TUdi Decision Support Tool to assist farmers in soil health management

• DOI: 10.5194/egusphere-egu25-12364
• Publication Date: 2025-03-15T00:43:01Z
• Authors: María-Llanos Lópe...; Elena Pareja; Iria Benavente-Fe...; Anna Osann; Javier Sánchez; Tomas Dostal; Josef Krasa; Raquel Falcão; Laura Zavattaro; Peter Strauss; Gunther Carl Lieb...; José Alfonso Gómez; Gema Guzmán; Ignacio Domenech; Csilla Hudek; Zsófia Bakacsi; Dimitre Nikolov; Xiaoping Zhang

The sustainable management of crops in areas at risk of soil health degradation is crucial, particularly given their vulnerability in the current context of climate change. Decision Support Tools (DSTs) designed specifically for farmers are essential for assessing risks, analyzing the impact of agricultural practices, and defining strategies to mitigate negative impacts on soil health. In response to this need, the TUdi DSTs were developed (in app and web format), integrating functionalities tailored to address different types of soil degradation processes by different approaches related to soil biology, erosion, compaction, structure, organic carbon and fertilization. These DSTs are designed to restore and enhance soil health, and to optimize the use of fertilizers at the user level.However, in scenarios of high soil health degradation, the tool’s results often highlight negative outcomes, potentially leading to rejection in its adoption. It is therefore crucial to assess user acceptance of such tools in advance. To tackle this challenge, in-person workshops were conducted, engaging both farmers and stakeholders from the agricultural sector. These workshops enabled the evaluation of the TUdi DST's acceptance, and the identification of improvements aimed at optimizing its usability and fostering its broader adoption. These efforts aim to ensure that the TUdi DST becomes an effective tool for supporting farmers in sustainable soil management, while contributing to the mitigation of climate change impacts on vulnerable agricultural systems.Acknowledgments: this work was supported by the research project TUdi (Horizon 2020, GA 101000224).