Combining farming systems and value chain analysis for agro-ecological intensification of smallholders’ crop-livestock farming systems in southern Mali.

By arouna.dissa , 19 June, 2017
Organizational Context
Name
Arouna Dissa
Chairgroup
Plan Production Systems (PPS)
Graduate school
PE&RC
Start date of project
Abstract

The population increase in southern Mali, in combination with complex challenges posed by the rainfed nature of agriculture, climate change and natural resource degradation, puts smallholder farmers under pressure to produce more food and improve their livelihoods. Agro-ecological intensification (AEI) holds potential for agricultural productivity, nutritious healthy ecosystems and better livelihoods. Indeed, the rising population offers opportunities to farmers to gain more income from increasing food demand, particularly in livestock and cereal value chains. Among the food value chain actors (e.g. producers, wholesalers, retailers and processors) farmers often represent a weaker or disadvantaged party in terms of resources and negotiation power, and their interests and priorities may be overlooked by their business partners and other stakeholders.

The overall aim of this study is to contribute to AEI and improving smallholder livelihoods through a better understanding of the role of collaboration and co-innovation among value chain actors of the cotton, maize, milk and sheep fattening; because, they can represent the diversity and complementarity of activities that people undertake for sustaining their livelihoods within the farming systems of the old cotton basin of Koutiala. The study will combine various concepts (farming systems, AEI, value chain and co-innovation) in iterative learning cycles with farmers and other stakeholders, using the DEED (Describe Explain Explore and Design) cycle. By so doing, it will focus on the selected value chains and AEI options that are tailored to the local specific context. Specifically, this study seeks to (1) investigate constraints and opportunities for the collaboration of farmers with other value chain actors; (2) adapt existing farm management tools that may foster smallholder market participation; (3) understand how the communication of farming systems research findings facilitates dialogue, negotiation and decision making among actors in VC; and (4) examine the contribution of co-innovation to the performance and functioning of smallholder farms.

Role supervisor

Dr. Katrien Descheemaeker (WUR) is the daily supervisor of the PhD research. Prof. Ken Giller (WUR) is the overall supervisor and the promoter of the PhD research. Dr. Ousmane Sanogo (IER), Dr. Jos Bijman (WUR) and Dr. Maja Slingerland (WUR) are involved in thematic research. For this, Dr. Ousmane Sanogo has expertise in crop-livestock farming systems of Southern Mali, he's involved in the adaptation of existing farm management tools (objective 2). Dr. Jos Bijman, as expert on horizontal and vertical coordination in agrifood value chains, is involved in the analysis of constraints and opportunities for the collaboration of farmers with other value chain actors (objective 1). Dr. Maja Slingerland has expertise in food security and development with farming systems as unit of analysis, she's involved in research of objective 1 and for understanding how the communication of farming systems research findings facilitates dialogue, negotiation and decision making among actors in value chains. 

Who's collecting the data

Most of the data will be collected by me (the PhD candidate). The PhD research is taking place in the frame of a research project called "Pathways to agro-ecological intensification of crop-livestock systems in southern Mali", of which phase I took place between 2012 and 2015, followed by a second phase (2016-2019). It's financed by the Mc Knight Foundation and implemented a set of partners composed of the WUR, ICRISAT, CIRAD, IER and ONG AMEDD. Technicians, interns and enumerators that will be recruited for the project activities will be involved, where possible, in data collection. Also data collected by the partners both from the first and the second phases will be used (under a shared responsibility of all of them). Some activities and data collection will be organized with another PhD candidate, Eva Huet. The PhD candidate is involved in the AfricaRISING project implementation in Mali, therefore some activities will be realized and data will be collected and shared for this project. 

Data on agricultural and livestock production in Mali may be received from governmental organizations (such as the Malian Company of textiles (CMDT), Regional Agricultural Department (DRA) and Regional Department Livestock and industrial Productions (DRPIA)).

Who's analysing the data

All the data will be analyzed by me (the PhD candidate) with assistance of my supervisors. Data will be analyzed using R as the scripting language. In case of joint activities, data analysis will be organized together with the other PhD candidate within the project, Eva Huet.

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: 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.

Backup procedure

The complete content of my local Thesis folder will be stored on my YoDa-drive.

During periods I'm abroad (mostly in Mali), I'll backup the complete content of my local Thesis folder to a Ms-OneDrive Thesis folder and share the contents. The Ms-OneDrive account is linked to my wur account (arouna.dissa@wur.nl). The content of this account should be transfered on my M-drive (once in Wageningen) or PPS backup server.

 

Research data with value for long term storage

Datasets, diagnostic and project reports used in my research, analysis reports, publications, posters generated, as well as the scripts will be stored for long term storage.

Research data excluded for long term storage. Why?

Data collected by different organisations and used in my research may be excluded from long term storage. Clear agreements regarding (long term) storage will be made with the owners of the data under confidentiality agreements.

Plans for sharing data?

As far as possible all data will be publically available in the PhD library website of PPS. 

How to access data once you leave?

Data will be stored in the PPS PhD library website. Data will also be shared with all the partners of the project. Data related to AfricaRISING project will be archived at the Harvard Dataverse site, as final (cleaned) data.

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

AfricaRISING requires data to be shared through the Harvard Dataverse site.

Other parties involved? Agreements on data sharing?

-

Other persons contributing (e.g. writing code)

People (Ousmane Sanogo (IER), Arouna Bayoko and Ousmane Dembele (ONG AMEDD)) who are involved in the implementation of the project.

Other persons with specific responsibility for data?

Data and information collected within the McKnight project are often a joint responsibility of all partner organizations (WUR, ICRISAT, CIRAD, IER, ONG AMEDD).

Privacy, security issues? How you deal with them?

All privacy sensitive data will be excluded from the datasets prior storing/sharing/publishing.