Leveraging long-term field experiments and other datasets to develop AI-ready decision support systems for sustainable soil management

Expected Outcome: Activities under this topic will help progress towards the objectives and targets of the Mission Soil and the EU Soil Strategy for 2030 . Activities should also contribute to the implementation of the Soil Monitoring and Resilience Directive after its adoption. Project results are expected to contribute to all the following expected outcomes: enhanced adoption of impactful sustainable soil management solutions and strategies supported by AI-powered decision support systems by land managers; harmonised, standard, robust, interoperable and accessible methods, protocols and logical architecture for long-term field experiments (LTEs) data collection and integration (including with other datasets) are in place; scientists, policymakers, and land managers gain enhanced access to comprehensive, high-quality soil data, enabling better research, informed decision-making, and effective land management practices. Scope: Long-term field experiments (LTEs) can be defined as “agricultural experiments for monitoring soil and crop properties under changing climate conditions and different management with a minimum duration of 20 years” [1] , [2] . LTEs are typically owned or managed by public research institutions. Long-term field experiments provide valuable information on soil health and sustainable soil management practices and can be considered critical infrastructure for agricultural research. However, LTEs present some limitations or needs to maximize their impact. There is a need for standardized methods in collecting and reporting soil data to ensure consistency and comparability across different studies and regions. Opportunities exist to integrate soil data from long-term field experiments with other data sources (including the Mission Soil projects results, but not only) to provide a more comprehensive understanding of soil health dynamics and trend, as well as response to policies and management strategies. Enhancing the accessibility and interoperability of soil data across platforms and sources can facilitate collaborative research and accelerate advancements in soil health management. More high-resolution temporal and spatial data are needed to capture short-term soil dynamics and site-specific variations that can influence broader interpretations of soil health trends. On the other hand, independent advisory services on soil health for land managers often face challenges such as limited access to comprehensive data, variability in expertise, and the inability to provide tailored recommendations specific to diverse local conditions. These services may struggle with integrating complex and dynamic factors that influence soil health, leading to generic advice that may not effectively address specific needs. There is an opportunity in utilizing cutting-edge technologies such as machine learning and artificial intelligence (AI) to analyse vast, complex soil data sets, extract meaningful patterns, and develop predictive models that can enhance the quality of advice provided, improving decision-making, and fostering more effective, sustainable soil management practices. Proposed activities should: design and implement standardised protocols and procedures for harmonised soil data collection, ensuring consistency and comparability, from different LTEs and regions across the EU and Horizon Europe Associated Countries; develop robust frameworks for integrating LTE data with other relevant soil health datasets, including outputs from Mission Soil projects, to create comprehensive soil health databases; develop open-access and user-friendly interoperable systems and platforms to improve data sharing and accessibility, allowing researchers, advisors, land managers and other stakeholders to easily access and utilize comprehensive soil health information; build a network of at least 50 LTEs covering most representative pedo-climatic regions in the EU and Associated Countries involving at least 7 owning institutions, to test and validate the developed infrastructure; promote the use of the developed infrastructure for widespread collection and integration of as many as possible soil-health relevant databases (LTEs and others) by, for example, developing intuitive interfaces and user-friendly platforms, partnering with relevant organizations managing LTEs and/or generating datasets, demonstration projects, feedback and improvement loops or training and support services; develop AI-driven tools to analyze integrated datasets (including publicly available such as CORDIS, SoilWise repository or repositories like Zenodo), extracting meaningful patterns, and generating predictive models that inform soil health dynamics and management strategies; examine potentially correlated explanatory covariates and their relative contribution to the outcome to facilitate spatial downscaling and forecasting in data poor regions and areas by using pre-trained deep learning models; develop and train open-source and/or modular AI components, providing comprehensive documentation and tutorials, and establish and nurture open-source communities by, for example, hosting hackathons, workshops, or online platforms to encourage the development, sharing, and integration of the developed modular AI components into commercial applications for land managers and advisors, with a focus on small-scale producers; mine large data from publicly available databases (e.g. CORDIS, SoilWise repository or repositories like Zenodo) to pre-train deep learning models and artificial intelligence mobile apps that will facilitate real-time soil status assessments. The project(s) must implement the multi-actor approach and ensure an adequate involvement of the primary production sector and all relevant actors (landowners, farmers, scientists, advisors, local/regional/national public authorities) throughout the different stages of project development and implementation. Beneficiaries may provide financial support to third parties (FSTP) to incentivise and support third-party developers to create or improve innovative AI-powered applications that deliver tailored advice to farmers and advisors, enhancing soil management practices and benefiting small-scale producers. Proposals should build on the work done by the SoilWise project and collaborate with the EU Soil Observatory . Proposals should include a dedicated task and appropriate resources to collaborate with other Mission Soil relevant projects developing soil information systems, in particular project African Union Soil Observatory (AUSO), and other projects that are being funded by other entities in the EU, Horizon Europe Associated Countries and in Africa, including philanthropic organisations. Participation of African organizations is encouraged. [1] Bonares_Series_2023_1_7ss0-zm41_V1.1.pdf [2] SOIL - Long-term field experiments in Germany: classification and spatial representation

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