Maize yield gaps and their mitigation in Ethiopia: an integrated assessment

By banchayehu.assefa , 20 April, 2016
Organizational Context
Name
Banchayehu Tessema Assefa
Chairgroup
Plant Production Systems
Graduate school
PE&RC
Start date of project
Abstract

Crop production in Sub-Saharan Africa is characterized by a large yield gap (Dzanku et al., 2015; Tittonell & Giller, 2013). Likewise, the crop yield gap in Ethiopia is high and persistent, which has resulted in food shortages and has made the country to depend on external food aids (Mann & Warner, 2015). In the last decade, however, crop yield has been improved, particularly of maize (Abate et al., 2015). Yet, actual maize yield is about 20-30% of water limited potential yield (Kassie et al., 2014; http://www.yieldgap.org/Ethiopia). This implies that there is a large potential to increase maize yield and improve food security in the country. Narrowing the yield gap requires identifying and explaining factors, which are time and area specific (Neumann et al., 2010; Tittonell & Giller, 2013; Van Ittersum et al., 2013). We propose an integrated analysis of the maize yield gap by considering crop management practices, access to agricultural technology, biophysical factors, farm(er) characteristics and socio-economic conditions.

By conducting household surveys and utilizing secondary data sources from maize growing areas of Ethiopia, this study aims at (i) analysing the maize yield gap by integrating biophysical, technological and crop management factors, (ii) investigating the determinants of the decision to adopt and the extent of maize technology adoption (mineral fertilizer and modern maize varieties) (iii) explaining the links between maize yield gaps and household food security and (iv) exploring the potential impacts of improved access to mineral fertilizers and  modern maize varieties on household food security. 

Role supervisor

Prof. Martin van Ittersum is the promotor and overall supervisor, and Dr Pytrik Reidsma is daily supervisor. Dr. Jordan Chamberlin is co-supervisor from the TAMASA project based in Ethiopia. Meeting with the full supervisory team will be on monthly basis. Prof. Martin has expertise in production ecology, yield gap analysis, integrated analysis of farming systems at different levels and bio-economic farm modelling. The expertise of Dr. Pytrik is in integrated assessment, farming systems analysis, yield gap analysis and bio-economic farm modelling. Dr. Jordan has expertise in economic theory, econometrics, agricultural economics and spatial analysis. If the composition of the supervisory team changes for some reason, appropriate substitution will be sought within WUR or other parties involved. 

Who's collecting the data

Data will be acquired from the ‘‘Sustainable intensification of Maize-Legume Cropping Systems for food security in Eastern and Southern Africa (SIMLESA)’’ project of International Maize and Wheat Improvement Center (CIMMYT); ‘‘Integrated assessment of the determinants of the MAize yield gap in Sub-Saharan Africa: towards farm INnovation and Enabling policies (IMAGINE)’’ project led by Wageningen UR and ‘‘Taking Maize Agronomy to Scale in Africa (TAMASA)’’ project led by CIMMYT. I will engage in IMAGINE and TAMASA data collections.    

Who's analysing the data

All the datasets are collected in larger projects, different people will analyze the data from these projects. I will analyze the data with regard to the research questions of my PhD thesis with support from supervisors where required. 

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 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 by storing it in the PhD Library Site of PPS. 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?

I will store my data in the PhD Library site of PPS. On this location, all data for each chapter of my thesis will be stored in a separate zip file.

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

No

Other parties involved? Agreements on data sharing?

Yes, see above. Data sharing agreements will be signed for data that have ownership issues. 

Other persons contributing (e.g. writing code)

People from TAMASA and WUR will be consulted where required. 

Other persons with specific responsibility for data?

Owners of the data will be contacted for data related questions. Moti Jaleta (CIMMYT) has provided the SIMLESA data, and will be involved and kept updated about the progress. Joao Silva (PhD student at PPS-WUR) will also be analysing these data, and therefore data cleaning and analysing may in some cases be done together. Jordan Chamberlin (CIMMYT) is responsible for the TAMASA household surveys. He is also a supervisor of this PhD project, to ensure good collaboration. The first round of IMAGINE data has been collected under coordination of Kindie Tesfaye (CIMMYT) and Joao Silva, and will be analysed by Marloes Van Loon (post-doc in PPS-WUR). The next round of data collection will be discussed with Kindie Tesfaye, and PhD supervisors. Workneh Kenea, who is also a PhD student within TAMASA in PPS-WUR, will also be collecting data on crop management practices, and collaboration will be needed specifically for the fourth objective, for which a bio-economic farm model will be developed and applied. 

Privacy, security issues? How you deal with them?

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