Proceedings of the 9th International Conference on Life Cycle Assessment in the Agri-Food Sector

Feed and Food Databases in LCA – An example of implementation Alexander Liedke1,*, Sabine Deimling1, Torsten Rehl1, Ulrike Bos2, Christian Peter Brandstetter2, Martin Baitz1 PE INTERNATIONAL AG, Hauptstraße 111-113, 70771 Leinfelden – Echterdingen, Germany University of Stuttgart, Chair of Building Physics, Department Life Cycle Engineering, Wankelstraße 5, 70563 Stuttgart, Germany  Corresponding author. E-mail: [email protected] 1 2

ABSTRACT New LCA databases in the feed and food sector have recently been published or are announced to be forthcoming. The objective of this publication is to present key aspects of implementation of one of these databases, the “GaBi feed and food database” (to be released in autumn 2014). Firstly, the GaBi LCA agrarian plant model, which has been continuously developed over the past decade and is embedded in a methodological and scientific framework, serves as a basis for modelling feed and food products. The model is based on a synthesis of relevant scientific and methodological approaches which are described in this publication. The specific approaches chosen to address the agricultural LCA modelling challenges of nitrogen cycle, reference system, CO2 uptake and storage in biomass, land use, land use change, irrigation and agricultural chemicals are described. Secondly, the approach to allow for a scientifically sound, feasible, consistent and applicable scaling of dataset generation for use in industry is explained in principle. Parameters in the model have been identified, analyzed and grouped and are determined via sequences of data collection. Thirdly, the modelling approach includes management, update and maintenance procedures to enable the data to follow the natural, agricultural, political and technical dynamics, prices and supply chain key parameters in time. The management and maintenance concept includes a professional systematic review process with yearly update schedules. Keywords: Feed and Food, LCA, agrarian plant model, database development, GaBi

1. Introduction The feed and food industry is considered to have a major contribution on the overall anthropogenic impact on the environment (FAO 2006, IPCC 2014a, IPCC 2014b). Several reports on strategies to abate this impact propose to focus efforts on energy efficiency, emissions efficiency and on a change and reduction of demand (IPPC 2014a). The latter implies action and interaction of consumers, governments and industry, for example to decrease the amount of lost, wasted, unused food (FAO 2006, FAO 2013). Producers of feed and food are also responsible for identifying and applying suitable technical solutions for a more sustainable and efficient use of resources. The possible benefits for companies are manifold, including potential cost savings and proactive mitigation of potential harm on their business by changing the environment and the environmental resources on which they depend (Callieri et al. 2008). Life cycle assessment (LCA) can be used to achieve these objectives: LCA is a tool used by companies to benchmark and implement sustainability measures over an improvement processes into their daily operations and business. Many companies that successfully implemented sustainability measures conclude that “you can only improve what you can measure” and that “only what gets measured gets managed” e.g. (Warsen 2013). The relevance of implementing applicable approaches in the agri-food practice is underlined by the estimate that around 25% of all LCA studies are related to agriculture (Blonk 2014a). Besides ethical and economic reasons for the sector to be actively engaged in sustainability, in particular LCA, several drivers increase the importance of LCA in the agri-food sector: policy developments, such as the green market initiative of the European Commission and its Product Environmental Footprint (PEF) pilots, sector specific initiatives, such as the ENVIFOOD protocol (ENVIFOOD 2013), FAO LEAP (FAO LEAP 2014), Feed LCA guidelines (IFIF 2014) and others offer new possibilities for the topic and for companies. Different stakeholders, including companies, universities and external data providers, are working independently on data solutions to respond to an increased data demand. Many databases were recently published or are forthcoming: “Agri-footprint®”, “AGRIBALYSE®”, “World food LCA database”, “Feed and food database” (see references for details on the databases). The main objective of this publication is to describe 3 key aspects of implementing the “Feed and food database”, which is released in autumn 2014. Many feed and food products are based on raw materials derived from agricultural plant systems. Different processing steps, agricultural management, farming and plant growing, handling, packaging, transport are required before a final product is ready for use (and, at end of life, specific disposal scenarios may need to be

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considered). The focus of this publication is on the plant system part that forms the data backbone of the “Feed and food database”. This is the major aspect covered in this publication. Agrarian plant systems belong to complex production systems within LCA due to their dependence on environmental conditions that are variable in time (e.g. within a year, from year to year) and in space (e.g. varies by country, region, local site conditions). Also the correlation between inputs (of fertilizers, pesticides, agricultural engineering, etc.) to outputs (of harvested crop, gaseous field emissions, leachate, etc.) is complex and often non-linear in nature. The following factors contribute to the complexity of agricultural modeling: - The variety of different locations, - Small scale soil variability within and between locations, - The large number of farms, - The variety of agricultural practices (e.g. conventional vs. organic) and equipments, - Technically, there is no well-defined border with the environment (´open system`), - Complex and indirect dependence of the output (harvest, emissions) from the input (fertilizers, location conditions etc.), - Variable weather conditions within and between different years, - Different cultivation periods (e.g. annual, perennial, plantations), - Quality and properties of products, - Multi-output systems (product(s) and by-products) and allocation diversity, - Various and big amount of Functional Units (FU), - Farming intensity (e.g. extensive vs. intensive), - Water availability and importance, - Variable pest populations (insects, weeds, disease pathogens, etc.), - Different cropping systems (e.g. monoculture, polyculture), and - Political conditions influencing cultivation. In addition to the appropriate consideration of these technical modelling aspects, customers of LCA datasets and databases want datasets to be highly reliable, to be easily understandable and marketable and to be delivered at a reasonable cost and time (Deimling et al. 2008). The transparency of the underlying model, of the raw data sources, of the applied QA procedures and of the external review processes are also of high importance to ensure the required professional credibility.

2. Methods The GaBi LCA agrarian plant model is the basis for all standard agrarian datasets that are provided in GaBi databases. The following requirements were identified by the authors during their experience over the last 12 years as being crucial for an integrated approach to develop new agri-food datasets: 1) 2) 3)

Scope: agrarian models (in this publication the focus is on terrestrial agrarian plant systems) Scale: agri-food data development and supply, based on clearly defined processes and quality assurance Management & maintenance: frequent updates, reviews and improvements of data and databases

These requirements are explained in more detail in the following sections. The precondition for these 3 requirements is a framework, which includes a collaborative review of suitable existing scientific methods, databases and inclusion of identified aspects (this requirement is not covered in this publication). 2.1. Scope Due to the inherent complex characteristics of an agricultural system, a relatively extensive and comprehensive, nonlinear, computing model for agrarian plant and plantation processes was developed and implemented in the GaBi software (Deimling et al. 2008). The model includes a multitude of input data, emission factors and parameters and its proper application of the model necessarily requires both agricultural expertise and LCA experience.

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Each processing step has been broken down into separate production modules such as soil cultivation and preparation, weed control, pest control, fertilization including organic fertilizers, harvest main product, byproduct etc., and others (i.e. irrigation). For each of these modules data on the inputs and outputs (i.e. fertilizers, pesticides, fuel for machinery, etc.) have to be collected and calculated. As shown in Figure 1, key characteristics of the model are different implicit mapped compartments (in particular: field operations, general agricultural process, land use change, clearing process, agrochemical processes, reference system land use, land use, allocation, carbon correction, soil leaching, irrigation process). This structure creates a comprehensive, flexible model that is highly parameterized and includes many background processes. As such, it is applicable for assessing any agrarian and plantation product of the world. Parameters can be fine-tuned to a high degree while the operating effort is reasonable. This allows inclusion of e.g. effects of crop rotation and periods with limited plant growth. The advantage and prerequisite is the delivery of consistent and exact results (Deimling et al 2008). Besides life cycle inventories, different life cycle impacts assessment methods for environmental indicators and impact categories can be applied.

Figure 1. Screenshot of the GaBi LCA agrarian plant model with illustration of different modelling modules. The system boundaries of the GaBi LCA agrarian plant model encompass agricultural production as well as post-harvest processes and transportation, and are shown in Figure 2. Some aspects were excluded from the GaBi LCA agrarian plant model as their relevance to the environmental profiles was found to be low, these include:  In case of human labor, social issues are outside the scope  Construction of capital equipment and maintenance of support equipment, and  Packaging materials for seeds, fertilizer, pesticides etc.

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Figure 2. System boundaries of the GaBi LCA agrarian plant model. To illustrate the background of the GaBi LCA agrarian plant model, seven key functionalities of the model are further explained below. Nitrogen cycle (as part of the modeling module “general agricultural process”): Nitrogen plays a fundamental role for agricultural productivity and is also a major driver for the environmental performance of an agricultural production system (Eickhout et al. 2006). For these reasons it is essential to evaluate all relevant nitrogen flows within, to and from the agricultural system. GaBi LCA agrarian plant model accounts for the nitrogen cycle that occurs in agricultural systems. The model ensures that nitrogen emissions are consistent for all cultivated species. Specifically the model includes emissions of nitrate (NO3-) in water and nitrous oxide (N2O), nitrogen oxide (NO) and ammonia (NH3) into air. The different N-based emissions are calculated as follows:  NH3 emissions to air from mineral and organic fertilizers are adapted from the model of rentrup and modeled specifically for the cropping system dependent on the fertilizer-NH4+ content, the soil-pH, rainfall and temperature. The following emission factors are used by default and adjusted in case more specific information is available. For ammonium nitrate, calcium ammonium nitrate, monoammonium phosphate, diammonium phosphate 2% of fertilizer N input, for ammonium sulphate 10%, for urea ammonium nitrate 8% and for urea 15% are emitted. These values are identical with data from Döhler et al. 2002 (with exception NH3-N).  N2 is the final product of denitrification. Denitrification is a process of microbial nitrate reduction that ultimately produces molecular nitrogen through a series of intermediate gaseous nitrogen oxide products. N2 emissions are assumed to be 9% of the N-fertilizer input based on a literature review made by Van Cleemput 1998. N2 emissons are also taken into consideration to determine the nitrate leaching potential.  NO is an intermediate product produced in microbial denitrification. NO emissions are calculated from the reference system after N-input from air plus 0.43% of the N-fertilizer input specific for the cultivation system as NO according to Bouwman et al. 2001.  N2O is an intermediate product produced in microbial denitrification. According to IPCC 2006, N2O emissions were calculated as 1% of all nitrogen available including nitrogen applied with fertilizers, atmospheric deposition, microbial nitrogen fixation, nitrogen available from previous crop cultivation and indirect emissions.

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 NO3- emission to groundwater is calculated based on available nitrogen (N not lost in gaseous form or taken up by the plant, stored in litter, storage in soil, etc.). Depending on the leaching water quantity and soil type, a fraction of this available nitrogen is calculated to be leached nitrate. When available N is calculated to be negative, (for instance due to a higher extraction than fertilizer input) a minimum Nloss factor is applied on the applied fertilizer and Nmin quantity.  Norg and NO3- emissions to water occur due to erosive surface run-off. It is very difficult to generalize erosion rates and deposition rates, as they are highly dependent on regional conditions such as climate, relief, soiltype, crop cultivated and vegetation. Soil erosion rates are estimated based on USDA 2003 and Wurbs and Steiniger 2011. It is assumed that 10% of the eroded soil accesses the waters, based on an evaluation of different literature sources Fuchs and Schwarz 2007, Hillebrand et al. 2005, Helbig et al. 2009, Nearing et al. 2005, while the rest accumulates to colluviums on other surfaces and is assumed irrelevant in the life cycle assessment. Compared to a pure N-balance model, this approach allows the illustration of N-losses in case of very low Nfertilization (e.g. N-deficit in rubber-tree plantations). In the case of high N-fertilization (e.g. intensive farming applications), the models correspond with the total N-balance approach. Emissions from erosion, the reference system, and nutrient transfers within crop rotations are also modeled consistently. Reference system (modelling module “Reference System Land Use”): Reinhardt 1998 (amongst others) illustrated the importance of usage of a reference systems in agricultural systems. The reference system is an inverse process used to assess the behavior of land that is not used agriculturally or influenced anthropogenically. In particular losses of nitrate to groundwater and emission of gaseous nitrogen compounds that result from nitrogen deposition onto this land are considered. This takes place in both the main cropping system as well as on land not under cultivation. Therefore not all occurring emissions can be assigned to the crop as they also occur on non-cultivated land, e.g. if this is fallow or a nature reserve. Here it is assumed that the nitrogen balance is neutral for the reference system, as any entry of nitrogen with rainfall is re-emitted from the systems in various forms into ground water and air. In addition to the emissions of nitrogen compounds, the soil erosion is mapped including the associated conditional entries of organic carbon contained in the soil and some heavy metals in surface waters. The same principle is applied that this erosion occurs to a lesser extent also in non-utilized natural systems and therefore cannot be assigned completely to the main crop. CO2 uptake and storage in biomass (part of modelling module “general agricultural process”; if allocations are applied, modelling compartment “carbon correction”): The product bound CO2 has to be accounted for directly as 100 % on the input side (flow: carbon dioxide resources) comparable to CO2 emissions into air on the output side (flow: carbon dioxide biotic). The CO2 quantities from renewables emitted during later stages in the life cycle (e.g. burning, composting etc.) have to be accounted as emissions to air (ILCD 2010). For fast moving consumer goods, this means that over the life cycle all bound CO2 is released at a later stage. Carbon emissions (besides CO2 e.g. CH4 and CO) during biomass production, its conversion and its end of life are also considered. In case of allocation, the carbon uptake of the product (after allocation) is corrected to the carbon stored in the product via an adjustment of the carbon dioxide resources flow. Land Use (modeling module “land use”): Further inventory data on land use is provided site specifically for the foreground system using the LANCA method (Beck et al. 2010). In order to include land use issues, the impacts on ecosystem services are considered, especially for the indicators erosion resistance, mechanical filtration, physicochemical filtration, groundwater replenishment and biotic production. Irrigation (modeling module “irrigation process”): water use is modeled based on the calculations of Pfister et al. 2011. A generic water model allows the selection of different plant water requirements and irrigation regimes depending on the specific regional conditions (e.g. precipitation, (fert)irrigation demand and irrigation technique). For details please refer to Pfister et al. 2011 and also Thylmann et al. 2012.

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Agricultural chemicals (modeling module “agrochemical processes”): eco-toxicity is an indicator that is becoming increasingly important in LCA. Therefore a close look at pesticide use and its environmental impact is necessary when performing a LCA of agricultural products. Both the production and use of pesticides contribute to their environmental burden. The life cycle inventories of pesticide production are based on literature data as primary data is only very rarely available. The LCIs cover all input and output flows relating to pesticide production. All pesticide LCIs are based on representative LCIs (background datasets) that are valid for all active ingredients within the same effect category (herbicide, fungicide, etc.). All available data on energy use in pesticide production is discussed in Audsley et al. 2009. A Pest LCI approach was integrated into the model based on the approach described by Dijkman et al. 2012. The Pest LCI model provides emission factors for active ingredients (AI) to water (ground and surface) and air. About 125 scenarios with different parameter settings for pesticide AI, crop and location were calculated and are available as parameters in the GaBi LCA agrarian plant model. In case of more specific data availability these parameters are specifically adjusted. Land use change (modeling module “land use change”): Emissions from direct land use change were calculated with the direct land use change assessment tool (Blonk 2014b) for the approach “weighted average” (as mandatorily required by the Envifood protocol and in line with WRI/WBCSD GHG protocol requirements) based on the approach from PAS 2050-1:2012 and WRI/WBCSD GHG protocol. This approach is crop-specific: The impacts from land use change are allocated to all crops, which increased in area harvested in a specific country, dependent on their respective share of area increase. According to all 3 standards, these emissions are distributed over a time period of 20 years. The tool works with statistical data from FAOSTAT for crop yields, harvested area of crops and area of forest and grassland, from FAO’s global forest resource assessment for carbon stocks (in case former land use is unknown) (FAO 2010), from EC JRC world map of climate types and world map of soil types (EC 2013a) and from IPCC for above ground mass carbon stock (if land use change is known), values of soil organic carbon stock and stock change factors (IPCC 2006a). Changes in soil organic carbon stock are taken into account with that methodology. The emissions are reported separately with a flow “carbon dioxide from land use change” as required by certain guidelines (e.g. ISO/TS 14067). Indirect land use change (please see EC 2010 for definition) is currently not considered as there are no international accounting standards available and while LCA is based on physical flows iLUC is based on market predictions (Finkbeiner 2014). 2.2. Scale Deimling et al. 2008 already concluded that some challenges remain as agricultural systems are complicated, so the model developed to assess them is also complicated and both data- and resource-intensive to use. Questions regarding the scalability of LCA in the feed and food industry were raised, especially with focus on retailers, who may stock thousands of food items but who do not manufacture these themselves. A need for “a streamlined data collection processes or central resource where the data requirements of the model can be easily accessed” was identified (Deimling et al. 2008). To ensure the high feasibility and applicability of the GaBi LCA agrarian plant model the significance of parameters was identified by the authors of this study, based on general LCA experience and specific experience with the GaBi LCA agrarian plant model and other literature (e.g. Nemecek and Schnetzer 2011). According to their importance, the parameters were prioritized by grouping them into three different groups, which are shown in Figure 3.

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Figure 3. Display of three different parameter groups for agrarian plant modelling with the GaBi LCA agrarian plant model. Sequences of data collection for parameter determination were defined, including flow diagrams for all “free” parameters. These flow diagrams and the parameter list will be made publically available in the documentation of the “feed and food database”, which is released in autumn 2014. The data collection process always starts with specific primary data - if available - before moving to more generic data. The most specific data are used preferentially. A framework for determination of sources is provided to the LCA practitioners in charge of data development. The most generic solution is an expert judgment of discrete, quantified steps which lie in a predefined and suitable bandwidth of values. This estimation of parameters is the last resort option if no better data can be obtained elsewhere. Full transparency of the approach is essential for all aspects relating to “management & maintenance” as described in the next section. 2.3. Management & Maintenance This chapter discusses how to convert agrarian plant LCA models into broadly usable data, while maintaining its scientifc soundness and technical quality over time. Feed and food data are the base and fuel for related LCA results. However in LCA different “planets” of users exist (Klöpffer and Heinrich 2001). These have different backgrounds but still talk about the “same” tasks and concepts. As a result, professional stakeholders have sought to develop approaches that can reduce misunderstandings and improve credibility (e.g. Rebitzer 2001). Even though different stakeholder backgrounds and some miscommunication still remains a decade later (Baitz et al. 2012), it is time to move another step forward. Stakeholders all aim to support LCA, but each one has his own, often a different interpretation of what this means. However for application in agroindustry it is important to consistently base their work on reliable and risk mitigating professional data solutions. Reliability, consistency and conformity of data are, besides scientific soundness, key for success for professional use. A professional environment necessarily includes reviews, verification, continuous improvement and yearly updates. To move from “scientific sound models” towards “scientific sound models that are constantly reviewed, maintained and updated” certain maintenance and quality assurance processes must be implemented. Consistency, continuity and reliability are core features of technology development, provision and maintenance for LCA software and database providers. The challenge is to manage the different inputs of stakeholders in a way that valuable data can be released and published in a consistent framework while immature or fuzzy data are filtered out. Data provision and use should be understood by any stakeholder as part of the “normal” management cycle: Plan-Do-Check-Act (or, to translate into the LCA data world, maybe: Plan—Implement— Review–Maintain). Figure 4 explains the external review and auditing process which is implemented in PE INTERNATIONAL and also true for the feed and food database. The figure shows the development of databases over time (for the time period 2012-2014; whereas the procedure continues after 2014). Jira is used as program for quality assurance to allow users and employees to report issues, improvements on the LCA software and LCA database content. The treatment of these reports issues is documented and continuously reviewed by an external auditing company (DEKRA). Spot checks were performed by ENEA and Ciemat on behalf of the European Commission

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(EC 2013b, ENEA 2014). Databases are updated once per year – inconsistencies are corrected immediately. Figure 5 shows the main review and audit aspects, displayed in a circle, which underpins the interaction and sequence of the different aspects.

Figure 5. Main review and audit aspects.

Figure 4. External Review and Auditing in a periodic continuous improvement setting of LCA databases (DB).

“Demand” is the core driver when planning data provision. New technologies, regulations, standards or new market regions are decisive. The core aspects concerning implementation of data are: overall relevancy, accuracy, methodological consistency and technical adequacy. Data implementation needs some LCI experts and many engineering and agri-food experts to generate adequate data results. Concerning maintenance of data, the frequency, the possibility of auto-updates of own-developed user systems and the proactive update or fade-out of older data is essential. A “review of the current situation” is closing the loop. Therefore also the “review of data” by suitable parties and the users groups with the related improvement input is a core aspect for the new planning of the next update cycle. The key of a professional review process is a systematic approach and continuous process embedded into well defined update routines covering - Basic or core technologies , e.g. power plants, refineries and water treatment units - Dependent datasets derived from these core models - Quality assurance processes This process increases the overall transparency of data generation (with the additional benefit of independent verification), strengthens quality and credibility and leads to high confidence by the users of the data. Feedback of stakeholders, demand determination of users and feedback from and into standardization processes and best practice guidelines is also important. Synergy of science and industry in database development is possible, if the different stakeholders stick to a modular system and their related responsibilities are understood and taken (Baitz et al. 2007).

3. Summary, conclusion and resulting actions Specific feed and food data needs feed and food specific knowhow. However, any sector database must be usable and understandable in all branches. It is a long way from field to retailer, with environmental impacts from many different branches along the chain (see Figure 6). Therefore, isolated or sector specific data that are only understandable, communicable or exchangeable within the same industry branch are of less value for the LCA environment.

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Field User

Figure 6. Data solutions in the network of branches. Three key aspects, as an example of implementation for food and feed LCA databases, are provided in this publication. Based on scientific and methodological approaches, the GaBi LCA agrarian plant model covers all relevant LCA agricultural aspects. Parameterization provides the necessary flexibility and applicability to cover all agricultural plant products with a global applicability. The scope of the model is constantly developed and improved. For a scientifically sound, feasible and applicable scaling of dataset generation for use in industry, model parameters are prioritized and grouped and are determined via sequences of data collection. Using reasonable operating efforts the model provides consistent and very accurate results for various agricultural and plantation products and differentiated adjustable farming practices. A management and maintenance concept allows the use of the scientifically sound agrarian plant LCA models in practice and with broadly usable data. This management and maintenance concept includes a professional systematic review process with well-defined update procedures. Ensuring the feasibility and practicability of a scientifically sound approach is essential for a professional database solution. Documentation of modeling approaches supports maximum transparency of various important aspects such as sources, quality assurance and review process. This approach is the basis and stepping stone towards regionalized (LCI and LCIA) dataset creation in the future. Similar approaches are to be applied for processing, by-products and other agricultural products with a special focus on the interconnection with other industry sectors.

4. References AGRIBALYSE®, Koch P and Salou T (2013) AGRIBALYSE®: Rapport Méthodologique – Version 1.0. Ed ADEME, Angers, 384 p Agri-footprint®, Blonk Agri Footprint BV, Gouda Audsley E, Stacey K, Parsons DJ, Williams AG (2009) Estimation of the greenhouse gas emissions from agricultural pesticide manufacture and use, Cranfield University, Cranfield, Bedford Baitz M et al. (2012) LCA’s theory and practice: like ebony and ivory living in perfect harmony? Editorial in the International Journal of Life Cycle Assessment, online first, DOI 10.1007/s11367-012-0476-x Baitz M, Gabriel R, Betz M, Deimling S (2007) Quality, reliability and trustworthiness of bio-fuel certificates, Econsense Workshop with Procedings, Berlin Beck T, Bos U, Wittstock B (2010) LANCA – Calculation of Land Use Indicator Values in Life Cycle Assessment. Lehrstuhl für Bauphysik (LBP), Universität Stuttgart

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Blonk (2014a) Why 25% of all published LCAs are agriculture related. Blonk Agri Footprint BV, Gouda http://blonkconsultants.nl/en/publications/2014/why-25-of-all-published-lcas-are-agriculture-related.html Accessed 10 May 2014 Blonk (2014b) Direct land use change assessment tool, version 2013.1 from Blonk Consultants, Gouda, 2013 Bouwman AF, Boumans LJM and Batjes NH (2002) Emissions of N2O and NO emissions from fertilized fields: Summary of available measurement data. In: Global Biogeochemical Cycles. 16(4), 1058 Brentrup F, Küsters J, Lammel J, Kuhlmann H (2000) Methods to Estimate On-field Nitrogen Emissions from Crop Production as an Input to LCA Studies in the Agricultural Sector. In: The International Journal of Life Cycle Assessment. 5(6), 349-357 Callieri C, Hauff J, Mahler D, O’Keefe J, Aulisi A, Cassara A, Davis C, Nogueron R, Rodgers J, Sauer A (2008) Rattling Supply Chains – The effect of Environmental Trends on Input Costs for the Fast-Moving Consumer Goods Industry, A.T. Kearney, World Resources Institute Ciemat Deimling S, Shonfield P, Bos U (2008) LCA and carbon footprints in agro-food: From theory to implementation in the food industry. 6th International Conference on LCA in the Agri-Food Sector, Zurich Dijkman TJ, Birkved M, Hauschild M (2012) PestLCI 2.0: a second generation model for estimating emissions of pesticides from arable land in LCA Döhler H, Eurisch-Menden B, Dämmgen U, Osterberg B, Lüttich M, Berg W, Brunsch R (2002) BMVEL/UBAAmmoniak-Emissionsinventar der deutschen Landwirtschaft und Minderungsszenarien bis zum Jahre 2010, UBA European Commission JRC (2013a) Soil Projects; Support to Renewable Energy Directive http://eusoils.jrc.ec.europa.eu/projects/RenewableEnergy/. Accessed 15 July 2014. European Commission JRC – Fazio S, Recchioni M, Mathieux F, Garrain D, de la Rùa C, Lechòn Y (2013b) Background analysis of the quality of the energy data to be considered for the European Reference Life Cycle Database (ELCD). Institute for Environment and Sustainability. European Commission (2010) Report from the commission on indirect land-use change related to biofuels and bioliquids (COM/2010/0811) Eickhout B, Bouwman AF, van Zeijts H (2006) The role of nitrogen in world food production and environmental sustainability. In: Agriculture, Ecosystems & Environment ENEA (2014), Centro Ricerca Bologna Technical report – Necessary steps and effort to make GaBi database meet the ILCD data network entry level requirements and PEF data requirements (not yet published) ENVIFOOD Protocol - Environmental Assessment of Food and Drink Protocol (2013) European Food Sustainable Consumption and Production Round Table (SCP RT), Working Group 1, Brussels FAO - Food and Agriculture Organization of the United Nations (2010) Global Forest Resource Assessment, FAO - Food and Agriculture Organization of the United Nations (2006) Livestock’s long shadow – environmental issues and options, Rome FAO - Food and Agriculture Organization of the United Nations (2013) Food wastage footprint – Impacts on natural resources, Rome FAO LEAP – Livestock Environmental Assessment and Performance Partnership by the Food and Agriculure Organization of the United Nations (2014) http://www.fao.org/partnerships/leap/en/ Accessed 10 May 2014 Feed and food database, PE INTERNATIONAL 2014, Stuttgart. Finkbeiner (2014) Indirect land use change – help beyond the hype? In: Biomass and Bioenergy. Elsevier, Vol.62, pp.218-221 Fuchs S and Schwarz M (2007) Ableitung naturraumtypischer Anreicherungsfaktoren zur Bestimmung des Phosphor- und Schwermetalleintrages in Oberflächengewässer durch Erosion. Universität Karlsruhe (TH), Institut für Wasser- und Gewässerentwicklung (IWG). GaBi (2013) GaBi Software and Databases, PE International AG, http://www.pe-international.com Green MB (1987) Energy in pesticide manufacture, distribution and use. In: HelseL, Z.R., (Ed.): Energy in Plant Nutrition and Pest Control. Amsterdam, Elsevier, pp. 165-177# Helbig H, Möller M, Schmidt G (2009) Bodenerosion durch Wasser in Sachsen-Anhalt - Ausmaß, Wirkungen und Vermeidungsstrategien. Erich Schmidt Verlag.

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This paper is from:

Proceedings of the 9th International Conference on Life Cycle Assessment in the Agri-Food Sector

8-10 October 2014 - San Francisco

Rita Schenck and Douglas Huizenga, Editors American Center for Life Cycle Assessment

The full proceedings document can be found here: http://lcacenter.org/lcafood2014/proceedings/LCA_Food_2014_Proceedings.pdf It should be cited as: Schenck, R., Huizenga, D. (Eds.), 2014. Proceedings of the 9th International Conference on Life Cycle Assessment in the Agri-Food Sector (LCA Food 2014), 8-10 October 2014, San Francisco, USA. ACLCA, Vashon, WA, USA. Questions and comments can be addressed to: [email protected]

ISBN: 978-0-9882145-7-6