Sustainable intensification through optimization of maize-pigeonpea/lablab cropping systems in Northern Tanzania

Submitted by esther.mugi on
    Organizational Context
    Name
    Esther Mugi
    Chairgroup
    PPS
    Graduate school
    PE&RC
    Start date of project
    Abstract

    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.

    Role supervisor

    Promotor: Prof. Ken Giller

    Co-promotor: Prof. Niels Anten

    Co-promotors/ daily supervisors: Dr. Lammert Bastiaans and Dr. Shamie Zingore

    Who's collecting the data

    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.

    Who's analysing the data

    All data analysis will be done by the PhD student with support from supervisors.

    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: DataModelPaper 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

    This include datasets used for my project,  publications and posters

    Research data excluded for long term storage. Why?

    None

    Plans for sharing data?

    Efforts will be made to ensure all data is available publicly.

    How to access data once you leave?

    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.

    Specific funders requirements for sharing data, or to impose embargo?

    None

    Other parties involved? Agreements on data sharing?

    None

    Other persons contributing (e.g. writing code)

    None

    Other persons with specific responsibility for data?

    No

    Privacy, security issues? How you deal with them?

    Any sensitive data will be excluded from datasets that will be availed publicly