein source are presented in Fig. 5B. In all scenarios of CAA inclusion and whatever the feeding program, scenarios based on soybean meal from CW showed higher TE impacts in Brazil (+6.73%) than scenarios based on soybean from SO but not in France (Fig. 5B). For France, increasing the number of feeding phases and IDF reduced the TE impact in all AA inclusion scenarios; the magnitude of the effect tended to decrease when AA inclusion increased. Conversely, for Brazil, increasing the number of feeding phases tended to slightly increase the TE impact in all situations. In both countries, IDF resulted in a reduced TE impact in all scenarios of feed formulation regardless of the protein source and soybean origin. Compared to 2P, IDF decreased the TE impact by about 21% in France and 8% in Brazil.

Land Occupation.

The values for LO ranged between the feeding programs from 2.33 to 2.52 m2∙yr per kg of BWG in Brazil and from 3.89 to 4.05 m2∙yr per kg of BWG in France (Table 5). According to the feeding program, the lowest LO impact was reached for IDF, both for soybean from SO and from CW. The highest impacts among the AA inclusion scenarios were observed for the noAA inclusion scenario in both countries regardless of the origin of soybean meal. Independently of the soybean origin and the geographical context of pig production, SOY showed higher LO impacts than MIX scenarios.

The main processes contributing to LO impact are displayed in Fig. 3. Taking into account that the LCI did not consider the land used by the buildings, feed production was almost the only contributing process to land occupation for both pig production contexts.

The detailed results with the interaction effects between feeding programs, CAA inclusion scenarios, and protein sources are presented in Fig. 5C. The variation of LO impact among feeding programs was rather similar for all CAA inclusion scenarios. Whatever the feeding program, the country, or CAA inclusion, scenarios based on soybean meal from CW showed 1.6% lower LO impacts than scenarios based on soybean from SO (Fig. 5C). There was an interaction between the protein source and CAA inclusion scenario in the French context. In the noAA scenario, SOY showed 2.1% lower LO impact than MIX, whereas in the low CP scenario, SOY showed 5.7% higher impact than MIX regardless of the origin of the soybean meal. The effect of the feeding program on LO was mainly affected by the protein source and by the level of CAA inclusion. In the French context, increasing the number of feeding phases increased the LO impact for diets based on MIX protein source by about 2%, whereas the opposite (about 2% decrease) was found when diets were based on SOY. For the Brazilian context, diets based on MIX showed a lower LO impact compared to SOY; this impact slightly increased with the increase in the number of feeding phases and decreased by about 5% in the IDF program. The largest variation between feeding programs with soybean from SO was observed for France in the withAA scenario, with a reduction of LO impact from 4.32 m2∙yr per kg of BWG for 2P to 4.02 m2∙yr for IDF.


DISCUSSION

Performance, Nitrogen and Phosphorus Balance, and Feed Cost

In agreement with Mosnier et al. (2011), Andretta et al. (2014), and Garcia-Launay et al. (2014), average dietary CP concentration over the growing-finishing period was reduced with IDF and when the number of feeding phases increased. In the same way, the strategy of CAA inclusion affected dietary CP, with the lowest level achieved for the lowCP scenario. This was expected since dietary nutrient utilization is a dynamic phenomenon resulting in a reduction of AA requirement relatively to energy over the fattening period (van Milgen et al., 2008). In line with the reduction of dietary protein content, nitrogen retention efficiency increased from 32% for 2P and noAA up to 47% for IDF and lowCP. Likewise, P retention efficiency increased from 37% for 2P and noAA up to 42% for IDF and lowCP. Andretta et al. (2014) observed a similar improvement of retention efficiency between 3-phase feeding and IDF from 34 to 38% for N and from 43 to 53% for P, respectively.

For both countries, comparison of pig performance between feeding programs showed better results for the IDF program. This feeding strategy allowed a daily supply close to the required amount of AA to each pig, taking into account better the effect of between-animal variation in requirements. Pomar et al. (2014a) also observed improved performance for IDF compared to 3-phase feeding, whereas Andretta et al. (2014) obtained similar results. Increasing the number of feeding phases tended to reduce performance compared to 2-phase feeding especially in Brazil. Brossard et al. (2009) indicated that depending on the variability of the population, more than 110% of the mean AA requirement had to be fed to achieve maximal growing performance. Pomar et al. (2014a) measured similar performance for feeding strategies with different numbers of feeding phases, but in their study, the highest AA supply was calculated to meet the requirements of the most demanding pigs. This seems to indicate that feeding 110% of the average pig SID lysine requirement may not be high enough according to the heterogeneity of the population.

The increase in the incorporation of CAA and phase feeding resulted in a significant reduction in feed cost in both countries, which is in agreement with the results of Mosnier et al. (2011) and Pomar et al. (2014b). The use of more diversified protein sources also reduced the feed cost. In the Brazilian context, this was related to the use of meat and bone meal, which was available at a cheaper cost than soybean meal (about 321 vs. 400 €/t). In addition, the inclusion of inorganic P, which was very costly (about 658 €/t), could also be reduced by using meat and bone meal. For France, the difference was mainly related to the lower cost of local protein sources, especially rapeseed meal (about 260 €/t).

The reduction in nitrogen and P excretion obtained with the multiphase and IDF programs was attributable to the reduction in nutrient supply and the better adjustment of nutrient level to pig requirements as already shown by Andretta et al. (2014). As reviewed by Dourmad and Jondreville (2007), reducing the dietary CP while balancing the diet with CAA is an effective way of reducing nitrogen excretion without affecting performance. These authors reported an 8.5% reduction in nitrogen excretion by pigs per percentage unit of reduction of CP, and Andretta et al. (2014) indicated that N excretion was reduced by 8.4% for each percentage unit of dietary CP for precision feeding in comparison with 3-phase feeding. In the present study, each percentage unit of dietary CP reduction due to CAA inclusion led to a decrease in nitrogen excretion of around 9.0% in the Brazilian context and 9.1% in the French context. The source of protein affected nitrogen and P excretion differently depending on the country. In the Brazilian context, the use of more diversified protein sources resulted in higher nitrogen and P excretion than with soybean meal only. Meat and bone meal was an interesting source of SID AA and digestible P, but this ingredient provided less lysine and more P per kilogram than soybean meal (Rostagno et al., 2011). In the French context, nitrogen excretion was lower for scenarios with diversified protein sources, whereas P excretion was higher in relation to the high indigestible P content in the rapeseed meal, which was almost twice the value in soybean meal (Sauvant et al., 2004).

Potential for Reducing Environmental Impacts Through Feeding Practices

The hypothesis for reducing the environmental impacts of pig production through the increase in the number of feeding phases and IDF was validated by our results for IDF, which always performed better, but it was not validated in all situations when increasing the number of feeding phases. Indeed, the results appeared to be dependent on the origin of the soybean and the country of pig production. The same dependency was observed for CAA inclusion scenarios, suggesting that incorporating CAA may reduce some of the environmental impacts of pig fattening when high-impact feed ingredients were used, whereas no effect or even the opposite was observed with low-impact ingredients.

Climate Change and Cumulative Energy Demand.

For the same manure management as in our study and with the hypothesis that 70% of the soybean was from CW and 30% from SO, Garcia-Launay et al. (2014) calculated a lower CC impact in France for a DMP program compared to a 2P feeding program. This was also the case in our study, in both countries, when soybean meal came from CW, whereas when it came from SO, the DMP program resulted in a higher CC impact than 2P (Table 5). This indicated that the effect of phase feeding on CC may depend on the origin of the soybean. In our study and in the studies of Garcia-Launay et al. (2014) and van Zanten et al. (2015), diets based on soybean meal showed higher impact than diets based on more diversified protein sources. The lower CC impact obtained with these diets was related in Brazil to the use of meat and bone meal, a coproduct with a low CC impact. For French conditions, rapeseed meal and pea were not associated with any deforestation processes, since this process occurred many centuries ago and, thus, was not taken into account in the LCA evaluation.

Concerning the interactions between the feeding programs, the incorporation of the CAA, and the soybean meal origin, the variation of CC impact among scenarios was clearly reduced with the increased inclusion of CAA (Fig. 4A), indicating that the effect of feeding programs on CC was more pronounced when no CAA were included. A more pronounced effect of feeding program was also observed when the soybean was from CW and especially when it was the sole protein source. In this situation, because of the high CC impact of CW soybean meal, increasing the number of feeding phases and CAA inclusion was very efficient in reducing CO2–eq emissions. This was not the case when the soybean was from SO. In addition, the differences related to the country were greater in scenarios without CAA addition because soybean meal was the main AA source and had a higher impact in France due to transport. This explained why the situation with the greatest decrease in CC impact due to changes in the feeding program was obtained with low or no CAA incorporation. Conversely, in Brazil with soybean from SO, CC impact tended to increase when the number of feeding sequences increased and it decreased only in the case of IDF.

In the same way, the effect of CAA inclusion was dependent on the country and the soybean origin. In France, increasing the incorporation of CAA always resulted in a decrease in CC impact, which was more marked with soybean from CW. In Brazil, CC impact decreased only with soybean from CW and even tended to increase with CAA inclusion when the soybean was from SO. These results clearly indicated that the effects on CC of the feeding program and the CAA supplementation strategy were highly dependent on the soybean origin, the availability of other protein sources, and the geographical location of production. Opposite results can even be obtained when contrasted situations are considered.

Effects of feeding strategy on CED were rather similar as for CC. The possibility of reducing CED impact by increasing the number of feeding phases was confirmed for diets based on soybean meal from CW but not for soybean from SO. Individual daily feeding only resulted in reduced CED impact in that situation. One reason for this result was the shorter distance of soybean transport when it came from SO, while the CAA (except L-lysine) had to be imported and had a high CED impact. In addition, CED tended to increase with the incorporation of CAA in association with soybean meal from SO. Conversely, Garcia-Launay et al. (2014) reported that CED always decreased with an increase in the number of feeding phases regardless of manure management and protein source. However, these authors did not consider animal variability, which was taken into account in the present study, and they used soybean mainly from CW.

Acidification and Eutrophication Potential Impacts.

Since both nitrogen and P contribute to eutrophication and nitrogen contributes to acidification by ammonia emissions (Guinée et al., 2002), the AP and EP impacts in both countries were always reduced by increasing the number of feeding phases and with the incorporation of CAA (Table 5). This was not surprising because both strategies reduced nitrogen and P excretion (Table 3) and, consequently, also reduced the NH3 emissions from animal housing and manure management and field application. These results were consistent with those reported by Garcia-Launay et al. (2014) who observed a decrease in AP and EP impacts when changing from a 2P to a DMP program with diets based on diversified protein sources. Moreover, as in the present study, these authors also reported that AP and EP impacts decreased when the incorporation of CAA increased. The use of diversified protein sources increased the AP impact in the Brazilian context, whereas it decreased it in the French context. These differences were probably due to the practices of crop nitrogen fertilization. In the Brazilian context, crop production was mainly fertilized by urea while in France ammonium nitrate was generally used. Because urea was applied to the soil in solution form and ammonium nitrate as a solid, a higher proportion of nitrogen could be lost during urea spreading. Moreover, urea first needed to be hydrolyzed to ammonium by the soil enzymes; this shifted the natural balance between NH4+ and NH3 to the latter form, resulting in volatilization losses (Jones et al., 2007). Since diets in the Brazilian context based on diversified protein sources included more maize with higher nitrogen fertilization than soybean meal, this may explain the higher AP impact.

Terrestrial Ecotoxicity.

Feeding strategies affected TE impacts only when high-impact feed ingredients were used. For this reason, the incorporation of CAA, the increase in the number of feeding phases, and IDF reduced TE impact in the French context but not in Brazil. The lower TE impact in Brazil compared to France was associated with the low TE impact of maize production, which represented more than 70% of feed composition in Brazilian formulations. In France, the lower TE impact of the lowCP scenario was a consequence of lower dietary CP. The same was reported by Garcia-Launay et al. (2014), who found a TE reduction of about 20% when moving the formulation scenarios from noAA to lowCP. For the Brazilian context, whatever the origin of soybean meal, increasing CAA inclusion increased the TE impact. This was due to the replacement of soybean, which had a low TE impact, with CAA and maize with higher TE impacts.

Land Occupation.

For the LO category, we clearly observed a reduction of impact with IDF and with the increase in CAA inclusion (Table 5). Moving from 2P to the DMP program tended to increase the LO impact in all situations, which was contrary to the result obtained by Garcia-Launay et al. (2014), indicating that this strategy did not bring any significant environmental benefits for LO when the effect of between-animal variability was considered. The decrease of LO as the result of CAA inclusion was the result of the lower soybean inclusion in the diets and the greater inclusion of cereals with lower LO impacts than soybean meal. Indeed, Prudêncio da Silva et al. (2014) reported an LO of soybean meal that was more than twice as high as that for maize in Brazilian grain production. The lower LO impact obtained with diets based on diversified protein sources was related in Brazil to the use of meat and bone meal, which has a low impact and, in France, to the inclusion of rapeseed meal with a lower impact than soybean meal.


CONCLUSION

The results of this study indicate that IDF would be the most effective approach for reducing the life cycle impact of pig fattening, whereas the potential of multiphase feeding programs depends on the impact considered, soybean origin, and the geographical context of pig production. The environmental benefits of phase feeding and the incorporation of CAA for reducing CC impact is limited in Brazil with soybean from SO, whereas it appears to be an efficient strategy with soybean from CW, especially in Europe. Potential eutrophication and acidification impacts are largely reduced by phase feeding and CAA addition in a rather similar way in all scenarios regarding protein source, protein origin, and the geographical context of pig production.

 

References

Footnotes


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