Organizational Context
The challenges facing smallholder farmers in Burkina Faso are many, including poor soil fertility combined with unpredictable weather conditions and weak market infrastructure. Several farming options and practices including agro-ecological options may aid smallholder farmers to withstand climate hazards and reverse soil degradation, potentially improving farming incomes and food security. To date, the limited success of many of these practices keeps production at low levels. This situation increases farmers’ exposure and limit their adaptive capacity that contributes to keeping them in poverty trap. The present study will explore the role of agro-ecological intensification options tailored to farmers’ specific contexts as a means to reduce farmers’ vulnerability and build their resilience to climate change and variability in the semi-arid and sub-humid area of Burkina Faso. To achieve this, resilience of farm systems under both present and future scenarios will be considered. Four research objectives organised in three phases are pursued in this research. The descriptive and diagnostic phase consists of two research objectives. The first objective will investigate the relationship farmers’ suitability criteria and their perception of climate risks and different farm types (objective 1). The second objective of this phase will assess farm system resilience under current conditions to reveal where interventions are needed (objective 2). Building on the outcomes of objective 2, in the following explorative phase, a dynamic model will be developed and used to explore the effects of different agro-ecological options for future resilience evaluated under various climate scenarios (objective3). The last phase will tailor local options and innovations to different farm types through three years of iterative interaction with farmers, farmers’ associations and extension workers (objective 4). This objective will consider findings from all the previous objectives.
Roles
Katrien Descheemaeker has expertise in crop-livestock systems, modelling and participatory approaches. She is the promotor of the PhD. David Berre has experience in Agronomy, Agricultural economics and modelling. He is the daily supervisor. Heidi Webber has expertise in Agronomy and crop modelling. She will help with the modelling aspect in Chapter 2 and 3.
I, the PhD student is responsible for collecting and managing data for my PhD research in collaboration with my supervisors. Some field technicians and interns will also help with the data collection under my supervision I may also use data collected by other organizations involved in the project. At the end of my PhD work, my data will be shared with my supervisors and made publicly available online if possible.
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Type of research data |
Specification |
Software choice |
File extension |
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Digital Maps |
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Study area location maps |
I will use the BNDT data to generate study area maps |
ArcGIS or QGIS |
.TIFF |
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Field data |
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Baseline data |
Those data were collected at the start of the project to characterize the sampled household. Collected data were about household socioeconomic data, agricultural and livestock and climate change perception data |
Excel |
.xls |
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Agronomic trial data |
Agronomic trial data are being collected yearly during the growing season. These data include crop yield, striga, phenological data and characterization and itinerary follow up data |
Excel |
.xls |
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Household survey data |
It is collected once on household risk perception and their criteria in selecting agroecological cropping systems. |
Excel |
.xls |
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Group discussion data |
Group discussion data were collected. They include evaluation of the tested options, takit game to identify criteria, identification of main perceived risks |
Excel |
.xls |
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Literature review summaries
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Will be done by me |
Word |
.docx |
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Method description |
Will be done by me |
word |
.docx |
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Paper’s draft |
Will be done by me (draft manuscript of the papers) |
word |
.docx |
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Others |
Administrative and course work documents |
Adode, word, Excel |
.pdf, .docx, .xlsx |
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).
Research data with values for long term storage may includes data used in writing papers for publication. The targeted journal may require storing the data in a given repository to make it available for crosschecking. Other repository for long term storage well knows by the WUR are DANS-EASY and 4TU Centre for Research Data. In any of the cases the WUR library will provide support in the data preparation and submission
Personal data
Data that other organizations working in the project did not give their authorization to share
Sharing and Ownership
Data sharing policy within the Fair_Sahel project is being developed but not yet completed.
I expect that others may want to use my data; therefore, I intend to make my data publicly available after the publication of my first paper on it.
The main funder of this project is the European Union which does not impose embargo but require that data should be store in a data verse for public use after sometimes.
The funder of the project requires to take all necessary measures to ensure the protection and the confidentiality of private data.
Organizations involved in the project which contributed to the data collection (INERA, AMSP, CIRAD, ZALF).
The funder of the project requires to take all necessary measures to ensure the protection and the confidentiality of private data.