Next Article in Journal
Risk Levels Classification of Near-Crashes in Naturalistic Driving Data
Previous Article in Journal
Applying the Technology Acceptance Model to Elucidate K-12 Teachers’ Use of Digital Learning Platforms in Thailand during the COVID-19 Pandemic
Previous Article in Special Issue
Global Dairy Sector: Trends, Prospects, and Challenges
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 14
Issue 10
10.3390/su14106028
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

LCA to Estimate the Environmental Impact of Dairy Farms: A Case Study

by
Sara Zanni
1,
Mariana Roccaro
2,*,
Federica Bocedi
3,
Angelo Peli
2 and
Alessandra Bonoli
4
1
Department of Management, University of Bologna, 40126 Bologna, Italy
2
Department for Life Quality Studies, University of Bologna, 47921 Rimini, Italy
3
Department of Veterinary Medical Sciences, University of Bologna, 40064 Bologna, Italy
4
Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, 40136 Bologna, Italy
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(10), 6028; https://doi.org/10.3390/su14106028
Submission received: 5 April 2022 / Revised: 9 May 2022 / Accepted: 13 May 2022 / Published: 16 May 2022
(This article belongs to the Special Issue Dairy Sector: Opportunities and Sustainability Challenges)
Browse Figures
Versions Notes

Abstract

:
Intensive farming is responsible for extreme environmental impacts under different aspects, among which global warming represents a major reason of concern. This is a quantitative problem linked to the farm size and a qualitative one, depending on farming methods and land management. The dairy sector is particularly relevant in terms of environmental impact, and new approaches to meeting sustainability goals at a global scale while meeting society’s needs are necessary. The present study was carried out to assess the environmental impact of dairy cattle farms based on a life cycle assessment (LCA) model applied to a case study. These preliminary results show the possibility of identifying the most relevant impacts in terms of supplied products, such as animal feed and plastic packaging, accounting for 19% and 15% of impacts, respectively, and processes, in terms of energy and fuel consumption, accounting for 53% of impacts overall. In particular, the local consumption of fossil fuels for operations within the farm represents the most relevant item of impact, with a small margin for improvement. On the other hand, remarkable opportunities to reduce the impact can be outlined from the perspective of stronger partnerships with suppliers to promote the circularity of packaging and the sourcing of animal feed. Future studies may include the impact of drug administration and the analysis of social aspects of LCA.

1. Introduction

Climate change, described as the long-term heating of our planet caused by human activities since the pre-industrial period, is among the main challenges on a global scale [1]. As it is well known, one of the main causes of increasing temperatures is greenhouse gas (GHG) emissions in the atmosphere.
GHG concentration affects the global temperature by absorbing strongly radiant electromagnetic energy at wavelengths capable of emitting quantities of elevated heat [2].
According to their global warming potential, the main gases responsible for the greenhouse effect are methane, carbon dioxide, used as a reference for the phenomenon, water vapor, and nitrous oxide, each with different global warming potential and specific sources. Methane, for example, is about 21 times more efficient in trapping heat in the atmosphere compared to CO2, while nitrous oxide is 296 times more efficient, with persistence in the atmosphere for up to 114 years [3]).
The agro-livestock sector is responsible for a relevant share of these gases due to direct and indirect emissions, accounting for about 17% of the global GHG emissions in 2018 [4].
In addition to the release of GHG into the atmosphere, farming contributes in part to the worsening of air quality through the production of mainly nitrogen compounds from manure, particulates obtained from combustion, and volatile organic compounds other than methane (NMVOC), carbon black (BC), heavy metals (i.e., chromium, copper, nickel, selenium, zinc, lead, cadmium, mercury), dioxins, polychlorinated biphenyls (PCBs), polycyclic aromatic hydrocarbons (PAHs), hexachlorobenzene (HCB), and of particulates, both less than 10 microns (PM10) and 2.5 microns (PM2.5), deriving from several farm-level operations [5].
Livestock production has a significant environmental impact as it affects several natural resources, including land and soil, water, air, and biodiversity. Nevertheless, growing populations and economics have led to increased demand for animal products and the consequent expansion of the livestock sector over the past decades. At the same time, in developed countries, consumers demand animal products that are both animal welfare- and environment-friendly [3].
In 2019, world milk production grew to about 852 Mt and was forecast to grow at 1.6% per year over the next decade, faster than most other main agricultural products [6]. Globally, cattle are the largest contributors to total livestock greenhouse gas (GHG) emissions, producing about 65% (4.6 Gt CO2eq annually) of sector emissions, with milk production contributing 20% of total sector emissions [7]. Besides direct emissions, livestock systems are responsible for indirect emissions arising from land-use change, fertilizer use, energy, and transport emissions related to livestock operations and supply chains [8]. The three largest sources of GHG from milk production are emissions from manure management (CH4 and N2O), emissions from feed production, processing, and transport (CO2 and N2O), and emissions from enteric fermentation (CH4), the latter accounting for more than half the total of emissions [9].
Although absolute emissions from the dairy sector have increased in the last decades due to production growth in response to the increasing demand, dairy farming is becoming more efficient considering that emissions per unit of product are decreasing [8].
Strategies to reduce GHG emissions include changes in feeding, breeding, and management practices, which essentially lead to the intensification of livestock farming. Strategies that aim to increase productivity are very promising ways to reduce environmental impact; however, in most cases, they are likely to negatively impact animal welfare. For example, intensive housing conditions increase the risk of social stress or hinder the expression of natural behavior [10].
The rising GHG emissions require shifting production systems toward carbon neutrality in order to take action to combat climate change and its impacts, which is one of the United Nations 2030 agenda for sustainable development goals (SDG 13). Moreover, sustainable food production systems and resilient agricultural practices are among the targets to achieve the “zero hunger” goal (SDG 2) and to promote new consumption and production models (SDG 12) by changing the way we produce and consume, mainly in the agri-food sector where these elements are so close, in the farm-to-fork perspective. At the same time, progress toward the sustainable use of natural resources is key to protecting biodiversity and ecosystems (SDG 15). Improving animal health and welfare and reducing GHG emissions through new farming techniques to meet societal demands on safe and sustainable food and contribute to climate change mitigation are among the nine key objectives of the 2023–2027 European common agricultural policy.
The relationship between livestock production and climate change is two-sided. Climate change, deriving from global warming, adversely impacts livestock production, with direct effects on animal health, reproductive efficiency, production performances, and behavior, but also indirect effects deriving from changes in the quality and quantity of feed, water availability, ecosystem alterations leading to changes in the biology and distribution of pathogens and vector-borne diseases [11]. An increase in temperatures negatively affects milk production, especially in cows with higher milk yield and milk quality, with a reduction in casein content [12].
Dairy cattle production systems need to adapt to climate change, but, on the other hand, they must commit to contributing to GHG reduction targets and minimize other negative environmental impacts while continuing to meet society’s needs. Agriculture is estimated to generate 11% of all global emissions [13], and dairy farm contribution to the overall impact of milk products is almost as high as 72% [14]. A special focus on this production phase is required to meet the global goals.
The dairy sector is an extremely complex system with numerous interacting components; consequently, determining the best strategies to reduce GHG emissions will depend on each farm’s local conditions and objectives. Therefore, a deeper understanding of the underlying driving factors of dairy cattle farming environmental impact is highly relevant to support strategies for limiting adverse effects on the environment and protecting the livestock sector [15].
Studies on the environmental impact assessment of dairy products have increased dramatically in the last decade and have been reviewed by several authors [16,17,18,19,20,21]. The main goals of these studies were either to assess the potential environmental impact of the product [22,23,24,25] or to compare different management systems [26,27,28]. However, very few of these have focused on Italian production systems [29,30,31,32] and even less on Italian Protected Designation of Origin (PDO) dairy products (e.g., Parmigiano Reggiano, Grana Padano) [33]. In Italy, milk production is concentrated in the northern plain regions, characterized by little rain during summer and a highly urbanized territory with low availability of agricultural land; cattle are kept mostly indoors, and maize silage is the main forage crop. Moreover, PDO product specifications impose restrictions on management strategies, sourcing, and type of animal feed.
Considering the peculiarity of the environmental conditions and farming practices, this study aimed to set up an LCA model specific for intensive dairy cattle farms involved in PDO production in Northern Italy. We aimed to evaluate the most relevant items of environmental impact triggered by this activity, considering its ability to impair animal health and welfare. To this end, a preliminary assessment was performed on a dairy cattle farm in the Emilia-Romagna region in Italy. The LCA model was realized using Simapro 8.5.1, including farm processes and main supplies, in terms of animal feed and end-of-life processes of the most relevant waste flows.
With the ultimate aim of integrating farm environmental impact and animal health and welfare, an interdisciplinary research approach combining engineering and veterinary expertise was carried out to evaluate farms operating conditions in terms of animal management and welfare, and specifically, to assess environmental impacts based on an LCA approach [34]. This approach offers grounds for future research and development of targeted sustainability strategies in the sector, specific to the studied geographical area, with a concrete possibility of application in the field.

2. Materials and Methods

The following sections report a detailed description of the site of the study (Section 2.1) and the conceptual model developed for the implementation of LCA (Section 2.2).

2.1. Site Description

The present study was carried out on an Italian dairy cattle farm producing Parmigiano Reggiano and soft cheeses in the Emilia Romagna Region.
Prior to the farm visit, a form for primary data collection was designed. During the farm visit (July 2020), data were collected on paper and subsequently transferred to Microsoft® Excel files. Data sources included the farm management software records, information collected by interviewing the farmer, and invoices.
For data that could not be collected on-farm, default values were defined based on processes available on Ecoinvent v.3.5 [35], with geographical reference to the European context, where possible, or Italian specific data, as in the case of the electricity production mix.
Information on farm size, number of animals, productivity, animal welfare, drug use, feed management, water consumption, fuel consumption, electric energy, manure management, and plastic waste were collected (as summarized in Table 1). It was not possible to quantify the use of chemical fertilizers and bedding material.
The farm is structured in four sites, located between Bologna and Modena provinces. Site A hosted calves and replacements up to 5 months of age, pregnant heifers (from two months pregnancy to delivery), and dry cows. Replacements from 5 months of age to the first month of pregnancy were hosted in site B. Site C hosted mostly primiparous milking cows, and site D hosted mostly multiparous milking cows. Animals are moved from site A to site C or D twice a week and from site A to site B twice a month.
The most represented breed was Holstein Friesian, but Italian Red Pied, Jersey, and Montbéliarde x Swedish Red and White crossbreds were also present.
Average daily milk production amounts to 35.15 kg for multiparous cows and 30.15 kg for primiparous cows. The milk is destined to produce Parmigiano Reggiano.
Cows are loose housed on deep litter with straw bedding (dry cows and heifers) or in pens with cubicles covered with sand (milking cows). Dry cows and heifers have access to external exercise areas. The facilities are equipped with ventilation systems consisting of ceiling fans for the resting areas and horizontal flow fans for the feeding zones. The waiting parlor is equipped with a ceiling fan and water sprayers.
Calves up to one month of age are reared in individual pens and then moved to group pens. They are fed through an automatic calf feeder and weaned at 70 days of age.
Site C and site D scored 70.33/100 and 71.31/100, respectively, for animal welfare in 2018, as certified by the Italian National Reference Center for Animal Welfare (CReNBA).
The diet consists of 60% fodder and 40% concentrates. Fodder (alfa-alfa, grass hay) and part of sorghum are self-produced; the other feeds are bought in. Given the production requirements for Parmigiano Reggiano, corn silage is only fed to heifers (site B). Daily average dry matter intake is 25 kg for milking cows, 15 kg for dry cows and pregnant heifers, and 8 kg for replacements.
The water comes from the aqueduct. Site C was also supplied from a well, but it was not possible to quantify the water used. There is no water recycling system in place.
The slurry is spread on the farm’s land intended for fodder production (470 ha). Figure 1 reports the data collection at the basis of the conceptual model definition.

2.2. Conceptual Model for the Assessment

The conceptual model at the basis of the LCA has been defined based on a cradle to farm gate perspective over a calendar year. Considering the specificity of the farm analyses, the model has been developed based on primary data, whenever possible, and previous studies in the field (e.g., Grassauer et al., 2022 [28]).
Several assumptions have been introduced aimed at reducing the uncertainties of the results and at providing a preliminary assessment of the generated impacts, as presented below:
-
Water for feeding and cleaning purposes was considered as deriving from the drinking water supply system;
-
Electricity consumption was considered entirely covered by energy from the grid, as the biogas generation plant was inoperative at the time of the study. The Italian country mix for medium voltage supply included in the Ecoinvent library was selected as representative for the case;
-
The processes related to calf growing were excluded from the model, as meat production, i.e., the co-production line, was out of the scope of the present study;
-
As manure management consisted in storage and spreading in the farm fields, it was included exclusively in terms of machinery diesel consumption and equipment involved;
-
The use of pharmaceuticals could not be included in the present model due to the lack of primary data about the impact of the specific drugs;
-
Carcass disposal was excluded from the boundaries of the present study.
A specific focus was dedicated to animal feed, which has been represented in the following terms, considering each upstream process and relative impacts:
-
The growing of sorghum and fodder in local sites was included in terms of machinery diesel consumption and equipment involved;
-
Purchased animal feed was detailed by typology, source location, and transportation (mean and distance).
Figure 1 shows the conceptual model in the form of a flowchart. The system boundaries are marked in light blue. Each process included in the analysis is connected through green arrows to the main line of “cattle farming” and “milk-production”. The excluded processes, either input (i.e., pharmaceuticals), outputs (i.e., calves growing), or both (i.e., energy production from biogas), are represented out of the system boundaries. Manure management, which lays on the system’s boundaries, was included in the analysis exclusively in direct terms, as fuel consumed by the farm’s machinery for spreading.
The LCA methodology was applied based on ISO 14040 [34] using the Simapro 8.5.1 software and the Ecoinvent 3.4. database [35]. The Functional Unit (FU) was set as 1 kg of Fat and Protein Corrected Milk (FPCM) produced in 2019, in accordance with similar studies in the field [24,25,33], and it is defined by [36] as
FPCM (kg) = raw milk (kg) × (0.337 + 0.116 × fat content (%) + 0.06 × protein content (%))
The average Italian national fat and protein contents reported in the year prior to the visit (2019) were used as reference values; hence, all milk was converted to FPCM with 3.8% fat and 3.4% protein [37].
Details about the processes included are reported in Appendix A. Table A1 shows the overall model, Table A2 shows the details regarding animal feed production, and Table A3 shows the details regarding the transport of animal feed.
Considering the relevance of GHG emissions from farming and their impact on the global warming issue, the selected calculation method was IPCC100a, i.e., the method developed by the International Panel on Climate Change, which provides the carbon footprint developed over a time horizon of one hundred years [38,39], but still integrated into an LCA framework. Previous studies in the field have indicated this as the most conservative approach [27].
Uncertainty analysis was performed, applying Monte Carlo simulation with a 95% confidence interval.

3. Results and Discussion

In developed countries, since more and more citizens are concerned about health, environmental, ethical, and animal welfare issues, consumption of animal products has been showing a reduction trend [3].
Despite the negative effects on the environment, our planet faces the challenge of feeding a rapidly growing global population, which is projected to reach 9.73 billion by 2050 and 11.2 billion by 2100 [40], while fulfilling the obligation to reduce greenhouse gas emissions [17].
For these reasons, the involvement of milk and dairy producers at the local level and their commitment to sustainability is strategic to meet the global goals.
The farm included in this case study is part of the nearly 3000 farms active in the Parmigiano Reggiano production area. It uses the produced milk for making both Parmigiano Reggiano and soft cheeses [41]. The production of this PDO cheese follows a specification linked to the characteristics of the production area.
With its 1368 heads, the investigated farm was part of the 4.5% of dairy farms in Emilia-Romagna with a consistency greater than 500 heads, while most dairy cattle farms in this region (39.2%) own between 100 and 499 heads [42]. The farm covers an area of 5.4 ha used for animal rearing and 560 ha for agriculture, compared to the national average of 18 ha, ranking it among the larger farms [43]. In terms of productivity, the farm has an average milk production of 30–35 kg/cow/day, reaching 38 kg/cow/day in winter. These values are significantly higher than the national average, which is around 25 kg of milk per cow per day [42].
Considering the peculiarities of the farm, also within the framework of the Parmigiano Reggiano production area, and the high environmental pressure in the region, we developed a dedicated LCA model, able to valorize the primary data collected.
The LCA applied in this case study allowed us to identify the most relevant items of impact in dairy cattle farming in terms of products, such as animal feed and plastic packaging, and processes, such as machinery fuel consumption for local sourcing of supplements and standard operations of the farm. Table 2 shows the results obtained by LCA in terms of impact generated over a year, considering the farm total production and consumptions reported to the FU (1 kg of FPCM).
Uncertainty analysis was performed based on the Monte Carlo simulation. With a 95% confidence interval, the results range between 1,506,532.347 and 1,887,999.032 kg CO2eq/year, with a standard deviation corresponding to about 6% of the mean value. Overall results of the simulation are available in Appendix B, Table A4.
The available literature shows high variability in the environmental impact of milk production due to the different assumptions and models used in the studies. Moreover, differences in the functional units, system boundaries, data sources, characterization factors, and allocation approaches add uncertainty to the comparisons [23,31]. For example, the study by Berton et al. [24] was based on data collected in different dairy farms of the Eastern Alps, therefore characterized by specific cattle breeds and farming and management systems. The tool proposed by Famiglietti et al. [33] outlines different system boundaries, including the cheese production phase and processes from different databases, to provide a comprehensive approach to the analysis.
However, the flows that mainly affect the results are the same, although their contribution to the total results differs due to the different assumptions and models used in each study. These include enteric fermentation, animal feed production, manure management, and spreading [44]. Nevertheless, it was evident that the case study presented discrepancies in terms of energy consumption, packaging, and animal feed compared to similar studies in the field. Consequently, the related impact results are higher than expected and suggest pathways for improvement.
As evident from the results, the diesel consumption for the farm operating machines is the most relevant item of impact, accounting for about 49% of the total impact, followed by the production of plastic packaging (27%) (Figure 2).
Considering the possible solutions to tackle these specific issues, we recognized the necessity to involve the supply chain in both cases. The fuel used by the farm operating machines is a factor that is currently difficult to mitigate, as the technologies for the production of electric machines are not sufficiently advanced to ensure the necessary power for the vehicles used in livestock farming and agriculture.
Waste production and management evaluation, particularly in terms of plastic packaging coming from livestock management activities, such as the polyethylene wrapping of hay and straw bales, and cleaning product containers, represent another important item for assessing the overall environmental impact of the farm. A potential solution for reducing the production of plastic waste is almost ready at hand. Figure 2 shows that the disposal of plastic waste through recycling reduces the impact of this item by 7%. The thin polyethylene film, which represents 90% of the plastic present in the farm, can be replaced, for example, with recyclable plastic, thus significantly reducing the environmental impact and working in accordance with the Single-use Plastics Directive (SUPs) [45]. This would require a strong commitment to sustainable farming and partnership with suppliers toward the eco-design of animal feed packaging [46]. In addition to this, clear pathways to make plastic recycling the everyday standard are required to avoid the diffuse practice of incineration or, worse, landfilling [47,48,49]. Moreover, local initiatives may provide an opportunity for the integration of new business lines [50], as the implementation of sustainability-related practices may boost the overall performance of small and medium enterprises [51].
Animal feed production and the related logistics resulted responsible for about 16% of the total impact, considering only the sourcing from third-party suppliers, as the internal cultivation was accounted for in the consumption items for the site. Figure 3 shows in detail the impact triggered by each component of animal feed. The production phase is evidently the most impactful, and maize represents the main item of impact. The amount supplied each year is comparable with sunflower, which displays an overall impact that is almost 60% lower.
Considering only the transport of purchased animal feed (Figure 4), it is evident how the local sourcing supports limited impacts, while overseas supply triggers about 75% of the overall impact of animal feed transport.
In this regard, improvement actions that could reduce the potential environmental impact include increasing the consumption of grass silage instead of maize silage and lowering the use of concentrates or using locally produced concentrates instead of imported ones [26]. Therefore, a revision of the supply chain is strongly recommended to maintain cost control in case of fluctuating prices of international logistics [52] and to secure the consistency of supplies in uncertain international conditions, as we are experiencing in relationship with the COVID-19 crisis [53]. This would allow keeping the commitment to the SDGs by securing food supply chains (SDG 2) and improving working conditions (SDG 8) as well as the sustainability of production and consumption modes (SDG 9), with the overarching aim of reaching the climate goals (SDG 13), regardless of contingent factors [54].
Table 3 summarizes the impact results for both animal feed production and transport.
As regards energy consumption, accounting for 4% of the total impact, according to the Institut de l’Elevage results [55], in the investigated farm, the following high-consumption areas could be identified: milk collection, refrigeration, and pumping; water heating for washing operations; overall lighting; cleaning and washing equipment.
Global warming is a major problem for livestock farming. It is particularly relevant in the Po Valley, where ensuring an adequate microclimate, which often requires putting in place air conditioning systems, is essential to reduce the risk of heat stress in cows [56]. In this case, about a quarter of the total energy consumed by the farm was related to the ventilation systems. Together with the consumption of water for summer cooling (72,000 L), this value reflects the robust farm commitment to ensuring a good welfare level for the cows.
Manure and sewage management is another fundamental issue for reducing GHG emissions, ensuring the environmental quality of rural areas, and protecting the aquifers and surrounding water basins. In this case, the lack of primary data on manure yearly production forced us to exclude the Tier 1 and Tier 2 emissions [38]. Therefore, the spreading, which the company carries out according to the guidelines of the Regional Agency for Prevention, Environment and Energy of Emilia-Romagna (Arpae), was the only treatment considered. In the modeled farm, sewage storage is carried out in uncovered basins and beds, trying to minimize the surface-volume ratio to avoid the transfer of dissolved organic carbon on the surface and reduce the related CO2 emissions. Finally, the production of biogas from the anaerobic digestion of sludge represents an important sustainability strategy of the company, but bottom-line production data are still lacking.

4. Implications

The relationship between farm animal welfare and environmental sustainability is complex, and these two research topics have historically been addressed separately [57]. Their integrated study would help to better understand the synergies and antitheses between these two pillars of livestock farming sustainability and may facilitate the identification of coordinated actions for improvement.
The theoretical implications that would derive from such a multidisciplinary approach can be found at a systemic level since environmental impact assessment could be included in an integrated risk-based farm classification system, like the one developed in Italy, which already includes data on animal health and welfare, biosecurity, antimicrobial usage and related antimicrobial resistance [58].
Practical implications would include integrating environmental impacts and animal welfare items into control dashboards for farmers for the smart management of dairy farms. This would benefit livestock farmers as it would enable them to monitor their management strategies, both short-term, related, for instance, to animal feed purchase, and long-term, considering, for example, machinery purchasing or the implementation of infrastructures, in view of reducing the environmental impact of their farms. In perspective, the collection of summary and aggregated data from dairy farms may feed the policy-making process, supporting it with updated information about the state-of-the-art and the impact of sector-specific policies to meet the global goals.

5. Conclusions

Climate change is an established problem, and cattle farming actively contributes by emitting 14.5% of greenhouse gases from human activities [40]. Stricter environmental policies triggered by the diffuse awareness of the pressing urgency of global challenges could lead to innovative solutions that will improve the competitiveness of the livestock sector in the long term [6].
The investigation of the most relevant items of impact in the dairy farm management represents, in this sense, the first and fundamental step to building awareness and measuring the results of sustainability-oriented actions. Considering the variety of farms, management strategies and supply chains involved into the livestock industry, it is now crucial to create a solid benchmark of cases tailored to the specific milk production scenarios of different areas, thus avoiding the “one-size-fits-all” approach and allowing to identify the main items of impact based on the different production approaches.
The study presents some inherent limitations: firstly, the process of growing calves and the disposal of effluents and animal waste were excluded from the analysis; secondly, pharmaceuticals were not accounted for. In the first case, future research development may broaden the system boundaries, considering the multi-output process, i.e., meat production. In the second case, the main constraint is represented by the lack of information about these products regarding environmental impacts. However, drug use has been carefully mapped. It appears to be highly promising as a possible hotspot of impact, both in input (for the production) and output (for the contaminant load carried into animal urine and, consequently, wastewater). Considering the social dimension of sustainability, future studies may also evaluate the impacts of dairy production on workers, local communities, and society at large.
This study outlines pathways for future research at different levels despite its limitations. Firstly, it provides a preliminary LCA that can be extended to include elements so far neglected, evaluating, for example, the impact of different scenarios of drug administration. Secondly, considering the extensive data collection performed through an interdisciplinary approach. It represents the first step towards the integration of LCA with other frameworks for the performance assessment of dairy cattle farming, namely oriented to include animal health and welfare, with the final aim of evaluating livestock farms from both the environmental sustainability and social sustainability point of views.

Author Contributions

Conceptualization, A.B. and A.P.; methodology, A.B. and A.P.; software, S.Z.; validation, M.R. and S.Z.; formal analysis, M.R. and S.Z.; investigation, F.B.; resources, A.B. and A.P.; data curation, F.B. and S.Z.; writing—original draft preparation, M.R. and S.Z.; writing—review and editing, A.B.; visualization, S.Z.; supervision, A.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

The farm manager signed an informed consent before the assessment started.

Data Availability Statement

All study data are presented in the article.

Acknowledgments

The authors would like to acknowledge the farmer for providing the case study and their precious collaboration throughout the project.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Details on the processes included are reported in the following tables.
Table
Table A1. Overall model, process details.
Table A1. Overall model, process details.
ProcessAmountUnitNotes
Resources
Water, well, in ground, IT72,000L
Materials/fuels
Diesel burned in agricultural machinery1,097,763.05kWhconsumption for farm processes (both feed production and animal husbandry)
Tap water {Europe without Switzerland}|market for|APOS, U28,035,555.2kg
Packaging film, low density polyethylene {GLO}|market for|APOS, U147,510kg
Packaging, for fertilizers or pesticides {GLO}|market for packaging, for fertilizers or pesticides|APOS, U16,390kg
Animal feed production1pSee Table A2
Electricity/heat
Electricity, medium voltage {IT}|market for|APOS, U141,840kWh
Animal feed transport1pSee Table A3
Final waste flows
Polyethylene waste163,900kg
Waste to treatment
PE (waste treatment) {GLO}|recycling of PE|APOS, U67,199kg41% of total waste, considering average regional waste disposal
Waste plastic, mixture {Europe without Switzerland}|treatment of waste plastic, mixture, municipal incineration|APOS, U73,755kg45% of total waste, considering average regional waste disposal
Waste plastic, mixture {Europe without Switzerland}|treatment of waste plastic, mixture, sanitary landfill|APOS, U22,946kg14% of total waste, considering average regional waste disposal
Table
Table A2. Animal feed—production, process details.
Table A2. Animal feed—production, process details.
Process DetailAmountUnit
Soybean, feed {GLO}|market for|APOS, U9148kg
Maize grain, feed {GLO}|market for|APOS, U181,598kg
Sunflower silage {GLO}|market for|APOS, U191,094kg
Protein feed, 100% crude {GLO}|market for|APOS, U22,042kg
Table
Table A3. Animal feed—transport, process details.
Table A3. Animal feed—transport, process details.
Process DetailDistanceUnitNotes
Transport, freight, sea, transoceanic tanker {GLO}|market for|APOS, U519,180.2tkmSunflowers’ transport—tanker, from Black Sea port (Ukraine) to Ravenna
Transport, freight, lorry 16–32 metric ton, EURO4 {GLO}|market for|APOS, U21,211.43tkmSunflower—road transport from Ravenna port
Transport, freight, lorry 16–32 metric ton, EURO4 {GLO}|market for|APOS, U493.99tkmSoy—road transport from production site (Modena province)
Transport, freight, lorry 16–32 metric ton, EURO4 {GLO}|market for|APOS, U9806.29tkmMaize—road transport from production site (Modena province)
Transport, freight, lorry 16–32 metric ton, EURO4 {GLO}|market for|APOS, U2997.71tkmProtein feed—road transport from production site (Cremona province)

Appendix B

Results of the uncertainty analysis.
Table
Table A4. Complete results of the Monte Carlo simulation for the overall process and for animal feed production and transport; other processes are integrated directly from Ecoinvent “SD”.
Table A4. Complete results of the Monte Carlo simulation for the overall process and for animal feed production and transport; other processes are integrated directly from Ecoinvent “SD”.
UnitMeanMedianStandard DeviationCoefficient of Variation2.5%97.5%Standard Error of the Mean
Total Yearly Impactkg CO2eq/year1,669,0741,661,037102,232.56.1251071,506,5321,887,9993232.877
Animal feed productionkg CO2eq/year243,847.2242,761.916,982.246.964294215,360.6279,755.3537.0255
Animal feed transportkg CO2eq/year8883.8018831.835586.7966.6052367890.04110,200.8518.55612

References

  1. Intergovernmental Panel on Climate Change—IPCC. IPCC SPECIAL REPORT: GLOBAL WARMING OF 1.5 °C (2018). Available online: https://www.ipcc.ch/sr15/ (accessed on 12 May 2022).
  2. Raven, P.H.; Lasos, J.B.; Singer, S.R.; Mason Kenneth, A.; Jonson, G.B. Biologia, 9th ed.; Piccinin Nuova Libreria: Padova, Italy, 2011; Volume 59, pp. 1316–1343. [Google Scholar]
  3. Steinfeld, H.; Gerber, P.; Wassenaar, T.; Castel, V.; Rosales, M.; De Haan, C. Livestock’s Long Shadow: Environmental Issues and Options; FAO: Rome, Italy, 2006; p. 390. [Google Scholar]
  4. FAO. Emissions Due to Agriculture. Global, Regional and Country Trends 2000–2018; FAOSTAT Analytical Brief Series No 18; FAO: Rome, Italy. Available online: https://www.fao.org/3/cb3808en/cb3808en.pdf (accessed on 20 February 2022).
  5. Taurino, E.; Bernetti, A.; Caputo, A.; Cordella, M.; De Lauretis, R.; D’Elia, I.; Di Cristofaro, E.; Gagna, A.; Gonella, B.; Moricci, F.; et al. Italian Emission Inventory Report 1990–2020 Informative Inventory Report 2022. Available online: https://www.isprambiente.gov.it/files2022/pubblicazioni/rapporti/iir_2022_italy-stampa-rev.pdf (accessed on 12 May 2022).
  6. OECD/FAO. OECD-FAO Agricultural Outlook 2020–2029; FAO, Rome/OECD Publishing: Paris, France, 2020; Available online: https://doi.org/10.1787/1112c23b-en (accessed on 8 February 2022).
  7. Gerber, P.J.; Steinfeld, H.; Henderson, B.; Mottet, A.; Opio, C.; Dijkman, J.; Falcucci, A.; Tempio, G. Tackling Climate Change Through Livestock: A Global Assessment of Emissions and Mitigation Opportunities; FAO: Rome, Italy, 2013. [Google Scholar]
  8. Reisinger, A.; Clark, H. How much do direct livestock emissions actually contribute to global warming? Glob. Chang. Biol. 2017, 24, 1–13. [Google Scholar] [CrossRef] [PubMed]
  9. FAO & Global Dairy Platform. Climate Change and the Global Dairy Cattle Sector: The role of the Dairy Sector in a Low-Carbon Future; FAO: Rome, Italy, 2019; Available online: http://www.fao.org/publications/card/en/c/CA2929EN/ (accessed on 10 February 2022).
  10. Llonch, P.; Haskell, M.J.; Dewhurst, R.J.; Turner, S.P. Current available strategies to mitigate greenhouse gas emissions in livestock systems: An animal welfare perspective. Anim. Int. J. Anim. Biosci. 2017, 11, 274–284. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  11. Henry, B.K.; Eckard, R.J.; Beauchemin, K.A. Review: Adaptation of ruminant livestock production systems to climate changes. Animal 2018, 12, 445–456. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Nardone, A.; Ronchi, B.; Lacetera, N.; Ranieri, M.S.; Bernabucci, U. Effects of climate changes on animal production and sustainability of livestock systems. Livest. Sci. 2010, 130, 57–69. [Google Scholar] [CrossRef]
  13. Smith, P.; Clark, H.; Dong, H.; Elsiddig, E.A.; Haberl, H.; Harper, R.; House, J.; Jafari, M.; Masera, O.; Mbow, C.; et al. Agriculture, Forestry and Other Land Use (AFOLU). 2014. Available online: https://www.ipcc.ch/site/assets/uploads/2018/02/ipcc_wg3_ar5_chapter11.pdf (accessed on 12 May 2022).
  14. Thoma, G.; Popp, J.; Shonnard, D.; Nutter, D.; Matlock, M.; Ulrich, R.; Hellogg, W.; Neiderman, Z.; Kemper, N.P.; Adom, F.X.; et al. Regional analysis of greenhouse gas emissions from USA dairy farms: A cradle to farm-gate assessment of the American dairy industry circa 2008. Int. Dairy J. 2013, 31, S29–S40. [Google Scholar] [CrossRef] [Green Version]
  15. Rotz, C.A. Modeling greenhouse gas emissions from dairy farms. J. Dairy Sci. 2018, 101, 6675–6690. [Google Scholar] [CrossRef]
  16. de Vries, M.; de Boer, I.J.M. Comparing Environmental Impacts for Livestock Products: A Review of Life Cycle Assessments. Live Sci. 2010, 128, 1–11. [Google Scholar] [CrossRef]
  17. Crosson, P.; Shalloo, L.; O’Brien, D.; Lanigan, G.J.; Foley, P.A.; Boland, T.M.; Kenny, D.A. A Review of Whole Farm Systems Models of Greenhouse Gas Emissions from Beef and Dairy Cattle Production Systems. Anim. Feed. Sci. Technol. 2011, 166–167, 29–45. [Google Scholar] [CrossRef]
  18. Hagemann, M.; Hemme, T.; Ndambi, A.; Alqaisi, O.; Sultana, M.N. Benchmarking of Greenhouse Gas Emissions of Bovine Milk Production Systems for 38 Countries. Anim. Feed Sci. Technol. 2011, 166–167, 46–58. [Google Scholar] [CrossRef]
  19. Milani, F.X.; Nutter, D.; Thoma, G. Invited Review: Environmental Impacts of Dairy Processing and Products: A Review. J. Dairy Sci. 2011, 94, 4243–4254. [Google Scholar] [CrossRef]
  20. Yan, M.-J.; Humphreys, J.; Holden, N.M. An Evaluation of Life Cycle Assessment of European Milk Production. J. Environ. Man. 2011, 92, 372–379. [Google Scholar] [CrossRef] [PubMed]
  21. Laca, A.; Gómez, N.; Laca, A.; Díaz, M. Overview on GHG Emissions of Raw Milk Production and a Comparison of Milk and Cheese Carbon Footprints of Two Different Systems from Northern Spain. Environ. Sci. Pollut. Res. 2020, 27, 1650–1666. [Google Scholar] [CrossRef] [PubMed]
  22. Castanheira, É.G.; Dias, A.C.; Arroja, L.; Amaro, R. The Environmental Performance of Milk Production on a Typical Portuguese Dairy Farm. Agri. Sys. 2010, 103, 498–507. [Google Scholar] [CrossRef]
  23. González-García, S.; Castanheira, E.G.; Dias, A.C.; Arroja, L. Using Life Cycle Assessment Methodology to Assess UHT Milk Production in Portugal. Sci. Total Environ. 2013, 442, 225–234. [Google Scholar] [CrossRef] [PubMed]
  24. Berton, M.; Bovolenta, S.; Corazzin, M.; Gallo, L.; Pinterits, S.; Ramanzin, M.; Ressi, W.; Spigarelli, C.; Zuliani, A.; Sturaro, E. Environmental impacts of milk production and processing in the Eastern Alps: A “cradle-to-dairy gate” LCA approach. J. Clean Prod. 2021, 303, 127056. [Google Scholar] [CrossRef]
  25. Kumar, M.; Kumar Choubey, V.; Deepak, A.; Gedam, V.V.; Raut, R.D. Life cycle assessment (LCA) of dairy processing industry: A case study of North India. J. Clean Prod. 2021, 326, 129331. [Google Scholar] [CrossRef]
  26. O’Brien, D.; Shalloo, L.; Patton, J.; Buckley, F.; Grainger, C.; Wallace, M. A Life Cycle Assessment of Seasonal Grass-Based and Confinement Dairy Farms. Agric. Syst. 2012, 107, 33–46. [Google Scholar] [CrossRef]
  27. Guerci, M.; Knudsen, M.T.; Bava, L.; Zucali, M.; Schönbach, P.; Kristensen, T. Parameters Affecting the Environmental Impact of a Range of Dairy Farming Systems in Denmark, Germany and Italy. J. Clean Prod. 2013, 54, 133–141. [Google Scholar] [CrossRef]
  28. Grassauer, F.; Herndl, M.; Nemecek, T.; Fritz, C.; Guggenberger, T.; Steinwidder, A.; Zollitsch, W. Assessing and improving eco-efficiency of multifunctional dairy farming: The need to address farms’ diversity. J. Clean Prod. 2022, 338, 130627. [Google Scholar] [CrossRef]
  29. Fantin, V.; Buttol, P.; Pergreffi, R.; Masoni, P. Life Cycle Assessment of Italian High Quality Milk Pro-duction. A Comparison with an EPD Study. J. Clean Prod. 2012, 28, 150–159. [Google Scholar] [CrossRef]
  30. Pirlo, G.; Carè, S. A Simplified Tool for Estimating Carbon Footprint of Dairy Cattle Milk. Ital. J. Anim. Sci. 2013, 4, e81. [Google Scholar] [CrossRef]
  31. Guerci, M.; Bava, L.; Zucali, M.; Tamburini, A.; Sandrucci, A. Effect of summer grazing on Carbon Footprint of Milk in Italian Alps: A Sensitivity Approach. J. Clean Prod. 2014, 73, 236–244. [Google Scholar] [CrossRef]
  32. Pirlo, G.; Lolli, S. Environmental Impact of Milk Production from Samples of Organic and Conventional Farms in Lombardy (Italy). J. Clean Prod. 2019, 211, 962–971. [Google Scholar] [CrossRef]
  33. Famiglietti, J.; Guerci, M.; Proserpio, C.; Ravaglia, P.; Motta, M. Development and testing of the Product Environmental Footprint Milk Tool: A comprehensive LCA tool for dairy products. Sci. Total Environ. 2019, 648, 1614–1626. [Google Scholar] [CrossRef] [PubMed]
  34. ISO 14044: Environmental Management—Life Cycle Assessment—Requirements and Guidelines. 2006. Available online: https://www.iso.org/standard/38498.html (accessed on 10 October 2021).
  35. Wernet, G.; Bauer, C.; Steubing, B.; Reinhard, J.; Moreno-Ruiz, E.; Weidema, B. The ecoinvent database version 3 (part I): Overview and methodology. Int. J. Life Cycle Assess 2016, 21, 1218–1230. Available online: http://link.springer.com/10.1007/s11367-016-1087-8 (accessed on 15 November 2021). [CrossRef]
  36. Gerber, P.; Vellinga, T.; Opio, C.; Steifeld, H. Productivity gains and greenhouse gas emissions intensity in dairy systems. Livest. Syst. 2011, 139, 100–108. [Google Scholar] [CrossRef]
  37. CLAL.it. Available online: https://www.clal.it/index.php?section=quadro_europa&country=IT (accessed on 12 January 2022).
  38. Intergovernmental Panel on Climate Change—IPCC. IPCC Fifth Assessment Report. The Physical Science Basis. 2013. Available online: http://www.ipcc.ch/report/ar5/wg1/ (accessed on 20 February 2022).
  39. PRé. SimaPro Database Manual—Methods Library, 4.15. 2020. Available online: https://simapro.com/wp-content/uploads/2020/06/DatabaseManualMethods.pdf (accessed on 20 September 2021).
  40. FAO. The Future of Food and Agriculture: Trends and Challenges; Food and Agriculture Organization of the United Nations: Rome, Italy, 2017; Available online: http://www.fao.org/publications/fofa/en/ (accessed on 12 May 2022).
  41. ISTAT. 2017. Available online: http://dati.istat.it/Index.aspx (accessed on 21 September 2020).
  42. CLAL. 2020. Available online: https://teseo.clal.it/?section=produttivita_capi (accessed on 21 September 2020).
  43. ISTAT. 2016. Available online: http://dati.istat.it/Index.aspx (accessed on 21 September 2020).
  44. Roma, R.; Corrado, S.; De Boni, A.; Forleo, M.B.; Fantin, V.; Moretti, M.; Palmieri, N.; Vitali, A.; Camillo, D.C. Life cycle assessment in the livestock and derived edible products sector. In Life Cycle Assessment in the Agri-food Sector; Springer: Cham, Switzerland, 2015; pp. 251–332. [Google Scholar]
  45. Directive (EU) 2019/of the European Parliament and of the Council of 5 June 2019 on the Reduction of the Impact of Certain Plastic Products on the Environment. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32019L0904 (accessed on 12 May 2022).
  46. Foschi, E.; Zanni, S.; Bonoli, A. Combining Eco-Design and LCA as Decision-Making Process to Prevent Plastics in Packaging Application. Sustainability 2020, 12, 9738. [Google Scholar] [CrossRef]
  47. Pubblicazioni: PlasticsEurope. Available online: https://plasticseurope.org/wp-content/uploads/2021/10/2019-Plastics-the-facts.pdf (accessed on 12 May 2022).
  48. Plastic Fantastic—Solving the Problem We Created for Ourselves|Heinrich Böll Foundation | Southeast 359 Asia Regional Office. Available online: https://th.boell.org/en/2019/10/31/plastic-fantastic-solving-problem-we-created-ourselves (accessed on 12 May 2022).
  49. Jambeck, J.R.; Geyer, R.; Wilcox, C.; Siegler, T.R.; Perryman, M.; Andrady, A.; Narayan, R.; Law, K.L. Plastic 357 waste inputs from land into the ocean. Science 2015, 347, 768–771. [Google Scholar] [CrossRef]
  50. Bonoli, A.; Zanni, A.; Awere, E. Organic waste composting and sustainability in low-income communities in Palestine: Lessons from a pilot project in the village. Intl. J. Rec. Org. Waste Agric. 2018, 8, 253–262. [Google Scholar] [CrossRef] [Green Version]
  51. Mura, M.; Longo, M.; Zanni, S. Circular economy in Italian SMEs: A multi-method study. J. Clear Prod. 2020, 245, 118821. [Google Scholar] [CrossRef]
  52. Kumar Tarei, P.; Kumar, G.; Ramkumar, M. A Mean-Variance robust model to minimize operational risk and supply chain cost under aleatory uncertainty: A real-life case application in petroleum supply chain. Comp. Ind. Eng. 2022, 166, 107949. [Google Scholar] [CrossRef]
  53. Anderson, J.D.; Mitchell, J.L.; Maples, J.G. Invited Review: Lessons from the COVID-19 pandemic for food supply chains**Presented as part of the ARPAS Symposium: Building a Resilient Food Production System in the US: What COVID-19 and Other Black Swan Events Have Exposed About Modern Food Production, July 2021. Appl. Animal Sci. 2021, 37, 738–747. [Google Scholar] [CrossRef]
  54. UN: SHARED RESPONSIBILITY, GLOBAL SOLIDARITY: Responding to the socio-economic impacts of COVID-19 March 2020. Available online: https://unsdg.un.org/sites/default/files/2020-03/SG-Report-Socio-Economic-Impact-of-Covid19.pdf (accessed on 12 May 2022).
  55. Institut de l’Elevage. Le Consommations d’Energie en Batiment d’Elevage Laitier. Repères de Consommations et Pistes D’économies. Collection: Synthèse, Janvier. 2009. Available online: http://www.bretagne.synagri.com/ca1/PJ.nsf/TECHPJPARCLEF/19296/$File/CONSOMMATION%20BAT%20ELEVAGE%20LAITIER%20ADEME.pdf?OpenElement (accessed on 28 March 2022).
  56. Bernabucci, U.; Biffani, S.; Buggiotti, L.; Vitali, A.; Lacetera, N.; Nardone, A. The effects of heat stress in Italian Holstein dairy cattle. J. Dairy Sci. 2014, 97, 471–486. [Google Scholar] [CrossRef] [PubMed]
  57. Arvidsson Segerkvist, K.; Hansson, H.; Sonesson, U.; Gunnarsson, S. Research on Environmental, Economic, and Social Sustainability in Dairy Farming: A Systematic Mapping of Current Literature. Sustainability 2020, 12, 5502. [Google Scholar] [CrossRef]
  58. Istituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia-Romagna “BRUNO UBERTINI”, Classyfarm. Available online: http://www.classyfarm.it (accessed on 22 April 2022).
Sustainability 14 06028 g001 550
Figure 1. Conceptual model flowchart.
Figure 1. Conceptual model flowchart.
Sustainability 14 06028 g001
Sustainability 14 06028 g002 550
Figure 2. LCA results in terms of the relative impacts of each process.
Figure 2. LCA results in terms of the relative impacts of each process.
Sustainability 14 06028 g002
Sustainability 14 06028 g003 550
Figure 3. Impact results for external production and transport of animal feed, in terms of kgCO2eq, referred to the FU.
Figure 3. Impact results for external production and transport of animal feed, in terms of kgCO2eq, referred to the FU.
Sustainability 14 06028 g003
Sustainability 14 06028 g004 550
Figure 4. Impact results, in terms of kgCO2eq, referred to the FU.
Figure 4. Impact results, in terms of kgCO2eq, referred to the FU.
Sustainability 14 06028 g004
Table
Table 1. Farm-specific primary data used to design the conceptual model for the assessment.
Table 1. Farm-specific primary data used to design the conceptual model for the assessment.
CategoryFarm-Specific InformationData
Farm sizeSite A2 ha
Site B0.5 ha
Site C1 ha
Site D1.9 ha
Cropland560 ha
Average number of animalsMilking cows564
Dry cows84
Replacements622
Calves97
Bulls1
Animal turnoverReplacement rate35%
Longevity2.5 deliveries
Adult mortality3%
Calf mortality1.87%
Productivity dataAnnual milk production66,000 quintals
Bought-in feed (source in brackets)Sunflower seeds (Black Sea)191,094 kg
Soybean (Modena province)9148 kg
Maize (Modena province)181,598 kg
Protein feed (Cremona province)22,042 kg
Water consumption (annual)Drinking water27,963,555.20 L
Cooling water72,000 L
Fuel consumption (annual)Agricultural e livestock farming machines181,995 L
Electric energy (annual)Ventilation system141,849 kWh
Other operations516,844 kWh
Manure managementPalatable slurry storage6 tanks (12,667 m3)
3 cesspits (1001 m3)
Non-palatable slurry storage9 open pits (12,229 m3)
Plastic wastePlastic wrapping and cleaning product containers163,900 kg
Table
Table 2. Impact calculated for the yearly production of the farm and the functional unit, i.e., 1 kg of FPCM, expressed in terms of kg CO2eq.
Table 2. Impact calculated for the yearly production of the farm and the functional unit, i.e., 1 kg of FPCM, expressed in terms of kg CO2eq.
ProcessTotal Yearly Impact
(kg CO2eq)		Impact
(kg CO2eq/1 kg of FPCM)
Farm operating machines822,797.9519.97
Electricity consumption61,470.301.49
Water consumption10,678.270.26
Animal feed244,051.515.92
Transport of animal feed8903.680.22
Packaging film, low density polyethylene (LDPE)443,183.5710.76
Packaging, for fertilizers or pesticides, cleaning products (PE)4234.030.10
PE recycling−108,227.27−2.63
Incineration of waste plastic175,486.474.26
Landfilling of waste plastic2337.880.06
Total1,664,916.3840.41
Table
Table 3. Impact calculated for the yearly supply of animal feed and the functional unit, i.e., 1 kg of FPCM, expressed in terms of kg CO2eq.
Table 3. Impact calculated for the yearly supply of animal feed and the functional unit, i.e., 1 kg of FPCM, expressed in terms of kg CO2eq.
ProcessTotal Yearly Impact
(kg CO2eq)		Impact
(kg CO2eq/1 kg of FPCM)
Production
Sunflower42,244.291.025
Maize128,295.483.114
Soybean35,211.730.855
Protein feed38,300.000.930
Transport
Sunflower6686.420.162
Maize1635.060.040
Soybean82.370.002
Protein feed499.830.012
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Zanni, S.; Roccaro, M.; Bocedi, F.; Peli, A.; Bonoli, A. LCA to Estimate the Environmental Impact of Dairy Farms: A Case Study. Sustainability 2022, 14, 6028. https://doi.org/10.3390/su14106028

AMA Style

Zanni S, Roccaro M, Bocedi F, Peli A, Bonoli A. LCA to Estimate the Environmental Impact of Dairy Farms: A Case Study. Sustainability. 2022; 14(10):6028. https://doi.org/10.3390/su14106028

Chicago/Turabian Style

Zanni, Sara, Mariana Roccaro, Federica Bocedi, Angelo Peli, and Alessandra Bonoli. 2022. "LCA to Estimate the Environmental Impact of Dairy Farms: A Case Study" Sustainability 14, no. 10: 6028. https://doi.org/10.3390/su14106028

APA Style

Zanni, S., Roccaro, M., Bocedi, F., Peli, A., & Bonoli, A. (2022). LCA to Estimate the Environmental Impact of Dairy Farms: A Case Study. Sustainability, 14(10), 6028. https://doi.org/10.3390/su14106028

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop