The Project Experience | Mobile Acquired Data

During the MAD Research Series, four projects were supported by Oikoi to scale out the adoption of Mobile Acquired Data (MAD) in research work across the region. Each of these provides a unique case study into the value and costs in adopting digital data collection including feedback from projects on the additional workloads, difficulties in using the technology and perspective from their frontline workers. The projects covered different types of studies, environments and with teams of varying technical aptitude. These pages provide an overview of each project (at a glance), along with what research activities adopted MAD technology, the difficulty reported by project staff, the experience designing, building and deploying their apps, and the associated benefits, challenges and costs.

Vietnam Vegetables - At a Glance

Location	Lao Cai province, Hanoi, Ho Chi Minh and Son La cities, Vietnam
Systems Studied	Smallholder vegetable production
Research Activities	2 x 2,500 household surveys (rural and urban, baseline and endline);
Lead Institution	The University of Adelaide
Partner Institutions	Australia: University of Queensland, NSW DPI, Department of Foreign Affairs and Trade Vietnam: Vietnam Women's Union, Institute of Policy and Strategy for Agriculture and Rural Development, Vietnam National University of Agriculture, Fruit and Vegetable Resource Institute, Soil and Fertiliser Research Institute, National Institute of Medicinal Materials, Plant Protection sub-department of Lao Cai
Disciplinary focus	Biophysical (vegetable resource and disease management practices) and socioeconomic (value change analysis and market settings)
Project stage during MAD implementation	3rd year of 4 year project, start of associated PhD study
Level of MAD experience of project staff	High technical capacity among researchers but untrained in MAD at the commencement of adoption
MAD activities	Rural household survey with short adoption time and urban household survey with a medium (2-3 months) adoption time
MAD feedback mechanism	In-app issue reporting form, real-time feedback via electronic messaging, daily reports to research managers, Prof Wendy Umberger and enumerator team as data was being collected
Key Lessons	Adopting MAD apps for this project reportedly saved an equivalent 1250 enumerator days of work Third party app builders should complete the application in a project space accessible to the project team. Additionally, there should be a requirements gathering and planning phase to ensure the project's needs will be met by the end product. When outsourcing enumeration have an exchange of policies before the application is built. High general technical capacity among researchers dramatically reduces the MAD training burden. Ad-hoc feedback and updates to the application (as opposed to scheduled, batched updates) increase the time required for testing, issue resolution and updates to translations. The use of devices for digital data collection should be leveraged to provide a platform for real time communication between enumerators, field supervisors and researchers.

Vietnam Vegetables: Full Case Study

Vanuatu Beef - At a Glance

Project Title	Increasing the productivity and market options of smallholder beef cattle farmers in Vanuatu
Location	Espiritu Santo, Vanuatu
Systems Studied	Smallholder beef production
Research Activities	On- and off-farm participatory research, demonstration and training, livelihoods and production systems monitoring
Lead Institution	The University of Queensland
Partner Institutions	Australia: Queensland Department of Agriculture, Fisheries and Forestry, Australian Ministry of Agriculture, Southern Cross University Vanuatu: Ministry of Trade, Commerce, Industry and Tourism, Vanuatu; Agricultural Research and Training Centre, Vanuatu Agriculture College, Ministry of Agriculture, Livestock, Forestry, Fisheries and Biosecurity
Disciplinary focus	Multidisciplinary; Livelihoods analysis, cattle economics and value chains, cattle and forage production
Project stage during MAD implementation	1st year of a 4-year project
Level of MAD experience of project staff	High technical capacity among researchers, exposure through the Masterclass, but untrained in MAD at the adoption
MAD activities	Small scale (36-100 households) livelihoods survey (baseline/endline), farm system survey (baseline/endline), longitudinal cattle production monitoring was developed with a 2-month lead time
MAD feedback mechanism	Informal phone conversations and emails, printouts of cattle information sheets for farmers
Key Lessons	It is important to consider practices in the field when designing application workflows Building in real time smallholder feedback strengthens the relationship between enumerators and smallholders and improves local perceptions of the project Difficulties in linking data between forms and cases can result from major changes to application structures after collection starts and a lack of real-time data monitoring Benefits of MAD are more obvious at scale (e.g. time saving increases with more fieldwork and longer surveys), but smaller scale projects could maximise benefits of MAD by selecting key parts of their data collection for MAD implementation Face to face intensive training is an effective method for rapid upskilling in app building

Vanuatu Beef: Full Case Study

Pakistan Dairy - At a Glance

Project Title	Improving smallholder dairy and beef profitability by enhancing farm production and value chain management in Pakistan
Location	Punjab and Sindh provinces, Pakistan
Systems Studied	Smallholder dairy and beef production within mixed crop-livestock systems
Research Activities	Meetings and discussions with potential partner organisations for scale-out activities of whole-family extension approach. Desktop survey for initial evaluations of organisation's management and objectives in extension. Review of current dairy-beef value chain literature in Pakistan. Value chain actor interviews to map beef value-chain of Punjab and Sindh. Review current understanding and efficiency in the dairy and beef operations on smallholders farms
Lead Institution	University of Melbourne, Australia
Partner Institutions	Pakistan: University of Animal and Veterinary Sciences (Lahore), Sindh Agriculture University Australia: University of Melbourne, Charles Sturt University
Disciplinary focus	Value chains research, extension and adoption
Project stage during MAD implementation	First year of a four-to-five-year project
Level of MAD experience of project staff	Project leader had one year of MAD experience, research staff were new to MAD
MAD activities	Survey of value chain actors (farmers, processors & NGOs), monitoring and evaluation of partner organisation extension activities, tracking farmer adoption after extension implementation.
MAD feedback mechanism	Surveys provided comparison of farmer-responses with baseline data collected in a previous project. Extension advice for different farmer responses was also pre-programmed into the MAD application and was provided during data collection.
Key Lessons	Allowing project teams (including a least one in-country) time to work together to learn application building under basic guidance hep to build local capacity and places emphasis on their understanding of how MAD applies to their work/research. Important to have clear targets about what is needed in app design otherwise the building process is drawn out. Start small with one or two builds and data collection activities and take the team through the whole process. This will help to build confidence/understanding.

Pakistan Dairy: Full Case Study

Myanmar Rice - At a Glance

Project Title	Diversification and intensification of rice-based systems in lower Myanmar
Location	Ayeyarwady delta, Myanmar
Systems Studied	Smallholder rice production
Research Activities	Household surveys, benchmarking farmers' fields and establishment of best practices, on-farm rice and rice-pulse trials, and postharvest management training.
Lead Institution	International Rice Research Institute, Philippines (IRRI)
Partner Institutions	Myanmar: Department of Agriculture, Department of Agricultural Research
Disciplinary focus	Agricultural Economics and Social Science
Project stage during MAD implementation	Extension year (4) of 3-year project
Level of MAD experience of project staff	Experience with 2 MAD platforms, SurveyBe and CSPro, prior to training in CommCare app building
MAD activities	250 x farmer data sheet (high level endline), 250 x household survey (in-depth endline), monitoring of participation in farmer training and meetings
MAD feedback mechanism	In-country researchers reviewed the information daily and debriefed in person with enumerators.
Key Lessons	MAD skills within an institution. Embedded calculations in apps increase the speed of data analysis and provide valuable feedback to farmers. Surveys developed for one project can become structural template for use in similar projects. Large surveys should be broken down by topic into multiple forms for targeted testing and to minimise the risk of data loss.

Myanmar Rice: Full Case Study

These implementation case studies can help projects understand what to expect from the MAD experience.