Organizational Context
This project will focus on farming systems in the Bougouni area of Southern Mali. These farming systems rely on a large land base to allow for long fallows, as well as to provide grazing areas for livestock and forest products. This project aims to characterize these farming systems and their evolution, and to work with farmers to identify pathways for continued intensification that lead to improved farm productivity and livelihoods while preserving the natural resource base. We will describe the current farming systems and their evolution using farm characterization and monitoring data, both existing historical and newly acquired. The relationship between farming system change and land use change will be assessed by combining household level data with remotely-sensed estimates of the evolution of cropland distribution. We will determine village-scale fodder productivity by assessing biomass production in rangeland and cropland and analyze its use with animal census and grazing data. Using farm characterization information we will explore options for farm-scale intensification for different types of farms and under a variety of future scenarios. The impact of different farm management practices and institutional changes such as local conventions on the natural resource base at village scale will be explored using participatory modeling techniques. These village-scale models will allow the study of interactions between farm systems and land use changes, in order to design options together with stakeholders that minimize trade-offs between farm productivity and conservation of the extensive natural areas present in the study area.
Roles
Katrien Descheemaeker, daily supervisor
Data sources include: Suivi et Evaluation Permanent, collected by the Institute d'Economie Rurale, ESPGRN Sikasso, currently supervised by Ousmane Sanogo the IFPRI AfricaRISING Baseline Survey, collected under supervision of Carlo Azzari and Beliyou Haile AfricaRISING Market survey, conducted by Ousmane Sanogo, IER-Sikasso AfricaRISING Farm Characterization study, which I supervised with assistance from Gilbert Dembele, AMEDD-Mali On-farm trial data (plant and soil), collected by me with assistance from field technicians Biomass assessment data (plant, soil, and species ID), collected under my supervision by Gilbert Dembele, AMEDD-Mali GPS tracking of herd locations, collected by me with assistance from field technicians Qualitative focus group data, collected by me Landsat remote sensing imagery, collected and pre-processed by USGS
With the exception of pre-processing of remote sensing data I am conducting data analysis myself.
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 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).
All datasets used for my project, analysis reports, publications, posters.
None
Sharing and Ownership
Data will be shared where possible.
Data related to AfricaRISING (i.e. all datasets I collect or supervise collection of) will be archived at the AfricaRISING CKAN site, as final (cleaned) data. Data will also be stored in the PPS PhD library.
USAID requires sharing AfricaRISING data through the ILRI site CKAN.
See above.
I am writing my own data analysis and model code.
Datasets listed above with other collecting organizations (IER, IFPRI) are the responsibility of the persons listed.
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.