Trajectories of agricultural change in southern Mali

Submitted by gatien.falconnier on
    Organizational Context
    Name
    Gatien Falconnier
    Chairgroup
    PPS
    Graduate school
    PE&RC
    Start date of project
    Abstract

    Smallholder agriculture in sub-Saharan Africa provides basis of rural livelihoods and food
    security, yet farmers have to cope with land constraints, variable rainfall and unstable
    institutional support. This study integrates a diversity of approaches (household typology and
    understanding of farm trajectories, on-farm trials, participatory ex-ante trade-off analysis) to
    design innovative farming systems to confront these challenges. We explored farm trajectories
    during two decades (1994 to 2010) in the Koutiala district in southern Mali, an area experiencing
    the land constraints that exert pressure in many other parts of sub-Saharan Africa. We classified
    farms into four types differing in land and labour productivity and food self-sufficiency status.
    During the past two decades, 17% of the farms stepped up to a farm type with greater
    productivity, while 70% of the farms remained in the same type, and only 13% of the farms
    experienced deteriorating farming conditions. Crop yields did not change significantly over time
    for any farm type and labour productivity decreased. Together with 132 farmers in the Koutiala
    district, we tested a range of options for sustainable intensification, including intensification of
    cereal (maize and sorghum) and legume (groundnut, soyabean and cowpea) sole crops and
    cereal-legume intercropping over three years and cropping seasons (2012-2014) through onfarm
    trials. Experiments were located across three soil types that farmers identified – namely
    black, sandy and gravelly soils. Enhanced agronomic performance was achieved when targeting
    legumes to a given soil type and/or place in the rotation: the biomass production of the cowpea
    fodder variety was doubled on black soils compared with gravelly soils and the additive
    maize/cowpea intercropping option after cotton or maize resulted in no maize grain penalty, and
    1.38 t ha−1 more cowpea fodder production compared with sole maize. Farm systems were redesigned
    together with the farmers involved in the trials. A cyclical learning model combining
    the on-farm testing and participatory ex-ante analysis was used during four years (2012-2015).
    In the first cycle of 2012-2014, farmers were disappointed by the results of the ex-ante trade-off
    analysis, i.e marginal improvement in gross margin when replacing sorghum with soybean and
    food self-sufficiency trade-offs when intercropping maize with cowpea. In a second cycle in
    2014-2015 the farm systems were re-designed using the niche-specific (soil type/previous crop
    combinations) information on yield and gross margin, which solved the concerns voiced by
    farmers during the first cycle. Farmers highlighted the saliency of the niches and the re-designed
    farm systems that increased farm gross margin by 9 to 29% (depending on farm type and
    viii
    options considered) without compromising food self-sufficiency. The involvement of farmers in
    the co-learning cycles allowed establishment of legitimate, credible and salient farm
    reconfiguration guidelines that could be scaled-out to other communities within the “old cotton
    basin”. Five medium-term contrasting socio-economic scenarios were built towards the year
    2027, including hypothetical trends in policy interventions and change towards agricultural
    intensification. A simulation framework was built to account for household demographic
    dynamics and crop/livestock production variability. In the current situation, 45% of the 99
    households of the study village were food self-sufficient and above the 1.25 US$ day-1 poverty
    line. Without change in farmer practices and additional policy intervention, only 16% of the
    farms would be both food self-sufficient and above the poverty line in 2027. In the case of
    diversification with legumes combined with intensification of livestock production and support
    to the milk sector, 27% of farms would be food self-sufficient and above the poverty line.
    Additional broader policy interventions to favour out-migration would be needed to lift 69% of
    the farms out of poverty. Other additional subsidies to favour yield gap narrowing of the main
    crops would lift 92% of the farm population out of poverty. Whilst sustainable intensification of
    farming clearly has a key role to play in ensuring food self-sufficiency, and is of great interest to
    local farmers, in the face of increasing population pressure other approaches are required to
    address rural poverty. These require strategic and multi-sectoral approaches that address
    employment within and beyond agriculture, in both rural and urban areas.

    Role supervisor

    Katrien Descheemaeker (daily supervisor PhD research)

    Who's collecting the data

    Gatien Falconnier

    AMEDD Mali

    ICRISAT Bamko/Mali

    Who's analysing the data

    Gatien Falconnier

    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 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

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    Research data excluded for long term storage. Why?

    -

    Plans for sharing data?

    I will upload the data available

    How to access data once you leave?

    The data will be uploaded

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

    -

    Other parties involved? Agreements on data sharing?

    IER owns the database on household monitoring (Chapter 2 of the thesis). The dataset should only be shared with WUR and Icrisat members

    Other persons contributing (e.g. writing code)

    Thomas Alexander Van Mourik

    Other persons with specific responsibility for data?

    Katrien Descheemaeker

    Ken Giller

    Privacy, security issues? How you deal with them?

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