Process information
| Key Data Set Information | |
| Location | AU |
| Geographical representativeness description | The dataset represents the cultivation of barley grain for Australia. Cultivation data from Australia is used to represent this region. The geographical representativeness (GeR) of parameters used for the process is considered. |
| Reference year | 2016 |
| Name |
Base name
; Treatment, standards, routes
; Mix and location types
Barley grain; ; technology mix; at farm
|
| Use advice for data set | It is intended for use in the feed PEFCR context and other PEF studies where applicable and compliant. |
| Technical purpose of product or process | Provision of a standard process according to the applied technology. |
| Classification |
Class name
:
Hierarchy level
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| General comment on data set | - |
| Copyright | Yes |
| Owner of data set | |
| Quantitative reference | |
| Reference flow(s) | |
| Time representativeness | |
| Data set valid until | 2020 |
| Time representativeness description | The DQR of the dataset reflects the quality of the data at reference year (2016). The most important time related parameter is the crop yield at agricultural cultivation (based on FAOStat, 2010-2014, for 5-years average data). Remaining parameters are based on country, crop and process specific data. |
| Technological representativeness | |
| Technology description including background system | This process describes the average production of barley grain on a farm in Australia. Cultivation process is based on the Agri-footprint methodology (2017). Considered activities include seeding and seed production, fertilizer and pesticide application rates and production, capital goods depreciation, energy use and transport for field management practices. The elementary flows include field emissions to the air and water, direct land use change, water use and emissions due to pesticide use.;Crop yields were derived mainly from 2010-2014 FAO statistics (FAOStat, FAO, 2016). Fertilizer application rates (in terms of N, P and K requirements) were derived from Pallière (2011), Rosas (2011) and FAOSTAT (2000). Specific fertilizer amounts are calculated based on crop specific nutrient requirements and country specific fertilizer mix derived from International Fertilizer Association (IFA) statistics (IFA, 2015).;Seeding rates are derived from 5-years average FOASTAT data or from the document “Technical Conversion Factors for Agricultural Commodities” document, FAOSTAT (2000). The production process of seeds is identical to this of crop cultivation with the only adjustment of the seed yield at seed production assumed to be 20% less that this of crop yield at farm.;Production of fertilizers and precursors have been modelled based on Kongshaug (1998) and Davis & Haglund (1999). The energy comes from Kongshaug (1998), while additional data on emissions was sourced from Davis and Haglund (1999). Other chemicals and auxiliary materials are modelled based on various data sources.;Animal manure is applied for soil maintenance based on the methodology described in appendix 4 of Vellinga et al. (2013). Heavy metals emissions due to manure and artificial fertilizer application have been calculated based on an adapted methodology from Nemecek & Schnetzer (2012). It is taking into account the heavy metal balance as a function of deposition, use of fertilizer and crop uptake using literature concerning heavy metal contents in manure (Amlinger et. al., 2004) and in fertilizers (Mels, Bisschops, & Swart, 2008).;Energy use was calculated based on data obtained from the farm simulation tool MEBOT (Schreuder, Dijk, Asperen, Boer, & Schoot, 2008).;With reference to the capital goods modelling, based on a literature review of Dutch farming practises we determined that the average material use from depreciation of machinery for arable farming equals 0.37 kg per litre diesel used at a farm. Average mass use of depreciation of machinery was determined using the mass of machinery (Williams, Audsley, & Sandars, 2006), repair factors for various machinery (Frischknecht et al., 2007), economic lifetime and utilisation rates of machinery (Wageningen UR, 2015a). The integration of capital good is analytically explain in the methodology report of the EC feed database (2017).;The pesticide production includes the active ingredient manufacturing (material and energy inputs) based on Green (1987), the inert ingredient production which comprises chemicals available by the ELCD database, the pesticide production which includes the mixing, blending and/or diluting the active ingredients with the inert ingredients and finally the packaging of marketable pesticide product. The integration of pesticide production is analytically explain in the methodology report of the EC feed database (2017). Pesticide application rates were taken from a large volume of literature sources.;Water use has been based on the "blue water" footprint (Mekonnen & Hoekstra, 2010). Land use change has been calculated using "Direct Land Use Change Assessment Tool (2015)" (Blonk Consultants, 2015).;The electricity, fuel production and transport data come from the EC dataset for energy and transport (“GaBi Energy&Transport LCI Datasets for EU Environmental Footprinting (EF) implementation 2016-2020”, of thinkstep AG).;[Dry Matter: 840 g/kg; Moisture: 160 g/kg; Gross Energy: 16.1 MJ/kg; Biogenic carbon of barley grain is 380.08 g/kg.] |
| Flow diagram(s) or picture(s) | |
Modelling and validation
Administrative information
Inputs and Outputs
LCIA results