| Autor: |
Bechoff, Aurélie, Mayanja, Sarah, Mvumi, Brighton, Nyanga, Loveness, Ngwenyama, Patrick, Stathers, Tanya, Shee, Apurba, Ferruzzi, Mario, Debelo, Hawi, De Bruyn, Julia, Arnold, Sarah, Rumney, Corinne, Tomlins, Keith |
| Jazyk: |
angličtina |
| Předmět: |
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| Popis: |
Postharvest losses and food security are important concerns in low- and middle-income countries (LMICs) and research in these areas has become a global priority. Measuring nutritional losses along the crop value chain (VC) can deepen quantitative and qualitative understanding of these losses, which is critical for understanding the contribution of agricultural interventions to nutritional improvement. Nutritional loss estimation methods and metrics can provide crucial information for improving food security strategies at local and global levels. The NUTRI-P-LOSS Project developed a methodology to estimate nutritional postharvest losses (NPHLs) throughout the VCs of key staple food crops (maize, sweet potato and cowpea) in two sub-Saharan African countries (Zimbabwe and Uganda). The project focused on key nutrient losses: energy, macronutrients (protein, lipid, carbohydrate, dietary fibre) and micronutrients, considered the most important in terms of deficiencies (vitamin A, zinc, and iron) in developing countries, especially sub-Saharan Africa.\ud We covered estimation of nutritional losses related to:\ud (1) physical weight losses (building on existing weight loss methodologies)\ud (2) other changes not associated with weight loss (i.e. quality losses).\ud Based on our experience during the NUTRI-P-LOSS Project, we are proposing a methodological approach for model dissemination to other countries and other commodities (i.e. other cereals, pulses and roots and tubers, as well as commodities from any other food group) in a cost-effective and sustainable way (e.g. through an open-line, open access platform such as APHLIS). We describe here the method and lessons learned, including challenges that arose in the development of the tool.\ud A key feature for the success of the NPHL estimate is obtaining reliable data on postharvest loss. A second key facet is managing the combination of the data in a coherent way to produce a reliable model estimate. This requires making choices on the presentation of the data (for example selecting field work data as opposed to laboratory data or vice versa) in order to produce a coherent model. Assumptions must be made carefully and stated clearly to ensure credibility. Lastly, in order to make an impact on nutritional outcomes, the model has to be integrated in an online platform (e.g. APHLIS) that can provide open access to data and enable stakeholders, such as LMIC policy makers, researchers and development partners, to obtain estimates of the nutritional postharvest losses in their focal country, VCs and contexts of interest. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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