Organizational Context
Many smallholder farmers in sub-Saharan Africa practice intercropping. To enable sustainable agricultural intensification, there is need for the intercrops to be optimized so as to enhance efficient use of available resources, which can be aided by spatio-temporal niche differentiation and facilitation of companion crops. In Northern Tanzania, smallholder farmers frequently intercrop maize with legumes such as pigeonpea or lablab. There is however insufficient understanding of the performance of these intercrops on-farm in contrasting agro-ecological zones (AEZs) and how they are influenced by socio-economic and biophysical factors of a farm household. There is also inadequate information on how the systems can be optimized to achieve multiple benefits from legumes both in the short term (e.g. legume grain yields) and in the long term (e.g. soil fertility) at minimal grain loss of the main crop (in this case maize). Management options for optimization that will be investigated in on-farm trials include choice of legume species, different crop varieties and nutrient status. The study therefore seeks to analyse the performance of pigeonpea\ lablab – maize intercrops and will combine farm surveys and on-farm trials with crop growth models on smallholder farming systems in contrasting AEZs of Northern Tanzania. The outcomes will inform intensification in Tanzania along sustainable trajectories for positive social, economic and environmental impacts.
Roles
Promotor: Prof. Ken Giller
Co-promotor: Prof. Niels Anten
Co-promotors/ daily supervisors: Dr. Lammert Bastiaans and Dr. Shamie Zingore
Survey data will be collected by the PhD student together with the SIIL/ TAMASA survey team in Tanzania.
Experimental data will be collected by the PhD student.
All data analysis will be done by the PhD student with support 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, 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 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).
This include datasets used for my project, publications and posters
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Sharing and Ownership
Efforts will be made to ensure all data is available publicly.
Through archiving all datasets in the PhD library site of PPS where I will upload per thesis chapter the raw data with detailed metadata, model source, codes and scripts.
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Any sensitive data will be excluded from datasets that will be availed publicly