Identifying context-specific and stakeholder-driven strategies for the agroecological transition in Northern African Living Labs

Submitted by marilena.reinhard on
    Organizational Context
    Name
    Marilena Reinhard-Kolempas
    Chairgroup
    Plant Production Systems
    Graduate school
    PE&RC
    Start date of project
    Abstract

    Northern African countries of the Mediterranean show a significant degradation risk of their natural resources and a high vulnerability towards climate change. The agroecological transition of food systems has been suggested to increase their sustainability and resilience towards climate change, ecosystem degradation and food crises. This transition entails a redesign and management of food systems based on ecological concepts and human values. Due to the ecological and social complexity of food systems and their uncertain future development, there are no ‘one-fits-all’- recipes for the agroecological transition. Interdisciplinary and participatory research is needed to develop appropriate, context-specific agroecological strategies and to evaluate their performance at various spatial and temporal food system scales.

    My PhD thesis follows the objective to co-design potential agroecological transition strategies with farmers and other stakeholders in the context of Northern African Living Labs in the context of the EU Horizon project NATAE (North African Transition to AgroEcology, https://www.natae-agroecology.eu/). Specific objectives are to identify place-based and stakeholder-driven agroecological interventions and to assess their effects on the sustainability and resilience of North African food systems (transformation knowledge), integrating both local and scientific knowledge sources and quantitative and qualitative methods. Qualitative methods comprise among others photo-elicitation, semi-structured interviews and focus group discussions. Quantitative methods include participatory ranking and multi-criteria assessment activities, agronomic experiments, and modelling. The used methods will be evaluated for their contribution to build transformational capacities among system actors (transformative knowledge).

    Who's collecting the data

    The data will be collected by me in collaboration with the local project partners in the Moroccan case studies.

    Additionally, farm household survey data are collected by the local project partners and share with NATAE project partners, including WU, in a pseudonymized database.

    Who's analysing the data

    Data analysis will be done by me with the feedback of my supervisors and in collaboration with NATAE project partners

    Location short term storage

    All data will be stored on my local harddisk in a folder called Thesis.

    Within this Thesis folder, I'll create per chapter the folders: DataModelPaper and Scripts. The Data folder has two sub-folders called: Raw and Processed.

    Folder contents:

    • Data - Raw sub-folder: Contains all raw data and meta-data (a description of your data).
    • Data - Processed sub-folder: Contains all processed data. 
    • Model folder: Complete listing of the model and the model results & analysis.
    • Paper folder: Text of a chapter / paper.  
    • Scripts folder: Contains all scripts used.
    Backup procedure

    The complete content of my local Thesis folder will be stored on my YoDa-drive. 

    During periods I'm abroad, I'll backup the complete content of my local Thesis folder to a Dropbox or MS-Onedrive Thesis folder and share the contents with my supervisor(s). 

    Research data with value for long term storage

    All final datasets and scripts used for my project, project reports, analysis reports, publications, posters.  

    How to access data once you leave?

    At the end of my PhD project my data will be made publicly available online if possible, at least my supervisors will receive a full copy of my data

    Other persons contributing (e.g. writing code)

    Collaboration with CIHEAM-IAMM: optimization modelling

    Collaboration with ENAM: experimental design and management, experimental data collection and analysis