Global dataset from 13,000 farm households now publicly available
The Rural Household Multiple Indicator Survey (RHoMIS) improves the process of gathering information from farming households in the rural developing world. We made the first batch of RHoMIS data and indicators, together with all open source software programs used, available in Scientific Data.
Agriculture is the most important livelihood option for most rural households in low- and middle-income countries. These rural farm households are highly diverse, varying in land area, amount of livestock present, crops grown, and farm management strategies. Amazingly enough, in this era of data revolution, robust big data to help the sustainable development of these farm households is lacking. In international ‘agriculture for development’ research there is a proliferation of survey tools and indicators leading to datasets which are often badly documented, incoherent, and with limited interoperability.
The Rural Household Multiple Indicator Survey (RHoMIS) was designed to improve the process of gathering information from farming households in the rural developing world. The tool is designed to minimise the burden on the rural household, to maximise the reliability of responses, and to improve consistency between different studies. Internationally recognised indicators are used, and reflexive learning since 2015 has led to a smooth and rapid questionnaire, which gathers considerable detail in a relatively short amount of time. The RHoMIS tool is built using open source software. The survey is delivered using Android mobile or tablet devices and the ODK software suite. Indicators are calculated and analyses returned using the R programming language.
In this publication in Scientific Data we made the first batch of harmonized RHoMIS data and all associated indicator values, together with all open source software programs used available. Data collection efforts took place during the years 2015, 2016, 2017 and the first three months of 2018, resulting in a dataset collected from 13,310 farm households across 21 low- and middle-income countries. The data were collected by academic, research for development and development organisations in a wide range of different projects, proving that the harmonized approach we proposed can be successfully implemented across a diverse range of settings and interests.
We hope this data is widely used to identify sustainable pathways towards improved food security of these vulnerable households, and underpin strategic studies trying to identify the drivers of diverse diets and possible trade offs between agricultural production intensification and key welfare indicators. We ourselves used the raw data and indicators already for a wide range of studies at site level, for regional analyses and even for continental analysis. Different aspects of smallholder households have been analysed, including gender equity, dietary diversity, nutritional gaps, poverty and greenhouse gas emissions in relation to production intensification, subsistence- versus market-orientated strategies, and on-farm vs. off-farm activities. A key opportunity for the future is to make these RHoMIS data ‘talk’ to other datasets, either remote sensing based, census data based or climate data based, to gain better insight in the opportunities to improve the welfare of smallholder households and to better match portfolios of new technologies to the needs of these vulnerable people.
RHoMIS is an on-going initiative, and we welcome interested parties to the community of practice (see www.rhomis.org for up-to-date information and downloadable survey questionnaires). Records continue to be submitted to the central data repository: in the latter part of 2018 more than 10,000 households were additionally interviewed, and their information added to the database. The RHoMIS team often works intensively with users of the tool in all kinds of applications to ensure consistency in the information collected and to make sure our analysis software runs smoothly, but a new exciting development is the ‘Do it Yourself’ RHoMIS, that everybody can use. We hope people will use this setup, and then contribute the data to our community of practice so that we can continue to make these data publicly available in new releases in Scientific Data, and open this rich vein of information to improve lives and save the planet.