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Components of APHLIS and how postharvest losses are calculated. Composants des APHLIS et des pertes post- récolte comment sont calculées. JRC. EUROPEAN COMMISSSION. What we will cover. What is APHLIS How the PHL calculator works The kinds of figures APHLIS produces

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  1. Components of APHLIS and how postharvest losses are calculated Composants des APHLIS et des pertes post- récolte comment sont calculées JRC EUROPEAN COMMISSSION

  2. What we will cover • What is APHLIS • How the PHL calculator works • The kinds of figures APHLIS produces • How we assess the quality of the loss estimates • Introduce the downloadable calculator

  3. What is APHLIS? • APHLIS is a unique service. It provides estimates of postharvest losses of cereal grains in sub-Saharan Africa. • It is based on a network of local experts who submit • data and verify loss estimates • It gives loss estimates by cereal, by country • and by province • Loss estimates are updated annually • The method and the data used to derive losses are • displayed so the system is fully transparent, and • Better loss data can easily be added to the system • so loss estimation can improve over time

  4. Components of APHLIS Network of local experts East and Southern Africa June 2008 to supply data and verify PHL estimates West and Central Africa April 2012

  5. PHL database Network of local experts • Stores key data • Production/yield • Rainfall • Climatic extremes etc • Is accessible by network • Annual up-dates

  6. PHL database PHL calculator • Estimates cumulative weight loss from production • Uses figures for loss from literature and from network • Network verifies loss estimates

  7. Data tables PHL calculator PHL tables PHLs by crop country and provinces Key agric. data GIS maps of PHLs and other data The web site Displays PHL estimates and key data http://www.phlosses.net

  8. Downloads Data tables PHL tables PHLs by crop country and provinces Key agric. data GIS maps of PHLs and other data Calculator spreadsheet • Allows users to enter own figures Users’ Guide

  9. How the PHL calculator works Comment la calculatrice de PHL fonctionne The PHL calculator determines a cumulative weight loss from production using loss figures for each link in the postharvest chain. A set of losses figures for the links of the postharvest chain is called a PHL profile Example of a PHL profile for maize grain Exemple d'un profil de PHL pour le grain de maïs Figures taken from the literature or contributed by network experts

  10. PHL Calculator contd • PHL profiles are specific for • Climate type (A – tropical, B - arid/desert, C – warm temperate) • Crop type (different cereals) • Scale of farming (subsistence/commercial) Five examples of PHL profiles

  11. PHL Calculator contd The PHL profile values are modified according to – Wet/damp weather at harvest Length of storage period (0-3, 4-6, >6 months) Larger grain borer infestation (for maize only) … and the PHL calculation takes into account – The number of harvests annually (1, 2 or 3) Amount of crop marketed or retained in farm storage NB PHL values are affected much more by the application of modifiers than by the initial selection of the PHL profile.

  12. The website Postharvest Losses Information System Home • Two ways to get PHL estimates • Consult the tables and/or maps on the website for losses by region, country or province • Download the PHL Calculator spreadsheet to enter user-preferred values for losses at a user defined geographical scale Losses estimates Losses maps (interactive) Literature Downloads PHL Network About us Contacts Links Production Yield Larger grain borer Average farm size

  13. Loss tables Regional losses for all cereals and by cereal type Estimated Postharvest Losses (%) 2003 - 2009 Click

  14. Loss tables by cereal type and country Estimated Postharvest Losses (%) 2003 - 2009 Click

  15. Loss tables by cereal type and province Estimated Postharvest Losses (%) 2003 - 2009 Click on one of these figures to get details of the loss calculation

  16. Calculation matrix documenting the PH loss calculation quality of data sources and references to sources Country: Malawi Province: Area under National Administration Climate: Humid subtropical (Cwa) Year: 2007 Crop: Maize Details of the loss calculation. 1. Production data by farm type and losses over seasons Annual production and losses % tonne Production Grain remaining Lost grain Seasonal production and losses Remaining (%) Losses (%) Season Farm type Production (t) Remaining (t) Losses (t) Production (%)

  17. PHL (%) calculation PHL (%) Calculation: Season: 1 Farm Type: small 20 Marketed at harvest (%) Marketed at harvest % - divides the harvest between what is stored on farm and what is sent to market. Details of the loss calculation 2. Factors modifying the PHL profile Rain at harvest – increases loss at harvest time. no data Rain at harvest no data Storage duration (months) Storage duration - loss increases with longer storage periods. Larger Grain Borer – LGB attack doubles farm storage losses. yes Larger grain borer

  18. Season 2 – smallholder only no grain marketed, all remains on farm. Details of the loss calculation 3. The PHL profile and loss increments PH profile (adjusted) Remaining grain Loss increment Stages Harvesting/field drying 6.4 93.6 6.4 4 89.8 3.7 Platform drying Threshing and shelling 1.2 88.7 1.1 - 88.7 0 Winnowing Transport to farm 2.3 86.7 2.1 Farm storage 9 78.9 7.8 Transport to market 1 78.9 0 Market storage 4 78.9 0 78.9 21.1 27.9 Total

  19. Details of the loss calculation 4. Quality of the data in the PH profile and references to data sources Origin of figure Datum not a measured estimate Datum not specific to maize References and individual loss figures % for small farms Stages Loss figure Reference Cereal Climate Farm type Method 2.0 9.9 5.8 9.5 5.0 Harvesting/field drying 6.4 Data overall specific to maize The reference to Boxall 1998 Data overall not measured

  20. The PHLs are also displayed on maps APHLlS

  21. There are also maps of LGB by year Locations where Larger Grain Borer (Prostephanustruncatus) was considered to be a significant pest in 2007 APhLlS

  22. Conclusions • In the initial stages, APHLIS may or may not produce loss • figures that are different from those currently in use and if they are different there will be no solid evidence that they will be more accurate. • However, the new system generates estimates for PHLs of cereal grains that are - • Transparent in the way they are calculated • Contributed (in part) and verified by local experts • Updated annually with the latest production figures • Based on the primary national unit (i.e. province) • Upgradeable as more (reliable) loss data become available

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