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How can farming intensification affect the environmental impact of milk production?

Open ArchivePublished:May 01, 2014DOI:https://doi.org/10.3168/jds.2013-7530

      Abstract

      The intensification process of the livestock sector has been characterized in recent decades by increasing output of product per hectare, increasing stocking rate, including more concentrated feed in the diet, and improving the genetic merit of the breeds. In dairy farming, the effects of intensification on the environmental impact of milk production are not completely clarified. The aim of the current study was to assess the environmental impacts of dairy production by a life cycle approach and to identify relations between farming intensity and environmental performances expressed on milk and land units. A group of 28 dairy farms located in northern Italy was involved in the study; data collected during personal interviews of farmers were analyzed to estimate emissions (global warming potential, acidification, and eutrophication potentials) and nonrenewable source consumption (energy and land use). The environmental impacts of milk production obtained from the life cycle assessment were similar to those of other recent studies and showed high variability among the farms. From a cluster analysis, 3 groups of farms were identified, characterized by different levels of production intensity. Clusters of farms showed similar environmental performances on product basis, despite important differences in terms of intensification level, management, and structural characteristics. Our study pointed out that, from a product perspective, the most environmentally friendly way to produce milk is not clearly identifiable. However, the principal component analysis showed that some characteristics related to farming intensification, such as milk production per cow, dairy efficiency, and stocking density, were negatively related to the impacts per kilogram of product, suggesting a role of these factors in the mitigation strategy of environmental burden of milk production on a global scale. Considering the environmental burden on a local perspective, the impacts per hectare were positively associated with the intensification level.

      Key words

      Introduction

      In recent decades, the European livestock sector has shown a general trend toward enlarging farm size and increasing intensification in terms of output per hectare. The intensification of production is generally characterized by increasing stocking rate, including more concentrated feed in the diet and improving the genetic merit of the breeds (
      • Alvarez A.
      • del Corral J.
      • Solís D.
      • Pérez J.A.
      Does intensification improve the economic efficiency of dairy farms?.
      ). Such evolution has also affected the Italian dairy sector, which has shown a strong decrease in the total number of dairy cows over the last 30 yr (from 2.6 million in 1980 to 1.6 million presently) and an increase in the number of cows per farm (from 7.9 to 31.8 in the same period;

      ISTAT. 2012. Istituto nazionale di statistica. Accessed Dec. 2012. http://www3.istat.it/

      ). Furthermore, in northern Italy, favorable climatic and infrastructure conditions have led to a very high livestock concentration with a consequent intensive utilization of natural resources (i.e., land, air, water) and high environmental pressure. Intensification of livestock production systems is generally considered detrimental from an environmental point of view. A study from New Zealand (
      • Basset-Mens C.
      • Ledgard S.
      • Boyes M.
      Eco-efficiency of intensification scenarios for milk production in New Zealand.
      ) showed that increasing the number of cows per land unit (with higher N-fertilization and more land used to grow maize for silage instead of permanent grass) reduced dairy farm eco-efficiency in terms of both milk production and land use functions.
      • Penati C.
      • Berentsen P.B.M.
      • Tamburini A.
      • Sandrucci A.
      • de Boer I.J.M.
      Effect of abandoning highland grazing on nutrient balances and economic performance of Italian Alpine dairy farms.
      , assessing environmental sustainability of a group of alpine dairy farms, found that the best environmental performances were obtained by the farms characterized by low stocking density, low production intensity, high feed self-sufficiency, and large land availability in the valley floor. But other results from the literature showed some positive effects of the intensification of livestock production in terms of environmental impact mitigation. A review study by
      • Crosson P.
      • Shalloo L.
      • O’Brien D.
      • Laniganc 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.
      concluded that increased output per hectare obtained through intensification can reduce emissions per kilogram of product.
      • Kristensen T.
      • Mogensen L.
      • Knudsen M.T.
      • Hermansen J.E.
      Effect of production system and farming strategy on greenhouse gas emissions from commercial dairy farms in a life cycle approach.
      identified herd efficiency and farming intensity as relevant strategies for environmental impact reduction .
      • Yan M.J.
      • Humphreys J.
      • Holden N.M.
      Life cycle assessment of milk production from commercial dairy farms: The influence of management tactics.
      found that, as milk production increases, a mitigation of environmental impact is observed.
      • Casey J.W.
      • Holden N.M.
      The relationship between greenhouse gas emissions and the intensity of milk production. Ireland.
      suggested that, to improve the environmental efficiency of dairy farms, a move toward fewer cows producing more milk at lower stocking rates is required. This represents an extensification in terms of area but an intensification in terms of animal husbandry systems.
      Life cycle assessment (LCA) is a generally accepted method for estimating the environmental impact of agricultural products on a global perspective. The main environmental effects quantified in LCA studies on dairy systems are the acidifying and eutrophic effects on watercourses, the global warming effect, and the utilization of resources such as land and nonrenewable energy during the production of milk (
      • 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.
      ).
      Even if climate change is a global issue, for environmental aspects with a local connotation (especially acidification and eutrophication), environmental impact should be evaluated not only per unit of product but also per hectare of land. In particular, eutrophication pertains directly to the leaching and run-off of nitrate and phosphate to the ground and surface water; therefore, this parameter contains a local aspect (
      • Oudshoorn V.
      • Sørensen C.A.G.
      • de Boer I.J.M.
      Economic and environmental evaluation of three goal-vision based scenarios for organic dairy farming in Denmark.
      ). Many authors showed significantly worse environmental performances of the intensive livestock systems when the impacts were expressed in terms of land unit (
      • Haas G.
      • Wetterich F.
      • Köpke U.
      Comparing intensive, extensified and organic grassland farming in southern Germany by process life cycle assessment.
      ;
      • Casey J.W.
      • Holden N.M.
      The relationship between greenhouse gas emissions and the intensity of milk production. Ireland.
      ).
      The first objective of the current study was to analyze the environmental performances of a sample of dairy farms, both on a global and on a local perspective, through an LCA approach. The second objective was to identify the relation between environmental impacts and main farm characteristics, focusing in particular on farming intensity.

      Materials and Methods

      System Description and Data Collection

      A group of 28 dairy cattle farms were involved in the current study. All the farms were located in northern Italy and were members of a cheese factory producing Grana Padano. All cows were Italian Holstein kept in permanent confinement without pasture. This rearing system is the most commonly used in the north of Italy.
      Data were collected through personal interviews of farmers. Questions were addressed to obtain precise information about cropping systems and field operations, fuel consumption, number of animals and housing systems, and manure storage and animal rations. Moreover, data regarding the inputs entering the farms were acquired, including amount of purchased feeds (both roughages and concentrates), fertilizers and pesticides, bedding materials, and number and origin of purchased replacement animals.
      In each farm, forages (hays and silages) and TMR were sampled and analyzed for the content of DM ash, CP, ether extract, and crude fiber by
      AOAC International
      methods; starch by
      AOAC International
      methods; NDF analyzed following the protocol of
      • Mertens D.R.
      Gravimetric determination of amylase-treated neutral detergent fiber in feeds using refluxing in beakers or crucibles: Collaborative study.
      ; and ADF and ADL by the method of
      • Van Soest P.J.
      • Robertson J.B.
      • Lewis B.A.
      Methods of dietary fiber, neutral detergent fiber and nonstarch polysaccharides in relation to animal nutrition.
      . Data obtained from the analyses were used for the estimation of digestibility of the feeding rations. The amount of milk produced by each farm was provided by the cheese factory, whereas the amount of meat (as animal liveweight) was estimated on the basis of the number of animals sold for slaughter and their liveweight declared by the farmers.
      Composition of concentrated feed was estimated on the basis of the raw materials reported on the commercial labels using CPM-Dairy Ration Analyzer Beta V3 software (

      Cornell-Penn-Miner. 2004. CPM Dairy. Dairy cattle ration analyzer, version 3.0.6. Cornell University, Ithaca, NY.

      ). Table 1 summarizes the inventory of the most important data used for impact assessment. All the data are expressed as the average value of the 28 dairy farms.
      Table 1Inventory data (average of the 28 farms)
      ItemUnitMeanSDMinimumMaximum
      Land
      Farm landha40.727.88.5120
      Permanent grassland% land52.123.812.9100
      Maize land for silage% land36.520.30.087.1
      NEL yieldMJ/ha74,96520,92434,901134,248
      Nitrogen yieldkg/ha18038.2103238
      N synthetic fertilizerskg/ha84.351.10.0202
      Pesticides (active substances)g/ha7916140.01,849
      Herd
      Dairy cowsn90.352.017.0195
      Livestock unit (LU)n14386.725.7308
      Milk productionkg of FPCM
      FPCM = fat- and protein-corrected milk.
      /cow per day
      27.14.318.135.4
      Production intensitykg of FPCM/ha19,7647,95512,00546,455
      Meat production
      Liveweight sold.
      kg/farm per year13045.653.1223
      Manure type
      Solid manure%40.338.00.0100.0
      Liquid slurry%59.738.00.0100.0
      Feed
      Feed produced on farmt of DM/LU per year3.81.21.66.5
      Purchased foragest of DM/LU per year0.30.40.01.4
      Purchased concentratest of DM/LU per year1.70.70.43.0
      Energy
      Diesel usekg/LU per year88.621.354.0141
      Electricity useKWH/LU per year21179.952.3336
      1 FPCM = fat- and protein-corrected milk.
      2 Liveweight sold.
      The income over feed cost (IOFC) was used as economic indicator of farm profitability, as proposed by

      Hutjens, M. F. 2007. Practical approaches to feed efficiency and applications on the farm. Penn State Dairy Cattle Nutrition Workshop, November 13–14, Grantville, PA. Accessed May 2012. http://www.dairyweb.ca/Resources/PDCNW2007/Hutjens.pdf

      , and it was calculated as the income from milk minus feeding costs (self-produced and purchased feed) per cow per day.

      Emission Estimation

      Greenhouse Gas Emissions On Farm

      Table 2 shows the models used for on-farm greenhouse gas emission (GHG) estimation. Methane (CH4) emissions from livestock enteric fermentations were estimated using an equation from
      • Ellis J.L.
      • Kebreab E.
      • Odongo N.E.
      • McBride B.W.
      • Okine E.K.
      • France J.
      Prediction of methane production from dairy and beef cattle.
      . To convert the energy of enteric methane in kilograms of methane emitted, the factor 55.65 MJ/kg of CH4 (

      IPCC (Intergovernmental Panel on Climate Change). 2006a. Emissions from livestock and manure management. Chapter 10 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock.pdf

      ) was used. Methane emissions from manure management were estimated using the Tier 2 method suggested by the Intergovernmental Panel on Climate Change (

      IPCC (Intergovernmental Panel on Climate Change). 2006a. Emissions from livestock and manure management. Chapter 10 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock.pdf

      ). Volatile solid excretion was estimated considering gross energy of the diets (kJ/kg of DM) evaluated using an equation of
      • Ewan R.C.
      Predicting the energy utilization of diets and feed ingredients by pigs.
      . Digestibility of the feed was estimated using a calculation model developed for each type of forage and concentrate feed on the basis of the equation proposed by

      INRA (Institut national de la recherche agronomique). 2007. Alimentation des bovins, ovins et caprins. Besoins des animaux—Valeurs des aliments. Tables Inra 2007. INRA, Versaille, France.

      . Feed nutritional characteristics were obtained from the laboratory analyses.
      Table 2Models and emission factors (EF) used for the estimation of greenhouse gas emissions on farm
      PollutantSourceAmount
      VS = daily volatile solid excreted (kg of DM /animal); B0 = maximum methane-producing capacity for manure (m3); MCF = methane conversion factors for each given manure management system (%); MS = fraction of livestock manure handled using each given manure management system (dimensionless); GE = gross energy intake (MJ/ d); DE% = energy digestibility of feed (%); (UE · GE) = urinary energy expressed as fraction of GE (dimensionless); EE% = ether extract of feed (% DM); Nex = annual N excretion (kg of N/animal); EF = emission factor for direct N2O emissions from a given manure management system (kg of N2O-N/kg of N in manure management system); Nsn = annual amount of synthetic fertilizer N applied to soils (kg of N); Non = annual amount of animal manure, compost, sewage sludge and other organic N additions applied to soils (kg of N); Ncr = annual amount of N in crop residues (above and below ground), including N-fixing crops, and from forage/pasture renewal, returned to soils (kg of N); Frac_loss = fraction of managed manure N that is lost in a given manure management system (%); N_bedding = annual amount of N from bedding (kg of N/animal); N volatilization = annual amount of manure N that is lost due to volatilization of NH3 and nitric oxide compounds (NOx; kg of N); Frac_GasMS = fraction of managed manure N that volatilizes as NH3 and NOx in a given manure management system (%); Frac_GasF = fraction of synthetic fertilizer N that volatilizes as NH3 and NOx (% ); Frac_GasM = fraction of applied organic N fertilizer materials and of urine and dung N deposited by grazing animals that volatilizes as NH3 and NOx (%); Frac_Leach = N fraction lost through leaching and runoff (%).
      Reference
      CH4EntericCH4 (MJ) = 2.16 (±1.62) + 0.493 (±0.192) ⋅ DMI (kg) − 1.36 (±0.631) ⋅ ADF (kg)

      + 1.97 (±0.561) ⋅ NDF (kg)
      • Ellis J.L.
      • Kebreab E.
      • Odongo N.E.
      • McBride B.W.
      • Okine E.K.
      • France J.
      Prediction of methane production from dairy and beef cattle.
      Manure storageCH4 = VS × B0 ⋅ 0.67 ⋅ MCF/100 ⋅ MSEquation 10.23 in

      IPCC (Intergovernmental Panel on Climate Change). 2006a. Emissions from livestock and manure management. Chapter 10 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock.pdf

      VS = [GE ⋅ (1 − DE/100) + (UE ⋅ GE)] ⋅ [(1 − Ash)/18.45]Equation 10.24 in

      IPCC (Intergovernmental Panel on Climate Change). 2006a. Emissions from livestock and manure management. Chapter 10 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock.pdf

      GE (kJ) = 17,350 + (234.46 ⋅ EE%) + (62.8 ⋅ CP%) − (184.22 ⋅ Ash %)
      • Ewan R.C.
      Predicting the energy utilization of diets and feed ingredients by pigs.
      DE: feed digestibility

      INRA (Institut national de la recherche agronomique). 2007. Alimentation des bovins, ovins et caprins. Besoins des animaux—Valeurs des aliments. Tables Inra 2007. INRA, Versaille, France.

      MCF solid storage: 4

      IPCC (Intergovernmental Panel on Climate Change). 2006a. Emissions from livestock and manure management. Chapter 10 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock.pdf

      MCF liquid slurry: 17
      MCF pit storage: 27
      N2O directManure storageN2O = Nex ⋅ MS ⋅ EF ⋅ 44/28Equation 10.25 in

      IPCC (Intergovernmental Panel on Climate Change). 2006a. Emissions from livestock and manure management. Chapter 10 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock.pdf

      Nex = Nintake ⋅ (1 − N retention)Equation 10.31 in

      IPCC (Intergovernmental Panel on Climate Change). 2006a. Emissions from livestock and manure management. Chapter 10 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock.pdf

      N intake: DMI ⋅ (CP %/100/6.25)
      N retention: N retained per animal with milk and weight gainEquation 10.33 in

      IPCC (Intergovernmental Panel on Climate Change). 2006a. Emissions from livestock and manure management. Chapter 10 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock.pdf

      EF solid storage: 0.005 (0.0027 − 0.01)Table 10.21 in

      IPCC (Intergovernmental Panel on Climate Change). 2006a. Emissions from livestock and manure management. Chapter 10 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock.pdf

      EF liquid slurry: 0.005
      EF pit storage: 0.002
      FieldN2O = (Nsn + Non + Ncr) ⋅ EF ⋅ 44/28Equation 11.2 in

      IPCC (Intergovernmental Panel on Climate Change). 2006b. N2O Emissions from managed soils, and CO2 emissions from lime and urea application. Chapter 11 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_11_Ch11_N2O&CO2.pdf

      Non: annual amount of N from managed animal manure applied to soil (Nex − Frac_loss + N bedding)Equation 10.34 in

      IPCC (Intergovernmental Panel on Climate Change). 2006a. Emissions from livestock and manure management. Chapter 10 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock.pdf

      Frac_loss solid storage: 40% (10 − 65)Table 10.23 in

      IPCC (Intergovernmental Panel on Climate Change). 2006a. Emissions from livestock and manure management. Chapter 10 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock.pdf

      Frac_loss liquid slurry: 40% (15 − 45)
      Frac_loss pit storage: 28% (10 − 40)
      EF: 0.01 (0.003 − 0.03)Table 11.1 in

      IPCC (Intergovernmental Panel on Climate Change). 2006b. N2O Emissions from managed soils, and CO2 emissions from lime and urea application. Chapter 11 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_11_Ch11_N2O&CO2.pdf

      N2O indirectManure storageN2OG = N volatilization ⋅ EF ⋅ 44/28Equation 10.27 in

      IPCC (Intergovernmental Panel on Climate Change). 2006a. Emissions from livestock and manure management. Chapter 10 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock.pdf

      Nvolatilization: Nex ⋅ MS ⋅ Frac_GasMS/100
      Frac_GasMS solid storage: 30 (10 − 40)Table 10.22 in

      IPCC (Intergovernmental Panel on Climate Change). 2006a. Emissions from livestock and manure management. Chapter 10 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock.pdf

      Frac_GasMS liquid slurry: 40 (15 − 45)
      Frac_GasMS pit storage: 28 (10 − 40)
      EF: 0.01 (0.002 − 0.05)Table 11.3 in

      IPCC (Intergovernmental Panel on Climate Change). 2006b. N2O Emissions from managed soils, and CO2 emissions from lime and urea application. Chapter 11 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_11_Ch11_N2O&CO2.pdf

      FieldN2O(ATDN) = [(Nsn ⋅ Frac_GasF) + (Non ⋅ Frac_GasM)] ⋅ EF ⋅ 44/28Equation 11.9 in

      IPCC (Intergovernmental Panel on Climate Change). 2006b. N2O Emissions from managed soils, and CO2 emissions from lime and urea application. Chapter 11 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_11_Ch11_N2O&CO2.pdf

      Frac_GasF: 0.1 (0.03 − 0.3)Table 11.3 in

      IPCC (Intergovernmental Panel on Climate Change). 2006b. N2O Emissions from managed soils, and CO2 emissions from lime and urea application. Chapter 11 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_11_Ch11_N2O&CO2.pdf

      Frac_GasM: 0.2 (0.05 − 0.5)Table 11.3 in

      IPCC (Intergovernmental Panel on Climate Change). 2006b. N2O Emissions from managed soils, and CO2 emissions from lime and urea application. Chapter 11 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_11_Ch11_N2O&CO2.pdf

      EF: 0.01 (0.002 − 0.05)Table 11.3 in

      IPCC (Intergovernmental Panel on Climate Change). 2006b. N2O Emissions from managed soils, and CO2 emissions from lime and urea application. Chapter 11 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_11_Ch11_N2O&CO2.pdf

      N2O(L) = (Nsn + Non) ⋅ Frac_Leach ⋅ EF ⋅ 44/28Equation 11.10 in

      IPCC (Intergovernmental Panel on Climate Change). 2006b. N2O Emissions from managed soils, and CO2 emissions from lime and urea application. Chapter 11 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_11_Ch11_N2O&CO2.pdf

      Frac_Leach: 0.3 (0.1 − 0.8)
      EF: 0.0075 (0.0005 − 0.025)Table 11.3 in

      IPCC (Intergovernmental Panel on Climate Change). 2006b. N2O Emissions from managed soils, and CO2 emissions from lime and urea application. Chapter 11 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_11_Ch11_N2O&CO2.pdf

      CO2Field operations

      Ecoinvent. 2007. Ecoinvent Centre 2007, Ecoinvent Data v2.0—Final reports Ecoinvent 2000 No. 1–15. Swiss Centre for Life Cycle Inventories, Dübendorf, Switzerland.

      Diesel combustion
      Excluding the quota used during field operations.
      CO2 = kg diesel ⋅ EF

      Nemecek, T., and T. Kägi. 2007. Life cycle inventories of Swiss and European agricultural production systems. Final Report Ecoinvent V2.0 No. 15a. Agroscope Reckenholz-Taenikon Research Station, Swiss Centre for Life Cycle Inventories, Zurich and Dübendorf, Switzerland

      EF: 3.12 kg of CO2/kg of diesel
      1 VS = daily volatile solid excreted (kg of DM /animal); B0 = maximum methane-producing capacity for manure (m3); MCF = methane conversion factors for each given manure management system (%); MS = fraction of livestock manure handled using each given manure management system (dimensionless); GE = gross energy intake (MJ/ d); DE% = energy digestibility of feed (%); (UE · GE) = urinary energy expressed as fraction of GE (dimensionless); EE% = ether extract of feed (% DM); Nex = annual N excretion (kg of N/animal); EF = emission factor for direct N2O emissions from a given manure management system (kg of N2O-N/kg of N in manure management system); Nsn = annual amount of synthetic fertilizer N applied to soils (kg of N); Non = annual amount of animal manure, compost, sewage sludge and other organic N additions applied to soils (kg of N); Ncr = annual amount of N in crop residues (above and below ground), including N-fixing crops, and from forage/pasture renewal, returned to soils (kg of N); Frac_loss = fraction of managed manure N that is lost in a given manure management system (%); N_bedding = annual amount of N from bedding (kg of N/animal); N volatilization = annual amount of manure N that is lost due to volatilization of NH3 and nitric oxide compounds (NOx; kg of N); Frac_GasMS = fraction of managed manure N that volatilizes as NH3 and NOx in a given manure management system (%); Frac_GasF = fraction of synthetic fertilizer N that volatilizes as NH3 and NOx (% ); Frac_GasM = fraction of applied organic N fertilizer materials and of urine and dung N deposited by grazing animals that volatilizes as NH3 and NOx (%); Frac_Leach = N fraction lost through leaching and runoff (%).
      2 Excluding the quota used during field operations.
      In the current study, animal nitrogen excretion was estimated as proposed by the

      IPCC (Intergovernmental Panel on Climate Change). 2006a. Emissions from livestock and manure management. Chapter 10 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock.pdf

      Tier 2 method considering the nitrogen intake (on the basis of CP% of the diet) minus the nitrogen retained by the animals and excreted with milk. Nitrous oxide (N2O) emissions from manure storages occurred in direct and indirect forms, and in both cases they were estimated using the Tier 2 method from

      IPCC (Intergovernmental Panel on Climate Change). 2006a. Emissions from livestock and manure management. Chapter 10 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_10_Ch10_Livestock.pdf

      . Direct and indirect N2O losses from fertilizer application were estimated following the Tier 2 and Tier 1 methods suggested by

      IPCC (Intergovernmental Panel on Climate Change). 2006b. N2O Emissions from managed soils, and CO2 emissions from lime and urea application. Chapter 11 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_11_Ch11_N2O&CO2.pdf

      , respectively; the amount of nitrogen applied to the soils from synthetic fertilizers and from manure (slurry and solid) plus the nitrogen from crop residues were accounted for in the estimation.
      Carbon dioxide (CO2) emissions from fuel combustion were estimated on the basis of fuel consumption of each farm. Emissions occurring during field operations (i.e., plowing, harrowing, sowing, harvesting, and so on) were estimated using the processes of the

      Ecoinvent. 2007. Ecoinvent Centre 2007, Ecoinvent Data v2.0—Final reports Ecoinvent 2000 No. 1–15. Swiss Centre for Life Cycle Inventories, Dübendorf, Switzerland.

      database; whereas, for other fuel consumptions (i.e., use for feeding mixer), the emission factor used was 3.12 kg of CO2/kg of diesel, as proposed by

      Nemecek, T., and T. Kägi. 2007. Life cycle inventories of Swiss and European agricultural production systems. Final Report Ecoinvent V2.0 No. 15a. Agroscope Reckenholz-Taenikon Research Station, Swiss Centre for Life Cycle Inventories, Zurich and Dübendorf, Switzerland

      . Emissions from livestock respiration and the variation in soil carbon stocks were not accounted for.

      Other Emissions On Farm

      Table 3 reports the models used for the estimation of acidifying and eutrophic substances emitted on farm. Ammonia (NH3) and nitrogen oxide (NOx) emissions that occur during animal housing, manure storage, and spreading were estimated following the method proposed by the European Environment Agency (

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      ,

      EEA (European Environment Agency). 2009b. 4.D Crop production and agricultural soils. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-d/4-d-crop-production-and-agricultural-soils.pdf/view

      ) on the basis of the total amount of nitrogen excreted by the animals. The Tier 2 method used a mass flow approach based on the concept of a flow of total ammonia nitrogen through the manure management systems. The NH3-N and NOx emission factors, as a proportion of total ammonia nitrogen, were specific for each manure type (slurry or solid) and each step in manure handling (

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      ). The NH3 and NOx emitted during manure spreading and application of synthetic fertilizers were estimated following

      EEA (European Environment Agency). 2009b. 4.D Crop production and agricultural soils. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-d/4-d-crop-production-and-agricultural-soils.pdf/view

      guidelines. The amount of nitrogen leached was estimated following the

      IPCC (Intergovernmental Panel on Climate Change). 2006b. N2O Emissions from managed soils, and CO2 emissions from lime and urea application. Chapter 11 in IPCC Guidelines for National Greenhouse Gas Inventories. Vol. 4: Agriculture, Forestry and Other Land Use. Accessed May 2012. http://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/4_Volume4/V4_11_Ch11_N2O&CO2.pdf

      model (Table 2). To estimate emissions of PO43-, the amount of phosphorus lost in dissolved form to surface water (run-off) and leached was considered as proposed by

      Nemecek, T., and T. Kägi. 2007. Life cycle inventories of Swiss and European agricultural production systems. Final Report Ecoinvent V2.0 No. 15a. Agroscope Reckenholz-Taenikon Research Station, Swiss Centre for Life Cycle Inventories, Zurich and Dübendorf, Switzerland

      .
      Table 3Models and emission factors (EF) for the estimation of ammonia, nitric oxide, and phosphate emissions on farm
      PollutantSourceAmount
      TAN = total ammoniacal-N; Nex = annual average N excretion per head (kg of N/animal); EF_TAN = emission factor of TAN; build_slurry = liquid slurry in the livestock buildings; build_solid = solid manure in the livestock buildings; storage_solid = solid manure in storages; storage_slurry = liquid slurry in storages; applic_slurry = application of liquid slurry to the field; applic_solid = application of solid manure to the field; NH3 applic_fert =emission from fertilizer application to the field; N fert_applic = total N from fertilizer application; EF fert_type = emission factor for fertilizer type; Amm nitr = ammonium nitrate; NPK = nitrogen-phosphorus-potassium fertilizer; Ts = mean spring temperature (°C); NOx = nitric oxide compounds (NO + NO2); Pgw = quantity of phosphorus leached to ground water (kg/ha); Pgwl = average quantity of phosphorus leached to ground water for each land use category (kg/ha); Fgw =correction factor for fertilization by slurry; Pro = quantity of phosphorus lost through runoff to rivers (kg/ha); Prol = average quantity of phosphorus lost through runoff to rivers for each land use category (kg/ha); Fro = correction factor for fertilization with each source of phosphorus.
      Reference
      NH3HousingTAN = Nex ⋅ EF_TANEquation 10 in

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      EF_TAN: 0.6Table 3–8 in

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      NH3build_slurry = TANbuild_slurry ⋅ EFbuild_slurry ⋅ 17/14Equation 15 in

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      EFbuild_slurry: 0.2Table 3–8 in

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      NH3build_solid = TANbuild_solid ⋅ EFbuild_solid ⋅ 17/14Equation 16 in

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      EFbuild_solid: 0.19Table 3–8 in

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      Manure storageNH3storage_solid = TANstorage_slurry ⋅ EFstorage_slurry ⋅ 17/14Equation 29 in

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      EFstorage_slurry: 0.20Table 3–8 in

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      NH3storage_solid = TANstorage_solid ⋅ EFstorage_solid ⋅ 17/14Equation 30 in

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      EFstorage_solid: 0.27Table 3–8 in

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      FieldNH3applic_slurry = TANslurry_applic ⋅ EFapplic_slurry ⋅ 17/14Equation 35 in

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      EFapplic_slurry: 0.55Table 3–8 in

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      NH3applic_solid = TANsolid_applic ⋅ EFapplic_solid ⋅ 17/14Equation 36 in

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      EFapplic_solid: 0.79Table 3–8 in

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      NH3applic_fert = Nfert_applic ⋅ EFfert_typeEquation 3 in

      EEA (European Environment Agency). 2009b. 4.D Crop production and agricultural soils. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-d/4-d-crop-production-and-agricultural-soils.pdf/view

      EF urea: 0.1067 + 0.0035 ⋅ TsTable 3–2 in

      EEA (European Environment Agency). 2009b. 4.D Crop production and agricultural soils. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-d/4-d-crop-production-and-agricultural-soils.pdf/view

      EFamm.nitr. and NPK: 0.0080 + 0.0001 ⋅ Ts
      NOxManure storageNOxstorage_solid = TANstorage_slurry ⋅ EFstorage_slurry ⋅ 17/14Equation 29 in

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      EFstorage_slurry: 0.0001Table 3–9 in

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      NOxstorage_solid = TANstorage_solid ⋅ EFstorage_solid ⋅ 17/14Equation 30 in

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      EFstorage_solid: 0.01Table 3–9 in

      EEA (European Environment Agency). 2009a. 4.B Animal husbandry and manure management GB2009 update. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-b/4-b-animal-husbandry-and-manure-management.pdf/view

      FieldNOxapplic_tot = (Nslurry_applic + Nsolid_applic + Nfert_applic) ⋅ EFapplic
      EFapplic: 0.026Table 3–1 in

      EEA (European Environment Agency). 2009b. 4.D Crop production and agricultural soils. From the EMEP/EEA air pollutant emission inventory guidebook 2009. Accessed May 2012. http://www.eea.europa.eu/publications/emep-eea-emission-inventory-guidebook-2009/part-b-sectoral-guidance-chapters/4-agriculture/4-d/4-d-crop-production-and-agricultural-soils.pdf/view

      PO3−4FieldPgw (leached to ground water) = Pgwl ⋅ FgwParagraph 4.4.3 in

      Nemecek, T., and T. Kägi. 2007. Life cycle inventories of Swiss and European agricultural production systems. Final Report Ecoinvent V2.0 No. 15a. Agroscope Reckenholz-Taenikon Research Station, Swiss Centre for Life Cycle Inventories, Zurich and Dübendorf, Switzerland

      Pgwl arable land: 0.07
      Pgwl permanent pasture and meadow: 0.06
      Fgw: 1 + 0.2/80 • P2O5slurry
      Pro (P lost through run-off to rivers) = Prol ⋅ Fro
      Prol open arable land: 0.175
      Prol extensive meadow: 0.25
      Frofert: 0.2/80 ⋅ P2O5fert
      Froslurry: 0.7/80 ⋅ P2O5slurry
      Fromanure: 0.4/80 ⋅ P2O5manure
      1 TAN = total ammoniacal-N; Nex = annual average N excretion per head (kg of N/animal); EF_TAN = emission factor of TAN; build_slurry = liquid slurry in the livestock buildings; build_solid = solid manure in the livestock buildings; storage_solid = solid manure in storages; storage_slurry = liquid slurry in storages; applic_slurry = application of liquid slurry to the field; applic_solid = application of solid manure to the field; NH3 applic_fert =emission from fertilizer application to the field; N fert_applic = total N from fertilizer application; EF fert_type = emission factor for fertilizer type; Amm nitr = ammonium nitrate; NPK = nitrogen-phosphorus-potassium fertilizer; Ts = mean spring temperature (°C); NOx = nitric oxide compounds (NO + NO2); Pgw = quantity of phosphorus leached to ground water (kg/ha); Pgwl = average quantity of phosphorus leached to ground water for each land use category (kg/ha); Fgw =correction factor for fertilization by slurry; Pro = quantity of phosphorus lost through runoff to rivers (kg/ha); Prol = average quantity of phosphorus lost through runoff to rivers for each land use category (kg/ha); Fro = correction factor for fertilization with each source of phosphorus.

      Off-Farm Processes

      The emissions related to off-farm activities were calculated using LCA software, Simapro PhD 7.3.3 (

      PRé Consultants. 2012. SimaPro 7.3.2 PhD, LCA software. Amersfoort, the Netherlands. Accessed May 2012. http://www.pre.nl

      ), and were modeled using the databases reported in Table 4. The processes considered included the production chain of commercial feed (from crop growing to feed factory processing), production of purchased forages and bedding material, rearing of purchased replacing heifers, production of chemical fertilizers and pesticides, and diesel and electricity used in the farms. Transportation was accounted for only in feed, bedding materials, and purchased replacement animals.
      Table 4Inventory of off-farm processes
      ProcessReference
      Feed production
      Crop

      Ecoinvent. 2007. Ecoinvent Centre 2007, Ecoinvent Data v2.0—Final reports Ecoinvent 2000 No. 1–15. Swiss Centre for Life Cycle Inventories, Dübendorf, Switzerland.

      ;

      Baldoni, R., and L. Giardini. 2002. Coltivazioni Erbacee, Foraggere e Tappeti Erbosi. Pàtron Editore, Bologna, Italy.

      ;
      • Ribaudo F.
      ; data from the current study
      Milk powderNielsen et al., 2007
      Feed processingNielsen et al., 2007
      Forage production

      Ecoinvent. 2007. Ecoinvent Centre 2007, Ecoinvent Data v2.0—Final reports Ecoinvent 2000 No. 1–15. Swiss Centre for Life Cycle Inventories, Dübendorf, Switzerland.

      ;

      Baldoni, R., and L. Giardini. 2002. Coltivazioni Erbacee, Foraggere e Tappeti Erbosi. Pàtron Editore, Bologna, Italy.

      ;
      • Ribaudo F.
      ; data from the current study
      Bedding material production

      Ecoinvent. 2007. Ecoinvent Centre 2007, Ecoinvent Data v2.0—Final reports Ecoinvent 2000 No. 1–15. Swiss Centre for Life Cycle Inventories, Dübendorf, Switzerland.

      Rearing animalsData from the current study
      Fertilizer production
      • Patyk A.
      • Reinhardt G.A.
      ;

      Ecoinvent. 2007. Ecoinvent Centre 2007, Ecoinvent Data v2.0—Final reports Ecoinvent 2000 No. 1–15. Swiss Centre for Life Cycle Inventories, Dübendorf, Switzerland.

      Pesticide production

      Ecoinvent. 2007. Ecoinvent Centre 2007, Ecoinvent Data v2.0—Final reports Ecoinvent 2000 No. 1–15. Swiss Centre for Life Cycle Inventories, Dübendorf, Switzerland.

      Energy production

      Ecoinvent. 2007. Ecoinvent Centre 2007, Ecoinvent Data v2.0—Final reports Ecoinvent 2000 No. 1–15. Swiss Centre for Life Cycle Inventories, Dübendorf, Switzerland.

      Transportation

      Ecoinvent. 2007. Ecoinvent Centre 2007, Ecoinvent Data v2.0—Final reports Ecoinvent 2000 No. 1–15. Swiss Centre for Life Cycle Inventories, Dübendorf, Switzerland.

      As farms bought a quota of their replacement heifers, a simplified LCA was performed to assess the impacts associated to heifer rearing, considering animals sold at 24 mo of age, average feed intake, average diet composition, standard housing conditions, and manure management.

      Impact Assessment

      The environmental impact of milk production in each dairy farm was evaluated through a detailed cradle-to-farm-gate LCA (
      • Belflower J.B.
      • Bernard J.K.
      • Gattie D.K.
      • Hancock D.W.
      • Risse L.M.
      • Rotz C.A.
      A case study of the potential environmental impacts of different dairy production systems in Georgia.
      ). The system boundaries included all the on-farm processes plus the off-farm activities linked to the production of external inputs (Figure 1).
      Figure thumbnail gr1
      Figure 1System boundaries. T = transportation.
      The selected environmental impact categories were global warming, acidification, eutrophication, nonrenewable energy use, and land use (
      • 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.
      ). The impact assessment was performed with the

      EPD (Environmental Product Declarations) 2008. SimaPro 7.7.3 PhD, Database\Professional\Methods\EPD 1.03 version. EPD, Amersfoort, the Netherlands.

      1.03 method, updated with

      IPCC (Intergovernmental Panel on Climate Change). 2007. Direct global warming potential. In IPCC Fourth Assessment Report. Climate Change 2007: Working Group I: The Physical Science Basis. Accessed June 2012. http://www.ipcc.ch/publications_and_data/ar4/wg1/en/ch2s2-10-2.html

      global-warming potential (GWP) conversion factors (100-yr time horizon). Land use was estimated on the basis of total area (on- and off-farm land).
      On a global perspective the functional unit (FU) was established as 1 kg of fat- and protein-corrected milk (FPCM) leaving the farm gate (
      • Thomassen M.A.
      • van Calker K.J.
      • Smits M.C.J.
      • Iepema G.L.
      • de Boer I.J.M.
      Life cycle assessment of conventional and organic milk production in the Netherlands.
      ) estimated using the formula FPCM (kg) = raw milk (kg) × (0.337 + 0.116 × % fat + 0.060 × % protein) from
      • Gerber P.
      • Vellinga T.
      • Opio C.
      • Henderson B.
      • Steinfeld H.
      Greenhouse Gas Emissions from the Dairy Sector.
      . The biological allocation method developed by

      IDF (International Dairy Federation).2010. A common carbon footprint approach for dairy. The IDF guide to standard lifecycle assessment methodology for the dairy sector. In the Bulletin of theIDFNo 445/2010. International Dairy Federation, Brussels, Belgium.

      for the dairy farming system was calculated using the formula AF = 1 − 5.7717 × R, where AF = allocation factor for milk; r = M meat/M milk; M meat = sum of liveweight of all animals sold, including bull calves and culled mature animals; and M milk = sum of sold FPCM. The environmental impacts were also estimated from a local point of view, assuming 1 ha of farm land as FU.

      Statistical Analysis

      Statistical analysis was performed using SAS 9.2 software (

      SAS Institute. 2001. User’s Guide, Version 9.0. SAS Institute Inc., Cary, NC.

      ) and was carried out in 3 steps. The first step was performed through a principal component analysis (PCA; PROC PRINCOMP) to study the relationships among total environmental impacts per kilogram of milk and per hectare, their on-farm contributions, and several quantitative variables related to farming intensity, including production level (kg of FPCM/cow per day), dairy efficiency (kg of FPCM/kg of DMI), number of dairy cows, stocking rate as livestock units (LU; LU/ha), total farm land (ha), shares of maize land for silage and grassland on total farm land, and IOFC (€/cow per day). In the second step, farms were grouped through a CLUSTER procedure (using average linkage method) considering as variables total farm land (ha), number of dairy cows, stocking rate (LU/ha), production level (kg of FPCM/cow per day), percentage of grass hay and maize silage on DMI, percentage of maize land for silage on farm land, dairy efficiency (kg of FPCM/kg of DMI), and feed self-sufficiency (expressed as the ratio between the DM produced on farm and the total DM used for animal feeding). For each cluster, average farm characteristics and environmental impacts on a global (FU = 1 kg of FPCM) and local (FU = 1 ha of farm land) scale were computed. Moreover a Pearson correlation analysis was used to identify the relationship between farm characteristics and each environmental impact expressed per hectare of farm land.

      Results

      Environmental Impacts of Milk Unit

      Table 5 reports the average results of the environmental impact assessment of milk production in the farms under consideration expressed per milk unit. The on-farm percentage of GHG emissions was much higher compared with the off-farm one. The most important contributor to global warming was enteric and manure storage emission (52.9 ± 4.40%), followed by emissions related to the production of concentrated feed (19.9 ± 6.78%). Almost all the acidification was due to on-farm activity and the main role was played by farm crop production (39.1 ± 8.54%), animal housing (22.7 ± 2.63%), and manure storage (22.5 ± 5.25%). Also, for eutrophication, on-farm contribution was the most important factor; in particular, farm crop production was the major driver (51.6 ± 7.89%), whereas, in off-farm processes, the production of concentrate feed accounted for 21.2 ± 7.66% of total eutrophication potential. In nonrenewable energy use, the on- and off-farm contributions were similar; the production of concentrated feed covered 46.6 ± 13.9% of the total energy consumption alone. Similar to energy consumption, land use did not show any important difference between on- and off-farm shares; crop production for purchased concentrated feed contributed 33.0 ± 10.8% of total impact alone, followed by growing of purchased forages (5.82 ± 7.58%).
      Table 5Total environmental impacts expressed per kilogram of fat- and protein-corrected milk (FPCM) for the 28 dairy farms and on-farm contributions
      Environmental impactLocationMeanSDMinimumMaximum
      Global warming (kg of CO2-equivalent)Total1.260.170.901.56
      On-farm %74.37.0561.187.8
      Acidification (g of SO2-equivalent)Total15.23.348.6321.7
      On-farm %86.65.6070.194.9
      Eutrophication (g of PO4-equivalent)Total7.331.395.009.69
      On-farm %74.68.1059.590.3
      Energy use (MJ)Total5.470.892.857.33
      On-farm %43.312.522.972.1
      Land use (m2)Total0.950.160.591.24
      On-farm %58.112.440.183.1
      Figure 2 shows the average contributions of different substances to GWP, acidification, and eutrophication. Overall, methane was responsible for 49.9 ± 3.64% of total GHG emission, followed by carbon dioxide and nitrous oxide, which had similar weights (25.4 ± 2.59 and 24.5 ± 3.25%, respectively). Enteric fermentation was the most important source of CH4, as 74.3 ± 8.87% of total methane was produced in the gastrointestinal tract of the animals.
      Figure thumbnail gr2
      Figure 2Contribution of different substances to the impact categories.
      Ammonia emission accounted for 88.8 ± 2.34% of acidification potential. Ammonia volatilized mainly during application of manure on farm soils (41.4 ± 9.36% of total ammonia emission) and during animal housing and manure storage (25.6 ± 2.87 and 25.1 ± 5.66% of total ammonia emission, respectively). Nitrate leaching was the main contributor to eutrophication potential (47.8 ± 4.01%), followed by volatilized NH3 (40.3 ± 4.61%), whereas the role of phosphate losses was less important (only 6.13 ± 1.43%). The percentage of nitrate leached during on-farm crop production was higher than the fraction related to purchased feed (concentrates and forages), at 67.1 ± 10.4 and 29.6 ± 10.9% of total nitrogen leached, respectively.

      Interaction Between Farm Characteristics and Environmental Impact

      The results obtained from the PCA are plotted in Figures 3 and 4 and the eigenvectors are reported in Tables 6 and 7. Figure 3 shows the multivariate correlation between farm characteristics and environmental impacts per kilogram of FPCM. The first dimension explains 38.7% of the total variance, whereas the second dimension explains 20.1%. Total impacts [total global warming (GWtot), total land use (LANDtot), and total energy use (ENERGYtot)] and their on-farm quotas, expressed in terms of kilograms of milk produced, are in the same area and highly correlated with each other and with feed self-sufficiency. On-farm land use and on-farm energy use are strongly related to feed self-sufficiency because the higher the quota of feed produced on farm, the higher their impact. Total and on-farm acidification and eutrophication are very close to the percentage of land used for maize silage production, which needs high N fertilization.
      Figure thumbnail gr3
      Figure 3Principal component analysis (environmental impact expressed per kilogram of fat- and protein-corrected milk). PC = principal component; GW = global warming (kg of CO2 equivalents); EUTr = eutrophication (g of PO4 equivalents); ACID = acidification (g of SO2 equivalents); LAND = land use (m2); ENERGY = energy use (MJ); tot = total impact; on = on-farm fraction of impact; IOFC = income over feed cost (€/cow per day); IOFC = income over feed cost (€/cow per day).
      Figure thumbnail gr4
      Figure 4Principal component analysis (environmental impact expressed per hectare of farm land). PC = principal component; GW = global warming (kg of CO2 equivalents); eutrophication (g of PO4 equivalents); ACID = acidification (g of SO2 equivalents); ENERGY = energy use (MJ); tot = total impact; on = on-farm fraction of impact.
      Table 6Eigenvectors corresponding to the principal components (PC) retained for the 28 dairy farms (impacts expressed per kilogram of fat- and protein-corrected milk; FPCM); the first 5 PC had eigenvalues greater than 1
      ItemUnitPC 1PC 2PC 3PC 4PC 5
      Farm landha0.070.330.290.00−0.52
      Maize land for silage% land0.090.26−0.29−0.250.07
      Permanent grassland% land−0.18−0.190.050.44−0.07
      Dairy cowsno.−0.010.400.08−0.02−0.55
      Stocking densityLU
      LU = livestock units.
      /ha
      −0.130.18−0.48−0.03−0.06
      Milk productionkg of FPCM/cow per day−0.220.320.170.120.27
      Dairy efficiencykg of milk/kg of DMI−0.200.270.23−0.010.33
      Feed self-sufficiency% of total feed DM0.23−0.120.26−0.420.00
      Global warming totalkg of CO2-equivalent0.300.00−0.140.35−0.06
      Global warming on farmkg of CO2-equivalent0.32−0.09−0.14−0.03−0.10
      Acidification totalg of SO2-equivalent0.270.28−0.080.070.17
      Acidification on farmg of SO2-equivalent0.270.27−0.08−0.040.15
      Eutrophication totalg of PO4-equivalent0.300.24−0.060.160.15
      Eutrophication on farmg of PO4-equivalent0.320.19−0.04−0.130.10
      Energy use totalMJ0.250.000.090.380.24
      Energy use on farmMJ0.26−0.260.05−0.280.04
      Land use totalm
      IOFC = income over feed cost.
      0.24−0.010.300.35−0.09
      Land use on farmm
      IOFC = income over feed cost.
      0.25−0.200.34−0.06−0.03
      IOFC
      IOFC = income over feed cost.
      €/cow per day−0.130.230.42−0.160.23
      1 LU = livestock units.
      2 IOFC = income over feed cost.
      Table 7Eigenvectors corresponding to the principal components (PC) retained for the 28 dairy farms (impacts expressed per hectare); the first 4 PC had eigenvalues greater than 1
      ItemUnitPC 1PC 2PC 3PC 4
      Farm landha−0.030.370.35−0.47
      Maize land for silage% land0.20−0.050.400.15
      Permanent grassland% land−0.060.03−0.58−0.32
      Dairy cowsLU
      LU = livestock units.
      /ha
      0.110.340.30−0.49
      Stocking densityno.0.31−0.08−0.08−0.10
      Milk productionkg of FPCM
      FPCM = fat- and protein-corrected milk.
      /cow per day
      0.120.46−0.200.17
      Dairy efficiencykg of milk/kg of DMI0.080.44−0.160.35
      Feed self-sufficiency% of total feed DM−0.21−0.080.410.25
      Global warming totalkg of CO2-equivalent0.33−0.04−0.10−0.09
      Global warming on farmkg of CO2-equivalent0.33−0.09−0.04−0.08
      Acidification totalg of SO2-equivalent0.330.030.030.05
      Acidification on farmg of SO2-equivalent0.320.020.060.06
      Eutrophication totalg of PO4-equivalent0.340.020.020.01
      Eutrophication on farmg of PO4-equivalent0.330.010.110.05
      Energy use totalMJ0.300.00−0.110.03
      Energy use on farmMJ0.22−0.280.100.18
      IOFC
      IOFC = income over feed cost
      €/cow per day−0.040.480.020.37
      1 LU = livestock units.
      2 FPCM = fat- and protein-corrected milk.
      3 IOFC = income over feed cost
      The farm characteristics enclosed in the upper-left area of Figure 3 are inversely related to the total impact per kilogram of milk. The distance between the variables on the first dimension of the graph means that improving milk production and dairy efficiency, on one hand, and increasing stocking density and the share of grassland on farm land, on the other hand, may result in a reduction of all the impacts per kilogram of product. Dairy efficiency is one of the parameters that mainly influences profitability, expressed as IOFC, of a dairy farm; in fact, they are in the same area of Figure 3. Stocking density and feed self-sufficiency are on the opposite sides of the graph in Figure 3 and inversely related as a consequence of the higher amount of feeds generally bought from the market in the high-stocking density farms. Figure 4 shows the multivariate correlation between farm characteristics and environmental impacts per hectare of land. All environmental impact categories are close to each other, to the percentage of land for maize silage, and to the stocking density. On the first dimension (principal component 1), which explains 50.8% of the variance, all impact categories, expressed on unit of land, are inversely related to feed self-sufficiency.
      The Pearson correlation analysis identified stocking density and feed self-sufficiency as the major drivers of environmental burden per hectare of farm land for all the impact categories. In particular, global warming (kg of CO2-equivalent/ha of farm land) showed a strong positive correlation with stocking density (r = 0.91; P < 0.001) and a negative correlation with feed self-sufficiency (r = −0.71; P < 0.001). Significant positive correlations were shown between the percentage of land used to grow maize for silage and all on-farm impact categories, especially acidification and eutrophication (0.55; P < 0.01 and 0.58; P < 0.01, respectively).
      Figure 5 shows the relationship between stocking density and eutrophication, expressed both per kilogram of FPCM and per hectare of farm land. The number of LU per hectare did not affect the emission per kilogram of milk, whereas it is a key point when the impact is expressed in land units.
      Figure thumbnail gr5
      Figure 5Relation between stocking density and eutrophication expressed per kilogram of fat- and protein-corrected milk (FPCM) and per hectare of farm land. Open circles (○) represent eutrophication per hectare and stocking density: y = 42.025x + 10.561; R2 = 0.74. Triangles (▲) represent eutrophication per kilogram of FPCM and stocking density: y = −0.0543x + 7.5317; R2 = 0.002. LU = livestock unit.

      Farming Intensity and Environmental Performances

      The cluster analysis clearly identified 3 groups of farms differing in terms of intensity level (Table 8). The first one (high) included 10 farms characterized by a high level of intensification: high milk production per hectare, high percentage of arable land on total land, large land area sowed with maize for silage, high stocking density, high milk yield per cow, high dairy efficiency, high use of concentrate and maize silage in the cow rations instead of grass hay. The second cluster (medium) consisted of 7 farms less intensive in comparison with the farms of first cluster. The third group (low) included 11 farms identified as the least intensive on the basis of their characteristics.
      Table 8Characteristics of the clusters
      ItemUnitIntensity level
      HighMediumLow
      MeanSDMeanSDMeanSD
      Farm10711
      Farm landha34.98.1781.421209.84
      Arable crops% of total land54.117.461.720.234.525.6
      Maize for silage% of total land45.718.638.513.326.922.5
      Livestock unit (LU)n15737.125836.15521.5
      Stocking densityLU
      LU = livestock units.
      /ha
      4.711.443.310.82.970.67
      Daily milk productionkg of FPCM
      FPCM = fat- and protein-corrected milk.
      /cow
      28.92.0328.12.2924.75.66
      Production intensitykg of FPCM/ha per year25,9179,04317,7714,87515,4394,668
      Dry matter intakekg/cow per day21.221.321.21.9212.02.12
      Dairy efficiencykg of milk/kg of DMI1.360.161.330.071.220.18
      Forage concentrate ratio1.330.331.370.582.211.56
      Maize silage intake% DMI30.416.8434.73.0422.7517.3
      Grass hay intake% DMI15.6411.517.99.2327.4714.4
      Feed self-sufficiency% of total feed DM54.6310.469.212.571.7214.4
      IOFC
      IOFC = income over feed cost.
      €/cow per day5.961.236.760.825.781.27
      1 LU = livestock units.
      2 FPCM = fat- and protein-corrected milk.
      3 IOFC = income over feed cost.
      Total farm land was different among the 3 groups, with the highest value in the medium cluster and lower values in the others. The percentage of arable land of the low group was lower compared with the other 2 groups; high had the higher quota of land used to grow maize for silage in comparison with the low group. The number of livestock units showed the same trend among the groups observed for the farm land. Stocking rate was generally high; in the high group it was particularly elevated compared with the other groups. The milk production levels of high and medium were higher compared with low; similarly, the dairy efficiency, which is strongly related to the level of productivity, showed better results in high and medium farms. High farms had lower feed self-sufficiency compared with the other groups which had similar values. Considering the economic performances, no differences were observed between the average IOFC of the 3 clusters.
      Analyzing the environmental impact on the product basis, only few differences were observed among the groups, and overall they should be considered similar (Table 9). Low farms showed higher on-farm energy use compared with the high ones. High farms had lower on-farm land use impact compared with the other 2 groups, whereas low had lower off-farm land use than high and medium.
      Table 9Environmental impacts expressed per kilogram of fat- and protein-corrected milk corresponding to each cluster of farms
      ItemLocationIntensity level
      HighMediumLow
      MeanSDMeanSDMeanSD
      Global warming (kg of CO2-equivalent)Total1.260.171.270.131.250.21
      On farm0.900.130.950.090.960.21
      Off farm0.360.090.320.080.290.13
      Acidification (g of SO2-equivalent)Total16.03.0716.24.0513.92.91
      On farm13.92.7914.34.2412.02.89
      Off farm2.130.511.940.481.861.09
      Eutrophication (g of PO4-equivalent)Total7.591.247.711.106.861.63
      On farm5.450.825.961.385.231.65
      Off farm2.140.561.750.511.630.74
      Energy use (MJ)Total5.441.175.440.665.510.80
      On farm2.000.422.190.382.770.91
      Off farm3.440.983.250.542.741.01
      Land use (m2)Total0.890.161.020.180.970.14
      On farm0.440.130.590.140.640.14
      Off farm0.450.090.430.110.320.13
      The results change widely if the environmental impacts are evaluated on land unit, as shown in Table 10. High had higher total environmental impact per hectare of farm land for all the categories in comparison to the other 2 groups, which were similar to each other. A similar trend was observed for on-farm GWP, acidification, and eutrophication, which were higher in high than in medium and low. No differences were found regarding on-farm energy use.
      Table 10Environmental impacts expressed per hectare corresponding to each cluster of farms
      ItemLocationIntensity level
      HighMediumLow
      MeanSDMeanSDMeanSD
      Global warming (kg of CO2-equivalent)Total36,26910,02626,0948,13022,4755,907
      On farm25,9927,50019,3845,54216,9353,618
      Off farm10,2773,0886,7112,8455,5403,442
      Acidification (g of SO2-equivalent)Total4641543165625285.5
      On farm40414127663.521666.6
      Off farm60.017.840.218.236.228.6
      Eutrophication (g of PO4-equivalent)Total21865.615531.612335.0
      On farm15851.711822.692.024.5
      Off farm60.318.237.119.830.718.3
      Energy use (MJ)Total152,32736,184111,27832,99799,31726,930
      On farm56,06312,71443,8448,65347,43610,663
      Off farm96,26428,11667,43426,86751,88130,655

      Discussion

      Environmental Impacts of Milk Unit

      The estimated GWP for the production of 1 kg of FPCM was comparable to the value found by
      • Guerci M.
      • Bava L.
      • Zucali M.
      • Sandrucci A.
      • Penati C.
      • Tamburini A.
      Effect of farming strategies on environmental impact of intensive dairy farms in Italy.
      and in agreement with
      • Castanheira É.G.
      • Dias A.C.
      • Arroja L.
      • Amaro R.
      The environmental performance of milk production on a typical Portuguese dairy farm.
      , who similarly obtained a higher contribution of on-farm activities to GHG emission compared with off-farm activities. The acidification and the eutrophication potentials were similar to
      • Castanheira É.G.
      • Dias A.C.
      • Arroja L.
      • Amaro R.
      The environmental performance of milk production on a typical Portuguese dairy farm.
      , but higher compared with findings reported by
      • 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.
      and
      • Basset-Mens C.
      • Ledgard S.
      • Boyes M.
      Eco-efficiency of intensification scenarios for milk production in New Zealand.
      . Total nonrenewable energy use was consistent with the results reported by
      • Thomassen M.A.
      • van Calker K.J.
      • Smits M.C.J.
      • Iepema G.L.
      • de Boer I.J.M.
      Life cycle assessment of conventional and organic milk production in the Netherlands.
      for conventional Dutch dairy farms. Considering land use, the total impact and on-farm contribution were similar to
      • 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.
      and
      • Basset-Mens C.
      • Ledgard S.
      • Boyes M.
      Eco-efficiency of intensification scenarios for milk production in New Zealand.
      . The contributions of the different substances to GWP were comparable with
      • Castanheira É.G.
      • Dias A.C.
      • Arroja L.
      • Amaro R.
      The environmental performance of milk production on a typical Portuguese dairy farm.
      . Similarly, several other studies reported CH4 to be the predominant contributor to the total climate change emissions (
      • Flysjö A.
      • Henriksson M.
      • Cederberg C.
      • Ledgard S.
      • Englund J.E.
      The impact of various parameters on the carbon footprint of milk production in New Zealand and Sweden.
      ;
      • Kristensen T.
      • Mogensen L.
      • Knudsen M.T.
      • Hermansen J.E.
      Effect of production system and farming strategy on greenhouse gas emissions from commercial dairy farms in a life cycle approach.
      ;
      • 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.
      ), whereas
      • Thomassen M.A.
      • van Calker K.J.
      • Smits M.C.J.
      • Iepema G.L.
      • de Boer I.J.M.
      Life cycle assessment of conventional and organic milk production in the Netherlands.
      found a methane contribution to total climate change of only 34% in the conventional system and of 43% in the organic system. Enteric methane is generally recognized as the major driver of GHG emissions in milk production, and the abatement of enterically derived CH4 is considered one of the most promising strategies for the reduction of GHG emissions from the dairy sector (
      • Mc Geough E.J.
      • Little S.M.
      • Janzen H.H.
      • McAllister T.A.
      • McGinn S.M.
      • Beauchemin K.A.
      Life-cycle assessment of greenhouse gas emissions from dairy production in eastern Canada: A case study.
      ).
      The main contribution of ammonia to total acidification potential was found also by
      • Thomassen M.A.
      • van Calker K.J.
      • Smits M.C.J.
      • Iepema G.L.
      • de Boer I.J.M.
      Life cycle assessment of conventional and organic milk production in the Netherlands.
      and
      • Castanheira É.G.
      • Dias A.C.
      • Arroja L.
      • Amaro R.
      The environmental performance of milk production on a typical Portuguese dairy farm.
      , who observed that NH3 emissions have a strong impact on the total acidification potential, whereas SO2 and NOx play a minor role.
      • Castanheira É.G.
      • Dias A.C.
      • Arroja L.
      • Amaro R.
      The environmental performance of milk production on a typical Portuguese dairy farm.
      reported NH3 and NO3 as the major contributors to total eutrophication potential, whereas
      • Thomassen M.A.
      • van Calker K.J.
      • Smits M.C.J.
      • Iepema G.L.
      • de Boer I.J.M.
      Life cycle assessment of conventional and organic milk production in the Netherlands.
      found phosphate to be more important in terms of impact on eutrophication. In the study of
      • 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.
      , nitrate losses occurring on-farm were around 90% for the seasonal grass-based dairy system, but only about 30% for the confinement dairy system.

      Interaction Between Farm Characteristics and Environmental Impact

      The negative relationships shown by the PCA between total environmental impact per milk unit, on one side, and dairy efficiency and milk production level, on the other, are in agreement with numerous results from the literature. In fact, feed conversion efficiency of the animals is known to be an effective strategy in mitigating the environmental impact per unit of product (
      • Hermansen J.E.
      • Kristensen T.
      Management options to reduce the carbon footprint of livestock products.
      ;
      • Yan M.J.
      • Humphreys J.
      • Holden N.M.
      Life cycle assessment of milk production from commercial dairy farms: The influence of management tactics.
      ); according to
      • Capper J.L.
      • Castañeda-Gutiérrez E.
      • Cady R.A.
      • Bauman D.E.
      The environmental impact of recombinant bovine somatotropin (rbST) use in dairy production.
      , a general increase in productivity might positively affect the environmental sustainability of milk.
      • Guerci M.
      • Bava L.
      • Zucali M.
      • Sandrucci A.
      • Penati C.
      • Tamburini A.
      Effect of farming strategies on environmental impact of intensive dairy farms in Italy.
      showed that farming strategies based on high production intensity and high dairy efficiency could mitigate environmental impact per kilogram of milk. The mitigation effect of enhancing dairy efficiency is based on the dilution of environmental costs associated with maintenance. Moreover high-producing cows usually receive low-fiber rations, reducing their methane emission per kilogram of milk.
      The negative correlation between stocking density and total impact (per kilogram of FPCM) is a little surprising, especially when eutrophication and acidification potentials are considered. But farms with high stocking density were also characterized by high production levels and high dairy efficiency.
      Grassland, instead of arable land, seemed to have a positive effect on the environmental impact, but its role was not clear due to the opposite effects of many factors. Generally grassland needs less fertilization than arable land and this has positive effects on GWP, eutrophication, and acidification, but arable crops (e.g., maize silage) have higher yield per hectare and require less field operations and less energy compared with grass hay production (
      • Rotz C.A.
      • Montes F.
      • Chianese D.S.
      The carbon footprint of dairy production systems through partial life cycle assessment.
      ).
      Profitability, expressed as IOFC, shows a negative relationship with total environmental impact per kilogram of FPCM. Farms with cows more efficient in converting feed to milk have higher income per cow and lower impacts per milk unit.
      Regarding environmental impact per hectare of land, PCA showed that the farm management characteristics mostly related to the different impact categories were stocking density and percentage of land for maize silage production. High LU per hectare means a high quantity of organic nitrogen on soil and low feed self-sufficiency. With respect to percentage of land for maize silage production, its positive relationship with environmental impact per hectare depends mainly on the high demand of maize in terms of nitrogen application (organic and chemical), which is positively related to environmental impact per unit of land, as found by
      • Casey J.W.
      • Holden N.M.
      The relationship between greenhouse gas emissions and the intensity of milk production. Ireland.
      .

      Farming Intensity and Environmental Performances

      The current study did not show any difference between the environmental impact per milk unit of the 3 clusters of farms, despite important differences among the groups in terms of farm characteristics and farming intensity. On the contrary, when the functional unit was the hectare of farm land, most of the impacts were much higher in the group of farms with high intensity levels. In general, intensification, defined as increased output per hectare, invariably led to increased emissions when expressed on an area basis; however, the result was less obvious when expressed on a product basis (
      • Crosson P.
      • Shalloo L.
      • O’Brien D.
      • Laniganc 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.
      ).
      • Basset-Mens C.
      • Ledgard S.
      • Boyes M.
      Eco-efficiency of intensification scenarios for milk production in New Zealand.
      highlighted better environmental performances for the low-input dairy systems compared with more intensive systems from both a product and local perspective; similar results were obtained by
      • 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.
      for a grass-based farm versus a confinement system.
      • van der Werf H.M.G.
      • Kanyarushoki C.
      • Corson M.S.
      An operational method for the evaluation of resource use and environmental impacts of dairy farms by life cycle assessment.
      observed no difference in terms of environmental impact between conventional and organic dairy systems when milk sold was considered as functional unit; however, on a land basis, the conventional systems showed a significantly higher environmental burden compared with the organic systems. Similar results were found by
      • Haas G.
      • Wetterich F.
      • Köpke U.
      Comparing intensive, extensified and organic grassland farming in southern Germany by process life cycle assessment.
      , who reported significantly worse environmental performances in the intensive system than in the extensive system when the impact was expressed on land unit.
      • Casey J.W.
      • Holden N.M.
      The relationship between greenhouse gas emissions and the intensity of milk production. Ireland.
      found a significant positive linear correlation between stocking rate and the amount of CO2-equivalent per hectare, but no relationship between stocking rate and GHG emissions per kilogram of milk. Similarly,
      • Oudshoorn V.
      • Sørensen C.A.G.
      • de Boer I.J.M.
      Economic and environmental evaluation of three goal-vision based scenarios for organic dairy farming in Denmark.
      found that no correlation between surplus N per hectare and emission of GHG per kilogram of ECM existed.
      As a consequence, when the environmental impacts related to the product unit are considered, the identification of the more sustainable production strategy seems to be difficult. Several studies compared organic versus conventional farms or grass-based versus confined farms; some authors attributed better environmental performances to the low-input systems (
      • Belflower J.B.
      • Bernard J.K.
      • Gattie D.K.
      • Hancock D.W.
      • Risse L.M.
      • Rotz C.A.
      A case study of the potential environmental impacts of different dairy production systems in Georgia.
      ;
      • 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.
      ), others associated the more intensive systems with a potential reduction of the environmental pressure (
      • Thomassen M.A.
      • van Calker K.J.
      • Smits M.C.J.
      • Iepema G.L.
      • de Boer I.J.M.
      Life cycle assessment of conventional and organic milk production in the Netherlands.
      ;
      • Kristensen T.
      • Mogensen L.
      • Knudsen M.T.
      • Hermansen J.E.
      Effect of production system and farming strategy on greenhouse gas emissions from commercial dairy farms in a life cycle approach.
      ), still other researchers reported different results depending on the impact category considered (
      • Cederberg C.
      • Mattsson B.
      Life cycle assessment of milk production—A comparison of conventional and organic farming.
      ).

      Conclusions

      When assessing the environmental impact per milk unit, it is difficult to clearly identify the relationship between farming intensity and environmental performances, despite important differences in terms of farm intensification level, management, and structural characteristics. However, the PCA showed that some characteristics related to farming intensification, particularly milk production per cow, dairy efficiency, and stocking density, were negatively related to the impact per kilogram of product; this suggests a role of these factors in the mitigation strategy of the environmental impact of milk production on a global scale. Besides an important role in global environmental impact (i.e., climate change), livestock systems are often responsible for local and not less important impacts (i.e., eutrophication of soils and water). Considering the environmental burden on a local perspective, the impacts were positively associated with the intensification level.

      Acknowledgements

      This research was supported by Plan for R&S (Research and Development), Region of Lombardy DG Agricoltura, Italy. Project no. 1726—Individuazione di modelli di aziende zootecniche per produzioni di eccellenza di latte e derivati.

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