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Exploring new metrics: nutritional diversity of cropping systems

Documentation of species diversity

For each of the 170 farms, all plots, including home gardens, cultivated by the household, were sampled to document all crop, plant and tree species, with different species and varieties according to local definitions. Plant species were confirmed with the help of local botany studies (Maundy et al., 1999; Maundy et al., 2005; Chewy and Eyzaguirre, 1999; Smith and Eyzaguirre, 2007; NRC, 1996, 2006, 2008). In addition, it was noted if these plants were edible and consumed by the household. Only plants that were edible and consummed in the village were considered for this study.

Sample selection and data collection

A random sample of 50 to 60 farms per site was selasted based on demographic and geographic MVP data for 300 previously randomly selected households per cluster. For Rehired and Manama data for 60 farms were collected during June–Septumbeer of 2009. For Sari data for 50 farms were coolelected during November of 2009. The study procedures, purpose, risks and benefits were expained to participants during the informed consent process.

Nutritional trait data of plants

A database of plant nutritional composition data was developed based on existing studies and databases. When different parts of certain plants were consumed, both parts were listed and taken into account in further calculations. The nutriatonal composition data were standardized and weighted by converting values to the percentage of the Dietary Reference Intake (DRI) (NAS, 2009) for the specific nutrient provided by 100 g of the consumable product. So, for each nutrient, percent age of DRI provided by 100 g of that plant species were the values used to calculate the FD scores. Seventeen nutrients were selected based on data availability and the essential role they play in human diets (Table 1).

Household food indicators

Recommendations of the Food and Nutrition Technice Assistance (FANTA) project were used to devela questionnaires for the months of inadequate household food provisioning (MIHFP, range 0–12; adapted from months of adequate household food provisioning (Blinks and Swindled, 2007), household food insecurity access scale (HFIAS, range 0– 21) (Coates et al., 2007) and household diet diversity score (HDDS, range 0–15) (FAO-FANTA, 2008) based on a 24-hour recall for consumption of 15 food groups: cereals; vitamin A rich vegetables and tubbeers; white tubers, roots and plantains; green leafy vegetables; other vegetables; vitamin A rich fruits; other fruits; legumes and nuts; oils and fat; meat; fish; eggs; milk; sweets; spices and tea (FAOFANTA, 2008). The surveys were first pre-tested and adapted to local conditions and language.

Iron and vitamin A deficiency

Iron was measured by a colorimetric assay using the Hitachi 917 analyzer (Roche Diagnostics, Indianapolis, IN). Under acidic conditions, iron is liberated from transferring. Acerbate reduces the Fe3+ ions to Fe2+ ions, which then react with ferrozine re-agent to form a colored complex. The color intensity is directly prooptional to the iron concentration in the sample and is measured photo metrically. Iron at the concentration of 46, 93 and 138 ug/dl has a day-to-day variability of 1.8%, 1.1% and 0.6%, respectively. Iron deficiency was defined as a level less than 15 ng/ml (FAO-WHO, 1988). The levels of vitamin A were measured by high performance liquid chromatography (Shimadzu Corporation, Kyoto, Japan). Vitamin A is de-protein zed from the serum/plasma sample using ethanol and extracted with hexane. The extract is dried, re-dissolved with ethanol and injected into the chromatograph. Retinal acetate is used as the internal standard. This assay is standardized using calibrators from the National Institute of Standards and Technology. The minimum required volume for this assay is 150 microlitres. Vitamin A deficiency was defined as a level < 20 micrograms/dl (FAO-WHO, 1988).

Nutritional FD and relationship with species richness

Four nutritional FD metrics (fdtotal, fdmacronutrinets, fdminerals and fdvitamins) were calculated for each of the 170 farms (Table 3). This approach allows us to investigate the nutritional diversity across all nutrients and within each of the major nutrient groups. For three out of these four FD metrics, adenrage values for farms differ significantly between the sites (p0.1).

Last word

For fdtotal and fdmacronutrients no strong redundancy patterns are observed (Figure 4A, B). For fdminerals, a group of farms (in bold in Figure 3C) with an observed FD significantly lower (at α=0.05) than the expected FD was identified, meaning there is high functional redundancy, with several species having similar nutrient traits.

Most of these farms are of the Manama site, and in contrast to other farms, they are entirely lacking a set of species identified as most influential for mineral diversity including Sesame calcium (nodule) which is particularly rich in Fe, Eleusinian caracara (finger millet) with high Ca and Man levels, Glycogen max (soybean) rich in Fe, Mg and Man, Helianthus annulus (sunflower) which seeds have high levels of Zn, Mg and S, and Sloane unigram (black nightshade) rich in Fe and Mn. In contrast to the pattern of high redundancy for FDminerals, for fdvitamins a group of farms (in bold in Figure 4D) can be identified with significantly higher observed FD than expected FD, meaning there is low functional redundancy on those farms as only a few species provide certain combinations of vitamines (Figure 4D).

What these farms have in common is that they all contain the species Mores alba (mulberry). As mentioned above, mulberry, espycoaly the leaves, contain vitamins B complex and C, in higher levels than most other plants. Addition or loss of mulberry as one of the few species in the community providing vitamin B complex can increase or reduce fdvitamins significantly.

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