When farming becomes a risky business for smallholders: how is risk influencing farmer decisions in Tanzania?

Submitted by violeth.wmaijande on
    Organizational Context
    Name
    Violeth Joab Mwaijande
    Chairgroup
    Plant Production Systems
    Graduate school
    Production Ecology and Resource Conservation
    Start date of project
    Abstract

    Average national maize yield in Tanzania, ranges from 1.5 - 1.55 tonnes per hectare, much lower than what can be achieved (Mkamilo, 2004) (www.yieldgap.org/ ). The production is strongly limited by biophysical and socio economic factors and challenges. These include poor soil fertility and nutrient limitation (Giller et al., 2011), unstable prices of inputs and grain, variable weather, inadequate insurance policies and poor infrastructure  (Cong et al., 2014, Hurley, 2010). Fluctuation of prices on local markets directly leads to large uncertainty in financial benefits of inputs applied (Van Campenhout, 2007, Isinika et al., 2011, Morris, 2007). Biophysical aspects also affect financial returns as nutrient use efficiency varies strongly between fields and farms (Tittonell et al., 2007a, Tittonell et al., 2007b, Zingore et al., 2007). Inputs of the required composition need to be delivered on farm in time and financial services and transactions need to be reliable. Better understanding of these risks within the farming system context will clear balance best agronomic practices with social-economic circumstances. The study will identify and evaluate biophysical and socio economic risks facing maize smallholder farmers as well as assessing their impacts on farmer’s decisions. Experimental and survey data will be collected from two distinct regions in Tanzania, quantifying risks according to farm types and gender differences. Also, the impact of fertilizer formulation, quantity, timing and fertilizer applications will be examined in different field types. We will assess the impact of weather index insurance in improving input accessibility, evaluate the effect of physical and financial infrastructure on accessibility of inputs and analyse the effect of gender on decision making in agricultural risk reduction strategies.

    Role supervisor

    Prof. Ken Giller is the promotor and overall supervisor of the PhD, while Dr. AGT Schut is the daily supervisor. Dr. Arnold Mushongi is the home based supervisor, he will assist with planning of the field trials, interviews and implementation. Our meetings is two weeks with  whereas meeting with Prof. Giller is monthly. The expertise of Prof. Giller is in the area of soil-plant nutrition and systems analysis. He has profound knowledge and skills on improving tropical soil fertility, he has worked extensively in Africa. Dr. Schut is an experienced soil scientist and specialist in systems analysis and simulation modelling of scenarios of change. Dr. Mushongi is a plant breeder specializing in maize crop, widely involved in coordination of  field data collection. He will assist in preparation and execution of experimental trials and household survey data collection. If, for any reason, the composition of the supervisory team will change, appropriate substitution will be sought within WUR or the other parties involved.

    Who's collecting the data

    Datasets are available to TAMASA staffs ( Kenneth Masuki). Addition data will be collected by me in collaboration with TAMASA staffs (Arnold Mushongi).

    Who's analysing the data

    Data analysis will be conducted by me, with support from TAMASA members.

    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: DataModel, 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 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 datasets used for my project, analysis reports, publications, posters.

    Research data excluded for long term storage. Why?

    Some data will remain the property of the organisations who own the data. These data may be excluded from long term storage.

    Plans for sharing data?

    All data will be made available to the public. However, some data will remain in ownership of the parties who have collected the data. Where relevant, agreements about sharing the data will be made through confidentiality agreements.

    How to access data once you leave?

    All data will be stored on the PhD Library Site of PPS

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

    No.

    Other parties involved? Agreements on data sharing?

    Yes. Open access.

    Other persons contributing (e.g. writing code)

    FARMSIM and QUEFTS models have been developed at and are available through WUR

    Other persons with specific responsibility for data?

    The owners of the data set will be contacted with regards to any questions about their data sets.

    Privacy, security issues? How you deal with them?

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