Assessing nutritional diversity of cropping systems in African villages

Abstract

Background: In Sub-Saharan Africa, 40% of children under five years in age are chronically undernourished. As new investments and attention galvanize action on African agriculture to reduce hunger, there is an urgent need for metrics that monitor agricultural progress beyond calories produced per capita and address nutritional diversity essential for human health. In this study we demonstrate how an ecological tool, functional diversity (FD), has potential to address this need and provide new insights on nutritional diversity of cropping systems in rural Africa.

Methods and findings: Data on edible plant species diversity, food security and diet diversity were collected for 170 farms in three rural settings in Sub-Saharan Africa. Nutritional FD metrics were calculated based on farm species composition and species nutritional composition. Iron and vitamin A deficiency were determined from blood samples of 90 adult women. Nutritional FD metrics summarized the diversity of nutrients provided by the farm and showed variability between farms and villages. Regression of nutritional FD against species richness and expected FD enabled identification of key species that add nutrient diversity to the system and assessed the degree of redundancy for nutrient traits. Nutritional FD analysis demonstrated that depending on the original composition of species on farm or village, adding or removing individual species can have radically different outcomes for nutritional diversity. While correlations between nutritional FD, food and nutrition indicators were not significant at household level, associations between these variables were observed at village level.

Conclusion: This study provides novel metrics to address nutritional diversity in farming systems and examples of how these metrics can help guide agricultural interventions towards adequate nutrient diversity. New hypotheses on the link between agro-diversity, food security and human nutrition are generated and strategies for future research are suggested calling for integration of agriculture, ecology, nutrition, and socio-economics.

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.

Figure 3. Nutritional functional diversity values are plotted against species richness for 170 household farms.

A: Nutritional FD = FD_total, summarizing functional diversity for all 17 nutrients listed in table 1; B: Nutritional FD = FD_{macronutrients} for the four macronutrients; C: Nutritional FD = FD_minerals for the seven minerals; D: Nutritional FD = FD_vitamins 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).