Measuring and tracking adaptation to climate change across scales in African Agriculture

By marcel.lubbers , 11 December, 2023
Organizational Context
Name
Andreea Nowak
Chairgroup
Plant Production Systems
Graduate school
x
Start date of project
Abstract

This research project focuses on measuring and tracking human-led adaptations to climate change across spatial and temporal scales, in the context of African agriculture sectors. It aims to explore if and how adaptation is taking place, who benefits from the outcomes, by how much, and how outcomes might change under different scenarios. The PhD project is organized into four chapters, which will explore the following research questions:

  1. How is climate adaptation measured and tracked at national scales? What types of adaptation pathways and priorities are being pursued by African governments? What indicators and data systems are used to track progress on adaptation in agriculture sectors? 
  2. How has climate adaptation been measured and tracked at local scales? What is the existing empirical evidence on outcomes from African smallholder farmers’ adaptations to climate? What are key adaptation pathways and how are they measured and quantified? 
  3. What outcomes emerge from climate adaptations undertaken by small-scale farmers in mixed rainfed systems in Senegal and Zambia? What are real and perceived adaptation outcomes from adaptations and the pathways to achieve them? What are the strengths and weaknesses of different approaches to track adaptation at farm and local scales? 
  4. Are small-scale farmers’ adaptations effective under different climate, socioeconomic and management scenarios? How will outcomes change under different conditions, who will benefit, and how? How can farm- and local-level measurements support national adaptation tracking efforts?  

Role supervisor

Dr. Pytrik Reidsma (main promotor), 

Prof. Dr. Julian Ramirez-Villegas (promotor), 

Dr. Todd Rosenstock (co-promotor/ daily supervisor)

Who's collecting the data

The research will use different types of data. Primary data will be collected by the PhD candidate through field surveys, focus group discussions, and key informant interviews. Field surveys will be conducted using Open Data Kit (ODK) software. The option of mobile phone surveys will also be explored. Secondary data will be collected from peer-reviewed publications indexed in major relevant databases, as well as from the grey literature, i.e., policy documents available in public domains. The research will also use data from publicly available datasets on climate, agriculture data, socio-economic data, etc

Who's analysing the data

Andreea Nowak

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 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

As strategy for long-term storage and accessibility, all data underlying publications of this research project (data processed, analyzed, source code, and metadata) will be published on CIAT / Harvard Dataverse (open access), to enable verification of the research and to facilitate accessibility and reusability of the data. This online repository also allows allocation of a digital object identifier (DOI), to identify the dataset permanently and ensure that user can locate the material. Code will also be stored in GitHub as a long-term strategy. Once the research is complete, the data underlying publications will also be registered in Pure, based on the publications’ DOI, as per WUR data policy.

Research data excluded for long term storage. Why?

None

How to access data once you leave?

All data underlying publications of this research project (data processed, analyzed, source code, and metadata) will be published on CIAT / Harvard Dataverse (open access), to enable verification of the research and to facilitate accessibility and reusability of the data. This online repository also allows allocation of a digital object identifier (DOI), to identify the dataset permanently and ensure that user can locate the material. Code will also be stored in GitHub as a long-term strategy. Once the research is complete, the data underlying publications will also be registered in Pure, based on the publications’ DOI, as per WUR data policy.