Organizational Context
Soybean is increasingly becoming a new cash crop in Malawi following government intervention to discourage tobacco production a former long-term cash crop in Malawi. Soybean has high potential for intensification in Malawi to close prevailing high yield gaps associated with crop diseases, poor soil fertility, poor varieties and climate change.
As an intervention, CGIAR has introduced agronomic innovations and advisory tools in soybean growing areas of central Malawi including planting windows, location specific fertilizer rates and advisory tool/systems. However, there is limited empirical evidence on implications of innovations on labour dynamics yet labour is an important factor of production especially in former large tobacco producing areas like central Malawi. This study therefore seeks to analyse soybean systems and implications of new crop, soyabean, and its management practices/innovations and the tool on labour dynamics to inform advisory systems in central Malawi. The study will characterize soybean farming systems focusing on historical trends and existing labour-based farm types. Also, the study will explain current farm labour dynamics focusing on labour requirements, availability, allocation, relations, and bottlenecks across crops, and operations, and time. In addition, this study will explore implications of innovations and advisory tool on labour dynamics, profitability and innovations adoptability across labour-based farm types, crops and gender groups. Furthermore, the study will co-design tailored innovations and advisory tool with stakeholders and farmers.
Roles
I have three supervisors on this Ph.D. Their names and responsibilities are highlighted below:
1. Katrien Descheemaeker: the promotor
2. Jens Andersson, the daily supervisory and co-promotor 1
3. Elke Vandamme, the co-promotor 2
Hyacinthe Nyirahabimana
Hyacinthe Nyirahabimana
Short and long 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.
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 the data from survey interviews and focused groups disscussions, scripts, and results.
None.
Sharing and Ownership
The data and outputs from the data will be shared to the relevant partners, University websites and journals.
All the data will be stored at YoDa: https://phd.pps.wur.nl/yoda-your-data-drive. Row and processed data will be stored in their specific files as recommended by PPS and WUR.
In addition, the data will be stored at 4TU for long-term storage.
The data collected by the Ph.D student should be store and made accessible to the funder International Potato Center (CIP) if possible in electronic form.
There are no other parties involved in the data sharing at the moment.
Privacy of the persons involved will be respected. Privale information and sensitive information like, names, photoes, videos, among others that could be taken in this research will not be publicised. Two data folders will be developed whereby personal data will be anonymised in the publicly accessible folder while the original raw data will be kept private for possible future project follow-up on the households and research continuity.
In addition, respondents will willingly participate in the surveys, FGDs, or any other data collection of this study after signing a concent form that will be provided to them.