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PurposeThe focus of the life-cycle assessment (LCA) of an agricultural plant product is typically on one crop. However, isolating one crop from the cropping system that it belongs to is often challenging because the crops are often interlinked with the other crops in the cropping system. The main objectives of this discussion article are as follows: (i) to discuss the characteristics of cropping systems which might affect the LCA methodology, (ii) to discuss the advantages and the disadvantages of the current available methods for the life-cycle assessment of cropping systems, and (iii) to offer a framework to carry out LCA of crops and cropping systems.MethodsThe definition of cropping systems is provided together with a description of two types of LCA: product LCA and system LCA. The LCA issues related to cropping system characteristics have been classified as (1) crop interrelationship, (2) crop management and emissions, and (3) functional unit issues. The LCA approaches presented are as follows: cropping system, allocation approaches, crop-by-crop approach, and combined approaches. The various approaches are described together with their advantages and disadvantages, applicability, comprehensiveness, and accuracy.Results and discussionThe cropping system approach is best suited for system LCA. For product LCA, none of the methods is fully exhaustive and accurate. The crop sequence approach takes into consideration the cropping system issues, if they happen within the year or season, and cannot be applied for intercropping and agroforestry systems. The allocation approaches take into account the cropping system effects by establishing a mathematical relationship between crops present in the cropping systems. The model for integrative life-cycle assessment in agriculture (MiLA) approach considers cropping system issues if they are related to multiproduct and nutrient cycling, while the crop-by-crop approach is highly affected by assumptions and considers cropping system issues only if they are related to the analyzed crop.ConclusionsEach LCA approach presents advantages and disadvantages. For system LCA, the cropping system approach is recommended. For product LCA, environmental burdens should be attributed applying the following hierarchy: (1) attributed to the crop if based on a clear causality, (2) attributed with combined approaches and specific criteria, and (3) attributed with allocation approaches and generic criteria. These approaches should be combined with the cropping system approach..

This article introduces the special issue “LCA of nutrition and food consumption” and 14 papers selected from the Ninth LCA Food Conference in San Francisco in October 2014. The scientific literature in the field of food LCA has increased more than ten times during the last 15 years. Nutrition has a high contribution to the total environmental impacts of consumption. Agricultural production often dominates the impacts, but its importance depends on the type of product, its production mode, transport, and processing. Local or domestic products reduce transports, but this advantage can be lost if the impacts of the raw material production are substantially increased. Diets containing less meat tend to be more environmentally friendly. Several studies concluded that respecting the dietary recommendations for a healthy diet would reduce the overall environmental impacts in the developed countries, although this is not a universal conclusion. Eight papers analyze the environmental impacts of catering and in-house food consumption and impacts on sectoral and national levels; four papers presents tools and methods to better assess the impacts of nutrition and to implement the results in practical decision-making. Finally, two contributions analyze the impacts of food waste and reduction options. (i) Comprehensive assessment. Most studies only analyze climate impacts, although data, methods, and tools are readily available for a more comprehensive analysis. (ii) Assessment of sustainability. The social dimension remains the weakest pillar. (iii) Data availability is still an obstacle, but significant progress has been made in recent years. (iv) Lack of harmonization of methodologies makes comparisons among studies difficult. (v) Land use. Enhanced consideration of land use impacts on biodiversity and ecosystem services is required in LCA. (vi) Defining the functional unit including nutritional aspects, food security, and health needs further work. (vii) Consumer behavior. Its impacts are still little assessed. (viii) Communication of the environmental impact assessment results to stakeholders including policy-makers and consumers needs additional efforts. (i) Development of holistic approaches for the assessment of sustainable food systems, (ii) assessment of land use related impacts and inclusion of ecosystem services, (iii) exploration of LCA results for policy support and decision-making, (iv) investigation of food consumption patterns in developing and emerging countries, and (v) harmonization of databases.

Purpose The emission of greenhouse gases (GHG) is a key criterion in the environmental assessment of biofuels. Life cycle inventories taking into account the latest methodological developments are an essential prerequisite for this assessment. In the last years, substantial progresses in the modelling of nitrogen emissions relevant for the climate as well as in modelling the emissions from land use change (LUC) have been achieved. Therefore, the biomass production inventories in the ecoinvent database were revised to take into account these developments.

The agricultural sector fulfils several functions such as the production of food energy and landscape conservation. An equilibrium between economic development and environmental protection should be found and research should aid political decision-making. In recent years, great efforts have been made to assess the environmental and economic implications of changes in both environmental and agricultural policies. Life cycle assessment (LCA) has been extended by cost functions and social parameters. The validation of suggestions for political measures can be improved by combining existing environmental and economic models. This approach is applied in this paper in order to compare an increased support for organic farming with specific environmental policy measures, focusing on the resulting impacts and socioeconomic indicators. The LCA tool Swiss Agricultural Life Cycle Assessment (SALCA) has been linked with the economic optimisation model Swiss Agricultural Sector Forecasting System (SILAS). Since the focus lies on agricultural production, the farm gate represents the system boundary of the LCA. By linking SALCA with SILAS, the classic LCA impact categories have been extended by socioeconomic indicators. A reference scenario representing the assumed development of economic conditions between 2008 and 2015 was varied by a support for organic farming and four specific policy measures (support for ecological compensation areas and a tax on energy, fertiliser and concentrates, respectively). To remain comparable, the changes in total payments were set to the same amount in all scenarios (100 million CHF). A support for organic farming would have favourable effects on several environmental and socioeconomic indicators, but the differences compared to a combination of the four specific measures are small. However, some of these single measures could reach parts of the targets more efficiently: an energy tax would stimulate the application of available energy-saving measures, and a tax on concentrates would lead to a shift from intensive animal husbandry to crop production, reducing imports of concentrates and emissions of ammonia. Overall, the predicted developments in product and factor prices combined with the assumed reductions of public payments until 2015, as represented by the reference scenario, have the greatest effect on environmental impacts. Theoretically, specific policy measures are more efficient than measures directed towards multiple goals. Especially in the case of agriculture, however, a specific measure not only has an impact on the associated target but affects all agricultural functions. The results of the model combination suggest that a support for organic farming lowers the risk of undesirable side effects on environmental and socioeconomic indicators. Before implementing more specific measures, their impacts on the different functions should be estimated. For this purpose, a combination of decision-support models with LCA may be an appropriate instrument.

Purpose Life cycle assessment (LCA) is widely used for the environmental assessment of food products, but difficulties arise when evaluating large portfolios of food products or when faced with a diversity of sources of ingredients and/or frequent changes of suppliers. In such situations, a specific, in-depth assessment of each ingredient is not feasible, and screening approaches using a few LCIA (Life Cycle Impact Assessment) results are not recommended. The goal of this paper is to propose an intermediate solution between a screening assessment using limited data and specific LCA for all products considering all sources of ingredients.

Introduction: This paper provides an overview on the content of the ecoinvent database and of selected metholodogical issues applied on the life cycle inventories implemented in the ecoinvent database. Goal, Scope and Background: In the year 2000, several Swiss Federal Offices and research institutes of the ETH domain agreed to a joint effort to harmonise and update life cycle inventory (LCI) data for its use in life cycle assessment (LCA). With the ecoinvent data-base and its actual data v1.1, a consistent set of more than 2'500 product and service LCIs is now available. Method: Nearly all process datasets are transparently documented on the level of unit process inputs and outputs. Methodological approaches have been applied consistently throughout the entire database content and thus guarantee for a coherent set of LCI data. This is particularly true for market and trade modelling (see, for example, electricity modelling), for the treatment of multi-out-put and of recycling processes, but also for the recording and reporting of elementary flows. The differentiation of diameter size for particulate matter emissions, for instance, allows for a more comprehensive impact assessment of human health effects. Data quality is quantitatively reported in terms of standard deviations of the amounts of input and output flows. In many cases qualitative indicators are reported additionally on the level of each individual input and output. The information sources used vary from extensive statistical works to individual (point) measurements or assumptions derived from process descriptions. However, all datasets passed the same quality control procedure and all information relevant and necessary to judge the suitability of a dataset in a certain context are provided in the database. Data documentation and exchange is based on the EcoSpold data format, which complies with the technical specification ISO/TS 14048. Free access to process information via the Internet helps the user to judge the appropriateness of a dataset. Concluding Remarks: The existence of the ecoinvent database proves that it is possible and feasible to build up a large interlinked system of LCI unit processes. The project work proved to be demanding in terms of co-ordination efforts required and consent identification. One main characteristic of the database is its transparency in reporting to enable individual assessment of data appropriateness and to support the plurality in methodological approaches. Outlook: Further work on the ecoinvent database may comprise work on the database content (new or more detailed data-sets covering existing or new economic sectors), LCI (modelling) methodology, the structure and features of the data-base system (e.g. extension of Monte Carlo simulation to the impact assessment phase) or improvements in eco-invent data supply and data query. Furthermore, the deepening and building up of international co-operations in LCI data collection and supply is in the focus of future activities

Life cycle inventories (LCIs) of agricultural products, infrastructure, inputs and processes are required to optimise food supply chains. In the past, the use of LCA was hindered by the limited availability of databases with LCIs for such agricultural inputs, processes and products in combination with LCIs of other major economic sectors. The ecoinvent database covers this need for the Swiss, and to an extent, the European context. A suitable approach had to be outlined for defining representative datasets for products from arable crops, since there was no comprehensive survey of agricultural production. No single data source was available for defining representative datasets for arable crops. It was therefore decided to define model crops on the basis of a variety of sources in collaboration with experts on the crops in question. The datasets were validated by experts and by comparison with literature. Field emissions were calculated using a set of models taking into account situation-specific parameters. Data defined by this procedure are more generally usable, but their definition is also more laborious. Selected results (inventories and impact assessment) are presented for infrastructure (buildings, machinery), work processes, fertilisers, pesticides, seed and arable crop products. Infrastructure has a higher share of environmental impacts than in typical industrial processes, often due to low utilisation rates. Energy use is dominated by mechanisation, the use of mineral fertilisers (particularly nitrogen) and grain drying. Eutrophication is caused mainly by nitrogen compounds. In general, field emissions are of decisive importance for many environmental impacts. The ecoinvent database provides representative agricultural data for the Swiss, and to an extent, the European context. It also provides the meta-information necessary for deciding whether a dataset is suitable for the purpose of a particular LCA study. To further improve the representativeness of the datasets, an environmental farm monitoring network is required.