Famers' choices in applying agroecological intensification options in southern Mali

By eva.huet , 18 June, 2018

Organizational Context

Name

Eva Huet

Chairgroup

PPS

Graduate school

PE&RC

Start date of project

Thu, 12/01/2016 - 12:00

Abstract

In the cotton zone of southern Mali agricultural activities are the main source of income for households. This agricultural system is pressured by increasing climatic variability, demographic pressure and a degrading environment. Agroecological intensification (AEI) is seen as a promising pathway to increase food production, to be resilient to climate stress, without causing environmental degradation. Past research tailored AEI options to farm types, but did not yet consider intra-household dynamics that influence decision making. Furthermore, the adoption of new technologies is not only guided by profitability but also by the perceived risks of their use, and hence adoption rates are often disappointing.

This research focuses on the choices farmers make about the use of AEI options, and their performance, in the light of the different risks farmers are facing. Both diversity within farms and between farms will be considered, since differences in constraints and opportunities might lead to other management strategies. Understanding these dynamics generates suggestions to inform farmer decision making, and insights for participatory research for development projects.

Survey information is combined with farm observations and trials, based on the DEED (Describe, Explain, Explore, Design) framework. Every season on-farm trials and demonstration fields are designed jointly by researchers and farmers to assess performance of AEI options according to several criteria. The results are discussed in village meetings, to stimulate co-learning. This data, and data from similar trials in previous years, is used as input in ex-ante modelling tools simulating performance of AEI options in an environment subject to risks.

Roles

Role supervisor

Katrien Descheemaeker, daily supervisor; Ken Giller, supervisor

Who's collecting the data

The PhD candidate is conducting research as part of a McKnight project: ' Pathways to agroecological intensification of crop-livestock systems in southern Mali', of which phase I took place between 2012-2015 followed by a second phase (2016-2019). The partners involved are WUR, ICRISAT, CIRAD, IER, AMEDD. The PhD candidate is also involved in AfricaRISING activities in Mali.

Agronomic trial and survey data is collected by the PhD candidate directly, AMEDD and ICRISAT technicians or by enumerators supervised by the PhD candidate. In case of joint activities, data collection will be organised together with the other PhD candidate within the project, Arouna Dissa.

Livestock trials are supervised by IER. Other collection of information and data within the project is a shared responsibility between all partners.

Who's analysing the data

All analyses will be done by the PhD candidate, with support by ICRISAT. Data analysis will be done in R. In case of joint activities, data analysis will be organised together with the other PhD candidate within the project, Arouna Dissa.

Short and long term storage

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 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

All final datasets used for my project, diagnostic and project reports, analysis reports, publications, posters. Also scripts will be stored for long term storage.

Research data excluded for long term storage. Why?

Some data will remain the property of different organisations who have obtained the data and are thus owners. These data 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.

All names and contact details from farmers will be removed from the dataset for privacy reasons.

Sharing and Ownership

How to access data once you leave?

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

Other persons contributing (e.g. writing code)

For the modelling exercises with FARMSIM, support is available from PPS (Mink Zijlstra, Willem Hekma)

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, AMEDD). APSIM, QUEFTS and FARMSIM models have been developed at and/or are available through WUR.

Privacy, security issues? How you deal with them?

All privacy sensitive information will be removed from the data prior to storage/sharing/publication. 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.