A Systemic View of Food Security for Early Warning Analysis: How Far Away?
A systemic view of
food security, for
analysis: how far
FEWS NET and Harvest
… all of these…
Quality of the local rainy season
Water source performance
Rainy season labor shortages
This seasonal harvest
Food availability in the marketplace
Market food price behavior
Local patterns of conflict
Crop and animal disease outbreaks
National, Regional, Global
Climate modes and trends
Sub-regional and national water demand
Global trends in food production
Regional and global food trade patterns
Global food demand and pricing impulses
Regional conflicts, global terrorism
Incentives/penalties for good governance
A systemic view of food security requires….
Evolution over time of
Early Warning inputs
Vacillating between local, national and global applications
Example: Informing the Local with Global
Global Datasets helped characterize
the Local, Sub-regional, and National
How close are we today?
Need more Local-Global integrated datasets
Within the FEWS NET Data Warehouse “Crop”
domain, the compilation of a Sub-National
Agricultural Statistics Archive has been jointly
supported by FEWS NET and Harvest.
Sub-national ag statistics are now being
compiled from original source documents
What Ag Stat data does it contain?
No. of countries w/Ag Stats being uploaded 165 (166 if USA)
Total years of data: 3,325
No. of sub-national ag stat reporting units: 14,390
No. of reported crops: 5,372
Final datapoint est. : 3,165,916 (w/o USA)
Datapoint = place/season/crop/area/yield/production. Notable missing countries: Tajikistan, Uzbekistan, PDRK, Timor
To use ag stats with remote
observations, we also need…
Time-series ag stat data without accurate shapefiles linked to the actual location
of each reporting unit on the earth, for every crop, every year and every season,
has limited value.
Solution? “Genealogical” profiles of the evolution in shapes/locations of each
country’s reporting units over time, are now being completed. They will identify
the evolution of “shapes” over time, and the right ones to overlay on the imagery
to match with the ag stats.
Preliminary research on the genealogies suggests that for ~96% of the historic
reporting units, historic shapefiles can be inferred from current shapes, and
generated on-the-fly for users as they select ag stats.
Example: A genealogy of reporting
unit shapes over time
What will we do with it?
Make better ag production estimates
Analyze synchronous crop failures due to weather/climate
Better understand food prices/production patterns
Global yield gap map
Inventory of national crop estimation procedures
Build global bulk food trade database, merge w/ crop stats
125 countries loaded; remaining ones being
cleaned before loading
For access, contact Harvest and/or FEWS NET