Productivity and economic value added drive swine production sustainability

Abstract

Context

Swine production plays a vital role in economic development; however, balancing productivity with environmental and financial sustainability remains a challenge.

Objective

This study introduces an integrated approach combining Life Cycle Assessment (LCA) and Economic Value Added (EVA) to assess eco-efficiency in Brazilian piglet production, a stage often underrepresented in sustainability research.

Methods

A Life Cycle Assessment was conducted to quantify environmental impacts, and EVA was used to evaluate financial performance. The analysis relied on data from 2016 to 2021, including greenhouse gas emissions, land use, and economic returns.

Results and conclusions

Results indicate that improvements in sow productivity led to lower GHG emissions per kilogram of piglet and that eco-efficiency varied with market conditions. In 2021, increased productivity and favorable prices resulted in positive EVA and a 7.8% land use reduction.

Significance

This study underscores the value of integrating environmental and economic assessments to support decision-making in swine systems. By applying the LCA–EVA framework to piglet production, this research provides insights to enhance the sustainability of pork value chains.

Introduction

Swine production plays a pivotal role in global food security and economic development. Brazil is among the world’s leading producers and exporters of pork, ranking fourth globally [2]. As the demand for more sustainable livestock systems grows, evaluating both the environmental and economic performance of pig production has become increasingly important. Life Cycle Assessment (LCA) is a widely recognized methodology for quantifying the environmental impacts of agricultural systems, including swine production [26, 51, 61]. Similarly, eco-efficiency analysis, defined as the integration of environmental and economic performance metrics, has emerged as a strategic tool for identifying trade-offs and synergies in sustainability [16].

Despite the growing relevance of swine production to the Brazilian economy, few studies have assessed the eco-efficiency of piglet production, a critical stage that significantly influences the sustainability of the entire supply chain. Although LCA and eco-efficiency approaches have been applied globally to swine production, research focused specifically on piglet production in Brazil remains scarce [30, 44].

This study addresses this gap by evaluating the eco-efficiency of weaned piglet production systems in Brazil through an integrated LCA–EVA (Economic Value Added) approach. As a foundational stage in the pork supply chain, piglet production directly affects both the environmental footprint and economic performance of subsequent stages. While previous studies have assessed environmental impacts across various production phases [30, 44], few have explored how these environmental indicators correlate with financial outcomes [54]. The integration of LCA and EVA enables a more comprehensive assessment of sustainability by linking emissions, land use, and resource consumption to value creation within piglet production systems.

Numerous studies have employed LCA to quantify greenhouse gas (GHG) emissions, land use, and resource efficiency in swine farming systems worldwide [26, 39, 45, 51]. In addition, research by Cherubini et al. [17] and Savian et al. [57] has offered insight into mitigation strategies in livestock production, while Monteiro et al. [42] examined the environmental footprint of swine systems. Moreover, studies by van der Meramo et al. [40] emphasize the importance of integrating LCA with economic performance metrics, highlighting the relevance of eco-efficiency assessments. Nevertheless, the joint application of environmental and financial indicators remains underexplored, particularly in Latin America. While LCA offers a structured approach to measuring environmental impacts, EVA complements this by quantifying economic viability [55], and their integration provides a holistic view of sustainability performance [59].

The central research question of this study is: How eco-efficient are weaned piglet production systems in Brazil when evaluated through an integrated LCA–EVA framework? To answer this, the study defines two specific objectives: (i) to quantify the environmental impacts of piglet production, focusing on feed, manure management, and land use, through LCA; and (ii) to assess the economic performance of these systems using the EVA metric. By jointly analyzing environmental and economic dimensions, the study seeks to determine the extent to which Brazilian piglet production systems can be considered eco-efficient. The alignment between these objectives and the research question ensures a coherent and comprehensive approach to understanding sustainability trade-offs and synergies in the sector.

In summary, this study proposes an innovative framework that integrates LCA and EVA to evaluate eco-efficiency at the piglet production stage, an area largely overlooked in the current literature. While previous research has examined LCA in swine production [17, 42], studies incorporating financial metrics remain limited, particularly in Latin America [53]. By providing a focused assessment within the Brazilian context, this study fills a critical knowledge gap and introduces a decision-support framework grounded in sustainability metrics. Furthermore, the study offers policy-oriented insights to help bridge the gap between scientific research and practical application, fostering more sustainable and economically viable practices in Brazilian swine production.

Literature overview

Swine farming has experienced significant growth in recent decades, driven by high global demand for pork, according to Sanz-Fernández et al. [56]. The authors note that pork constitutes over 30% of global animal protein consumption. Maher et al. [37] emphasize that this high demand poses environmental and economic challenges for global swine farming. Achieving sustainability requires more than regulatory compliance,it demands technological innovations to enhance efficiency and ensure the sector’s security and resilience [61].

Achieving sustainability in swine farming requires integrating diverse environmental and economic analysis methods to provide reliable data for decision-making. The eco-efficiency approach, rooted in sustainability principles, balances environmental and economic outcomes to meet the growing demand for comprehensive assessments [67]. As a strategic tool, eco-efficiency employs thorough environmental and economic evaluations to promote sustainable practices [38].

Life Cycle Assessment is a key tool in eco-efficiency studies, providing quantitative indicators to measure environmental efficiency in production [26]. Economic Value Added measures the value generated by a product for the company, enabling precise analysis of the financial performance of swine production [20]. This tool addresses the needs of shareholders and managers by assessing business value generation, surpassing traditional financial metrics [21].

The modernization of swine farming seeks to integrate sustainability and productive efficiency through innovative practices that minimize environmental impacts while maintaining profitability [29]. Despite consumer demand for more sustainable products, swine production has adapted to meet evolving market expectations, balancing financial gains with environmental responsibility [37].

Swine production remains a key livestock sector, adapting to global transformations and sustainability demands. The pursuit of an efficient and environmentally responsible production model has driven the development of strategies increasingly aligned with sustainable production principles [43].

Data and methodology

The rationale for selecting LCA and EVA lies in their complementary strengths in evaluating sustainability within livestock systems. LCA provides a structured and standardized methodology for quantifying environmental impacts, such as greenhouse gas emissions and land use, across all stages of piglet production. Concurrently, EVA offers an economic perspective by assessing whether production systems generate returns above the cost of invested capital, considering market fluctuations and investment risk. By integrating these two approaches, the study captures both environmental performance and financial viability, enabling a comprehensive assessment of eco-efficiency. This combined methodology is consistent with prior research [55], which emphasizes the need for multidimensional sustainability assessment tools to guide producers and policymakers toward resource-efficient and economically viable farming systems.

Data sources

This study integrates both primary and secondary data sources to construct the LCA and EVA models. Primary data were gathered from a commercial piglet production farm in Brazil, selected for its representativeness of standard industry practices. These include records on feed consumption, manure management, energy use, and production outputs. All animal handling and data collection followed national and institutional guidelines on animal welfare, specifically those established by the Brazilian National Council for the Control of Animal Experimentation [19], which permits the use of observational data from commercial farms without requiring additional ethical approval. No experimental procedures were conducted beyond routine farm operations.

Secondary data were drawn from various national and international sources, including:

• The Brazilian Institute of Geography and Statistics (IBGE) for national swine production data.

• Environmental databases such as Ecoinvent v3.1 to support LCA parameters.

• The Agri-footprint database (v1.0) for detailed feed composition.

• Feed formulation datasets from Embrapa for assessing diet-related environmental impacts.

• Market reports and industry publications for EVA-related economic inputs.

Life cycle assessment framework

The environmental performance of piglet production was assessed using an attributional LCA, conducted in accordance with ISO 14040 and ISO 14044 standards. The functional unit was defined as one weaned piglet, ensuring comparability with previous studies [17, 57]. The system boundary encompassed all processes from feed production to the weaning phase, including:

• Feed production cultivation, processing, and transportation of feed ingredients;

• Piglet rearing housing infrastructure, manure management practices, and on-farm energy use;

• Emissions accounting quantification of methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂) emissions.

The LCA was performed using SimaPro software. The ReCiPe method was selected due to its harmonization capacity across midpoint and endpoint categories and its applicability to average conditions in animal production systems [35].

Economic value added calculation

The economic sustainability of piglet production was assessed using the EVA method, which evaluates profitability while incorporating environmental costs. The key components of the EVA calculation included:

• Revenue from piglet sales, based on prevailing market prices;

• Operating costs, including expenditures on feed, labor, energy, and veterinary services;

• Environmental externalities monetized market prices, which positively impacted Net Operating Profit.

Integrating economic and environmental indicators within an eco-efficiency framework necessitates evaluating financial outcomes through the EVA methodology [55]. Eco-efficiency requires a comprehensive assessment of life cycle processes that simultaneously consider environmental burdens and economic performance, as illustrated in Fig. 1. Accordingly, data collection in this study was structured into two distinct phases.

[See PDF for image]

Fig. 1

Economic Value Added and related indicators in weaned piglet production (Diagram illustrating the components used to calculate EVA, such as Return on Investment (ROI), Invested Capital, and Weighted Average Cost of Capital (WACC), within the context of swine production systems). *Land Use is a subcategory of Land use change category; CH₄, Methane; ROI, Return of investment; EVA, Economic value added; CAPM, Capital asset pricing model

The first phase involved conducting a Life Cycle Assessment to quantify environmental impacts. This analysis followed ISO 14040 and 14044 standards [27], ensuring methodological consistency with the defined goal and scope. The LCA aimed to assess greenhouse gas emissions, land use, and other environmental indicators based on the functional unit (FU) of 1 kg of weaned piglet live weight.

The second phase comprised the application of the EVA method for economic evaluation. This approach determines the financial value generated by production systems beyond the minimum required return for investors and stakeholders. EVA was calculated using data from financial reports, including Net Operating Profit After Taxes (NOPAT) and Invested Capital (IC), which encompassed both equity and debt. The Weighted Average Cost of Capital (WACC) was also calculated to determine the cost of invested capital and evaluate the system’s economic viability.

The integration of LCA and EVA results enabled the assessment of eco-efficiency, following the methodology proposed by the World Business Council for Sustainable Development [66]. This approach facilitated a comprehensive evaluation of the environmental and economic trade-offs inherent in weaned piglet production systems.

Scope and goal

The primary goal of this study is to conduct an eco-efficiency analysis of Brazilian piglet production systems by integrating environmental and economic perspectives. The scope of the analysis encompasses the entire production chain, with a focus on identifying the most significant environmental impact categories and key economic indicators relevant to weaned piglet production.

Although LCA enables a comprehensive evaluation of environmental impacts, the selection of impact categories was guided by their relevance to the swine sector and the availability of reliable data. The decision to concentrate on specific categories, such as greenhouse gas emissions, land use, and resource consumption, was based on their critical importance to the sustainability of piglet production and their frequent adoption in previous studies [17, 26, 51]. Moreover, including all possible impact categories would have substantially increased the complexity of the analysis without necessarily yielding proportional gains in understanding the main environmental challenges within the production chain [9].

To achieve its objectives, the study adopted a multi-phase approach:

1. Environmental assessment This phase estimated the potential environmental impacts associated with the production process. Global Warming Potential (GWP) and Land Use (LU) were selected as key impact categories, based on survey findings and the Swine LCA Guidelines developed by the Food and Agriculture Organization [27]. Additionally, primary productivity indices were established for weaned piglet farms (WPF), including metrics such as sow performance, feed conversion efficiency, and reproductive indicators.

2. Economic evaluation The second phase focused on economic analysis by identifying critical financial indicators. The EVA method was employed using financial data from piglet production systems, enabling a detailed assessment of profitability and value creation within the production chain.

3. Eco-efficiency assessment In the final phase, results from the LCA and EVA analyses were integrated using the methodology proposed by the World Business Council for Sustainable Development [66]. This framework evaluates the balance between economic performance and environmental impacts, providing a quantitative measure of sustainability.

Data from Brazilian piglet production systems were used to evaluate these key indicators and metrics. The findings contribute to a deeper understanding of how eco-efficiency can be improved through optimized resource use, reduced environmental impacts, and enhanced economic performance. The overarching objective is to provide actionable insights for decision-makers, promoting more sustainable practices in piglet production while addressing global challenges such as climate change and resource scarcity.

Study location

Brazil is the fourth-largest global producer and exporter of pork, with technologically advanced piglet production systems that commonly feature individual sow housing during gestation and lactation. For the purposes of this study, it is assumed that such technologies are uniformly adopted across Brazilian farms.

To conduct the analysis, a representative weaned piglet production unit was selected based on the availability of reliable zootechnical and financial data. This unit provided key reports, including the Exercise Results Demonstrative (ERD), Patrimonial Balance Sheet (PB), and Sales Reports (SR). The selected farm accounts for approximately 0.49% of the Brazilian sow population dedicated to weaned piglet production, which totaled 2.039 million sows in 2018 [25]. The farm’s partnership with a leading genetic improvement company also supported its selection, given the company’s involvement in global swine production, contributing to approximately 130 million pigs slaughtered annually [1].

The selected production unit demonstrates a high degree of technical and economic representativeness, making it a suitable model for extrapolation to the broader Brazilian context. Its adoption of standardized housing systems, modern genetic lines, and performance monitoring tools aligns with prevailing industry practices across the country’s main swine-producing regions. Additionally, the farm’s inclusion in national benchmarking systems, such as those managed by Agriness®, validates its alignment with average or above-average performance metrics within the sector. By integrating primary data from this farm with aggregated secondary data from comprehensive national databases, the study ensures both internal validity and broader applicability of its findings. This methodological approach enables the generation of insights that are not only grounded in real-world farm operations but also scalable to inform sustainability strategies across Brazil’s piglet production sector.

Life cycle assessment

Life Cycle Assessment studies involve evaluating all inputs and outputs of productive resources across each phase of the production cycle [27]. To ensure consistency and comparability among results, this study adopted a functional unit of one kilogram of weaned piglet live weight as the basis for standardizing the analysis.

The methodological framework required detailed data on natural resource extraction, raw material processing, and final production stages, with a specific focus on weaned piglet production. Partial datasets were sourced from Ecoinvent® (v3.1) and Agri-footprint® (v1.0). Information related to the animal feeding process was obtained from a model farm authorized by the Brazilian Ministry of Agriculture (MAPA), while animal-specific performance data were extracted from technical reports provided by commercial Brazilian piglet production farms.

To calculate life cycle impacts related to feed manufacturing, the SimaPro® software was employed. For animal-related impact assessment, the ReCiPe® method was selected due to its harmonization capabilities and its alignment with average conditions in livestock production systems. This method assesses 16 environmental impact categories, providing a robust basis for decision-making processes [35].

A comprehensive life cycle inventory was compiled, covering all relevant inputs and outputs associated with the production of one kilogram of weaned piglet. As illustrated in Fig. 2, SimaPro® was used to calculate CO₂-equivalent emissions and land use (expressed in square meters per kilogram of feed) for each animal category within the production system.

[See PDF for image]

Fig. 2

Diagram of the Weaned Piglet Production System in Brazil (The system covers the reproductive phases of pre-conception, gestation (115 days), and lactation, alongside key components such as feed input, manure management, housing, energy use, and GHG emissions (CH₄, N₂O, CO₂), within the Life Cycle Assessment boundaries). By authors, Pre-conception—Period in which the female (sow or gilt) is under management prior to insemination; Gestation—A 115-day period during which the female undergoes several stages of pregnancy; Lactation—The period in which the sow provides milk to her nursing piglets

Productive data

Primary data were obtained from Economic Index Reports for the years 2016, 2017, and 2018, and from Animal Production Index Reports covering the period from 2016 to 2021. In addition, feed production data for each animal category were essential components of the analysis.

The animal production indexes included a range of performance metrics, such as: parturition rate, reproductive loss rate, non-productive days, wean-to-estrus interval, total piglets born, piglets born alive, nursery piglet mortality rate, weaned piglets per sow per year, and parturitions per sow per year.

These data were derived from Brazilian sow production records spanning 2016 to 2021, with a particular emphasis on sow reproductive performance indicators (see Table 1). This comprehensive dataset supported both the environmental and economic components of the analysis, ensuring methodological robustness and temporal comparability.

Table 1. Productivity inventory for weaned piglets in brazilian piglet farms (2016–2021)

	2016	2017	2018	2019	2020	2021	M	SD
FBS	787.100	886.362	882.721	909.855	975.563	897.113	889.786	60.697
WEI	6.410	6.530	6.380	6.530	7.110	6.140	6.517	0.324
RL (%)	9.430	8.240	9.570	9.960	7.160	9.320	8.947	1.047
FR (%)	87.340	87.700	87.240	83.600	86.690	87.810	86.730	1.584
DG	114.930	115.350	115.060	114.180	115.280	115.170	114.995	0.427
SFY	2.350	2.290	2.350	2.360	2.330	2.350	2.338	0.026
TBP	13.760	14.090	14.210	12.790	13.670	14.990	13.918	0.724
TBPA	12.660	12.980	13.050	11.840	12.580	13.750	12.810	0.630
LW1	1.390	1.280	1.370	1.300	1.390	1.370	1.350	0.048
LD	25.620	30.370	25.340	24.110	26.890	25.200	26.255	2.204
MT (%)	8.370	7.430	8.690	11.700	8.060	9.240	8.915	1.493
WPS	11.550	11.950	11.870	10.460	11.500	12.480	11.635	0.674
WPSY	27.143	27.366	27.990	24.686	26.795	29.410	27.231	1.550
TWP2	21.364	24.256	24.707	22.460	26.140	26.384	24.219	1.993
LDWG	0.207	0.228	0.210	0.191	0.237	0.210	0.214	0.016
LW2	6.700	8.210	6.700	6.010	7.740	6.670	7.005	0.810
DNP	15.840	16.348	14.980	23.225	17.178	14.860	17.072	3.136

FBS, Female breeding stock; WEI”Wean to Estrus Interval; RL, Reproductive Loss; FR, Farrowing rate; DG, Days of Gestation; SF/Y, Sow Farrowing per Year; TBP, Total Born Piglet; TBPA, Total Born Piglet Alive; LW1, Live weight on first life day; LD, Lactation Duration; MT, Mortality rate during lactation phase; TWP, Total Weaned Piglet; WPS, Weaned piglet per sow; WPSY, weaned piglets/sow/year; LDWG, Lactation Daily Weight Gain; LW2, Live weight on last day of lactation period; NPD, non-productive days; M, mean; SD, standard deviation

For feed production, each processing phase was meticulously identified, including the layout and operational parameters of the swine feed factory, as authorized by the Brazilian Ministry of Agriculture (MAPA). It was assumed that all swine feed factories operating within Brazil’s commercial pig production systems comply with MAPA regulations and technical requirements.

Bromatological indicators of feed composition for each reproductive phase were essential to the analysis, ensuring an accurate representation of nutrient content and feed quality across production stages.

The primary animal production indexes used in the study are presented in Fig. 2, while the system boundaries, encompassing all relevant inputs and outputs across the life cycle, are illustrated in Fig. 3.

[See PDF for image]

Fig. 3

Animal production indicators across reproductive phases (Illustration of key zootechnical indicators, such as wean-to-estrus interval, farrowing rate, and weaned piglets per sow, used to assess performance during pre-conception, gestation, and lactation in piglet production). By Authors, WEI—Wean to Estrus Interval (in days); SFY—Sow Farrowing per Year; WPS—Weaned Piglet/sow; WPSY—Weaned Piglet/sow/year; LWSY—Live weight of weaner (In this study, “weaner” refers to a piglet at the moment of weaning management, whereas “weaned” denotes a piglet that has already undergone weaning. This distinction is important because certain indicators are measured specifically at the time of weaning (e.g., number of piglets per female at weaning), while others are assessed over a longer period (e.g., total number of piglets weaned per female per year).) piglet/sow/year; LD—Lactation days; NPD—Non-productive days

Life cycle inventory (LCI)

This study adopts a quantitative survey methodology designed to organize, summarize, characterize, evaluate, and interpret collected data. The analysis spans the period from 2016 to 2021, specifically chosen to capture the dynamics of the sector following the economic downturn that began in 2015.

Characterization

• Characterization factors from the ReCiPe model were used to convert LCI data into environmental impacts for each selected category.

• Global Warming Potential was calculated in accordance with the guidelines provided by the Intergovernmental Panel on Climate Change (IPCC).

• Land use was assessed using the direct method, relying on IPCC-derived indices and emission factors.

Potential environmental impact categories

Röös et al. [51] analyzed greenhouse gas emissions alongside other environmental impact categories by reviewing 23 studies encompassing 53 different animal production scenarios. However, the substantial variability across these systems limited the accuracy and consistency of the results.

To overcome this issue, the present study focused exclusively on Brazilian swine production systems, which exhibit greater homogeneity in terms of genetics, feed composition, and manure management practices. This targeted approach was designed to enhance the precision of the analysis, particularly regarding the climate change impact category, and to enable a more accurate estimation of land use impacts. By narrowing the scope to a more consistent national context, the study aims to reduce systemic variability and generate more reliable insights into the environmental performance of swine production systems.

Climate change potential from animal production

The assessment of climate change potential focused on estimating GHG emissions, primarily CH₄ and CO₂, generated through physiological processes in swine, such as enteric fermentation [34]. Mathematical models were applied to each production phase to allocate these emissions accurately across the production system.

In accordance with ISO 14040 and 14044 standards [27], the study applied three levels of modeling approaches:

• Tier 1 Utilizes global default values for GHG emissions from standardized international datasets.

• Tier 2 Relies on country- or region-specific data to estimate emissions.

• Tier 3 Incorporates detailed, diet-based indices linked to animal physiology, including parameters related to digestion, heat loss, and respiration.

In this study, Tier 1 modeling was applied to evaluate emissions related to feed manufacturing, while Tier 2 modeling was employed for estimating emissions from the various phases of animal production. This hybrid approach allowed for a more accurate assessment of climate-related environmental impacts under real-world Brazilian production conditions.

Carbon dioxide emissions from sows via enteric fermentation

Emissions of CH₄ and CO₂ from sows were calculated using mathematical models based on the methodology proposed by Rigolot et al. [50]. The CH₄ estimation was derived from digestible nutrient intake, including organic matter, crude fiber, crude protein, starch, ether extract, and mechanical energy (expressed in megajoules) for each animal category. Specifically, 5.6657 MJ of energy corresponds to 1 kg of CH₄.

Digestible nutrient values used in the model were obtained from the Brazilian Nutritional Tables compiled by Rostagno et al. [52]. Once CH₄ emissions were calculated, they were converted into CO₂-eq. by multiplying each kilogram of CH₄ by a factor of 23, in accordance with FAO [27] recommendations.

Land use occupation

Land occupation for diet grain production can be assessed using two primary approaches: direct and indirect methods [64]. This study adopted the direct method, which assumes that the land used for crop production had already been cleared and cultivated prior to the period under analysis, thereby excluding emissions associated with land use change (LUC).

The analysis relied on land use indices developed by the IPCC. Environmental impacts were assessed using SimaPro® software, which provided access to a robust database for general impact calculations. However, to remain consistent with the assumptions of the direct method, region-specific inferences for Brazilian production systems were excluded from the modeling process.

Economic value added

The economic sustainability of piglet production was evaluated using the EVA method, which measures profitability while incorporating environmental costs. EVA was calculated following the methodology proposed by Assaf Neto [8].

Implementing the EVA approach requires access to detailed financial data from the production systems under analysis. These data include the components necessary to calculate Net Operating Profit After Taxes (NOPAT) and Invested Capital (IC), which encompasses both equity and third-party financing. Typically, this information is sourced from Statements of Income, Financial Performance Reports, Equity Balance Sheets, and Sales Reports corresponding to the years under study.

Once NOPAT and IC were identified, the study calculated the Return on Investment (ROI) by dividing NOPAT by IC and multiplying the result by 100. To determine the Cost of Equity Capital (CEC), the Capital Asset Pricing Model (CAPM) was applied. This model was chosen to address some of the known limitations of traditional CAPM approaches, particularly in the context of emerging markets (Mossin 1966).

Various adaptations of CAPM have been proposed by financial experts to account for the specific conditions of developing economies. These include:

• the Local CAPM (L-CAPM) tailored for emerging markets,

• the Hybrid Capital Asset Pricing Model [38],

• the Benchmark CAPM Model developed by Assaf Neto [7].

For this study, the Assaf Neto [7] model was selected due to its methodological soundness and its applicability to the Brazilian economic context. This version of CAPM employs representative and stable economic indicators from the United States (USA) as reference benchmarks for projecting future performance, while adjusting for country-specific risk and inflation factors. Notably, it excludes U.S. inflation and integrates Brazilian macroeconomic variables, enhancing its robustness for calculating capital costs in Brazil.

The Cost of Equity Capital (Ke) was determined using the following adapted CAPM formula:

1

In which:

• R_f represents the rate free of global risk, determined by the interest rate which is paid with bonds issued by ten-year historical series of the United States Government Treasury;

• β represents the unleveraged beta of comparable investments on the global market—specifically from the Farming/Agriculture sector;

• (R_m − R_f) represents the premium by the market risk, given by the difference between global market return and the rate free of global risk.

• INFBR—INFUSA this term adjusts the cost of equity for differences between the inflation rates of Brazil (INFBR) and the United States (INFUSA);

• RISKBR this is an additional premium required by investors due to the country-specific risks (economic, political, and currency risks).

Consequently, the Cost of Equity was calculated by applying the CAPM framework to the period from 2016 to 2021, using the indicator values presented in Table 2. The risk-free rate was derived from U.S. Treasury Bonds, which serve as a global benchmark for financial stability. Core variables such as unlevered beta, market return, and the country risk premium were obtained from global financial datasets compiled by Damodaran [22].

Table 2. Capital asset pricing model variables for calculating equity costs in brazilian piglet farming (2016–2021)

Indexes	2016	2017	2018	2019	2020	2021
Unleveraged betaa	0.63	0.56	0.7	0.7	0.53	0.63
Rm—S&P500a	9.50%	19.09%	− 6.02%	28.28%	16.44%	20.28%
Rf—T-bonds 10 yearsb	0.69%	0.78%	0.97%	0.73%	− 0.15%	− 0.31%
Market Risk Premium BRa	4.27%	3.46%	4.17%	2.96%	2.91%	2.97%
Inflation BRc	5.35%	2.86%	3.78%	4.19%	4.56%	10.38%
Inflation EUAc	2.74%	2.21%	1.52%	2.34%	1.68%	7.87%
Ke	13.74%	15.00%	3.13%	25.36%	15.17%	18.98%

By authors, ^ahttps://pages.stern.nyu.edu/~adamodar/; ^bhttps://www.treasury.gov; ^chttps://www.global-rates.com. Accessed on March 22, 2022

After determining the Cost of Equity Capital, calculating the Weighted Average Cost of Capital (WACC) became essential, given that a portion of the invested capital originated from third-party financing subject to associated borrowing costs. Therefore, it was necessary to identify the capital structure, i.e., the proportion of equity and debt relative to the total invested capital. The WACC was then calculated according to the standard formulation, as illustrated in Fig. 4.

[See PDF for image]

Fig. 4

Equation for Calculating the Weighted Average Cost of Capital (Formula illustrating the components used to compute WACC, including equity (Ke), debt (Kd), and capital structure proportions, applied to assess economic viability in piglet production systems). By authors, WACC, Weighted average cost capital

Consequently, the WACC rates were calculated using the annual Cost of Equity and debt financing values provided by the productive unit, as detailed in Table 3.

Table 3. Weighted average cost of capital (WACC) for Brazilian Weaned Piglet Farming (2016–2021)

Var	2016	2017	2018	2019	2020	2021
Ke	13.74%	16.00%	3.13%	25.36%	15.17%	18.98%
Kd	8.75%	8.75%	8.75%	8.75%	8.75%	8.75%
E	9,712,422.59	9,722,251.87	7,959,131.82	6,184,408.23	5,128,576.86	5,832,909.55
D	2,153,917.20	1,545,870.83	803,277.82	572,816.40	401,074.48	87,189.24
IC (E + D)	11,866,339.79	11,268,122.7	8,762,409.64	6,757,224.62	5,529,651.34	5,920,098.79
WACC	12.83%	15.01%	3.65%	23.95%	14.70%	18.83%

By authors, Var, Variables; Ke, Property capital cost; Kd, Third-party debt costs; E (equity), Total of property capital; D (debt), Total debt; IC, Invested capital; WACC, Weighted average cost capital

Given that the current legal status of the production unit classifies it as a cooperative, the income tax rate was excluded from the calculation of the economic-financial results.

Finally, the EVA was calculated as the product of the difference between the Return on Investment (ROI) [13] and the WACC, multiplied by the Invested Capital, in accordance with the model proposed by Assaf Neto [8]. The calculation is expressed as follows:

Cost of investment

2

Following the application of the EVA mathematical model, the results represent the economic value generated per kilogram of live weight for weaned piglets, as presented in Table 3.

Eco-efficiency of piglet production for finishing systems

The concept of eco-efficiency refers to a quantitative economic analysis that assesses the extent to which environmental impacts affect the economic value generated by a given activity. This study adopted the methodology proposed by the World Business Council for Sustainable Development [66], which calculates eco-efficiency by dividing the EVA by the corresponding environmental impact value (EIV), as presented in Eq. 3.

The EIV is calculated by aggregating the environmental impacts derived from the LCA, following the formulation proposed by Guinée [32], as shown below:

3

where:

• Ei represents the environmental impact score for each category (e.g., global warming potential, acidification, eutrophication).

• Wi represents the weighting factor for each impact category based on its relative importance.

• n is the total number of impact categories.

Eco-efficiency is then determined using the relationship:

4

In wich:

• Eef represents the eco-efficiency

• EVA represents the economic value added

• EIV represents the environmental impact value

Correlation

The concept of eco-efficiency is grounded in the integration of environmental and economic indicators, including LCA results, EVA, and ROI. Environmental outcomes, such as GHG emissions and land use, can be directly compared to the economic value generated along the production chain (Table 4).

Table 4. Weaned piglet productivity and environmental impact indicators (2016–2021)

Years	CO2eq. feed process and enteric emissions (kg/kg LW)	Pre-conception FI (kg/kg LW2)	Gestation FI (kg/kg LW2)	Lactation FI (kg/kg LW2)	Wr LU (m2/kg LW2)
2016	2.502	0.184	4.010	1.457	16.569
2017	2.426	0.176	3.904	1.399	16.078
2018	2.420	0.176	3.893	1.397	16.041
2019	2.450	0.272	3.869	1.394	16.165
2020	2.344	0.166	3.768	1.359	15.530
2021	2.309	0.161	3.725	1.328	15.314
M	2.409	0.189	3.862	1.389	15.950
SD	0.071	0.042	0.102	0.043	0.455

LW2, Life Weight of weaner piglets; Wr, Weaner; FI, Feed intake; LU, Land Use; M, Mean; SD, Standard deviation

To explore these interrelationships, Pearson correlation analysis was conducted to assess the degree of association between environmental and economic variables. This approach provides insights into the level of synchrony between the two dimensions, offering a deeper understanding of how environmental performance influences, or is influenced by, economic outcomes.

Results

Brazilian weaned piglet production showed significant improvements between 2016 and 2021. By 2021, the average number of weaned piglets per sow had increased by nearly two piglets compared to 2016 (Table 5). This upward trend is consistent with the findings of Bonesmo and Enger [12], who investigated the effects of genetic progress on Norwegian pork production between 2014 and 2019, reporting an increase in the number of weaned piglets per sow per year from 24.3 to 27.9.

Table 5. Productivity and environmental outcomes from weaner to growing stages (2016–2021)

Years	WPSY	CO2eq. feed process and enteric emissions (kg/kg LW)	WTGY LU (m2/kg LW)
2016	27.142	2.502	16.569
2017	27.366	2.426	16.078
2018	27.990	2.420	16.041
2019	24.686	2.450	16.165
2020	26.795	2.344	15.530
2021	29.410	2.309	15.314
M	27.231	2.409	15.950
SD	1.550	0.071	0.455

WPSY, Weaner piglet/sow/year; LW, Live weight on weaning; LU, Land Use; M, mean; SD, Standard deviation

The results highlight a consistent improvement in Brazilian sow productivity between 2016 and 2021, particularly in terms of weaned piglets per sow per year (WPSY). By 2021, the WPSY increased from 27.1 in 2016 to 29.4, reflecting gains in reproductive efficiency and improvements in herd management practices. However, this increase in productivity did not result in significant changes in the live weight of weaned piglets. Although annual fluctuations were observed, ranging from a low of 6.01 kg in 2019 to a high of 8.21 kg in 2017, the overall variation was modest. The average weight remained approximately 7.00 kg, with values ranging from 6.70 kg in 2016 to 6.67 kg in 2021 (Table 1).

The rise in WPSY suggests a more efficient conversion of inputs into outputs, signaling improvements in biological productivity. While piglet weight remained relatively stable, the increased number of piglets produced per sow per year contributed to cost-efficiency and enhanced the potential for positive economic performance, especially in years with favorable market conditions. These productivity gains also influenced environmental indicators by distributing environmental burdens, such as GHG emissions and land use, across a larger number of piglets, thereby improving environmental performance per unit produced and contributing to the overall eco-efficiency of the system.

The mean WPSY over the period was 27.23, while GHG emissions per kilogram of weaned piglet averaged 2.41 kg CO₂eq, as shown in Table 5. Notably, emissions decreased from 2.50 kg CO₂eq/kg in 2016 to 2.31 kg CO₂eq/kg in 2021, reflecting a positive trend in environmental sustainability. This reduction is partially attributable to the increased productivity, which diluted the environmental impacts across more piglets. By quantifying these effects through LCA, the study fulfills its first objective: to evaluate the environmental impacts of piglet production.

The results also underscore that while biological efficiency plays a key role in lowering per-unit emissions, broader management practices, including feed formulation, manure handling, and resource use, remain critical leverage points for improving environmental performance. In this context, LCA functions not only as a monitoring tool but also as a strategic instrument for identifying sustainability hotspots within the production system.

According to the Food and Agriculture Organization (FAO), global demand for animal protein is projected to rise significantly, driven by population growth and changing dietary patterns [30]. However, meeting this demand must be aligned with substantial reductions in environmental impacts [12]. Animal agriculture operates within a complex web of environmental pressures, with feed production for confined systems standing out as a major contributor to human-induced climate change. Key concerns include GHG emissions, particularly from energy-intensive crop cultivation, and land use change associated with expanding feed crop areas.

This study revealed that producing 1 kg of weaned piglet required an average of 5.44 kg of feed, corresponding to the use of approximately 15,950 m² of arable land (Table 5). These figures emphasize the critical need for efficiency improvements in feed use and land management to mitigate environmental burdens associated with piglet production.

Economic results are presented in Table 6, which details the EVA for Brazilian weaned piglet production over the analyzed period. In the initial years, EVA values were negative, reflecting financial losses or returns below the cost of capital. However, gradual improvements in production efficiency led to a progressive increase in EVA, culminating in a positive result in 2021. This turnaround reflects a 74% increase in total piglet output over the period, driven by improvements in technical productivity and system efficiency. The shift from negative to positive EVA illustrates how gains in biological and operational performance can translate into tangible economic value, reinforcing the relevance of integrated eco-efficiency assessments.

Table 6. Economic value added (EVA) evaluation for Brazilian Weaned piglet production (2016–2021)

Years	Wra	Wr weight	U$ paid/KG	Total revenue (U$)a	Invested (U$/kg)	Net operational resultsa	ROI	WACC	Total EVAa	Ton Wrb	EVA/kg Wr
2016	26.000	6.410	3.369	586.916	7.743	− 453.326	− 0.336	0.1374	− 638.650	174.199	− 3.666
2017	31.945	6.530	3.278	859.697	4.596	− 273.985	− 0.227	0.1600	− 466.884	262.267	− 1.780
2018	37.728	6.380	2.624	663.218	4.531	− 304.274	− 0.266	0.0313	− 340.131	252.779	− 1.346
2019	37.509	6.530	3.679	829.371	3.001	1.072	0.002	0.2536	− 170.505	225.427	− 0.756
2020	44.177	7.110	2.900	991.531	1.542	54.908	0.104	0.1517	− 25.065	341.930	− 0.073
2021	45.249	6.140	3.758	1134.086	2.783	370.833	0.442	0.1898	211.408	301.809	0.700
M	37.101	6.517	3.268	844.136	4.033	− 100.796	− 0.047	0.154	− 238.305	259.735	-1.154
SD	7.305	0.324	0.440	202.559	2.152	300.909	0.292	0.073	308.286	58.437	1.517

ROI, Return on Investment; WACC, Weighted Average Cost Capital; EVA, Economic Value Added; Wr, weaner piglet; M, mean; SD, Standard deviation

^amultiplied by 1,000,000;

^b1000 ton

Another contributing factor to the improved economic performance in the latter part of the study period was the increase in live weight (LW) market prices, which positively impacted Net Operating Profit After Taxes and Return on Investment. Despite ROI turning positive from 2019 onwards, the EVA remained negative in both 2019 and 2020. This result is largely attributable to the high WACC associated with swine production, as shown in Table 3. WACC, a critical parameter in investment decision-making, exhibited substantial variability during the period due to fluctuations in the international financial market, which, in turn, affected the EVA performance of the weaned piglet sector.

This study also observed a slight decline in average piglet live weight after 2016, coinciding with an increase in overall productivity, particularly the rise in weaned piglets per sow per year. However, since 2020, the live weight of weaned piglets has shown signs of recovery, which contributed to a modest improvement in system-level EVA. Additionally, the elevated cost of animal protein during the COVID-19 global pandemic likely influenced the financial dynamics of the Brazilian swine industry, resulting in improved profitability across the supply chain and positively affecting EVA outcomes [41].

The progression of EVA values, from strongly negative in the early years to positive in 2021, underscores the importance of market conditions and operational efficiency in determining the economic sustainability of piglet production. While gains in biological productivity enhanced several performance indicators, the study shows that eco-efficiency, as measured through the integrated LCA–EVA framework, is only fully realized when EVA surpasses WACC. This finding directly supports the study’s second objective: to assess the economic performance of piglet production systems and identify trade-offs between profitability and environmental sustainability.

The results also reinforce the role of EVA as a valuable decision-support tool in sustainability assessments, particularly for aligning environmental performance with financial viability. Favorable market prices and optimized technical productivity create a pathway through which environmental gains (e.g., reduced emissions and land use) can be aligned with economic benefits, reinforcing the strategic importance of efficiency at both biological and financial levels.

These findings are in line with Collins et al. [18], who reported that weaning weight significantly influences post-weaning piglet performance. Their results indicate that heavier piglets at weaning are associated with lower feed costs per unit of weight gain, especially when animals are finished on low-cost diets, a dynamic that may enhance both economic and environmental indicators.

Between 2016 and 2021, piglet productivity increased by 8.36%, while GHG emissions per unit produced declined by 8.16%. Improvements in sow performance also contributed to a 7.8% reduction in the land area required for piglet production. These changes, combined with higher total revenue in 2021 (Table 6), led to a positive EVA, reduced environmental impacts, and marginally positive eco-efficiency results (Table 7). The increase in total revenue was primarily driven by a substantial rise in WPSY between 2016 and 2019, demonstrating how technical productivity and market conditions synergistically enhance the sustainability of piglet production systems.

Table 7. Eco-efficiency results for weaned piglet production (2016–2021)

Years	Eco-efficiency Wr (U$/Kg CO2eq x Kg−1 LW)	Eco-efficiency Wr (U$/m2 x Kg−1 LW)
2016	− 0.838	− 0.212
2017	− 0.529	− 0.130
2018	− 0.320	− 0.080
2019	− 0.158	− 0.040
2020	− 0.021	− 0.005
2021	0.175	0.044
M	− 0.282	− 0.071
SD	0.364	0.091

LW, Live weight; OR, Operational results; EI, Environmental impact; EVA, Economic value added; M, mean; SD, Standard deviation

Table 8 presents the correlation between eco-efficiency, Economic Value Added, and the price paid per piglet, expressed in USD per kilogram of live weight for weaned piglets (Wr). The results indicate that higher economic returns are positively associated with improved sustainability performance, suggesting that financial profitability can reinforce eco-efficiency outcomes. This observation is consistent with the findings of Afonso [4], who reported that larger litter sizes in sows were associated with lower greenhouse gas (GHG) emissions per piglet, compared to smaller litters, thus linking productivity gains with environmental benefits.

Table 8. Pearson correlation between economic, environmental, and eco-efficiency variables in weaned piglet production

	EVA Wr (U$/kg LW)	Eco-efficiency Wr (U$/ Kg CO2eq × Kg−1 LW)	Eco-efficiency Wr (U$/ m2 × Kg−1 LW)
Total CO2eq.(kg/kg Wr)	− 0.906	-0.885	− 0.888
Wr LU (m2/kg LW2)	− 0.927	− 0.908	− 0.911
U$ paid/kg Wr	0.161	0.180	0.177
EVA Wr (U$/kg LW)		0.991	0.993

EVA, Economic value added; Wr, weaner piglet; LW, Live weight

Among the analyzed variables, the variation in the USD value paid per functional unit was identified as a key factor influencing eco-efficiency scores. This reinforces the relevance of market dynamics in shaping the overall sustainability of piglet production systems, as shifts in pricing directly affect both economic performance and the distribution of environmental burdens across the production chain.

The assessment of eco-efficiency using real-world operational data provides valuable insights into the actual performance of production systems. Analyses based on historical data are particularly important for identifying and diagnosing aspects of eco-efficiency that may not be readily apparent through routine management or traditional accounting practices. This type of evaluation not only functions as a strategic decision-support tool, but also highlights opportunities for integrated improvements that enhance both economic performance and environmental sustainability. By bridging the gap between operational results and sustainability indicators, such assessments contribute to more informed, resilient, and future-oriented production strategies.

Discussion

Between 2016 and 2021, this study found that emission reduction was a secondary factor in improving economic performance. However, Vonderohe et al. [65] argue that economic sustainability often conflicts with environmental sustainability. They note that many systems designed to mitigate environmental impacts increase production costs without delivering significant economic returns.

This study provides novel insights into the eco-efficiency of piglet production systems in Brazil by integrating LCA and EVA methodologies. Notable gains in productivity, reductions in GHG emissions, and improved financial performance align with global sustainability goals. These findings align with prior research concluding that efficient livestock systems can simultaneously reduce environmental impacts and enhance profitability [17, 18].

The results support the theoretical proposition that biological efficiency, such as increased numbers of weaned piglets per sow, provides environmental benefits by distributing emissions and land use across a larger output. Afonso [4] and Sanz-Fernández et al. [56] argue that enhancing female biological efficiency through genetic improvement programs can promote sustainability in weaned piglet production. Similarly, Garcia-Launay et al. [30] highlight the role of feed and reproductive efficiency in reducing environmental footprints [47, 48].

The observed decline in GHG emissions per kilogram of live piglet and the improvement in EVA in recent years mirror trends identified in Norwegian and French swine systems, where technological and managerial advancements have delivered synergistic sustainability outcomes [12, 39]. However, the study also reveals that environmental improvements alone are insufficient to guarantee economic viability.

Farmer [28] emphasizes that environmental impact is a critical aspect of productivity, but biological challenges, such as suckling piglet mortality, still require scientific and technological advancements. These challenges depend on management practices and female biology, as the number of piglets born does not fully reflect weaned piglets per sow annually (WPSA). Female milk production, influenced by feeding programs, is a key factor [28]. Feed not only affects environmental [63] and economic [31] outcomes but also plays a vital role in determining milk production during lactation, thereby improving suckling piglet survival and weaning weight [33].

Ali et al. [5] note that variable feed costs in Brazil necessitate productivity goals for breeding females, focusing on prolificacy and offspring feed conversion efficiency. Beyond feed costs and their return on animal productivity, Ali et al. [6] highlight that the environmental costs of feed production contribute to the economic value of swine products. Integrating Economic Value Added (EVA) to link environmental impacts with product value generation shows that investments in productivity, encompassing feed, genetics, and infrastructure, yield both economic and environmental benefits.

Despite reduced emissions, EVA remained negative until market conditions converged with productivity gains in 2021. This underscores the critical role of market dynamics, particularly input costs and piglet prices, in determining financial sustainability. These findings are consistent with Cadéro et al. [15] and Utnik-Banás et al. [62], whose highlighted the sensitivity of economic viability in pig production to fluctuations in feed prices and output markets.

Another notable contribution is the correlation analysis between environmental indicators and EVA, which confirms that lower emissions and reduced land use are strongly associated with improved economic outcomes. This finding aligns with Besson et al. [11], Ojo et al. [46] and Zhang et al. [68] whose reported that selecting for feed efficiency not only mitigates emissions but also enhances profit margins in livestock systems.

By employing a combined LCA–EVA framework, this study provides a comprehensive evaluation of the eco-efficiency of weaned piglet production systems in Brazil. Similar integrated approaches have been adopted by Ruviaro et al. [53], who applied Life Cycle Cost Analysis (LCCA) to Brazilian dairy systems and underscored the importance of concurrently assessing environmental and economic dimensions in livestock production. This dual-perspective approach facilitates the identification of efficiency bottlenecks and supports the development of strategies aimed at improving production system sustainability.

While the study contributes valuable insights into the trade-offs and synergies between environmental and economic performance in swine production, it is essential to recognize both its strengths and limitations. The integrated methodological approach enhances analytical depth and practical relevance. However, certain constraints, such as the representativeness of the case study unit and the use of secondary data for specific parameters, may limit the generalizability of the findings. Nevertheless, the framework presented herein lays a strong foundation for future applications of integrated sustainability assessments in animal production systems.

Strengths and limitations

A key strength of this study lies in the integration of LCA and EVA methodologies, enabling a robust assessment of eco-efficiency by linking environmental impacts with economic performance. This dual approach provides a valuable decision-making tool for stakeholders, grounded in real-world, farm-level data that enhances the validity of the findings by capturing actual production dynamics.

However, some limitations should be acknowledged. The analysis is based on a single model farm, which, despite its technical rigor, may not represent the heterogeneity of piglet production systems across Brazil. Differences in farm size, management, technology use, and regional conditions could lead to distinct eco-efficiency outcomes [10].

Additionally, the study does not consider seasonal variations in productivity, which affect reproductive performance, feed conversion, and emissions. Ignoring temporal dynamics may distort certain indicators by masking intra-annual fluctuations [68]. The absence of regional variation in feed sourcing and formulation is another limitation, as differences in ingredient origin, transport, and nutritional strategies significantly influence environmental impacts [36].

Furthermore, socio-economic heterogeneity among producers, such as disparities in access to credit, technical assistance, infrastructure, and market integration, is not explicitly addressed. These factors can shape both financial viability and the adoption of sustainable practices yet are not fully captured in the EVA or policy analysis [23, 24].

Future research should include a broader and more diverse sample of farms across Brazilian regions and production scales, using stratified or regional case-study approaches. Incorporating multi-year and multi-regional data would allow for the analysis of seasonal effects and socio-economic conditions, leading to more comprehensive and policy-relevant insights into eco-efficiency in piglet production [14, 60].

Policy recommendations

The results of this study offer strategic insights for policymaking aimed at enhancing the eco-efficiency of piglet production in Brazil. Improving environmental performance is crucial not only for mitigating impacts but also for bolstering economic resilience amid growing market and regulatory demands. Based on the findings, the following policy recommendations are proposed:

• Encourage low-impact feed ingredients Given the significant contribution of feed to environmental impacts, policies should promote low-carbon feed alternatives and the use of agro-industrial by-products, thereby reducing GHG emissions and land use [17].

• Support precision nutrition Incentivizing precision feeding technologies can enhance feed efficiency and reduce nutrient losses. Financial incentives, tax benefits, or technical assistance can accelerate adoption, especially among small and medium-scale producers [57].

• Implement carbon pricing and certification Mechanisms such as carbon credits, environmental taxes, and eco-certification programs can stimulate sustainable practices and improve the market positioning of environmentally responsible farms [42].

• Invest in manure management technologies Promoting anaerobic digestion, composting, and nutrient recovery can reduce GHG emissions and generate renewable energy or organic fertilizers. Public funding or subsidized credit can drive adoption, especially in regions with dense swine production ( [3]).

• Improve data transparency and reporting Establishing standardized frameworks for monitoring and reporting sustainability metrics enables evidence-based decisions, benchmarking, and greater transparency across the value chain [49].

By adopting these measures, Brazil can advance toward a swine sector that is more sustainable, efficient, and aligned with global sustainability demands, enhancing both domestic environmental performance and the competitiveness of its pork exports.

Absence of uncertainty analysis

Although uncertainty analysis is a critical component of LCA studies, it was not conducted in this research due to data limitations and computational constraints. The primary dataset was based on a single, high-quality case study farm, supplemented by secondary data from national and international sources. The limited variability in the dataset and the lack of replication across multiple systems restricted the feasibility of a meaningful uncertainty analysis [58].

Moreover, the integration of LCA and EVA already represents a methodologically complex endeavor, requiring the harmonization of diverse environmental and financial datasets. Conducting a full uncertainty analysis across both dimensions would demand a level of data granularity and temporal-spatial resolution that was not available within the scope of this study.

Future research should aim to include a larger number of production units and integrate both sensitivity and uncertainty assessments to enhance the robustness and generalizability of eco-efficiency evaluations in piglet production systems.

Conclusions

This study addressed the limited understanding of eco-efficiency in Brazilian piglet production systems by integrating Life Cycle Assessment and the Economic Value-Added approach. From 2016 to 2021, increased sow productivity reduced GHG emissions per unit of production, while favorable market conditions and improved economic indicators contributed to higher EVA, signaling enhanced financial sustainability.

The findings demonstrate that efficient management practices can reduce environmental impacts and improve economic performance. A 7.8% reduction in land use per weaned piglet exemplifies the potential of technological advancements in mitigating the environmental footprint of swine farming.

This study offers four original contributions: (a) It introduces an integrated LCA–EVA framework to assess eco-efficiency holistically, addressing trade-offs and synergies between environmental and economic dimensions; (b) It focuses on the underrepresented piglet production phase, offering targeted insights; (c) It generates locally relevant evidence for Brazilian livestock systems, where integrated approaches remain scarce; and (d) It provides actionable recommendations for producers and policymakers to foster sustainable practices in swine production.

The study’s implications go beyond academia. For producers, it reinforces the benefits of strategies such as precision nutrition and optimized manure management. For policymakers, it supports designing incentives that reward integrated environmental and economic performance. The dual-method approach also offers a replicable model for other livestock systems.

Future research should build on this framework by incorporating larger datasets, addressing uncertainty, and integrating additional sustainability dimensions, such as animal welfare, biodiversity, and social indicators, to further enhance the robustness and relevance of eco-efficiency assessments.

Author contribution

Rita Therezinha Rolim Pietramale: Conceptualization, Formal analysis, Writing—Original Draft. Carolina Obregão da Rosa: Writing—Review and Editing. Daiane Pereira de Souza: Methodology, Software, Validation. Gabriela Vilela dos Santos Mantovani: Project administration, Supervision. Fabiana Ribeiro Caldara: Investigation, Visualization. Clandio Favarini Ruviaro: Supervision.

Funding

This study was supported by the Coordination for the Improvement of Higher Education Personnel (CAPES) and the Support Foundation for the Development of Education, Science, and Technology of the State of Mato Grosso do Sul (FUNDECT) under Chamada FUNDECT 18/2021—MS Carbono Neutro.

Data availability

Data are available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent to publish

Not applicable.

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) used Deep-L to language and grammar text revision. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

Competing interests

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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