The objective of this work was to quantify and understand the impacts of intensive pig production at family-farm level. A case study from the west of Santa Catarina State was used to identify adverse issues (hotspots) of pig production by integrating the assessment of the environmental, economic, and social aspects of the system. The quantitative and qualitative indicators calculated can guide and support the decision-making processes for a variety of stakeholders and actors.
Methods
The environmental performance of the pig production system was assessed from cradle-to-farm gate using environmental Life Cycle Assessment methodology set out in ISO 14040 (ISO 2006a). The functional unit (FU) was 1 kg of Liveweight (kg-LW). The structure of the Life Cycle Sustainability Assessment (LCSA) was based on Neugebauer et al. (J Clean Prod 102:165–176, 2015) and Chen and Holden (J Clean Prod 172:1169–1179, 2018), who proposed a tiered framework to evaluate the impacts on the environmental, social, and financial aspects of a product. The economic dimension or Life Cycle Cost (Hunkeler et al. in Environmental life cycle costing. Crc Press, London, 2008) focused on farm-level activities. The social impact was calculated based on the UNEP/SETAC (2009) guidelines.
Results and discussion
The environmental performance of the finishing pig production was slightly lower than reference value for climate impacts, acidification, and eutrophication. The economic impacts tended to be positive, reflecting the efforts of the farmer and employee to maintain high productivity and reduce the number of pig losses in comparison with the reference values. However, this effort did not result in greater profitability, causing low farm income. The impacts of low profitability were not transferred to the employee since the wage were above the reference value. There is a need for more education for small farmers, which is known to have a positive correlation with the adoption of new technologies, thus reducing adverse environmental and social impacts and increasing economic return.
Conclusions
The interaction of social and economic factors suggests it is unlikely that the farm can achieve better environmental performance. The limited economic return and low level of education have a negative impact on the farmer’s capacity to adopt new technologies to improve environmental outcomes. The use of LCSA, based on a consistent model across the three aspects of sustainability, made it possible to understand the interaction of these factors.
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1 Introduction
Pork is one of the world’s most widely eaten meats, with consumption expected to increase (OECD-FAO Agricultural 2022). Brazil is the 4th largest producer globally, and one of the largest exporters (Brazil for business 2022). Pork is also systematically important to Brazil, especially for the southern states where much of the production is located (IBGE 2022). Santa Catarina State has the greatest concentration of producers, responsible for 26% of total national pork production, and for 45% of interstate movement of breeding animals (Sebrae 2016). More than 78% of swine herds and 51% of slaughterhouses are concentrated in the western region of the state, spread across an area of 27,289 km2, with 7.3 million pigs slaughtered in 2018 (Giehl and Mondardo 2020). Concentration is very much a feature of the Brazilian pork industry, with data indicating that 50% of slaughtered pigs in Brazil are controlled by three large meat companies (Guimarães et al. 2017). Over the last 30 years, large companies have intensified the vertical integration of pig production causing small-scale farmers to specialize in a specific phase of the production process, e.g. finishing pig units (Miele 2017). The large companies also control the production of inputs (e.g. feed and genetics), and coordinate value chain logistics, at national level. At regional level, smaller companies called “integrating companies” coordinate the pig production units (e.g. growing to finishing pig production stage), providing logistics and agronomic advice. These integrating companies are the link between the farm and the slaughtering and processing stages of the pig production. According to the Brazilian Association of Animal Protein (ABPA 2023), 90% of national pork production is carried out by integrated producers. The farms are responsible for providing services, i.e. safe-guardianship and husbandry of the animals for specific phases of the rearing, notwithstanding that the animals always remain the property of the integrating companies (Sebrae 2016). The farmer is contracted to take care of the pigs, under the control of the integrator, and to deliver the product during the agreed period in exchange for a payment. Therefore, the pig farmer bears the investment costs (facilities and equipment), provides labour, water, electricity, and is responsible for waste management and all the environmental risks on farm. In the western region of Santa Catarina, around 90% of the agricultural land is held by small-to-medium sized family farms of 50 hectares or less (Carballo et al. 2013). According to the 2017 Brazilian Agricultural census (IBGE 2018), these family farmers are dominated by male leaders (91%), aged between 45 and 65 years old (58%) with little formal education, and more than 10 years of experience running the farm. These family farms create a significant number of rural jobs, help maintain food security and help to support the regional economy.
The vertical integration and intensification of the pig production system has had a direct impact on family farms. Giongo et al. (2017) studied the experiences of pleasure and suffering of 16 integrated pig farmers. The main suffering experiences were “overwork, constant pressure to increase productivity, remuneration practices, and the absence of speech and listening spaces”. The pleasure experiences were related to “maintaining the family tradition and the care of the animals”. Alves and Mattei (2006) have shown that selection criteria used by the large companies exclude many small farms, selectively increasing poverty for some in western Santa Catarina. Small family farms cannot afford to invest in the facilities and equipment to achieve the productivity required by the integrating company. As a result of this exclusion, some family farms have gone out of business, and a population movement from rural areas to urban centres has become evident (Alves and Mattei 2006). In addition, independent pig farms often struggle to compete with large agri-businesses in offering quality products at competitive prices (Coletti and Lins 2011). On the other hand, many farms have successfully remained in the system, but have become economically dependent on the large companies (Kern 2021).
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The intensification of pig production has led to a variety of environmental impacts, not just for the Southern states, but also more widely in states where feed is grown. The expansion of Brazilian agriculture to address a growing global demand for meat and animal feed is directly responsible for land-use change (LUC) in the Central-West region of the country (Caro et al. 2018), where most of the feed ingredients are cultivated, especially soybean. In the Southern states, animal density is a major contributor to many environmental issues, from climate change and air pollution, to land, soil and water degradation, biodiversity loss, and to the depletion of water and natural resources (c.f., Arogo et al. 2003; Basset-Mens and van der Werf 2005; Reckmann et al. 2013; Cherubini et al. 2015; Monteiro et al. 2017).
The concepts of “Sustainability” and “Sustainable Development” have come to be seen as ideals to aim for by contemporary societies, states, and trans-national bodies (Mebratu 1998; Hahn and Kühnen 2013; Harlow et al. 2013). Going hand in hand with this aspiration is the need for useful systems of measurement. Life Cycle Sustainability Assessment has been presented as a tool that could be used to better understand sustainability (Valdivia et al. 2013). It has been defined as (Life Cycle Initiative 2022), “… the evaluation of all environmental, social and economic negative impacts and benefits in decision-making processes towards more sustainable products throughout their life cycle”. The three pillars of sustainability (environmental, economic, and social) were considered by Kloepffer (2008) formulated as:
where LCA is the Life Cycle Assessment (environmental aspect); LCC is the Life Cycle Costing; and SLCA is the Social Life Cycle Assessment. Of the techniques and methodologies required, LCA ISO14040 (ISO 2006a) and 14044 (ISO 2006b) is the most established, having been used for over three decades to evaluate products, processes, and services (Guinée et al. 2011). LCC has been around longer than LCA, but the lack of international standards has hindered its wider adoption. According to Hunkeler et al. (2008), an LCC is: “An assessment of all costs associated with the life cycle of a product that are directly covered by any one or more of the actors in the product life cycle (e.g., supplier, manufacturer, user or consumer, or end of life actor) with complementary inclusion of externalities that are anticipated to be internalized in the decision-relevant future”. The third element of Kloepffer’s formula, SLCA, evaluates social and socioeconomic impacts (UNEP/SETAC 2009). International standardization of measurement and assessment is still in development (e.g. UNEP/SETAC 2009). The concept of the LCSA accords with the typical modelling structures of LCAs, LCC, and SLCA, following the life cycle of a product (process or service), with structures as envisaged by ISO 14040—Goal and Scope, Life Cycle Inventory, Life Cycle Impact, and Interpretation (ISO 2006a). One of the advantages of LCSA is that all three aspects are integrated using the same system boundary, functional unit and assumptions, instead of approaching them separately (Valdivia et al. 2013). Implementation is not always easy as in practice LCSA requires the integration of a variety of different methods, tools, and disciplines, involving the different dimensions of sustainability, but with no formally agreed system of frameworks, methodologies, and tools (Finkbeiner et al. 2010; Keller et al. 2015; Neugebauer et al. 2015; Guinée 2016; Onat et al. 2017; Chen and Holden 2018). To solve these issues, Neugebauer et al. (2015) developed an indicator hierarchy, proposing the idea of a stepped implementation concept using a tiered approach. This was subsequently adopted by Chen and Holden (2018) who applied an LCSA framework to an Irish Dairy Farm, where they integrated the three sustainability dimensions (environmental, economic, and social) into a sustainability score. The present study used the tiered LCSA framework of Chen and Holden (2018) to align the data requirements by temporal and spatial scale, decrease uncertainty, and ultimately enhance the value of the sustainability indicators.
The widespread intensification of pig production in Brazil in recent decades has led to substantial impacts in regions where pig farms are located. The environmental, social, and economic consequences need to be identified and evaluated in an integrated way for greater awareness of sustainability performance of the agricultural activities fundamental for safeguarding the long-term survival of family farms and rural communities. There have only been a small number of studies that have evaluated (separately) the environmental, social, and economic aspects of pig production at the farm level. Unfortunately, a life cycle approach has been used in only a few environmental LCA case studies in Brazil. A systematic approach to understanding pork sustainability issues is needed to allow actors to make intelligent decisions about sustainable production. The objective of this research was to quantify and understand the impacts of family farm intensive pig production at farm level, using a case study from the west of Santa Catarina State, to identify adverse issues (hotspots) of pig production by integrating the assessment of the environmental, economic, and social aspects of the system. The quantitative and qualitative indicators calculated can guide and support the decision-making processes for a variety of stakeholders and actors by providing insights from a typical farm in the region.
2 Methods
The structure of the LCSA was based on Neugebauer et al. (2015) using a Tier II approach (Chen and Holden 2018), i.e. using selected indicators for each dimension. The pragmatic decision to limit the number of indicators was taken during the study because of the data scarcity typical of developing countries, (Ndzabandzaba 2015). The environmental indicators calculated were climate change, acidification and eutrophication impacts using midpoint CML 2001 methods (Guinée et al. 2002). The economic indicators were production cost, profitability and labour productivity calculated using methods of Hunkeler et al. (2008). The social aspect focused on two stakeholders: the farmer (the owner and manager of the farm), and the employee (the hired worker). The social indicators were fair wage (for labour), farm income, working hours per year, education level and age structure calculated using the methods of the guidelines for social LCA (UNEP/SETAC 2009) and Chen and Holden (2017).
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To integrate the environmental, economic, and social aspects of the model, the method presented by Chen and Holden (2018) was used for the following:
(a)
Indicator calculation, for the social qualitative indicators
(b)
Normalization factors and benchmarking, which compared indicators to selected reference values
(c)
Weighing the three dimensions, which in this case gave all indicators equal weight
(d)
Integrating environmental, economic, and social aspects into a graphical representation, using a Life Cycle Sustainability Triangle (LCST), giving each aspect equal weight
For qualitative social indicators, that provide detail of the indicators using words, a scoring system was applied before the normalization step. The scoring system was based on the indicator subcategory, which describes the indicator performance from the worst scenario to the best one, attributing scores from 0 (worst) to 100 (best). The distribution of the scores assumed a reference value at the midpoint (50 points) (see Supplementary Materials). The normalization step was applied to all environmental, social, and economic indicators using a reference value (benchmark) using either Eqs. 1a (more is better) or 1b (less is better):
where: Ua is the normalized indicator value; Vb is the benchmark value; and Vc is the calculated value for the indicator (Chen and Holden 2018). More is better was applied to fair wage, farm income, education level, profitability, and labour productivity. Less is better was applied to working hours, age range, climate change, acidification and eutrophication. The results of the normalization steps were multiplied by 100 to range from 0 to 100.
The Life Cycle Sustainability Assessment (LCSA) was conducted, following the structure of ISO 14040 (2006a): (a) Goal and scope; (b) Life Cycle Inventory; (c) Life Cycle Impact Assessment; and (d) Life Cycle Interpretation. The ELCA was modelled using Gabi software (sphera.com) including all inputs, outputs, and processes from crop production (cradle) to finished pig at the farm gate. The economic and social aspects only considered the finishing farm, to better understand the role played by family farmers in the pig production chain, and in the sustainability of this specific activity. Details of the goal (summarized in the introduction above) and scope (briefly described below) are presented in the Supplementary Information.
The case study family farm was part of a vertical integrated production system, for which the integrating company provides feed, genetics, medicine, logistics, piglets and agronomic services. The farm’s function is to produce live pigs for slaughter to enter the food chain. The pig production facilities on the farm have a capacity to raise up to 6420 animals per year, from growing to finishing stages (20 kg to 130 kg body weight, respectively). The case study far is characterized as an intensive growing to finishing pig system, where the animals are always kept indoors for around 110 days. The animals are fed with concentrated feed, which is produced outside the farm. The buildings are equipped with automatic pig feeders and water supply facilities. The herd is raised in groups, in concrete buildings, open on the sides, which allow natural ventilation. No bedding is used, and the pigs are housed on a 5% sloped solid concrete floor, so liquid slurry (manure) flows to a drainage system and into storage tanks. Manure is stored in open tanks for 120 days, before spreading on the surrounding land for crop nutrition. According to Cherubini et al. (2015) “The most common manure management system (MMS), which is used in 80% of integrated farms, is the storage of manure in open slurry tanks without a natural crust cover”.
The system boundary (Fig. 1) includes all the major inputs and outputs required to produce pig liveweight, so the functional unit (FU) was taken as 1-kg liveweight of pig at the farm gate produced in 2015, ready to be transported to the abattoir. The whole system includes crop production (maize and soybeans), grain drying and cleaning processes, feed production at the feed factory, growing to finished pig, and manure management and spreading on surrounding land. The piglet production, transport and energy used in all processes were also included into the system. For the economic and social assessments, the boundary was restricted to the growing to finishing pig unit and manure management, including land spreading (also shown in Fig. 1).
Fig. 1
Flow diagram of the growing to finishing pig production system, which includes the main processes of the system. The environmental system boundary is represented by a red line, and the blue line represents the economic/social system boundary
×
The inventory data are presented in the Supplementary Information. For the environmental Life Cycle Inventory (LCI), input and output data of the growing to finishing pigs were collected from farm records and integrating company records. Background data were collected from Brazilian statistical databases (IBGE Census), LCA databases (Ecoinvent, Gabi database), the Brazilian National Swine and Poultry Research Centre (EMBRAPA) and from the Santa Catarina State Environmental Agency (IMA). These sources were used as data sources for feed manufacturing, and breeding and rearing piglets. Specifically, secondary data for crop production (maize and soybean) were taken from peer-reviewed journal papers, considering geographical, temporal, and technical relevance. According to the integrating company, in 2014/2015, maize and soybean were purchased from the Brazilian state of Parana to produce concentrated feed. Soybean production data were taken from Prudêncio da Silva et al. (2010), because this study considered grains from the South of Brazil. For maize production, data were gathered from Prudêncio et al. (2014), who performed an LCA study of broiler chicken production in the south of Brazil, using maize cultivated in this region.
Data for the economic and social aspects of the growing to finishing pig production unit were collected from the farm, detailed in the Supplementary Information. All currency values were converted from Brazilian Reais, (R$) to US Dollars ($) based on the 2015 exchange rate of USS $1 = R$3.33. Water costs were not included in the production cost, since the water used for this specific pig production facility (pig drinking water and cleaning water) was sourced from a spring at no cost.
Environmental, social and economic reference values (Vb, Eqs. 1a and b) for each indicator were gathered from regional and national statistics, and journal papers. Three studies published between 2015 and 2018 (Cherubini et al. 2015; Andretta et al. 2018; Monteiro et al. 2017) were used to define environmental indicator reference values, since they all were based on CML method. Due to database scarcity, the economic reference values were estimated, using: (a) data from Agricultural Census 2017 (IBGE 2018), which provided the average price paid by Integrating Companies for a live finished swine, as US$ 6.91in the Western region of Santa Catarina; (b) total production cost to produce 1 kg of liveweight in 2015 (Embrapa 2018), which was US$0.973/kg, considering feed intake, drugs, labour, etc.; (c) cost-share with the integrating company and the finishing pig farm as 95.28% and 4.72%, based on Talamini et al. (2006); (d) labour productivity and mortality rates obtained from Embrapa (2015) referring to 2015 average values for finishing pig farming in the state of Santa Catarina. The social reference value indicators were obtained from the Brazilian Institute of Geography and Statistics (IBGE). Since data from the 2017 Agricultural Census was used, it was possible to collect the average values for farm income, age structure, and level of education of the head of family farms from the western part of Santa Catarina specifically. Fair wage of the employee was based on data from the Brazilian Association of Swine Breeders (ABCS 2018).
To facilitate interpretation, sensitivity analysis was used to assess the robustness of results and establish the sensitivity of the model to uncertain factors in the LCI and LCIA. The analysis was focused on input parameters related to assumptions, seasonal variation and the variability reported in secondary data sources.
3 Results
3.1 Environmental impacts of the case study farm
The climate change impact of the growing to finishing pig production facility (Fig. 2a) was estimated to be 2.22 kg CO2-eq. per kg of liveweight (kg-LW). Feed production was the major contributor (1.23 kg CO2-eq. per kg-LW) representing 68.5% of GHG emissions, especially relating to nitrogen losses (N2O) during crop production. Manure management was the second largest source of GHG emissions (26.6%) due to the release of methane (CH4) during storage. Emissions from fattening the pigs (from piglet to farm gate) represented 4.8% of the total GHG emissions. Avoided mineral fertilizer accounted for − 0.12 kg CO2-eq. per kg-LW.
Fig. 2
The hotspots of a climate change, b acidification, and c eutrophication impact during pig production system per kg-LW
×
The total acidification impact (Fig. 2b) was estimated at 53.56 g SO2 eq. per kg-LW. The major contributor was crop production, responsible for 67.6% of total acidification (36.21 g SO2 eq. per kg-LW), due to sulphur dioxide (SO2) and ammonia emissions from chemical fertilizer production and utilization on land. The growing to finishing stage of the pig production accounted for 11.99 g SO2 eq. per kg-LW, accounting for 22.38% of the total emissions. From this total, ammonia loss to the atmosphere from the pig building accounted for 6.93 g SO2 eq. per kg-LW and ammonia from manure storage and spreading contributed 9.44% of acidification (5.06 g SO2 eq per kg-LW). Avoided fertilizer accounted for − 6.2 g SO2 eq. per kg-LW.
The eutrophication impact (Fig. 2c) was estimated at 24.20 g PO43− eq. per kg-LW. Phosphorus (P) and Nitrogen (N) losses during grain production (including inorganic fertilizer production) were the main sources of eutrophication (71%). Manure management, mostly land spreading, was responsible for 17% of the total impact (4.02 g PO43− eq. per kg-LW), with avoided fertilizer accounting for − 7.59 g PO43− eq. per kg-LW. Ammonia, from pig housing, was also a source of eutrophication, contributing 9%, or 2.29 g PO43− eq. per kg-LW.
The sensitivity analyses (Table 1) show the influence that key parameters had on the environmental impact of the case study farm. Slightly more than doubling the transport distance of maize, the heaviest cargo, from the cleaning/drying facility to the feed factory, led to an increase in GHG emissions from fuel consumption from 0.08 kg CO2 eq. to 0.11 kg CO2 eq. per kg-LW, a 1.35% increase system wide. The model was not sensitive to energy (i.e. using wood fuel and renewable electricity) because energy consumption only caused 2.4% of the total climate change, 0.97% of the total acidification, and 0.22% of the total eutrophication impacts. Uncertainty due to energy costs/availability are unlikely to significantly affect the overall environmental results. The model was sensitive to how impact was calculated for manure management. The choice of equation using Hutchings et al. (2013) or Rigolot et al. (2010) caused a 1.35% difference in climate impacts, a 0.09% difference in acidification impacts and a 0.54% difference in eutrophication impacts. The model was most sensitive to the assumed source of soybean. Using the data provided by Prudêncio da Silva et al. (2010) to produce 1 kg of soybean in Mato Grosso state caused a 12.61% increase in climate impacts, a 3.29% increase in acidification and a 5.41% increase in eutrophication compared to sourcing from the adjacent state of Parana. This can be explained by the deforestation in Mato Grosso associated with soybean production. Although producing feed in Mato Grosso increases environmental burdens, the production cost in this state is 30% lower than in Santa Catarina (Embrapa 2015), thus influencing demand by integrating companies.
Table 1
Results of the LCA sensitivity analysis
Description
Parameter
Variation
GWP
Acidification
Eutrophication
kg CO2 eq.
g SO2 eq.
g PO4−3 eq.
Case study farm base scenario results
2.22
53.56
24.20
Transport distance—maize production to feed factory (assumption)
Soybeans produced in South of Brazil, no deforestation
Soybeans produced in Center West of Brazil, deforestation
2.5
55.32
25.51
%
12.61(+)
3.29(+)
5.41(+)
aTAN_h = TAN_0 + MR_h*ON_0, where TANh = Available TAN in housing (h) (k gm−3); TANO = TAN excreted (kg m−3); MRh. Mineralized rate (kg kg−1), is the proportion of the organic N entering the house (h) that is mineralized; ONO. N-organic excreted (kg m−3). And NH3_h = TAN_h*e_1, where NH3_h = NH3 emissions in house (h) (kg NH3-N); e_1 Emission factor for NH3 emitted in housing (kg kg−1) in Hutchings et al. (2013)
The family farm received US$ 39,742 in 2015 from the integrating company for growing to finishing 6308 pigs. Table 2 details the total production cost for the business and the corresponding profit. The production cost included: labour costs, slurry costs (cost associated with renting land and slurry land spreading), electricity costs, funeral costs, finance costs, investment depreciation and maintenance costs. Labour costs included wages, payroll taxes and social security costs for one employee. However, the farmer labour does not receive the labour benefits.
Table 2
Results of the Life Cycle Cost (LCC) of the growing to finishing pig production unit in 2015
Growing to finishing pig production costs 2015
Revenue
US$
39,742.3
100
%
Direct cost
Labour farmer
US$
4,924.05
12.4
%
Labour costs
US$
9,774.21
24.6
%
Slurry cost
US$
2,743.94
6.9
%
Electricity and energy cost
US$
1,718.23
4.3
%
Funeral
US$
993.55
2.5
%
Finance cost
US$
6,607.14
16.6
%
Overhead cost
Investment depreciation
US$
3,237.17
8.1
%
Maintenance
US$
1,077.21
2.7
%
Production cost
US$
31,075.50
78.2
%
Profit unit
US$
8,666.80
21.8
%
The integrating company makes payments every 4 months, based on quality and quantities of the product in terms of liveweight of pig for slaughtering. Thus, the value of a finishing pig herd depends on the performance of the pig group in terms of daily liveweight gain (g/day), feed conversion ratio and mortality rates of the animals. The average physical performance measures of the case study farm are presented in Table 3.
Table 3
Results of the physical performance of the growing to finishing pig production unit in 2015
Physical performance of the growing to finishing pig unit in 2015
Pigs sold (animals)
6,308
Mortality (%)
1.74
Daily liveweight gain (g/day)
978.73
Feed conversion ratio
2.40
Average liveweight at slaughter (kg)
129.65
The economic sensitivity analysis (Table 4) used the maximum and minimum values of mortality rates during 2015, in comparison with the average mortality of that period, which was the base scenario. Mortality percentage had a direct influence on the number of losses and profitability, since mortality rate is a productivity performance indicator, and affects the price paid by the integrating company. It also impacts production cost, due to the costs of disposal of dead animals. Mortality rate can cause > 50% change in economic outcome for the farm, thus the profitability is very sensitive to this parameter.
Table 4
Economic Sensitivity Analysis of the growing to finishing pig production unit, considering different mortality rates indexes in 2015
Parameter/impact category
Average mortality rate (2015)
Lowest mortality rate (2015)
Highest mortality rate (2015)
Mortality rate (%)
1.74
1.43
2.38
Production cost (US$/kg-LW)
0.0451
0.0447
0.0461
Profitability (US$/kg-LW)
0.0126
0.0196
0.0078
Labour productivity (seconds/kg-LW)
14.12
14.08
14.26
Number of losses (pigs/kg-LW)
0.00016
0.00013
0.00022
Profit unit (US$/year)
8,666.80
13,532.31
5,339.95
Variation (%)
0.00
+ 56.14
− 38.39
3.3 Social impact of the case study farm
The family farm comprises four people, a couple plus two children (age 14 and 21). Both children were studying (part time in secondary school and full time in university) and were not included in the labour estimates for the farm. Neither the farmer nor his wife had finished primary education, thus each had < 4 years of schooling. The children do not intend to take over the farm, and with their education can secure much higher income in cities or working for larger agricultural companies.
The farm income was US$ 1,133 per month from finishing pigs. Other agricultural activities developed on the farm by the wife, increased the farm income to US$ 1,532 (Table 5). The farmer worked 3,052 h in 2015, mainly spent cleaning the barns, checking the health of the animals, and dealing with manure management. Although pig manure is considered an organic fertilizer, because there are so many animal producers in this region, manure is treated as waste as there is insufficient land for commercial land spreading. To overcome this issue, the farmer rents some additional land to spread the manure and cultivate maize and other silage crops.
Table 5
Results of the social Life Cycle Assessment (SLCA) of the growing to finishing pig production unit in 2015
Stakeholders categories
Impact categories
Result
Farm worker
Fair wage
US$ 591.25/month and US$ 223.30/month labour benefits
Farmer
Farm income
US$ 1,532/month including growing to finishing pig production and other farm products
Working hours year
3,052 h per year of working on-farm
Education level
Primary school initiated
Age range
50 years old in 2015
The farmer shared the activities of growing to finishing pigs with one employee with a monthly wage of US$ 591.25, plus US$ 223.3 of labour benefits (Table 5) for a shift of 44 h weekly. The Brazilian minimum wage in 2015 was US$ 236.63 per month plus benefits. According to 2010 Brazilian Census, in the western region of Santa Catarina 7% of the population lives on half the minimum wage per month, 21% of the population have one minimum wage, and the vast majority, 42%, receive between one and two minimum wages per month. In this sense, the family farm business was making a positive social contribution.
4 Discussion
Implementing the tiered LCSA approach developed by Chen and Holden (2018) for a Brazilian pig production system was not straightforward. The main limitation was data scarcity, either because the data were never collected or due to an unwillingness to share. A good example of this was the social indicator “Health and Safety” of the worker and farmer, which could not be considered due to lack of both farm records and national or regional statistics. Available activity data were compared with the detail specified for each of the tiers proposed by Chen and Holden (2018), to determine that tier II was the most appropriate for this case study. This procedure enabled the study to be consistent with the resources available at national and regional level, as well as with the selections of indicators that could be used. Details of the assumptions and limitations are presented in the Supplementary Information, most pertinently only one publication was found that presented both the production cost and profitability for growing to finishing pig stage in Santa Catarina. However, the Brazilian Census of Agriculture 2017, which was used for most of social data and to obtain the price paid by the integrator to the pig farmer, is comprehensive and high quality. It presents the national and regional census on land practices, statistics on farm structure, farmer demographics, including the characterization of family farms in relation to the total population of farms.
4.1 Comparisons with previous research and reference values
4.1.1 Environmental impacts
Only four Brazilian pig production ELCA studies have been published to date, summarized in Table 6 for comparison with this study. The different functional units, system boundaries, emissions factors, and assumptions make direct comparison difficult. The aspects in common among these studies are the geographical location of the systems, which is Santa Catarina, and the three impact categories were analysed: climate change, acidification, and eutrophication, estimated using CML methodology. The environmental impacts reported in these studies, from 2013 to 2018, are of the same order of magnitude as climate impact, ranging from 1.8 to 2.6 kg CO2 eq., acidification impact from 32 to 56 g SO2 eq., and eutrophication impact from 13 to 25.7 g PO4-3 eq.
Table 6
Environmental impacts results of previous pig LCA studies and the case study farm
1 kg of body weight gain over fattening period (30–115 kg)
2.3–2.5 kg
55–62 g
16–19 g
Case study farm
1 kg of liveweight at the farm gate
2.20 kg
53.6 g
24.3 g
Notwithstanding the challenge of post hoc comparison due to differences of modelling approach, some statements can be made. The production of feedstuff is the largest contributor to environmental burdens in all the pig production systems analysed, followed by manure management. The origin of the soybean used explains some of the difference in the environmental impacts between Cherubini et al. (2015), where 98% of the soybeans came from the Brazilian state of Mato Grosso, while in this study the soybean was produced in the adjacent state of Parana. Mato Grosso state is by far the largest soy producer with yields of 3663 kg/ha in 2022 (Conab 2022), but this output is associated with high levels of deforestation. Between 2015 and the end of 2022, an area of tropical forest equivalent to the size of Germany, The Netherlands and half of Belgium combined was lost in this state (Wagner et al. 2022). Therefore, the sensitivity analysis showed that changing soybean supply to Mato Grosso would increase the climate impact for the case study farm by about 13%, and the other impacts by 3% (acidification) and 5% (eutrophication). The system reported by Spies (2003) assumed that the feed maize was produced locally, so the climate impact reported by these authors is much lower than the results of Cherubini et al. (2015). If it is assumed that all the soybean for the pig feed came from the Central West of Brazil, the results of the sensitivity analysis show that, when compared with the environmental impacts estimated by Cherubini et al. (2015), the performance of the case study farm is equal or better (Fig. 3). While the farmer is doing well environmentally, there is scope for improvement that will not be realized while social and economic constraints remain.
Fig. 3
Environmental performance of the growing to finishing pig production facility, using data from the sensitivity analysis, and Cherubini et al. (2015) as a reference value
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4.1.2 Economic impacts
Lack of data was a major limitation for analysing the economic dimension of the case study farm. Only one publication (Talamini et al. 2006) presented both the production cost and profitability for growing to finishing pig stage in Santa Catarina (Table 7). Labour cost was the main contributor of the total production cost, which represented 45% of cost reported by Talamini et al., and 37% for the case study farm. The technology difference between 2006 and 2015 for raising pigs (e.g. automatic pig feeders and water supply facilities) could explain the higher labour cost in the earlier study. The discrepancy in mortality rates and profitability is very marked, probably reflecting the change in the management of the value chain in the intervening decade.
Table 7
Financial estimation of the growing to finishing pig farm in Santa Catarina in 2006 and the case study farm in 2015 (US$)
It is also possible to compare the case study farm with economic data reported in the Agricultural Census (IBGE 2018) and with prices reported by InterPig, which compares the costs of pig production in 14 European countries, Brazil and the USA (Embrapa 2015) (Table 8). The comparison shows the production cost of 1 kg-LW pig.
Table 8
The annual average cost to produce 1 kg of lightweight pig in 2015 for some countries of the EU, the USA, Santa Catarina state (SC), and Mato Grosso state (MT) in Brazil
The production cost calculated for the growing to finishing pig unit was US$ 0.0451 per kg-LW, while profitability was US$ 0.0126 /kg-LW. Labour productivity was calculated at 20 s/kg-LW. Compared to the reference values (Table 9) the case study farm had almost 2.5% lower Production Cost but received around 10% less for a live finishing pig. The case study farm was 24% less profitable than the reference value despite having better performance in terms of mortality rate and labour productivity. This underpayment could be explained by the fact that the farmer sells the pigs to an integrating company, which than sells to the slaughtering stage and then to the food processing stage. Historically, independent producers achieved greater profit (Rocha et al. 2007; Gollo et al. 2013; Engelage et al. 2015). Furthermore, pig farming systems in Brazil are cyclical, meaning supply and cost of maize and soybean and the price paid for liveweight vary throughout the year and also from year to year. Therefore, integrated system pig producers have a more stable financial situation, but with reduced possibility of high returns. Meanwhile, independent producers face economic losses in downturns, but large rewards during periods of high activity. According to Rocha et al. (2007) this can be explained by the difference in the structure of the production chains. The independent pig farming relationship is typical of open market structures while the integrated system means the integrating company takes the risk but reaps the reward.
Table 9
Economic Impact Indicators and Productivity Parameters of the Case Study Farm and the Reference Values
Impact indicator and parametres
Case study farm
Reference value
Source reference value
Price paid by integrated company per finishing pig in the region (US$)
When comparing the physical performance of finishing pigs around the world with the case study farm (Table 10), the case study farm stands out in terms of its efficiency in raising the animals, nationally and internationally. This case study has the lower finishing mortality of 1.7%. The range of mortality rates for growing to finishing pig herds in the literature ranged from 1.73% (Stoffel and Rambo 2022) to 3.5% (Talamini et al. 2006), and the most prevalent causes of death are pneumonia (33%) and gastric ulcers (15.4%), according to Piva et al. (2020).In addition to the low mortality rates, the farm’s records show a high daily liveweight gain of 978 g/day, resulting from an average feed conversion ratio of 2.4. Unfortunately, for the case study farm, the outstanding husbanding does not result in monetary rewards.
Table 10
Physical performance of finishing pigs in some countries of the EU, the USA, Santa Catarina State (SC), and Mato Grosso State (MT) in Brazil
The Social Life Cycle Impact Assessment for the growing to finishing pig production unit (Table 11) revealed that the employee wage was superior to most agricultural workers in the Western region of Santa Catarina and the average wage of a worker in the pork sector. The farmer was working 14 h per week more than the official working week of 44 h. This was necessary to maintain high productivity.
Table 11
Social impact indicators of the case study farm and reference values
Impact indicator
Case study farm
Reference values and sources
Fai wage—US$/month
591.25
565.17
Associação Brasileira dos Criadores De Suinos – ABCS (2018)
In terms of the farm income, although raising pigs is the major source for the family, diversification of the business is important for three reasons: (i) the farm is not totally dependent on the relationship with the integrating company and/or the overall performance of the pig sector; (ii) the farmer only receives payment from the integrator every 4 months, so having other sources of income allows better cash flow management; and (iii) other activities allow the farmer and family to increase their total income. However, the extra work comes with downsides for health, for safety through the increased likelihood of accidents, and for quality of life. As shown in Fig. 4, the farm income was slightly lower than the reference value even when the income from pig finishing and of other agricultural activities are combined.
Fig. 4
Social performance of the growing to finishing pig farm in comparison with the reference values
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To place the educational level of the farmer, i.e. the head of the family farm, in context, it is important to understand the current social-educational profile of Brazil, especially in rural areas. According to data from the Brazilian Institute of Geography and Statistics 2017 Agricultural Census (IBGE 2018), there is a population of over 15 million agricultural workers. Of these, 15.4% are illiterate, 14.1% have < 1 year of schooling, and 23.7% have 1 to 4 years of schooling. More than half of the rural population did not finish primary education and < 1% have a graduate qualification. The farmer’s education level is typical, but low. According to the Education for All Global Monitoring Report 2008 (The United Nations Children’s Fund 2008), an adult person who has less than 4 years of schooling is typically considered functionally illiterate, with low income and poor access to employment. Moreover, lower levels of education are associated with low likelihood of adopting new technologies, benefiting from agricultural policies, integrating into wider markets, and belonging to a support organization such as a union or cooperative (Medina et al. 2015). While the farmer in the case study is not that old (50) compared to the regional age distribution (60 years old), he is operating in the context of an exodus of young adults to the cities, fleeing from poverty. His children have a higher level of education so are looking for better opportunities than the long work hours and poor return offered by the farm.
5 Integrating environmental, economic, and social impact
Pig production systems are complex, involving many different processes, from fertilizers and pesticides production, crop cultivation, transportation to and from farms, feed manufacturing, pig breeding and fattening, to the final meat production. Therefore, the LCSA of pig production is also complex. Looking at the graphical representation of the environmental, economic, and social indicator categories (Fig. 5) it is possible to see how the different impacts compare. The environmental performance is slightly lower than the reference values, indicating that the case study farm has higher impact than is typical for the industry. The economic impacts provide a mixed message. The efforts of the farmer and worker to maintain high productivity and minimize animal mortality are not reflected in farm income (presented here as a social impact, but it can also be regarded as economic) and profit. The profit could be increased if the employee were paid less, but this would have an unacceptable social consequence. The social impacts tend to be unfavourable compared to those typical for the industry.
Fig. 5
The three-dimension impact indicators of the growing to finishing pig production unit represented in a Pie Radar Graphic
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An integrated picture is gained from a Life Cycle Sustainability Triangle, where each aspect has equal weighting, showing that the three dimensions of sustainability are in balance (Fig. 6). While the economic aspects perform a little better than reference values, this is not reflected by farm income. The workload required to keep mortality rates low and labour productivity high is not reflected in farmer wellbeing and is not reflected in better sustainability performance than the average of the region. Working hours had a negative social impact and farm income was significantly lower than similar farms in the region. An improvement in the environmental aspects of pig production would improve the life quality of the family and workers, e.g. health, quality of water resources, among many others, and it would also benefit the local rural community. For the farmer to be able to increase the environmental performance of his production, adoption of technological solutions would be necessary (e.g. manure treatment), which, in most cases, would involve capital investments and greater educational attainment. The farmer’s education is typical of the region, and it can be posited that if the farmer had more education, there would be capacity to adopt technology and processes that could reduce environmental impact and increase income. However, excessively long working hours (+ 30%) and small profits (21%) make it unlikely that the farmer will be able to reduce these impacts further. The scenario of overwork and low financial return is not attractive to the farmer’s children. According to Fischer et al. (2016), only 25.3% of the rural properties in the west of Santa Catarina had an inheritor who intended to continue the family business, even though most of the farmers were willing to continue in agriculture. The interrelationships and linkages between the three dimensions of the LCSA, the degree of synergies, and the right balance between these three elements is certainly the biggest challenge when evaluating sustainability.
Fig. 6
Life Cycle Sustainability Triangle (LCST) of the growing to finishing pig production unit
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The pig chain represents a major value-added activity in the economy of the west of Santa Catarina, and it is a major contributor to the overall national economy (Embrapa 2018). It generates income/revenue for a multitude of actors and activities from grain producers, livestock farming, feed factories and the pharmaceutical industry, to transport services, slaughtering and the food processing industries, to the distribution segment right up to the final consumer. The increased yields and efficiency and lower costs (ABCS 2018) have resulted in pork becoming widely available and cheaper to buy. However, this development model has had downsides, especially in relation to the social and environmental impacts of this production system on local family farms and rural communities.
While this study investigated only one family farm, 90% of the agricultural land in Santa Catarina is held by similar small family farms of 50 ha or less. The case study farm is typical (Feuz and Skold 1992) of the many family farms that raise animals in this region. For the region of Western Santa Catarina, taking 118 municipalities located in the area from the Agricultural Census 2017 (IBGE 2018) the case study farm, clearly represents the typology of the family farm business: male leadership > 45 years of age, < 4 years of formal education, overworked, and problems of succession. The results reflect a general understanding of the occupational hazards of pig production (Hafer et al. 1996; Hurley et al. 2000; Hamscher et al. 2003, Chmielowiec-Korzeniowska et al. 2017), but for Brazil, there are few studies (e.g. Costa et al. 2007) and no national or regional statistics. The eutrophication and acidification impact of the case study farm reflect the excessive release of nitrogen and phosphorus reported by Broetto et al. (2014) and the poor water quality reported by the Agricultural Research and Rural Extension Company of Santa Catarina (Baldissera and Borsatto 2004).
Finally, it should be noted that the baseline used for sustainability assessment of the case study farm uses the typical values for the region. If the case study farm were compared to best practice, which was not possible due to lack of data, the message would be far less optimistic.
6 Conclusions
The objective of this work was to quantify and understand the implications of environmental, economic, and social impacts of family farm intensive pig production in Santa Catarina State, Brazil. It was concluded that:
The production of feedstuff is the largest contributor to environmental impacts (climate change, acidification, and eutrophication). The farmer’s control over feed choice is limited because of the control of the large meat and integrator companies over the production system.
Manure management and the ammonia emissions from the pig buildings are important drivers of acidification impact. The farmer can influence these impacts, but given limited farm income and education, might not have the capacity to adapt the production system.
Profitability was poor despite production cost and labour productivity being better than typically achieved in the region. The farmer has little control over this because there are only a small number of large meat companies that dictate prices.
The high productivity was only possible because of very long working hours required to maintain low animal mortality. The farmer is not making sufficient income to hire more labour and does not have the capacity (due to available capital, time, and education) to use technology to address the issue.
The combination of overwork, limited economic return and low level of education has a direct negative impact on the farmer’s capacity to adopt new technologies to improve environmental, economic and social outcomes.
The interaction of social factors, driving economic factors, reveals that it is unlikely that the farmer can improve the sustainability of pig production. The LCSA revealed that addressing this issue would most likely require structural change in the pork industry in the region.
Declarations
Conflict of interest
The authors declare no competing interests.
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