Fig. 6

PCA plot consisting of all the variables (35) measured in this study, showing their relative influence on the variation observed in the dataset, which is indicated as  % on the first dimension (Dim 1) and the second dimension (Dim 2). This PCA also gives an indication of the grouping between all the variables measured. Variable labels: “Kcal_balance” = energy balance; “Fe_balance” = iron balance; “Zn_balance” = zinc balance; “Total_crops_m2” = total cropping area; “m2_person” = total cropping area per capita; “Tubers_m2_pcapita” = per capita area cultivated with tubers; “Cereals_m2_pcapita” = per capita area cultivated with cereals; “Legumes_m2_pcapita” = per capita area cultivated with legumes; “No_migrant” = number of household members migrating; “Mig_months” = number of months off-farm for migrating members. Livestock labels refer to total number of animals. Crop labels refer to total surface area in square meters (m2) and total outputs in kilograms (kg). Arrow length is proportionate to the contribution made by an individual variable to the variation in the dataset. Directionality relative to other variables illustrates the nature of correlations (positive or negative) among variables. Crop outputs and per capita cropping areas (tubers, cereals, legumes) are shown to not only be largely driving variation in the data space (long arrows) but to also be positively correlated to energy, iron and zinc balances (same direction). The fact that livestock and household (size, migration) parameters are placed relatively far from the remaining variables further suggests that crop parameters have the strongest and most influential relations to household balance outcomes