Changes in the eco-efficiencies of the New Zealand Dairy Industry over six years C. L. Flemmer ∗ School of Engineering and Advanced Technology, Massey University, Turitea Campus, Palmerston North, New Zealand 4442
ABSTRACT Over a six year period, New Zealand milk production increased from 11.4 to 15.1 million tonnes and production of dairy products increased from 1.9 million tonnes to 2.6 million tonnes. This work presents physical data (land, electricity, fertilizer and water use, production of milk and processed dairy products, water effluent and greenhouse gas emissions) for the entire New Zealand dairy farming and dairy processing sectors and estimates of the accuracy of this data. The dairy processing data came directly from the records of Fonterra, which processed 95% of the country’s milk and is the world’s largest exporter of dairy products, so it is very accurate. Eco-efficiencies are used to compare the performance of the dairy industry over a period of six years. Dairy farming became significantly more efficient in terms of land (-27%), electricity (-12%), water (-21%) and lime use (-16%) and produced significantly less effluent (-20%). At the same time fuel and fertilizer use were slightly less efficient (2% and 6% respectively). The dairy processing industry used 21% less water and discharged 21% less effluent. Fuel used in milk transportation was 14% more efficient. Key words: Dairy farming, dairy processing, eco-efficiency
INTRODUCTION Physical inputs and outputs were collected for the entire New Zealand dairy farming and dairy processing sectors for the year 2004 1and a careful assessment of the data accuracy was done. Physical inputs are the physical resources used directly by the sector in order to operate. For dairy farming, these include land (in hectares), electricity (in gigajoules), water, fertilizer and fuel (in tonnes). Physical outputs are the tonnes of useful product (milk and processed dairy product) as well as the tonnes of effluent water and greenhouse gases produced directly from the dairy farming and processing operations. The term ‘directly’ means that embodied resources are not included. For example, energy use is the amount purchased by the sector and does not include the energy embodied in other inputs, such as fertilizers or chemicals, used by the sector. This differentiates the data reported here from data in Life Cycle Assessment because the latter is based on total inputs and outputs (i.e. it encompasses both direct and embodied contributions). A further point of difference is that in New Zealand milk collection from the dairy farms is done by the dairy processing sector so the inputs and outputs arising from this activity are attributable to the processing sector rather than the farming sector. The 2004 data was compared with baseline data collected for the year 1998 reported in Flemmer et al. (2005) and Flemmer (2012). There were significant changes in the structure of the New Zealand dairy processing industry over the six years (see Figure 1); in 1997 there were 14 major dairy processing companies and dairy exports were handled by the New Zealand Dairy Board. In 2004 there were only three major processing companies (Fonterra, Westland and Tatua Cooperatives), each in charge of its own export business. Fonterra is significantly larger than the other two cooperatives, processing about 95% of the country’s milk. A comparison of 2004 and 1998 parameters is still valid
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Corresponding author:
[email protected] Most of the data covered the period from April 2003 to May 2004, so strictly speaking the data is for the year 2003/04. This is abbreviated throughout as 2004.
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for dairy processing despite the major structural changes because the processing facilities themselves did not change significantly; they merely changed corporate governance.
Company Northland
Kiwi Co-op
Otago Cheese Co-op SIDFa Tasman Milk Products Kaikoura Co-op Marlborough Cheese Co-op SID Co-op Alpine Dairy Products Southland Dairy Co-op NZDGb Co-op
Anchor Bay Products
Westland Coop Tatua Co-op a
Site Dargaville Kauri Maungatoroto Longburn Pahiatua Whareroa Hawera
1997
1998
1999
2000
Kiwi
2001-2004
Fonterra
Stirling SIDF Takaka Kaikoura Tua Marina SID Co-op Clandeboye SIDC Edendale NZDG Edgecumbe Hautapu Lichfield Morrinsville Paerata Reporoa TeAwamutu TeRapa Tirau Waitoa Waharoa Hokitika Karamea Tatuanui
NZDG
Westland Co-op Tatua Co-op
: South Island Dairy Farmers; b: New Zealand Dairy Group;
Figure 1. Changes in the structure of New Zealand Dairy Processing from 1997 to 2004
The work reported here makes several noteworthy contributions to the field. Firstly, most published studies of this type are based on a small number of farms or on a few products from a single processing plant. The scale of this data is nationwide; the farm data is based on all 3.9 million dairy cows in New Zealand and the dairy processing data is for 2.6 million tonnes of assorted dairy products produced in 2004. Secondly, it is rare to find any information on the accuracy of published data. The accuracy of the data presented here is carefully assessed and quantified. Thirdly, the author was in the unique position of being seconded to Fonterra for 3 years, with full access to their records on the processing of 95% of the country’s milk, so the quality of the processing data is excellent. Finally, most studies provide a single snapshot in time. Here the data is compared with similar data 2
from 1998 allowing an assessment of the changes in the performance of the New Zealand dairy industry over the six years. The assessment period (1998 to 2004) might be considered to be so long ago that the data is obsolete. However, this is not the case. The process of collecting such data is very lengthy and, in the case of dairy processing, is further delayed by commercial sensitivity issues. For this reason, all similar work published in the last two years (deVries and de Boer, 2010; Yan, Humphries and Holden, 2011; Milani, Nutter and Thoma, 2011, and Flemmer, 2012) is based on data for the period 1996 to 2005.
LITERATURE REVIEW Growing global demand for pastoral products is driving farmers to intensify production. At the same time, there is increasing awareness that this has significant environmental consequences, for example, in terms of soil quality, water use and greenhouse gas emissions. Within New Zealand agricultural intensification has arisen from steady genetic improvement, better feed quality, improved animal health and increased pasture growth (Mulet-Marquis and Fairweather, 2008, and MacLeod and Moller, 2006) and the benefits and problems in the New Zealand dairy industry arising from this intensification are discussed in Conforte et al. (2008). There has also been a push towards accountability in the marketing of food products (Crosson et al., 2011; Garnett, 2008; McDowell, 2008). This has led to many Life Cycle Assessment (LCA) studies on dairy farming. There are several comparative reviews, for example, LCA studies on dairy farming from 1996 to 2005 in OECD countries by deVries and de Boer (2010), on European milk production by Yan, Humphries and Holden (2011), on milk production and its impact on dairy processing by Milani, Nutter and Thoma (2011) and a comparison of methods used to estimate GHG emissions in milk by Crosson et al. (2011). New LCA research into dairy farming includes: • • • • • •
A carbon footprint for milk produced in New Zealand and in Sweden in 2004/05 with various scenarios for allocation to milk and meat products by Flysjo et al. (2010). An assessment of dairy farms in Spain from 2000 to 2002 using both LCA and Data Envelopment Analysis (DEA) by Iribarren et al. (2011). Allocation of dairy farm greenhouse gas (GHG) emissions from 2001 to 2003 to dairy and meat products by Kristensen et al. (2011). Energy use on 14 US dairy farms in 2006/07 by Capareda et al. (2010). The effect of methodology on assessment of dairy farming from 2001 to 2003 by O’Brien et al. (2011). A study on water use and climate change associated with dairy farming in the MurrayDarling Basin in Australia by Coats (2008).
Assessments of dairy processing are much scarcer because of commercial sensitivity issues (the factories are controlled by entities which want to protect their own competitive edge). Early work is reviewed by Flemmer (2012) and recent studies include: • • • • • •
A review of the environmental impact of dairy processing, focussing on cheese manufacture from 1996 to 2005, by Milani, Nutter and Thoma (2011). LCA on Dutch cheese production in 2007 to determine Global Warming Potential (GWP) eco-efficiency by van Middelaar et al. (2011). Review of LCA on food production including milk and cheese in the 1990’s by Roy et al. (2009). A carbon footprint for New Zealand milk and dairy products by Fonterra (2010) and Lundie et al. (2009). Production of dairy products in New Zealand by Stringleman and Scrimgeour (2011). Dairy processing waste treatment by Frenkel et al. (2009).
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Researchers agree that there are “hot spots” within dairy farming, i.e., areas which are of particular concern because they have significant adverse environmental impacts. These include use of fertiliser (leading to eutrophication), water and energy and production of effluent and GHG emissions. The top three contributors to GHG emissions are on-farm dry matter intake, emission of nitrous oxide due to excreta during grazing and emission of methane by cows during digestion. Dairy processing inherits many of its “hot spots” from milk production, i.e., dairy farming, and there are significant environmental impacts from its own operation, particularly in packaging, waste management and cleaning processes. It uses a lot of energy and water and discharges effluent with high organic components and GHG emissions from energy use. Another common finding is that it is very difficult to compare the different studies because of varying practices, assumptions, boundaries, data quality and methods of allocating impacts to multiple products such as milk and meat produced on a dairy farm (Crosson et al., 2011; Yan, Humphries and Holden, 2011). The question of allocation is even more complicated for dairy processing where plants commonly produce multiple products and in New Zealand there are also substantial intersiteintermediate product transfers (Lundie et al., 2009). The functional unit used for eco-efficiency differs from one country to the next; in New Zealand, eco-efficiencies are usually expressed per kilogram of milk (Basset-Mens et al., 2009), in Europe the functional unit is energy corrected milk or ECM (De Vries and de Boer, 2010) and in Holland fat and protein corrected milk (FPCM) is used 2. Park et al. (2010) note that no matter which metric is used, eco-efficiencies may be poor indicators of environmental damage because they merely indicate the trend in the amount of resource used or the amount of effluent/emission. For example, farming may be using less fertiliser per kg milk over time and show decreasing fertiliser eco-efficiency numbers, but the soil may still suffer progressive damage. Finally, the published data has varying accuracy. This is generally not reported but is recognised as important (Yan, Humphries and Holden, 2011). The exception is work done by Basset Mens et al. (2009) who provide a very careful discussion and analysis of the accuracy and sensitivity of computation of GHG emissions in New Zealand milk production.
MATERIALS AND METHODS Sources of data include government reports, corporate reports, internal company datasets, company sales data, research articles and personal communication with engineers, accountants, environmental strategists and agricultural sales representatives. The main sources of data, assumptions and reliability/accuracy of the data are discussed in the following sections. Procedure for getting dairy farm data The sources of dairy farm data are summarised in Table 1. Procedure for getting dairy processing data Fonterra provided access to their internal databases on annual (2004) financial statements, energy surveys, production surveys, transport accounts, waste reduction programme and major site water balances (the latter for 2002). The economic data from Fonterra corresponded to the processing of 95.3% of the total milk collected and this data was scaled up to account for the processing of domestic milk (1.7%) and for the processing by Tatua and Westland (3%) (MAF, 2004b). Fonterra reviewed the resulting economic and physical data and only allowed a small subset of the data to be published because of commercial sensitivity constraints. Other data sources which were used included: • 2
The Producer Price Index from Statistics New Zealand (2005).
Yan, Humphries and Holden, 2011, provide equations for ECM and FPCM
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• •
The Livestock Improvement Corporation (LIC) (2004), annual report for milk processed and tonnage of export products. The MAF (2004) Situation and Outlook for New Zealand Agriculture and Forestry (SONZAF) report for estimates of tonnage of export products.
Table 1. Data type and source for New Zealand dairy farming in 2004 Data type Source Land 3 New Zealand Ministry of Agriculture and Forestry (MAF), 2004, Situation and Outlook for New Zealand Agriculture and Forestry (SONZAF). Electricity New Zealand Ministry of Agriculture and Forestry (MAF), 2004 Farm Monitoring Report for regional annual spend on electricity and the method outlined in the Appendix to convert this to national annual spend. Electricity cost from the farm budget represents only the electricity used for dairy farming (and not for residential use) and is classified as commercial use. The Ministry for Economic Development (MED) 2011 for the commercial rate for electricity in 2004. Water use Flemmer and Flemmer, 2007. Lime and fertilizer Statistics New Zealand, 2003, Agricultural Statistics for the tonnes of seven fertilizers used in 2002 and Statistics New Zealand, 2007, Agricultural Production Census for the tonnes of fertilizers used in 2006. Interpolation to get the tonnes of fertilizers in 2004. Fuel (petrol and MAF 2004 Farm Monitoring Report for regional annual spend on fuel and the method diesel) outline in the Appendix to convert this to national annual spend. Farm fuel use was assumed to be about 80% diesel and 20% petrol (by volume) based on anecdotal evidence from local dairy farmers 4. The Ministry for Economic Development (MED) 2011 report cites historical fuel prices. Raw milk The Livestock Improvement Corporation (LIC), 2004, Annual Report for annual regional milksolids production data. Water effluent Flemmer and Flemmer, 2008. Greenhouse gas Greenhouse gas emissions were estimated using LIC 2004 for the total number of cows emissions and Global and the procedure described in the Ministry for the Environment (MFE) 2005 report. Warming Potential The 100-year Global Warming Potential (GWP) was computed according to Houghton (GWP) et al., 1995 as: GWP (mass equivalent CO2) = mass of CO2 + 21 x mass CH4 + 310 x mass N2O where, CO2 is carbon dioxide, CH4 is methane and N2O is nitrous oxide The definition of 100-year GWP has been modified since then and the most current definition has a factor of 25 for methane and 298 for nitrous oxide (Forster et al. 2007), but the earlier definition was used in order to compare the result with that of 1998.
Data quality assessment In presenting data, it is important to form a judgement on the quality of the data and, ideally, to quantify the accuracy of the data. However, this is a very difficult task. For example, the economic data for dairy farming was derived from the MAF (2004) dairy farm monitoring report. The report merely states that the data for each of 5 regions came from 20 farmers with input from agribusiness and is thought to be representative of about 83% of the farms in a particular region. There is no information on the standard deviation of the data, either within the 83% of farms or over the region as a whole. The New Zealand government collects this and other data annually and uses it to monitor the country’s agricultural production, which implies that the data is important and valid but still gives no quantification of its accuracy. In a personal communication, the MAF officer responsible for the 3
Land refers to all land used for dairy farming including land for grazing replacement stock, producing silage, roads, buildings, etc. 4 It is recognised that the error in this assumption may be large since Barnett and Russel, 2010 report that on dairy farms 97.2% of fossil fuel use is from diesel and 2.8% from petrol
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Canterbury region data suggested that the most current (2011) data was accurate to roughly +/- 5% over the region. In some cases, a second source of data was found and used to judge the accuracy of the reported data. However, even this method has the intrinsic problem that generally there is an error in the second source of data and usually this error is not reported. Despite the difficulties and limitations, best estimates of the accuracy of the data are reported in the results section.
RESULTS The New Zealand Dairy Farming Industry Tables 2 and 3 show the physical inputs and outputs for the New Zealand dairy farming industry. Table 4 presents estimates of the accuracy of the data and justification of the estimated accuracy. Table 2. Physical Inputs for the New Zealand Dairy Farming Industry in 2004 Input land electricity water (stock drinking, dairy shed wash and irrigation) Lime Fertilizer (not lime) Urea DAPb Ammonium Sulphate Other N fertilisers Phosphatic Fertiliserc Potassic Fertilisersd Total
Quantity 1.964 x 106 ha 2.97 PJa 981 x 106 tonnes 573 x 103 tonnes 128 x 103 tonnes 74.3 x 103 tonnes 17.6 x 103 tonnes 93.1 x 103 tonnes 501 x 103 tonnes 146 x 103 tonnes 960 x 103 tonnes 17.7 x 103 tonnes 79.3 x 103 tonnes
fuel -petrol fuel - diesel a PJ: Peta Joules (1015 Joules) b : diammonium phosphate; c for example, triple superphospate; d for example, potash
Table 3. Physical Outputs for the New Zealand Dairy Farming Industry in 2004 Output
Quantity (tonnes) milk 15.1 x 106 From petrol 54 x 103 a From diesel 249 x 103 CO2 From lime 839 x 103 Total 1,142 x 103 From fuel 34 From animal 373 x 103 Methane Total 373 x 103 From fuel 3 From animal 14.8 x 103 N2Ob Total 14.8 x 103 GWP (tonne equivalents CO2) 13.6 x 106 Farm dairy effluent 47.1 x 106 Total water discharge/runoff 918 x 106 a b : carbon dioxide, : nitrous oxide Table 4. Estimated accuracy of physical dairy farming data
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Input or Output Land
Accuracy (% +/-) 3
Electricity
13
Water input and output
nd
Lime and other fertilizer
