The carbon footprint of integrated milk production and renewable energy systems – A case study

Science of the Total Environment 609 (2017) 1286–1294

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

The carbon footprint of integrated milk production and renewable energy systems – A case study Elisabetta Vida, Doriana Eurosia Angela Tedesco ⁎ Department of Environmental Science and Policy, University of Milan, Via Celoria 2, 20133 Milan, Italy

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• The Carbon footprint of milk was calculated building the Life Cycle Inventory according to ISO 14040 and 14044. • The main environmental hotspots were enteric methane (CH4) emissions. • Anaerobic digestion and photovoltaic systems were the mitigation strategies. • Implementing renewable energy systems allowed to reduce carbon footprint of milk.

a r t i c l e

i n f o

Article history: Received 12 May 2017 Received in revised form 23 July 2017 Accepted 30 July 2017 Available online xxxx Editor: D. Barcelo Keywords: Dairy farm Manure management Anaerobic digestion Photovoltaic system Carbon credits

a b s t r a c t Dairy farms have been widely acknowledged as a source of greenhouse gas (GHG) emissions. The need for a more environmentally friendly milk production system will likely be important going forward. Whereas methane (CH4) enteric emissions can only be reduced to a limited extent, CH4 manure emissions can be reduced by implementing mitigation strategies, such as the use of an anaerobic digestion (AD). Furthermore, implementing a photovoltaic (PV) electricity generation system could mitigate the fossil fuels used to cover the electrical needs of farms. In the present study to detect the main environmental hotspots of milk production, a Life Cycle Assessment was adopted to build the Life Cycle Inventory according to ISO 14040 and 14044 in a conventional dairy farm (1368 animals) provided by AD and PV systems. The Intergovernmental Panel on Climate Change tiered approach was adopted to associate the level of emission with each item in the life cycle inventory. The functional unit refers to 1 kg of fat-and-protein-corrected-milk (FPCM). In addition to milk products, other important coproducts need to be considered: meat and renewable energy production from AD and PV systems. A physical allocation was applied to attribute GHG emissions among milk and meat products. Renewable energy production from AD and PV systems was considered, discounting carbon credits due to lower CH4 manure emissions and to the minor exploitation of fossil energy. The CF of this farm scenario was 1.11 kg CO2eq/kg FPCM. The inclusion of AD allowed for the reduction of GHG emissions from milk production by 0.26 kg CO2eq/kg FPCM. The PV system contribution was negligible due to the small dimensions of the technology. The results obtained in this study confirm that integrating milk production with other co-products, originated from more efficient manure management, is a successful strategy to mitigate the environmental impact of dairy production. © 2017 Elsevier B.V. All rights reserved.

⁎ Corresponding author. E-mail address: [email protected] (D.E.A. Tedesco).

http://dx.doi.org/10.1016/j.scitotenv.2017.07.271 0048-9697/© 2017 Elsevier B.V. All rights reserved.

E. Vida, D.E.A. Tedesco / Science of the Total Environment 609 (2017) 1286–1294

1. Introduction In thinking about the future, the world's growth population is alarming because humankind currently consumes more resources than the planet can generate (United Nations, 2015). The perceived limit to produce food for a growing global population and the sustainability of food production systems represent a source of concern. Doubt has been cast on the possibility to continue using high levels of external inputs in production, increasing the share of livestock in total output, expanding cultivated land, irrigation and transporting products over long distances (Alexandratos and Bruinsma, 2012). In this scenario, livestock supply chains contribute to total greenhouse gas (GHG) emissions, representing 14.5% of all human-induced emissions (IPCC, 2007). Cattle milk contributes 20% of total sector emissions (Gerber et al., 2013), which consists mostly of methane (CH4) from gastroenteric fermentation. The reduction of CH4 formation can be mostly achieved by approaches that improve feed efficiency. It is well known that poor quality diets of ruminant farm animals lead to large emissions of CH4 per unit of useful animal product (Ward et al., 1993). Nevertheless, the potential to reduce CH4 formation through nutrition and feeding management is modest because the range of alteration is restricted by the delicate rumen ecosystem compromising milk yield. Methanogens are a crucial part of rumen ecology. In ruminants, the rumen microorganisms degrade fiber as an energy source for the animal. Complex carbohydrates are fermented to volatile fatty acids through multiple-step pathways that produce hydrogen (H2). Methanogens convert H2 to CH4, which is removed by the cow through eructation and breathing (Knapp et al., 2014). Indeed, this essential rumen microorganism symbiotic relationship has energy inefficiencies (Van Nevel and Demeyer, 1988). Milk production and the impact generated to produce milk need to be evaluated with attention because milk and dairy products are a vital source of human nutrition. Optimizing resource use can maximize the profitability of confinement dairy systems and improve the environmental sustainability of milk production (Capper et al., 2009). To achieve dairy farm sustainability, it is important to implement different strategies for mitigating the environmental impact of milk production at the farm level, specifically GHG emissions from CH4, nitrous oxide (N2O) and carbon dioxide (CO2). The challenge is very difficult; however, as suggested by Gerber et al. (2013), GHG emissions by the livestock sector could be cut by 30% through the wider use of existing best practices and technologies. The management and improvement of dairy farms has led to steady change and modernization (Castro et al., 2012), along with greater attention to GHG emissions. Increasing animal productivity seems to be a way to reduce CH4 released due to the decreased numbers of cows required to maintain a given milk production (Moss et al., 2000). Moreover, an increase in milk yield per lactation was observed when milking frequencies increased from 2 to 3 times per day (DePeters et al., 1985; Klei et al., 1997). Automatic milking systems increase voluntary milking frequency and consequently milk yield. Furthermore, Yan et al. (2010) indicated that CH4 energy output as a proportion of gross energy intake or milk energy output was negatively related to levels of milk production. This suggests that in the future, cows will cope with higher yields, at least partly, a high milking frequency (Løvendahl and Chagunda, 2011) and, in turn, a reduction in CH4 emissions per unit product (Moss et al., 2000; Yan et al., 2010). Poor fertility means that more cows are required to maintain a given milk yield; therefore, total herd emissions will increase. Total herd emissions of CH4 can be reduced if fertility is improved to an ideal level, which should reduce CH4 emissions on a herd basis and also per liter of milk produced (Garnsworthy, 2004). Moreover, maintaining healthy dairy cows and preventing diseases, such as mastitis, allow for reduced GHG emissions due to unproductive periods. In dairy system production, the natural degradation of manure is a further contributor to uncontrolled CH4 emissions during storage,

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which is undesirable because of the global warming effects (Appels et al., 2011). Processing dairy manure in an anaerobic digestion (AD) to generate biogas can reduce GHG emissions during storage and land application (Aguirre-Villegas et al., 2015). Following digestion, the digestate can still be land applied as fertilizer and emit less CH4 than non-digested manure during storage, as it contains less carbon and volatile solids (Aguirre-Villegas et al., 2014). Renewable energy production from manure is a good mitigation policy to improve dairy farm sustainability (Bacenetti et al., 2016b; Lijó et al., 2017). Fossil fuels are replaced by the cogeneration of biogas into electricity to counteract dairy farm GHG emissions. Another way to reduce the exploitation of fossil fuels and GHG emissions is the use of photovoltaic system (PV) technology that converts solar energy into electricity. Lighting, milking, pumping and cooling of milk are the daily activities requiring electrical appliances and consumption. Generally speaking, PV technology directly generates electricity from solar energy, is fossil fuel free and saves on energy consumption and GHG emissions during operation (Mahesh and Shoba Jasmin, 2013; Peng et al., 2013). The PV system represents a promising source of electricity generation in dairy farms. Hence, the dairy farm becomes an electricity producer instead of an electricity consumer, reducing CO2 emissions with less environmental damage (Bey et al., 2016; Peng et al., 2013). Measuring the GHG emissions of milk production on farms is difficult, and an accurate GHG inventory is important to determine the emission profile of the livestock sector considering parameters such as productivity, feed characteristics, feed intake and manure management systems (Cunha et al., 2016). To quantify the main GHG emissions, carbon footprint (CF) of a product allow to indicate its impacts on global warming. In order to evaluate the CF, the ISO/TS 14067 (2014) specifies principles, requirements and guidelines for the quantification and communication of the CF of a product, based on International Standards on Life Cycle Assessment (LCA) (ISO 14040 and ISO 14044) for the quantification of the main GHG emissions (ISO, 2006a; ISO, 2006b).To achieve a better reproducibility of the milk production from LCA and CF assessments, the Intergovernmental Panel on Climate Change (IPCC) provides standardized GHG emission factors and equations (IPCC, 2006). To follow the “from cradle to farm gate” procedure, LCA methodology helps to simulate emissions associated with milk before it is sold by the farm. The farm management scenarios delineate the emissions profile of specific milk production chains. The model calculates the annual on- and off-farm GHG emissions (CO2eq) from imported inputs, until milk is sold by the farm (O'Brien et al., 2014b). The present case study aimed to empirically assess the CF of milk production from a dairy farming system that intentionally directs its efforts to reduce environmental impact by using different mitigation option strategies. This is primarily accomplished by renewable energy production through AD and PV systems. The use of renewable energy in the milk production process can rectify the high demand of fossil fuel and reduce the CF of milk production, allowing for the ability to scale credits for lower CH4 manure emissions and renewable energy production. To perform the LCA evaluation, a combination of Tier 1 and 2 manure-based emission factor and equation (IPCC, 2006) procedures have been considered, taking into account the interactions between the dairy and renewable energy systems. Thus, the primary data from the case farm were divided into three main systems: 1) farm system, 2) AD system and 3) PV system. 2. Materials and methods 2.1. Farm description The environmental impact assessment was carried out following an intensive dairy farm located in the province of Bergamo (Italy) (45°29′ 1″N and 9°48′33″E), a highly concentrated area of farms and fields used for the cultivation of raw materials, primarily animal feed. The farm primarily produced milk from dairy cows and meat from male calves that

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were not designated for internal replacement. The farm was a conventional dairy herd of Italian Frisian cows with an annual 30% replacement rate. The herd included 1368 animals that were divided in different physiological groups: 460 lactating cows, 78 dry cows, 292 heifers (2– 15 months), 100 heifers (15 months-partum), 187 fattening calves and 251 pre-weaning calves. 2.2. Goal and scope definition The intended purpose of this study was to evaluate the environmental impact of milk production in the case farm. The study also aimed to evaluate the mitigation potential of integrating milk production with renewable energy systems, particularly AD and PV systems, on CF of kg fat-and-protein-corrected milk (FPCM). Therefore, the scope of the current study was to consider the GHG mitigation opportunity in a dairy farm system through improved manure management performed to avoid GHG emissions and to reduce the use of fossil electricity.The CF was calculated following the ISO/TS 14067 (2014), building the Life Cycle Inventory according to ISO 14040 and 14044 (ISO, 2006a, 2006b) and then calculating the emissions through the Tier 1 and 2 approach provided by IPCC (2006). 2.3. Functional unit, system boundary and allocation The approach chosen for the study was “from cradle to farm gate”. This choice allowed us to more closely detect the environmental hotspots of the dairy farm (Baldini et al., 2017). The functional unit (FU) represents the reference to which GHG emissions are attributed. The FU referred to a kilogram of milk, and it has been standardized to 4% fat and to 3.3% protein (FPCM) according to the following equation: FPCM (kg) = raw milk (kg)* (0.1226*fat% + 0.0776*protein% + 0.2534) (IDF, 2010).

On this farm, attributional LCA was chosen as more appropriate because this type of analysis gives a description of resource flows and emissions attributed to the functional unit (Meier et al., 2015). The system boundaries represent input and output flows considered in the evaluation. Fig. 1 summarizes the system boundaries considered in this study. The LCA system boundary refers to the first phase of a dairy supply chain, namely dairy farming. The boundary considers the whole life cycle of cows and calves inside the dairy farm, including the agricultural processes of feedstuffs (Palmieri et al., 2017). The choice of allocation procedure may affect the results of the LCA study because different allocation approaches lead to a wide range of results (Brankatschk and Finkbeiner, 2014). Hence, when referring to a given dairy system, the selection of a particular allocation method could be influenced by the advantages and disadvantages that are entailed with this choice, thus leading to possible distortions (Baldini et al., 2017). The application of LCA methodology to animal products is one of the most controversial issues because, in addition to the main product, there are co-products that need to be considered. Thus, it is important to divide the environmental burden among each analyzed product and co-product using the most suitable allocation method of emissions (IDF, 2010). For the dairy farm system, the meat generated from surplus calves and culled dairy cows was an important co-product. According to the International Dairy Federation (IDF, 2015), total emissions were allocated between milk and meat, yielding a physical allocation of 88% and 12%, respectively. Physical allocation was used instead of the economic allocation, because is not influenced by price variability of milk and meat. Moreover, system expansion approach was applied to dissolve multi functionality of the renewable energy systems, as reported by the ISO 14044 (2006b). After collection, the manure from the livestock stabled on straw was stored in stock piles before feeding the AD system; slurry, from the tanks below the recovery floor, was collected in the storage tank of the AD system. This approach is also known as the “avoided

Fig. 1. System boundaries of the farm system.

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burdens” method and has been mostly used for systems where a coproduct can replace one or more other products (Azapagic and Clift, 1999). Thus, according to Aguirre-Villegas et al. (2014), the energy coproducts ethanol, biodiesel and biogas, provide the same function as gasoline, diesel and natural gas. Negative emissions indicate that AD is able to displace more grid electricity than the emissions coming from the manure handling processes. AD significantly reduces GHG emissions even if displaced grid emissions were not considered (Aguirre-Villegas and Larson, 2017). With this approach, renewable energy production from AD and PV systems was accounted for as co-product to scale credits due to the minor fossil fuel exploitation and GHG emissions. 2.4. Life cycle inventory

Table 1 Data collected in the farm: overview and productions on yearly basis. System

Category

Farm system

Livestock

Components

Lactating cows Dry cows Heifers Pre weaning calves Fattening calves Production Milk Milk fat Milk protein Milk/animal/day Meat Purchased Seeds products Feed Inorganic fertilizer Pesticides/herbicides Other products Farm forages Maize silage Alfalfa silage Moisture corn silage Sorghum silage Ryegrass hay Wheat Diesel Energetic consumptions Electricity Biodigestion Energetic Electricity system consumptions Animals Slurry excreta

Biomass Energy production Photovoltaic Energy system production

Table 2 List of default factors used in the GHG emissions evaluated on yearly basis. Default factor

Value

Reference

DEa UEb Bo c

45 0.04 0.24 (dairy cows) 0.18 (other cattle) 0.2 (slurry system without natural crust cover at 12 °C) 0.02 (solid storage at 12 °C) 35.7 (dairy cows, slurry storage) 36.8 (dairy cows, solid storage) 25.2 (other cattle, slurry storage) 39.0 (other cattle, slurry storage) 83 (dairy cows) 36 (heifers) 33.6 (other cattle) 0.2 40 (dairy cows, slurry storage) 30 (dairy cows, solid storage) 35 (other cattle, slurry storage) 10 0.1 0.2 0.3

IPCC (2006a) IPCC (2006a) IPCC (2006a)

MCF(S,k)d

MS(T,S,k)e

Nex(T)f

The inventory data include all inputs and outputs flows relevant to the framework of this study and refer to the year 2015. Primary data regarding the case farm were collected by means of interviews with farmer and purchase invoices and were reported in Table 1. The primary data from the case farm were divided into three main systems: 1) farm system 2) AD system and 3) PV system. List of default factors used in the GHG emissions evaluation on yearly basis were reported in Table 2. System 1 involved livestock management (herd composition and housing systems) and production (milk and meat). Animals were kept in loose housing stable indoor systems, where lactating cows were housed on slatted floors with cubicles on pelleted straw; dry cows, heifers 13 months-partum and calves of 3–5 months were housed on straw litter; heifers of 5–12 months and fattening calves were housed on slatted floors; and pre-weaning calves were housed on straw litter in a single box. Lactating cows were milked three times a day. System 1 also involved the cultivation of farm feed such as alfalfa silage,

Manure Maize silage Mineral supplement Electric energy Thermal energy Electric energy

Amount 460 78 392 251 187 5, 943.159 t 3.54% 3.09% 35 kg 120 t liveweight 0.25 t 4160.18 t 1.2 t 0.22 t 376.07 t 80 ha 20 ha 50 ha 40 ha 20 ha 13 ha 75,000 L 227 MWh 204 MWh 21,900 m3 slurry (2555–2,920 m3 water washing of the milking parlor) 1825 t 73 t 3.0 t 2190 MWh 91.25 MWh 140–150 MWh

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MS(T,S)g FracGasMSh

FracleachMSi FracGASFj FracGASMk FracLEACH-(H)l

IPCC (2006a)

IPCC (2006a)

Regione Lombardia (Italy), 2016. DGR X/5171 IPCC (2006a) IPCC (2006a)

IPCC (2006a) IPCC (2006b) IPCC (2006b) IPCC (2006b)

a

Digestible energy (%). Urinary energy excretion. Maximum methane producing capacity for manure produced by livestock category T 3 (m CH4 kg−1 of VS excreted). d Methane conversion factors for each manure management system S by climate region k (%). e Fraction of livestock category T's manure handled using manure management system S in climate region k (Western Europe) (%). f Annual average N excretion per head of category T in the country (kg N animal−1 yr−1). g Fraction of total annual N excretion for each livestock category T that is managed in manure management system S (%). h Percent of managed manure nitrogen for livestock category T that volatilizes as NH3 and NOx in the manure management system S (%). i Percent of managed manure nitrogen losses for livestock category T due to runoff and leaching during solid and liquid storage of manure (%). j Fraction of synthetic fertilizer N that volatilizes as NH3 and NOx applied to soils (kg of N applied)−1. k Fraction of applied organic N fertilizer that volatizes as NH3 and NOx (kg N volatilized (kg of N applied)−1. l Fraction of all N added to/mineralized in managed soils in regions where leaching occurs (kg N (kg of N additions)−1). b c

ryegrass hay, sorghum silage, maize silage, moisture corn silage, and wheat, divided into a farm agricultural area of 160 ha. Purchased products (e.g., roughages and concentrates, fertilizers, pesticides, herbicides, seeds) and energy consumption were considered in system 1 and were recorded based on supplier specifications. Energy consumption involved all the fossil fuel (electricity and diesel) costs due to the different farm activities: field operations, silage processing, feeding, milking, refrigeration, house cooling and lighting. The dry matter content of total mixed rations for each physiological group was estimated based on the daily feed ration. System 2 involved the AD energy consumption and production (electricity and heat) from manure and biomass feedstock digestion. System 3 involved the PV system used for energy consumption and production. In the systems 2 and 3 for energy production, the infrastructure and their life cycle were not included in the life cycle inventory. 2.5. Mitigation strategies 2.5.1. Biogas energy system The AD system considered for this evaluation was composed of a storage tank (600 m3) and an anaerobic digester plant (4000 m3). AD had an electrical power of 250 kW used for combined heat and power production (CHP). The system included co-digestion of mainly slurry and manure and partially maize silage daily removed from the silo

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exposed surfaces, together with mineral supplement for the production of biogas in the farm-scale anaerobic digester. Slurries were removed daily from the tanks below the recovery floor with an automatic pump system linked to the storage tank (600 m3). The slurry storage was b24 h and moved to the anaerobic chamber due to the level sensors that maintain a constant inside volume. The AD operated in anaerobic and thermophilic conditions (50 °C). Consequently, the biogas was used for CHP generation with a daily production of 6000 kWh: 10.5% offsets the system needed on a yearly basis, and the remaining was sold to the national electric grid. The system also produced 91.25 MWh of thermal heat, which was used to heat the anaerobic chamber and the farmer's home. Digestate was stored in a covered tank and employed as an organic fertilizer for field application after storage for 180 days. 2.5.2. Photovoltaic system The photovoltaic system implemented in this farm was monocrystalline (mono-Si) solar cells, roof-integrated collector and gridconnected. The PV studied had a production scale module of 140–150 MWh yr−1; of this amount, approximately 45% contributed to cover the electrical needs of the farm, and the remaining wattage was sold to the national grid. 2.6. Emissions and impacts modeling The IPCC's Tiers approach (IPCC, 2006) was adopted to associate a level of emission with each item in the life cycle inventory. In this study, Tier 1 and Tier 2 methods were mainly used depending on the availability of the farm's economic and management data. The impact of the main GHG on global warming was calculated for a 100-year time horizon using a CO2 equivalence factor of 265 for N2O and of 28 for CH4 (Myhre et al., 2013). Country-specific default factors and Table 3 Emission factors used for estimating GHG emissions on the farm. GHG emissions

Emission factor (EF)

Reference

N2OD(mm)a N2OG(mm)b

0.005 (kg N2O-N/kg N) 0.01 (kg N2O-N (kg NH3-N + NOx-N volatilized)−1) 0.0075 (kg N2O-N (kg N leaching)−1) 0.01 (kg N2O-N (kg N input)−1) 8 (kg N2O-N ha−1 yr−1) 0.01 (kg N-N2O (kg NH3-N + NOx-N volatilized)−1) 0.0075 (kg N2O-N (kg N leached)−1) 0.2 (tons C (ton of urea)−1)

IPCC (2006a) IPCC (2006a)

N2OL(mm)c N2ODirect-Nd N2O(ATD)-Ne N2O(L)-Nf CO2-C emissionsg CO2-fossil fuel CO2-electric energy CO2-transport

IPCC (2006a) IPCC (2006b) IPCC (2006b) IPCC (2006b) IPCC (2006b)

3.2 (kg CO2eq (l diesel)−1) 0.061 (kg CO2eq kW h−1)

Opio et al. (2013) Opio et al. (2013)

0.375 (kg CO2eq ton−1 km−1)

Kristensen et al. (2015) Kiefer et al. (2015) Mogensen et al. (2014) Kristensen et al. (2015) O'Brien et al. (2014a)

CO2-soybean

0.5 (kg CO2eq (kg soybean−1))

CO2-maize

0.47 (kg CO2eq (kg ss−1))

CO2-sunflower

0.47 (kg CO2eq (kg ss−1))

CO2-barley

0.35 (kg CO2eq (kg ss−1))

Direct N2O emissions from manure management in the country (kg N2O yr−1). Indirect N2O emissions due to volatilization of N from manure management in the country (kg N2O yr−1). c Indirect N2O emissions due to leaching from manure management in the country (kg N2O yr−1). d Annual direct N2O-N emissions produced from managed soils (kg N2O-N yr−1). e Annual amount of N2O-N produced from atmospheric deposition of N volatilized from managed soils (kg N2O-N yr−1). f Annual amount of N2O-N produced from leaching of N additions to managed soils in regions where leaching occurs (kg N2O-N yr−1). g Annual C emissions from urea application (tons C yr−1).

emission factors used in the equations are presented in Tables 2 and 3, respectively. Briefly, farm animals were divided into physiological groups with an average weight per group (reported in Table 4), and a feed intake was calculated on the specific ration for the physiological phase. Pre-weaning calves were not considered in the evaluation. All farm data were automatically recorded by AfiFarm herd management software (Total Dairy Management, S. Paolo (BS), Italy). 2.6.1. CH4 emissions CH4 emissions were estimated according to the Tier 2 method (IPCC, 2006). CH4 from enteric fermentation was evaluated based on gross energy intake, a feed energy density of 18.45 MJ per kg dry matter and a methane conversion factor (Ym), calculated according to Gerber et al. (2013). CH4 emissions from manure management were calculated based on the volatile solid excretion rates and applying the CH4 emission factor from Tier 2 method (IPCC, 2006). The ash content of slurry was 0.08 of the dry matter feed intake, which was estimated by taking the mean values of three samples of slurry in the storage tank. Methane emissions from manure management were calculated only for the livestock categories stabled on straw because manure was stored in stockpiles, whereas the slurry emissions were not considered due to the bioconversion to biogas from the slurry in AD system. Methane emission factors for digestate were not considered in this evaluation due to the fact that digestate storage takes place in covered tanks. 2.6.2. N2O emissions Direct N2O emissions from manure management were computed according to the Tier 1 method (IPCC, 2006), based on the annual average N excretion (Nex(T)) per head of category in the country (Regione Lombardia, Italy, DGR X/5171, 2016). The fraction of total annual nitrogen (N) excretion for each livestock category was based on three samples of slurry from the storage tank of each animal category (MS(T,S)). Indirect N2O emissions, due to the volatilization of N and to the leaching from manure management, were, respectively computed according to the Tier 1 and Tier 2 methods, based on the amount of manure N lost during manure management (IPCC, 2006). N2O emissions associated with crop production were due to the N fertilizers to crops, accounting for both direct and indirect emissions. Direct emissions from managed soils were estimated according to the Tier 1 method (IPCC, 2006) and based on 1) the annual amount of N synthetic fertilizer, 2) the annual amount of animal N manure and, 3) the annual amount of N in crop residues and in mineral soils. The annual amount of N synthetic fertilizer applied to soils was 900 kg N year−1(about 5 kg N ha−1), which was calculated based on the amount of inorganic fertilizer bought in one year. The annual amount of animal N manure applied to soils was 65,145.39 kg N year−1, which was evaluated based on the amount of slurry produced daily. N2O emissions were computed on total amount of manure, although the digestate was applied on farm soil as an organic fertilizer up to the nitrogen amount limit for Region Lombardia, Italy (Decision EU 2016/1040, Region of Lombardia, 2016) and the surplus, exceeding the limits of nitrogen application, was spread in contiguous arable farms. The annual amount of N in crop residues and in mineral soils was, respectively 20,999.3 kg N year−1 and 78.73 kg N year−1, which was estimated based on the N content in crop residues (above and below ground) reported by Mogensen et al. (2014) for each farm

a

b

Table 4 Physiological groups and average weight of livestock. Category

Number

Average weight (kg)

Lactating cows Dry cows Heifers 2–15 months Heifers 15 months-partum Fattening calves

460 78 292 100 187

650 700 235 540 365

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crop. Indirect emissions from atmospheric deposition of N volatilized/ leaching from managed soils were computed according to the Tier 1 method (IPCC, 2006). 2.6.3. CO2 emissions Carbon dioxide emissions from urea application were computed according to the Tier 1 method (IPCC, 2006), based on the annual amount of urea fertilization used (0.6 ton year−1). Energy (electricity, fossil fuels) was used by the farm for field operations, silage processing, feeding, milking, refrigeration, house cooling and lighting. The CO2 emissions were calculated by multiplying the annual resource consumption with the corresponding emission factors. The impact related to the transport of purchased raw materials (compounder feedstuffs, forages, pesticides/herbicides, seeds, detergents, disinfectants, fertilizer) was estimated assuming a 16-t capacity diesel truck traveling an average distance of 4 km. The CO2 emissions due to the production process of off-farm protein and energy sources were estimated by multiplying the amount of dry matter with the corresponding emission factors.

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Table 5 GHG emissions inventory of the integrated dairy farm. Source

Tons CO2eq yr−1

Percentage of the total GHG

CH4 enterica CH4 manureb N2OD(mm)c N2OG(mm)d N2OL(mm)e N2ODirect-Nf N2O(ATD)-Ng N2O(L)-Nh Fossil fuel Electric energy CO2-C emissioni Transport of purchased inputs Production of purchased feedstock Emission t CO2eq Credits from energy generation Total Emission t CO2eq

5418 11.83 0.07 0.02 0.01 1230.91 54.63 81.63 240 9.76 0.44 14.17 685.91 0.7747.38 −787.36 6960.02

69.93 0.15 b0.01 b0.01 b0.01 15.89 0.71 1.05 3.10 0.13 b0.01 0.18 8.85 100%

a

CH4 emissions from enteric fermentation. CH4 emissions from manure management. Direct N2O emissions from manure management in the country. d Indirect N2O emissions due to volatilization of N from manure management in the country. e Indirect N2O emissions due to leaching from manure management in the country. f Annual direct N2O-N emissions produced from managed soils. g Annual amount of N2O-N produced from atmospheric deposition of N volatilized from managed soils. h Annual amount of N2O-N produced from leaching of N additions to managed soils in regions where leaching occurs. i Annual C emissions from urea application. b c

2.6.4. Carbon credits The first mitigation strategy involved the AD system, where the collected manure and a mixture of maize silage and supplements were processed in biogas and digestate. The electricity costs for biodigester and PV functionality were already accounted for in the general computing of energy consumption of the farm. For this scenario, it was assumed that all CH4 from biogas was converted to electricity through combustion (Aguirre-Villegas et al., 2015). Carbon credits from the AD system were estimated based on the amount of CH4 that would be emitted on the farm if the digester was not implemented. Moreover, the combustion of biogas displaces the use of fossil fuels for energy generation and, thus, contributes to additional carbon credits (Wang et al., 2010). The credits for surplus energy generated by the AD system, were calculated using an average emission factor of 0.386 kg CO2 per kWh of electricity and heat generation from different energy sources in Italy in 2009 (IEA, 2011). The second mitigation strategy involved the PV system that directly generated renewable electricity from solar energy. Carbon credits from the PV system were evaluated computing GHG emissions released for the exploitation of fossil fuel if PV was not implemented on the farm. The reduced environmental impact with the surplus energy from PV system was calculated using an average emission factor of 0.386 kg CO2 per kWh of electricity and heat generation from different energy sources produced in Italy in 2009 (IEA, 2011). Therefore, in order to verify the magnitude of environmental benefit associate with mitigation procedure the non-integrated scenario was compared to the integrated scenario as a mitigation option.

for promoting the correct rumen functionality. Nonetheless, the utilization of fiber substrate produces H2 as an intermediate product that would inhibit the utilization of feed in the rumen (Moss et al., 2000). Methanogenesis is a physiological process that allows methanogens to reduce and remove H2, promoting digestion processes. While improving feed intake and feed digestibility may partially reduce the problem, it is not enough to stem the emissions. Nutrition and feeding approaches have modest (2.5 to 15%) potential to reduce CH4 emissions, whereas rumen modifiers have very little success in terms of sustained CH4 reductions without compromising milk production (Knapp et al., 2014) or milk fat and protein content (Mc Geough et al., 2012). The second highest source of GHG was direct N2O emissions due to the treatment of soils with organic and inorganic fertilizer. However, whereas frequent manure application increases N2O (Chadwick et al., 2011; Jokela et al., 2004), it reduces GHG emissions due to the shorter manure storage (Aguirre-Villegas and Larson, 2017). This was confirmed by the lower GHG emissions observed for CH4 and direct and indirect N2O emissions from manure management.

3. Results and discussion 3.2. Carbon credits 3.1. GHG emissions GHG emissions were calculated to assess the contribution of each input to the total CF and to identify the main sources of environmental impact. In the current study, LCA allowed for the detection of the major critical environmental issues in the production chain of 1 kg FPCM. The inventory of GHG emissions from the sources considered in the evaluation is reported in Table 5. As expected, cattle production was dominated by CH4 from enteric fermentation, which has the greatest contribution to climate change, as reported by many authors (Bacenetti et al., 2016a; Battini et al., 2016; Kristensen et al., 2015; O'Brien et al., 2014a; O'Brien et al., 2014b; Vellinga et al., 2011; Wang et al., 2016;). Although enteric fermentation is the main process contributing to climate change, in ruminants the production of CH4 is physiological and the impact could be reduced to a limited extent. Furthermore, the use of fiber substrate in ruminant diets is essential

The effect of the implementation of mitigation strategies on the farm could be marked by comparing GHG emissions before and after considering carbon credits for electricity production. In fact, without considering the mitigation potential of AD and PV systems, total GHG emissions were 8592 ton CO2eq yr−1. Including these strategies in the computing phase, GHG emissions were 6960 ton CO2eq yr−1 with a difference of 1632 ton CO2eq yr−1. The contribution to environmental sustainability of including AD and PV systems on the case farm reduced GHG emissions from milk production by 0.26 kg CO2eq/kg FPCM. The results obtained from this study show that integrated dairy farms with the anaerobic digestion reduced the environmental burden of milk production. In fact, the AD can reduce CH4 emissions related to manure management by N50%, mostly in the form of CH4 during storage (Amon et al., 2006). For the manure-management scenario with AD, reductions from manure are mostly from the capture of CH4 during digestion which

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is then converted to CO2 during combustion (Aguirre-Villegas and Larson, 2017). The main environmental benefit from biogas energy systems compared to energy supply from fossil fuels is the potential to mitigate the global warming potential (GWP) impact category by recycling and converting CH4 emissions to electricity and offsetting fossil fuel emissions (Aguirre-Villegas et al., 2015; Bacenetti et al., 2016a; Ebner et al., 2015; Hijazi et al., 2016). The 0.26 kg CO2eq/kg FPCM mitigation is comparable to 0.23 kg CO2eq/kg FPCM reported in a study by AguirreVillegas et al. (2015). Similarly, on this case farm, 55% of the reduction was attributed to the avoided emissions from fossil energy exploitation, and 45% was attributed to the lower CH4 manure emissions. In this case study, the contribution of the PV system was negligible due to the small dimensions of the technology. Nevertheless, PV systems are a promising source of electricity generation for energy resource savings and covering electrical farm needs. The PV system has proved to be sustainable and environmentally friendly because solar is abundant, safe, clean and renewable (Breyer et al., 2015; Peng et al., 2013; Wang et al., 2014). There is a need for improvement in the implementation of these technologies to further reduce climate change and improve the environmental sustainability of dairy farms. Other mitigation options could be composting, solid-liquid separation, covering slurry storage, flaring CH4 and reducing methanogen inoculum by complete emptying of slurry storage at spring application (Jayasundara et al., 2016). 3.3. Carbon footprint of milk production The CF of the case farm was 1.11 kg CO2eq/kg FPCM. Analyzing similar studies in the literature reveals that the CF of this farm was 1) lower than estimates reported in other farms, such as the 1.35 to 1.50 kg CO2eq/kg FPCM range reported by Battini et al. (2016) and the 1.31 to 2.08 kg CO2eq/kg FPCM range reported by Wang et al. (2016); 2) comparable to the 0.92 kg CO2eq/kg FPCM reported by Mc Geough et al. (2012) and; 3) higher than the 0.63 to 0.77 kg CO2eq/kg FPCM range reported by Aguirre-Villegas et al. (2015). Battini et al. (2016) performed LCA of four typical milk production systems of the Po Valley, which was comparable to the case farm in terms of milk yield and feeding system. In their study, the dairy farm with the lowest GHG emissions was due to the low impact of land use change and a high milk yield per cow, resulting on a value of 1.35 kg CO2eq/kg FPCM. Wang et al. (2016) quantified GHG emissions and land use of eight confinement dairy farms that covered different milk production levels, herd structures and diet compositions. In their study GHG emissions are prevalently influenced by the combination of milk productivity and herd structure. Mc Geough et al. (2012) conducted an LCA of GHG emissions from a typical nongrazing dairy production system in eastern Canada, assessing several methods of allocation among co-products. The results of their study showed that the choice of co-product allocation method affected the relative GHG emissions attributed to milk and/or meat. AguirreVillegas et al. (2015) evaluated the effect of integrating dairy and bioenergy systems on land use, net energy intensity (NEI) and GHG emissions. As in our study, reductions in GHG emissions and NEI came mainly from the credits of avoided emissions, and primary energy from displaced fossil fuels and GHG emissions were further reduced when implementing AD as a manure management practice. However, it should be noted that making an absolute comparison of the results from other studies is not currently correct because each LCA performed considers different functional units, allocation procedures, system boundaries and computing methods. Thus, harmonization among LCA studies applied to the milk sector is still a goal to be achieved (Baldini et al., 2017). For example, the IPCC may update the equations system and provide a more detailed methodology for the sources of emissions to obtain a more standardized CF value with useful insights. Beyond the need of a standardized methodology, there is the requirement of a

constant improvement in a more comprehensive identification of hotspots and trade-offs and of the complementarity among different science domains (such as technological, environmental and territorial features, social and economic domains) (Sala et al., 2017). The average GHG emissions discount of this case farm was attributed to the reduced exploitation of fossil electricity and to the reduction of CH4 manure achieved by the integration of milk production with mitigation strategies (AD and PV systems). In order to verify the magnitude of environmental benefit associate with mitigation procedure the nonintegrated scenario was compared to the integrated scenario as a mitigation option (Fig. 2). Total GHG emissions decreased by 19% in the scenario with mitigation option. In the integrated dairy farm, the analysis showed that the GHG emissions from manure decreased by 98.60%, from the electric energy used in the farm decreased by 29% and, from the renewable energy production, the carbon credits were 9.8%. Thus, promoting integrated systems that produce renewable energy products alongside the primary production is desirable and necessary. Nevertheless, the resulting benefit is sustainable if farms allow better amortization of plant costs at a large scale. Although larger farms handle larger manure volumes, this difference reinforces the economic barriers that small farms face to invest in infrastructure (Aguirre-Villegas and Larson, 2017). The mitigation potential of AD and PV systems needs to be further improved by considering other management solutions that maximize their functionality, as well as an additional reduction margin. Further, cows were milked three times daily and, according to Erdman and Varner (1995), increasing milking frequency promotes an increased daily milk yield. This would mean that GHG emissions are allocated to a greater amount of milk resulting in an ameliorated CF per kg of FPCM. Moreover, future developments on mitigation procedures would involve the production of algae-based products as feed supplement in livestock diet using digested dairy manure as grown substrate. This would be an alternative to the current practice of spraying dairy manure effluents on agricultural fields converting N and P manure into valuable algal biomass. Furthermore, due to their protein content, carbohydrates, lipids, and fatty acids, algae may be useful as animal feed (Dibenedetto et al., 2016; Yaakob et al., 2014), thus contributing to dairy farm sustainability. Integrating milk production with other co-products originated from more efficient manure management needs further evaluation, although it is now considered one of the successful strategies to mitigate the environmental impact of milk production and to make the livestock sector more sustainable. Thus, ecologically sound manure management could minimize N2O consequences due to the application of organic and inorganic fertilizers to soils and allow for the implementation of a policy of circular economy of recovery and recycling (Markou and Georgakakis, 2011; Mulbry et al., 2008; Mulbry et al., 2005; Wang et al., 2010). 4. Conclusions The aim of the study was to evaluate GHG emissions on a conventional and high-performing dairy farm. The environmental evaluation was performed using the “from cradle to farm gate” LCA, a physical allocation of impacts and FPCM as a functional unit. On this farm, two main mitigation strategies were implemented and considered in the GWP assessment. The most impactful GHG source is enteric CH4, followed by direct N2O emissions due to the treatment of soils with organic and inorganic fertilizers. Whereas CH4 produced through enteric fermentation is an intrinsic property of the cow and an inevitable emission source of the dairy production system, the improved management of manure represents a sizeable GHG mitigation opportunity. The AD used in the case study resulted in an effective solution measure due to its contribution to environmental sustainability of − 0.26 kg CO2eq/kg FPCM. The climate change relief was principally due to the reduction of CH4 emissions from manure management and to the carbon credits cogenerated into renewable electric energy.

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Fig. 2. Comparison of the carbon footprints of milk in non-integrated and integranted dairy farm.

Environmental sustainability could be further improved by utilizing long-term storage of manure and digestate, allowing for the reduction in direct N2O emissions due to the treatment of soils with organic and inorganic fertilizers. The PV system demonstrated a contribution to environmental sustainability, even though it was negligible in this case study due to the low dimensions of the technology implemented. The contribution could be improved in light of improved solar panels technologies or simply by the number of PV systems. The results of the present study confirm that integrating milk production with other co-products, originated from more efficient manure management, is a successful strategy to mitigate the environmental impact of milk production to make the livestock sector more sustainable. The standardization of the LCA procedure is desirable and necessary for underlining the trade-offs among different environmental impact categories, avoiding the shift of the environmental burden.

Acknowledgements The authors would like to acknowledge the Cipolla Giovanni Luigi E Marino farm (Cascina Barona Sopra, 15, Antegnate, (BG), Italy) for supplying the data. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References Aguirre-Villegas, H.A., Larson, R.A., Reinemann, D.J., 2014. From waste-to-worth: energy, emissions, and nutrient implications of manure processing pathways. Biofuels Bioprod. Biorefin. 8, 770–793. Aguirre-Villegas, H.A., Passos-Fonseca, T.H., Reinemann, D.J., Armentano, L.E., Wattiaux, M.A., Cabrera, V.E., Norman, J.M., Larson, R., 2015. Green cheese: partial life cycle assessment of greenhouse gas emissions and energy intensity of integrated dairy production and bioenergy systems. J. Dairy Sci. 98, 1571–1592. Aguirre-Villegas, H.A., Larson, R.A., 2017. Evaluating greenhouse gas emissions from dairy manure management practices using survey data and lifecycle tools. J. Clean. Prod. 143, 169–179. Alexandratos, N., Bruinsma, J., 2012. World agriculture towards 2030/2050: the 2012 revision. ESA Working Paper No. 12-03. FAO, Rome. Amon, B., Kryvoruchko, V., Amon, T., Zechmeister-Boltenstern, S., 2006. Methane, nitrous oxide and ammonia emissions during storage and after application of dairy cattle slurry and influence of slurry treatment. Agric. Ecosyst. Environ. 112, 153–162. Appels, L., Lauwers, J., Degrève, J., Helsen, L., Lievens, B., Willems, K., Van Impe, J., Dewil, R., 2011. Anaerobic digestion in global bio-energy production: potential and research challenges. Renew. Sust. Energ. Rev. 15, 4295–4301. Azapagic, A., Clift, R., 1999. Allocation of environmental burdens in multiple-function systems. J. Clean. Prod. 7, 101–119.

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