Transcript 幻灯片 1 - APHLIS

Postharvest Losses Information System
APHLIS
for improved
Food Security Planning
JRC
EUROPEAN COMMISSSION
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
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
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
The Problem
Soaring food prices and the
economic recession
are
PHLs have
negative impacts on hunger,
hampering efforts
reduce income generation and
poverty to
alleviation,
poverty. 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
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
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
APHLIS – the System in a nutshell
PHL database
Network of local experts
PHL calculator
Download
Data
tables
Agric. data
PHL
tables
PHLs by crop
GIS maps of
PHLs etc country and province
Calculator
spreadsheet
APHLIS network of experts – its most important
resources
Network of local experts
to supply data and
verify PHL estimates
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
Drying
Shelling/threshing
Winnowing
Transport to store
Storage
Transport to market
Market storage
6.4
4.0
1.2
2.3
5.3
1.0
4.0
Figures taken
from the literature
or contributed by
network experts
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
Crop type
Scale of farming
Harvesting/field drying
Drying
Shelling/threshing
Winnowing
Transport to store
Storage
Transport to market
Market storage
A
Maize
Small
C
Maize
Large
B
Sorghum
Small
B
Millet
Small
A
Rice
Small
6.4
4.0
1.2
2.3
5.3
1.0
4.0
2.0
3.5
2.3
1.9
2.1
1.0
4.0
4.9
4.0
2.1
2.2
1.0
4.0
3.5
2.5
2.5
1.1
1.0
4.0
4.3
2.6
2.5
1.3
1.2
1.0
4.0
PHL Calculator contd
The PHL profile values are modified according to –
1. Wet/damp weather at harvest
2. Length of storage period (0-3, 4-6, >6 months)
3. Larger grain borer infestation (for maize only)
… and the PHL calculation takes into account –
4. The number of harvests annually (1, 2 or 3)
5. 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.
How to get a PHL estimate
Postharvest Losses Information System
Home
Losses estimates
Losses maps (interactive)
Literature
Downloads
PHL Network
About us Contacts Links
Production
Yield
Larger grain borer
Average farm size
Two ways to get PHL estimates
 Consult the tables and/or maps on the
website for losses by region, country or
province
Loss tables
Regional losses for all cereals and by cereal type
Estimated Postharvest Losses (%) 2003 - 2009
Click
APHLlS
Loss tables by cereal type and country
Estimated Postharvest Losses (%) 2003 - 2009
Click
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
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
Season Farm type Production (t) Remaining (t) Losses (t) Production (%) Remaining (%) Losses (%)
PHL (%) calculation
PHL (%) Calculation: Season: 1 Farm Type: small
Marketed at
harvest (%)
20
Details of the loss
calculation
2. Factors modifying
the PHL profile
Rain at harvest
no data
Storage duration
(months)
no data
Larger grain borer
Marketed at harvest % - divides the
harvest between what is stored on
farm and what is sent to market.
Rain at harvest – increases loss at
harvest time.
Storage duration - loss increases
with longer storage periods.
yes
Larger Grain Borer – LGB attack
doubles farm storage losses.
Details of the loss calculation
3. The PHL profile and loss increments
Stages
Harvesting/field
drying
Platform drying
Threshing and
shelling
Winnowing
Transport to
farm
Farm storage
Transport to
market
Market storage
Total
PH profile
(adjusted)
Remaining grain Loss increment
6.4
69.5
4.8
4
66.8
2.8
1.2
66
0.8
-
66
0
2.3
64.4
1.5
9
58.6
5.8
1
58.6
0
4
58.6
0
58.6
15.7
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
Origin of figure
Stages
Loss figure
Reference
Cereal Climate Farm type Method
2.0
9.9
5.8
9.5
Harvesting/field drying
5.0
6.4
The reference to
Boxall 1998
1
Data overall specific to maize
0
Data overall not measured
The PHLs are also displayed on maps
PHL values in 2007
Maize
Sorghum
Wheat
APHLlS
There are also maps of LGB by year
Locations where Larger Grain Borer (Prostephanus truncatus) was
considered to be a significant pest in 2007
AFRICA-PHL
LGB 2007
APHLlS
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
The downloadable calculator – front page
Change language
Open calculator
You can change the default figures (in blue)
…………..changing the defaults
You can change any of the default figures (in blue)
……… observing the calculation
PHL profiles for
large-scale
& small -scale
maize farming in
Cwa climate
Cumulative
annual loss for
one season
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
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