Table 1.
Nutrients and nutrient groups taken into account for calculation of FD metrics.
Table 2.
Site characteristics.
Figure 1.
Schematic model of how to assess nutritional functional diversity.
Two data sets are required: a species by trait matrix (1), and a farm or site by species matrix (2). From the species×trait matrix, the multivariate distances between crop species are calculated (3), where distance is a function of distinctness in nutrient composition and content. The distances between species are used to cluster species into a dendrogram (4). Based on the crop species present in a given farm, the branch lengths of the dendrogram are summed (5). Example Farms A and C illustrate how nutritional functional diversity can differ even when species richness is identical, depending on the nutritional distinctiveness of the crop species present.
Figure 2.
Schematic model to assess degree of redundancy by modeling observed versus expected functional diversity for a given species richness.
If a set of communities has a large range of species richness, but shows little variation in functional diversity, then the species pool in that set of communities has high functional redundancy. In contrast, a set of communities with low functional redundancy may exhibit large changes in functional diversity with only small changes in species richness.
Table 3.
Indicator outcomes per site.
Figure 3.
Nutritional functional diversity values are plotted against species richness for 170 household farms.
A: Nutritional FD = FDtotal, summarizing functional diversity for all 17 nutrients listed in table 1; B: Nutritional FD = FDmacronutrients for the four macronutrients; C: Nutritional FD = FDminerals for the seven minerals; D: Nutritional FD = FDvitamins for the six vitamins (table 1). Farms in Mwandama are shown as triangles, farms in Sauri as squares, and farms in Ruhiira as circles.
Figure 4.
Observed values for nutritional diversity are plotted against simulated expected nutritional FD values for 170 household farms.
Farms that have observed FD values significantly different from expected FD values are marked in bold. Farms in Mwandama are shown as triangles, farms in Sauri as squares, and farms in Ruhiira as circles.
Figure 5.
Suggested strategy for future research on nutritional functional diversity.
The overall objective of the strategy is to guide agricultural and landscape interventions towards more balanced nutritional outcomes. Three major fronts for research are suggested: study of potential determinants and barriers of nutritional FD and identify the ones that can be controlled (1); collection of new and mobilization of existing data that enable a more comprehensive calculation of nutritional FD and this at a landscape and village level (2); establishing linkages with consumption and human health outcomes of agricultural systems through integrated datasets that include health and socio-economics (3); and integrated modeling and analysis of potential synergies and tradeoffs between nutritional diversity and other outcomes from agriculture (4).