Influence of age, farming site, and boiling on pro-vitamin A content in sweet potato(&lt;i&gt;Ipomoea batats (L.)&lt;/i&gt;Lam.) storage roots

K'Osambo, L.; Carey, E.E.; Misra, Arup Kumar; Wilkes, Jonathan M.; Hagenlmana, V

doi:10.4314/jfta.v4i3.48066

Cited by 2 publications

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“…For fresh sweet potato, we made the assumption that there was little qualitative nutritional losses. Minor qualitative nutritional losses were observed in fresh sweet potato (sugars, carotenoids, starch) stored over long periods of time (over five months) (Ezell & Wilcox, 1952;Kósambo et al, 1999). In practice, fresh sweet potato in Uganda could be sold within a week after harvest.…”

Section: Development Of the Quantitative Modelmentioning

confidence: 99%

Estimation of nutritional postharvest losses along food value chains: A case study of three key food security commodities in sub-Saharan Africa

et al. 2022

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Postharvest losses (PHLs) amplify food insecurity and reduce the amount of nutrients available to vulnerable populations, especially in the world's Low and Middle Income Countries (LMICs). However, little is known about nutrient loss at the various postharvest stages. The objective of our study was to develop a methodology and a tool to estimate nutritional postharvest losses (NPHLs) along food value chains for three distinct food commodities in sub-Saharan Africa. The study used a combination of literature, laboratory and field data to investigate NPHLs caused by both changes in quantity and quality of food material (quantitative and qualitative NPHLs, respectively). The method can be expanded to various other food value chains. A user-friendly predictive tool was developed for case studies involving maize and cowpea in Zimbabwe, and for sweet potato in Uganda. Quantitative and qualitative NPHLs were combined and converted into predicted nutrient loss and nutritional requirement lost due to postharvest losses. The number of people who may not meet their daily nutritional needs, as a result of the food and nutrient losses at country level, was estimated. The estimates consider nutritionally vulnerable groups such as children under five years and pregnant women. The nutrient density of the harvested food material, the level of food production, the postharvest stages along the food value chain, the levels of pest damage along the value chain, and the susceptibility of the nutrients to degradation e.g. during storage, are all important factors that affect NPHLs. Our modelling work suggests that reducing PHLs along food value chains could significantly improve access to nutritious food for populations in LMICs.

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Section: Development Of the Quantitative Modelmentioning

confidence: 99%