Periodic Reporting for period 1 - REFOREST (Agroforestry at the forefront of farming sustainability in multifunctional landscapes in Europe)
Reporting period: 2022-07-01 to 2023-12-31
In Europe, traditional agroforestry practices, combining trees with farming, have been sidelined by modern agriculture. However, the pressing need for sustainable food production, carbon capture, and biodiversity conservation is prompting a re-evaluation of agroforestry. The ReForest project aims to empower farmers across Europe to adopt agroforestry, addressing barriers and fostering innovation. Through an open science approach, the project seeks to bridge the knowledge gap, enhance digitalisation, and create an interactive platform for stakeholders. We use Living Labs and interaction with stakeholders to co-create metrics, financial tools, and efficient monitoring systems, ReForest aims to make agroforestry an attractive and economically sustainable choice for farmers. The project's holistic approach, focusing on education, key ecosystem services, and policy and financial innovation, aims to ensure a sustainable learning system for agroforestry, making it a cornerstone of modern agriculture and ensuring a resilient and sustainable food supply in Europe.
The REFOREST project has already achieved several significant milestones in advancing agroforestry in Europe. The co-creation and engagement platform is being promoted to a range of stakeholders and will serve as a hub for community building and knowledge exchange. The project has successfully completed the AF Co-creation Guidelines and Factsheets, engaging all partners in community-building. The Factsheets will be available via the Platform in a number of languages, making them directly accessible by farmers and advisors alike. The innovation network and ongoing networking impact assessments demonstrate the project's commitment to collaborative effort and to local innovation adapted to the specific environment. The conceptual graphical model of agroforestry systems has been approved, and efforts are underway to develop a dynamic management tool tracking innovation impact. They have started to adapt it to individual Living Labs. Establishing Living Labs and the kickoff workshops in each have laid the groundwork for data collection, knowledge typology reports, and socioeconomic value chain assessments. The development of the FarmTree Tool with climate projections, biomass to carbon indicators, biodiversity indicators, and multilingual support is nearing completion and enhances the understanding of agroforestry systems. The project's focus on creating datasets, neural network training, and remote sensing tools contributes to accurately predicting carbon capture and biodiversity potential. Progress in mapping value chains, adapting the Public Goods Tool, and exploring consultancy business models adds depth to the project. Project's outcomes to date emphasise the potential of agroforestry in achieving carbon neutrality and the need for a robust policy framework that integrates food systems, agriculture, forestry, and rural development for sustainability.
The ReForest project has achieved progress beyond the current state-of-the-art, with a key focus on developing essential tools. The co-creation and engagement platform serves as a pivotal hub for community building and knowledge exchange, enabling stakeholders to interact and share insights.
The FarmTree Tool has been successfully adapted for European conditions, including tree species characteristics. Non-experts can use the model to assess the impact of climate projections, biomass-to-carbon indicators, biodiversity metrics, and features multilingual support. This tool provides a comprehensive understanding of agroforestry systems and contributes to decision-making regarding tree-crop interactions.
The project emphasises creating datasets of remote sensing and ground data, facilitating the development of automated and semi-automated agroforestry assessment and verification capability. The neural network training dataset, a repository of remote images from agroforestry case study sites, is instrumental in assessing the performance and cost-effectiveness of various imaging platforms, ensuring the widespread applicability of the methodology.
The FarmTree Tool has been successfully adapted for European conditions, including tree species characteristics. Non-experts can use the model to assess the impact of climate projections, biomass-to-carbon indicators, biodiversity metrics, and features multilingual support. This tool provides a comprehensive understanding of agroforestry systems and contributes to decision-making regarding tree-crop interactions.
The project emphasises creating datasets of remote sensing and ground data, facilitating the development of automated and semi-automated agroforestry assessment and verification capability. The neural network training dataset, a repository of remote images from agroforestry case study sites, is instrumental in assessing the performance and cost-effectiveness of various imaging platforms, ensuring the widespread applicability of the methodology.