DOI: 10.6084/m9.figshare.14619279.v1
title: Data file

description: These data are associated with the publication "Food insecurity related to agricultural practices and household characteristics in rural communities of northeast Madagascar", accepted for publication at the journal Food Security, May 2021.<br>The explanation of variables provided is given in a ReadMe file. Only de-identified and processed data are provided, in accordance with our International Review Board ethical protocol. Not all variables collected during the study can be provided publicly to ensure the confidentiality of our participants.<br>Data were collected using the following methods:<b>Study design: </b>The survey team included RJY and NAFR as the primary enumerators, local Malagasy researchers from each village, and AO, CF, and MM. Data were collected during the months of June and August, in Mandena in 2018, and in Manantenina and Matsobe in 2019. In each of the three communities, we conducted social surveys for randomly selected households. In Mandena, a drone image of the village was overlaid with a grid system (100X100m cells), which was used to first randomly sample grids, and then randomly sample households within grids, in proportion to the number of houses in those grids. In Manantenina, a member of the research team who lived in the village provided a complete list of all village households, which was used to randomly sample households. In Matsobe, 2018-2019 census data were used to randomly select households. If no members of the randomly selected household could be found, that household was substituted for an available neighbor.All surveys were administered in the local dialect of Malagasy, and informed consent was given by all study participants prior to taking the survey. RJY or AFR and/or a local research team member, fluent in the local dialect, conducted the informed consent and survey with the study participant. The survey was conducted using Qualtrics software on Samsung tablets, and had an average duration of 60 minutes to complete. Study participants were compensated with 1,000 Ariary (MGA, approximately 0.30 USD) in mobile phone credit upon survey completion.<b><i>Food insecurity</i></b><b>: </b>Questions about food insecurity were modified from a prior study of agrarian socioeconomics in Malawi (Ward et al. 2018). We asked respondents if they had times when there was not enough food to feed the family over the past three years. We note that in Malagasy culture, when referring to food security generally, the interpretation is whether there was enough rice for the household, since rice is the staple food. To address the causes of food insecurity, options on the survey included small land size, lack of money, the cost of food in the local markets, extreme natural events (i.e., cyclones, droughts, insect or rodent pest outbreaks), as well as allowing the respondent to give any other reason for food insecurity.<b><i>Socioeconomic characteristics</i></b><b>: </b>Standard data on demographics of households were collected using a survey adapted from the Demographic and Health Survey instrument (ICF_International, 2012). These variables included the number of individuals in the household, their ages in years, gender, education level, and main activity (farming, wage labor, etc.), and whether farmers reported other wage-earning activities other than their subsistence farming. To assess material wealth, we also collected data on the ownership of common household assets, such as radio, television, telephone, generator, solar panels, and farming tools including shovels, axes, plows. We asked about the household materials used to build the walls, roof, and floor, including natural products that were collected such as bamboo, raffia, and <i>Ravinala</i>, and purchased materials including wood planks, aluminum sheets, or cement. To create composite asset indicators, we used principal components analysis (PCA) to summarize the data on household assets and household building materials into orthogonal axes that best captured the variance in the data. As an alternative measure, we summed the number of assets the respondent reported. Households were classified as having a single female head if the respondent was female, identified herself as the head of the household, and reported that she was either not married nor living with a partner, or was a widow.<b><i>Agricultural practices</i></b><b>: </b>Questions about agricultural practices included the types of crops grown, how farmers grew rice (low-land flooded paddies, on hillsides, or both), and domestic animal ownership (the number of animals owned for livestock, poultry, and other animals, enumerated between 1-5 or more than 5 individual animals). The size of farm land was assessed by asking farmers about the input of rice that would be required to grow rice on their land, based on a conversion that approximately 15kg is used to farm one ha (pers. comm. with local stakeholders). Rice and vanilla harvests were calculated in kg.To calculate crop diversification, we enumerated the total number of crops the farmers reported growing in the last year, as well as the total number of cash crops (coffee, cloves, cacao, and vanilla). We also calculated the proportion of the top five crops grown by the respondent, based on the five most commonly grown crops across all respondents. We divided these proportions among the top five food and cash crops. Lastly, we used a PCA to summarize the crop data into two axes that best separated farmers according to those that grow similar crops. We quantified variation in domestic animal ownership as the sum of domestic animals owned, as well as the richness and diversity (Shannon diversity index) of all domestic animals owned (cattle, pigs, goats, poultry). We also conducted a PCA of domestic animals owned to use the first PC as a composite score of domestic animal ownership.