Quantis is helping sow the seeds for a sustainable future for agriculture. EIT Climate-KIC, Europe’s largest public-private partnership addressing climate change through innovation, has awarded Quantis a grant to launch the GeoFootprint Project. The ground-breaking, two-year project will deliver comprehensive and site-specific data via a publicly available, web-based platform to a diverse set of actors across crop-based value chains. The goal of the project is to foster more effective measurement, monitoring and management of local sustainable agricultural practices.
Agriculture is the second largest emitter of the greenhouse gas emissions driving climate change (accounting for 17% of global emissions) and is a major contributor to deforestation, water scarcity, soil degradation and land use change. Leadership in this sector, particularly at farm-level, is vital to reduce impacts, mitigate risk and safeguard future growth. To do this, companies and producers need greater transparency on the effectiveness of their sustainability initiatives.
By converging Life Cycle Assessment (LCA) datasets and spatial data from Geographic Information Systems (GIS), the GeoFootprint Project will provide companies, public authorities and various actors at farm level with instant access to the spatially-sensitive footprint of major commodities everywhere in the world.
With this information, actors across crop-based industries will have a more holistic understanding of farm-level impacts and be better positioned to replicate value-added sustainability practices.
Building Better Data
Life Cycle Assessment is the leading decision-making support tool for the environmental management of agricultural commodities. Until now, it has relied on data derived from average meteorological conditions at the country level to identify drivers of environmental impacts and evaluate measures for reducing them. Practices can be informed by local or national governmental policies, market trends and pressures from other supply chain actors; however, most agricultural practices are highly context specific and largely determined by micro-spatial parameters such as soil properties, precipitation and slope.
Integrating data from GIS into LCA calculations allows a product’s spatial context to be taken into consideration when evaluating impacts. This ultimately offers a more accurate way of assessing the efficiency of farm-level cultivation practices and their mitigation potential (click here for a deep dive into Quantis’ existing work on regionalizing agricultural LCAs).
Over the next two years, the project partners, guided by Quantis, will produce:
+ A computational regionalization engine that seamlessly integrates spatial data into environmental footprinting;
+ A Land Use Change calculation module that will operationalize the Land Use Change Guidance and link its effects to their corresponding drivers; and
+ A Land Use calculation module that enables the monitoring and modeling of the effects of different land use management practices on Soil Organic Carbon.
+ Scaling Impact
Decarbonizing the agricultural sector requires participation at all levels of the supply chain. But in a global economy where thousands of miles separate companies from their producers and suppliers, it can be difficult to link brand-level strategy to field-level changes. The GeoFootprint Project aims to bridge this gap by providing brand owners with enhanced visibility of on-the-ground impacts to better manage their supply chains and foster greater collaboration between key players across the value chain.
Another key aim of the project is to democratize access to climate risk information with a focus on land use and agriculture. A significant portion of the data, as well as the web platform’s overwriting function, will be made publically available to encourage all relevant stakeholders to engage in the decarbonization of the agricultural sector.
To learn more about the GeoFootprint Project, how it will shape the future of impact accounting and agriculture, or how to participate, contact Carole Dubois.