Teunis Johannes Dijkman

PestLCI 2.0: a second generation model for estimating emissions of pesticides from arable land in LCA

PurposeThe spatial dependency of pesticide emissions to air, surface water and groundwater is illustrated and quantified using PestLCI 2.0, an updated and expanded version of PestLCI 1.0.MethodsPestLCI is a model capable of estimating pesticide emissions to air, surface water and groundwater for use in life cycle inventory (LCI) modelling of field applications. After calculating the primary distribution of pesticides between crop and soil, specific modules calculate the pesticide’s fate, thus determining the pesticide emission pattern for the application. PestLCI 2.0 was developed to overcome the limitations of the first model version, replacement of fate calculation equations and introducing new modules for macropore flow and effects of tillage. The accompanying pesticide database was expanded, the meteorological and soil databases were extended to include a range of European climatic zones and soil profiles. Environmental emissions calculated by PestLCI 2.0 were compared to results from the risk assessment models SWASH (surface water emissions), FOCUSPEARL (groundwater via matrix leaching) and MACRO (groundwater including macropore flow, only one scenario available) to partially validate the updated model. A case study was carried out to demonstrate the spatial variation of pesticide emission patterns due to dependency on meteorological and soil conditions.ResultsCompared to PestLCI 1.0, PestLCI 2.0 calculated lower emissions to surface water and higher emissions to groundwater. Both changes were expected due to new pesticide fate calculation approaches and the inclusion of macropore flow. Differences between the SWASH and FOCUSPEARL and PestLCI 2.0 emission estimates were generally lower than 2 orders of magnitude, with PestLCI generally calculating lower emissions. This is attributed to the LCA approach to quantify average cases, contrasting with the worst-case risk assessment approach inherent to risk assessment. Compared to MACRO, the PestLCI 2.0 estimates for emissions to groundwater were higher, suggesting that PestLCI 2.0 estimates of fractions leached to groundwater may be slightly conservative as a consequence of the chosen macropore modelling approach. The case study showed that the distribution of pesticide emissions between environmental compartments strongly depends on local climate and soil characteristics.ConclusionsPestLCI 2.0 is partly validated in this paper. Judging from the validation data and case study, PestLCI 2.0 is a pesticide emission model in acceptable accordance with both state-of-the-art pesticide risk assessment models. The case study underlines that the common pesticide emission estimation practice in LCI may lead to misestimating the toxicity impacts of pesticide use in LCA.

The Glasgow consensus on the delineation between pesticide emission inventory and impact assessment for LCA

PurposePesticides are applied to agricultural fields to optimise crop yield and their global use is substantial. Their consideration in life cycle assessment (LCA) is affected by important inconsistencies between the emission inventory and impact assessment phases of LCA. A clear definition of the delineation between the product system model (life cycle inventory—LCI, technosphere) and the natural environment (life cycle impact assessment—LCIA, ecosphere) is missing and could be established via consensus building.MethodsA workshop held in 2013 in Glasgow, UK, had the goal of establishing consensus and creating clear guidelines in the following topics: (1) boundary between emission inventory and impact characterisation model, (2) spatial dimensions and the time periods assumed for the application of substances to open agricultural fields or in greenhouses and (3) emissions to the natural environment and their potential impacts. More than 30 specialists in agrifood LCI, LCIA, risk assessment and ecotoxicology, representing industry, government and academia from 15 countries and four continents, met to discuss and reach consensus. The resulting guidelines target LCA practitioners, data (base) and characterisation method developers, and decision makers.Results and discussionThe focus was on defining a clear interface between LCI and LCIA, capable of supporting any goal and scope requirements while avoiding double counting or exclusion of important emission flows/impacts. Consensus was reached accordingly on distinct sets of recommendations for LCI and LCIA, respectively, recommending, for example, that buffer zones should be considered as part of the crop production system and the change in yield be considered. While the spatial dimensions of the field were not fixed, the temporal boundary between dynamic LCI fate modelling and steady-state LCIA fate modelling needs to be defined.Conclusions and recommendationsFor pesticide application, the inventory should report pesticide identification, crop, mass applied per active ingredient, application method or formulation type, presence of buffer zones, location/country, application time before harvest and crop growth stage during application, adherence with Good Agricultural Practice, and whether the field is considered part of the technosphere or the ecosphere. Additionally, emission fractions to environmental media on-field and off-field should be reported. For LCIA, the directly concerned impact categories and a list of relevant fate and exposure processes were identified. Next steps were identified: (1) establishing default emission fractions to environmental media for integration into LCI databases and (2) interaction among impact model developers to extend current methods with new elements/processes mentioned in the recommendations.

Pesticide emission modelling and freshwater ecotoxicity assessment for Grapevine LCA: adaptation of PestLCI 2.0 to viticulture

PurposeConsumption of high quantities of pesticides in viticulture emphasizes the importance of including pesticide emissions and impacts hereof in viticulture LCAs. This paper addresses the lack of inventory models and characterization factors suited for the quantification of emissions and ecotoxicological impacts of pesticides applied to viticulture. The paper presents (i) a tailored version of PestLCI 2.0, (ii) corresponding characterization factors for freshwater ecotoxicity characterization and (iii) result comparison with other inventory approaches. The purpose of this paper is hence to present a viticulture customized version of PestLCI 2.0 and illustrate the application of this customized version on a viticulture case study.MethodsThe customization of the PestLCI 2.0 model for viticulture includes (i) addition of 29 pesticide active ingredients commonly used in vineyards, (ii) addition of 9 viticulture type specific spraying equipment and accounting the number of rows treated in one pass, and (iii) accounting for mixed canopy (vine/cover crop) pesticide interception. Applying USEtox™, the PestLCI 2.0 customization is further supported by the calculation of freshwater ecotoxicity characterization factors for active ingredients relevant for viticulture. Case studies on three different vineyard technical management routes illustrate the application of the inventory model. The inventory and freshwater ecotoxicity results are compared to two existing simplified emission modelling approaches.Results and discussionThe assessment results show considerably different emission fractions, quantities emitted and freshwater ecotoxicity impacts between the different active ingredient applications. Three out of 21 active ingredients dominate the overall freshwater ecotoxicity: Aclonifen, Fluopicolide and Cymoxanil. The comparison with two simplified emission modelling approaches, considering field soil and air as part of the ecosphere, shows that PestLCI 2.0 yields considerable lower emissions and, consequently, lower freshwater ecotoxicity. The sensitivity analyses reveal the importance of soil and climate characteristics, canopies (vine and cover crop) development and sprayer type on the emission results. These parameters should therefore be obtained with site-specific data, while literature or generic data that are acceptable inputs for parameters whose uncertainties have less influence on the result.ConclusionsImportant specificities of viticulture have been added to the state-of-the-art inventory model PestLCI 2.0. They cover vertically trained vineyards, the most common vineyard training form; they are relevant for other perennial or bush crops provided equipment, shape of the canopy and pesticide active ingredients stay in the range of available options. A similar and compatible model is needed for inorganic pesticide active ingredients emission quantification, especially for organic viticulture impacts accounting.

LCA of Food and Agriculture

This chapter deals with the application of Life Cycle Assessment to evaluate the environmental sustainability of agriculture and food processing. The life cycle of a food product is split into six stages: production and transportation of inputs to the farm, cultivation, processing, distribution, consumption and waste management. A large number of LCA studies focus on the two first stages in cradle-to-farm gate studies, as they are the stages where most impacts typically occur, due to animal husbandry and manure handling, production and use of fertilisers and the consumption of fuel to operate farm machinery. In the processing step, the raw agricultural product leaving the farm gate is converted to a food item that can be consumed by the user. Distribution includes transportation of the food product before and after processing. In the consumption stage, environmental impacts arise due to storage, preparation and waste of the food. In the waste management stage, food waste can be handled using a number of technologies, such as landfilling, incineration, composting or digestion. A number of case studies are looked at here where the life cycles of typical food products (meat, cheese, bread, tomatoes, etc.), and an entire diet are discussed. Other case studies deal with what LCA can conclude on the differences between conventional and organic farming, and the perceived advantages of local food items. Finally, methodological issues in agricultural LCA are discussed: the choice of functional unit, setting the boundary between technosphere and ecosphere, modelling flows of nutrients and pesticides, and the generally limited number of impact categories included in LCA studies.

How to assess sustainability in automated manufacturing

The aim of this paper is to describe how sustainability in automation can be assessed. The assessment method is illustrated using a case study of a robot. Three aspects of sustainability assessment in automation are identified. Firstly, we consider automation as part of a larger system that fulfills the market demand for a given functionality. Secondly, three aspects of sustainability have to be assessed: environment, economy, and society. Thirdly, automation is part of a system with many levels, with different actors on each level, resulting in meeting the market demand. In this system, (sustainability) specifications move top-down, which helps avoiding sub-optimization and problem shifting. From these three aspects, sustainable automation is defined as automation that contributes to products that fulfill a market demand in a more sustainable way. The case study presents the carbon footprints of a robot, a production cell, a production line and the final product. The case study results illustrate that, depending on the actor and the level he/she acts at, sustainability and the actions that can be taken to contribute to a more sustainable product are perceived differently: even though the robot is a minor contributor to the carbon footprint at cell or line level, from the perspective of a robot producer reducing the electricity consumption during the robots use stage can be a considerable improvement in the carbon footprint of a robot, and thus in the sustainability profile of the robot.