WP1: Data Foundations
Lead Partner: ETHZ
Objectives: This WP aims to address SO1, SO3 and SO5. We do this by solidifying and enhancing the data foundation for food footprinting and in turn, create the basis for deriving PEF compliant datasets in WP2 and fill most relevant gaps in life cycle impact assessment methods.
Objectives: This WP aims to address SO1, SO3 and SO5. We do this by solidifying and enhancing the data foundation for food footprinting and in turn, create the basis for deriving PEF compliant datasets in WP2 and fill most relevant gaps in life cycle impact assessment methods.
Tasks
T1.1: Breaking new ground: overcoming spatial and temporal limitations in agricultural production data
Lead: SEI (10PM).
Effort by contributors: ETHZ (10PM), UOY (6PM), DDS (2PM), NILU (0.5PM). Timing: M1-19. Mapping of the subnational (municipality, district, province) agricultural production of all key global crops, including their tonnage, area and yield. The higher accuracy and spatial-explicitness of this dataset allows important improvements in all following other analyses, particularly on resource and energy use, and as well as on environmental impacts of food production. We will build upon the current research of this consortium that developed the GSAP (Global Subnational Agricultural Production) database, covering the subnational regions of the vast majority of countries. Remaining agricultural products will be modelled based on other datasets and research of consortium members, such as the HESTIA.earth database, Cropgrids, and MapSPAM. Contributes to:
D1.1 (M18).
This task is linked to the following milestones: MS1.1 (M18).
T1.2: Providing new-generation quantification of resource and energy use and eutrophying emissions in crop production
Lead: ETHZ (10PM).
Effort by contributors: SEI (5PM), ULEI (3PM), UOY (3PM), NILU (0.5PM). Timing: M6-36. Based on Task 1.1, we develop a detailed crop growth model using remote sensing based information in combination with growth and yield statistics for establishing more accurate inventory flows of crop production for key processes and biosphere exchanges. This allows us to add details on land use and land use change, with a special focus on tropical deforestation associated with food imports. Based on the detailed land use data, we will enhance water consumption estimates through an irrigation model (with increased level of detail based on previous work of the consortium). We will close the gap of detailed nutrient use and emission inventories, by enhancing fertiliser application models, differentiating artificial and organic fertiliser (for organic production) and model related N and P emissions. This also allows better estimating energy use in food supply chains due to fertiliser application. We extend model greenhouse / vertical farming production models to provide detailed information on land use, water consumption, heat and electricity demand, and material use. Contributes to:
D1.2 (M36).
T1.3: Improving footprinting of feed and other resource use for animal products
Lead: ETHZ (10PM).
Effort by contributors: ULEI (4PM), UOY (4PM), DDS (2PM). Timing: M1-37. We will utilise GLEAM to assess resource use and environmental impacts of livestock products by simulating the entire livestock production system from feed production to final product output. It considers various factors such as animal genetics, management practices, and feed composition to estimate resource inputs and environmental outputs such as greenhouse gas emissions, nutrient losses, and water use. For aquatic food, we will extend feed and energy use models, and add a model on antibiotics use in aquaculture, and assess key emissions (ULEI). For wild catch, we will explore novel parameters related to capture fisheries, including locations, fishing effort magnitude, and fishing gear type, and also consider related by-catch and trawling in benthic habitat areas. The Inventory results generated in this Task and Task 1.2 will be coupled with enhanced, regionalized impact assessment methods. Detailed assessment of land use change via use of spatially explicit inventory data and geospatial/remote-sensing information will also facilitate better modelling of related greenhouse gas (GHG) emissions, biodiversity loss, and assessment of soil health impacts due to erosion and compaction as well as loss of ecosystem services. Based on the enhanced fertiliser model, we will assess freshwater eutrophication and will improve the model for marine eutrophication. Existing models will be improved based on ongoing work at ULEI, CNRS and ETHZ. Ammonia emissions from fertiliser application will be addressed with a regionalized PM model developed by ETHZ and NILU. Continuing ongoing work at CNRS, ULEI and ETHZ, water scarcity impact assessment will be extended at the global scale distinguishing soil moisture, surface water and groundwater). Impact assessment models of wild catch are so far limited in scope and functionality. We will assess impacts of fisheries on fish stock depletion and related impacts on the marine ecosystem based on ongoing work at SRC, ETHZ, ULEI and UOY. Contributes to:
D1.3 (M36).
T1.4: Modelling domestic and international trade and transport modes and emissions
Lead: ULEI (0PM).
Effort by contributors: ETHZ (9PM), SEI (9PM), UOY (5PM), NILU (1PM), DDS (1PM). Timing: M10-48. This task develops a comprehensive spatially explicit approach to analyse the supply chains and footprint of agricultural products. This integrated approach will involve analysing subnational commodity flows combining Trase for selected high-impact crops with FABIO/MRIO for full coverage of all international trade flows. About 85% of all traded products are transported by sea, a number that increases for the specific case of staple foods given their bulky nature. We will model global maritime transport of food trade and assess associated maritime shipping emissions. For all other transportation modes, we will develop a detailed multi-modal transport model, incorporating assessment of greenhouse gas and particulate matter emissions. Regarding IPR, the Trase data is all open, however some input data may be restricted. While the data product used to obtain results might not be shared, the final outputs that are part of the project will be freely shareable as long as we make sure they are not traceable back to the input data. Contributes to:
D1.4 (M40).
T1.5: Align nutritional and environmental footprint data for easier direct comparison of environmental and dietary profile of foods
Lead: ZHAW (11PM).
Effort by contributors: ETHZ (9PM), ULEI (3PM). Timing: M6-36. This task includes the following elements: (1) Provide information about nutritional contents of food products and food categories based on existing literature and databases; (2) Literature review to include relevant nutritional aspects (e.g. protein quality, bio-availability) with a focus on new plant-based protein sources and novel foods; (3) Literature research on existing data on the influence of different processing methods on the nutritional composition (for main crops); (4) Compile nutrient requirements of population; (5) Integrate assessment approaches or methodologies to assess how well a menu meets nutritional requirements; (6) Data preparation, harmonisation and conversion of the results into a previously defined and compatible data format for the subsequent work packages. Contributes to:
D1.2 (M24), D1.3 (M32).
T1.6: Including Food Waste and Processing Into Food LCA Results
Lead: ZHAW (11PM).
Effort by contributors: ETHZ (9PM). Timing: M10-46. This task includes the following elements: (1) Updated review of existing literature and databases with information about the amounts of FLW; (2) at each level of the food value chain (FVC) including the stages of agricultural production (including harvest and postharvest losses), wholesale (including transport, storage, and postharvest losses), processing (including overproduction and edible side streams), the food service industry as well as households in Europe (with a focus on Switzerland) and in developing countries and in different food categories; (3) A compilation of aspects to consider in order to make data comparable; (4) Implications of different definitions used in various contexts (e.g. edible/potentially edible/inedible, avoidable/possibly avoidable/unavoidable, food losses/waste, including/excluding use as animal feed etc.); (5) Methodologies how to consider FLW in LCA analysis depending on the scope of the assessment. Contributes to:
D1.5 (M42), D1.1 (M21).
T1.7: Technical implementation and data merging for a PEF-compliant database
Lead: ZHAW (11PM).
Effort by contributors: ETHZ (9PM), INTEC (2PM), NILU (1PM). Timing: M30-42. Datasets generated in this Work package into an LCA database that meets the standards of PEF. The task will link and further develop models and datasets to enhance data sharing and validation, increase visibility and relevance, and improve data utilisation. The results of WP1 form the basis to consistently include environmental impacts of processes and FLW at the various stages of the FVC and their impact reduction potential as well as nutritional aspects in the methodologies developed in WP2, into EU-wide modelling in WP3, case studies in WP4, stakeholder involvement in WP5, and management in WP6. Contributes to:
D1.5 (M42).
Milestones
Table 3.1d: List of Milestones
| Number | Name | Work Package No. | Lead Beneficiary | Means of Verification | Due Date (in months) |
|---|---|---|---|---|---|
| MS1.1 | Preliminary results of all data for initial implementation in WP2 | 1 | ETHZ | Data available on project workspace or repository | 18 |
Deliverables
| Number | Relative Number in WP | Name | Lead Beneficiary | Type | Dissemination Level | Due Date (in months) |
|---|---|---|---|---|---|---|
| D1.1 | 1 | Sub-national crop dataset | SEI | DATA | PU | 18 |
| D2.1 | 1 | Data assessment report | UOY | R | PU | 21 |
| D1.2 | 2 | LCIs of crops and animal products | ETHZ | DATA | PU | 36 |
| D2.2 | 2 | Recommendations sheets | UOY | OTHER | PU | 24 |
| D1.3 | 3 | Regionalized LCIA data | ETHZ | DATA | PU | 36 |
| D2.3 | 3 | FAIR data recommendations | UOY | R | PU | 32 |
| D1.4 | 4 | Trade and transport model | SEI | OTHER | PU | 40 |
| D1.5 | 5 | PEF-compliant LCI data of food | ZHAW | DATA | PU | 42 |