APHLIS

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Postharvest Losses Information System
APHLlS
APHLIS for improved Food Security Planning
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APHLIS the slideshow

• What is APHLIS and what problems does it
• address
• How you can get PHL estimates from the
• system
• How you can generate your own PHL
• estimates
• The way forward

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APHLIS - a unique service

• APHLIS generates estimates of postharvest losses
  (PHLs) of cereals in East and Southern Africa and
  is
• Based on a network of local experts who submit
• data and verify loss estimates
• Built on a complete survey of the literature on
  PHLs

• APHLIS provides
• Loss estimates by cereal, by country and by
  province
• that are updated annually
• A display of the data used to derive losses so
  the
• system is fully transparent, and
• The opportunity to add better loss data
• so that loss estimation can improve over time

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What are Postharvest Losses (PHLs)?
PHLs (of cereals) are the cumulative weight
losses from production from each link in the
postharvest chain (including all grain not fit
for human consumption but not PHLs from
processing e.g. milling).
Maize weight losses 2007 from provinces of
Zimbabwe and Ethiopia
Postharvest chain
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The Problem
Soaring food prices and the economic recession
are hampering efforts to reduce poverty.

• PHLs have negative impacts on hunger, poverty
  alleviation, income generation and economic
  growth. Yet the magnitude and location of such
  losses are poorly understood because PHL figures
  are
• mostly guesstimates
• relatively difficult to trace for both logic
• and info source, and
• the sources themselves may not be very
• reliable

APHLlS
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The advantages of better PHL estimates

• By improving PHL estimates it will be possible in
  the short
• term to -
• Improve food security arrangements by
  calculating food
• supply estimates more reliably from production
  figures
• .and long-term to target loss reduction
  interventions at
• the most affected areas (geographically)
• the most affected links in the postharvest chain
  or those
• that would be most cost effective to address,
  and

APHLlS
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A system for getting better PhL estimates

• The main elements of APHLIS are
• Local expert network providing data and
  verifying PHLs
• Database with access to local experts, by
  country,
• PHL Calculator (model) that estimates
  losses
• Web site for display of loss data by
  cereal for each
• country and each province, in tables and
  in maps
• Downloadable calculator for PHL
  estimation at any
• geographical scale

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APHLIS the System in a nutshell
Download
PHL tables
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APHLIS network of experts its most important
resources
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How the PHL calculator works
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
Harvesting/field drying 6.4
Drying 4.0
Shelling/threshing 1.2
Winnowing -
Transport to store 2.3
Storage 5.3
Transport to market 1.0
Market storage 4.0
Figures taken from the literature or contributed
by network experts
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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
Climate type A C B B A
Crop type Maize Maize Sorghum Millet Rice
Scale of farming Small Large Small Small Small
Harvesting/field drying 6.4 2.0 4.9 3.5 4.3
Drying 4.0 3.5 - - -
Shelling/threshing 1.2 2.3 4.0 2.5 2.6
Winnowing - - - - 2.5
Transport to store 2.3 1.9 2.1 2.5 1.3
Storage 5.3 2.1 2.2 1.1 1.2
Transport to market 1.0 1.0 1.0 1.0 1.0
Market storage 4.0 4.0 4.0 4.0 4.0
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PHL Calculator contd

• The PHL profile values are modified according to
  
• Wet/damp weather at harvest
• Length of storage period (0-3, 4-6, gt6 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.
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How to get a PHL estimate
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

Losses estimates
Losses maps (interactive)
Literature
Downloads
PHL Network
About us Contacts Links
Production
Yield
Larger grain borer
Average farm size
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Loss tables
Regional losses for all cereals and by cereal type
Estimated Postharvest Losses () 2003 - 2009
APHLlS
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Loss tables by cereal type and country
Estimated Postharvest Losses () 2003 - 2009
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Loss tables by cereal type and province
Estimated Postharvest Losses () 2003 - 2009
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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)
Losses (t)
Production ()
Remaining (t)
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PHL () calculation
PHL () Calculation Season 1 Farm Type small
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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
Storage duration (months)
no data
Storage duration - loss increases with longer
storage periods.
Larger Grain Borer LGB attack doubles farm
storage losses.
yes
Larger grain borer
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Details of the loss calculation 3. The PHL
profile and loss increments
PH profile (adjusted)
Remaining grain
Loss increment
Stages
Harvesting/field drying
69.5
4.8
6.4
Platform drying
66.8
2.8
4
Threshing and shelling
66
0.8
1.2
Winnowing
66
0
-
Transport to farm
64.4
1.5
2.3
Farm storage
58.6
5.8
9
Transport to market
58.6
0
1
Market storage
58.6
0
4
Total
58.6
15.7
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Details of the loss calculation 4. Quality of the
data in the PH profile and references to data
sources
Datum not a measured estimate
0
Datum not specific to maize
0
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
6.4
Harvesting/field drying
Data overall specific to maize
1
Data overall not measured
0
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The PHLs are also displayed on maps

PHL values
in 2007













Maize

Sorghum

Wheat




APHLlS
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There are also maps of LGB by year
Locations where Larger Grain Borer (Prostephanus
truncatus) was considered to be a significant
pest in 2007
APHLlS
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Getting your own PHL estimate- using the
downloadable calculator

• The downloadable calculator lets you enter your
  own figures. It can
• Work at whatever geographical scale is relevant
• See all the details of the calculation
• Assess the reliability and see the origin of data
• Record multiple estimates and obtain weighted
  average PHLs

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The downloadable calculator front page
You can change the default figures (in blue)
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..changing the defaults
You can change any of the default figures (in
blue)
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observing the calculation
PHL profiles for large-scale small -scale
maize farming in Cwa climate
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Conclusions

• APHLIS generates PHL estimates for 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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For the future

• For the future APHLIS ..
• Would benefit from an effort to generate more
  PHL
• data.
• Should be made sustainable by efforts of the
• international community.
• Could be expanded in geographical range (W.
  Africa,
• Asia, S. America) and technical content (e.g.
  pulses)
• May be used in new ways, for example as
  unseasonal rain
• becomes more common the impact of this on
  PHLs can be
• predicted