animals Article Carbon Footprint Assessment of Spanish Dairy Cattle Farms: Effectiveness of Dietary and Farm Management Practices as a Mitigation Strategy Ridha Ibidhi 1,2 and Sergio Calsamiglia 3, * 1 Department of Eco-Friendly Livestock Science, Institute of Green Bio Science and Technology, Seoul National University, Pyeongchang, Gangwon 08826, Korea; ridha@snu.ac.kr or ibidhi_ridha@hotmail.fr 2 National Institute of Agronomic Recherche of Tunisia (INRAT), Laboratory of Animal Production and Forages, Université de Carthage, rue Hédi Karray, Ariana 2049, Tunisia 3 Animal Nutrition and Welfare Service (SNiBA), Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain * Correspondence: Sergio.Calsamiglia@uab.cat; Tel.: +34-93581495  Received: 21 September 2020; Accepted: 5 November 2020; Published: 10 November 2020  Simple Summary: Livestock production has been identified as an important source of greenhouse gas emissions. The current study was conducted to quantify the carbon footprint of Spanish dairy farms and to evaluate the potential of nutritional and management practices for mitigating methane emissions at farm level. The carbon footprint ranged from 0.67 to 0.98 kg CO2 -eq/kg of energy corrected milk. Simulation scenarios showed that methane emissions and the carbon footprint of milk could be reduced more through management practices rather than dietary strategies. Modelling may provide policy makers, farmers and stakeholders valuable information for planning and developing strategies to reduce the carbon footprint associated with milk production. Abstract: Greenhouse gas emissions and the carbon footprint (CF) were estimated in twelve Spanish dairy farms selected from three regions (Mediterranean, MED; Cantabric, CAN; and Central, CEN) using a partial life cycle assessment through the Integrated Farm System Model (IFSM). The functional unit was 1 kg of energy corrected milk (ECM). Methane emissions accounted for the largest contribution to the total greenhouse gas (GHG) emissions. The average CF (kg CO2 -eq/kg of ECM) was 0.84, being the highest in MED (0.98), intermediate in CEN (0.84), and the lowest in CAN (0.67). Two extreme farms were selected for further simulations: one with the highest non-enteric methane (MED1), and another with the highest enteric methane (CAN2). Changes in management scenarios (increase milk production, change manure collection systems, change manure-type storage method, change bedding type and installation of an anaerobic digester) in MED1 were evaluated with the IFSM model. Changes in feeding strategies (reduce the forage: concentrate ratio, improve forage quality, use of ionophores) in CAN2 were evaluated with the Cornell Net Carbohydrate and Protein System model. Results indicate that changes in management (up to 27.5% reduction) were more efficient than changes in dietary practices (up to 3.5% reduction) in reducing the carbon footprint. Keywords: greenhouse gas; carbon footprint; dairy farm; methane Animals 2020, 10, 2083; doi:10.3390/ani10112083 www.mdpi.com/journal/animals Animals 2020, 10, 2083 2 of 15 1. Introduction Climate change is a worldwide concern and the carbon footprint (CF) was proposed as an indicator to tackle the greenhouse gas (GHG) emissions from anthropogenic activities to the atmosphere [1]. Agricultural production, in general, and livestock in particular is recognized as an important contributor to GHG production [2]. The global livestock population contributes about 14.5% to global GHG emissions and the dairy sector is estimated to contribute by 4% of all anthropogenic GHG emissions [3,4]. Driven by population growth and diet changes towards an increase meat and dairy consumption, the production of livestock products globally is increasing. This increase will carry significant environmental costs through the accumulation of GHG in the atmosphere [5]. The main sources and types of GHG from ruminants include methane (CH4 ) production from enteric fermentation and animal manure, carbon dioxide (CO2 ) from land use, and nitrous oxide (N2 O) from manure and slurry management [6]. Mathematical modelling approaches, including life cycle assessment (LCA), Intergovernmental Panel on Climate Change (IPCC) guidelines [6,7], and predictive models (empirical and mechanistic), have been used to evaluate the contribution of livestock production to GHG emissions [8,9]. Many studies have been conducted to quantify GHG emissions from individual farm sources, but few have provided estimates of total farm-level emissions [10]. Therefore, whole farm predictive models were proposed to estimate the CF and the profile of GHG emissions by production stage integrating all sources of emissions [9,11]. In addition, whole farms models can be used as an effective tool to evaluate the overall impact of management strategies to reduce farm gate GHG emissions. The Integrated Farm System Model (IFSM) is a process-based mechanistic whole-farm simulation model that incorporates soil processes, crop growth, tillage, planting and harvest operations, feed storage, feeding, herd production, manure storage, and economics [12]. The IFSM was originally used in dairy farms in the United States [13] and successfully adapted to be used in dairy farms in Australia and Canada [14,15]. The Spanish dairy sector is a major industry with about 850,000 milking cows producing 7,100,000 ton/year of raw milk [16]. Furthermore, Spain is committed to the United Nations Framework Convention on Climate Change (UNFCCC) and annually reports its national emissions inventory following guidelines promoted by the IPCC [6] to limit or reduce GHG from different sectors through national measures. Previous studies conducted in Spain used different methods to estimate the CF of milk from dairy cattle [17,18]. Del Prado et al. [17] assessed the CF of milk from 17 commercial confined dairy farms in the Basque Country (northern Spain) using a combination of LCA [19], NGAUGE [20], and LANDDAIRY [13,21] models. Results revealed that the CF of milk ranged from 0.84 to 2.07 kg CO2 -eq/kg energy corrected milk (ECM). Laca et al. [18] also reported that the milk CF (CO2 -eq/kg fat-protein corrected milk) was higher in the semi-confined system (1.22 kg) compared with the pasture-based system (0.99 kg) in northern Spain. Despite the significant contributions of these studies, none has yet compared results from different regions and assess the impact of feeding and management changes on CH4 emissions and the milk CF. The objectives of the current study were: (1) to predict GHG emissions and assess the CF from twelve dairy farms from the three most productive regions in Spain; (2) to evaluate the diet contribution on enteric CH4 emissions; and (3) to evaluate the impact of dietary and management modifications on CH4 emissions and the milk CF. Animals 2020, 10, 2083 3 of 15 2. Materials and Methods 2.1. Farm Selection and Data Collection This study was carried out in the three most important regions in dairy production in Spain and with the highest potential for future expansion: Mediterranean (MED: Catalonia, Valencia and Murcia), Cantabric (CAN: Galicia, Asturias and Cantabria) and Central (CEN: Castilla-La Mancha, Castilla-Leon, Madrid and Aragon). MED has predominantly a Mediterranean climate, CAN has an Atlantic climate and CEN has a temperate continental climate [22]. CAN is home of 52% of the total dairy cattle milk production, while MED and CEN represent 14 and 20% of the total milk production, respectively [16]. Farm data were collected through a questionnaire from twelve Holstein dairy farms in Spain, four from each region. Selected dairy farm represented a significant number of dairy farms in a region in terms of size, forage and crops grown, livestock systems, labor organization, management practices and production technology used [23]. Within each region, farms were selected in a two-step process: In the first step, technical advisors with a sound knowledge of local conditions and with good contacts with farm managers were contacted. The main characteristics of farms were discussed with the aim to describe a typical farm for each region. In the second step, selected farms data were obtained by visiting the farms and completing a structured questionnaire. The questionnaire collected information from different areas: crop and soil, grazing, machinery, tillage and planting, crop harvest, feed storage, facilities, bedding type, herd productivity, feeding strategies and manure management. Diet composition from each cattle group of each herd was also requested (type and amounts of forages and concentrates) together with nutritional management strategies (feeding groups for each herd, feeding system, grazing activity and mineral supplementation). The general characteristics of the selected dairy farms from each region are presented in Table 1. Animals 2020, 10, 2083 4 of 15 Table 1. Characteristics of the selected dairy farms in Spain by region. Mediterranean Region Farms Cantabric Region Farms Central Region Farms Parameters Mean Max Min SD Mean Max Min SD Mean Max Min SD Farm structure Farm size (ha) 154 330 25 127.7 56 82 25 26.5 105 180 60 52.3 Grazing area (ha) - - - - 14.5 40.0 0.0 19.00 - - - - Italian Ryegrass (ha) 13.5 26.0 0.0 11.70 14.8 26.0 4.0 12.42 - - - - Alfalfa (ha) 8.3 24.0 0.0 11.32 0.0 0.0 0.0 0.00 10.3 25.0 0.0 12.39 Maize (ha) 27.5 60.0 0.0 32.02 14.3 36.0 0.0 17.56 12.5 40.0 0.0 18.93 Sorghum (ha) 8.8 35.0 0.0 17.50 - - - - - - - - Oat (ha) 29.3 70.0 0.0 35.06 - - - - 70.5 180.0 25.0 73.29 Herd and production characteristics Number of animals 280 440 106 154.9 161 240 64 72.6 319 400 198 88.3 Number of lactating cows 133 220 50 80.0 85 112 42 30.9 185 220 130 38.7 Number of dry cows 22 36 9 11.1 8 15 4 5.2 13 20 7 5.6 Heifers (1–2 years) 58 78 22 26.0 33 51 10 17.4 49 70 20 22.6 Calves 67 106 25 40.7 32 57 8 20.2 73 100 41 25.0 Milk yield (Tm/cow/year) 10.4 11.0 9.6 0.71 10.6 12.1 8.5 1.52 11.4 12.2 10.5 0.75 Milk protein (%) 3.6 3.9 3.4 0.23 3.4 3.8 3.1 0.30 3.3 3.5 3.2 0.15 Milk fat (%) 3.8 3.9 3.6 0.13 3.7 4.2 3.3 0.43 3.3 3.6 3.1 0.21 Feed composition and intake DMI 1 (kg/dairy cow/day) 24.5 25.8 22.3 1.53 23.5 25.2 21.3 1.64 25.6 27.2 23.8 1.73 % concentrates feeds 36 39 34 2.4 44 55 32 10.2 44 50 40 4.7 % forages 64 66 61 2.4 56 68 45 10.2 56 60 50 4.8 Manure and fertilizer application Liquid slurry application (m3 /ha) 45 50 40 4.1 27 50 0 20.7 49 60 40 8.3 NPK 2 (kg/year) 264 300 230 30.4 219 300 0 146.4 194 275 80 82.2 1 DMI; dry matter intake; 2 NPK: nitrogen, phosphorus and potassium. requested (type and amounts of forages and concentrates) together with nutritional management strategies (feeding groups for each herd, feeding system, grazing activity and mineral supplementation). The general characteristics of the selected dairy farms from each region are presented in Table 1. Animals 2020, 10, 2083 5 of 15 2.2. Modeling Procedure Farm emissions were simulated using the IFSM, a mechanistic model developed and validated 2.2. Modelinget by Chianese Procedure al. [12] to calculate CH4, N2O, CO2 and total GHG emissions from dairy cattle production Farm emissionsthrough systems a partial using were simulated LCA. the TheIFSM, IFSMawas further refined mechanistic model into a process‐based developed whole and validated by farm simulation Chianese including et al. [12] major to calculate CH4components for total , N2 O, CO2 and soil GHG processes, cropfrom emissions growth, dairytillage, planting, cattle production harvesting, feed storage, systems through feeding, a partial LCA. TheherdIFSM production, manure was further storage refined intoand economics in dairy a process-based wholefarms farm (Figure 1) [24]. The functional unit considered in this study was 1 kg of ECM at the simulation including major components for soil processes, crop growth, tillage, planting, harvesting, farm gate. The system boundaries feed storage, feeding, contained all relevant herd production, activities manure storagefrom andcradle to farm economics gate farms in dairy including on‐farm (Figure 1) [24]. activities, the production of farm inputs and carbon sequestration. Emissions The functional unit considered in this study was 1 kg of ECM at the farm gate. The system boundaries attributable to imported feeds and its transportation were excluded. In addition, the model was adapted contained all relevant activities from cradle to farm gate including on-farm activities, the production of to be used in Spanish farm inputsconditions and carbon such as soil type,Emissions sequestration. nutritional characteristics attributable of forages to imported feedsand andconcentrates, and its transportation the 10‐year average daily local weather. Weather variables included annual temperature, were excluded. In addition, the model was adapted to be used in Spanish conditions such as soil type, relative humidity, nutritionalwind velocity, mean characteristics precipitation of forages and solar radiation and concentrates, from three and the 10-year meteorological average daily local stations weather. corresponding to each region. Weather variables included annual temperature, relative humidity, wind velocity, mean precipitation and solar radiation from three meteorological stations corresponding to each region. Figure 1. System boundaries, primary and secondary greenhouse gas emissions sources and sinks for a partial1.life Figure cycle boundaries, System assessment of the carbon primary and footprint secondary(adopted fromgas greenhouse Rotz et al. [13]). emissions sources and sinks for a partial life cycle assessment of the carbon footprint (adopted from Rotz et al. [13]). In addition, the Cornell Net Carbohydrate and Protein System Model (CNCPS version 6.1; [25]) was used to evaluate the contribution of diets to enteric CH4 emissions of lactating cows on each farm and explore the impact of dietary modifications on CH4 emissions. The CNCPS is a mechanistic mathematical model that estimates cattle requirements and nutrient supply based on animal, environment, and feed composition information and predicts nitrogen (N), phosphorus (P) and CH4 emissions, enabling its integration in a whole-farm nutrient management plans [25,26]. 2.3. Methane Mitigation Scenarios Simulation To assess how changing dietary and management practices affects the enteric, non-enteric CH4 emissions and milk CF, different scenarios were compared. Two extreme dairy farms were selected for further simulations: one with the largest enteric CH4 emissions, and another with the largest non-enteric CH4 emissions. Animals 2020, 10, 2083 6 of 15 The CNCPS model [25] was used in the farm with the largest share of enteric CH4 emissions to evaluate three changes in the diet composition of milking cows to determine how these changes would affect enteric CH4 emissions: (i) Reducing by 10% the forage to concentrate ratio; (ii) Improving forages quality: the original ryegrass silage (9% crude protein, CP; 65% neural detergent fiber, NDF; and 8% lignin, ADL) was changed by an improved ryegrass silage (21% CP, 50% NDF and 7% ADL); (iii) Supplying 330 mg/day of monensin. The IFSM model was used in the farm with the largest share of non-enteric CH4 emissions to analyze the impact of five management changes on CH4 emissions: (i) Changing milk production from 9565 to 11,000 (+13%) and 8000 (−15%) kg milk/cow/year; (ii) Comparing five manure collection systems: top-loaded lined earthen basin, top-loaded lined concrete tank, bottom-loaded tank, covered tank and enclosed tank; (iii) Comparing four types of manure storage: semi-solid (12–14% dry matter; DM), solid (20% DM), slurry (8–10% DM) and liquid-slurry (5–7% DM); (iv) Comparing five types of bedding: straw, chopped straw, sand, sawdust and manure solids. (v) Installing an anaerobic digester. 3. Results 3.1. Greenhouse Gas Emissions Profile and the Carbon Footprint of the Selected Dairy Farms The estimated annual averages for CH4 , N2 O, CO2 , total GHG emissions and CF of milk (kg of CO2 -eq/kg of ECM) from dairy cattle farms by source of emissions (animal housing, manure storage, manure field application, grazing, fuel combustion and secondary emissions) from MED, CAN and CEN regions are presented in Table 2. The annual average CH4 emissions were the highest in MED (328 ± 25.5 kg/cow), intermediate in CEN (273 ± 52.0 kg/cow) and the lowest in CAN (251 ± 48.0 kg/cow). The highest annual average N2 O emissions were in MED (6.5 ± 1.81 kg/cow), intermediate in CEN (4.0 ± 1.52 kg/cow) and the lowest in CAN (3.2 ± 1.66 kg/cow). Negative values were obtained for the annual average net CO2 emissions on all farms as the positive contributions from animal and housing, manure storage, grazing and fuel combustion were offset by the net feed production. The average CF of milk in selected farms was 0.87 kg CO2 -eq/kg of ECM, being the highest in MED (1.0 ± 0.12), intermediate in CEN (0.8 ± 0.08) and the lowest in CAN (0.7 ± 0.07 kg of CO2 -eq/kg of ECM). The breakdown of total GHG emissions into component gases (enteric CH4 , non-enteric CH4 , N2 O and CO2 ) was examined in absolute terms and relative to each other for all farms. CH4 emissions from enteric fermentation and manure handling (non-enteric fermentation) represented the largest share of GHG emissions, accounting for more than 70% of total farm emissions. Animals 2020, 10, 2083 7 of 15 Table 2. The profile of greenhouse gas by source of emissions from the selected dairy farms by region. Mediterranean Region Farms Cantabric Region Farms Central Region Farms Greenhouse Gas Emissions Mean Max Min SD Mean Max Min SD Mean Max Min SD CH4 (kg/cow) 1 Animals and housing 202 217 180 17.2 110 194 25 93.3 187 205 168 17.0 Manure storage 126 152 96 23.8 56 91 14 40.3 86 166 33 60.8 Field-applied manure 0.2 0.4 0.1 0.14 0.1 0.2 0.0 0.08 0.3 0.6 0.1 0.24 Grazing 0.0 0.0 0.0 0.00 84.5 196.4 0.0 100.07 0.0 0.0 0.0 0.00 Total 328 352 292 25.5 251 284 180 48.0 273 335 223 52.0 N2 O (kg/cow) 2 Animals and housing 3.1 3.6 1.8 0.86 0.6 1.2 0.0 0.67 2.3 3.2 0.0 1.52 Manure storage 0.2 0.8 0.0 0.40 0.0 0.0 0.0 0.00 0.7 1.4 0.0 0.58 Cropland 3.2 4.8 2.4 1.13 2.7 5.7 1.0 2.17 1.0 1.6 0.6 0.43 Total 6.5 8.4 4.2 1.81 3.2 5.7 2.2 1.66 4.0 5.6 2.0 1.52 CO2 (kg /cow) 3 Animals and housing 6391 6850 6107 340 3776 6721 751 334 6106 6474 5806 308 Manure storage 418 503 357 71.2 199 321 50 140.4 333 649 131 248.9 Net feed production −10,553 −10,155 −10,776 275 −9551 −7431 −10,301 1414 −9681 −8843 −10,400 738 Fuel combustion 239 313 169 60.2 163 239 61 74.9 204 372 89 128.7 Grazing animals 0.0 0.0 0.0 0.00 2399 5541 0.0 2835.6 0.0 0.0 0.0 0.00 Net Emissions −3506 −3075 −3909 465 −3014 −2516 −3398 367 −3038 −2787 −3162 173 Total GHG (ton CO2 -eq 4 ) Animals and housing 670 1036 288 370.2 414 579 162 186.6 849 1045 536 218.8 Manure storage 554 841 182 338.6 161 310 19 134.4 1769 5143 348 2259.8 Net Feed production 122 204 36 68.8 67 172 30 70.0 78 184 5 78.5 Fuel combustion 38 49 7 20.4 15 25 2 9.4 48 75 26 19.9 Secondary sources 549 991 213 352.0 217 310 98 91.5 562 772 167 271.2 CF (kg of CO2 -eq/kg of ECM) 5 1.0 1.1 0.8 0.12 0.7 0.8 0.6 0.07 0.8 0.9 0.8 0.08 1 CH4 : methane; 2 N2 O: nitrous oxide; 3 CO2 : carbon dioxide; 4 CO2 -eq: CO2 equivalent; 5 CF: carbon footprint expressed as kg of CO2 per kg of energy corrected milk with 3.5% fat and 3.1% protein concentrations. Animals 2020, 10, 2083 8 of 15 3.2. Diet Evaluation with the CNCPS Model and Its Contribution to Enteric Methane Emissions Diet evaluation of each farm with the CNCPS model and its contribution to enteric CH4 emissions per unit of milk produced are presented in Table 3. The highest enteric CH4 emissions (g/kg of milk) were recorded in CAN farms with an average of 13.6 and a range from 12.2 in the CAN1 to 16.2 in the CAN2 farms. Intermediate CH4 emissions (g/kg of milk) were recorded in MED with an average of 12.5 and a range from 11.2 to 13.3 in the MED3 and MED4 farms, respectively. The lowest CH4 emissions (g/kg of milk) were recorded in CEN farms with an average of 12.4 and a range from 10.7 to 14.4 in CEN4 and CEN1 farms, respectively. Table 3. Enteric methane emissions evaluation from feed intake in the selected farms using the CNCPS 1 model. Farm Number of Milking Cows Milk Yield (kg/day) Enteric Methane (g/kg Milk) Mediterranean Region (MED) MED1 82 32.0 12.3 MED2 59 36.5 11.2 MED3 180 34.5 13.1 MED4 220 33.0 13.3 Mean 135 34.0 12.5 Cantabric Region (CAN) CAN1 85 36.5 12.2 CAN2 42 28.5 16.2 CAN3 102 36.0 13.4 CAN4 112 40.0 12.4 Mean 85 35.3 13.6 Central Region (CEN) CEN1 220 36.5 14.4 CEN2 200 40.0 11.2 CEN3 190 34.0 13.2 CEN4 130 38.0 10.7 Mean 185 37.1 12.4 1 CNCPS: Cornell Net Carbohydrate and Protein System Model. 3.3. Characteristics of the Two Selected Extreme Farms Two extreme dairy farms in term of CH4 emissions were selected for further simulations (Table 4): one with the largest non-enteric CH4 emissions (MED1), and the other with the largest enteric CH4 emissions (CAN2). The first farm was used to simulate the effects of changes in management on non-enteric CH4 emissions using the IFSM model. Farm MED1 is located on a 330 ha clay loam soil in Girona that is used to provide most of the forage required for feeding cows (ryegrass and corn silage). It has 82 milking Holstein cows plus replacement heifers housed in a free stall barn and bedded pack barns, respectively. Average intake was 26.5 kg DM of a 55:45 forage to concentrate ratio, and average production was 32.0 L/cow/day. The second farm was used to simulate the effects of changes in diet composition on enteric CH4 emissions using the CNCPS model. The CAN2 farm is on a 25 ha of loam soil in Lugo (northern Spain) that produces all required ryegrass and forage for pasture. It has 42 milking Holstein cows housed in a free stall barn with access to pasture. Average estimated intake and production of milking cows was 21.3 kg DM (68:32 forage to concentrate ratio) and 28.5 L/cow/day of milk, respectively. Animals 2020, 10, 2083 9 of 15 Table 4. Characteristics of the selected two extreme dairy farms in term of enteric and non-enteric methane emissions. Selected Dairy Farms Parameters Farm MED1 1 Farm CAN2 2 Farm area (ha) 330 25 Soil type Clay loam Loam Number of animals 197 64 Number of lactating cows 82 42 Number of dry cows 21 4 Heifers (1–2 years) 54 10 Calves 40 8 Calving strategy Whole year Whole year Housing of animals Free stall Free stall Milk yield (kg/cow/year) 9565 8500 Corn, sorghum and ryegrass Pasture, ryegrass silage and Primary feed silage; and concentrates concentrates Grazing time 0 Whole day Intake (kg DM 3 /cow/day) 26.5 21.3 Type of manure Semi-solid (12−14% DM) Liquid-slurry (5−7% DM) Type of storage pit Top-loaded lined earthen basin Covered basin Storage period for manure 6 months 4 months Bedding type Straw (4 kg) None Enteric CH4 4 emissions (g/kg milk) 12.3 16.2 Enteric CH4 emissions (kg/cow/year) 145 167 Non-enteric CH4 emissions (kg/cow/year) 191 29 1 Mediterranean region farm 1; 2 Cantabric region farm 2; 3 DM: dry matter; 4 CH4 : methane. 3.4. Dietary and Management Changes Scenarios to Reduce Enteric and Non-Enteric Emissions 3.4.1. Dietary Change Scenarios The CNCPS model was used to analyze three potential changes in diet composition and quality in the CAN2 farm to determine how these changes affected their enteric CH4 emissions. Results of the simulated scenarios are shown in Table 5. Table 5. Potential of dietary strategies on the methane emissions change in the Cantabric region farm (CAN2). Baseline Scenario 1 Scenario 2 Scenario 3 Parameters Ratio F/C 1 Silage Quality 2 Ionophores 3 Dry matter intake (kg/day/cow) 21.6 21.6 21.6 21.6 Ration forage: concentrate 68/32 58/42 68/32 68/32 Milk yield kg/cow/day 28.5 31.2 28.7 28.6 CH4 4 (g/kg milk) 16.4 15.6 16.3 16.3 Total CH4 production (kg/cow/year) 170 177 170 170.4 % of CH4 change per kg of milk 0.0 −5.0 −0.5 −0.3 CF 5 (kg CO2 -eq/kg ECM) 0.59 0.57 0.59 0.59 % CF change - 3.5 0.0 0.0 1 F/C: 10% reduction in the forage/concentrate ratio; 2 Improved silage quality; 3 Supplement 330 mg monensin/day; 4 CH4 : methane; 5 CF: carbon footprint. The 10% reduction in the forage: concentrate ratio reduced CH4 emissions from 16.4 to 15.6 g/kg milk, representing a 5% reduction in CH4 emissions, but only 3.5% of the milk CF. In contrast, change in the nutritional value of the ryegrass silage had a small impact on CH4 emissions which decreased from 16.4 to 16.3 g/kg milk. Finally, the addition of 330 mg/day monensin also had small effects on CH4 emissions from 16.4 to 16.3 g/kg milk. The impact of these two dietary factors on the milk CF was negligible. Animals 2020, 10, 2083 10 of 15 3.4.2. Management Change Scenarios The effects of management changes on CH4 emissions and the CF in the MED1 farm are presented in Table 6. Simulation with the IFSM revealed that the increased milk production by 13% reduced slightly CH4 emissions (−0.2%), but the impact on the milk CF was moderate (−6.4%). Change in bedding types from straw to sand had a larger effect, reducing CH4 emissions by 10% and the milk CF by 13.8%. Farm MED1 has already a semi-solid manure management system that it is rather efficient, reducing CH4 emissions around 32% and the milk CF by 7.4% compared with slurry or liquid-slurry systems. The utilization of a solid manure management system would further reduce the milk CF by 17.4%. The construction and use of an anaerobic digester would reduce CH4 emissions by 29.9% and the milk CF by 24.8%. The highest reduction potential would be obtained if the top-loaded lined earthen basin is changed into an enclosed tank, with a 37.5% and 27.5% reduction in CH4 emissions and the milk CF, respectively. Table 6. Potential of management practices change on methane emissions in the Mediterranean region farm (MED1). CH4 2 CF 3 (kg Simulation % CH4 % CF Baseline and Changes Details Emissions CO2 -eq/kg Scenario Change Change (kg/cow/year) ECM 4 ) Change in milk Baseline 9565 335.5 - 1.09 - production Change 1 8000 335.1 +0.1 1.16 +6.4 (kg/cow/year) Change 2 11,000 334.8 −0.2 1.02 −6.4 Baseline Semi-solid 230.4 - 1.09 - Change in manure Change 1 Solid 226.6 −1.6 0.90 −17.4 collection system 1 Change 2 Slurry 335.1 +31.2 1.17 +7.4 Change 3 Liquid-slurry 338.5 +31.9 1.17 +7.4 Baseline Top-loaded lined earthen basin 334.8 - 1.09 - Change 1 Top-loaded lined concrete tank 332.6 −0.7 1.09 0.0 Change in manure Change 2 Bottom-loaded tank or basin 301.9 −9.8 1.01 −7.3 storage method Change 3 Covered tank or basin 264.7 −20.9 0.93 −14.7 Change 4 Enclosed tank 215.3 −35.7 0.79 −27.5 Baseline Straw 335.5 - 1.09 - Change 1 Chopped straw 334.7 −0.2 1.09 0.0 Change in Change 2 Sawdust 334.8 −0.2 1.08 −1.0 bedding type Change 3 Sand 302.2 −9.9 0.94 −13.8 Change 4 Manure solids 302.5 −9.8 1.00 −8.3 Anaerobic digester Baseline No anaerobic digester 335.6 - 1.09 - installation Change 1 With anaerobic digestor 235.2 −29.9 0.82 −24.8 1 Manure collection system: Semi-solid (12–14% DM); Solid (20% DM); Slurry (8–10%DM); Liquid-slurry (5–7% DM); 2 CH4 : methane; 3 CF: carbon footprint; 4 ECM: energy corrected milk with 3.5% fat and 3.1% protein concentrations. 4. Discussion 4.1. The Carbon Footprint The milk CF among all farms ranged from 0.6 to 1.1 kg CO2 -eq/kg ECM and was within the range reported in earlier LCA studies using the same model for conventional dairy farms [13,15]. Del Prado et al. [17] reported a slightly higher CF (0.84 to 2.07 kg CO2 -eq/kg ECM) in 17 commercial confined dairy farms in the Basque Country (northern Spain) using a combination of sub-models approaches. Laca et al. [18] also reported a higher CF of milk in semi-confined dairy cows in northern Spain (1.22 kg CO2 -eq/kg ECM). Differences compared with our results are explained by the different system boundaries delimitation used, where emissions from transport and purchased feeds were included in these previous studies but not in ours, which justifies higher values. In contrast, the milk CF in Pennsylvania and California farms were lower (0.60, 0.55 and 0.61 kg CO2- eq/kg ECM, respectively) [13] than results reported herein. Belflower et al. [27] found that the CF in conventional United States (USA) farms was about 0.87 kg CO2 -eq/kg ECM, closer to our estimates, but variation was much larger in Holstein cows in Australia (range from 0.54 to 1.35 kg CO2 -eq/kg ECM; [15]). Results obtained in our simulations are within the range of those reported elsewhere in similar conditions. Animals 2020, 10, 2083 11 of 15 4.2. Methane and Carbon Footprint Mitigation Strategies CH4 is one the main GHG produced at farm gate from enteric and non-enteric fermentation in dairy farms which mitigation may help reduce the CF [28–30]. It is very important to identify which strategies may have the highest impact on the reduction of CH4 emissions and the milk CF, so policy-makers, farmers and stakeholders may direct their policies toward the most efficient processes. In the current study, we evaluated the effects of dietary and management changes on CH4 emissions and milk CF using CNCPS and IFSM, respectively. 4.2.1. Dietary Change Scenarios Dietary changes in the CAN2 farm were simulated with the CNCPS model, and reformulated to optimize production using the ingredients available in the farm. Our results suggest that CH4 emissions originated from enteric fermentation and the CF of milk could be reduced up to 5.0% and 3.5%, respectively, when the proportion of forage was reduced by 10%. Feeding grain increases ruminal propionate while lowering acetate levels from microbial fermentation [29–31]. While fibrolytic bacteria ferment toward acetate, which releases a carbon that methanogenic bacteria reduce with metabolic hydrogens to produce CH4 , amylolytic bacteria ferment towards propionate that consumes metabolic hydrogens. Therefore, when the fermentation is shifted towards a lower acetate to propionate ratio, CH4 production is reduced and justifies the results observed. Also, the increase in concentrate in the diet resulted in an increase in milk production that contributed to the dilution of the amount of CH4 per kg of milk. In contrast, improving silage quality had negligible impact on reducing CH4 emissions and the milk CF. The small impact of an improved silage quality was expected. Most dairy diets in Spain contain under 55% forage in their diets, and quality is already reasonably high. Increasing forage quality implies a better digestibility of the fiber components, but probably has a small impact on the fermentation pathways, producing similar proportions of CH4 . There are few in vivo data exploring the impact of forage quality on CH4 emissions and the milk CF. Boadi and Wittenberg [32] reported that CH4 emissions in dairy heifers were reduced by 23.4 and 42.5% when forage quality improved from low to medium and high quality silage, respectively. However, the high proportional impact is counterbalance with the small overall contribution of young non-producing animals to the overall farm GHG production. Finally, the impact of monensin on CH4 emissions observed in our report was small. Monensin decreased the acetate-to-propionate ratio in rumen fluid through increasing the flow of reducing equivalents to form propionate [33]. In fact, results from a meta-analysis conducted based on 11 experiments on lactating dairy cows showed that monensin supplementation reduced CH4 production by about 2−3% [34]. It is possible that the small changes reported herein result from an underestimation of the effects of monensin on CH4 emissions in the CNCPS model. However, the effects of monensin, even considering in vivo evaluations, are relatively small. All dietary changes proposed herein are within reasonable changes to be implemented in a dairy farm. Therefore, the overall potential of dietary changes appears to have a relatively small impact of up to 5% in the reduction of CH4 emissions and up to 3.5% in the CF. 4.2.2. Management Change Scenarios On the other hand, the IFSM model was used to simulate management scenarios in the MED1 farm with the highest non-enteric CH4 emissions. Three major strategies should be considered: increase productivity to dilute fixed GHG emissions; use bedding material that produce less emissions; and modify manure management practices. Increasing productivity of milking cows is an effective strategy to mitigate GHG emissions, which may allow a reduction in animal numbers providing the same edible product output at a reduced CF. We estimated that an increase of around 1.500 L/cow/year would reduce the CF of milk by 6.4%. Boadi and Wittenberg [32] also reported that increasing milk production of dairy cows from 5000 to 10,000 L/cow/year in the European Union would increase total CH4 production per animal by 23%, Animals 2020, 10, 2083 12 of 15 but the production/ECM would be reduced up to 40%. In fact, the sustained increase in productivity was the single most influential mitigation factor which reduced the CF of milk in the United States dairy industry from 1944 to 2007 [35]. Bedding material may also have a relevant impact on CH4 emissions and the milk CF. The MED1 farm had a straw-type bedding. While differences in CF compared with other organic alternatives were small, the use of sand would have a moderate, yet relevant, impact, reducing CH4 emissions by 9.8% and the milk CF by 13.8%. The potential impact of bedding type on the CF reported herein is relevant and within the ranges observed by Aguirre-Villegas and Larson [36]. Manure is the second largest source of GHG emissions from dairy farms [36]. Therefore, manure handling strategies can be effective strategies to reduce non-enteric CH4 emissions and the milk CF. In the current study, the MED1 farm had a semi-solid manure collection system. The solid collection systems are the least contaminating in terms of GHG emissions compared with semi-solid, multiple and liquid systems [36,37]. Solid manure has less available water and is usually stored in stock piles that promote aeration, which reduces CH4 emissions. In contrast, liquid manure is stored on pits that promote anaerobic conditions, increasing CH4 emissions. Compared with slurry or liquid-slurry systems, MED1 farm has already reduced CH4 emissions and the CF by around 32 and 7%, respectively. However, moving MED1 semi-solid manure into a solid system would have an additional important impact on the CF (17.4% reduction). The installation of an anaerobic digestor could reduce enteric CH4 and the milk CF by 29.9 and 24.8%, respectively. Aguirre-Villegas and Larson [36] reported that anaerobic digestion can reduce GHG emissions related to manure management by more than 50%, mostly in the form of CH4 during storage. However, the largest contribution to the reduction of the milk CF would come from shifting an uncovered storage tank to an enclosed one that would reduce non-enteric CH4 and the CF of milk up to 35.7 and 27.5%, respectively. This large impact of an enclosed manure handling system agrees with Rotz et al. [13], who reported that enclosing manure storage with a flare to burn the escaping biogas would reduce by 39% the milk CF. Overall, manure handling has the largest potential to effectively contribute to the reduction of CH4 and the milk CF compared with those obtained through changes in dietary strategies. This study has several limitations. For example, the costs of the proposed mitigating strategies were not considered. Therefore, future studies aimed at estimating the cost of implementation of each strategy will help clarify the cost effectiveness of these mitigation strategies. The uncertainties and assumption in primary and secondary input data such as the average body weight, feed dry matter intake, ingredient and chemical composition of diets, fertilizers and manure application to croplands and pasture emissions factors may affect the accuracy of the results of this analysis using the IFSM. Rotz et al. [37] reported that uncertainties for IFSM predictions of GHG emissions were ±10 and ±20% for enteric and manure handling CH4 , respectively, ±50% for N2 O, and ±20% for fuel combustion and upstream emissions. In addition, the current analysis considered all relevant activities from cradle to farm gate, but excluded emissions attributable to imported feeds and its transportation. Finally, strategies to reduce CH4 emissions could be associated with other environmental impact such as water contamination, denitrification and eutrophication. Therefore, the milk CF analysis should be integrated with other footprint indicators (water, land and energy) in order to cover other environmental impacts spectrum of dairy farming. 5. Conclusions The IFSM was used in this study to quantify the profile of GHG emissions in twelve dairy cattle farms from three Spanish regions. This study revealed that Cantabric farms are more efficient in term of GHG emissions compared with Mediterranean and Central dairy farms. CH4 was found to be the major contributor to the milk CF. Mitigation efforts are frequently targeted to changes in dietary strategies while management efforts are often ignored. However, simulation scenarios showed that CH4 emissions and the CF of milk could be reduced more through management practices compared Animals 2020, 10, 2083 13 of 15 with dietary strategies. Modelling may provide policy makers, farmers and stakeholders valuable information for planning and developing strategies to reduce the CF associated with milk production. Author Contributions: Conceptualization, R.I. and S.C.; methodology, R.I. and S.C.; software, R.I. and S.C.; formal analysis, R.I.; investigation, R.I. and S.C.; data curation, R.I.; writing—original draft preparation, R.I.; writing—review and editing, S.C; supervision, project administration, S.C.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Acknowledgments: Authors would like to thank dairy farmers for their collaboration. Conflicts of Interest: The authors declare no conflict of interest. References 1. Persson, U.M.; Johansson, D.J.A.; Cederberg, C.; Hedenus, F.; Bryngelsson, D. Climate metrics and the carbon footprint of livestock products: Where’s the beef? Environ. Res. Lett. 2015, 10, 034005. 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