Replication Data for: Food-sourcing from on-farm trees mediates positive relationships between tree cover and dietary quality in Malawi

Data and code for the replication of the study: "Food-sourcing from on-farm trees mediates positive relationships between tree cover and dietary quality in Malawi". Food security policies often overlook the potential of trees to provide micronutrient-rich foods. Through causal mediation analysis, we show the positive effect of tree cover on micronutrient adequacy, explained by people sourcing food from on-farm trees. Detailed survey data (n = 460 households with repeated surveys) from rural Malawi were linked to high-resolution (3m) tree cover data to capture forest and non-forest trees. Our findings support tree-based landscape restoration policies for nature and nutrition. These files contain data collected from 460 Malawian households in October 2021 (dry season) and March 2022 (wet season). It also contains a list of unique food items collected through the 24-hr dietary recall survey. Using publicly available nutrition data (see below), the nutrient value of each food item/recipe was recorded. This study-specific food composition table (FCT) was used to calculate the micronutrient adequacy of individual respondents. We have also included the code necessary to re-produce the causal mediation analysis (and associated sensitivity analyses) featured in the study (format: R Script). Code/Software: The data to be uploaded in R is included as an Excel file (Excel 2016). Note: if you want to run the code, you will have to save the Excel sheets containing dry season data and wet season data as separate files with names that correspond to the R Script. To examine the relationship between tree cover, use of on-farm food trees, and dietary quality, we cleaned and analyzed our data in R (version 4.2.2). The code included here includes the linear regressions, controlling for a selection of covariates, that we ran to examine these relationships. Specifically, we used the ‘mediation’ package in R to evaluate the causal mediation (i.e. indirect) effect of our food tree variable. This package also allowed us to evaluate the robustness of our average causal mediation effect estimates by testing for the possible confounding effect of unmeasured pretreatment variables. We also tested for the impact of omitted variables using the ‘sensmakr’ package in R. Other R packages used to prepare the data: (openxlsx)(readxl) (dplyr) (scales) (regclass) (forcats). The code for the causal mediation analysis and associated sensitivity checks is formatted and uploaded as an R Script file. More details can be found in the Instructions document. R, 4.2.2