Data collection with farmers in bean pest and disease management using the Open Data Kit
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This document summarizes a project that uses the Open Data Kit (ODK) for data collection with farmers on bean pest and disease management. The project aims to guide pest and disease management efforts and increase technology adoption. It was previously hindered by issues like decentralized data, costly collection methods, and errors. The project now pilots ODK, which allows simultaneous digitized collection and aggregation of data, including multimedia. This reduces costs and improves quality. Lessons learned include the importance of training, testing the tool, and using a suitable smartphone. Future research will compare ODK to paper methods to address participants' concerns about cloud data loss.
Data collection with farmers in bean pest and disease management using the Open Data Kit
- 1. Data collection with farmers in bean pest and disease management using the Open Data Kit Warren Arinaitwe, Valentine Aritua & Rachel Muthoni-A w.arinaitwe@cigar.org; v.aritua@cigar.org 14th October, 2015 Kampala , Uganda
- 2. Project background • Data collection with ODK part of a 3 –year project aimed at Supporting investments for upscaling of grain legumes in western Kenya through assessing and modeling the threat of biotic stressors. • Its funded by the McKnight Foundation and coordinated by CIAT- Uganda • It’s a multi- partner project with 5 Kenya based partners including 2 universities, 2 Kenya agricultural and livestock research organizations, 1 development partner and 8 farmer based research networks • The major goal of the project is to guide ongoing and future legume pest and disease management efforts, improve strategic decision making, and increase the likelihood of integrated pest and disease management technology adoption • Target crops include common beans, cowpea, ground nuts and soybeans
- 4. Project data management plan Obstacles: •Data was not centrally managed- so data sharing became difficult. •Data collection and entry was not aggregated- so the system became costly •Numerous post data collection errors • The project is engaged in development of a digital data management system(DDS). • Under DDS, the project has explored several options including; • the traditional- Pen and paper , relational databases such as MS Access and other platforms such as ODK A. Pen and paper- at the project inception in 2012
- 5. ……Data management Continued • Moving forward, the project is piloting ODK as an alternative to the old systems Obstacles: • Data collection and entry was not aggregated- so the system became costly •Actual database developed was costly. •Numerous entry errors •Data processing required expertise •Data sharing among partners was difficult. B. MS access database- 2013 &2014
- 6. …….Data management Continued C. Open Data Kit(ODK): • Its an array of open source tools to enable data collection using android based devices and data submission to an online server(cloud). • It’s a stepwise process
- 7. ODK processes 1. Data form development : Relevant pest and disease questions are generated by subject matter specialists and uploaded on ODK platform
- 8. ODK processes 2. Download forms to ODK
- 9. …..ODK processes 3. Train enumerators and pretest the tool.
- 10. ODK processes https://ona.io/warren 4. View data on the server
- 11. Results The project has successfully piloted and adopted ODK in their data management systems
- 12. Lessons learnt 1.Improved data quality: Data collection and entry error controls were embedded into the collection tool 2.Cost reduction: Simultaneous collection and digitization of data has been achieved which has reduced post field data processing costs 3. Rich data set obtained: Complementation of collected data with multi-media data such as videos, photos, audio recordings and GPS data has been achieved. 4.The system is more interactive- This has made data sharing and further processing by the project team members more flexible!. Other lessons 1.Extensive training of enumerators is important 2.The tool needs to be pretested before actual data collection 3.Not every smartphone will work. A good smartphone with functional GPS and a longer battery life is important 4.The system can be complex with complex questionnaires. The instrument should be as precise as possible.
- 13. Future research Problem: Some Project participants have held on to the use of paper and pen just in case their data is lost on the cloud. Proposed research: The project will pursue research that demonstrates the value proposition on the use of paper and pen and the mobile device. The research will strengthen the use of ODK in research, and understand the perceptions of different project partners towards the new data capture and management system.