Organizational Context
Strategies to alleviate hunger and poverty among the rural poor often revolve around intensifying farming. Agricultural development projects employ a wide variety of interventions to achieve livelihood improvements, but not all farmers benefit equally and it has often been concluded that the poorest are left behind. The poor – vulnerable through their powerlessness and limited resources – are arguably prone to losing out in a competitive environment. For the poor to benefit from interventions, it is important to understand (1) what mechanisms lead to differentiated outcomes of interventions, and (2) whether these livelihood outcomes contribute to resilience, so that they are meaningful for vulnerable intended beneficiaries. The objective of this study is to describe and explain the distribution of benefits from diverse agricultural interventions, and to explore and design alternative intervention pathways with equitable and resilient livelihood outcomes. The literature review will provide an overview of diverse agricultural interventions and their unequal impacts. In the African study villages I will carry out interactive exercises with farmers to determine what interventions mean for their livelihoods, and how interventions influence access to resources and interdependencies. Locally relevant shocks and constraints will be identified through interviews. Agent-based modelling allows for exploring population properties that emerge from the behaviour of individuals: persons in a network with varying access to resources. Agricultural interventions and shocks will be introduced to the model population, to explore pathways towards equitable outcomes and resilience. A modified version of the model – a game – will be developed and tested with farmers, to facilitate co-design of agricultural interventions for a heterogeneous population.
Roles
The data will be collected by me.
Data analysis will be conducted by me, with feedback from supervisors.
Short and long term storage
All data will be stored on my local harddisk in a folder called Thesis.
Within this Thesis folder, 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.
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).
All final datasets used for my project, diagnostic and project reports, analysis reports, publications, posters. Also scripts will be stored for long term storage.
All privacy sensitive information will be removed from the data prior to storage/sharing/publication. All survey and crop trial data will be anonymized, with farmers identified by codes only. GPS coordinates of fields and households will only be shared on request by parties with legitimate interests in re-contacting the same farmers, and with the requirement that they in turn will anonymize any data they collect.
Sharing and Ownership
For all data and outputs, there is the intention to make these publicly available.
Data will also be shared with all the partners of the project: data related to the Africa RISING project will be archived at the Harvard Dataverse site, as final (cleaned, anonymized) data.
Data, scripts and models will be stored on my WUR Yo-Da drive.