USDA-GIS-101117_ENVIRONMENT*******************

Harlan Shannon
Meteorologist
U.S. Department of Agriculture
Office of the Chief Economist
World Agricultural Outlook Board
Washington D.C., U.S.A.
An Overview of the USDA World Agricultural Outlook
Board Geographic Information System
Agricultural Weather Assessments

• Several analytical techniques can be
used to assess agricultural weather
– time series analyses
– analog comparisons
– maps
• Historically, many maps were static &
depicted just one variable, making it
difficult to:
– overlay data sets
– visualize and evaluate relationships
– easily assess crop weather conditions
• Geographic Information Systems (GIS)
have helped overcome these obstacles
0
25
50
75
100
125
83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 00 01 02
5 - WESTERN CORN BELT
Percent of Normal Precipitation: May 1 to Dec 31
Introduction
INDIAN
OC EAN
Tasmania
Victoria
New South W ales
Queensland
S. Australia
N. Territory
W. Australia
Precipitation (m m)
0 - 1
1 - 10
10 - 25
25 - 50
50 - 100
100 - 200
200 - 400
400 - 1000
1000 +
Precipitation (m m)
0 - 1
1 - 10
10 - 25
25 - 50
50 - 100
100 - 200
200 - 400
400 - 1000
1000 +
USDA World Agricultural Outlook Board
Australia
Total Precipitation (mm)
March 4, 2010 - March 10, 2010

• GIS defined: integrated system for
organizing, displaying, and analyzing
spatial data using computers and
compatible technologies
• GIS incorporates quantitative data
directly into the system, helping users:
– overlay multiple data sets
– create precise maps
– statistically evaluate relationships
• Numerous organizations use GIS to
study, monitor, and model agricultural
and meteorological processes
GIS Described
Simple GIS
Robust GIS

• Software
– ArcView 3.x – older software still used
to run some operational applications
– ArcGIS 9.x – majority of operations
transitioned to this newer software
• Hardware
– Pentium D desktop computers
• 3.4 GHz processing speed
• 2 GB RAM
• Windows XP operating system
– PCs connected via local area network
– Oracle 11g database
WAOB Geographic Information System

1. Manually generate maps of
important agricultural &
meteorological variables
2. Automate product creation to
maximum extent possible
3. Overlay & analyze
agrometeorological data sets,
establish relationships, & determine
impacts where applicable
GIS Activities
CA
AZ
NM
TX LA
AR
MS AL
FL
Ver.
Puebla
Zacatecas
Sinaloa
Colima
Tab.
Campeche
Quintana
Roo
Yucatan
Chiapas
Oaxaca
Guerrero
Mich.
Jalisco
Nayarit
San Luis
Potosi
Tamaulipas
Nuevo
Leon
Baja
Sur
B aja
Norte
Coahuila
Durango
Chihuahua
Sonora
PACIFIC
OC EAN
GULF
OF
MEXICO
GU AT.
BELIZE
HONDURAS
EL SAL.
NICARAGU A
Percent of N orm al
< 25 %
25 - 50 %
50 - 75 %
75 - 100 %
100 - 150 %
150 - 200 %
200 - 400 %
> 400 %
Precip. = 0
Percent of N orm al
< 25 %
25 - 50 %
50 - 75 %
75 - 100 %
100 - 150 %
150 - 200 %
200 - 400 %
> 400 %
Precip. = 0 USDA World Agricultural Outlook Board
Mexico
Percent of Normal Precipitation
May 1, 2002 - December 31, 2002
1
5.8
1.78
0.44
1.69
0.83
0.16
4.36
0.73
1.05
4.26
0.23
0.4
0.9
0.32
1.02
0.43
0.37
0.01
0.43
2.23
0.43
1.87
2.57
3.55
0.85
5.73
0.43
3.38
0.43 0.43
3.22
5.97
0.37
0.13
0.43
2.37
0.43
0.02
0.48
2.78
2.46
0.79
0.89
0.43
0
0
0
0.2
0.37
0.12
1.59
0.37
0.37
0.43
0.37
0.37
0.44
2.44
0.37
0.37
0.15
0.22
2.07
0.37
0.37
0.43
0.06
0.43
0.37
0.77
0.43
0.02
0.43
0.37
10.62
1.51
0.43
Tropical Storm Gaston
Rainfall in Mid-Atlantic Cotton Growing Areas
Joint Agricultural Weather Facility
Virginia
North
Carolina
South
Carolina
Atlantic
Ocean
Cotton Percent Bolls Opening
North Carolina
South Carolina
34%
20%
Data obtained from NASS weekly Crop Progress
report (released August 30, 2004)
Virginia 54%
#
Charleston
Raleigh
Wilmington
Richmond
Norfolk
8 PM, Saturday, Aug 29 - 8 AM, Tuesday, Aug 31
(Rainfall reported in inches)
Legend
Major Growing Areas
Minor Growing Areas
Storm Track (general)

manual labor…
GIS Activities

Crop Production Data – Internet
NASS crop production data
posted on the Internet

Crop Production Data – Excel
Data can be converted into a
GIS-compatible format using
a spreadsheet

Crop Production Data – ArcView
Agricultural data can then be
imported into GIS and mapped

Crop Maps – United States

Crop Maps – Brazil

Crop Maps – Russia
Russia: Wheat
JOINT AGRICULTURAL WEATHER FACILITY (JAWF)
Major growing areas
Minor growing areas
Legend
Percentages indicate each region's contribution
to total national production. Regions not numbered
contribute less than 1% to the national total.
22
8
26
28
15
Siberia
Region
Urals
Region
Northern
Region
Volga
Region
Central
Region
Southern
Region
Russia
Ukraine
Belarus
Kazakhstan
Area depicted in map above
Lakes
FEB MAR
Wheat crop calendar for most of Russia
JAN MAY
MAR APR DEC
AUG
JUN JUL SEP OCT NOV
PLANT
PLANT
HEAD
HEAD
HARVEST HARVEST
#
#
Major and minor growing areas combined account for
99% of total national production
Major growing areas combined account for 75% of total
national production
Major and minor growing areas and country production
percentages based upon averaged oblast-level data from
1996-2000.
#
Source: Sovecon Agrokhleb Bulletin Statistics and Forecasts
February 18, 2003 Issue No.4 (41), 2002.
Pink = spring wheat
Blue = winter w heat

• Crop production areas shift slowly
over time, thus maps of major crop
areas are updated infrequently
– automated updates not necessary
• In contrast, crop progress & conditions
vary on much shorter time scales,
requiring more frequent map updates
– manual map production can be time
consuming and tedious if a large
number of products are required
• Automation is necessary to effectively
and efficiently produce such products
Automating Applications

5
27
24
12
46 75
4
3
47
13
75
12
6
13
7
67
75
0
National Progress
Since last week
Versus 5-yr average
Behind the 5-yr average by at least 15 percentage points
Ahead of the 5-yr average by at least 15 percentage points
#
#
Joint Agricultural Weather Facility (JAWF)
U.S. Corn Progress
U.S. Corn Progress
Portion of Crop Mature
Portion of Crop Mature
September 2, 2001
September 2, 2001
Mature
Mature 20
20
+8
+8
+4
+4
Crop Progress & Condition Maps
Crop progress and condition
maps previously prepared
by hand

VBA Macros
• Object-oriented programming
language, enables users to automate
various tasks associated with mapping
• Examples of automation:
– importing data
– spatial analysis (e.g., contouring)
– defining map scale/extent
– annotation
– creating a map legend
– exporting/printing a map

Programming significantly
reduces product generation
time and ensures product
consistency from run-to-run

Once initial programming
accomplished, often little
additional code needed to
expand/enhance suite of
products

same principles apply to automating
weather analyses…
Automating More Applications

Maps of Daily Data
Blue number = minimum temperature
Red number = maximum temperature
Green number = precipitation

Color Contour Maps
CA
AZ
NM
TX LA
AR
MS AL
FL
Ver.
Puebla
Zacatecas
Sinaloa
Colima
Tab.
Campeche
Quintana
Roo
Yucatan
Chiapas
Oaxaca
Guerrero
Mich.
Jalisco
Nayarit
San Luis
Potosi
Tamaulipas
Nuevo
Leon
Baja
Sur
B aja
Norte
Coahuila
Durango
Chihuahua
Sonora
PACIFIC
OC EAN
GULF
OF
MEXICO
GU AT.
BELIZE
HONDURAS
EL SAL.
NICARAGU A
Precipitation (m m )
0 - 1
1 - 10
10 - 25
25 - 50
50 - 100
100 - 200
200 - 400
400 - 1000
1000 +
Precipitation (m m )
0 - 1
1 - 10
10 - 25
25 - 50
50 - 100
100 - 200
200 - 400
400 - 1000
1000 +
USDA World Agricultural Outlook Board
Mexico
Total Precipitation (mm )
October 11, 2009 - October 17, 2009

Color Contour Maps
Western
Australia
Northern
Territory
South
Australia
Queensland
New South
Wales
Victoria
Tas.
Java
INDONESIA
Coral
Sea
Gulf
of
Carpentaria
Timor
Sea
INDIAN
OCEAN
INDIAN
OCEAN Australia
Total Precipitation (mm)
August 30, 2010 - September 5, 2010
25 to 50
75 to 100
125 to 150
200 to 225
250 to 275
50 to 75
100 to 125
175 to 200
225 to 250
275 to 300
150 to 175
300 to 350
400 to 450
500 to 600
350 to 400
450 to 500
600 to 800
Above 800
Precipitation (mm)
0 to 1
5 to 10
1 to 5
10 to 25
Data Source: Supplemental

Crop Moisture Index

MODIS Crop Condition Analyses
Western Australia
September 3, 2007
Western Australia
September 2, 2008

MODIS Winterkill Analyses

real power in GIS is ability to
overlay and analyze multiple data sets…
Overlaying & Analyzing Data

Percent of Cotton Bolls
Percent of Cotton Bolls
Open by Sep 26
Open by Sep 26
TOP ## = Percent Bolls Open
TOP ## = Percent Bolls Open
[BOTTOM ##] = Change from
[BOTTOM ##] = Change from
5-yr average
5-yr average
Data Sources:
- Rainfall estimates obtained from the National Weather Service
- Agricultural areas are delineated using NASS county-level crop data
84
84
[+10]
[+10]
92
92
[+22]
[+22]
81
81
[+8]
[+8]
91
91
[+8]
[+8]
71
71
[-14]
[-14]
95
95
[+12]
[+12]
Major Cotton Areas
Minor Cotton Areas
8 AM EDT Sep 25, 2010 - 8 AM EDT Oct 2, 2010
Remnants of Nicole combine with
a front to soak East Coast cotton
Rainfall (inches)
0.1 - 1.0
1.0 - 2.0
2.0 - 4.0
4.0 - 6.0
10.0 - 16.0
6.0 - 8.0
8.0 - 10.0
More than 16.0

Thank You !

USDA-GIS-101117_ENVIRONMENT*******************

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USDA-GIS-101117_ENVIRONMENT*******************