WP1: Project Management & Stakeholder Involvement
AIM: Coordinating and managing project strategies and activities; establishing European-wide project-compatible stakeholder network
DESCRIPTION: ETH will coordinate and manage the project, promote efficient execution of all planned activities, oversee the work, ensure proper communication among the project partner, amongst many others. ELO, on the other hand, will lead the stakeholder involvement part.
LEAD: ETH & ELO
WP2: Natural European Climate Gradient Progression
AIM: Provisioning of regional climate projects; identification of European ecoregions with forecasted climate progression; final selection of sites via stakeholder engagement.
DESCRIPTION: Plain raw data retrieved from Copernicus, one of Europe’s largest investment programmes, gains value once analysed and enriched with data. The Copernicus Climate Change services collect a pool of data to monitor the climate and predict how it will change in the future, with applications assessing and mapping the continuing northward migration of agro-climate zones in Europe over the past decade as well as forecasting such shifts over the next decades. This information can be utilised to examine climate change impacts across humanity activities, for example in the case of MICROSERVICES in regards to soil microbial diversity across shifting climatic zones.
NOA is developing regional climate models to identify climatically distinct ecoregions across the continent, where forecasted progressions in climate change predict that a specific ecoregion will become climatically significant to another one in the future to the northward migration of climatic conditions. Hopefully, within each ecoregion, multiple wheat sites will be identified that also cover – as far as possible – different agricultural management regimes.
WP3: Wheat Microbiomes, Crop-Microbiome Interactions, and Ecosystem Multifunctionality Patterns
AIM: Assessing diversity of wheat-associated microbiomes, wheat microbiome interactions, and ecosystem multifunctionality across the climate gradients.
DESCRIPTION: Exploring and understanding cases in which microbiome data can be integrated into ‘smart farming’ practices is only just beginning. Following the advent of next-generation DNA sequencing, researchers have started characterizing microbial communities associated with wheat and other crops: soil type, management regime, and crop genotype have been shown to shape these microbiomes. Climate change is also another factor affecting plant-microbe interactions, in the form of altering carbon and nitrogen fluxes, with such interactions turning modulate resistance and resilience of crops towards climate change stressors.
However, one major limitation of studies on the effects of climate change on microbial diversity and abundance is that they ignore region-specific climate change traits and the interactive aspects of microbial communities, crops, and ecosystem functions and services.
Soil samples collected across the continent will be sent to WP leader, INRAE (French National Institute of Agricultural and Environmental Research) in Dijon for downstream analysis. Further analysis will be split between ETH (Swiss Federal Institute of Technology) Zurich in collaboration with LEITAT Technological Center, as well as UoK (University of Kassel). Combined, these analyses will assess ecosystem multifunctionality, covering several ecosystem services (e.g. food and fibre production, soil fertility and nutrient cycling, climate regulation etc.). Statistical analysis will be performed to assess biodiversity shifts and ecosystem multifunctionality.
WP4: Drought Simulation Effects on Crop-Soil-Microbiome Nexus under Different Management Regimes
AIM: Assessing the impact of simulated drought on the crop-soil-microbiome nexus in a long-term agricultural field experiment; evaluating the capacity of organic agricultural practice to increase resistance and resilience of the crop-soil-microbiome nexus towards drought.
DESCRIPTION: Intensive industrial agriculture has led to an oversimplification of agricultural ecosystems and a decrease in soil fertility across Europe, resulting in a loss of aboveground biodiversity and the streamlined flow of nutrients and energy through the system. In the context of climate change, conservational practices (ones that enhance soil biodiversity and its functions through agroecosystem diversification) also aim at increasing climate resilience (adaptation) and reducing the contribution of agricultural practices to greenhouse gas emissions (mitigation).
Led by Agroscope, the impact of drought under conventional and organic farming regimes will be simulated in rainout-shelters within the DOK agricultural long-term trial in Switzerland.
The DOK is an experiment that compares biodynamic, organic, and conventional cultivation of arable crops (i.e. wheat, potatoes, maize, etc.) since 1978 at the same site. This provides a unique opportunity in assessing the long-term impacts of various agricultural practices on various ecosystem functionalities. The DOK was chosen to assess the impact of drought as an important future climatic event linked to climate change on the crop-soil-microbiome nexus. Rainout sheltering will take place during the main growing season.
Soil samples from the DOK will be analysed in coordination between ETH, INRAE, and LEITAT.
LEAD: UoK & AGROSCOPE
WP5: Machine Learning Predictions on Biodiversity and Ecosystem Services Based on Climatic Shifts
AIM: Developing a machine learning-based regression model to pave the way for future tools to accurately forecast region-specific soil biodiversity dynamics and cascading effects on key ecosystem services in agricultural systems under future environment conditions.
DESCRIPTION: Artificial intelligence in the form of machine learning algorithms is enabling real-time diagnostics and forecast of crop physiological conditions based on climate and soil physiochemical data. However, there are very few studies on assessing the potential of such algorithms being used to predict crop productivity thanks to soil microbiome parameters. MICROSERVICES will evaluate the potential of machine learning to combine region-specific climate change models, rhizosphere, microbiome diversity, adaptive plant traits, and ecosystem services data to generate an algorithm capable of forecasting the effect of future environmental scenarios to ecosystem functionality. Further down the line, policymakers and key stakeholders across jurisdictions can benefit from this information by obtaining predictions of biodiversity shifts and their resulting consequences for agriculture under future climatic scenarios.
Data across the various work packages will be harmonised and formatted for subsequent analysis by LEITAT alongside NOA. A single data structure will be created, containing information about soil sites, climate, biodiversity, and ecosystem multifunctionality. Patterns and correlations defined among region-specific climate conditions, microbial diversity, and ecosystem multifunctionality will be used as a starting point for modelling a predicted regression algorithm. The accuracy in predicting ecosystem services dynamics in relation to climate conditions and microbial diversity will be validated with data from the work done at the DOK in Switzerland.
WP6: Dissemination, Outreach, and Policy Impact
AIM: Disseminating research findings to the scientific community and stakeholders; transforming project results to impact policy agendas;reaching out to the public to increase awareness among society.
DESCRIPTION: WP6 will make the results operative and accessible for a wide range of stakeholders by focusing on communication and outreach in a collaborative framework. ELO (the European Landowners’ Organization) will lead this work package. Alongside stakeholders and policymakers, MICROSERVICES project partners will impact ongoing policy debates on climate action by highlighting both the short- and long-term implication of MICROSERVICES results. These will then be implemented at the national and European level through a wide range of channels and tools.
LEAD: ETH & ELO
In this section, you can see a rough outline of key moments of the project, and by when we expect to get them completed:
|Project Milestone||Estimated completion by*|
|Project kick-off meeting (held virtually due to COVID-19 restrictions)||April 2021|
|Installation of rain-out shelters in the DOK in Switzerland||November 2021|
|Establish regional climate models and climate gradients thanks to Earth-Observation data||December 2021|
|Collect samples across the site network||August 2022|
|Collect samples in the DOK experiment completed||September 2022|
|Generate data and analyse samples from the various climate gradients||March 2023|
|Generate data and analyse samples from the DOK in Switzerland||March 2023|
|Generate patterns and correlation amongst climate, biodiversity, and ecosystem multifunctionality according to the gathered data||August 2023|
|Validate the machine-learning based prediction regression model||February 2024|
|Publish academic articles based on the interpretation of the results of the climate gradient analysis||February 2024|
|Publish academic articles based on the interpretation of the results of the drought simulation in the DOK analysis||February 2024|
|Disseminate project findings to a wide range of stakeholders from policymakers to the public||February 2024|
|Present final results and larger implications of the project to stakeholders, policymakers, and scientists at a wider symposium||February 2024|