Integrated resilience assessment of farming systems: case-studies from Europe

By wim.paas , 13 June, 2018

Organizational Context

Name

Wim Paas

Chairgroup

Plant Production Systems Group, Business Economics Group

Graduate school

Production Ecology and Resource Conservation

Start date of project

Thu, 06/08/2017 - 12:00

Abstract

In a variable production context, European agriculture needs to become more resilient and sustainable. Where describing and measuring sustainability is well addressed in agricultural research, operationalizing the resilience thinking is more difficult. One reason is that resilience thinking needs the concepts of robustness, adaptability as well as transformability to properly describe dynamic behaviour under stress and shocks. Second, the indicators that describe actual resilience are hard to measure. To accommodate for this, resilience enhancing attributes (proxies for resilient behaviour) for farming systems have been proposed. However, the applicability of these attributes specified for robustness, adaptability and transformability in different contexts is not known.

This PhD-proposal aims to study resilience and identify resilience enhancing attributes of the essential functions of farms and farming systems for different case-study areas in Europe. Objective 1 aims to identify resilience enhancing attributes for arable farms in the Netherlands, highlighting two specific case-study areas. Objective 2 aims to identify resilience enhancing attributes for arable farming systems in the Netherlands. For objective 1 and 2, a statistical approach is taken depending on available (national) datasets. Objective 3 aims to assess the impacts of shocks, stressors and resilience enhancing attributes on essential functions of farming systems for the current and future situations. Objective 3 takes a participatory approach and depends greatly on stakeholder input from 11 case-study areas in Europe. These 11 case-studies include both arable and livestock farming systems. Objective 4 aims to identify the safe operating space of farming systems in current and future situations. Objective 4 takes a quantitative modelling approach and includes a case-study with arable farming from the Netherlands and a case study from at least one other European country.

Roles

Role supervisor

Daily supervision will be done by the supervisors Dr Pytrik Reidsma and Dr Miranda Meuwissen. Prof. Martin van Ittersum is the overall supervisor and promotor of the PhD student. The full supervisory team will meet with the PhD student every month.

The role of Dr Miranda Meuwissen is in the area of resilience analysis, with an emphasis on financial aspects. Miranda Meuwissen is the leader of the SURE-Farm consortium, a EU 2020-Horizon project, in which this PhD-project is embedded. The role of Dr Pytrik Reidsma is in the area of integrated resilience assessment and system analysis (at farm, and farming system level). Pytrik is the work package leader to which this PhD-project is contributing to. Dr. Van Ittersum has expertise on methods for integrated assessment of plant production systems at multiple scales, including methods to assess input-output relationships of alternative production systems. If for some reason the composition of the supervisory team will change, appropriate substitution will be sought withing WUR.

Who's collecting the data

Individual farm data is available via Wageningen Economic Research (WEcR) and via the Directorate General for Agriculture and Rural Development (DG-Agri). Dr Pytrik Reidsma, myself and SURE-Farm consortium partners will collect additional data in participatory workshops in 11 case-study areas in Europe.

Who's analysing the data

All analyses will be done by me.

Short and long term storage

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: Data, Model, Paper 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 M-drive or if the amount of data exceeds 50 Gb on the backup server of PPS.

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

Research data with value for long term storage

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

Research data excluded for long term storage. Why?

Datasets with individual farm data from WEcR and DG-Agri, because they contain privacy sensitive information. Datasets from WEcR are only available via remote access; access stops after the research is finished. Datasets of DG-Agri will be deleted from my local hard drive after the research is finished, accordingly to the confidentiality agreement that I signed.

Sharing and Ownership

How to access data once you leave?

The PhD Library site of PPS, on this location as far as possible all data for each chapter of my thesis will be stored in a separate zip file.

The Models Library Site of PPS for storing the model data.

Other persons contributing (e.g. writing code)

To be determined

Other persons with specific responsibility for data?

I'm responsible for collecting and managing the data used in my research, in consultation with my supervisor. At the end of my PhD project my data will be made publically available online if possible, at least my supervisors will receive a full copy of my data.

Privacy, security issues? How you deal with them?

All privacy sensitive data will be excluded from my datasets.