Greenhouse gas emissions and land use from confinement dairy farms in the Guanzhong plain of China – using a life cycle assessment approach

Journal of Cleaner Production xxx (2015) 1e10

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Greenhouse gas emissions and land use from confinement dairy farms in the Guanzhong plain of China e using a life cycle assessment approach Xiaoqin Wang a, *, Troels Kristensen b, Lisbeth Mogensen b, Marie Trydeman Knudsen b, Xudong Wang a a b

College of Resources and Environment, Northwest Agriculture and Forestry University, Yangling, 712100, China Department of Agroecology, Aarhus University, Blichers Alle 20, Post Box 50, DK-8830, Tjele, Denmark

a r t i c l e i n f o

a b s t r a c t

Article history: Received 15 February 2015 Received in revised form 21 October 2015 Accepted 29 November 2015 Available online xxx

The objective of this study were to quantify greenhouse gas (GHG) emissions and land use of milk production and evaluate the potential mitigation options at farm level in the Guanzhong plain, which is the main dairy farming region in China. The life cycle assessment methodology was used to analyse the GHG emissions of eight confinement dairy farms that covered different milk production levels, herd structures and diet compositions. The GHG emission per kg of fat and protein corrected milk (FPCM) ranged from 1.31 to 2.08 kg CO2 eq. after farm GHG emissions had been allocated to milk, meat and manure. Enteric methane and emissions related to feed production and manure management were the three major GHG emission sources from the eight farms and contributed 54%e60%, 21%e30% and 8% e10%, respectively, to total emissions. Land use per kg of FPCM was 1.81 m2 with the largest contribution from feed production. GHG emissions associated with the production of per kg of FPCM decreased with increasing herd milk productivity and herd feed efficiency. Sensitivity analyses showed that the GHG emissions were significantly affected by metric values of global warming potential (GWP), with increases of about 4% and 12% for the calculation based on the IPCC (2013) GWP values, compared to IPCC (2007) and IPCC (1996) values, respectively; and soybean meal from imported soybeans would increase GHG emissions per kg of FPCM significantly due to the emissions related to land use change. Lowering Nfertilizer use per ha for feed crop production, improving milk productivity and herd structure, and encouraging farmers to use manure for crop production are promising abatement strategies. Due to the low percentage of grass (average 14%) in the diet for dairy cows in the Guanzhong plain, replacing part of the concentrates with alfalfa hay would increase milk productivity and decrease GHG emissions per kg of FPCM. © 2015 Elsevier Ltd. All rights reserved.

Keywords: GHG emissions LCA Confinement Dairy Manure management Milk

1. Introduction Our planet is experiencing extreme weather events, the intensity and frequency of which have increased (IPCC, 2007; Pirlo, 2012). Over the last decade, anthropogenic greenhouse gas (GHG) emissions have come to be accepted as the main cause of climate
et al., 2013). Livestock is one of the main anthrochange (Verge pogenic sources of GHG emissions, accounting for about 15% of annual global emissions (Gerber et al., 2013). Dairy cattle are the

* Corresponding author. Tel./fax: þ86 029 87080282. E-mail address: [email protected] (X. Wang).

second largest of these livestock sources; therefore dairy farming systems are under particular scrutiny (Pirlo, 2012) with a growing number of studies evaluating GHG emissions and mitigation strategies, typically based on life cycle assessments (Thomassen et al., 2008; Castanheira et al., 2010; de Vries and de Boer, 2010; Pirlo, 2012; Asselin-Balencon et al., 2013; van Middelaar et al., 2013; Baek et al., 2014; Gollnow et al., 2014). However, there is a large variation in the results from these studies, ranging from 0.41 to 2.46 kg CO2 eq. per kg of milk (Pirlo, 2012; Asselin-Balencon et al., 2013) due to the difference in the methods applied, data sources and dairy farming systems. The largest difference in GHG emissions per kg of milk is between developed countries and developing countries (Gerber et al., 2013; Hagemann et al., 2012). Most of the

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Please cite this article in press as: Wang, X., et al., Greenhouse gas emissions and land use from confinement dairy farms in the Guanzhong plain of China e using a life cycle assessment approach, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.11.099

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X. Wang et al. / Journal of Cleaner Production xxx (2015) 1e10

latest studies focus on dairy systems in developed countries and only very few on developing countries, especially China. The studies of Gerber et al. (2013) and Hagemann et al. (2012) do involve developing countries. However, both studies are at global level, with the former using aggregated data from different sources for production practices and productivity and the latter typical farm data, but the analysis used the same GHG emission factors for concentrates for all the countries and a one-size-fits-all formula to estimate emissions. All this makes their results less accurate than studies at farm level. Weiler et al. (2014) and Bartl et al. (2011) studied the dairy systems of two developing countries e Kenya and Peru, respectively, but they both focused on smallholder dairy systems. To our knowledge, no study has focused on GHG emissions of intensive dairy farming systems in China based on survey data at farm level. China is the third-largest dairy producer in the world (Qian et al., 2011; Hagemann et al., 2012; Garnett and Wilkes, 2014). Production systems and milk productivity in China are very different from other major dairy-producing countries such as India, USA or New Zealand, and large differences also exist between farms within China itself. In China, 33% of all cows are reared on farms with 100 head or more (DAC, 2012), typically confinement dairy farms. Most confinement dairy farms do not have any cropland and import feed from other areas. Average annual milk yields are about 5000 kg per cow, which together with the low-quality feed can result in high GHG emissions per kg of milk. Since Chinese milk production is still growing (OECD and FAO, 2013; Garnett and Wilkes, 2014), therefore to achieve emission reduction targets of the nation it is necessary to quantify and reduce GHG emissions per unit of milk from Chinese dairy systems. Most studies on the carbon footprint of milk production have not considered the effect of land use, although it can be argued that all use of land for crop production increases the pressure on land use, thus causing land use change and greenhouse gas emissions somewhere in the world (e.g. Audsley et al., 2009). On the other hand, China is suffering from the pressure of arable land shortage as the arable land per capita is low compared to average global level. It is therefore especially important for China to get the most efficient use of the land while meeting emission reduction targets. The objective of this study was to evaluate GHG emissions and land use from milk production in the Guanzhong plain, which is an important dairy farming region in China. The effect of the feed ration, herd structure and manure management on GHG emissions was analysed in detail, and mitigation options for dairy farming systems in the Guanzhong plain were discussed. 2. Materials and methods Based on the ISO 14040/14044 principles (ISO, 2006a,b), life cycle assessment (LCA) was used to analyse GHG emissions from cradle to farm gate based on case studies at eight private dairy farms representing different herd structures, feed supplies and herd productivity rates. 2.1. Description of dairy farms in the Guanzhong plain The Guanzhong plain is one of the main dairy farming regions in China (Cao et al., 2013). It is situated in the northwestern part of China and has a mean annual temperature of 12  C. All dairy farms are confinement dairy systems. The differences in herd size, feed composition and productivity are large (Cao et al., 2013). In order to cover different production levels, eight dairy farms were selected where production levels per cow (kg milk delivered to dairy) ranged from 15 to 22 kg milk per cow per day (Table 1). Input and output data were collected for the 2010/2011 financial year. All

eight farms bought all feed from outside the dairy farm. Manure was collected once a day and sold to orchards and arable farms nearby where the manure was composted with infrequent turning for mixing and aeration. After 1e3 months, the manure was spread in the orchards or vegetable fields. None of the eight dairy farms reared their bull calves. The bull calves were sold for meat production after birth, while female calves were reared as replacement dairy cows. The main characteristics of the eight dairy farms are listed in Table 1.

2.2. System boundary definition The system boundary defines the relevant activities from cradle to farm gate. As illustrated in Fig. 1, it included a number of processes: 1) feed crop production and concentrates processing which covered fertilizer production and energy consumption, 2) transport of feed to dairy farms, 3) enteric fermentation, 4) energy use for farm operations and 5) manure management during storage.

2.3. Functional unit and allocation The functional unit (FU) of the study was 1 kg of fat and protein corrected milk (FPCM) (IDF, 2010) delivered to the dairy at the farm gate. Due to data for milk fat and protein content not being available for individual farms, average protein and fat contents in milk of 3.22% and 3.66%, respectively, for the Shaanxi province (DAC, 2013) were used for all eight farms. Since manure was sold to orchards and vegetable farms, manure was considered a co-product of the dairy farm system, and GHG emissions from composting manure were included in the dairy systems. In addition to milk, the dairy farms produced meat from bull calves and culled cows. Total GHG emissions at the farm gate were partitioned into milk, meat and manure, using the economic allocation method. Unlike milk, bull calves and culled cows, the price of manure differed markedly between farms depending on the farmers' awareness of the value of manure as a fertilizer and local market conditions. Therefore, in this study, the economic value of manure was calculated based on synthetic N fertilizer equivalents (Weiler et al., 2014; Alary et al., 2011):

MANURE ¼ fertilizer price  Nm where MANURE is the economic value of manure, the fertilizer price is a standard N fertilizer price (3.26 Yuan per kg N was used in this study) and Nm is kg N in manure used as fertilizer which is calculated by subtracting the N losses in manure management from N ex animal. The method of calculating N losses and N ex animal is provided in Section 2.4.2. Economic allocation was also used for partitioning the GHG emissions from feed production where more than one product was produced per crop. For example, the GHG emissions of maize grain and maize straw were calculated by partitioning the total GHG emission from growing maize according to the economic value of the maize grain and the maize straw. Similarly, the GHG emission of wheat bran was determined based on the price of wheat flour and wheat bran.

2.4. Inventory analysis Inventory data were collected for the 2010/2011 financial year and therefore the GHG emissions modelled were representative for this period.

Please cite this article in press as: Wang, X., et al., Greenhouse gas emissions and land use from confinement dairy farms in the Guanzhong plain of China e using a life cycle assessment approach, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.11.099

X. Wang et al. / Journal of Cleaner Production xxx (2015) 1e10

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Table 1 Characteristics of eight dairy farms in the Guanzhong plain of China (ascending order according to milk productivity, kg milk per dairy cow). Item Livestock Total cattle Dairy cows Milk productiona Bull Calves soldb Cull cows soldb Feed consumption Concentratec Maize silage Maize straw silage Alfalfa hay Wheat straw Energy use on farm Electricity Coal Diesel a b c

Unit per year

Farm 1

Farm 2

Farm 3

Farm 4

Farm 5

Farm 6

Farm 7

Farm 8

head head tonne head head

49 34 152 18 7

240 135 617 50 20

450 300 1555 80 45

110 47 258 20 9

220 130 780 50 19

300 200 1200 45 30

420 170 1037 50 50

950 550 3690 200 83

tonne tonne tonne tonne tonne

150 0 189 42 4

496 0 2190 18 142

976 1600 2400 62 73

180 650 650 20 28

496 0 1424 160 0

584 0 3600 219 0

720 3650 0 240 0

2044 2000 6000 675 0

kWh tonne l

7200 0.4 28

51,200 12 1029

76,600 17 1714

14,000 13 214

40,000 18 2372

96,000 28 3429

97,900 17 1714

192,000 85 6171

The price of milk varied from 3 to 4 Yuan per kg of milk depending on farm. The price of bull calves and cull cows sold is 400 and 5000 Yuan per head, respectively. Ingredients of concentrate: maize grain, wheat bran, soybean meal and others account for 50%, 20%, 22% and 8% of the total, respectively.

2.4.1. Feed production Greenhouse gas emissions from feed production included crop production and processing of concentrates. Emissions associated with feed production were from four sources: 1) CO2 from fertilizer production; 2) N2O from application of N fertilizers to crops, accounting for both direct and indirect emissions; 3) CO2 from energy consumption related to field operations and 4) CO2 from energy consumption associated with the processing of concentrates, including manufacture of wheat bran and soybean meal from wheat and soybean, and the milling and mixing of concentrate ingredients at the feed factories. Maize straw and whole maize crop (for silage) were harvested manually and were chopped for silage on the dairy farms. The energy consumption and associated emissions for silage processing was included in the total energy consumption and emissions from the dairy farms. The agricultural land used for feed production for the dairy farms in this study had been under cultivation for hundreds of years; therefore there were no emissions associated with direct land use change. Soil carbon changes due to crop production and indirect land use change were not included. Data for fertilizer input and crop yield were sourced from NDRC (2010) and data on energy consumption for field operations from Liang et al. (2009) (Table 2). Most of the farms do not use fertilizer for alfalfa and use manual labour for harvesting, so for alfalfa hay only the emission from transportation was included. Calculations of N2O from application of N fertilizers to crops were based on IPCC

(2006). All emission factors for GHG from fertilizers and energy sources are given in Table 3. Based on yield and price, 95% and 5% of the emission from maize crop production was allocated to maize grain and maize straw, respectively, and 97% and 3% of emissions from wheat crop production was allocated to wheat grain and wheat straw, respectively. Based on the price and yield ratios of wheat flour to wheat bran, and soy oil to soy meal, 14% of total emissions from wheat grain production and wheat flour processing was allocated to wheat bran, while 59% of total emissions from soybean production and soy oil processing was allocated to soy meal. The electricity consumptions for processing soy oil, wheat flour and concentrate were 30, 48 and 30 kWh per 1000 kg, respectively (Bai, 2007). Greenhouse gas emissions from feed production at the dairy farm were calculated as the annual feedstuff consumption (including animal refusals) multiplied by the GHG emission per unit produced. Land use was calculated as the sum of the farmed area and land for production of feed. The allocation method for land use was same as that used for GHG emissions. 2.4.2. Emissions from transportation of feed Emissions from transportation were calculated by multiplying diesel consumption with its emission factor. The standard of 20 L diesel for a 10-t lorry driving 100 km was used. The distances for transporting maize silage, maize straw, wheat straw and alfalfa hay

Feed production

Electricity

Concentrates process

Feed crops production

Dairy farm Farm operation

Concentrates Roughages

Diesel Fertilizer

Transportation

Feeding

Enteric fermentation

Electricity Diesel Coal

Manure compost Milk Meat Fertilizer(Manure)

Fig. 1. Schematic of system boundary for the milk production system in the Guanzhong plain of China. The GHG emissions of milk production include emissions from feed production, transport of feed, enteric fermentation, energy use on farm and manure management.

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Table 2 Annual resource use and output from cultivation of crops on 1 ha. Feed

Maize grain

Input Mineral fertilizer, kg N/ha Mineral fertilizer, kg P2O5/ha Mineral fertilizer, kg K2O/ha Diesel, L/ha Electricity, kWh/ha Output Grain yield, kg/ha Straw or silage yield, kg/ha a b c

272 67 14 45a 732a 6013 7516

Wheat

Soybean

214 162 8 45a 1177a

56 56 24 28b 0b

5915 4317

1908 e

Maize silage

Alfalfa hay

200 150 0 45a 732a e 79,454

4650c

Liang et al. (2009). Knudsen et al. (2010). Based on interviews to 33 farmers.

Table 3 Factors used for estimating emissions from feed crop production and energy consumption. Pollutant

Emission Factor (EF)

Reference

N2Odirect, kg NH3eN, kg

Application of fertilizer (per kg N) Application of fertilizer (per kg N)

Zhang et al. (2010) Wang et al. (2002)

NO3eN, kg N2Oindirect, kg

Application of fertilizer (per kg N) From NH3 (per kg NH3eN) From leaching (per kg NO3eN) N in fertilizer (per kg N) P in fertilizer (per kg P) K in fertilizer (per kg K) Diesel (per L) Coal (per kg) Electricity (per kWh)

0.0105 0.049a 0.036b 0.056c 0.25 0.01 0.0075 4.77 4.63 0.596 2.76 2.08 0.943

GHG, kg CO2 eq.

a b c

Maize. Wheat. Soybean.

from their production place to dairy farms were 20, 10, 30 and 1500 km (10-t lorry), respectively. Concentrates were transported 15e30 km from feed factories to dairy farms (10-t lorry). One exception was soybean meal which was transported from the Heilongjiang province, 2290 km away (10-t lorry). Maize grain and wheat bran were bought from places nearby (10 km) feed factories, so only transportation for soybean meal was included, while transportation for maize grain and wheat bran was omitted. 2.4.3. CH4 emissions from enteric fermentation Enteric CH4 emissions were estimated according to the Tier 2 method (IPCC, 2006), based on a gross energy intake (GE) of 18.45 MJ per kg dry matter intake (DMI) and a methane conversion factor (Ym) of 6.5%. Dry matter intake was calculated from the sum of each feedlot intake (expressed as fresh weight, Table 1)

Table 4 Feed characteristics for feed used on all eight farms.a Feed

DM (%)b

DE (%)c

ASH (%)d

CP (%)e

Maize grain Wheat bran Soybean meal Maize silage Maize straw silage Wheat straw Alfalfa hay

86 87 89 26 29 91 88

85.8 77.1 92.2 67.7 67.7 45.2 62.7

1.3 4.8 6.1 6 7 8 8

9.1 16.4 49.7 8 6 3 16

a b c d e

Zhao et al. (2009) IPCC (2006) IPCC (2006) Wang et al. (2012) EcoInvent (2010) EcoInvent (2010) Xie et al. (2008) Xie et al. (2008) Di et al. (2007)

Data from Chinese Feed Database (IAS, 2013). Dry matter content as a fraction of feed. Digestibility of feed dry matter in %. Ash content as a fraction of dry matter feed. Crude protein content as a fraction of dry matter feed.

multiplied by the standard dry matter content of feedlots (DM%, Table 4). Each feedlot intake was from recordings of annual feed consumption of farms. 2.4.4. Emissions from manure management On the eight farms, manure management was based on passive composting. Emissions of CH4 and N2O from manure management were estimated based on the Tier 2 and Tier 1 methods, respectively (IPCC, 2006). Factors for CH4 and N2O emissions from manure management were taken from IPCC (2006) for Asian dairy cows (Table 5) and 0.13 m3 CH4 per kg of volatile solids was used as the maximum methane producing capacity (B0). Factors for calculating volatile solids are set out in Table 4. N intake was calculated from the dry matter intake and crude protein (CP) in each feed stuff, with a standard content of 16% nitrogen. Excretion of nitrogen in manure was calculated as the difference between N in the feed intake and N in produced milk and live weight change. The N content in milk was calculated from the protein content of milk and converted to N by

Table 5 Manure management factors from IPCC (2006). Manure management system

MCFa at 12  C

EF3b

FracGasc

EF4d

Wind-row composting, passive Daily spread Slurry

0.005 0.001 0.13

0.01 0 0.005

0.4 0.07 0.4

0.01 0.01 0.01

a

Methane conversion factor for manure management system. Direct emission factor of N2O for manure management system. c Fraction of manure N that volatilizes in manure management system. d Indirect emission factor for N2O emissions from volatilization (kg N2OeN kg1 NH3eN þ NOx-N volatilized). b

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X. Wang et al. / Journal of Cleaner Production xxx (2015) 1e10

dividing by 6.38. Nitrogen in live weight gain was set to 26 g N per kg of live weight (Kristensen et al., 2011). 2.4.5. Emissions from energy consumption on farm Energy was used on the farms for milking, refrigeration, silage processing, feed mixing and feeding. CO2 emissions due to energy use on the farms were calculated by multiplying the annual energy resource consumption (Table 1) with the emission factors (Table 3). Emissions from other production inputs like pesticides, plastics and cleaning materials were not included, since no information was available, and neither were emissions associated with halocarbon losses from refrigerants included. 2.5. Impact assessment Greenhouse gas emissions from the system were expressed in CO2 equivalents (CO2 eq.). The global warming potential (GWP) based on the 5# Assessment Report of the IPCC (IPCC, 2013) was used to convert CH4 and N2O to CO2 eq. terms. Consequently, a GWP of 265 was used for N2O, 30 for fossil CH4 and 28 for biogenic CH4. 2.6. Statistical and sensitivity analysis The GHG emission model is conceptualized as a spreadsheet package using Microsoft Excel. SPSS 18.0 was used for regression analysis. A number of sensitivity analyses were conducted to test the effect of GWP values, a feed strategy, origin of soybeans, and manure management on GHG emissions and land use per kg of FPCM. For the effect of GWP values on the results, three different metric values were applied, based on IPCC 1996 (21 for CH4 and 310 for N2O), IPCC 2007 (25 for CH4 and 298 for N2O) and IPCC 2013 (28 for CH4 and 265 for N2O). Dairy cows are fed less than 3 kg alfalfa hay per cow per day on most Chinese dairy farms, which is not good for the health and milk productivity of cows (Liu et al., 2013). Studies have reported that replacing some of the concentrates in the diet with alfalfa hay can increase milk productivity (Li et al., 2003; Yue et al., 2009; Guo, 2010). An alternative diet replacing 1.5 kg concentrates with 3 kg alfalfa hay was assumed to be applied on the eight dairy farms to check the effect on GHG emissions. This resulted in an increased milk production of 2 kg per dairy cow per day (Liu et al., 2013). We assumed two different scenarios for the alfalfa production: 1) that an increasing demand for alfalfa does not result in a higher use of fertilizer, but in a higher use of land (situation excluding use of fertilizer), 2) that fertilizer is used to enhance yield of alfalfa per ha (situation including use of fertilizer). For the situation including fertilizer use, American values were used for GHG emissions and land use kg1 alfalfa hay (DM) of 0.17 kg CO2 eq. (Adom et al., 2012) and 1.19 m2 (calculated based on yield, USDA NASS, 2011a), respectively. Except for locally-produced soybeans mainly in North China (such as the Heilongjiang province), China gets respectively 42, 39 and 15% of its total soybean import from America, Brazil and Argentina (Anonymous, 2011; Yang, 2014). Regarding emissions from the crushing of soybean, they were assumed to be the same for the different countries. The value for GHG emissions per kg of soybean cake from soybeans produced in America of 0.46 kg CO2 eq. was taken from Adom et al. (2012). Emissions from soybean cake of soybeans produced in Brazil and Argentina consisted of two parts: emissions from the production process and emissions related to land use change, since soybeans produced in Brazil are entirely associated with deforestation, and in Argentina partially associated with the conversion of pasture and shrubland to cropland (FAO,

5

2010). Emissions from the production process in Brazil and Argentina were assumed to be the same as the American due to a lack of data. The values for GHG emissions related to land use change of 7.69 and 0.93 per kg of soybean cake from Brazil and Argentina, respectively, were taken from FAO (2010). The yields of soybean meal per ha for calculating land use for America, Brazil and Argentina were 2396 kg (USDA NASS, 2011b), 2000 kg (Lehuger et al., 2009) and 1600 kg (DM basis, Bartl et al., 2011) based on the yield of soybeans, respectively. For manure management, the effect of other manure management systems (‘Daily spread’ and ‘Slurry’) was compared with ‘Passive wind-row composting’ as a mitigation option. A situation where the manure is heaped but not used was also assessed. Since there are no emission factors for this situation, the factors for ‘passive composting’ were used, but the total GHG emission was partitioned into milk and meat only. 3. Results and discussion 3.1. Dairy farm technical performance There were large differences between the eight farms in milk production, structure, feeding and efficiency (Table 6). FPCM production per dairy cow ranged from 4261 to 6375 kg per year, with a total number of heads (cows and heifers) per cow ranging from 1.4 to 2.4. FPCM production per head did not show any significant relation (r2 ¼ 0.14) to FPCM production per dairy cow, due to the large difference in herd structure between farms. Dry matter intake (kg) per year per head ranged from 4133 to 5667, with percentage of concentrate in the ration (% of DMI) at herd level ranged from 27% to 57% and percentage of alfalfa from 1% to 16%. The crude protein content ranged from 9.9% to 14.4% of DMI. The level of crude protein of most farms was below the recommended minimum of 12.5%, which may influence farm performance. Due to the difference in herd structure and diet, the feeding efficiency at herd level (kg DMI per kg of FPCM) ranged from 1.28 to 2 kg dry matter per kg of FPCM. 3.2. Emissions from feed production and transport Concentrates had a higher GHG emission per kg dry matter compared with roughages (Table 7). The GHG emission from the production and use of fertilizer was 63e79% of the total GHG emissions related to feed production and transport. Compared to the studies of Adom et al. (2012); Asselin-Balencon et al. (2013) and Mogensen et al. (2014), GHG emissions per kg of maize and wheat are high in the Guanzhong plain. One reason is that excessive Nfertilizer is used in the Guanzhong plain (Zhang et al., 2011), which is higher than in America (Asselin-Balencon et al., 2013) and Denmark (Mogensen et al., 2014). Another reason is that the GHG emissions of N-fertilizer production is high in China compared to other places (Adom et al., 2012; Wang et al., 2012; O'Brien et al., 2014; Meul et al., 2014; Mogensen et al., 2014). So, decreasing the use of N-fertilizer could decrease the GHG emission from feed production. In China alfalfa is almost always grown as a permanent crop with no or low fertilizer input and with manual harvesting, leading to zero GHG emission from its production. However, for the eight farms, the GHG emission from transportation of alfalfa was high as the cultivation of alfalfa took place a long distance from the farms. Soybean meal requires the largest area of 4.24 m2 for production per kg of dry matter since the yield of soybeans is the lowest of the feedlots. The land required for per kg dry matter of alfalfa hay is 2.44 m2 due to the low yield of alfalfa hay per ha in China, with negligible inputs of fertilizer and irrigation water. Maize grain

Please cite this article in press as: Wang, X., et al., Greenhouse gas emissions and land use from confinement dairy farms in the Guanzhong plain of China e using a life cycle assessment approach, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.11.099

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Table 6 Production, herd structure, feeding and efficiency for eight Chinese dairy farms (per year). Farm, no Per dairy cow FPCMa, kg Total no. of heads Per cattle head FPCM, kg Meat, kg live weightb Dry matter intake, kg Concentrate, % of DMIc Alfalfa, % of DMI Crude protein in DMI, g DMI per kg of FPCM Manure, kg N a b c

1

2

3

4

5

6

7

8

4261 1.4

4346 1.7

4926 1.5

5216 2.3

5700 1.6

5700 1.5

5795 2.4

6375 1.7

2956 92 4543 57 16 144 1.75 87

2444 53 4895 33 1 99 2.00 63

3284 61 4469 38 2 112 1.36 62

2229 51 4943 26 3 100 2.22 66

3368 56 4316 41 14 125 1.28 67

3800 60 5667 27 11 104 1.49 73

2070 70 4133 33 12 123 2.00 67

3691 56 4718 35 13 119 1.28 69

FPCM ¼ fat and protein corrected milk. Bull calf 38 kg; Culled cow 550 kg. DMI ¼ Dry matter intake.

Table 7 GHG emission (g CO2 eq.) and land use (m2) per kg dry matter. GHG emission

Maize grain Wheat bran Soybean meal Maize silage Maize straw silage Wheat straw Alfalfa hay

Land use

Fertilizer production

Cultivation, N emissions

Cultivation, energy

245 123 159 58 34 11 0

279 116 137 56 39 10 0

139 112 43 38 19 10 0

Processing 26 23

0

a

Transport

Total

109 3 1 1 72

664 376 470 155 93 32 72

1.83 0.94 4.24 0.48 0.25 0.08 2.44

a

The GHG emission from processing of maize silage, maize straw silage and wheat straw was included in emissions from on-farm energy consumption, since they were processed on the dairy farms.

required the third-largest land area, which was high compared to maize silage that had a higher yield. Maize straw silage and wheat straw required less land since they are a high-yield agricultural waste.

3.3. Greenhouse gas emissions from dairy farming systems in the Guanzhong plain The primary source of GHG emissions, accounting for 54%e60% of total emissions, was enteric fermentation. Feed production was the second-highest at 21%e30% of total emissions, manure management the third-highest at 8%e10%, and GHG emissions from onfarm energy use and feed transportation were 3%e9% and 1%e2% of total emissions, respectively. Farm 1 had the lowest GHG emission from energy use, since it was using manpower instead of machinery and energy resources. When total farm GHG emissions were allocated to milk, average GHG emissions per kg of FPCM were 1.71 kg CO2 eq. with a span of values from 1.37 to 2.26 kg (Table 8). When total emissions were allocated to milk, meat and manure, using economic allocation, the average GHG emission per kg of FPCM was 1.60 kg CO2 eq. with a span from 1.31 to 2.08 kg. Emissions per kg meat (live weight) ranged from 3.40 to 6.13 CO2 eq. and emissions per kg manure (N) from 1.19 to 2.15 CO2 eq. The total area used for production of 1 kg of FPCM was an average of 1.81 m2, ranging from 1.45 to 2.43 m2 after allocation. The largest part of this area was external feed production, which was responsible for 97%e99% of total land use, since these eight dairy farms do not have cropland on the farm and bought in all their feeds e a common practice in China.

3.4. Greenhouse gas emissions of this study compared with other studies In the present study, enteric methane emissions and emissions related to feed production and manure management were the three main sources of GHG emissions, as also found in other studies (Castanheira et al., 2010; McGeough et al., 2012; Asselin-Balencon et al., 2013; O'Brien et al., 2014). Feed production was the secondlargest source of emissions, which is in accordance with the studies of Castanheira et al. (2010) and Asselin-Balencon et al. (2013), who found GHG emissions from feed production accounted for 27% and 33%, respectively. The total GHG emission from the dairy system was allocated to milk, meat and manure according to the economic contribution. The allocation factors applied to milk ranged from 0.92 to 0.95, which is similar to those found in the studies of Cederberg and Stadig (2003), Thomassen et al. (2008) and McGeough et al. (2012). To enable a comparison of the GHG emission results in our study with results expressed in different FUs based on different values of GWP, in addition to using IPCC (2013) and FPCM values we also calculated the GHG emissions of milk production based on different versions of GWP values and FUs (Table 9). The GHG emission per kg of FU was below the average global emission intensities of 2.8 kg CO2 eq. per kg of FPCM (Gerber et al., 2013), it was within the range of 0.74e2.46 kg CO2 eq. kg1 FPCM measured by Asselin-Balencon et al. (2013), but higher than values from studies of more intensive € et al., 2011; dairy systems (de Vries and de Boer, 2010; Flysjo Kristensen et al., 2011; McGeough et al., 2012; Baek et al., 2014; O'Brien et al., 2014). One reason for the large difference in GHG emission per kg FU could be differences in cow productivity. Milk productivity in our study was higher than the average global

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X. Wang et al. / Journal of Cleaner Production xxx (2015) 1e10

7

Table 8 GHG emissions (kg CO2 eq.) and land use (m2) of eight dairy farms, expressed per kg product.

Emission, per kg of FPCM Enteric fermentation Feed production Manure management Energy use on farm Feed transportation Total After allocationa Per kg of FPCM Per kg of live weight Per kg of manure (N) Land use, per kg of FPCM Farmland Feed production Total before allocation After allocation a

Farm 1

Farm 2

Farm 3

Farm 4

Farm 5

Farm 6

Farm 7

Farm 8

0.95 0.53 0.18 0.05 0.04 1.74

1.23 0.49 0.16 0.13 0.02 2.04

0.84 0.39 0.11 0.08 0.02 1.44

1.36 0.51 0.18 0.17 0.03 2.26

0.79 0.35 0.12 0.11 0.03 1.41

0.92 0.32 0.12 0.14 0.03 1.52

1.08 0.48 0.17 0.13 0.04 1.91

0.79 0.33 0.11 0.11 0.03 1.37

1.61 3.55 1.24

1.90 5.09 1.78

1.36 3.51 1.24

2.08 6.13 2.15

1.33 3.48 1.22

1.45 3.73 1.32

1.75 4.50 1.60

1.31 3.40 1.19

0.02 2.62 2.64 2.43

0.02 1.85 1.87 1.74

0.02 1.52 1.54 1.45

0.07 2.02 2.09 1.92

0.02 1.79 1.81 1.71

0.05 1.54 1.59 1.51

0.03 2.30 2.33 2.14

0.02 1.64 1.66 1.58

92%e95%, 4%e7% and 1%e2% of total GHG emission was allocated to milk, meat and manure, respectively, depending on farm sales.

Table 9 GHG emissions (kg CO2 eq.) expressed per kg product based on different GWP values.

GHG emission per kg FPCM per kg ECM per kg milk

GWP values based on IPCC 1996 CH4 ¼ 21 N2O ¼ 310

GWP values based on IPCC 2007 CH4 ¼ 25 N2O ¼ 298

GWP values based on IPCC 2013 CH4 ¼ 28 N2O ¼ 265

(kg CO2 eq.) 1.41

1.53

1.60

1.42 1.34

1.54 1.46

1.61 1.52

productivity (Gerber et al., 2013), it was within the range of that found by Asselin-Balencon et al. (2013), but was less than in other studies of more intensive dairy systems (de Vries and de Boer, 2010; € et al., 2011; Kristensen et al., 2011; McGeough et al., 2012; Flysjo Baek et al., 2014; O'Brien et al., 2014). Compared to our study, Hagemann et al. (2012) reported lower GHG emissions per kg of ECM for North Chinese dairy farms of 1.44 kg CO2 eq. (the value of GWP was based on IPCC, 2007). However, as discussed, this study calculated emissions using emission factors from non-Chinese studies. Average land use per kg of FU in our study was within the range of 1.2e1.93 m2 measured in European countries (de Vries and de Boer, 2010; Kristensen et al., 2011). Chinese dairy farming systems (which have a lower productivity than their European counterparts) had high GHG emissions per kg of milk, but the land area occupied was no higher than in Europe partly because agricultural waste such as wheat straw and maize straw silage is used in Chinese dairy farming systems.

3.5. Factors that affect the GHG emission from milk production in the Guanzhong plain 3.5.1. Effect of milk productivity and herd structure on GHG emission There was a significant relation (r2 ¼ 0.87) of dairy cow milk productivity (FPCM per cow) and herd structure (the proportion of dairy cows in the herd) with GHG emission per kg of FPCM. The results showed that for similar herd structures, farms with a high dairy cow milk productivity tended to have lower GHG emissions per kg of FPCM; and for similar milk production levels per cow, dairy farms with a lower percentage of dairy cows had higher GHG emissions per kg of FPCM compared with farms with a higher percentage of dairy cows. This can partly explain why farms 4 and 7 had higher GHG emissions expressed per kg of milk, and farm 1 had

a lower GHG emission than farms with similar milk production levels per cow. The combined impact of dairy cow milk productivity and herd structure can be expressed in an indicator ‘herd milk productivity’ (FPCM per head). Milk productivity at herd level showed a significant linear relation (r2 ¼ 0.77) with GHG emission per kg of milk (Fig. 2), which was also the case for the contribution from enteric methane, feed production and manure management. The emission from on-farm energy use, on the other hand, was not correlated to herd productivity, as also found in the global study by FAO (2010). This means that farms can decrease the GHG emission per kg of FPCM by improving dairy cow milk productivity and herd structure. The milk productivity of dairy cows can be increased by improving diet. To improve the herd structure, some researchers (Horn et al., 2012; Lehmann et al., 2014) have suggested increasing the interval between calvings to reduce the number of replacement heifers while maintaining milk yields. However, this is only relevant in high-yielding herds. Capper et al. (2009) reported that genetic improvement has been a major contributor to the increase in productivity in USA and has also benefited herd structure. At the level of production in the Chinese herds, genetic improvement could be one of the measures used to improve milk productivity and herd structure.

3.5.2. Effect of feed ration on GHG emission The relationship between the average relative daily intake of dry matter from concentrate, alfalfa and silage and GHG emission per kg of milk and herd milk productivity can be illustrated by simple correlation coefficients (Table 10). Neither GHG emission per kg of FPCM nor herd milk productivity showed any significant correlation with the level of concentrate, silage or alfalfa intake or the content of crude protein in the diet. These results might not give the total picture due to the difference in herd structure between farms, the small number of farms

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X. Wang et al. / Journal of Cleaner Production xxx (2015) 1e10

G HG emissions (CO 2eq. per kg of FPCM)

8

2.4 2.2 2.0 1.8 1.6 1.4 1.2 1.0

R2 = 0.77

Total

R2 = 0.63

Enteric

R2 = 0.86

Feed

R2 = 0.83

Manure

R2 = 0.04

Energy on farm

0.8 0.6 0.4 0.2 0.0 2000

3000

4000

FPCM per head, kg annually Fig. 2. The relationship between GHG emission per kg of FPCM and milk productivity at herd level (FPCM per head). R2 stands for a coefficient of determination between milk productivity per head and GHG emissions from total, enteric methane, feed production, manure management and energy use on farm, respectively.

Table 10 Correlation between feed parameters and milk yield per head and GHG emission per kg of FPCM.

Concentrate, % of DMI Alfalfa, % of DMI Silage (straw and maize) % of DMI Crude protein, g per kg DMI Feed efficiency, kg DMI per kg of FPCM

FPCM, kg per head

Emission, kg CO2 eq. per kg of FPCM

0.02 0.13 0.06

0.08 0.22 0.16

0.01 0.82

0.26 0.98

(<10) that may not be representative, and the method used to estimate enteric methane. A potential effect of the starch and fat content of different types of feed (roughage vs. concentrate) was not included in the method used in this study. However, we found the total use of feed per kg of FPCM at herd level (herd feed efficiency) was significantly correlated to both milk yield and GHG emission per kg of milk, in agreement with Kristensen et al. (2011) and Thoma et al. (2013). It is well known that the herd feed efficiency was affected by milk production, herd structure and diet. Compared with American higheperformance confinement dairy farms (O'Brien et al., 2014) and typical east-Canadian confinement dairy farms (McGeough et al., 2012), the percentage of grass used for silage or hay for dairy cows on the eight farms was low (14% on average vs. 30% and 27.5%, respectively, based on DMI). Both the American and Canadian dairy farms had higher milk productivity (12,506 and 8530 kg milk per cow per year, respectively) and lower GHG emissions per kg of milk (0.89 and 0.84 kg CO2 eq. kg1 FPCM, respectively, calculated in ECM) than the eight Chinese farms, illustrating the potential of improved productivity and feed ration composition for reducing emissions. Alfalfa hay is known as high quality forage that can increase milk productivity. The sensitivity analysis showed when replacing 1.5 kg concentrates with 3 kg alfalfa hay on the eight Chinese farms, total GHG emissions increased by about 2% and 5%, while GHG emission per kg of FPCM decreased by about 8% and 5% for a situation without and with fertilizer, respectively, due to a milk production increase of about 11% compared to the present situation with a low use of alfalfa hay. For land use, the addition of alfalfa hay increased total land use by about 11% and land use per kg FPCM by 0.08% when no fertilizer was used. However, both total land use and land

use per kg FPCM decreased, by 8% and 17%, respectively when fertilizer was used, due to the larger yield of alfalfa hay per ha. Soybean meal is an important protein source of cow's diet, while growing imports of soybeans would cause increasing of GHG emissions from milk production. The sensitivity analysis showed that changing the soybeans from domestic to imported increased GHG emissions related to land use change by an average of 0.5 kg CO2 eq. per kg FPCM. There was a smaller decrease for total land use per kg of FPCM, although domestic cropland occupation decreased by 0.6 m2 per kg of FPCM.

3.5.3. Effect of manure management on GHG emission The results showed that a daily spreading of manure would result in the lowest GHG emission per kg of FPCM, and the slurry system would give the highest emissions of the three manure management options. Compared with a passive composting system, a daily manure spreading system would decrease GHG emission per kg of FPCM by 8%e11% for the eight farms, but the slurry system would increase GHG emission per kg of FPCM by 3%e7%. Although the passive composting system emits larger amounts of GHGs than the daily spread of manure, the latter is impractical because most dairy farms are located close to cities and do not have their own cropland. The passive composting system emits less GHG compared with the slurry system, which predominates in Europe. China traditionally composts manure before spreading it on fields. However, currently only orchards, melon and vegetable fields use manure and maize and wheat fields do not because it is inconvenient and smelly compared with using N-fertilizer. In the Guanzhong plain, dairy manure excretion exceeds the demand of the orchards, melon and vegetable fields. Some dairy farmers simply pile the manure not used in heaps or dump it in the wild. In this situation, the GHG emission per kg of FPCM would increase by at least 1%e2% compared with its use for crop production. In addition, the situation of manure not being used would cause other environmental issues, such as pollution of rivers or croplands, and dumping manure also has an economic cost. To reduce GHG emissions from the manure, it is important that farmers use deodorising techniques for the composting process and are encouraged to use manure in maize and wheat fields. To get a more accurate picture of other environmental impacts, more studies should be performed on the situation where manure is not used, and LCA could be used to compare the environmental impact of this situation with the situation where the manure is used.

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X. Wang et al. / Journal of Cleaner Production xxx (2015) 1e10

3.6. Effect of global warming potentials The difference in GWP values assigned caused large differences in GHG emissions expressed per kg of FPCM. When based on the IPCC (2013) GWP values, GHG emissions increased by about 4% and 12% compared with IPCC (2007) and IPCC (1996) GWP values, respectively. Emissions from enteric fermentation increased by 12% and 25%, respectively, while emissions from manure decreased by 10% and 15% and from feed production decreased by 4% and 6%, respectively. 4. Conclusion The GHG emissions per kg of FPCM for the farms ranged from 1.31 to 2.08 kg CO2 eq. after allocation, which was lower than the global average. The average land use per kg of FPCM was 1.81 m2, most of which was contributed by feed production. The GHG emissions are influenced by the combination of cow's milk productivity and herd structure. Farms with a high ‘herd milk productivity’ tend to have lower emission intensity. The sensitivity analysis showed that soybean meal from imported soybeans cause significant increase of GHG emissions due to land use change, while a small decrease for total land use per kg of FPCM. The results of GHG emissions expressed per kg of FPCM in our study increased about 4% and 12% for the calculation based on IPCC (2013) compared with IPCC (2007) and IPCC (1996) values, respectively. Abatement measures for Chinese dairy farms should focus on decreasing N-fertilizer use per ha for feed crop production, improving diet, milk productivity and herd structure, encouraging farmers using domestic soybean or other low emission intensity protein feeds and using manure on cropland. There is a large potential in mitigating GHG emissions from Chinese milk production. The present study gave a general insight into the GHG emissions from dairy farms in the Guanzhong plain of China, but more work needs to be done quantifying the abatement. Additionally, although global warming is one of important environmental impacts from dairy systems, decision making should weigh potential trade-offs with other environmental impacts. Acknowledgements The authors would like to thank the managers of the eight dairy farms for data support. This study was supported by the National Natural Science Foundation of China (Grant No. 41201588), the Fundamental Research Funds for the Central Universities (Grant No. QN2012039), and the National Science & Technology Pillar Program during the 12th Five-year Plan Period (Grant No. 2012BAD14B11). References Adom, F., Maes, A., Workman, C., Clayton-Nierderman, Z., Thoma, G., Shonnard, D., 2012. Regional carbon footprint analysis of dairy feeds for milk production in the USA. Int. J. Life Cycle Assess. 17, 520e534. Alary, V., Corniaux, C., Gautier, D., 2011. Livestock's contribution to poverty alleviation: how to measure it? World Dev. 39 (9), 1638e1648. Anonymous, 2011. A Report of Soybean Imported to China in 2011. Online at. http:// www.doc88.com/p-3813719556956.html. Asselin-Balencon, A.C., Popp, J., Henderson, A., Heller, M., Thoma, G., Jolliet, O., 2013. Dairy farm greenhouse gas impacts: a parsimonious model fora farmer's decision support tool. Int. Dairy J. 31, S65eS77. Audsley, E., Brander, M., Chatterton, J., Murphy-Bokern, D., Webster, C., Williams, A., 2009. How low can we go? An assessment of greenhouse gas emissions from the UK food system and the scope for to reduction them by 2050. Food Clim. Res. Netw. (FCRN) WWF-UK 80. Baek, C.Y., Lee, K.M., Park, K.H., 2014. Quantification and control of the greenhouse gas emissions from a dairy cow system. J. Clean. Prod. 70, 50e60. Bai, L., 2007. The Study on LCA of Pork Production for Cleaner Production and Key Technologies of Swine Manure Treatment. Si Chuan Agricultural University, Ya'an, pp. 60e61. China (in Chinese).

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Please cite this article in press as: Wang, X., et al., Greenhouse gas emissions and land use from confinement dairy farms in the Guanzhong plain of China e using a life cycle assessment approach, Journal of Cleaner Production (2015), http://dx.doi.org/10.1016/j.jclepro.2015.11.099